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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 1,081 records · Page 60
All sourcesNASA NTRSDOE OSTI
DOE OSTI2025

A GPU‐Based Ocean Dynamical Core for Routine Mesoscale‐Resolving Climate Simulations

Abstract We describe an ocean hydrostatic dynamical core implemented in Oceananigans optimized for Graphical Processing Unit (GPU) architectures. On 64 A100 GPUs, equivalent to 16 computational nodes in current state‐of‐the‐art supercomputers, our dynamical core can simulate a decade of near‐global ocean dynamics per wall‐clock day at an 8‐km horizontal resolution; a resolution adequate to resolve the ocean's mesoscale eddy field. Such efficiency, achieved with relatively modest hardware resources, suggests that climate simulations on GPUs can incorporate fully eddy‐resolving ocean models. This removes a major source of systematic bias in current IPCC coupled model projections, the parameterization of ocean eddies, and represents a major advance in climate modeling. We discuss the computational strategies, focusing on GPU‐specific optimization and numerical implementation details that enable such high performance.

Silvestri, Simone [Massachusetts Institute of Tech↗
DOE OSTI2025

LBNF Material Activation Study

This poster presents a study on material activation at the Fermilab Long-Baseline Neutrino Facility (LBNF), focusing on understanding how high-energy beams interact with surrounding materials to produce radioactive isotopes. Using a simplified model of the LBNF-20 bunker and the FLUKA Monte Carlo simulation tool, the project quantifies isotope production at key locations inside and outside the shielding structures. The study employs a two-step simulation process, tracking primary and secondary particles, and explores the impact of different geometric and material configurations on activation rates. Results include preliminary comparisons of simulated activation rates and suggest methods for refining geometry, improving accuracy, and efficiently simulating future configurations. This work informs the design and operation of the LBNF facility, aiding in radiation safety and shielding optimization. Future efforts will focus on sample iteration, automated simulations, and detailed residual isotope analyses to further enhance the understanding of material activation in high-energy physics environments.

Pietraszek, Jacob [Unlisted, US]↗
NASA NTRS2006

Evolutionary Design of a Phased Array Antenna Element

We present an evolved S-band phased array antenna element design that meets the requirements of NASA's TDRS-C communications satellite scheduled for launch early next decade. The original specification called for two types of elements, one for receive only and one for transmit/receive. We were able to evolve a single element design that meets both specifications thereby simplifying the antenna and reducing testing and integration costs. The highest performance antenna found using a genetic algorithm and stochastic hill-climbing has been fabricated and tested. Laboratory results are largely consistent with simulation. Researchers have been investigating evolutionary antenna design and optimization since the early 1990s, and the field has grown in recent years its computer speed has increased and electromagnetic simulators have improved. Many antenna types have been investigated, including wire antennas, antenna arrays and quadrifilar helical antennas. In particular, our laboratory evolved a wire antenna design for NASA's Space Technology 5 (ST5) spacecraft. This antenna has been fabricated, tested, and is scheduled for launch on the three spacecraft in 2006.

Globus, Al↗
NASA NTRS2010

Effect of Turbulence Modeling on an Excited Jet

The flow dynamics in a high-speed jet are dominated by unsteady turbulent flow structures in the plume. Jet excitation seeks to control these flow structures through the natural instabilities present in the initial shear layer of the jet. Understanding and optimizing the excitation input, for jet noise reduction or plume mixing enhancement, requires many trials that may be done experimentally or computationally at a significant cost savings. Numerical simulations, which model various parts of the unsteady dynamics to reduce the computational expense of the simulation, must adequately capture the unsteady flow dynamics in the excited jet for the results are to be used. Four CFD methods are considered for use in an excited jet problem, including two turbulence models with an Unsteady Reynolds Averaged Navier-Stokes (URANS) solver, one Large Eddy Simulation (LES) solver, and one URANS/LES hybrid method. Each method is used to simulate a simplified excited jet and the results are evaluated based on the flow data, computation time, and numerical stability. The knowledge gained about the effect of turbulence modeling and CFD methods from these basic simulations will guide and assist future three-dimensional (3-D) simulations that will be used to understand and optimize a realistic excited jet for a particular application.

