Publications
My research spans high-fidelity CFD, machine learning for engineering, open datasets, turbulence modelling, and scalable computing. Search the publication record or use a topic filter.
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2026
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Editorial for “Fusing Data and Physics: Machine Learning for Computational Fluid Dynamics”
Computers & Fluids Editorial 2026
BibTeX
@article{ashton2026fusingdataeditorial, author = {Ashton, Neil and Dwight, Richard P. and Cinnella, Paola}, title = {Editorial for {``Fusing Data and Physics: Machine Learning for Computational Fluid Dynamics''}}, journal = {Computers \& Fluids}, year = {2026}, month = jul, pages = {107233}, doi = {10.1016/j.compfluid.2026.107233}, issn = {0045-7930}, publisher = {Elsevier}, note = {Editorial}, } -
Data Fusion Techniques for Optimizing Motorsport Aeromap Generation: A Survey
SAE Technical Paper Series 2026
Abstract
This paper reviews data-fusion strategies for generating aerodynamic databases and evaluates their suitability for motorsport aeromaps, with emphasis on the operational constraints specific to Formula One. It surveys surrogate-agnostic, Kriging-based, and neural-network-based approaches, and discusses their use for combining legacy and current aerodynamic datasets while limiting negative transfer.
BibTeX
@inproceedings{ongley2026datafusion, author = {Ongley, Thomas James Henry and Teschner, Tom-Robin and Ashton, Neil and Siampis, Efstathios}, title = {Data Fusion Techniques for Optimizing Motorsport Aeromap Generation: A Survey}, booktitle = {SAE Technical Paper Series}, year = {2026}, month = jul, number = {2026-01-5058}, doi = {10.4271/2026-01-5058}, publisher = {SAE International}, issn = {0148-7191}, } -
HiLiftAeroML: High-Fidelity Computational Fluid Dynamics Dataset for High-Lift Aircraft Aerodynamics
arxiv.org 2026
BibTeX
@article{ashton2026hiliftaeromlhighfidelitycomputationalfluid, title = {HiLiftAeroML: High-Fidelity Computational Fluid Dynamics Dataset for High-Lift Aircraft Aerodynamics}, author = {Ashton, Neil and Clark, Adam and Heidt, Liam and Ivey, Christopher and Bose, Sanjeeb and Agrawal, Rahul and Goc, Konrad and Ranade, Rishi and Adams, Corey and Sharpe, Peter and Nidhan, Sheel and Akkurt, Semit and Leibovici, Daniel and Kossaifi, Jean}, year = {2026}, url = {https://arxiv.org/abs/2605.19565}, journal = {arxiv.org}, } -
High-Fidelity CFD Data Generation for HiLiftAeroML using Solution-Adapted WMLES
AIAA SCITECH 2026 Forum 2026
Abstract
This paper describes the data generation effort for HiLiftAeroML; an open-source high-fidelity CFD dataset of a high-lift aircraft for the purpose of AI surrogate model development. The dataset is composed of 1800 samples, arising from 180 geometry variants and 10 angles of attack for the high-lift NASA Common Research Model (CRM) geometry, used within the AIAA High-Lift Prediction Workshop series. One of the novelties of this dataset is the use of a GPU-accelerated, high-fidelity, explicit, wall-modeled LES approach for each simulation, using solution-adapted grids containing 300-500 million control volumes. This ensures reasonable accuracy across the lift curve, given known challenges in steady-state RANS approaches for these portions of the flight envelope. This article describes the geometry parametrization, simulation workflow and validation efforts.
2025
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Fluid Intelligence: A Forward Look on AI Foundation Models in Computational Fluid Dynamics
arxiv.org 2025
Abstract
Driven by the advancement of GPUs and AI, the field of Computational Fluid Dynamics (CFD) is undergoing significant transformations. This paper bridges the gap between the machine learning and CFD communities by deconstructing industrial-scale CFD simulations into their core components. Our main contribution is to propose the first scaling law that incorporates CFD inputs for both data generation and model training to outline the unique challenges of developing and deploying these next-generation AI models for complex fluid dynamics problems. Using our new scaling law, we establish quantitative estimates for the large-scale limit, distinguishing between regimes where the cost of data generation is the dominant factor in total compute versus where the cost of model training prevails. We conclude that the incorporation of high-fidelity transient data provides the optimum route to a foundation model. We constrain our theory with concrete numbers, providing the first public estimates on the computational cost and time to build a foundation model for CFD.
BibTeX
@article{Ashton2025, title = {Fluid Intelligence: A Forward Look on AI Foundation Models in Computational Fluid Dynamics}, author = {Ashton, Neil and Brandstetter, Johannes and Mishra, Siddhartha}, year = {2025}, url = {https://arxiv.org/abs/2511.20455}, journal = {arxiv.org} } -
A Benchmarking Framework for AI models in Automotive Aerodynamics
arxiv.org 2025
Abstract
In this paper, we introduce a benchmarking framework within the open-source NVIDIA PhysicsNeMo-CFD framework designed to systematically assess the accuracy, performance, scalability, and generalization capabilities of AI models for automotive aerodynamics predictions. The open extensible framework enables incorporation of a diverse set of metrics relevant to the Computer-Aided Engineering (CAE) community. By providing a standardized methodology for comparing AI models, the framework enhances transparency and consistency in performance assessment, with the overarching goal of improving the understanding and development of these models to accelerate research and innovation in the field. To demonstrate its utility, the framework includes evaluation of both surface and volumetric flow field predictions on three AI models: DoMINO, X-MeshGraphNet, and FIGConvNet using the DrivAerML dataset. It also includes guidelines for integrating additional models and datasets, making it extensible for physically consistent metrics. This benchmarking study aims to enable researchers and industry professionals in selecting, refining, and advancing AI-driven aerodynamic modeling approaches, ultimately fostering the development of more efficient, accurate, and interpretable solutions in automotive aerodynamics
BibTeX
@article{Tangsali2025, title = {A Benchmarking Framework for AI models in Automotive Aerodynamics}, author = {Tangsali, Kaustubh and Ranade, Rishikesh and Nabian, Mohammad Amin and Kamenev, Alexey and Sharpe, Peter and Ashton, Neil and Cherukuri, Ram and Choudhry, Sanjay}, year = {2025}, url = {https://arxiv.org/abs/2507.10747}, journal = {arxiv.org} } -
High-Lift Prediction Workshop 5: Hybrid RANS/LES Technology Focus Group Summary
AIAA SciTech 2025 2025
Abstract
This study summarizes the work of the Hybrid RANS/LES (HRLES) Technology Focus Group (TFG) for the 5th High-Lift Prediction Workshop (HLPW-5). Results indicate that, as shown in HLPW-4, HRLES methodologies continue to consistently outperform SA-based RANS models in predicting maximum lift coefficients and flow-field correlations for a range of high-lift aircraft geometry variations - from clean wing to those with flap, slat and engine nacelles. However, challenges persist in simulating low angle-of-attack configurations with deployed flaps, as well as the transitional flow in some of the lower-Reynold-number cases studied. This work also highlights the importance of mesh resolution and low-dissipation numerical schemes in achieving consistent results across different HRLES implementations. While HRLES methods demonstrates promise for industrial applications, the work of the HLPW-5 HRLES TFG underscores the need for further research to enhance the maturity and widespread applicability of these methods.
