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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 1,171 records · Page 65
All sourcesNASA NTRSDOE OSTI
DOE OSTI2021

Efficient exascale discretizations: High-order finite element methods

Efficient exploitation of exascale architectures requires rethinking of the numerical algorithms used in many large-scale applications. These architectures favor algorithms that expose ultra fine-grain parallelism and maximize the ratio of floating point operations to energy intensive data movement. One of the few viable approaches to achieve high efficiency in the area of PDE discretizations on unstructured grids is to use matrix-free/partially assembled high-order finite element methods, since these methods can increase the accuracy and/or lower the computational time due to reduced data motion. In this paper we provide an overview of the research and development activities in the Center for Efficient Exascale Discretizations (CEED), a co-design center in the Exascale Computing Project that is focused on the development of next-generation discretization software and algorithms to enable a wide range of finite element applications to run efficiently on future hardware. CEED is a research partnership involving more than 30 computational scientists from two US national labs and five universities, including members of the Nek5000, MFEM, MAGMA and PETSc projects. We discuss the CEED co-design activities based on targeted benchmarks, miniapps and discretization libraries and our work on performance optimizations for large-scale GPU architectures. We also provide a broad overview of research and development activities in areas such as unstructured adaptive mesh refinement algorithms, matrix-free linear solvers, high-order data visualization, and list examples of collaborations with several ECP and external applications.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2023

Energy Characterization of the UMD Electron Accelerator

Multiple different groups at Los Alamos National Laboratory have a cited interest in understanding the effects of ionizing radiation in materials. Of especial interest to the ISR division is understanding the effects in materials that are used in the space environment, where radiation is omnipresent and impossible to fully shield from. Development is under way to simulate the effects of radiation damage in detectors and electronics, primarily with particle transport codes such as Geant4 and RAMPART, a code that provides a user friendly interface to Geant4. However, these simulations need benchmarking to experimental tests, some of which have been performed at a variety of small electron accelerator facilities. One of these facilities is the Linear Electron Accelerator at the University of Maryland Radiation Facilities. This facility can provide electron beams on the order of 100 mA delivered in tight macropulse bunches, which allows for experiments where the behavior of electronics is monitored as a function of pulse number. While the beam rates per macropulse of this accelerator is monitored during an experiment via a current pick-off connected to a large graphite beamstop, the energy distribution of this electron beam is not measured during the experiment and has not been well characterized. The energy distribution of the beam is pertinent for these types of experiments, since dose is directly related to energy deposited in a medium. Further, these experiments are performed in open air and sample placement in the room can dictate what dose rate they receive per macropulse. We have created simulated dosemaps for the UMD vault room, but these simulations assume a given energy of the beam, both for calculating the dose and to simulate how much the beam diverges in the air. There have been two attempts now to characterize this accelerator beam energy with a plate type spectrometer, including the most recent experiment earlier this year to quantify the beam energy distribution. These results will also be compared to a dosimetry experiment performed by collaborators at the UMD facility, the details of which are described in Reference. Understanding the electron beam energy is important for future experiments, because the energy directly impacts the dose and radiation damage given to a sample and is important for properly simulating the electron beam when comparing simulation to experiment.

43 PARTICLE ACCELERATORS↗
DOE OSTI2021

Efficient Learning of Accurate Surrogates for Simulations of Complex Systems

Machine learning methods are increasingly deployed to construct surrogate models for complex physical systems at a reduced computational cost. However, the predictive capability of these surrogates degrades in the presence of noisy, sparse, or dynamic data. We introduce an online learning method empowered by optimizer-driven sampling that has two advantages over current approaches: it ensures that all local extrema (including endpoints) of the model response surface are included in the training data and it employs a continuous validation and update process in which surrogates undergo retraining when their performance falls below a validity threshold. We find, using benchmark functions, that optimizer-directed sampling generally outperforms traditional sampling methods in terms of accuracy around local extrema even when the scoring metric is biased towards assessing overall accuracy. Finally, the application to dense nuclear matter demonstrates that highly accurate surrogates for a nuclear equation of state model can be reliably auto-generated from expensive calculations using few model evaluations.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2025