Brown, Clifford A.↗
NASA NTRS2002

Raytracing for Multi-Spacecraft Missions

The numerical simulation of in-situ instruments allows the instrument design to be optimized for particle throughput and allows for the iteration of the design parameters before fabrication. This effort leads to greater efficiency since the hardware itself does not have to be machined and fabricated to test each design change. More importantly for multi-spacecraft missions, the numerical raytracing allows the assessment of manufacturing tolerances so that a low relative accuracy can be obtained between the instruments. We will discuss our experience in optimizing and raytracing our charged particle instruments, obtaining the geometry factor from these simulations, and the comparison with the results of laboratory testing. This discussion with others will support the ISSI Scientific Report on Calibration Techniques for In-Situ Plasma Instrumentation.

Coffey, Victoria↗
DOE OSTI2026

Chasing Gamma-Ray Signals from Binary Neutron Star Coalescences with the Cherenkov Telescope Array: Prospects and Observing Strategies

The detection of gravitational waves (GWs) from a binary neutron star (BNS) merger by Advanced LIGO and Advanced Virgo (GW170817), together with its electromagnetic counterpart, the short gamma-ray burst GRB 170817A, heralded the birth of multimessenger astronomy. The detection of TeV emission from GRBs motivates follow-up observations with the Cherenkov Telescope Array Observatory (CTAO), which is ideal for detecting such signals due to its unprecedented sensitivity, rapid response, and wide-field survey capabilities. The aim of this work is to evaluate GeV–TeV GW follow-up strategies for CTAO using a multistep simulation pipeline and to estimate the expected rate of joint GW–GRB detections during observing run O5. Using a simulated sample of BNS systems with corresponding GW detections, gamma-ray emission is simulated through phenomenological prescriptions based on the observed population of short GRBs, including off-axis jet scenarios. CTAO observations are simulated to account for instrument response, sky tiling strategies, integration times, and varying observing conditions. Strategies with variable and constant integration times are investigated. We find that, via an optimized follow-up strategy, about 5% of simulated GW-associated short GRBs produce GeV–TeV radiation detectable by CTAO. Detectability is strongly influenced by the jet opening angle and viewing angle, suggesting that even rough estimates of the viewing angle in GW alerts could enhance targeting. This framework motivates future follow-ups of GW-detectable events, including neutron star–black hole mergers, and further supports the development of advanced strategies incorporating galaxy distributions and synergies with future detectors such as the Einstein Telescope.

Abe, S. [University of Tokyo] (ORCID:0000000172503↗
DOE OSTI2024

Multigrid Reduction in Time for Chaotic and Hyperbolic Problems (Final Report)

The coming massive parallelism of exascale computing presents a pressing challenge for the many DOE simulations of time-dependent partial differential equations (PDEs), which typically use traditional sequential time stepping methods. Since this traditional approach is inherently serial, it presents a sequential bottleneck when moving to exascale computing, because future performance gains will come through greater concurrency, not faster clock speeds. Thus, the goal of this work is to research parallelism in time, i.e., methods that compute multiple time values simultaneously, not sequentially. The focus will be on hyperbolic and chaotic problems of interest to DOE, with the goal of enabling scalable simulations of time-dependent hyperbolic and chaotic problems on future architectures. The chosen methodology for solving these problems parallel-in-time is multigrid, because multigrid (when it works) is a powerful, optimal, and scalable solver for discretized PDEs. Multigrid is already commonly used in many DOE simulations for scalably and optimally solving space-only PDE problems. The areas of hyperbolic and chaotic problems are chosen because of their relevance to problems of programmatic interest to DOE. However, these problems are also well-known to be difficult for parallel-in-time methods, with the most common method, parareal, diverging in many cases. The current state of-the-art for parallel-in-time at LLNL is the multigrid reduction in time (MGRIT) XBraid package, which also struggles for such problems, while still showing some improvement over parareal. In summary, new methods are needed for an efficient parallel-in-time scheme for hyperbolic and chaotic problems, and this work shall research promising new multigrid methods in this area. In particular, this work shall continue researching the directions from the current collaboration with Dr. Falgout, which are laid out in the work Toward Parallel in Time for Chaotic Dynamical Systems and showed the first known results of a parallel-in-time speedup for a chaotic problem. This work outlines two key improvements to XBraid for chaotic problems, the so-called “theta” and “delta-correction” methods. Here, these two improvements will be further researched and improved (including with a new relaxation method inspired by on Least Squares Shadowing (LSS)) and explored for more complicated problems.

