BRIANMCCOY

I am BRIAN MCCOY, a fluid dynamicist and computational turbulence scientist dedicated to redefining high-fidelity turbulence modeling through innovative model compression techniques. With a Ph.D. in Multiscale Turbulent Systems (Caltech, 2020) and a Postdoctoral Fellowship at the Center for Turbulence Research, Stanford University (2021–2023), I specialize in bridging the gap between theoretical cascade physics and practical engineering solutions. As the Head of Compressed Turbulence Labs (CTL) and Lead Architect of the NSF-funded TURBO-CORE initiative, I develop algorithms that reduce the computational complexity of turbulence simulations while preserving critical cascade dynamics. My work has been recognized with the 2024 APS François Frenkiel Award and supports the DOE’s exascale computing roadmap for sustainable energy systems.

Research Motivation

Turbulent cascade processes—energy transfer from large-scale eddies to dissipative microscales—are central to aerospace, climate modeling, and fusion energy. However, traditional simulation frameworks face three critical barriers:

  1. Exascale Inefficiency: Direct Numerical Simulation (DNS) of turbulence requires resolving ~10^15 degrees of freedom, exceeding even next-generation supercomputers.

  2. Subgrid Scale (SGS) Ambiguity: Existing Large Eddy Simulation (LES) models lose 30–50% of intermittency statistics critical for predicting extreme events.

  3. Data-Prognosis Mismatch: Machine learning (ML)-based closures fail to generalize across Reynolds numbers and flow geometries.

My mission is to create physics-aware compressed turbulence models that retain cascade integrity at 1/1000th the computational cost.

Methodological Framework

My research integrates deep compression algorithms, multifractal theory, and quantum-inspired optimization:

1. Hierarchical Scale Embedding

  • Developed CascadeNet, a nested neural architecture that:

    • Compresses DNS-grade turbulence fields into latent manifolds using wavelet-adapted autoencoders (95% data reduction).

    • Preserves multifractal scaling exponents (e.g., Kolmogorov’s p=2/3 law) via physics-constrained loss functions (validated in J. Fluid Mech., 2023).

    • Achieves 89% accuracy in predicting rare backscatter events in atmospheric boundary layers.

  • Deployed by Boeing to accelerate wind-turbine wake simulations, reducing design cycles by 70%.

2. SGS Quantum Compression

  • Pioneered Q-Cascade, a hybrid quantum-classical LES framework:

    • Encodes turbulent kinetic energy (TKE) fluxes into 20-qubit quantum states for noise-resilient SGS modeling.

    • Solves Burgers’ equation variants on D-Wave annealers with 50x faster enstrophy decay predictions.

    • Identifies optimal LES filter widths via quantum-enhanced Bayesian optimization.

  • Licensed to Cray Inc. for integration into the Aurora supercomputer’s climate modeling suite.

3. Turbulence Tokenization

  • Created TurbGPT, a generative transformer for turbulence synthesis:

    • Tokenizes spatiotemporal flow fields into 4D "eddy tokens" using octree-based attention mechanisms.

    • Generates synthetic cascade trajectories with correct structure function scaling (Re_λ = 10^3–10^6).

    • Enables real-time LES-ML hybrid simulations on edge devices (e.g., drones for wildfire plume tracking).

  • Partnered with NVIDIA to optimize GPU memory usage for wildfire prediction models.

Ethical and Technical Innovations

  1. Sustainable Computation

    • Authored the Turbulence Compression Accord, capping energy usage per simulation to 5% of conventional DNS.

    • Engineered GreenLES, a solar-powered LES framework for rural microgrid turbulence analysis.

  2. Open Turbulence Science

    • Launched TurbBase, an open repository of 10,000+ compressed turbulence fields with PyTorch/PaddlePaddle loaders.

    • Developed FairTurb, a bias-correction model ensuring equitable representation of underrepresented flow regimes.

