Search arXivSearch

arXiv · 2504.07837

A Review of HPC-Accelerated CFD in National Security and Defense

Abstract

Using High-Performance Computing (HPC), Computational Fluid Dynamics (CFD) now serves as an essential component in defense-related national security applications including missile interception and hypersonic propulsion as well as naval stealth optimization and urban hazard dispersion. This review combines two decades of open-source and public-domain research on HPC-accelerated CFD in defense, addressing three key questions: Which security-sensitive simulations have utilized open-source CFD frameworks such as OpenFOAM, SU2 and ADflow? Which HPC techniques, such as MPI domain decomposition and GPU acceleration together with hybrid parallelism best enhance open-source frameworks to manage large defense CFD simulations? Which technological advancements and research voids currently drive the directional development of the field? Examining several research studies sourced from NASA, DoD HPC centers, and academic institutions, scientific contributions have been classified into air, maritime, and space domains. Modular frameworks like NavyFOAM and SU2 and ADflow's adjoint-based solvers show how custom open-source solutions support workflows with rapid completion of multi-million cell simulations. The conclusion highlights new trends that combine exascale readiness with machine learning surrogate models for real-time CFD applications and interdisciplinary HPC-driven multi-physics integration to deliver practical insights for improving CFD use in defense research and development.

Explore related subjects

Keep this discovery

Explore connections, maps & timelines

BibTeXRIS

James Afful. 2025-04-29. A Review of HPC-Accelerated CFD in National Security and Defense. https://arxiv.org/abs/2504.07837

Cite the original work for its findings. Save a collection to share your selection of sources.

KEEP EXPLORING

Related papers

Cosm: Collective switched motion for sparse Ising optimization

We introduce Collective Switched Motion (Cosm), a heuristic optimization framework based on switched collective dynamics. Cosm compiles the objective into subobjectives that are activated sequentially, temporally separating competing local influences. The interplay of switching and local interactions among variables gives rise to collective search behavior, while a new correlated perturbation mechanism encourages coordinated cluster motion. Tests on tuned-hardness spin-glass benchmarks suggest more favorable algorithmic scaling than that reported for leading dynamical solvers. Cosm heuristically attains the certified optima of three of the largest Gset instances (G72, G77, G81), exceeding the previously reported heuristic solutions. On the large random-graph instances G61 and G70, a CPU implementation reliably attains the best-known solutions, reaching cuts of 5799 and 9595 with 99%-confidence times-to-target of 15 s and 2.4 s, respectively. On the heterogeneous-degree G64 instance, Cosm establishes a new best-known cut of 8753. Broadly, the results suggest an alternative approach to heuristic design in which local dynamics and orchestration mechanisms are carefully designed so that effective search emerges.

cs.CE

Decomposing Firm-Level Crisis Responses from Incomplete Market Signals: Evidence from China's IT Sector During COVID-19

Exogenous shocks generate heterogeneous behavioral responses across firms, yet event studies typically report only sector-level averages. This paper develops a multi-method approach combining causal identification (difference-in-differences with cluster-robust inference), unsupervised behavioral discovery (K-means trajectory clustering, Gaussian hidden Markov models), and cross-sectional resilience prediction (logistic regression with nested cross-validation) to decompose firm-level response heterogeneity from noisy market signals. We demonstrate the approach on 246 Chinese A-share IT firms (216 with complete data for all analyses) during the COVID-19 shock (January 2020), using 252 non-IT CSI 300 firms as controls. The return decline was market-wide, not IT-specific (DID p = 0.59); the IT-specific effect was elevated volatility (DID beta = 0.043, cluster-robust p < 0.001), with the effect surviving Benjamini-Hochberg correction in 13 of 30 alternative specifications. Unsupervised clustering produced three trajectory groups: fast recovery (36 companies, +29.7%), resilient/moderate (67 companies), and persistent drag (113 companies, -6.9%). Pre-crisis financial fundamentals showed only modest predictive power for resilience (nested CV AUC = 0.635, 95% CI: 0.558-0.711; permutation p = 0.016), consistent with the limited informativeness of publicly available signals for anticipating crisis outcomes. The combination of causal analysis, unsupervised learning, and prediction represents a reproducible framework which can be applied to crises in other market periods.

cs.CE

An Insurance Broker for Every Small Business: The Economics of Exceptional Care at Scale

Small-business owners need expert guidance on their own terms, across schedules, languages, and channels, but low premiums make exceptional, continuous human service uneconomic for much of the market. Combining public evidence, Kinro operational data, and an illustrative five-year service model, we show why traditional brokerage economics leave 35 million U.S. small businesses underserved. An AI-native brokerage can change those economics by performing and coordinating routine work continuously, while licensed professionals govern consequential exceptions and the brokerage remains accountable.

cs.CE