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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 55 records · Page 3
All sourcesNASA NTRSDOE OSTI
DOE OSTI2026

MFC 5.0: An exascale many-physics flow solver

Many problems of interest in engineering, medicine, and the fundamental sciences rely on high-fidelity flow simulation, making performant computational fluid dynamics solvers a mainstay of the open-source software community. Previous work MFC 3.0 was made a published, documented, and open-source solver via Bryngelson et al. Comp. Phys. Comm. (2021) with numerous physical features, numerical methods, and scalable infrastructure. MFC 5.0 is a significant update to MFC 3.0, featuring a broad set of well-established and novel physical models and numerical methods, as well as the introduction of GPU and APU (or superchip) acceleration. Here, we exhibit state-of-the-art performance and ideal scaling on the first two exascale supercomputers, OLCF Frontier and LLNL El Capitan. Combined with MFC’s single-accelerator performance, MFC achieves exascale computation in practice, and achieved the largest-to-date public CFD simulation at 200 trillion grid points as a 2025 ACM Gordon Bell Prize finalist. New physical features include the immersed boundary method, N-fluid phase change, Euler–Euler and Euler–Lagrange sub-grid bubble models, fluid-structure interaction, hypo- and hyper-elastic materials, chemically reacting flow, two-material surface tension, magnetohydrodynamics (MHD), and more. Numerical techniques now represent the current state-of-the-art, including general relaxation characteristic boundary conditions, WENO variants, Strang splitting for stiff sub-grid flow features, and low Mach number treatments. Weak scaling to tens of thousands of GPUs on OLCF Summit and Frontier and LLNL El Capitan achieves efficiencies within 5% of ideal to over 90% of their respective system sizes. Strong scaling results for a 16-times increase in device count show parallel efficiencies over 90% on OLCF Frontier. MFC’s software stack has undergone further improvements, including continuous integration, which ensures code resilience and correctness through over 300 regression tests; metaprogramming, which reduces code length while maintaining performance portability; and code generation for computing chemical reactions

Computational fluid dynamics↗
DOE OSTI2023

Design and operating principles for high-performing anion exchange membrane water electrolyzers

Anion-exchange-membrane water electrolyzers (AEMWEs) provide a promising pathway to utilize low-carbon renewable electricity to produce clean hydrogen at high efficiency and purity, while maintaining low system costs compared to incumbent technologies. Though significant progress has been made in developing membranes and catalysts, AEMWEs still require better performance and durability to realize widespread deployment. Here, we overcome these challenges by decoupling anode and cathode polarization behavior via integration of a reference electrode in the membrane-electrode assembly. This measurement identified that the mass-transport losses dominate the cathode overpotential if feeding with electrolytes, while kinetic losses dominate the anode overpotential. These losses are mitigated by varying electrode properties and operating strategies, where a more hydrophobic, optimal loaded cathode, a high porosity anode, and operating with the cathode dry exhibited the best performance. These findings eventually enabled achieving a high-performing and durable complete PGM-free AEMWE operating at 1.5 Acm –2 for over 500h with negligible degradation, demonstrating significant progress for AEMWEs.

25 ENERGY STORAGE↗
DOE OSTI2023

Carbon dioxide reduction in solid oxide electrolyzer cells using transition metals infiltrated into Gd 0.1 Ce 0.9 O 1.95 (GDC10) scaffolds

Here, this study reports the catalytic activity of transition metal electrocatalysts (Co, Ni, and Cu) incorporated into Gd 0.1 Ce 0.9 O 1.95 (GDC10) cathodes for the electroreduction of CO 2 in solid oxide electrolyzer cells (SOECs). CO 2 electroreduction performance of cells having porous and non-infiltrated GDC10 cathodes was compared with the performance of cells containing transition metal electrocatalysts infiltrated into porous GDC10 cathodes at 750, 800, and 850°C. Results showed that cells with Co infiltrated cathodes had the best catalytic activity towards CO 2 electroreduction. Furthermore, these cells displayed good stability towards CO electroreduction, having a faradaic efficiency value close to 100% with insignificant voltage increase when tested for 48h at 750 and 850°C under the current densities of 0.2, and 0.4Acm -2 , respectively.

