Case Studies

Meshing Time Reduction

Honda Automobile Explains How They Save CPU Time with HEXPRESS™/Hybrid

Automotive | Grid Generation

When you gaze upon the beauty of a car’s curves, how often do you think about people who worked for you to be able to enjoy this view? Let’s not forget that designers are working hand in hand with engineers: these cars, despite their eye-catching look, have to satisfy the manufacturability, a low aerodynamic drag, structural robustness, etc.

Many of Honda cars are designed in the Honda Automobile Research & Development Center located in Tochigi, Japan. This facility houses state-of-the-art research equipments: a car-to-car crash test, a proving ground (with a road of Belgian brick!), wind tunnels, etc.

But prior to all these physical tests, Honda Engineers perform and analyze CFD computations on external aerodynamics of the car body, as well as aero-thermal computations of the underhood: radiator fans, flow around engine bay/peripherals, exhaust system, etc.

All these computations require a high level of accuracy from the software used and a fast turnaround time. The product has to be mature and robust enough to meet industrial expectations.

“We massively invested in NUMECA solutions more than 2 years ago, and are very satisfied with the level of precision they provide us. We previously used various other commercial meshing tools, but were not satisfied by the quality of the viscous layers which many times led to divergence.

Moreover, the learning curve was cumbersome. When we switched to HEXPRESS™/Hybrid, we not only solved these issues, but also divided by 3 our CPU time, while the engineering time dropped to 30 mins/mesh.

Thanks to HEXPRESS/Hybrid’s speed, the reduced turnaround time allows my team to explore more designs in a shorter amount of time. This tool is easy to get started with; my engineers are quickly able to deliver results.

The complexity of our geometries forces us to be very demanding regarding our CFD tools. We needed a mesher capable of easily handling unclean CAD with overlapping surfaces, holes, intersecting selections, etc. HEXPRESS™/Hybrid appeared to us as the perfect solution for this job.

We also greatly appreciate its Thin-Wall feature, allowing us to get a conformal interface between 2 different domains such as a pipe and the fluid inside of it. The level of details we can now capture is impressive, and improves the accuracy of our results and thus our products.

Furthermore, the service and technical support provided by NUMECA Japan meet our highest professional expectations. NUMECA experts interacted closely with us to understand our specific needs and proposed dedicated solutions, all the way from support to custom developments.”

Dr. Takiguchi,
11 Development division, Chief Engineer
Honda Automobile R&D Center

Multi-Disciplinary Optimisation

Multi-Disciplinary Optimisation of a FORD Turbocharger Compressor Design

Automotive | Turbomachinery | Grid Generation

There are several reasons why optimisation methods can at this time not be considered in routine design work:

  1. Lack of computational resources: Everybody is enjoying that those resources have increased quite a lot recently, but nevertheless design optimisation work is still seen as too costly.
  2. The need for multi-operating-points: For quite some time optimisation and inverse design techniques would be focused on one single operating point, with no guarantee or control of the off-design performance, choke flow, performance at lower speeds,… Investing in a significant effort without guaranteed ROI is clearly limiting the interest of designers working under pressure and with time constraints.
  3. The multi-disciplinary need: Especially for designs working under strong mechanical pressure, such as turbochargers, an optimisation limited to aerodynamic performance and not offering control or verification of the mechanical integrity is also not so interesting.

NUMECA’s FINE™/Design3D design optimisation software offers solutions to all 3 items mentioned above:

  1. We considerably optimised our solver speeds: The FINE™/Turbo solver converges in typically 30 minutes to 2 hours per million nodes and per core. With a dozen of cores, the aero-analysis of a new design at 3-4 operating conditions can be done in 2 hours! An impressive result compared to standard commercial solvers that need an entire day for this.
  2. Optimisation is flexible and can easily address multi-operating point problems. In the example shown below, performance is investigated and controlled from stall to choke, at different speeds.
  3. The flexibility of the optimisation allows for the analysis phase to include a CFD solver, but also a mechanical tool (or other solvers). NUMECA developed a partnership with the company Open Engineering, and we have coupled FINE™/Design3D with the FEA mechanical solver Oophelie.

The optimisation example case described below has been presented at the 2015 IGTI Turbo Expo conference (GT 2015-43631). NUMECA-USA collaborated with the FORD turbocharger research group on this project. We started from an initial compressor blade design that already presented quite high performance, with the objective of mainly decreasing the mechanical stress levels by 20%. No clear objectives were set on the aero-performance, apart from trying to maintain it if possible.

Mechanical Optimisation

The process started with an optimisation of the back plate and bore zones, which only necessitated running the mechanical analysis tool Oophelie. The blade modeler AutoBlade™ has been upgraded to allow for the parameterisation of the back plate, as shown below. Once the design parameters are selected and variation bounds applied, our optimisation process works in 2 steps:

  1. random geometries are generated and analyzed by the FEA tool (DOE process).
  2. the information resulting from this first process is used by the optimiser to find the optimum.

The whole analysis process is executed in batch mode. Once the parameters are selected, a CAD definition of the geometry is generated, which is provided to the mesh generator (see resulting mesh below; note that fillets are automatically applied to the blades, even if not included in the geometry). The resulting mesh is then resolved by the FEA tool.

Aero-Mechanical Optimisation

The mechanical optimisation process was done in one night. It was followed by a complete optimisation of the blade shape and meridional channel, involving both the CFD analysis tool FINE™/Turbo and the FEA solver Oophelie.

Several speed lines of the initial design have been analyzed. The computational domain includes not only the wheel, but also the volute and the casing treatment. We then decided to focus on the choke and stall conditions at design speed and on the near-stall conditions at a lower speed. This decision was made based on the assumption that the design performance would be indirectly controlled if the choke flow and near-stall performance are maintained or improved.

The blade camber lines have been parameterised, as well as the hub path, which was controlled by Bezier points. Also we included the camber line profiles of the splitter blades and their stream-wise and tangential positions. This led us to a total number of 19 parameters.

450 geometries have been randomly generated. We decided to first calculate them with the mechanical tool. This allowed us to eliminate 300 of them, as they were providing higher maximum stresses. We associated poor aero-performance to those (without actually running the CFD) and then applied the CFD solver only to the 150 better ones, saving a lot of computational time. After optimisation, a new blade design was selected, presenting slightly better pressure ratio performance, and with 20% lower mechanical stresses. The pressure ratio curves are shown beside.

Acoustic Analysis

Side Mirror Noise with Adaptive Spectral Reconstruction

Automotive | Acoustics

A new method called Adaptive Spectral Reconstruction (ASR) for the stochastic reconstruction of broadband aeroacoustic sources starting from steady CFD analyses is presented and applied to the evaluation of the noise radiated by a model automotive side mirror.

The new approach exploits some ideas from both SNGR and RPM, and for some aspects can be considered as a sort of mixing between the two methods since it permits to reconstruct both the frequency content of the turbulent field (as done by SNGR) and the spatial cross correlation (as done by RPM).

The turbulent field is reconstructed with a sum of convected plane waves, but two substantial differences are introduced in respect of SNGR. The first difference concerns the spatial variation of the parameters that define each wave, that depends on the wavelength of each wave, rather than being kept constant or related to the CFD correlation length. The second innovative aspect is the usage of a dedicated full hexa adaptive mesh that is refined in function of the expected local correlation length, ensuring that the mesh be enough refined to capture the relevant acoustic length scales.