Unified Universal Control and Coordination of Inverter-Based Resources, and Validation for a PV + Battery Hybrid Plant

As renewable energy deployment grows, hybrid power plants (HPPs) combining photovoltaic (PV) and battery systems must evolve to offer both energy and grid stability services. These systems typically include a mix of grid-following (GFL) and grid-forming (GFM) inverters, presenting unique coordination and control challenges. This Department of Energy–funded project developed and validated a Unified Universal Control and Coordination (UUCC) framework for such PV + battery hybrid plants, enabling seamless and stable operation, including ultrafast black start, autonomous synchronization, and robust frequency and voltage regulation, under different grid conditions. The project significantly advanced the understanding of inverter-based resource (IBR) control by developing and validating three complementary system-level approaches for hybrid GFL/GFM operation: 1. A combined Virtual Resistance (VR)-based GFL and Virtual Oscillator Control (VOC)-based GFM method, where each inverter type is governed by a specialized control strategy. Together, these achieve stable, fast-response coordination, eliminating inrush current and enabling smooth black start and grid synchronization across a wide range of grid strengths. 2. A Deadbeat-based UUCC strategy, which uses discrete-time, switching-cycle-level control for both GFL and GFM inverters. This approach replaces traditional PI/PLL control with a control parameter-free, high-bandwidth framework that supports stable LVRT and instantaneous synchronization under all conditions. 3. A benchmark comparison with Siemens’ commercial GFM microgrid controller, which provided a fast baseline platform. The commercial approach decoupled v & f control was implemented on a commercial microgrid controller.The baseline commercial benchmark helped highlight superior transient response and black start performance offered by the deadbeat and VOC approaches. These technical contributions offer substantial improvements over conventional inverter control schemes, which often rely on slow phase-locked loop (PLL)-based synchronization, require careful control parameters tuning, and prone to unstable in weak grids with GFL inverters and in stiff grid with GFM inverters therefore challenging for hybrid GFL+GFM under all grid conditions. The deadbeat-based UUCC framework enables simpler, faster, and more robust operation of hybrid IBR systems using wide-bandgap (WBG) devices such as SiC power semiconductors. The rapid expansion of hybrid distributed energy resources (DERs), including residential and commercial PV-BESS installations such as Tesla Powerwall, PV with vehicle-to-grid (V2G) capability, and other integrated configurations, presents complex operational challenges for medium-voltage radial distribution feeders. These networks are subject to frequent disturbances such as faults, switching operations, rapid reclosing sequences, and feeder reconfigurations, all of which introduce dynamic stress on IBRs. In addition, planned feeder segmentation and deliberate islanding for resilience will require DERs that can autonomously perform blackstart, establish voltage and frequency references, and resynchronize with the main grid. The advanced deadbeat-based UUCC control and blackstart functionalities developed in this project directly address these requirements, enabling decentralized and autonomous operation of inverter-dominated DERs in distribution systems under a wide range of fault and reconfiguration scenarios. From a public benefit perspective, these innovations enable more reliable and cost-effective integration of renewable energy into distribution networks. The ability to autonomously black start and stabilize grids under varying grid conditions support accelerates recovery from outages and support decentralized resilient energy systems. By reducing system complexity and improving performance, this project lays critical groundwork for future inverter-dominated power grids that are clean, reliable, and accessible to all.