25 ENERGY STORAGE↗
DOE OSTI2020

Cluster structure of light nuclei

We review recent studies of the cluster structure of light nuclei within the framework of the algebraic cluster model (ACM) for nuclei composed of k α-particles and within the framework of the cluster shell model (CSM) for nuclei composed of k α-particles plus x additional nucleons. The calculations, based on symmetry considerations and thus for the most part given in analytic form, are compared with experiments in light cluster nuclei. The comparison shows evidence for Z 2 , D 3h and T d symmetry in the even–even nuclei 8 Be (k = 2), 12 C (k = 3) and 16 O (k = 4), respectively, and for the associated double groups Z' 2 and D' 3h in the odd nuclei 9 Be, 9 B (k = 2, x = 1) and 13 C (k = 3, x = 1), respectively.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2023

Highly Robust and Efficient Blechert-Type Cyclic(alkyl)(amino)carbene Ruthenium Complexes for Olefin Metathesis

The first Blechert-type ruthenium complexes containing cyclic(alkyl)(amino)carbene (CAAC) ligands are reported. Furthermore, these catalysts demonstrate remarkable thermal stability in solution and excellent catalytic performances at low catalytic loading (up to 0.005 mol%) in ring-closing metathesis (RCM), macro-RCM, ring-closing enyne metathesis (RCEYM), cross-metathesis (CM), ethenolysis and ring-opening cross metathesis (ROCM). Moreover, up to 95% ee was obtained in asymmetric ring-opening cross metathesis (AROCM) and 57% ee asymmetric cross-metathesis (ACM)

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2020

Sensitivity of EDM experiments in paramagnetic atoms and molecules to hadronic CP violation

Experiments searching for the electric dipole moment (EDM) of the electron d e utilize atomic/molecular states with one or more uncompensated electron spins, and these paramagnetic systems have recently achieved remarkable sensitivity to d e . If the source of CP violation resides entirely in the hadronic sector, the two-photon exchange processes between electrons and the nucleus induce CP-odd semileptonic interactions, parametrised by the Wilson coefficient C SP , and provide the dominant source of EDMs in paramagnetic systems instead of d e . We evaluate the C SP coefficients induced by the leading hadronic sources of CP violation, namely nucleon EDMs and CP-odd pion-nucleon couplings, by calculating the nucleon-number-enhanced CP-odd nuclear scalar polarisability, employing chiral perturbation theory at the nucleon level and the Fermi-gas model for the nucleus. This allows us to translate the ACME EDM limits from paramagnetic ThO into novel independent constraints on the QCD theta term |$\bar θ$| < 3 × 10 –8 , proton EDM |d p | < 2 × 10 –23 e cm, isoscalar CP-odd pion-nucleon coupling |$\bar g$$^{(1)}_{πNN}$| < 4 × 10 –10 , and color EDMs of quarks |$\tilde d$ u -$\tilde $d d | < 2 × 10 –24 cm. We note that further experimental progress with EDM experiments in paramagnetic systems may allow them to rival the sensitivity of EDM experiments with neutrons and diamagnetic atoms to these quantities.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2020

Effective Gaps Are Not Effective: Quasipolynomial Classical Simulation of Obstructed Stoquastic Hamiltonians

All known examples suggesting an exponential separation between classical simulation algorithms and stoquastic adiabatic quantum computing (StoqAQC) exploit symmetries that constrain adiabatic dynamics to effective, symmetric subspaces. The symmetries produce large effective eigenvalue gaps, which in turn make adiabatic computation efficient. We present a classical algorithm to subexponentially sample from an effective subspace of any k -local stoquastic Hamiltonian H , without a priori knowledge of its symmetries (or near symmetries). Our algorithm maps any k -local Hamiltonian to a graph G = ( V , E ) with | V | = O (poly (n)) , where n is the number of qubits. Given the well-known result of Babai [Graph isomorphism in quasipolynomial time, in Proceedings of the Forty-Eighth Annual ACM Symposium on Theory of Computing (2016), pp. 684–697], we exploit graph isomorphism to study the automorphisms of G and arrive at an algorithm quasipolynomial in | V | for producing samples from effective subspace eigenstates of H . Our results rule out exponential separations between StoqAQC and classical computation that arise from hidden symmetries in k -local Hamiltonians. Our graph representation of H is not limited to stoquastic Hamiltonians and may rule out corresponding obstructions in nonstoquastic cases, or be useful in studying additional properties of k -local Hamiltonians.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2021