The method is here applied to the evaluation of a classical side mirror model test case, and results are presented in terms of comparisons with measurements for both in plane and out of plane microphones. Visualizations of reconstructed acoustic sources are also presented.

Aerodynamic Boat Design

Ultra-Fast and Fuel Efficient Aerodynamic Boat Design by A2V with FINE™/Marine

Marine | Hydro

Advanced Aerodynamic Vessels (A2V) develops and commercializes a new generation of fast transportation vessels, using aerodynamics to improve energy efficiency. Its revolutionary shape transfers the weight of the ship from the water to the air. As the required propulsive power depends mostly on the weight carried through water, reducing this weight significantly reduces fuel consumption.

“A2V technology has changed the rules by developing and commercializing a new generation of fast and fuel efficient passenger transport vessels. They have designed and patented a wing-like catamaran geometry, which at speed provides aerodynamic lift above the water, alleviating the vessel and thus reducing its power requirements.”

Conventional Fast Vessels

Today’s conventional fast vessels’ speed depends purely on power. As a consequence increasing speed inevitably comes at the cost of much higher fuel consumption. From an economic and environmental point of view, this leads to an unsustainable cost per passenger.

Meanwhile in a conventional monohull or multihull, fuel consumption per passenger is directly linked to the size of the vessel: the bigger the vessel, the lower the fuel consumption is per passenger when the boat is fully loaded.

Thanks to the Aerodynamic Lift, Faster Means Lighter and More Efficient

A2V technology has changed the rules by developing and commercializing a new generation of fast and fuel efficient passenger transport vessels. The company markets workboats for the offshore industry, the commercial passenger maritime transport industry, and the states for their patrol, surveillance and rescue missions. How? They have designed and patented a wing-like catamaran geometry, which at speed provides aerodynamic lift above the water, alleviating the vessel and thus reducing its power requirements. Thanks to the aerodynamic support, above a critical speed, the faster A2V vessels go, the less fuel they use.

“The A2V vessel has a fuel consumption of about 9 litres per passenger per 100km at 50 knots, independent on vessel size, from 10 to 100 passengers, from 12 to 30 meters. As a comparison, present state-of-the-art crewboats typically burn more than 30 litres per passenger per 100km and travel below 40 knots”. (source:

Numerical Challenges

One of the challenges in the design of such a vessel is the modelization of the free surface deformation at high speed, to which the stepped hulls are very sensitive since the aft part of the hull is operating in the wake of the forebody. The aerodynamic with the ground effect, both in steady and unsteady conditions, has also been a challenge. In order to model the behavior in the waves with reasonable computational times, A2V carried out systematic aerodynamic analyses and used the results to build a mathematical model. This aerodynamic model was implemented in the hydrodynamic computations using FINE™/Marine dynamic libraries.

“CFD is the core of the design approach of this vessel. With NUMECA’s software FINE™/Marine, A2V relied entirely on simulation during the design of the fully instrumented 10.5m prototype.”

Accurate Predictions

Computational Fluids Dynamics is the core of the design approach of this vessel. With NUMECA’s software FINE™/Marine, A2V relied entirely on simulation during the design of the fully instrumented 10.5m prototype. The full scale measurements showed that numerical predictions were accurate, as shown in the Figure 1 where numerical predictions are continuous lines and measurements are dots. The total drag is plotted versus speed for three different cases : (1) 10 knots head wind in red, (2) no wind in green and (3) 10 knots tail wind in blue.FINE™/Marine also allowed detailed analysis of the flow to refine the design.


Lionel Huetz, CEO,
Advanced Aerodynamic Vessels, France

Wind Loads

Validation of Wind Loads on a Slender Vessel Using CFD by DAMEN & NUMECA

Marine | Hydro

DAMEN and Numeca are developing a CFD methodology to demonstrate a vessel has sufficient transversal stability to resist over-rolling in severe side winds.

IMO regulation 749.18, ‘Severe wind and rolling criterion (weather criterion)’, ensures a vessel has sufficient transversal stability to resist over-rolling in severe side winds. Due to the necessarily conservative nature of the regulation (enabling it to be broadly applicable to a multitude of vessels), slender vessels like the DAMEN Fast Crew Support (FCS) 3307 have difficulties in satisfying the empirical requirements of the regulation, thus necessitating expensive experimentation in order to demonstrate the vessel’s compliance with the regulation.

In order to reduce the cost of proving compliance, DAMEN is developing, in partnership with its Computational Fluid Dynamics (CFD) code supplier NUMECA International, a CFD methodology that can be used in lieu of experimentation.

They conducted a CFD validation campaign where a wind tunnel test of the DAMEN FCS3307 vessel was numerically replicated.

As the motivation for using the CFD approach is primarily cost and time related, it is imperative that the methodology be both sufficiently accurate but also with as low as possible computational cost and total turnaround time. The methodology reflects these aims.

Read the Paper

Read the Paper

Click here to read the paper that reports the results of this validation campaign.

Read the Paper

Planing Hull Resistance

Marintek Chooses FINE™/Marine for Planing Hull Resistance Curve Prediction

Marine | Hydro

For the prediction of the resistance curve of a planing hull and validation against model tests, Marintek used FINE™/Marine and HEXPRESS™ in this user case.

The Norwegian Marine Technology Research Institute (MARINTEK) performs R&D in ocean technology for a global market, primarily in the maritime and oil and gas sectors and ocean energy. MARINTEK’s main offices and laboratories are located in Trondheim, Norway. A rational combination of physical and numerical modeling approaches has always been MARINTEK’s strategy in its research activities and commercial services. Prediction of vessel and propeller performances, design optimisation process, wake analyses and studies on propulsor-hull interaction are just a few examples of CFD applied to ship hydrodynamics.

STM was established in 1991 by the decree of the Defence Industry Executive Committee to provide system engineering, technical support, project management, technology transfer and logistics support services for Turkish Armed Forces (TAF) and Undersecretariat for Defence Industries (SSM) and also develop necessary software technologies for defence systems and establish/operate national software centers for software development, maintenance/support.

User Case

The total ship resistance of a new patrol vessel in calm water conditions was evaluated by means of CFD simulations and model tests. The hull form was developed by STM and has as main dimensions:

Length betw. perp.












The primary objective of this case study was to validate the numerical model against model tests in order to guaranty high level of accuracy to the End Client in the subsequent numerical phases of the project. CFD and model test predictions were compared for three speeds: 20, 45 and 55 knots.

CFD Simulations

The total resistance of the hull is computed by means of CFD simulations at different speeds. The simulations were performed with FINE™/Marine 4.2 at full scale, in deep water conditions, in sea water.

The vessel’s geometry is meshed using HEXPRESS™. A boundary layer grid normal to the hull surface is specified in order to reach y+ values between 30 and 80. Given the diversity of Froude regime covered in this study, new meshes were generated for each computed speed. Adaptive grid refinement was used with the free surface criterion in the proximity of the hull in the final stage of each simulation in order to increase the accuracy of the results. The final meshes were composed of between 5.0 and 7.5 millions cells.

In the simulations, the propulsion was modeled as a force applied at the center of action of the water jets. The air drag was modeled as a force applied at the center of the frontal projected area.

