Machine Learning and Automatic Mesh Optimization: Watershed Technologies for Heat Transfer and Fluid Flow Optimal Simulations
Bibliographic record
Abstract
Many areas of CFD and CHT require, or ought to be using, large samplings to perform parametric explorations and, ultimately, optimization of flow-based components or processes.This is demanding in 3D and even more so for multidisciplinary problems combining CFD, CHT, and, often, CSD.Nowhere is this problem more apparent than in the certification of aircraft, rotorcraft, and jet engines for flying into known icing.The required analyses involve the simultaneous simulation of high-Mach external aerodynamics over the aircraft, small and large droplets and ice crystals impingement, low-Mach internal aerodynamics inside ice protection systems or in engines, conjugate heat transfer across multiple fluid-structure interfaces, liquid-to-ice-to-liquid-crystals phase changes, changing geometries due to ice accretion or ablation on external and internal components, fluid-structure interaction induced deformations, and ice cracking and tracking.These complexities have made component optimization, the ultimate aim of any simulation capability a rarity in this field.While keeping the approach applicable to a wide variety of problems, the Lecture will thus use in-flight icing as a relevant application example.The Lecture will review aspects of modern CFD-Aero and CFD-Icing that straddle the analysis, design, testing, and certification processes, via a Reduced Order Modeling (ROM) calculation for a complete aircraft flow + supercooled droplets or ice crystals impingement + ice accretion + performance degradation, in seconds or minutes and not days!The methodology is based on Proper Orthogonal Decomposition, multi-dimensional interpolation, and machine learning algorithms, along with an error-driven iterative sampling method to adaptively select an optimal set of snapshots.Hundreds of such snapshots (full 3D solutions) can be obtained within a day on a supercomputer at a fraction of the cost of a day in a tunnel.The methodology can provide engineers and certification consultants with a CFD simulator and no need for a CAD system, a CFD code, a mesh generator, running codes, adjusting parameters, and monitoring solution and mesh convergence.This gamechanger ought to allow OEMs and their second-tier suppliers to use the same toolset without divulging proprietary geometries which is currently a serious obstacle.The lecture will also demonstrate an alternative to the recommendations of using systematic mesh refinement to demonstrate grid convergence, a quasi-impossibility in industry where the motivation for carrying out CFD is not publishing papers but improving productsThe combined ROM + Mesh Optimization methodologies will be demonstrated on "a complete aircraft" going through its combined aerodynamic and icing certification envelopes, providing rich complementary data to dry/icing tunnels or natural ice flights and a path to the optimization of hot air and electrical ice protection systems.Finally, "Gappy-ROM" will be demonstrated for using ROM in enriching fluid and heat transfer experimental data and reducing test models' complexity.Machine Learning paves the way for any organization to analyze/optimize components with data as rich as, and compatible with, the associate OEM.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".