14 SOLAR ENERGY↗
DOE OSTI2025

2D Modeling of Plasma Streamer and Glow Phases at Ammonia-Air Flame Conditions

Streamer and glow plasma phases have been modeled at two thermochemical states of an ammonia-air flame: fresh reactants and burnt products. A new AMReX-based solver has been verified against benchmarks in the literature and has been used to perform these simulations. A Helmholtz-equation based photoionization model with parameters accounting for the presence of NH3 in air has been coupled with the solver to accurately model the streamer propagation phase. A detailed plasma kinetics mechanism has been compiled and used to predict the evolution of electrons, excited states, ions, and radicals during streamer propagation and glow formation. The propagation velocity of streamers was found increase by almost two-fold when the mixture was changed from the fresh reactants to the burnt products. Moreover, vibrational excitation was found to be limited to the streamer body, whereas ionization predominantly occurred at the streamer head, as is expected. Finally, the differences in the pathways of O and H radical production during the streamer propagation and glow phases have been briefly discussed.

ammonia-air flame↗
DOE OSTI2020

Enhanced Co-Expression Extrapolation (COXEN) Gene Selection Method for Building Anti-Cancer Drug Response Prediction Models

The co-expression extrapolation (COXEN) method has been successfully used in multiple studies to select genes for predicting the response of tumor cells to a specific drug treatment. Here, we enhance the COXEN method to select genes that are predictive of the efficacies of multiple drugs for building general drug response prediction models that are not specific to a particular drug. The enhanced COXEN method first ranks the genes according to their prediction power for each individual drug and then takes a union of top predictive genes of all the drugs, among which the algorithm further selects genes whose co-expression patterns are well preserved between cancer cases for building prediction models. We apply the proposed method on benchmark in vitro drug screening datasets and compare the performance of prediction models built based on the genes selected by the enhanced COXEN method to that of models built on genes selected by the original COXEN method and randomly picked genes. Models built with the enhanced COXEN method always present a statistically significantly improved prediction performance (adjusted p-value ≤ 0.05). Our results demonstrate the enhanced COXEN method can dramatically increase the power of gene expression data for predicting drug response.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2026

Strategy and performance of the CMS long-lived particle trigger program in proton-proton collisions at $\sqrt{s}$ = 13.6 TeV

In the physics program of the CMS experiment during the CERN LHC Run 3, which started in 2022, the long-lived particle triggers have been improved and extended to expand the scope of the corresponding searches. These dedicated triggers and their performance are described in this paper, using several theoretical benchmark models that extend the standard model of particle physics. The results are based on proton-proton collision data collected with the CMS detector during 2022$-$2024 at a center-of-mass energy of 13.6 TeV, corresponding to integrated luminosities of up to 123 fb$^{-1}$.

FOS: Physical sciences↗
NASA NTRS1981

Experimental and analytical studies of a model helicopter rotor in hover

A benchmark test to aid the development of various rotor performance codes was conducted. Simultaneous blade pressure measurements and tip vortex surveys were made for a wide range of tip Mach numbers including the transonic flow regime. The measured tip vortex strength and geometry permit effective blade loading predictions when used as input to a prescribed wake lifting surface code. It is also shown that with proper inflow and boundary layer modeling, the supercritical flow regime can be accurately predicted.

Caradonna, F. X.↗
NASA NTRS1991

Characterizing Computers And Predicting Computing Times

Improved method for evaluation and comparison of computers running same or different FORTRAN programs devised. Enables one to predict time necessary to run given "benchmark" or other standard program on given computer, in scalar mode and without optimization of codes generated by compiler. Such "benchmark" running times are principal measures used to characterize performances of computers; of interest to designers, manufacturers, programmers, and users.

Saavedra-Barrera, Rafael H.↗
NASA NTRS1992

Development of a CFD code for casting simulation

The task of developing a computational fluid dynamics (CFD) code to accurately model the mold filling phase of a casting operation was accomplished in a systematic manner. First the state-of-the-art was determined through a literature search, a code search, and participation with casting industry personnel involved in consortium startups. From this material and inputs from industry personnel, an evaluation of the currently available codes was made. It was determined that a few of the codes already contained sophisticated CFD algorithms and further validation of one of these codes could preclude the development of a new CFD code for this purpose. With industry concurrence, ProCAST was chosen for further evaluation. Two benchmark cases were used to evaluate the code's performance using a Silicon Graphics Personal Iris system. The results of these limited evaluations (because of machine and time constraints) are presented along with discussions of possible improvements and recommendations for further evaluation.