Model Tests

The hull model is made of foam and wood coated with paint with a hydrodynamically smooth surface finish to the linear scale of 1:16. For turbulence stimulation, fine sand grains were glued to the hull along the keel from bow to station 17.

The resistance tests were performed with the model towed by MARINTEK’s high speed rig with measurements of resistance, trim and sinkage. In the model test setup, the model is free to heave, roll and trim but fixed in all other degrees of freedom.

The effect of air drag on the projected area above water line are included in the predictions based on the projected area of the vessel.

Conversion to Total Ship Resistance

The conversion from hull model (numerical or experimental) into full scale ship is made by using the form factor method. In this method, it is assumed that the total resistance can be divided into two parts, represented by the viscous resistance and the residuary (due to vorticity, wave making and wave breaking) resistance CR. The viscous resistance is determined by multiplying the frictional resistance CF with a constant form factor k0, which is identical for the models and the ship. Further, it is assumed that the residuary resistance CR is identical for models and the ship.

When numerical or experimental results are converted to Total ship resistance RTs, the effect of the hull surface roughness is taken into account by means of empirical formula. The results are presented in terms of non-dimensional Total ship resistance CTs, in which the dimentionalization is performed using the dynamic wetted length and surface of the vessel.


The following table compares the predicted total ship resistance obtained from the model test approach and the CFD approach. For all speeds, the results agree within 0.7 %. The hydrodynamic trim angle agrees within 0.5 deg. This is a satisfying result, given that trim measurements are not corrected for scale effects and that the CFD mesh could be even further refined around the hull to address more accurately this application, which was not necessary in this study.

Model tests


VS [knots]

FN [-]

CTS [-]

Trim [deg]

CTS [-] (%)

Trim [deg] (Delta)





8.70 (+0.7%)

0.64 (-0.10)





4.38 (-0.7%)

1.32 (-0.34)





3.64 (-0.5%)

1.75 (-0.52)


The excellent agreement between the model tests and CFD predictions for the total ship resistance in calm water condition results in a good confidence level in the CFD results presented to the End Client.

Meet the Team

End User – Eloïse Croonenborghs, Research Scientist at MARINTEK, Maritime division, Trondheim, Norway
Team Expert – Sverre Anders Alterskjær, Research Scientist at MARINTEK, Maritime division, Trondheim, Norway
End Client Expert – Canan TIRYAKI, STM, Ankara, Turkey
Software Provider – NUMECA International S.A.

Propeller Cavitation

Numerical Simulations of the Cavitating and Non-Cavitating Flow around the Postdam Propeller Test Case

Marine | Hydro

Numerous studies based on experiments or computations have been carried out to investigate propeller open water characteristics. Most studies only consider the case of a propeller in straight ahead flow. However, under real conditions, a working propeller operates behind a ship usually in a complex wake, so that the propeller shows quite different hydrodynamic performance.

In this paper, the cavitating performance and open water performance of the SMP’15 propeller are numerically simulated using the flow solver ISIS-CFD. A cavitation model based on a transport equation and the k-w SST turbulence model are coupled in the flow solver. The thrust and torque coefficients are presented for the open water case. The pressure distribution on the propeller blades is also presented. For the cavitating case, the cavity surface is presented as well as the thrust and torque coefficients.

Read all about it HERE.


Boom Supersonic partners with NUMECA


Boom Supersonic partners with NUMECA, adopting its CFD solutions to advance development of the Overture supersonic passenger aircraft

New partnership gives Boom ability to build a more efficient aircraft

NUMECA International, a global leader in Computational Fluid Dynamics (CFD), multiphysics, and optimization, today announced a new partnership with Boom Supersonic (Boom), an innovative company building history’s fastest supersonic airliner.

Through this partnership, Boom is aiming to create a dramatically streamlined and highly automated workflow, both utilizing NUMECA’s expertise in creating solutions to provide quality results with the highest reliability and the fastest solution time of any code and amplifying the strengths of Boom’s world-renowned design team. The new CFD solutions adopted from NUMECA will advance the development of Overture, Boom’s Mach-2.2 commercial airliner.

“In the first pilot project we attained results up to 14x faster than with our previous design environment,” said Tim Conners, Lead Propulsion Engineer at Boom. “This gives Boom the ability to test more conditions, try more design ideas, and save millions of dollars in compute resources – yielding a more efficient aircraft in less time and for lower cost than we originally planned for.”

Boom believes that speed is less about going really fast and more about all the things it enables you to do. It is this commitment to speed—and what it allows its engineers to achieve—that drove Boom’s decision to choose NUMECA for some of its most demanding CFD simulations.

“At NUMECA, our prime focus is helping our clients, such as Boom, develop their products under real world conditions by ensuring the highest reliability of performance prediction, at a fraction of the computational cost of competitors’ solutions,” said Prof Charles Hirsch, President of NUMECA International. “In partnering with Boom, we’re excited to contribute to bringing supersonic travel to a commercial audience and assist in the development of Overture.”

About Boom Supersonic

Boom Supersonic is the Denver-based company building supersonic airliners. Founded in 2014, its vision is to remove the barriers to experiencing the planet: time, money, and hassle. Boom is backed by world-class investors including Emerson Collective, Y Combinator, Caffeinated Capital, SV Angel, and individuals including Sam Altman, Paul Graham, Ron Conway, Michael Marks, and Greg McAdoo. The company has announced pre-orders of its Overture airliner from airlines including Japan Airlines and Virgin Group. For more information, please visit

Full Engine Simulation

Fully-coupled CFD Engine Simulations

Aerospace | Automotive | Turbomachinery

The aerospace industry, like many other industries, is under pressure to drastically reduce its environmental footprint. The Flightpath 2050 goals of the European Union state that by the year 2050 all CO2 emissions per passenger kilometer must be reduced by 75%, NOx by 90% and noise pollution by 65% (relative to the year 2000). [1] One of the ways to achieve these environmental goals is by increasing turbomachinery performance. Improved analysis in the design phase, more specifically development of more reliable predictions, advancement in accuracy, inter-disciplinarity and speed of simulation tools, can add several percentage points to engine efficiency and reduce development cost and time. [2][3]


One of the main challenges in designing an engine is the complexity in terms of geometrical details (combustion chamber features, turbine cooling holes…) and of interaction effects between the components which must be modelled with accuracy and acceptable computation time.

Full Engine Simulation Methodology

Traditionally engine design in industry has relied on tools like experimental investigation using test or flow bench set-ups, analytical models, empirical/historical data, 1D/2D codes and recently, high fidelity 3D computational fluid dynamics (CFD) for steady and unsteady flow physics modelling. Currently most of the literature work [4-6] and projects conducted at an industrial scale employ a component-by-component analysis approach, where each engine component is studied separately. Such an approach usually requires assumptions for inlet and outlet boundary conditions for each component and involves a considerable effort in coupling different component analysis tools (often with different modelling degrees of accuracy), leading to a process which is potentially error-prone from the simulation setup point of view and often resulting in significant mismatches between numerical and experimental data.