97 MATHEMATICS AND COMPUTING↗
NASA NTRS2010

Particle Swarm Optimization Toolbox

The Particle Swarm Optimization Toolbox is a library of evolutionary optimization tools developed in the MATLAB environment. The algorithms contained in the library include a genetic algorithm (GA), a single-objective particle swarm optimizer (SOPSO), and a multi-objective particle swarm optimizer (MOPSO). Development focused on both the SOPSO and MOPSO. A GA was included mainly for comparison purposes, and the particle swarm optimizers appeared to perform better for a wide variety of optimization problems. All algorithms are capable of performing unconstrained and constrained optimization. The particle swarm optimizers are capable of performing single and multi-objective optimization. The SOPSO and MOPSO algorithms are based on swarming theory and bird-flocking patterns to search the trade space for the optimal solution or optimal trade in competing objectives. The MOPSO generates Pareto fronts for objectives that are in competition. A GA, based on Darwin evolutionary theory, is also included in the library. The GA consists of individuals that form a population in the design space. The population mates to form offspring at new locations in the design space. These offspring contain traits from both of the parents. The algorithm is based on this combination of traits from parents to hopefully provide an improved solution than either of the original parents. As the algorithm progresses, individuals that hold these optimal traits will emerge as the optimal solutions. Due to the generic design of all optimization algorithms, each algorithm interfaces with a user-supplied objective function. This function serves as a "black-box" to the optimizers in which the only purpose of this function is to evaluate solutions provided by the optimizers. Hence, the user-supplied function can be numerical simulations, analytical functions, etc., since the specific detail of this function is of no concern to the optimizer. These algorithms were originally developed to support entry trajectory and guidance design for the Mars Science Laboratory mission but may be applied to any optimization problem.

Grant, Michael J.↗
NASA NTRS1972

Adaptive control of stochastic linear systems with unknown parameters

The problem of optimal control of linear discrete-time stochastic dynamical system with unknown and, possibly, stochastically varying parameters is considered on the basis of noisy measurements. It is desired to minimize the expected value of a quadratic cost functional. Since the simultaneous estimation of the state and plant parameters is a nonlinear filtering problem, the extended Kalman filter algorithm is used. Several qualitative and asymptotic properties of the open loop feedback optimal control and the enforced separation scheme are discussed. Simulation results via Monte Carlo method show that, in terms of the performance measure, for stable systems the open loop feedback optimal control system is slightly better than the enforced separation scheme, while for unstable systems the latter scheme is far better.

Ku, R. T.↗
NASA NTRS1974

Simulation of low thrust guidance problems

The determination of optimal rocket control profiles and the guidance procedures used to approximate these profiles have received much attention. Simple guidance procedures have sufficed for the Apollo flights. A modified version of the linear tangent guidance law is developed which can be used for a low-thrust orbital maneuvering system such as the space shuttle. The modification allows the estimation of gravity over long thrusting arcs which is necessary for low thrust systems.

Dawkins, G. S.↗
NASA NTRS1994

Lidar performance analysis

Section 1 details the theory used to build the lidar model, provides results of using the model to evaluate AEOLUS design instrument designs, and provides snapshots of the visual appearance of the coded model. Appendix A contains a Fortran program to calculate various forms of the refractive index structure function. This program was used to determine the refractive index structure function used in the main lidar simulation code. Appendix B contains a memo on the optimization of the lidar telescope geometry for a line-scan geometry. Appendix C contains the code for the main lidar simulation and brief instruction on running the code. Appendix D contains a Fortran code to calculate the maximum permissible exposure for the eye from the ANSI Z136.1-1992 eye safety standards. Appendix E contains a paper on the eye safety analysis of a space-based coherent lidar presented at the 7th Coherent Laser Radar Applications and Technology Conference, Paris, France, 19-23 July 1993.