Murph, Jesse E.↗
NASA NTRS1992

DSMC analysis in a heterogeneous parallel computing environment

A methodology for implementing parallel DSMC codes in a heterogeneous computing environment is described. The methodology involves the use of a common message-passing software library together with recently developed software that handles the actual interprocessor communications in a standard manner across a variety of computing platforms. Benchmark tests using a simple DSMC model problem were performed on an Intel iPSC/860, a Cray-YMP and a group of Sun workstations. The approach was found to give speedups that scaled linearly with problem size on all the computing platforms tested. This methodology was then incorporated into a production-type DSMC code to allow the simulation of problems that would not otherwise have been practical. The application of this production code to simulations of hypersonic shear flows and shock-lip interactions under near-continuum conditions is described. Synchronous and asynchronous models for implementing parallelism into DSMC simulations are also described and both models are shown to produce the same steady-state result.

Wilmoth, R. G.↗
NASA NTRS1993

Assessment and development of second order turbulence models

Second order turbulence models describe the effect of mean flow and external agencies (such as buoyancy) on the evolution of turbulence. Therefore, in principle, these models give a more accurate description of complicated flow fields (e.g. flows with large anisotropy in turbulence, such as near the leading edge of a turbine blade) than the two equation models. The objectives of this document are to assess the performance of the various second order turbulence models in benchmark flows and to seek improvements where necessary in models for the pressure correlation term in the scalar flux equation and for the scalar dissipation equation.

Shabbir, Aamir↗
NASA NTRS1994

Empirical study of parallel LRU simulation algorithms

This paper reports on the performance of five parallel algorithms for simulating a fully associative cache operating under the LRU (Least-Recently-Used) replacement policy. Three of the algorithms are SIMD, and are implemented on the MasPar MP-2 architecture. Two other algorithms are parallelizations of an efficient serial algorithm on the Intel Paragon. One SIMD algorithm is quite simple, but its cost is linear in the cache size. The two other SIMD algorithm are more complex, but have costs that are independent on the cache size. Both the second and third SIMD algorithms compute all stack distances; the second SIMD algorithm is completely general, whereas the third SIMD algorithm presumes and takes advantage of bounds on the range of reference tags. Both MIMD algorithm implemented on the Paragon are general and compute all stack distances; they differ in one step that may affect their respective scalability. We assess the strengths and weaknesses of these algorithms as a function of problem size and characteristics, and compare their performance on traces derived from execution of three SPEC benchmark programs.

Carr, Eric↗
NASA NTRS2005

Evolutionary optimization of a Genetically Refined Truss

Structural optimization is a field of research that has experienced noteworthy growth for many years. Researchers in this area have developed optimization tools to successfully design and model structures, typically minimizing mass while maintaining certain deflection and stress constraints. Numerous optimization studies have been performed to minimize mass, deflection and stress on a benchmark cantilever truss problem. Predominantly traditional optimization theory is applied to this problem. The cross-sectional area of each member is optimized to minimize the aforementioned objectives. This paper will present a structural optimization technique that has been previously applied to compliant mechanism design. This technique demonstrates a method that combines topology optimization, geometric refinement, finite element analysis, and two forms of evolutionary computation: Genetic Algorithms and Differential Evolution to successfully optimize a benchmark structural optimization problem. An non-traditional solution to the benchmark problem is presented in this paper, specifically a geometrically refined topological solution. The design process begins with an alternate control mesh formulation, multilevel geometric smoothing operation, and an elastostatic structural analysis. The design process is wrapped in an evolutionary computing optimization toolset.