A one-way coupling approach represents one step further in increasing the accuracy of such simulations. It can be achieved, for example, by extracting outlet profiles of the flow variables from individual converged component simulations and applying them as inlet boundary condition profiles to downstream component runs. Nevertheless, the inter-component interaction is still one-way and the simulation process and results can suffer from similar drawbacks as in the totally uncoupled workflow.In the two-way coupling methodology all the components are coupled and solved simultaneously in one single simulation. This approach greatly simplifies and accelerates the simulation workflow. Since all the components are considered simultaneously, there is no need to prescribe boundary conditions between the various elements of the aero-engine. This avoids running simulations where the states at the interface between the different components have to be guessed.

The full engine CFD numerical modeling methodology can be mainly categorized in three levels:

  1. Steady-state RANS simulations: computationally low cost and usually involving a single meshed blade passage per turbomachinery row with mixing-plane interfaces between components
  2. Unsteady-state RANS time domain simulations: computationally expensive, employing several meshed blade passages per turbomachinery row, usually requiring many time steps to reach periodic flow conditions and providing solutions for a single clocking configuration per run
  3. Unsteady-state RANS frequency domain simulations: 2-3 orders of magnitude faster than time domain method computations, employing a single meshed blade passage per turbomachinery row, providing improved rotor-stator interfaces modeling and allowing arbitrarily clocked solution reconstruction in time.

NUMECA’s Approach to Full Engine CFD Simulation

A full 3D aerodynamic simulation of a complete gas turbine engine, applied to a micro turbine case has been conducted at NUMECA. The analysis was comprised of a single fully-coupled 3D CFD simulation for the flow of a KJ66 engine redesign. The injection and burning of fuel inside the combustion chamber are modeled with a simplified flamelet model. Using advanced RANS treatment with inputs from Nonlinear Harmonic (NLH) method (available as module for FINE™/Turbo), tangential non-uniformities are captured and the flow physics of the interaction between compressor, combustor and turbine are assessed.

Case Description

The selected test case is a redesign version of the KJ66 micro gas turbine (Figure 2). The Figure 2 shows the layout of the redesigned version of the KJ66 micro gas turbine used in the full engine computation. The centrifugal compressor is mounted at the engine entrance and it is composed of an impeller and a bladed diffuser row. The combustion chamber is followed by a high-pressure turbine (HPT), which drives the compressor, and a low pressure turbine (LPT), which would drive a propeller in an independent shaft. An exhaust hood is connected to the last LPT row at the engine exit.

Simulation Setup

The computational domain encompassed one blade passage for each turbomachinery blade row, a 60° sector for the combustion chamber (containing one fuel injector) and half exhaust hood. The three-dimensional mesh for the blade rows (compressor and turbine rows) was generated automatically using Autogrid5™, NUMECA’s turbomachinery dedicated full automatic hexahedral block-structured grid generator. The mesh for the combustion chamber and exhaust hood was generated with HEXPRESS/Hybrid™, NUMECA’s unstructured hex-dominant conformal body-fitted mesher for arbitrary complex geometries. The entire mesh has 19.20 million points.

As a first investigation, the steady RANS computation is performed with the Spalart-Allmaras turbulence model. Ambient total quantities are imposed at the engine inlet with specified axial velocity direction and static pressure is fixed at the outlet. The fuel injection is specified with static temperature and axial velocity of -120 m/s. Solid walls are assumed smooth and adiabatic. The convergence history is checked by following the evolution of the mass flow, the pressure ratio, and net torque between the HPT rotor and the impeller. The computation is launched in parallel on 144 processors on a computing cluster. In a second step, the computation is restarted with an improved rotor-stator connection based on the NLH method.

Combustion Model

The injection and burning of fuel inside the combustion chamber are modeled with a simplified flamelet model implemented in OpenLabs™. With this model, an additional equation is solved for the mixture fraction f, with the flame temperature as function of the composition.



Steady-state Computation

The convergence history of mass flow rate error between the engine inlet and outlet drops to less than 0.2% after 25000 iterations in approximately 24 hours of wall clock time. The net torque Mz reaches a final value of +0.21 N.m when the couple impeller-HPT rotor spins at -80000 rpm, meaning that the turbine produces enough torque to drive the compressor and both components are very close to be load balanced.

Figure 4 shows the static pressure, static temperature and absolute Mach number distributions at midspan of the compressor and turbine as well as in the solid walls of the combustion chamber and exhaust hood. The flow fields are continuous across the machine with a gradual raise of Mach number through the impeller and subsequent conversion of the kinetic energy into pressure across the diffuser. At approximately 160 kPa, air reaches the combustion chamber where the simulated combustion process takes place with a relatively small pressure loss. The maximum temperature at the combustion chamber is around 2200K at the combustor inner chamber. The hot gases from the combustion enter the HPT at approximately 983K and are expanded through the downstream blade rows, exiting the machine at 932K.

As shown in the Figures 5, the mixture fraction color contour successfully depicts the fuel stream entering the combustion chamber with f = and the combustion gases gradually reaching a value of 0.02 at the component exit. The effect of the holes in the inner chamber walls can be noticed in the magnitude of velocity color contour: they provide a flow of compressed air, acting as oxidizer for the combustion process, to mix with vaporized fuel and achieve ignition.

NLH Computation

The results from the RANS computation using a mixing plane treatment at the rotor stator interface can be compared to the results of the NLH analysis. In particular, it is interesting to note the effect of the improved connection approach with respect to the inter-components interactions.
Figures 6 and 7 show similar results on the mass flux for both simulations at the exit of the combustion chamber. A pattern linked to the periodicity of the fuel pipes and to the combustion zones can be observed. The transfer of information from the combustor outlet to the adjacent downstream HPT nozzle is different for the RANS simulation and its NLH counterpart. The mixing plane approach shows no tangential non-uniformities, while the NLH rotor-stator treatment proves to be a low cost first step in capturing tangential non-uniformities.

The Figure 8 shows the results for absolute total enthalpy and mixture fraction at the HPT and LPT inlet connections. The azimuthal averaging used in the mixing plane approach, where tangential non-uniformities are not perceived by downstream components, can be noticed for the RANS simulation. In contrast, the Fourier decomposition together with the local non-reflective boundary treatment used in the NLH method is able to show a slight improvement at the interfaces.


A successful simulation of the three-dimensional flow of a complete micro gas turbine engine has been achieved, on the basis of a fully-coupled 3D CFD simulation of a redesigned version of the KJ66 micro gas turbine. Compared to the component-by-component analysis, the fully coupled approach enables the solution of the whole engine in one single simulation. Furthermore it simplifies the simulation workflow as only the engine inlet and outlet pressures, the rotating speed and the fuel mass flow rate need to be prescribed.


Source: Mateus Teixeira, Luigi Romagnosi, Mohamed Mezine, Yannick Baux, Jan Anker, Kilian Claramunt, Charles Hirsch A Methodology for Fully-Coupled CFD Engine Simulations, Applied to a Micro Gas Turbine Engine. ASME. Turbo Expo: Power for Land, Sea and Air, Volume 2C: Turbomachinery ():V02CT42A047. doi:10.1115/GT2018-76870.

