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Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

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At least 19 records
All sourcesNASA NTRSDOE OSTI
DOE OSTI2026

Efficient many-jet event generation with flow matching

We apply for the first time, to the best of our knowledge, the flow matching method to the problem of phase-space sampling for event generation in high-energy collider physics. By training the model to remap the random numbers used to generate the momenta and helicities of the scattering matrix elements as implemented in the portable partonic event generator pepper, we find substantial efficiency improvements in the studied processes. We focus our study on the highest final-state multiplicities in Drell-Yan and top-antitop pair production used in simulated samples for the Large Hadron Collider, which computationally are the most relevant ones. We find that the unweighting efficiencies improve by factors of 184 and 25, respectively, when compared to the standard approach of using a vegas-based optimization. We also compare continuous normalizing flows trained with flow matching against the previously studied normalizing flows based on coupling layers and find that the former leads to better results, faster training and a better scaling behavior across the studied multiplicity range, while the latter evaluate faster. When combining the advantages of both methods using the regflow approach, we find parton-level unweighted event generation walltime gains of about a factor of 10 at the highest final-state multiplicities.

Bothmann, E. [CERN; Gottingen U.] (ORCID:000000016↗
DOE OSTI2026

Flow matching meets biology and life science: a survey

Over the past decade, advances in generative modeling, such as generative adversarial networks, masked autoencoders, and diffusion models, have significantly transformed biological research and discovery, enabling breakthroughs in molecule design, protein generation, catalysis discovery, drug discovery, and beyond. At the same time, biological applications have served as valuable testbeds for evaluating the capabilities of generative models. Recently, flow matching has emerged as a powerful and efficient alternative to diffusion-based generative modeling, with growing interest in its application to problems in biology and life sciences. This paper presents the first comprehensive survey of recent developments in flow matching and its applications in biological domains. We begin by systematically reviewing the foundations and variants of flow matching, and then categorize its applications into three major areas: biological sequence modeling, molecule generation and design, and peptide and protein generation. For each, we provide an in-depth review of recent progress. We also summarize commonly used datasets and software tools, and conclude with a discussion of potential future directions.

59 BASIC BIOLOGICAL SCIENCES↗
NASA NTRS2010

Initial Flow Matching Results of MHD Energy Bypass on a Supersonic Turbojet Engine Using the Numerical Propulsion System Simulation (NPSS) Environment

Preliminary flow matching has been demonstrated for a MHD energy bypass system on a supersonic turbojet engine. The Numerical Propulsion System Simulation (NPSS) environment was used to perform a thermodynamic cycle analysis to properly match the flows from an inlet to a MHD generator and from the exit of a supersonic turbojet to a MHD accelerator. Working with various operating conditions such as the enthalpy extraction ratio and isentropic efficiency of the MHD generator and MHD accelerator, interfacing studies were conducted between the pre-ionizers, the MHD generator, the turbojet engine, and the MHD accelerator. This paper briefly describes the NPSS environment used in this analysis and describes the NPSS analysis of a supersonic turbojet engine with a MHD generator/accelerator energy bypass system. Results from this study have shown that using MHD energy bypass in the flow path of a supersonic turbojet engine increases the useful Mach number operating range from 0 to 3.0 Mach (not using MHD) to an explored and desired range of 0 to 7.0 Mach.

Benyo, Theresa L.↗
NASA NTRS2011

Flow Matching Results of an MHD Energy Bypass System on a Supersonic Turbojet Engine Using the Numerical Propulsion System Simulation (NPSS) Environment