Spiers, Gary D.↗
DOE OSTI2024

Modeling and Optimization of Zeolites for Contaminant Removal from Coal Combustion Impoundment Leachates

Coal Combustion Residual (CCR) landfills in the U.S. can lead to elevated contaminant concentrations in groundwater and runoff, particularly of arsenic, boron, and selenium. Zeolites can be readily synthesized from materials contained within the coal combustion ash impoundments and can be cation-exchanged to enhance adsorption capacity, selectivity, and reactivity. However, the optimization of zeolites becomes a daunting task when accounting for the variety of Si:Al ratios, the species of extra-framework cations present, and the zeolite pore structure. Molecular simulations provide methods to study and guide the design of zeolites for the sorption of contaminants from aqueous solution. In this work, models that predict the sorption of arsenic, boron, and selenium from water using cation-exchanged zeolites. Because of the lack of experimental adsorption data for these species of contaminants, models were parametrized to reproduce the results of first-principles calculations and then used to predict sorption for zeolites for a dataset containing 6000 combinations of zeolites and sorption conditions. Machine learning was used to train a model to predict sorption for materials in this database based on the results of the molecular simulations. Next, a genetic algorithm was used to optimize zeolites for the removal of each contaminant from aqueous solution for individual impoundment sites based on contaminant concentrations reported by the Electric Power Research Institute.

Findley, John↗
NASA NTRS2024

Revisiting Optimal Guidance Solution for Vertical Landing and Take-Off

The vertical landing and take-off are crucial phases of a rocket-powered spacecraft's powered descent and ascent trajectories, during which its motion is confined to a single (vertical) axis. It is well-known that the minimum-propellant guidance solutions for vertical landing and take-off in a vacuum, consist of only one coast and one full-thrust arc and no singular arc exists in these problems. However, an exact solution for the vertical descent problem is not found in the literature. In the present work, exact guidance solutions for the optimal vertical landing and take-off are presented that are valid for arbitrary boundary conditions. The time-based and switching function-based guidance law implementations are discussed and their performance is compared using lunar descent and ascent simulations.

powered descent↗
NASA NTRS2005

Large-Scale NASA Science Applications on the Columbia Supercluster

Columbia, NASA's newest 61 teraflops supercomputer that became operational late last year, is a highly integrated Altix cluster of 10,240 processors, and was named to honor the crew of the Space Shuttle lost in early 2003. Constructed in just four months, Columbia increased NASA's computing capability ten-fold, and revitalized the Agency's high-end computing efforts. Significant cutting-edge science and engineering simulations in the areas of space and Earth sciences, as well as aeronautics and space operations, are already occurring on this largest operational Linux supercomputer, demonstrating its capacity and capability to accelerate NASA's space exploration vision. The presentation will describe how an integrated environment consisting not only of next-generation systems, but also modeling and simulation, high-speed networking, parallel performance optimization, and advanced data analysis and visualization, is being used to reduce design cycle time, accelerate scientific discovery, conduct parametric analysis of multiple scenarios, and enhance safety during the life cycle of NASA missions. The talk will conclude by discussing how NAS partnered with various NASA centers, other government agencies, computer industry, and academia, to create a national resource in large-scale modeling and simulation.

Brooks, Walter↗
NASA NTRSDate not supplied

Efficient Implementation for Unitary Coupled Cluster State Preparation for Near-Term Quantum Computers

Unitary coupled cluster theory (UCC) is a common wave function ansatz for quantum simulation of molecular electronic structure using the variational quantum eigenvalue solver (VQE). Even for small molecules using a double-ζ basis, the number of variational parameters required to minimize the electronic energy (i.e., optimize the circuit) is large and beyond the reach of current quantum computers. For example, a circuit simulating C2 using the UCCSD ansatz and the cc-pVDZ basis set with frozen-core will require over 10,000 variational parameters and a Hilbert space of over 10^8 determinants. To make progress on simulating such molecular systems on near-term quantum computers, we explore how much of the optimization can be approximately prepared with classical simulation while reducing the number of optimization steps performed on a quantum device. Recently, Chen, Cheng, and Freericks [J. Chem. Theory Comput. 2021, 17, 841-847] presented an algorithm for the factorized form of the UCC ansatz that allows for efficient UCC optimizations on classical hardware. We flip the algorithm around and use it to prepare approximate quantum circuits for systems that require a large number of qubits to represent. We will present results from our implementation and discuss strategies for incorporating this implementation for algorithms involving near-term quantum computers.

