Never miss a story from Computational Fluid Dynamics. Subscribe for in-depth analysis and articles.
Today, a significant challenge in applied fluid dynamics is the lack of understanding of turbulence-dependent features. Leaving us with sparse industrial confidence in applying CFD to applications such as flow detachment over an aircraft wing, shock-boundary layer interactions, etc. Improving the capabilities of complex fluid flow models can reduce energy consumption, greenhouse gas (GHG) emissions, and noise radiations from aircraft, cars, and ships. These complex models can favorably impact the economy and industrial leadership in a highly competitive manner. Hence, understanding, modeling, and predicting turbulence phenomena is key for efficient and environmentally safe design. To make this a reality, the HiFi-TURB project sets out on a highly ambitious and innovative program to address some deficiencies in advanced turbulence models.
The HiFi-TURB project rests on the following pillars of excellence :
This consortium is a collaboration between major aeronautical companies and software editors, with Cadence Design Systems acting as a coordinator. Well-known research centers and academic groups, including ERCOFTAC, act as a source of turbulence expertise.
The large-scale availability of high-performance computing (HPC) opens the door to a truly novel approach to turbulence model development. This study applies artificial intelligence (AI) and machine learning (ML) techniques to a database of high-fidelity, scale-resolving test case simulations containing features of separated flow regions or complex 3D flows. Figure 1 shows an example of a flow field used as the basis for the turbulence modeling task.
Figure 1. Flow structure of T161 cascade (typical flow field used as input for the turbulence model improvement).
The huge amount of data generated in these simulations requires a new data mining approach. This is where neural concept brings in its toolchain based on deep learning to analyze large amounts of data provided by 3D scale-resolving simulations. Using geometry-based variational auto-encoders (VAE), Cadence CFD was able to gain insights into correlations between averaged flow variables. The VAE compresses the data physically meaningfully into ‘embeddings’ and then reconstructs the original input from the compressed data. This is done with a high accuracy, which allows the use of the ML models as a replacement (surrogates) for the original data. The advantages are easier data handling, possible exploitation of data mining, and analysis techniques that can provide insights into the physics involved in the study.
Figure 2. Investigated simulation domain (left) and statistical analysis of quantities (right).
Figure 2 is an example of the possible analysis. The symbol colors on the 2D plot correspond to the ‘embedding’ value and are the same in the 3D view (left) and in the 2D plot (right). Points of the same color have the same value for all the considered physical quantities. The 3D view colored by the embedding value is a global statistical representation for several physical quantities over the investigated domain. Both plots provide a new perspective on the flow behavior using a machine learning model.
Work Package 1: Management
Task 1: General Coordination/project steering Task 2: Knowledge exchange (inter-communication / website) Task 3: Dissemination/exploitation
Work Package 2: Further improvement of HOM towards reduced CPU cost and curved grid generation
Task 1: Reducing CPU cost for high-fidelity LES/DNSTask 2: Towards industrial curved-grid generation techniques
Work Package 3: Generation of high-fidelity LES/DNS data sets for identified physical phenomena
Task 1: Selection of basic and industrial relevant test cases with identified physical propertiesTask 2: Generating new h - High-fidelity LES/DNS data setsTask 3: Evaluation, reliability, and quality of LES/DNS data
Work Package 4: Feature detection and advanced analysis of LES/DNS data
Task 1: Analysis of basic turbulence- averaged data via data-driving methodologiesTask 2: Analysis of time turbulence data via AI and deep learning methodologies connected to HRLM and WMLES
Work Package 5: Turbulence modeling assessments and improvements – monitored by WP5 Task Group
Task 1: Develop, improve, and assess EARSM turbulence modelsTask 2: Develop, improve, and assess DRSM turbulence modelsTask 3: Develop, improve, and assess wall models for WMLES and Hybrid RANS-LES
Work Package 6: Validation of new turbulence models applied to representative and industrial-relevant test cases
Task 1: Validation on external flow configurations (High-Lift and Drag Pred. WS cases) - baseline to newTask 2: Validation of internal flow configurations for fixed (diffusor) and rotating cases - baseline to newTask 3: Assessment and recommendations
Work Package 7: Management of the LES/DNS databases for open accessibility (ERCOFTAC)
Task 1: Definition of database criteria and implementation rulesTask 2: Creation and management of the LES/DNS databaseTask 3: Integrating results of WP6 in the ERCOFTAC Wiki Knowledge Base
If you would like to try scale-resolving simulations using Fidelity CFD for your CFD applications, request a demo today!