Hull, Patrick V.↗
NASA NTRS2007

Evolutionary Optimization of a Geometrically Refined Truss

Structural optimization is a field of research that has experienced noteworthy growth for many years. Researchers in this area have developed optimization tools to successfully design and model structures, typically minimizing mass while maintaining certain deflection and stress constraints. Numerous optimization studies have been performed to minimize mass, deflection, and stress on a benchmark cantilever truss problem. Predominantly traditional optimization theory is applied to this problem. The cross-sectional area of each member is optimized to minimize the aforementioned objectives. This Technical Publication (TP) presents a structural optimization technique that has been previously applied to compliant mechanism design. This technique demonstrates a method that combines topology optimization, geometric refinement, finite element analysis, and two forms of evolutionary computation: genetic algorithms and differential evolution to successfully optimize a benchmark structural optimization problem. A nontraditional solution to the benchmark problem is presented in this TP, specifically a geometrically refined topological solution. The design process begins with an alternate control mesh formulation, multilevel geometric smoothing operation, and an elastostatic structural analysis. The design process is wrapped in an evolutionary computing optimization toolset.

Hull, P. V.↗
NASA NTRS2009

Fan Noise Prediction with Applications to Aircraft System Noise Assessment

This paper describes an assessment of current fan noise prediction tools by comparing measured and predicted sideline acoustic levels from a benchmark fan noise wind tunnel test. Specifically, an empirical method and newly developed coupled computational approach are utilized to predict aft fan noise for a benchmark test configuration. Comparisons with sideline noise measurements are performed to assess the relative merits of the two approaches. The study identifies issues entailed in coupling the source and propagation codes, as well as provides insight into the capabilities of the tools in predicting the fan noise source and subsequent propagation and radiation. In contrast to the empirical method, the new coupled computational approach provides the ability to investigate acoustic near-field effects. The potential benefits/costs of these new methods are also compared with the existing capabilities in a current aircraft noise system prediction tool. The knowledge gained in this work provides a basis for improved fan source specification in overall aircraft system noise studies.

Nark, Douglas M.↗
NASA NTRS2012

Evaluating the Efficacy of the Cloud for Cluster Computation

Computing requirements vary by industry, and it follows that NASA and other research organizations have computing demands that fall outside the mainstream. While cloud computing made rapid inroads for tasks such as powering web applications, performance issues on highly distributed tasks hindered early adoption for scientific computation. One venture to address this problem is Nebula, NASA's homegrown cloud project tasked with delivering science-quality cloud computing resources. However, another industry development is Amazon's high-performance computing (HPC) instances on Elastic Cloud Compute (EC2) that promises improved performance for cluster computation. This paper presents results from a series of benchmarks run on Amazon EC2 and discusses the efficacy of current commercial cloud technology for running scientific applications across a cluster. In particular, a 240-core cluster of cloud instances achieved 2 TFLOPS on High-Performance Linpack (HPL) at 70% of theoretical computational performance. The cluster's local network also demonstrated sub-100 ?s inter-process latency with sustained inter-node throughput in excess of 8 Gbps. Beyond HPL, a real-world Hadoop image processing task from NASA's Lunar Mapping and Modeling Project (LMMP) was run on a 29 instance cluster to process lunar and Martian surface images with sizes on the order of tens of gigapixels. These results demonstrate that while not a rival of dedicated supercomputing clusters, commercial cloud technology is now a feasible option for moderately demanding scientific workloads.

Cloud Computing↗
NASA NTRSDate not supplied

Language Model For Earth Science: Exploring Potential Downstream Applications As Well As Current Challenges

The use of deep learning techniques to build transformer language models such as SciBERT and GPT3 have transformed the natural language technology (NLT) landscape. These new NLTs are being used in speech to text and vice versa, auto-mated text classification, sentiment analysis, topic modeling, text summarization, and cognitive assistants. While Earth science has no shortage of unstructured data such as journal and conference papers, little efforts have focused on harnessing NLTs for knowledge extraction and supporting the scientific process. This paper surveys the use of language models in different science. BERT-E, a new Earth science-specific language model, is presented. BERT-E is generated using a transfer learning solution. A language model that has already been trained for general Science (SciBERT) is fine-tuned using abstracts and full text extracted from various Earth science-related articles. A downstream keywords classification application is used for evaluation, and the use of BERT-E shows improved performance. The need to develop a robust set of benchmarks in evaluating the language model such as BERT-E is discussed. Finally, example applications are presented to inspire additional ideas for applications using domain-specific language models.

R Ramachandran↗