Flow matching has been successfully achieved for an MHD energy bypass system on a supersonic turbojet engine. The Numerical Propulsion System Simulation (NPSS) environment helped perform a thermodynamic cycle analysis to properly match the flows from an inlet employing a MHD energy bypass system (consisting of an MHD generator and MHD accelerator) on a supersonic turbojet engine. Working with various operating conditions (such as the applied magnetic field, MHD generator length and flow conductivity), interfacing studies were conducted between the MHD generator, the turbojet engine, and the MHD accelerator. This paper briefly describes the NPSS environment used in this analysis. This paper further describes the analysis of a supersonic turbojet engine with an MHD generator/accelerator energy bypass system. Results from this study have shown that using MHD energy bypass in the flow path of a supersonic turbojet engine increases the useful Mach number operating range from 0 to 3.0 Mach (not using MHD) to a range of 0 to 7.0 Mach with specific net thrust range of 740 N-s/kg (at ambient Mach = 3.25) to 70 N-s/kg (at ambient Mach = 7). These results were achieved with an applied magnetic field of 2.5 Tesla and conductivity levels in a range from 2 mhos/m (ambient Mach = 7) to 5.5 mhos/m (ambient Mach = 3.5) for an MHD generator length of 3 m.

Benyo, Theresa L.↗
DOE OSTI2025

Flow matching beyond kinematics: Generating jets with particle identification and trajectory displacement information

We introduce the first generative model trained on the etlass dataset. Our model generates jets at the constituent level, and it is a permutation-equivariant continuous normalizing flow (CNF) trained with the flow matching technique. It is conditioned on the jet type, so that a single model can be used to generate the ten different jet types of etlass. For the first time, we also introduce a generative model that goes beyond the kinematic features of jet constituents. The etlass dataset includes more features, such as particle-ID and track impact parameter, and we demonstrate that our CNF can accurately model all of these additional features as well. Our generative model for etlass expands on the versatility of existing jet generation techniques, enhancing their potential utility in high-energy physics research, and offering a more comprehensive understanding of the generated jets. Published by the American Physical Society 2025

Birk, Joschka (ORCID:0000000219310127)↗
DOE OSTI2025

A comparison of probabilistic generative frameworks for molecular simulations

Generative artificial intelligence is now a widely used tool in molecular science. Despite the popularity of probabilistic generative models, numerical experiments benchmarking their performance on molecular data are lacking. Here, in this work, we introduce and explain several classes of generative models, broadly sorted into two categories: flow-based models and diffusion models. We select three representative models: neural spline flows, conditional flow matching, and denoising diffusion probabilistic models, and examine their accuracy, computational cost, and generation speed across datasets with tunable dimensionality, complexity, and modal asymmetry. Our findings are varied, with no one framework being the best for all purposes. In a nutshell, (i) neural spline flows do best at capturing mode asymmetry present in low-dimensional data, (ii) conditional flow matching outperforms other models for high-dimensional data with low complexity, and (iii) denoising diffusion probabilistic models appear the best for low-dimensional data with high complexity. Our datasets include a Gaussian mixture model and the dihedral torsion angle distribution of the Aib9 peptide, generated via a molecular dynamics simulation. We hope our taxonomy of probabilistic generative frameworks and numerical results may guide model selection for a wide range of molecular tasks.

Artificial intelligence↗
NASA NTRS1985

Flow in a model turbine stator

In view of the complex nature of the flowfield in the hot section of gas turbine engines, the need to predict heat transfer and flow losses, the possible appearance of separation and strong secondary flows, etc., the present effort is focusing upon a Navier-Stokes approach to the three dimensional turbine stator problem. The advantages of a full Navier-Stokes approach are clear since when combined with a suitable turbulence model these equations represent the flow and heat transfer physics. In particular, the Navier-Stokes equations accurately represent possible separated regions and regions of significant secondary flow. In addition, the Navier-Stokes approach allows representation of the entire flow field by a single set of equations, thus avoiding problems associated with representing different regions of the flow by different equations and then matching flow regions.

Buggeln, R. C.↗
NASA NTRS2000

Suppression of Cavity-Driven Flow Separation in a Simulated Mixed Compression Inlet

A test facility designed to simulate a bifurcated subsonic diffuser operating within a mixed compression inlet is described. The subsonic diffuser in this facility modeled a bypass cavity feature often used in mixed compression inlets for engine flow matching and normal shock control. A bypass cavity-driven flow separation was seen to occur in the subsonic diffuser without applied flow control. Flow control in the form of vortex generators and/or a partitioned bypass cavity cover plate were used to eliminate this flow separation, providing a 2% increase in area-averaged total pressure recovery, and a 70% reduction in circumferential distortion intensity.