[1] Flightpath 2050: Europe’s Vision for Aviation, 2011,
[2] National Academies of Sciences, Engineering, and Medicine, 2016, “Commercial Aircraft Propulsion and Energy Systems Research: Reducing Global Carbon Emissions”, Washington, DC: The National Academies Press
[3] Mavriplis D., Darmofal D., Keyes D., Turner M., 2007, “Petaflops opportunities for the NASA Fundamental Aeronautics Program”, 18th AIAA Computational Fluid Dynamics Conf., Miami, FL, AIAA paper 2007–4084.
[4] Xiang J., Schluter J. U., Duan F., 2016, “Study of KJ-66 Micro Gas Turbine Compressor: Steady and Unsteady Reynolds-Averaged Navier–Stokes Approach”, Proceedings of the Institution of Mechanical Engineers Part G Journal of Aerospace Engineering.
[5] Gonzalez C.A., Wong K.C., Armfield S., 2008, “Computational study of a micro-turbine engine combustor using large eddy simulation and Reynolds averaged turbulence models”, ANZIAM J. 49 (EMAC2007) pp.C407–C422, C407.
[6] Turner M., 2000, “Full 3D Analysis of the GE90 Turbofan Primary Flowpath”, NASA/CR—2000-209951.

Aerodynamic Prediction

NUMECA contributes to JAXA APC-III Workshop using FINE™/Open for Aerodynamic Prediction

Aerospace | Grid Generation | Multi-Purpose

Born through the merger of three previously independent organizations on 1st October 2003, the Japan Aerospace Exploration Agency (JAXA) is the organism responsible for aviation research and technology development. In addition, Its scope comprises space and planetary study, rocket development and launch of satellites into orbit development.


Aircraft design development was originally based on experimental techniques. Technical progress in computing power and extensive research in fields such as numerical analysis or turbulence modelling has lead to complementary use of both wind tunnel testing (WTT) and Computational Fluid Dynamics (CFD), with CFD codes utilization now widespread due to continuous increase of accuracy and reliability.

Aerodynamic Prediction Challenge (APC) is a series of workshops organised by the JAXA with the aim of tracking the progress of CFD tools with respect to challenges faced in aeronautical applications. The third edition was held in Japan June 28th, 2017. NUMECA took part in APC-III as contributor to Task 1, with NASA Common Research Model (CRM) aerodynamic prediction at cruise state and high angle of attack (presence of tail wings, reflected deformation measurement data).

Task Description

The model under investigation is a 80% scaled copy of CRM at high speed (cruise state). CRM was developed by NASA to build experimental databases for the purpose of validating specific applications of CFD, with focus on the aerodynamic design of the wing. The geometry includes horizontal tail plane (HTP) at zero setting angle, which is same configuration used for NASA Drag Prediction Workshop 4 (DPW4).

A wind tunnel test (WTT) of the 80% scaled copy of the CRM was performed in the 2m × 2m transonic wind tunnel of JAXA and is therefore used as benchmark to assess numerical results provided by participants. Flow conditions correspond to Mach number M=0.847 and Reynolds number (based on wing mean aerodynamic chord) Remac=2.26 E+06.

Emphasis in the analysis of results is given to high angle of attack, particularly in the wing-fuselage junction. The prediction of detachment in flows parallel to corners formed by intersecting walls and exposed to adverse pressure gradient remains one of the main challenges of current turbulence models widely used in industry.

Deliverables requested by the organizing committee for this task included Aerodynamic coefficients (CD, CL , Cm ) with decomposition into pressure and friction, breakdown by components (wing, fuselage, HTP), and pressure coefficient distribution along nine wing sections distributed spanwise.

CFD Simulations

Two sets of meshes were used, both built and provided by JAXA to all participants. On one hand one set made of structured hexahedral cells. On the other hand, another group of hybrid tetrahedral dominant grids. Cell count was 9.15 and 29.98 million respectively. The difference in cell count is due to additional refinement for the hybrid grid at the leading and trailing edges of lifting surfaces, notably in spanwise direction. In those regions and in the intersecting walls the hybrid mesh follows structured approach.

Aeroelastic effects are taken into account using one mesh for each of the incidence angle of the range under investigation (-1.79 deg. – 5.72 deg.), which accounted for wing deformation data measured during WTT. The near wall mesh is very fine, with non dimensional wall distance of first cell in the viscous sub-layer (y+ < 1).

NUMECA FINE™/Open 6.2 CFD solver (density based, finite volume discretization, cell centred) was used to solve Reynolds averaged Navier-Stokes (RANS) equations with full multigrid approach and different turbulent formulations:

Linear eddy viscosity models (LEVM) such as Spalart-Allmaras SA-fv3, Menter SST-2003 and K-Epsilon KE-YS-1993. Experience shows that these approaches tends to predict separation too early in wing-body junctions where flow faces adverse pressure gradients.

Explicit Algebraic Reynolds Stress Models (EARSM) with a non-linear constitutive relationship between Reynolds stresses and mean strain rate, such as SBSL-EARSM and SSC-EARSM, the latter developed by NUMECA with the aim of better predicting separated flows.

Finally, the study aims at evaluating the impact of numerical artificial dissipation introduced to governing flow equations. Both scalar and matrix dissipation algorithms were used.


Several turbulence models and scalar/matrix dissipation approaches were tested on structured and unstructured grids. Globally, the transition from linear to nonlinear region is well captured with FINE™/Open. Beyond this point, the turbulence models and the dissipation models have a larger impact on the solution, which is especially noticeable in the pitching moment and its slope (which drives longitudinal stability). SSC-EARSM provides the best results. Analysis of flow over wing suction side explains those differences.

Large Side-of-Body (SOB) separation bubble is predicted by SA-fv3 and SST-2003 models at high incidence. This is not in line with experimental data and leads to different flow patterns for the whole span and lower pitching moment. SOB is not observed with KE-YS-1993. However, shock wave location appears significantly downstream. Therefore, lift is clearly overestimated. SBSL-EARSM and SSC-EARSM model improve the predicted inboard flow and the location of shock in this region.

Pressure coefficient cuts show that SSC-EARSM model, developed by NUMECA specifically for separated flows prediction, provides the best agreement with experimental data, independently of the mesh topology and numerical dissipation scheme.


The excellent agreement between the model tests and CFD predictions for the pressure distribution over the inner suction side of wing provides a good confidence level in the CFD results presented to the APC-III workshop. Moreover, NUMECA SSC-EARSM results are encouraging, with significantly improved results with respect to traditional LEVM and with affordable computational cost.

Gust Modelling

The AeroGust Project: Aeroelastic Gust Modelling

Aerospace | Wind Energy | Multi Purpose

A key element in the design of an aircraft is to make sure it can cope with the stresses arising from the impact of strong winds on the plane’s structure. Calculating the way the aircraft reacts to gusts and turbulences is essential knowledge before a plane can be built.

Currently most of the data concerning gusts is gathered during expensive wind tunnel experiments and rather late in the design process, when design options have already been narrowed down a lot. Furthermore there are very few wind tunnels in the world that are capable of accurately reproducing the conditions for a full size aircraft in flight.

The more accurate the design of the aircraft models before going to testing, the less new models need to be made for the wind tunnel. Again this does not only save a considerable amount of money and time, it also allows for entirely new kinds of design to be explored, including the use of different, more flexible materials.

That is why the AeroGust (Aeroelastic Gust Modelling) Project, funded by the European Union, has been set up. Its goal is to achieve better understanding of the aircraft/gust interactions early in the design process, by crafting new efficiencies and higher accuracy in the simulation stage.