Grand Unification of Quantum Algorithms

Quantum algorithms offer significant speed-ups over their classical counterparts for a variety of problems. The strongest arguments for this advantage are borne by algorithms for quantum search, quantum phase estimation, and Hamiltonian simulation, which appear as subroutines for large families of composite quantum algorithms. A number of these quantum algorithms have recently been tied together by a novel technique known as the quantum singular value transformation (QSVT), which enables one to perform a polynomial transformation of the singular values of a linear operator embedded in a unitary matrix. In the seminal GSLW’19 paper on the QSVT [Gilyén et al., ACM STOC 2019], many algorithms are encompassed, including amplitude amplification, methods for the quantum linear systems problem, and quantum simulation. Here, we provide a pedagogical tutorial through these developments, first illustrating how quantum signal processing may be generalized to the quantum eigenvalue transform, from which the QSVT naturally emerges. Paralleling GSLW’19, we then employ the QSVT to construct intuitive quantum algorithms for search, phase estimation, and Hamiltonian simulation, and also showcase algorithms for the eigenvalue threshold problem and matrix inversion. This overview illustrates how the QSVT is a single framework comprising the three major quantum algorithms, suggesting a grand unification of quantum algorithms.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2020

Dedicated short range communications used as fail-safe in autonomous navigation

Here in this paper, we have analyzed scenarios for leader-follower vehicle convoys that have the potential to be unsafe, hazardous, or even fatal in order to provide insight on dangerous driving conditions for autonomous vehicle platoons. These scenarios were created in a simulation software called Prescan, and are made to reflect the behavior of the vehicle dynamics and sensor characteristics of a real-world vehicle convoy that is currently being tested at the American Center for Mobility (ACM) and sponsored by the Department of Energy (DOE), National Renewable Energy Laboratory (NREL), the U.S. Army Combat Capabilities Development Command - Ground Vehicle Systems Center (U.S. Army CC-DEVCOM) and the Michigan Department of Transportation (MDOT). The follower vehicle in this study is tested under multiple scenarios, equipped with a Dedicated Short Range vehicle-to-vehicle communication system (DSRC), a radar-based adaptive cruise control system (ACC) and a precision global positioning system (GPS).

42 ENGINEERING↗
DOE OSTI2023

Adaptive Time Step Control for Multirate Infinitesimal Methods

Multirate methods have been used for decades to temporally evolve initial-value problems in which different components evolve on distinct time scales, and thus use of different step sizes for these components can result in increased computational efficiency. Generally, such methods select these different step sizes based on experimentation or stability considerations. For problems that evolve on a single time scale, adaptivity approaches that strive to control local temporal error are widely used to achieve numerical results of a desired accuracy with minimal computational effort, while alleviating the need for manual experimentation with different time step sizes. However, there is a notable gap in the publication record on the development of adaptive time step controllers for multirate methods. In this paper, we extend the single-rate controller work of Gustafsson [ACM Trans. Math. Software, 20 (1994), pp. 496-517] to the multirate method setting. Specifically, we develop controllers based on polynomial approximations to the principal error functions for both the "fast" and "slow" time scales within multirate infinitesimal (MRI) methods. We additionally investigate a variety of approaches for estimating the errors arising from each time scale within MRI methods. We then numerically evaluate the proposed multirate controllers and error estimation strategies on a range of multirate test problems, comparing their performance against an estimated optimal performance. Through this work, we combine the most performant of these approaches to arrive at a set of multirate adaptive time step controllers that robustly achieve desired solution accuracy with minimal computational effort.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2022

Fragile Earth: AI for Climate Mitigation, Adaptation, and Environmental Justice

The Fragile EarthWorkshop is a recurring event that gathers the research community to find and explore howdata science can measure and progress climate and social issues, following the framework of the United Nations Sustainable Development Goals (SDGs).Fragile Earth 2022: AI for Climate Mitigation, Adaptation, and Environmental Justice is a workshop taking place as part of the ACM's KDD 2022 Conference on research in knowledge discovery and data mining and their applications. The dates for the Conference are August 14-18, 2022.