Wendt, Bruce J.↗
NASA NTRS1983

Comparison of visualized turbine endwall secondary flows and measured heat transfer patterns

Various flow visualization techniques were used to define the secondary flows near the endwall in a large heat transfer data. A comparison of the visualized flow patterns and the measured Stanton number distribution was made for cases where the inlet Reynolds number and exit Mach number were matched. Flows were visualized by using neutrally buoyant helium-filled soap bubbles, by using smoke from oil soaked cigars, and by a few techniques using permanent marker pen ink dots and synthetic wintergreen oil. Details of the horseshoe vortex and secondary flows can be directly compared with heat transfer distribution. Near the cascade entrance there is an obvious correlation between the two sets of data, but well into the passage the effect of secondary flow is not as obvious.

Gaugler, R. E.↗
NASA NTRS1984

Comparison of visualized turbine endwall secondary flows and measured heat transfer patterns

Various flow visualization techniques were used to define the seondary flows near the endwall in a large heat transfer data. A comparison of the visualized flow patterns and the measured Stanton number distribution was made for cases where the inlet Reynolds number and exit Mach number were matched. Flows were visualized by using neutrally buoyant helium-filled soap bubbles, by using smoke from oil soaked cigars, and by a few techniques using permanent marker pen ink dots and synthetic wintergreen oil. Details of the horseshoe vortex and secondary flows can be directly compared with heat transfer distribution. Near the cascade entrance there is an obvious correlation between the two sets of data, but well into the passage the effect of secondary flow is not as obvious. Previously announced in STAR as N83-14435

Gaugler, R. E.↗
NASA NTRS1985

Numerical approach for the aerodynamic analysis if airfoils with laminar separation

A numerical method for simultaneously and efficiently coupling an external subsonic potential flow and an interior viscous flow such that the two flows match at an interfacing boundary is discussed. Both a panel method and a simple point compressible vortex model are used for the outer potential field. The interior flow solvers which were used are the Navier-Stokes and Euler codes of T. J. Coakley and the Euler code of A. Verhoff. In order to test compatibility, the panel method is coupled to the less expensive Euler codes since the coupling procedure is identical with the Navier-Stokes code. The results show significant efficiency improvements can be obtained over the uncoupled approach. Results also indicate the outer potential flow is best represented by the simple point compressible vortex model. The panel method couples smoothly to Coakley's implicit code but is numerically incompatible as coupled with the explicit Euler code. An improved Navier-Stokes code is under initial development which extends the Euler code to include the necessary viscous terms. Results are shown for all infinite length channel with one wavy periodic wall with and without laminar separation.

Halt, D. W.↗
DOE OSTI2026

Monte Carlo Event Generation with Continuous Normalizing Flows

We apply continuous normalizing flows trained with the flow matching method to the problem of phase-space sampling in Monte Carlo event generation for high-energy collider physics. Focusing on lepton-pair and top-quark pair production with multiple jets, the two computationally most expensive processes at the Large Hadron Collider, we train helicity-conditioned continuous normalizing flows to remap the random numbers used in matrix element evaluation. Compared to standard methods, we achieve unweighting efficiency improvements by factors of up to 184 and 25 for the two processes at their respective highest jet number, at the cost of an increased evaluation time. When combining the advantages of continuous normalizing flows with the fast evaluation times of coupling-layer-based flows, using the RegFlow approach, we find parton-level unweighted event generation walltime gains of about a factor of 10 at the highest jet numbers. These substantial gains highlight the promise of samplers based on machine learning for next-generation collider experiments.