BibTeX
@inproceedings{ashton2025a, author = {Ashton, Neil and Batten, Paul and Skaperdas, Vangelis and Holst, Kevin R.}, booktitle = {AIAA SciTech 2025}, title = {High-Lift Prediction Workshop 5: Hybrid RANS/LES Technology Focus Group Summary}, year = {2025}, doi = {10.2514/6.2025-0048}, } -
Hybrid RANS/LES Computations of the HLPW-5 Test Cases Using CFD++
AIAA SciTech 2025 2025
Abstract
This paper presents a summary of results from the 5th High-Lift Prediction Workshop computed using CFD++. The major focus of this paper is on the performance of hybrid RANS/LES methods on high-lift aircraft configurations, but results are also compared against statistically steady-state RANS and transition-prediction simulations in order to demonstrate the effect of switching to a hybrid RANS/LES model based on the same underlying RANS framework, and to help shed light on remaining discrepancies in two of the test cases - discrepancies that were not observed in HLPW-4. This work confirms conclusions from earlier studies on the High-Lift Common Research Model (CRM-HL) at high angles of attack with hybrid RANS/LES which suggests a definitive overall improvement in predictive accuracy with respect to conventional RANS models, particularly at or near stall. These studies also suggest that, at least on the finer meshes used for HLPW-5 case 1, there is a benefit to the use of enhanced shielding functions that go beyond those in standard DDES. Lastly, transition-prediction simulations suggest that the addition of the flaps in configurations 2.3 and 2.4 may be responsible for a delay in the transition onset over the main element. Future work could usefully address this, either in tunnel measurements, CFD simulations, or both.
BibTeX
@inproceedings{batten2025a, author = {Batten, Paul and Ashton, Neil and Skaperdas, Vangelis}, booktitle = {AIAA SciTech 2025}, title = {Hybrid RANS/LES Computations of the HLPW-5 Test Cases Using CFD++}, year = {2025}, doi = {10.2514/6.2025-0685}, } -
Immersed Boundary Wall-Modelled Large Eddy Simulations for the 5th High-Lift Prediction Workshop
AIAA Scitech 2025 2025
Abstract
A new Wall-Modelled Large Eddy Simulation (WMLES) framework, Volcano ScaLES, is evaluated on high-lift aircraft configurations as part of the fifth AIAA High Lift Prediction Workshop (HLPW-5). Excellent “blind” CLMax predictions are obtained for multiple configura- tions involving slats, flaps, and the nacelle, with errors less than 1 - 2% compared to the ONERA experimental data. Modest grid convergence is shown via a sequence of consistently refined grids, and some qualitative differences between the use of constant-coefficient Vreman and the dynamic Smagorinsky subgrid-scale models are reported. Utilization of appropriate mixed precision numerical formulations along with flow-optimized grids allow for highly efficient use of consumer-grade GPUs such as the Nvidia A10g. We show that even with grids using as many as 1.6 billion degrees of freedom, WMLES is cost-competitive with conventional steady-state RANS models. Reynolds number sensitivity study shows monotonically increasing lift going from a subscale Reynolds number of ReMAC = 5.49 x106 to a flight-scale Reynolds number of ReMAC = 30x106 with reasonable grid convergence obtained at all three Reynolds numbers studied.
BibTeX
@inproceedings{ashton2025b, author = {Ashton, Neil and Ghate, Aditya and Kenway, Gaetan and Angel, Jordan and Wong, Man Long and Kiris, Cetin}, booktitle = {AIAA Scitech 2025}, title = {Immersed Boundary Wall-Modelled Large Eddy Simulations for the 5th High-Lift Prediction Workshop}, year = {2025}, doi = {10.2514/6.2025-0888}, }
2024
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Immersed Boundary Wall-Modelled Large Eddy Simulations for Automotive Aerodynamics
AIAA Aviation 2024 2024
Abstract
This paper presents preliminary validation results of a new immersed boundary Wall- Modelled Large Eddy Simulation (WMLES) framework, Volcano ScaLES, for two automotive configurations as part of the 4th Automotive CFD Prediction Workshop (AutoCFD 4): the Windsor Squareback body and the DrivAer Notchback model. For the Windsor body, grids of up to 363 million cells were run to assess the sensitivity to spatial resolution and showed favorable correlation to experimental pressure and velocity data. This paper also presents preliminary results for the simulation of the DrivAer model which more closely represents the complexity of a production road-vehicle. We assess the sensitivity to the sub-grid scale closure model as well as the influence of mesh refinement on solution accuracy by exploring grids of more than half a billion cells. For both cases we demonstrate how the use of WMLES based upon a Cartesian immersed boundary method built for GPUs from the ground-up can provide both excellent correlation to experimental data as well as computational efficiency; only requiring the use of desktop-grade GPUs such as the Nvidia GeForce RTX 4090 or A10g series. To the authors knowledge, this is one of the first examples of a WMLES using a Navier–Stokes based Cartesian finite-difference code with immersed-boundary method being used to simulate a complex road-vehicle.
BibTeX
@inproceedings{angel2024, author = {Angel, Jordan and Ghate, Aditya and Kenway, Gaetan and Wong, Man Long and Kiris, Cetin and Ashton, Neil}, booktitle = {AIAA Aviation 2024}, title = {Immersed Boundary Wall-Modelled Large Eddy Simulations for Automotive Aerodynamics}, year = {2024}, doi = {10.2514/6.2024-4172}, } -
Assessing the HPC performance of CODA for the NASA Common Research Model
AIAA Aviation 2024 2024
Abstract
In this paper we study the HPC performance of the new CFD software by ONERA, DLR and Airbus (CODA) on a range of CPU hardware types for the NASA Common Research Model, using meshes up to 192M cells. We explore both the weak and strong scaling, as well as the influence of the number of OMP threads and MPI ranks on the overall runtime. A number of different generations of AMD-based CPU architectures are assessed and compared against two generations of Arm-based CPUs. It is found that the code demonstrates excellent weak and strong scaling, with greater than linear scaling down to 2000 cells per CPU core. As expected the newer generation of AMD CPUs with greater memory bandwidth and higher cores per node, result in better over runtime performance. We see as good or better parallel efficiency using ethernet-based Elastic Fabric Adapter (EFA) interconnect versus Infiniband. The single-socket Arm64-based processors offer similar performance to the x86-based alternatives which suggests those more power-efficient processors can help to reach both performance and sustainability targets. Future work will be to extend this comparison to GPUs and Arm64 based Graviton4, which offer even greater potential for performance improvements.
