Enterprise AI Analysis: Fluid Dynamics
Full Domain Analysis in Fluid Dynamics
This paper introduces Full Domain Analysis (FDA), a methodological framework synthesizing evolutionary computation, machine learning, and simulation for comprehensive exploration of complex engineering design spaces. FDA aims to efficiently determine the full space of solutions in a problem domain, enabling deep understanding, innovation, and robust design. The framework defines key components: encodings, search, CFD, efficiency, and visualization, demonstrating its utility through a 2D fluid dynamics example. It shows how surrogate-assisted quality-diversity search, combined with generative models, enables efficient generation and interactive analysis of vast and diverse solution sets, paving the way for scaling to complex 3D turbulent flow problems.
Executive Impact & ROI
Our analysis reveals the transformative potential of Full Domain Analysis for accelerating discovery and optimizing complex systems, leading to significant gains in efficiency and innovation.
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Solutions Evaluated (approx.)
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CFD Simulations Run
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Representative Designs Discovered
Deep Analysis & Enterprise Applications
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Encodings
Efficient representations of shapes are crucial. Direct encodings are simple but limited. Indirect encodings like CPPNs offer flexibility but are hard to predict. Latent-generative models, using VAEs/GANs, capture complex design characteristics from data, providing a low-dimensional search space and enabling innovation. This paper highlights the trade-offs in reachability, validity, searchability, predictability, understanding, and prior knowledge integration across these encoding types for FDA.
Search & Optimization
FDA requires algorithms that generate diverse, high-performing solution sets, not just single optima. Multiobjective Optimization (MOO) finds Pareto fronts, but diversity is limited to trade-offs. Multimodal Optimization (MMO) explores local optima across the parameter space. Quality-Diversity (QD) is most suitable, aiming for diversity in solution behavior/morphology, even for lower-performing individuals. Surrogate-assisted QD (SPHEN) significantly improves efficiency by predicting fitness and characteristics, making FDA feasible for expensive simulations.
CFD & Efficiency
Computational Fluid Dynamics (CFD) simulations are computationally expensive. FDA demands robust, stable solvers for vast numbers of unsupervised simulations, even at the cost of some accuracy. Hybrid RANS-LES and data-driven turbulence models are promising for industrial scales. Efficiency is boosted by surrogate models (Bayesian Optimization, GP models) to reduce real simulation count, and generative surrogates (diffusion models) which directly predict flow fields with physical consistency, achieving 100-1000x speed-ups for LES-grade data.
Simulation Efficiency Gain
Up to 1000x Times fewer real simulations needed with SPHEN + SurrogatesEnterprise Process Flow
Define Parameters & Objectives
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Generate Diverse Designs (QD)
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Evaluate & Simulate (CFD)
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Analyze & Learn (ML/VAE)
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Refine & Iterate
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2D Building Footprint Optimization
Using FDA, 2D building footprints were optimized to minimize maximum flow velocity (umax) and turbulence (enstrophy E) for wind nuisance compliance. SPHEN with GP surrogates efficiently generated 1,000 solutions from 2,250,000 proposed designs. A VAE was then trained to discover morphological features correlating to flow characteristics, enabling users to interactively explore design variations and understand the underlying physics. This demonstrated FDA's ability to reveal trade-offs (e.g., larger area often leading to higher umax) and identify prototypes.
Outcome: FDA successfully identified 100 high-performing prototypes, revealed correlations between shape morphology and flow features, and allowed for interactive exploration of the design space, demonstrating significant insights beyond single-point optimization.
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