Bothmann, Enrico [CERN; Gottingen U.] (ORCID:00000↗
DOE OSTI2025

Universal energy-speed-accuracy trade-offs in driven nonequilibrium systems

The connection between measure theoretic optimal transport and dissipative nonequilibrium dynamics provides a language for quantifying nonequilibrium control costs, leading to a collection of thermodynamic speed limits, which rely on the assumption that the target probability distribution is perfectly realized. This is almost never the case in experiments or numerical simulations, so here we address the situation in which the external controller is imperfect. We obtain a lower bound for the dissipated work in generic nonequilibrium control problems that (1) is asymptotically tight and (2) matches the thermodynamic speed limit in the case of optimal driving. Along with analytically solvable examples, we refine this imperfect driving notion to systems in which the controlled degrees of freedom are slow relative to the nonequilibrium relaxation rate, and identify independent energy contributions from fast and slow degrees of freedom. Furthermore, we develop a strategy for optimizing minimally dissipative protocols based on optimal transport flow matching, a generative machine learning technique. Furthermore, this latter approach ensures the scalability of both the theoretical and computational framework we put forth. Crucially, we demonstrate that we can compute the terms in our bound numerically using efficient algorithms from the computational optimal transport literature and that the protocols we learn saturate the bound.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2026

CaloTrilogy: Toward a Breakthrough in One-Step, End-to-End, Physics-Guided Shower Generation for Modern Calorimeters

High-precision calorimeter simulation at current and future colliders imposes rapidly growing computational demands, motivating the development of machine-learning surrogates for traditional Monte Carlo tools such as Geant4. Flow matching and diffusion-based generative models have become leading approaches for high-dimensional fast simulation because of their sample quality, but typically require ${\cal O}(100)$ function evaluations at inference and often rely on auxiliary networks to constrain global observables, compromising streamlined end-to-end generation. We introduce a unified framework that improves the balance between speed, shower quality, and physics fidelity. The method combines: (i) an average velocity field integrator that enables sampling in one or a few evaluations; (ii) a learned generative prior in shower space, constructed from data rather than random noise; and (iii) physics-guided loss terms that impose inductive biases on key observables during training. These elements are training time regularizers, preserving end-to-end inference with no additional cost. With only one or a few evaluation steps, the model achieves shower quality competitive with state-of-the-art flow and diffusion approaches, tested on several public high granularity calorimeter datasets. The results demonstrate inter-layer shower structure consistent with the underlying physics, providing a strong candidate for future fast simulation workflows.

Jiang, Cheng [Edinburgh U.]↗
DOE OSTI2025

CaloChallenge 2022: a community challenge for fast calorimeter simulation

Here, we present the results of the ‘Fast Calorimeter Simulation Challenge 2022’—the CaloChallenge. We study state-of-the-art generative models on four calorimeter shower datasets of increasing dimensionality, ranging from a few hundred voxels to a few tens of thousand voxels. The 31 individual submissions span a wide range of current popular generative architectures, including variational autoencoders (VAEs), generative adversarial networks (GANs), normalizing flows, diffusion models, and models based on conditional flow matching. We compare all submissions in terms of quality of generated calorimeter showers, as well as shower generation time and model size. To assess the quality we use a broad range of different metrics including differences in one-dimensional histograms of observables, KPD/FPD scores, AUCs of binary classifiers, and the log-posterior of a multiclass classifier. The results of the CaloChallenge provide the most complete and comprehensive survey of cutting-edge approaches to calorimeter fast simulation to date. In addition, our work provides a uniquely detailed perspective on the important problem of how to evaluate generative models. As such, the results presented here should be applicable for other domains that use generative AI and require fast and faithful generation of samples in a large phase space.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
NASA NTRS1974

Performance of a highly loaded two stage axial-flow fan

A two-stage axial-flow fan with a tip speed of 1450 ft/sec (442 m/sec) and an overall pressure ratio of 2.8 was designed, built, and tested. At design speed and pressure ratio, the measured flow matched the design value of 184.2 lbm/sec (83.55kg/sec). The adiabatic efficiency at the design operating point was 85.7 percent. The stall margin at design speed was 10 percent. A first-bending-mode flutter of the second-stage rotor blades was encountered near stall at speeds between 77 and 93 percent of design, and also at high pressure ratios at speeds above 105 percent of design. A 5 deg closed reset of the first-stage stator eliminated second-stage flutter for all but a narrow speed range near 90 percent of design.

Ruggeri, R. S.↗