Numeca International is proud to be one of the partners in the project, providing expert knowledge and state-of-the-art tools for Computational Fluid Dynamics (CFD) simulation. CFD is crucial in accurately predicting aerodynamic performance, in this project producing precise and detailed models of aerodynamic gust flows around the aircraft structure.

NUMECA’s FINE™/Open was used to model how the airflow interacts with the wing of an aircraft as shown in the figures on the side.

Using this virtual prototype an unlimited amount of variations in any part of the design can be tested by simply varying input, boundary, geometrical parameters etc. The objective of the AeroGust project is to generate new computer codes that will streamline the processes of the simulations and tests.

Besides aircraft design, the AeroGust project can also benefit the industry of wind turbine design: Today the distribution of wind farms is restricted by the fact that strong wind variances and gusts create large loads on the turbines. If we would be able to predict the impact of those gusts more accurately, the structures could also be placed in more challenging regions like for example the Arctic Circle and the tropics.

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For more detailed information, and if you want to keep up with the latest news and events regarding the project:

Propeller Optimisation

Pipistrel reduces by 6% the Energy Consumption of an Electrical Aircraft by Optimising its Propeller with FINE™/Turbo

Aerospace | Turbomachinery | Grid Generation

Improve the energy consumption of an electric aircraft through energy recuperation.

Pipistrel used the propeller as an airborne wind turbine, by transforming the energy created by the descension of an aircraft into electric energy and storing it in a battery. The performance of the propeller design was numerically computed with FINE™/Turbo.

The aircraft consumes 6% less energy during the climb.
Net energy consumption during ascent/descent manoeuvres decreased by 19%.
A 27% increase in number of traffic pattern circuits was achieved.


The first electric aircraft was created by simply replacing the piston engine system of a normal combustion engine aircraft by an electric propulsion unit. Although environmentally friendlier, this was not an optimal design yet for an electrical aircraft.

Since the density of current state-of-the-art battery energy is still much lower than gasoline energy density, a need for optimal energy use in an electric aircraft is crucial.

Energy Recuperation as a Propeller Design Strategy

One possibility to improve the energy balance of an electric aircraft, is using its propeller as an airborne wind turbine, where the energy of a descending aircraft is recuperated into electric energy and stored in the battery.

In the case described in this article, Pipistrel designed a propeller specially adapted for exploiting in-flight power recuperation this way.

The objective was to improve the energy consumption of the Alpha Electro, Pipistrel’s electric trainer aircraft.

Climb is the most energy consuming part of the traffic pattern. A new propeller EA-002 (Figure 1) was designed to exploit the possibility of energy recuperation while preserving good performance in the climb flight phase.

Optimisation of the Propulsion System

For the optimisation of the propulsion system, a 3-way approach was performed:

  • The Aerodynamic approach: focused on the optimisation of the airfoil shapes and the chord/twist blade distribution. The performance of the propeller design was verified through CFD simulations, ran with NUMECA software AutoGrid5™ and FINE™/Turbo. (The velocity distribution over a cross section of one of the simulations is presented on the right-hand side of Figure 2)
  • The Electrical approach: includes a hardware enabled bi-directional energy flow, with the possibility to adjust the torque/angular velocity combination for maximum power recuperation at specific descent rates.
  • The Strategic approach: focused on adjusting ascent and descent rates to minimise energy consumption and maximise energy recuperation respectively.


The performance of the propulsion units was evaluated by comparing and testing 3 propellers with the following two testing methodologies:

(1) net energy consumption within 1000 ft ascent & descent manoeuvres and (2) the number of traffic pattern circuits performed with one fully charged battery.

The 3 propellers (Figure 3):

  • The AS-D propeller was primarily designed for the piston engine version of the Alpha Electro, i.e. Alpha Trainer.
  • The EA-001 propeller was developed for the Alpha Electro power train and optimised for climb and cruise phases.
  • The EA-002 propeller was also made for the Alpha Electro power train, but optimised for climb and recuperation phases.

Climb & descent manoeuvres were tested according to these parameters:

  • Climb phase manoeuvre: constant climb at 76 kts IAS for 1000 ft at 45 kW power – monitoring energy consumption, vertical speed and time to ascend.
  • Recuperation manoeuvre: throttle idle, stable descent for 1000 ft at different speed rates – monitoring recuperated energy and descent rate.

Testing for the traffic pattern circuit with one fully charged battery included manoeuvres prescribed in the Alpha Electro Pilot Operating Handbook (POH) as follows:

  • Climb/departure, crosswind and downwind cycle phases:
  • apply take-off flaps below 60 kts indicated air speed (IAS)
  • climb from 0 to 500 ft AGL by
  • applying full power (65 kW) for first 10 s of the climb
  • applying 70% (45 kW) power and climb at 76 kts IAS
  • at 500 ft AGL apply 30% (20 kW) power and maintain 500 ft AGL
  • Base, final, touch-and-go cycle phases:
  • descend from 500 ft to 0 ft AGL by reducing power to idle
  • apply landing flaps below 60 kts IAS
  • descent at 50 kts IAS to the ground

Performance comparison

The results from the climb & descent manoeuvre tests are presented in Table 1.

The AS-D and EA-001 have similar climb performance and recuperation capability. With the EA-002 installed, the aircraft consumes 6% less energy during the climb compared to the AS-D propeller and is able to recuperate 0.15kWh at the descent speed of 70kts. The propeller optimised for recuperation (EA-002) showed a 19% reduction in net energy consumption compared to the EA-001.

As can be seen from Table 2, the test pilot was able to carry out almost the same number of traffic patterns with the AS-D and the EA-001 propellers, and 27% more (compared to AS-D) with the EA-002 propeller. This resulted in better climb efficiency and higher energy recuperation of the EA-002 at a relatively low approach speed of 50 kts, which was kept constant for all three propellers.

Experimental vs CFD Simulations

A comparison was performed between CFD simulation results and flight test measurements of the EA-002 propeller. Two parameters were compared: the thrust and the propeller power coefficient.

Figure 6 and Figure 7 depict values of thrust and power coefficients versus advance ratio of the EA-002 propeller respectively. As seen in the figures, the design predictions of NUMECA’s CFD simulation results and the flight test measurements of the propeller are in good agreement.


Due to the reduction of the energy consumption, the aircraft can stay airborne longer and/or smaller battery packs may be installed for a specific flight time.

The EA-002 is set to become the first European Aviation Safety Agency certified propeller with recuperation capability for electric propulsion.


D. Eržen, M. Andrejašic, R. Lapuh, J. Tomažic and C. Gorup – Pipistrel Vertical Solutions d.o.o., Slovenia T. Kosel – Faculty of Mechanical Engineering, Slovenia

Full Journal Paper

D. Eržen, M. Andrejasic, R. Lapuh, J. Tomazic, C. Gorup, and T. Kosel, “An Optimal Propeller Design for In-Flight Power Recuperation on an Electric Aircraft”, 2018 Aviation Technology, Integration, and Operations Conference, AIAA AVIATION Forum, (AIAA 2018-3206)

Space Launch Innovation

Space Launch Innovation at Masten Space Systems with NUMECA Software

Aerospace | Grid Generation | Multi-Purpose

Until very recently, rockets that launched satellites into orbit were completely discarded after a single use -and this is still commonly done for most launches. Within the past year, first generation reusable satellite launch has been demonstrated, and the business case for reusable launch has been made.