BibTeX
@inproceedings{patel2024, author = {Patel, Hirav and Gerhold, Thomas and Ashton, Neil}, booktitle = {AIAA Aviation 2024}, title = {Assessing the HPC performance of CODA for the NASA Common Research Model}, year = {2024}, doi = {10.2514/6.2024-3792}, } -
DrivAerML: High-Fidelity Computational Fluid Dynamics Dataset for Road-Car External Aerodynamics
arxiv.org 2024
Abstract
Machine Learning (ML) has the potential to revolutionise the field of automotive aerodynamics, enabling split-second flow predictions early in the design process. However, the lack of open-source training data for realistic road cars, using high-fidelity CFD methods, represents a barrier to their development. To address this, a high-fidelity open-source (CC-BY-SA) public dataset for automotive aerodynamics has been generated, based on 500 parametrically morphed variants of the widely-used DrivAer notchback generic vehicle. Mesh generation and scale-resolving CFD was executed using consistent and validated automatic workflows representative of the industrial state-of-the-art. Geometries and rich aerodynamic data are published in open-source formats. To our knowledge, this is the first large, public-domain dataset for complex automotive configurations generated using high-fidelity CFD.
BibTeX
@article{ashton2024drivaer, title = {DrivAerML: High-Fidelity Computational Fluid Dynamics Dataset for Road-Car External Aerodynamics}, year = {2024}, url = {https://arxiv.org/abs/2408.11969}, journal = {arxiv.org}, author = {Ashton, Neil and Mockett, Charles and Fuchs, Marian and Fliessbach, Louis and Hetmann, Hendrik and Knacke, Thilo and Schonwald, Norbert and Skaperdas, Vangelis and Fotiadis, Grigoris and Walle, Astrid and Hupertz, Burkhard and Maddix, Danielle}, } -
AhmedML: High-Fidelity Computational Fluid Dynamics Dataset for Incompressible, Low-Speed Bluff Body Aerodynamics
arxiv.org 2024
Abstract
The development of Machine Learning (ML) methods for Computational Fluid Dynamics (CFD) is currently limited by the lack of openly available training data. This paper presents a new open-source dataset comprising of high fidelity, scale-resolving CFD simulations of 500 geometric variations of the Ahmed Car Body - a simplified car-like shape that exhibits many of the flow topologies that are present on bluff bodies such as road vehicles. The dataset contains simulation results that exhibit a broad set of fundamental flow physics such as geometry and pressure-induced flow separation as well as 3D vortical structures. Each variation of the Ahmed car body were run using a high-fidelity, time-accurate, hybrid Reynolds-Averaged Navier-Stokes (RANS) - Large-Eddy Simulation (LES) turbulence modelling approach using the open-source CFD code OpenFOAM. The dataset contains boundary, volume, geometry, and time-averaged forces/moments in widely used open-source formats. In addition, the OpenFOAM case setup is provided so that others can reproduce or extend the dataset. This represents to the authors knowledge, the first open-source large-scale dataset using high-fidelity CFD methods for the widely used Ahmed car body that is available to freely download with a permissive license (CC-BY-SA).
BibTeX
@article{ashton2024ahmed, title = {AhmedML: High-Fidelity Computational Fluid Dynamics Dataset for Incompressible, Low-Speed Bluff Body Aerodynamics}, year = {2024}, journal = {arxiv.org}, author = {Ashton, Neil and Maddix, Danielle and Gundry, Samuel and Shabestari, Parisa}, url = {https://arxiv.org/abs/2407.20801}, } -
WindsorML: High-Fidelity Computational Fluid Dynamics Dataset for Automotive Aerodynamics
Advances in Neural Information Processing Systems 37 2024
Abstract
This paper presents a new open-source high-fidelity dataset for Machine Learning (ML) containing 355 geometric variants of the Windsor body, to help the development and testing of ML surrogate models for external automotive aerodynamics. Each Computational Fluid Dynamics (CFD) simulation was run with a GPU-native high-fidelity Wall-Modeled Large-Eddy Simulations (WMLES) using a Cartesian immersed-boundary method using more than 280M cells to ensure the greatest possible accuracy. The dataset contains geometry variants that exhibits a wide range of flow characteristics that are representative of those observed on road-cars. The dataset itself contains the 3D time-averaged volume & boundary data as well as the geometry and force & moment coefficients. This paper discusses the validation of the underlying CFD methods as well as contents and structure of the dataset. To the authors knowledge, this represents the first, large-scale high-fidelity CFD dataset for the Windsor body with a permissive open-source license (CC-BY-SA).
BibTeX
@inproceedings{ashton2024windsor, title = {WindsorML: High-Fidelity Computational Fluid Dynamics Dataset for Automotive Aerodynamics}, year = {2024}, booktitle = {Advances in Neural Information Processing Systems 37}, doi = {10.52202/079017-1194}, author = {Ashton, Neil and Angel, Jordan and Ghate, Aditya and Kenway, Gaetan and Wong, Man Long and Kiris, Cetin and Walle, Astrid and Maddix, Danielle and Page, Gary}, pages = {37823-37835}, url = {https://proceedings.neurips.cc/paper_files/paper/2024/hash/42a59a5f35b1b3c3fd648397c88a7164-Abstract-Datasets_and_Benchmarks_Track.html}, } -
Machine Learning for Road Vehicle Aerodynamics
SAE World Congress 2024
Abstract
This paper discusses an emerging area of applying machine 1 learning (ML) methods to augment traditional Computational 2 Fluid Dynamics (CFD) simulations of road vehicle aerodynam-3 ics. ML methods have the potential to both reduce the com-4 putational effort to predict a new geometry or car condition 5 and to explore a greater number of design parameters with the 6 same computational budget. Similar to traditional CFD meth-7 ods, there exists a broad range of approaches. In particular, 8 the accuracy and computational efficiency of a CFD simula-9 tion vary greatly depending on the choice of turbulence model 10 (DNS, LES, RANS) and the underlying spatial and temporal nu-11 merical discretizations. Similarly, the end-user must select the 12 correct ML method depending on the use-case, the available in-13 put data, and the trade-off between accuracy and computational 14 cost. In this paper, we showcase several case studies using var-15 ious data-driven ML methods to highlight the promise of these 16 approaches. Whilst these case studies are not comprehensive 17 investigations of the underlying methods and do not include all 18 possible ML approaches (i.e., physics-driven), they highlight 19 the ability of these models to in general predict new designs in 20 near real-time (i.e., less than 5 seconds), after typically less than 21 1 hour of training on a single GPU. There still exists a need for 22 high quality training data from traditional CFD methods and 23 high-fidelity CFD simulations to validate the ML predictions.