Abe, Naoki↗
DOE OSTI2024

Remark on Algorithm 1012: Computing Projections with Large Datasets

In ACM TOMS Algorithm 1012, the DELAUNAYSPARSE software is given for performing Delaunay interpolation in medium to high dimensions. When extrapolating outside the convex hull of the training set, DELAUNAYSPARSE calls the nonnegative least squares solver DWNNLS to compute projections onto the convex hull. However, DWNNLS and many other available sum-of-squares optimization solvers were not intended for usage with many variable problems, which result from the large training sets that are typical in machine learning applications. Thus, a new PROJECT subroutine is given, based on the highly customizable quadratic program solver BQPD. This solution is shown to be as robust as DELAUNAYSPARSE for projection onto both synthetic and real-world datasets, where other available solvers frequently fail. Although it is intended as an update for DELAUNAYSPARSE, due to the difficulty and prevalence of the problem, this solution is likely to be of external interest as well.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2025

Data Readiness for AI: A 360-Degree Survey

Artificial Intelligence (AI) applications critically depend on data. Poor-quality data produces inaccurate and ineffective AI models that may lead to incorrect or unsafe use. Evaluation of data readiness is a crucial step in improving the quality and appropriateness of data usage for AI. R&D efforts have been spent on improving data quality. However, standardized metrics for evaluating data readiness for use in AI training are still evolving. In this study, we perform a comprehensive survey of metrics used to verify data readiness for AI training. This survey examines more than 140 papers published by ACM Digital Library, IEEE Xplore, journals such as Nature, Springer, and Science Direct, and online articles published by prominent AI experts. This survey aims to propose a taxonomy of data readiness for AI (DRAI) metrics for structured and unstructured datasets. We anticipate that this taxonomy will lead to new standards for DRAI metrics that would be used for enhancing the quality, accuracy, and fairness of AI training and inference.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2026

Trilinos: Enabling Scientific Computing across Diverse Hardware Architectures at Scale

Trilinos is a community-developed, open-source software framework that facilitates building large-scale, complex, multiscale, multiphysics simulation code bases for scientific and engineering problems. Since the Trilinos framework has undergone substantial changes to support new applications and new hardware architectures, this document is an update to “An Overview of the Trilinos project” by Heroux et al. (ACM Transactions on Mathematical Software, 31(3):397–423, 2005). It describes the design of Trilinos, introduces its new organization in product areas, and highlights established and new features available in Trilinos. Particular focus is put on the modernized software stack based on the Kokkos ecosystem to deliver performance portability across heterogeneous hardware architectures. This article also outlines the organization of the Trilinos community and the contribution model to help onboard interested users and contributors.

Heterogeneous Hardware Architectures↗
DOE OSTI2021

Kohn-Sham Solver (KSSOLV) v2.0

KSSOLV is a MATLAB toolbox for solving Kohn-Sham density functional theory based electronic structure eigenvalue problems. It uses an object oriented features of MATLAB to represent atom, molecules, wavefunctions and Hamiltonians and their operations. It is designed to make it easier for users to prototype and test new algorithms for solving the Kohn-Sham problem. KSSOLV2.0 contains significant improvement over the original KSSOLV described in a paper published in ACM Transaction on Mathematical Software (attached). In addition to performing ground state calculation for small molecules, it can also perform geometry optimization for both molecules and solids. It uses standard pseudopotentials and implements local density approximation, generalized gradient approximation and hybrid functionals. Future releases will also include time-dependent DFT and post DFT calculations such as the GW quasi-particle energy calculation and Bethe-Salpeter equation solver for optical absorption.

Yang, Chao↗
DOE OSTI2020

Prediction of tissue optical properties using the Monte Carlo modeling of photon transport in turbid media and integrating spheres

Monte Carlo methods are an established technique for simulating light transport in biological tissue. Integrating spheres make experimental measurements of the reflectance and transmittance of a sample straightforward and inexpensive. This work presents an extension to existing Monte Carlo photon transport methods to simulate integrating sphere experiments. Crosstalk between spheres in dual-sphere experiments is accounted for in the method. Analytical models, previous works on Monte Carlo photon transport, and experimental measurements of a synthetic tissue phantom validate this method. We present two approaches for using this method to back-calculate the optical properties of samples. Experimental and simulation uncertainties are propagated through both methods. Both back-calculation methods find the optical properties of a sample accurately and precisely. Our model is implemented in standard Python 3 and CUDA C++ [J. Nickolls, I. Buck, M. Garland, and K. Skadron, ACM Queue 6 , 40 ( 2008 ) ] and is publicly available in Code 1.

Cook, Patrick D. (ORCID:0000000279345428)↗