J Wayne Mullinax↗
NASA NTRSDate not supplied

Efficient Implementation for Unitary Coupled Cluster State Preparation for Near-Term Quantum Computers

Unitary coupled cluster theory (UCC) is a common wave function ansatz for quantum simulation of molecular electronic structure using the variational quantum eigenvalue solver (VQE). Even for small molecules using a double-ζ basis, the number of variational parameters required to minimize the electronic energy (i.e., optimize the circuit) is large and beyond the reach of current quantum computers. For example, a circuit simulating C2 using the UCCSD ansatz and the cc-pVDZ basis set with frozen-core will require over 10,000 variational parameters and a Hilbert space of over 10^(8) determinants. To make progress on simulating such molecular systems on near-term quantum computers, we explore how much of the optimization can be approximately prepared with classical simulation while reducing the number of optimization steps performed on a quantum device. Recently, Chen, Cheng, and Freericks [J. Chem. Theory Comput. 2021, 17, 841-847] presented an algorithm for the factorized form of the UCC ansatz that allows for efficient UCC optimizations on classical hardware. We flip the algorithm around and use it to prepare approximate quantum circuits for systems that require a large number of qubits to represent. We will present results from our implementation and discuss strategies for incorporating this implementation for algorithms involving near-term quantum computers.

Quantum Computing↗
NASA NTRSDate not supplied

Efficient Implementation for Unitary Coupled Cluster State Preparation for Near-Term Quantum Computers

Unitary coupled cluster theory (UCC) is a common wave function ansatz for quantum simulation of molecular electronic structure using the variational quantum eigenvalue solver (VQE). Even for small molecules using a double-ζ basis, the number of variational parameters required to minimize the electronic energy (i.e., optimize the circuit) is large and beyond the reach of current quantum computers. For example, a circuit simulating C2 using the UCCSD ansatz and the cc-pVDZ basis set with frozen-core will require over 10,000 variational parameters and a Hilbert space of over 10^(8) determinants. To make progress on simulating such molecular systems on near-term quantum computers, we explore how much of the optimization can be approximately prepared with classical simulation while reducing the number of optimization steps performed on a quantum device. Recently, Chen, Cheng, and Freericks [J. Chem. Theory Comput. 2021, 17, 841-847] presented an algorithm for the factorized form of the UCC ansatz that allows for efficient UCC optimizations on classical hardware. We flip the algorithm around and use it to prepare approximate quantum circuits for systems that require a large number of qubits to represent. We will present results from our implementation and discuss strategies for incorporating this implementation for algorithms involving near-term quantum computers.

Quantum Computing↗
NASA NTRSDate not supplied

Propulsion-Airframe Integration for Conceptual Redesign of a Low-Boom Supersonic Transport

A low-boom supersonic transport was designed for a cruise Mach of 1.7 and 40 passengers. This low-boom aircraft, referred to as the Mach 1.7 40-PAX concept, was generated using computational fluid dynamics (CFD) based sonic boom analysis at the start of overland cruise (SOC). The engine for the Mach 1.7 40-PAX concept was designed using the Numerical Propulsion System Simulation (NPSS) and modeled as a flow-through nacelle for CFD analysis. To understand how the engine plume affects the undertrack ground signature, the Mach 1.7 40-PAX concept is redesigned using an aeropropulsive CFD simulation. A process is developed for approximation of the NPSS engine at SOC by a CFD engine for aeropropulsive CFD simulation. The generated CFD engine has the identical nozzle boundary conditions and approximately the same mass flow and thrust as those of the NPSS engine at SOC. Then, the configuration with the CFD engines is optimized to approximately restore the low-boom characteristics of the Mach 1.7 40-PAX concept. Finally, the CFD simulation data for the optimized concept with the CFD engines is used to calibrate the low-fidelity aerodynamic analyses for mission analysis of this concept. The cyclic dependency of the involved disciplinary analyses is resolved using an iteration method for a consistent coupling of the mission analysis, NPSS engine analysis, and low-boom redesign using the aeropropulsive CFD simulation. This low-boom redesign study is used as an example to demonstrate how the propulsion-airframe integration could be implemented for conceptual design of low-boom supersonic transports.

Low-boom supersonic transport↗