BibTeX
@inproceedings{ananthan2024, author = {Ananthan, Vidyasagar and Ashton, Neil and Chadwick, Nate and Lizarraga, Mariano and Maddix, Danielle C and Maheswaran, Satheesh and Moreno, Pablo Hermoso and Shabestari, Parisa M and Sovani, Sandeep and Subramanian, Shreyas and Tadepalli, Srinivas and Yu, Peter}, isbn = {0148-7191}, issn = {2688-3627}, booktitle = {SAE World Congress}, title = {Machine Learning for Road Vehicle Aerodynamics}, year = {2024}, doi = {10.4271/2024-01-2529}, url = {https://www.sae.org/publications/technical-papers/content/2024-01-2529/}, } -
Summary of the 4th High-Lift Prediction Workshop Hybrid RANS/LES Technology Focus Group
Journal of Aircraft 2024
Abstract
This paper summarizes the collective efforts of multiple teams that contributed to the hybrid RANS/LES technical focus group for the 4th AIAA CFD High Lift Prediction Workshop (HLPW-4), which took place on January 7, 2022, in San Diego, California. The overall conclusion is that turbulence-resolving methods such as hybrid RANS/LES (HRLES) do offer improved predictions for these high-lift geometries, with respect to the underlying RANS models, but there are nuances, and some unresolved issues remain that should be the focus of future work. In particular, while HRLES methods appear to show clearly improved predictions at higher angles of attack, there is some tendency for HRLES methods to return slightly worse moment predictions at lower angles of attack, suggesting that prediction of the shallow separation from the flaps might need further research. Computing cost also remains a significant issue, with HRLES methods requiring roughly nine times more high-performance computing central processing unit core hours than steady-state RANS methods, indicating that future algorithmic and computational optimization could be beneficial. Finally, there are strong indications that modeling the wind tunnel has a positive impact on correlation with experimental measurements, suggesting that future work might be better focused on in-tunnel simulations.
BibTeX
@article{ashton2023b, author = {Ashton, Neil and Batten, Paul and Cary, Andrew and Holst, Kevin}, doi = {10.2514/1.C037329}, issn = {0021-8669}, journal = {Journal of Aircraft}, issue = {1}, pages = {86-115}, title = {Summary of the 4th High-Lift Prediction Workshop Hybrid RANS/LES Technology Focus Group}, url = {https://arc.aiaa.org/doi/10.2514/1.C037329}, volume = {61}, year = {2024}, }
2023
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Performance Study of Convolutional Neural Network Architectures for 3D Incompressible Flow Simulations
Proceedings of the Platform for Advanced Scientific Computing Conference, PASC 2023 2023
Abstract
Recently, correctly handling spatial information from multiple scales has proven to be essential in Machine Learning (ML) applications on Computational Fluid Dynamics (CFD) problems. For these type of applications, Convolutional Neural Networks (CNN) that use Multiple Downsampled Branches (MDBs) to efficiently encode spatial information from different spatial scales have proven to be some of the most successful architectures. However, not many guidelines exist to build these architectures, particularly when applied to more challenging 3D configurations. Thus, this work focuses on studying the impact of the choice of the number of down-sampled branches, accuracy and performance-wise in 3D incompressible fluid test cases, where a CNN is used to solve the Poisson equation. The influence of this parameter is assessed by performing multiple trainings of Unet architectures with varying MDBs on a cloud-computing environment. These trained networks are then tested on two 3D CFD problems: a plume and a Von Karman vortex street at various operating points, where the solution of the neural network is coupled to a nonlinear advection equation.
BibTeX
@inproceedings{Illarramendi2023, author = {Ajuria Illarramendi, Ekhi and Bauerheim, Michael and Ashton, Neil and Lapeyre, Corentin and Cuenot, Bénédicte}, doi = {10.1145/3592979.3593416}, isbn = {9798400701900}, booktitle = {Proceedings of the Platform for Advanced Scientific Computing Conference, PASC 2023}, keywords = {CFD,CNN,Poisson equation,incompressible flows}, month = jun, pages = {1-11}, publisher = {Association for Computing Machinery, Inc}, title = {Performance Study of Convolutional Neural Network Architectures for 3D Incompressible Flow Simulations}, year = {2023}, } -
Overview and Summary of the First Automotive CFD Prediction Workshop: DrivAer Model
SAE International Journal of Commercial Vehicles 2023
Abstract
The First Automotive CFD Prediction Workshop was held in December 2019 at St Anne’s College at the University of Oxford with the aim to assess the ability of a broad range of computational fluid dynamics (CFD) methods to predict the flow over realistic automotive geometries. Here, results from 53 simulation data sets from 9 separate groups are analyzed for the open-source automotive DrivAer model (in the fastback and estate variants). The represented CFD approaches include Reynolds-averaged Navier-Stokes (RANS) approaches with a broad range of turbulence models, as well as scale-resolving approaches such as wall-modelled large-eddy simulation (WMLES) and hybrid RANS-LES methods (HRLM). A range of CFD codes was used, including commercial, academic, and open source. Compared to the two experimental data points, there was a large spread of CFD results. The difference between drag predictions among HRLM and RANS methods is significant, with an even larger mismatch for lift. The differences are found to be more significant for the estate geometry than for the fastback, with the former having larger areas of flow separation. In general it is found that the spread of HRLM is smaller than those for RANS approaches, with HRLM grouping closer to the range of experimental values. However, for HRLM, there is a systematic underprediction of the front lift coefficient that is irrespective of the mesh, turbulence model, and CFD code. Given that the majority of participants used the same mesh and boundary conditions, and in some cases the same CFD code, it suggests that also user choices around numerical schemes, convergence, and turbulence model coefficients may have a sizable impact, which was not possible to fully control in this first workshop. It is worth noting as well that the CFD simulations were conducted in a free-air environment and did not model the wind-tunnel geometry itself, which may also be an area requiring further study. The DrivAer model exhibits numerous complex flow physics, i.e., laminar/turbulent separation, diffusion of momentum in turbulent shear layers, and interactions of turbulent wakes with boundary layers. However, the nature of a community-driven workshop and the lack of extensive experimental data means that this article can only report the current state of the art and serve as a reference for future workshops and a springboard for more focused future studies where topics can be explored in greater detail.