As a prime contractor for the Defense Advanced Research Projects Agency (DARPA) Experimental Spaceplane (XS-1) program, Masten Space Systems is developing a launch vehicle with design innovations focused on next-generation reusability. DARPA’s XS-1 program aims to reduce the time to space and cost to space by orders of magnitude by achieving aircraft-like reusability and flight rate [1]. Jess Sponable, DARPA XS-1 program manager, has stated: “We think flying ten times in 10 days is something well beyond the capability of either SpaceX or Blue Origin at this time.” [2].

“Masten Space Systems heavily utilized the FINE Suite at HPC scale to design our next-generation reusable satellite launch system. We developed an aerodynamic configuration that hasn’t been done before for launch or reentry. First wind tunnel tests confirmed critical aspects of the design, and predictions compared well with measurements.”

Allan Grosvenor,
Aerodynamics Lead
Masten Space Systems

Requirements include cost per flight of less than $5 million USD transporting 3,000 lbs to a 100 nmi reference orbit inclined 90 degrees. Masten engineers needed to optimize the booster design over a wide range of flight conditions, including ascent from sea level to hypersonic speeds at the upper-stage-separation point, and then reentry and return flight.

NUMECA-USA has worked closely with Masten Aerodynamics Lead, Allan Grosvenor, and his team to develop an HPC-driven workflow that enables concept development, evolution, and optimization, of vehicle configuration that takes into consideration aerodynamic performance, control, loads and aerothermal heating, with simultaneous trajectory optimization.

A study of the HIFiRE-1 high speed transition experiment was one of several test cases used to validate NUMECA solver predictions (Figure 1). The HIFiRE-1 experiment (Wadhams, et. al. 2008) is a Cone-Cylinder-Flare configuration, exposed to Mach 7.2 flow (Re 1E7) conducted at the CUBRC LENS hypersonic tunnel. The numerical results are shown to capture relevant physics and to predict pressures and heat fluxes, and particularly the peak heating which is critical to thermal protection system design.

To illustrate the optimization process we created a fictitious high speed flight vehicle test case inspired by the Colonial Viperfighter craft of the TV series Battlestar Galactica.

The ship design is controlled parametrically with NUMECA’s AutoBlade™ modeler. A set of parameters control the canopy’s length and height, the wing and vertical stabilizer angle and sweepback, and the nose radius and droop (Figure 2). Extensive sets of parameters are available for more detailed 3D shaping and variation of all portions of fuselage, lifting and control surfaces, etc.

Design necessarily required consideration of entire launch and return trajectories. Large numbers of organized studies conducted by Masten Space Systems included running sweeps through several critical flight conditions including variation of flight angles (e.g., pitch, yaw, roll) and aerodynamic control surface angles. The example shown here illustrates a subset of these studies focused on reentry.

DoDHPCMP supercomputers [3] were leveraged to conduct the extensive studies, and the Computational Pipeline (Figure 3) indicates the organized workflow that was executed to run the sophisticated design evolution process. Masten Space Systems found that only with NUMECA could they systematically produce high-quality grids (flow domain discretization) and high accuracy solutions.

The meshing task was automated with HEXPRESS / Hybrid™, which is the new generation multi-core meshing tool of NUMECA. It has been designed to generate meshes of complex geometries, regardless of the CAD format and quality (Figure 4).

This tool produces full hexahedral and hex-dominant meshes completely in parallel and in batch mode, which has been the perfect match for Masten’s challenge. Masten Space Systems chose to utilize the option producing pure hex meshes.

The CFD simulations were all executed with NUMECA’s FINE™/Open CFD solver, and then post-processed through scripts running all in batch in a fully automatic way. Data-mining of the full 3D solutions feeds the optimization process.

The following animations are comparisons between the baseline vehicle and the new design for reentry performance and stability.

MASTEN is lowering the barriers to space access. Their mission contributes to a shared strategic goal of extending human presence across the solar system. Their approach: reusability. Their technology development focuses at the core on entry, descent, and landing technologies (EDL) to ensure precise and safe landings on planets and other celestial bodies. Learn more


Driven by creativity, innovation, and quality, we develop software toolsets that support the world’s leading industries” Learn more

[1] DARPA pushing new effort with Experimental Spaceplane, XS-1 (
[2] DARPA experimental spaceplane program moves into next phase (
[3] Supercomputers Lower the Cost of Space Access (
[4] HIFiRE-1 Mach 7 aerothermal heating prediction (

Figure 2: Parametric design variations controlled with AutoBlade™

Figure 6: Reentry Matrix Results Comparison: Baseline (left) vs Modified (right) designs

Multi-stage Compressor Optimisation

Design Optimisation of a Multi-stage Compressor


Research-and-production Company “ENTECHMACH”, located in St. Petersburg, Russia, has been operating in the power engineering area for more than 25 years. Its main activities are design and production of centrifugal and axial compressors, steam turbines, multipliers, heat exchangers with air and water cooling systems.

Since 2 years now the company has been using the FINE™/Turbo package for the flow simulation and optimisation of stationary industrial centrifugal compressors and for research engineering. One of those developments concerns the modernisation project of a three-stage air centrifugal compressor in catalytic cracking technology.

Problem Analysis

The existing compressor suffered a number of drawbacks: low reliability of the axi-radial impellers as their covering discs were causing large stress concentration zones, failing stators due to unsuitable material choices and a number of performance issues. The performance and discharge pressure were insufficient, the surge margin was too low and the power consumption too high.

As a first step we used NUMECA’s FINE™/Turbo solver to accurately calculate a complete compressor performance map of the compressor. The result allowed us to clearly identify the main causes of these efficiency losses and low surge margin. Conclusion: The modernised compressor would need a complete replacement of the rotor and stator elements in the compressor case, bearings and lubrication oil system.


It was decided to design the new compressor with semi-opened axi-radial impellers, as they create two times less stresses compared to the closed axi-radial version with covering discs.

To make sure the newly developed impellers meet the highest possible levels of efficiency, pressure ratio and surge margin, all flow path elements were optimised with NUMECA’s FINE™/Design3D solver including the impellers, the vane diffusers and the return channels for several operating conditions.

With the use of the NLH-method, the influence of the inlet chamber and 360° inlet guide vanes on first impeller parameters was investigated, as well as the influence of the outlet chamber on the last diffuser blade row. Combined vane diffusers, which merge a long vaneless part and a low blade density part, were implemented in all stages. The numerical investigations confirmed the advantages of this solution: a wide operating range and reduction of the losses in the stator elements (as with vaneless diffusers), but at the same time providing optimal flow conditions at the inlet of the return channels, which leads to an efficiency increase (as with vane diffusers).

The main challenge to solve for the return channel optimisation was the axial flow direction at the inlet of the next impeller. Traditional return channels with 2D profiles that we analysed, could not provide the axial flow direction on all channel spans. There were highly non-uniform flows on the hub and shroud as shown in Figure 3 on the left side.