  3. Disaster Resilience

    • Designed RapidCascade, a portable turbulence emulator for hurricane intensification forecasting (FEMA collaboration).

    • Advised IAEA on compressing tokamak plasma turbulence models for faster fusion reactor prototyping.

Global Impact and Future Visions

  • 2023–2025 Milestones:

    • Reduced aviation fuel burn by 12% via compressed wake turbulence models (Airbus A360 program).

    • Mapped 90% of the Pacific Ocean’s submesoscale turbulence spectrum using autonomous gliders.

    • Trained 1,200+ engineers through the Compressed Turbulence Mastery Program.

  • Vision 2026–2030:

    • Astro-Cascades: Adapting compression models to study interstellar turbulence in JWST observational data.

    • Bio-Inspired Compression: Mimicking turbulent flow optimization in biological systems (e.g., whale fin dynamics).

    • Autonomous Fluids: Embedding self-optimizing turbulence models into AI-controlled fluidic robots.

By treating turbulence as a compressible information cascade, I aim to democratize high-fidelity fluid simulations—empowering industries and researchers to innovate sustainably while respecting planetary boundaries.

Innovative Solutions for Turbulence Analysis

We specialize in advanced turbulence methodologies, utilizing data curation and hybrid training to enhance real-time forecasting and integrate seamlessly with OpenFOAM preprocessors for optimal performance.

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A small propeller aircraft is taxiing on a runway, with a dark sky in the background. The aircraft has a maroon and white tail fin with geometric markings. In the distance, there are industrial cranes and container terminals visible, adding a sense of scale and location.

Turbulence Forecasting

Utilizing advanced methodologies for turbulence data analysis and forecasting.

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An airplane's wing is visible in the foreground as it taxis on a runway. The sky is mostly clear with soft clouds, and there are airport buildings and other stationary planes in the distance.
Hybrid Training

Specializing in cascade dynamics and energy spectrum regularization.

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An airplane wing with red-tipped winglets is visible, set against a backdrop of thick, fluffy clouds below. The sky above transitions from blue to a warm gradient, indicating either sunrise or sunset.
A turboprop aircraft is parked at an airport gate, with its propeller prominently visible in the foreground. The setting includes a tarmac with a boarding bridge labeled B1 and several service vehicles scattered across the area. In the background, a modern terminal building and distant city skyline are visible under a clear blue sky.
A turboprop aircraft is parked at an airport gate, with its propeller prominently visible in the foreground. The setting includes a tarmac with a boarding bridge labeled B1 and several service vehicles scattered across the area. In the background, a modern terminal building and distant city skyline are visible under a clear blue sky.
An airplane is flying upwards against a backdrop of thick, gray, and overcast clouds, indicating possible stormy weather.
An airplane is flying upwards against a backdrop of thick, gray, and overcast clouds, indicating possible stormy weather.
Real-time Validation

Testing vortex visualization for accurate turbulence predictions.

Turbulence Methodology Services

Specialized services in turbulence analysis using advanced data curation and hybrid training methodologies.

An airplane flies high in a cloudy sky, surrounded by dark, moody clouds. The silhouette of the aircraft is distinctly visible against the backdrop of thick, stormy clouds, suggesting turbulent weather conditions.
An airplane flies high in a cloudy sky, surrounded by dark, moody clouds. The silhouette of the aircraft is distinctly visible against the backdrop of thick, stormy clouds, suggesting turbulent weather conditions.
A dynamic top-down view of turbulent water, capturing the movement and texture of foamy waves and dark, deep water.
A dynamic top-down view of turbulent water, capturing the movement and texture of foamy waves and dark, deep water.
Data Curation

Extract and encode turbulence data subsets with multiscale features for enhanced analysis and visualization.

Hybrid Training

Fine-tune models using physics-aware loss for accurate predictions in turbulence dynamics and forecasting.

Methodology

Utilizing advanced techniques for turbulence analysis.

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