BibTeX
@article{ashton2022g, author = {Ashton, Neil and van Noordt, William}, doi = {10.4271/02-16-01-0005}, issn = {19463928}, issue = {1}, journal = {SAE International Journal of Commercial Vehicles}, keywords = {AutoCFD1,Automotive,DrivAer,Hybrid RANS-LES,RANS,WMLES}, publisher = {SAE International}, pages = {61-85}, title = {Overview and Summary of the First Automotive CFD Prediction Workshop: DrivAer Model}, volume = {16}, year = {2023}, }
2022
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Towards best-practices for Hybrid RANS-LES simulations of high-lift aircraft geometries
AIAA Aviation 2022 2022
BibTeX
@conference{ashton2022, author = {Ashton, Neil and Eberhardt, Scott and Batten, Paul and Skaperdas, Vangelis}, booktitle = {AIAA Aviation 2022}, title = {Towards best-practices for Hybrid RANS-LES simulations of high-lift aircraft geometries}, year = {2022}, doi = {10.2514/6.2022-3524}, } -
Towards a Standardized Assessment of Automotive Aerodynamic CFD Prediction Capability - AutoCFD 2: Ford DrivAer Test Case Summary
SAE Technical Papers 2022
Abstract
The 2nd Automotive CFD Prediction workshop (AutoCFD2) was organized to improve the state-of-the-art in automotive aerodynamic prediction. It is the mission of the workshop organizing committee to drive the development and validation of enhanced CFD methods by establishing publicly available standard test cases for which high quality on- and off-body wind tunnel test data is available. This paper reports on the AutoCFD2 workshop for the Ford DrivAer test case. Since its introduction, the DrivAer quickly became the quasi-standard for CFD method development and correlation. The Ford DrivAer has been chosen due to the proven, high-quality experimental data available, which includes integral aerodynamic forces, 209 surface pressures, 11 velocity profiles and 4 flow field planes. For the workshop, the notchback version of the DrivAer in a closed cooling, static floor test condition has been selected. For a better comparability of CFD results, two carefully designed control meshes were provided. Both meshes share identical distributions in the flow field volume but differ in near wall spacing to allow for wall-modelled and wall-resolved solutions. The 65 results, which were submitted by 22 participants, revealed a very significant variability of the aerodynamic force predictions even when using the same turbulence model on the control grids. While individual simulations using scale-resolving hybrid turbulence models correlated very well to the experimental flow field data, other analyses using almost identical simulation approaches resulted in very different predictions. The comparison of transient versus steady state analysis confirmed that transient simulations deliver more accurate flow field predictions. A significant impact of the near wall mesh resolution could not be confirmed by the results submitted for the DrivAer test case.
BibTeX
@inproceedings{hupertz2022, author = {Hupertz, Burkhard and Lewington, Neil and Mockett, Charles and Ashton, Neil and Duan, Lian}, doi = {10.4271/2022-01-0886}, issn = {01487191}, issue = {2022}, booktitle = {SAE Technical Papers}, month = mar, publisher = {SAE International}, title = {Towards a Standardized Assessment of Automotive Aerodynamic CFD Prediction Capability - AutoCFD 2: Ford DrivAer Test Case Summary}, year = {2022}, } -
HLPW-4/GMGW-3: Hybrid RANS/LES Technology Focus Group Workshop Summary
AIAA Aviation 2022 2022
2021
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Performance of CPU and GPU HPC Architectures for Off-Design Aircraft Simulations
AIAA Scitech 2021 Forum 2021
Abstract
© 2021, American Institute of Aeronautics and Astronautics Inc, AIAA. All rights reserved. This paper presents a detailed analysis of the relative performance and cost of GPU and CPU architectures for a full aircraft RANS simulation using the CFD code zCFD. Using Amazon Web Services as the platform, several generations of NVIDIA GPUs are assessed (T4, V100, and A100) and compared to x86 Intel Broadwell and Skylake CPUs. It is found that for the same computational setup and mesh, one Amazon EC2 p4d.24xlarge instance (containing eight NVIDIA A100 GPUs) provides the same run-time as 2,160 cores (60 nodes) of the Intel Skylake Amazon EC2 c5n.18xlarge. At the maximum number of nodes tested (100 CPU nodes and 10 GPU nodes-each containing 8 GPUs) the GPU-based instance is 2.8x faster than the x86 Intel CPU-based instance (Amazon EC2 c5n.18xlarge) for half the cost. It is hoped these results will provide extra evidence to support the continued focus on CFD code development to support GPUs and will help to make high-fidelity simulations more practical for engineering companies.
BibTeX
@inproceedings{Turner2021, author = {Appa, J. and Turner, M. and Ashton, N.}, doi = {10.2514/6.2021-0141}, isbn = {9781624106095}, booktitle = {AIAA Scitech 2021 Forum}, title = {Performance of CPU and GPU HPC Architectures for Off-Design Aircraft Simulations}, year = {2021}, }
2020
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Coherence Analysis of Rotating Turbulent Pipe Flow
AIAA Scitech 2020 Forum 2020
Abstract
Rotating and swirling turbulence comprises an important class of turbulent flows, not only due to the complex physics that occur, but also due to their relevance to many engineering applications, such as combustion, cyclone separation, mixing, etc. In these types of flows, rotation strongly affects the characteristics and structure of turbulence. The underlying turbulent flow phenomena are complex and currently not well understood. The axially rotating pipe flow is a well-suited prototypical case for studying rotation effects in turbulence due to its simple geometry and the ability to be reproduced experimentally in a controlled environment. By examining the complex interaction of turbulent structures within rotating turbulent pipe flow, insight can be gained into the behavior of rotating flows relevant to engineering applications. Direct numerical simulations are conducted at a bulk Reynolds number of ReD = 19,000 with rotation numbers ranging from N = 0 to 3. In addition to providing turbulence statistics, proper orthogonal decomposition is used to identify the relevant (highest energy) modes of the flow and obtain an understanding about the coherence in the flow.
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Towards High-Fidelity CFD on the Cloud for the Automotive and Motorsport sectors
SAE World Congress 2020
BibTeX
@inproceedings{ashton2020, author = {Ashton, N and Sachs, S and Foti, L and Eberhardt, S}, city = {detroit}, booktitle = {SAE World Congress}, title = {Towards High-Fidelity CFD on the Cloud for the Automotive and Motorsport sectors}, year = {2020}, doi = {10.4271/2020-01-0665}, }
2019
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Assessment of the Elliptic Blending Reynolds Stress Model for a Rotating Turbulent Pipe Flow Using New DNS Data
AIAA Aviation 2019 Forum 2019
Abstract
New direct numerical simulation data of a fully-developed axially rotating pipe at Re = 5300 and Re = 19, 000 is used to examine the performance of the second-moment closure elliptic blending Reynolds stress model for a range of rotation rates from N=0 to N=3. In agreement with previous studies (using alternative second-moment closure models), the turbulence suppression observed by the DNS is over-predicted. This over-prediction is greatest at Re = 5, 300 and most noticeable in the poor prediction of the u′ w′ turbulent shear-stress component. At N=3 the flow is completely relaminarized in contrast to the DNS that is only partly relaminarized. The accuracy of the second-moment closure model is superior to the two-equation k − ω SST model which predicts pure solid-body rotation, however, both are equally poor at the highest rotation rates. The accuracy of each model is also assessed for the initial portion of a rotating pipe where in contrast to the fully-developed rotating pipe flow the turbulent suppression is under-predicted compared to the DNS. It is clear that greater work is required to understand the root cause of the poor prediction by these second-moment closure models and further DNS and experimental work is underway to assist this effort.