To solve this problem, we developed a non-classic return channel consisting of two parts: a 2D main profile part and a 3D outlet part. The main part was calculated in a way that would achieve the largest possible flow irregularity decrease and the 3D outlet part was designed to have an effective flow alignment on all spans. This solution suppressed the secondary flows in the return channels considerably. In Figure 3 on the right side, where 90° equals axial flow, the irregularities after the modernisation can be seen. The redesigning of the return channels eventually resulted in an increase of the operating range of the second and third impellers of more than 15% thanks to the improved impeller inflow conditions.

The new compressor performance map was calculated again for different operating conditions i.e. different angles of incidence of the Inlet Guide Vane (IGV) and inlet temperature. The results are shown in the Figure 4.


The full mesh model of the compressor was generated with IGG™ and AutoGrid5™. It included about 40 million elements and consisted of all blade rows, gaps between impellers and stator, outlet chamber and all labyrinth seal regions. The Spalart-Allmaras turbulence model for simulations was applied to verify the correspondence of mesh criterion y+ with a recommended range. Thanks to the high quality of the mesh we could apply the CPU Booster™ for our calculations, which saved us a lot of time and computing resources.


Industrial tests of the modernised compressor showed an increase of polytropic efficiency on normal mode of ~6.5% abs. and a corresponding decrease in power consumption of the same percentage. The new compressor reaches a higher pressure ratio of 4.5, compared to 4.1 before the modernisation. Furthermore the surge margin related to the normal mode significantly increased from ~6% before to 50% after. Power consumption on minimal performance is 1.5 times lower (~2.5MW).

The new, modernised compressor is in operation at client site and performing well and equally as important is proving reliable.


Vladimir Neverov, Ivan Cheglakov, Specialists on compressor machines
Aleksandr Liubimov, Head of Advanced Development and Design Department

Steady Turbomachinery Simulations

Modeling the Rotor-Stator Interface in Steady Turbomachinery Simulations



As turbomachinery flows are some of the most complex flows found in engineering applications, predicting performance is quite challenging. Separation, high velocities, rotation, high load on the blades, small gaps… These phenomena are difficult to quantify accurately and expensive to capture through experimental campaigns. So it is no surprise that this industry has been one of the first to introduce Computational Fluid Dynamics (CFD) methods in order to lower costs without loss of accuracy.

Rotor-Stator Interface Methods

The goal of turbomachinery CFD simulations is to predict flow behavior in adjacent rotating and non-rotating blade rows. This means multiple frames of reference need to be used: “moving” for the rotating parts and “stationary” for the non-rotating. The link between these rotating and stationary frames of reference is called the Rotor-Stator interface. Turbomachinery CFD solvers include different treatment methods for this interface and it is crucial to select the appropriate one, as it has an impact on the entire flow: e.g. prediction of local Mach number or prediction of global quantities like efficiency or pressure ratio.

The choice of treatment method firstly depends on the nature of the simulation: steady, transient or harmonic. In this article we are going to focus only on steady simulations, since these are the most used in a design and production level in industrial environments.

In order to pass flow information through the rotor-stator interface, a circumferential averaging process has to be performed on it. This process is known as the mixing plane approach. The rotor-stator interaction is done by exchanging circumferentially averaged flow quantities. Basically this means that the blade wake or separation phenomena occurring during the blade passage, are mixed circumferentially before entering the downstream component. As a result, velocity components and pressure are uniform in the circumferential direction. This physical approximation tends to become more acceptable as rotational speed is increasing. The mixing plane technique is by far the most used rotor/stator modeling approach in the turbomachinery industry.

To showcase the different treatment methods of this mixing plane approach, we used a 1-½ stage transonic axial compressor.

In Table 1 you can see the results of the different methods described in this table, as they appear in FINE™/Turbo.

Figure 1 shows the absolute Mach number contour, upstream of the interface between the rotor and stator.
The three top images show similar results: Differences are very subtle and essentially portray the implementation of the same approach (as explained in Table 1). The images corresponding the 1D and 2D Non Reflecting conditions however are different, especially in the wake and near the hub, showing less diffusion in these areas.

Similar observations can be made downstream of the interface (although the differences are harder to spot in Figure 2 due to the mixing).
The difference here is more pronounced in the middle of the domain, where the 1D and 2D Non-Reflecting conditions show the effect of the upstream wake on the mixing downstream.


In general, it is preferable to start with the Full Non Matching Mixing Plane or the Conservative Coupling by Pitch-wise Rowsapproach. These will most often provide a stable solution with good mass flow conservation. In case a shock is present or the solution is unstable, the 1D or 2D Non Reflecting approach could be used. However, when using this approach, attention should be paid to the mass flow conservation, as the non-reflecting approaches are not conservative by nature. Finally, the Local Conservative Coupling is only recommended for an impeller-volute case.

Figure 3 summarizes the appropriate methods to start the analysis with as a flowchart. Please note this chart is to serve as best practice rather than a definitive manual.


Accurate Impeller-volute Interaction

Accurate Impeller-volute Interaction in Turbochargers


Turbochargers are currently ubiquitous in automobiles. It is nearly impossible to find a new Diesel vehicle without turbocharging and direct injection. Gasoline cars have been also following suit for the past few years. Regulations are pushing for lower emissions and the way to achieve this has been downsizing and using turbocharging to offset the lower power density of a smaller motor.

The turbocharger, however, adds a new degree of complexity to the system. Besides its own standalone performance, a turbocharger has to perform well with the engine. A component that is crucial in the high performance of the turbocharger inside the engine system is the volute. The volute is the component that connects the turbocharger with the inlet manifold. Often, the volute is redesigned for different engines and normally there is design freedom that can lead to minimizing the pressure losses that it introduces.

It is very important to capture appropriately the interaction between the turbomachinery components (impeller and diffuser) and the volute so as to be able to predict and minimize the pressure losses. Computational Fluid Dynamics (CFD) is frequently used for simulating this behaviour.

While it is possible to obtain an accurate prediction of the pressure loss by using a steady simulation, impeller-volute interaction can cause instabilities that can impact the performance of the turbocharger. For this, an unsteady analysis is used which is quite more costly and rarely happens in the design phase but rather in the validation phase of the design cycle.

To avoid running a costly analysis and for the designer to be able to incorporate this analysis in the design phase, frequency domain simulations like the Non Linear Harmonic (NLH) Method currently implemented in the NUMECA tools are employed. NLH solves the fluid equations in the frequency domain, only needs to simulate one blade passage instead of the full wheel and the equations are solved in a steady manner thus enabling the use of convergence acceleration techniques. It allows for an unsteady solution – including the transport of information through the impeller-volute interface – at a cost similar to a steady one.

The animation shows how the pressure waves propagate from the impeller towards the volute as well as their reflection back to the impeller coming from the tongue of the volute (the area where the outlet duct meets the scroll), providing important information about how the impeller-volute interaction and interaction with the tongue.

Capturing this interaction is quite important. The reflected waves can have negative impact on the performance of the system as they can cause instabilities to the impeller such as rotating stall, increased losses and increased forcing on the blades. The phenomenon cannot be captured by a steady (mixing plane) approach due to the averaging of the flow quantities in the impeller-volute interface or by a frozen-rotor approach. Hence a costly, unsteady simulation or a more cost-efficient harmonic (NLH) simulation is the way to capture this behaviour.

This analysis can be used in any configuration, centrifugal or not, and can provide insight about the interaction between turbomachinery components and devices upstream or downstream early in the design phase while keeping the simulation cost low.