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A DNS Study to Investigate Turbulence Suppression in Rotating Pipe Flows
AIAA Aviation 2019 2019
2018
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Real-World Variability in the Prediction of Intracranial Aneurysm Wall Shear Stress: The 2015 International Aneurysm CFD Challenge
Cardiovascular Engineering and Technology 2018
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Assessing the Sensitivity of Hybrid RANS-LES Simulations to Mesh Resolution, Numerical Schemes and Turbulence Modelling within an Industrial CFD Process
SAE Technical Papers 2018
Abstract
© 2018 SAE International. All Rights Reserved. A wide-ranging investigation into the sensitivity of the hybrid RANS-LES based OpenFOAM CFD process at Audi was undertaken. For a range of cars (A1, TT, Q3 & A4) the influence of the computational grid resolution, turbulence model formulation and spatial & temporal discretization is assessed. It is shown that SnappyHexMesh, the Cartesian-prismatic built-in OpenFOAM mesher is unable to generate low y + grids of sufficient quality for the production Audi car geometries. For high y + grids there was not a consistent trend of additional refinement leading to improved correlation between CFD and experimental data. Similar conclusions were found for the turbulence models and numerical schemes, where consistent improvements over the baseline setup for all aerodynamic force coefficients were in general not possible. The A1 vehicle exhibited the greatest sensitivity to methodology changes, with the TT showing the least sensitivity. The overall correlation from CFD to the wind-tunnel was still very good with only 1 drag count difference for the A1 & Q3 and 6 drag counts for the TT and A4. The lift correlation was poorer and is the subject of continued research, in particular into the generation of high-quality low y+ meshes and improved turbulence modelling. This paper demonstrates the challenges of finding the optimum setup for hybrid RANS-LES simulations and the large number of influencing parameters.
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Towards a viscous wall model for immersed boundary methods
AIAA Aerospace Sciences Meeting, 2018 2018
Abstract
© 2018, AIAA Aerospace Sciences Meeting. All rights reserved. Immersed boundary methods have drawn wide attention over the past decades due to the fact that the mesh generation process can be automated independent of the complexity of the geometry. While many immersed boundary approaches have been developed, there remains one key challenge that has yet to be solved. The primary shortcoming of Cartesian mesh immersed boundary methods is the inability of efficiently resolving thin turbulent boundary layers encountered in high-Reynolds number flow applications. The inefficiency of resolving this thin region of the flow is associated with the use of constant aspect ratio Cartesian grid cells. Conventional CFD approaches can efficiently resolve the large wall-normal gradients by employing grid stretching towards the wall thereby creating large aspect ratio cells near the wall. This paper discusses different types of local wall modeling approaches that were proposed in previous research studies and combines them with an immersed boundary method. Standard subsonic turbulence modeling test cases, i.e., flow past the NACA0012 airfoil and flow over a bump in a channel, are used to test different implementations. A key focus of this paper is to investigate the limitations of the different wall models, numerical implementation details of the immersed boundary method and the coupling effects.
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3rd High-Lift Workshop Summary Paper – OpenFOAM, STAR-CCM+ & LAVA Simulations on Unstructured Grids
AIAA Aerospace Sciences Meeting, 2018 2018
Abstract
© 2018 by the American Institute of Aeronautics and Astronautics, Inc. All rights reserved. This paper presents a summary of the performance of three CFD codes; OpenFOAM, STAR-CCM+ and LAVA using unstructured grids for the third AIAA High-Lift prediction workshop. Three test-cases were computed; a 2D DSMA661 airfoil, the NASA high-lift Common-Research Model (CRM) and the JAXA Standard Model (JSM) high-lift model. Each solver used a choice among several turbulence models including the Spalart-Allmaras, SST and the k − ε lag elliptic blending model. When the same mesh and turbulence model were used, such as in the CRM case simulated at the University of Oxford with STAR-CCM+ and OpenFOAM, the agreement was very close. In the case of the JAXA Standard Model, the three solvers used different grids and turbulence models so that only basic trends could be identified. The three codes predicted similar flow structures, typically under-predicting inboard separation and over-predicting outboard separation. In general it was found that none of the RANS models tested was able to capture the post-stall region, suggesting further research should focus on transient hybrid RANS-LES or wall-modeled LES methods in combination with investigating transition effects.
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Development of high-quality hybrid unstructured meshes for the GMGW-1 workshop using ANSA
AIAA Aerospace Sciences Meeting, 2018 2018
Abstract
© 2018, American Institute of Aeronautics and Astronautics Inc, AIAA. All rights reserved. The accuracy of Computational Fluid Dynamics (CFD) simulations ultimately depend on the underlying computational grid that the solution is computed upon. The choice of turbulence modelling closure and numerical algorithms have a key role in the accuracy but can only perform as well as the mesh itself. For complex flows, such as the flow over a high-lift aircraft, the development of a high-quality mesh is non-trivial, requiring both a consideration on the level of mesh refinement to reduce numerical dissipation but also mesh quality to ensure robustness and stability. In this work an unstructured meshing approach is taken using the commercial software ANSA, developed by BETA-CAE Systems. This paper describes the meshing process within ANSA as well as an analysis of the unstructured grids produced for the 1st AIAA Geometry and Meshing Workshop and 3 rd AIAA High-Lift Workshop. Particular focus is made on the process to generate suitable grids for various CFD codes including OpenFOAM.
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EC135 Helicopter Fuselage
Go4Hybrid: Grey Area Mitigation for Hybrid RANS-LES Methods 2018
Abstract
© Springer International Publishing AG 2018. The EC135 helicopter fuselage represents a realistic and challenging test-case with a range of complex flow physics, such as 3D separation and an unsteady vortical wake. Whilst developing new turbulence models naturally begins with simple test-cases, the potential impact of any new approach for industry cannot truly be measured until they are assessed on such representative complex geometries.
2017
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Recalibrating Delayed Detached-Eddy Simulation to eliminate modelled-stress depletion
23rd AIAA Computational Fluid Dynamics Conference 2017
2016
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Computation of Turbulent Flow in a Rotating Pipe using the Elliptic Blending Reynolds Stress Model
46th AIAA Fluid Dynamics Conference 2016
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Computational hemodynamics of abdominal aortic aneurysms : Three-dimensional ultrasound versus computed tomography
Journal of Engineering in Medicine 2016
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Assessment of RANS and DES methods for realistic automotive models
Computers & Fluids 2016
Abstract
This paper presents a comprehensive investigation of RANS and DES models for the Ahmed car body and a realistic automotive vehicle; the DrivAer model. A variety of RANS models, from the 1-equation Spalart Allmaras model to a low-Reynolds number Reynolds Stress model have shown an inability to consistently correctly capture the flow field for both the Ahmed car body and DrivAer model, with the under-prediction of the turbulence in the initial separated shear layer found as a key deficiency. It has been shown that the use of a hybrid RANS-LES model (in this case, Detached Eddy Simulation) offers an advantage over RANS models in terms of the force coefficients, and general flow field for both the Ahmed car body and the DrivAer model. However, for both cases even at the finest mesh level hybrid RANS-LES methods still exhibited inaccuracies. Suggestions are made on possible improvements, in particular on the use of embedded LES with synthetic turbulence generation. Finally the computational cost of each approach is compared, which shows that whilst hybrid RANS-LES offer a clear benefit over RANS models for automotive relevant flows they do so at a much increased cost.
BibTeX
@article{Ashton2016Automotive, author = {Ashton, N. and West, A. and Lardeau, S. and Revell, A.}, doi = {10.1016/j.compfluid.2016.01.008}, issn = {00457930}, journal = {Computers & Fluids}, pages = {1-15}, title = {Assessment of RANS and DES methods for realistic automotive models}, volume = {128}, url = {https://doi.org/10.1016/j.compfluid.2016.01.008}, year = {2016}, }
2015
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Slat Noise Prediction using Hybrid RANS-LES Methods on Structured and Unstructured Grids
21st AIAA/CEAS Aeroacoustics Conference 2015
Abstract
© 2015, American Institute of Aeronautics and Astronautics Inc, AIAA. All Rights Reserved. This paper presents work to assess the noise emissions from a high-lift three-element airfoil using hybrid RANS-LES methods on both structured and unstructured meshes. The primary purpose of this work is to assess the sensitivity of the grid type and resolution using the same finite-volume code, numerical scheme and turbulence model. It has been shown that whilst the structured mesh provides better correlation to experimental data, an unstructured grid provides a good prediction of both the aerodynamic and acoustic data. The difierences between both meshes is generally small and explained by a combi- nation of mesh resolution, RANS/LES activation and numerical error. In particular, the initial separated shear layer is identified as the most challenging area to capture correctly. Furthermore estimates are made on the computational efiort to compute an entire aircraft using hybrid RANS-LES methods, based upon the spatial and temporal resolution of this work.
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Application of an elliptic blending Reynolds stress model in attached and separated flows
22nd AIAA Computational Fluid Dynamics Conference 2015
Abstract
© 2015, American Institute of Aeronautics and Astronautics Inc, AIAA. All rights reserved. A novel Reynolds stress model is presented that can be integrated up to the wall through the use of an elliptic blending of a near wall model for the pressure redistribution term and a homogeneous model farther away from the wall. The main novelty of the proposed model consists in using the homogeneous dissipation rate of turbulent kinetic energy εh instead of ε to model the anisotropic dissipation rate tensor. The model parameters are calibrated by using DNS results for a plane channel flow with a friction Reynolds number of ReT = 2000. The accuracy of the model is further demonstrated for the channel flow at ReT = 1000,4200, and 52000 through comparison with DNS data. To test the model performance in separated flows the periodic hill flow at several Reynolds numbers (Reb = 2800, 5600, 10600, 37000) is considered. For the hill flow, the model performs reasonable well showing slightly better results when compared to results obtained with the Menterhen compared to results obtained with thes SST k - ω model although both models fail to reproduce the high turbulence levels in the initial separated shear layer. Two external aerodynamics problems are studied in addition to the internal flows: the NACA 0012 at varying angles of attack and the NACA 4412 with a trailing edge separation (Aoa = 12). The flow over the NACA 0012 airfoil is considered for Rec = 106 at angles of attack AoA = 0, 5,10,15,16,17,18,19. For the lower angles of attack AoA < 17 the new Reynolds stress model predictions for lift and drag are in close agreement with the experimental values and also agree with the SST model predictions. For the highest angle of attack AoA = 19 the Reynolds stress model predicts flow separation and thus the lift and drag results are closer to the experimental values when compared to the SST model results. A similar result is found for the 2D NACA 4412 airfoil with trailing edge separation case (AoA = 12o). For the cases considered in this study the new Reynolds stress model was very robust but provided only small improvements over the two-equation model results in the flows with separation. Further calibration of the model parameters in a flow with separation could improve the results and will be considered as a next step.
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Grey-Area Mitigation for the Ahmed Car Body Using Embedded DES
Progress in Hybrid RANS-LES Modelling 2015
Abstract
© Springer International Publishing Switzerland 2015. The Ahmed car body represents a generic car geometry which exhibits many of the flow features found in real-life cars despite its simplified geometry. It is a challenging test case for the turbulence modelling community as it combines both 3D separation and the formation of counter-rotating vortices, which interact together to produce a recirculation region behind the car body. It is shown that none of the RANS models tested are able to correctly predict the size of the recirculation region, regardless of modelling level, mesh resolution or the choice of the length scale (i.e. ω or ε). All of these models under-predict the turbulence levels over the slanted back and as a consequence over-predict the separation region. The DDES simulations (regardless of the under lying URANS model) offer an improved predictive capability compared to the RANS models when the mesh resolution is sufficient. When the mesh resolution is insufficient the DDES models produces worse results than either of the URANS models. In both cases, the grey area problem is demonstrated, wherein a lack of both modelled and resolved turbulence in the initial separated shear layer results in an over-prediction of the separation region. A one-way embedded DDES approach is shown to give the best compromise between accuracy and simulation cost. It accurately predicts the level of resolved turbulence in the initial separated shear layer and thus compared to non-embedded DDES and URANS, the injection of synthetic turbulence upstream of the separation point allows for the correct level of turbulence at the onset of separation. The resulting separation zone is correctly predicted and the grey-area problem is reduced.
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Comparison of RANS and des methods for the DrivAer automotive body
SAE Technical Papers 2015
Abstract
Copyright © 2015 SAE International. Computational Fluid Dynamics (CFD) is now one of the most important design tools for the automotive industry. Reliable CFD simulations of the complex separated turbulent flow around vehicles is becoming an ever more crucial goal to increase fuel efficiency and reduce noise emissions. In this study Reynolds Averaged Navier-Stokes (RANS) models (both at eddy-viscosity and second-moment closure levels) are compared to hybrid RANS-LES methods (Detached-Eddy Simulation). The application is the DrivAer model; a new open-source realistic car model which aims to bridge the gap between simple Ahmed body and MIRA/SAE Reference car models and actual car geometries in use by the major car manufacturers. To date, many hybrid RANS-LES studies on complex geometries have been under-resolved compared to more academic cases, due to a limit on computational resources available. In this work a thorough assessment of grid resolution up to 300 million cells is conducted together with a discussion of mesh metrics to assess grid resolution. It is found that no RANS model can successfully capture the correct flow field for all car configurations, even with fine meshes using advanced Reynolds Stress models. This failure is attributed to an under-prediction of the turbulence levels in the initial separated shear layer, which leads to an over-prediction of the recirculation size. The use of DES shows a clear improvement in terms of the drag coefficient and pressure distribution for each configuration. Whilst the results are still not in perfect agreement with the experimental data, the trends between the difference car models are in excellent agreement with the experimental data. Finally suggestions for further improvements are also discussed and similarities between the DrivAer models and the Ahmed car body are presented.
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The Computational Fluid Dynamics Rupture Challenge 2013-Phase II: Variability of Hemodynamic Simulations in Two Intracranial Aneurysms.
Journal of Biomechanical Engineering 2015
Abstract
With the increased availability of computational resources, the past decade has seen a rise in the use of computational fluid dynamics (CFD) for medical applications. There has been an increase in the application of CFD to attempt to predict the rupture of intracranial aneurysms, however, while many hemodynamic parameters can be obtained from these computations, to date, no consistent methodology for the prediction of the rupture has been identified. One particular challenge to CFD is that many factors contribute to its accuracy; the mesh resolution and spatial/temporal discretization can alone contribute to a variation in accuracy. This failure to identify the importance of these factors and identify a methodology for the prediction of ruptures has limited the acceptance of CFD among physicians for rupture prediction. The International CFD Rupture Challenge 2013 seeks to comment on the sensitivity of these various CFD assumptions to predict the rupture by undertaking a comparison of the rupture and blood-flow predictions from a wide range of independent participants utilizing a range of CFD approaches. Twenty-six groups from 15 countries took part in the challenge. Participants were provided with surface models of two intracranial aneurysms and asked to carry out the corresponding hemodynamics simulations, free to choose their own mesh, solver, and temporal discretization. They were requested to submit velocity and pressure predictions along the centerline and on specified planes. The first phase of the challenge, described in a separate paper, was aimed at predicting which of the two aneurysms had previously ruptured and where the rupture site was located. The second phase, described in this paper, aims to assess the variability of the solutions and the sensitivity to the modeling assumptions. Participants were free to choose boundary conditions in the first phase, whereas they were prescribed in the second phase but all other CFD modeling parameters were not prescribed. In order to compare the computational results of one representative group with experimental results, steady-flow measurements using particle image velocimetry (PIV) were carried out in a silicone model of one of the provided aneurysms. Approximately 80% of the participating groups generated similar results. Both velocity and pressure computations were in good agreement with each other for cycle-averaged and peak-systolic predictions. Most apparent "outliers" (results that stand out of the collective) were observed to have underestimated velocity levels compared to the majority of solutions, but nevertheless identified comparable flow structures. In only two cases, the results deviate by over 35% from the mean solution of all the participants. Results of steady CFD simulations of the representative group and PIV experiments were in good agreement. The study demonstrated that while a range of numerical schemes, mesh resolution, and solvers was used, similar flow predictions were observed in the majority of cases. To further validate the computational results, it is suggested that time-dependent measurements should be conducted in the future. However, it is recognized that this study does not include the biological aspects of the aneurysm, which needs to be considered to be able to more precisely identify the specific rupture risk of an intracranial aneurysm.
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Key factors in the use of DDES for the flow around a simplified car
International Journal of Heat and Fluid Flow 2015
2014
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Investigation into the Predictive Capability of Advanced Reynolds-Averaged Navier–Stokes Models for the DrivAer Automotive Model
The International Vehicle Aerodynamics Conference 2014
BibTeX
@incollection{ashton2014drivaerrans, author = {Ashton, Neil and Revell, Alistair}, title = {Investigation into the Predictive Capability of Advanced Reynolds-Averaged Navier--Stokes Models for the {DrivAer} Automotive Model}, booktitle = {The International Vehicle Aerodynamics Conference}, year = {2014}, pages = {125--137}, publisher = {Woodhead Publishing}, address = {Loughborough, UK}, isbn = {978-0-08-100199-8}, doi = {10.1533/9780081002452.4.125}, }
2013
2012
2011
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The Numerical Simulation of the Flow over an EC145 Helicopter Fuselage Using HPC Facilities
23rd International Conference on Parallel Computational Fluid Dynamics 2011
BibTeX
@inproceedings{ashton2011ec145, author = {Ashton, Neil and Billard, Flavien and Moulinec, Charles and Prosser, Robert and Revell, Alistair and Uribe, Juan C.}, title = {The Numerical Simulation of the Flow over an {EC145} Helicopter Fuselage Using {HPC} Facilities}, booktitle = {23rd International Conference on Parallel Computational Fluid Dynamics}, year = {2011}, month = may, address = {Barcelona, Spain}, } -
A hybrid numerical scheme for a new formulation of delayed detached-eddy simulation (DDES) based on elliptic relaxation
Journal of Physics: Conference Series 2011
2010
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Prediction of the Unsteady Mean Turbulent Flow around Tandem Cylinders Using URANS Turbulence Models
Proceedings of the Fifth European Conference on Computational Fluid Dynamics (ECCOMAS CFD 2010) 2010
BibTeX
@inproceedings{ashton2010tandemcylinders, author = {Ashton, Neil and Revell, Alistair and Prosser, Robert}, title = {Prediction of the Unsteady Mean Turbulent Flow around Tandem Cylinders Using {URANS} Turbulence Models}, booktitle = {Proceedings of the Fifth European Conference on Computational Fluid Dynamics ({ECCOMAS CFD} 2010)}, year = {2010}, month = jun, address = {Lisbon, Portugal}, isbn = {978-989-96778-1-4}, }
Thesis
PhD thesis · 2013
Development, Implementation and Testing of an Alternative DDES Formulation Based on Elliptic Relaxation
University of Manchester
Research artifacts & technical reports
Dataset · 2026
WindsorML dataset
Open high-fidelity automotive-aerodynamics data · DOI 10.57967/hf/7644
Dataset · 2025
AhmedML dataset
Open high-fidelity bluff-body aerodynamics data · DOI 10.57967/hf/5002
Technical report · 2018
Optimizing the I/O Performance of OpenFOAM for Massively Parallel High-Fidelity CFD Simulations
ARCHER eCSE07-15