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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 829 records · Page 46
All sourcesNASA NTRSDOE OSTI
DOE OSTI2018

Realistic shell-model calculations for p -shell nuclei including contributions of a chiral three-body force

In this study we present an evolution of our derivation of the shell-model effective Hamiltonian, namely introducing effects of three-body contributions. More precisely, we consider a three-body potential at next-to-next-to-leading order in chiral perturbation theory, and the induced three-body forces that arise from many-body correlations among valence nucleons. The first one is included, in the derivation of the effective Hamiltonian for one- and two-valence nucleon-systems, at first order in the many-body perturbation theory. Namely, we include only the three-body interaction between one or two valence nucleons and those belonging to the core. For nuclei with more than two valence particles, both induced—turned on by the two-body potential—and genuine three-body forces come into play. Since it is difficult to perform shell-model calculations with three-body forces, these contributions are estimated for the ground-state energy only. To establish the reliability of our approximations, we focus attention on nuclei belonging to the p shell, aiming to benchmark our calculations against those performed with the ab initio no-core shell model. The obtained results are satisfactory, and pave the way to the application of our approach to nuclear systems with heavier masses.

6 ≤ A ≤ 19↗
DOE OSTI2019

Computational Fluid Dynamics Modeling and Verification of a Submersible Pump-Turbine Runner: Preprint

An innovative reversible pump-turbine runner with a 180-degree flow direction reversal has been designed and optimized using Computational Fluid Dynamics (CFD) modeling. The reversible pump-turbine configuration provides a cost-effective means of achieving adequate submergence below tailwater, thereby permitting higher-speed turbine and generator operation and reduced equipment size. Results from CFD modeling and analysis of the hydraulics and runner design are presented. The benchmarks for these parameters are the performance parameters of conventional pump-turbines with scroll-case turbine inlets. The model efficiencies were independently verified, and a sensitivity analysis of the results is presented. The results do not reveal a region of pump instability that is characteristic of pumps and pump-turbines with scroll cases. CFD modeling and analysis of the hydraulics and runner design are presented. The benchmarks for these parameters are the performance parameters of conventional pump-turbines with scroll-case turbine inlets. The model efficiencies were independently verified, and a sensitivity analysis of the results is presented. The results do not reveal a region of pump instability that is characteristic of pumps and pump-turbines with scroll cases.

13 HYDRO ENERGY↗
DOE OSTI2025

An HPC benchmark survey and taxonomy for characterization

The field of High-Performance Computing (HPC) is defined by providing computing devices with highest performance for a variety of demanding scientific users. The tight co-design relationship between HPC providers and users propels the field forward, paired with technological improvements, achieving continuously higher performance and resource utilization. A key device for system architects, architecture researchers, and scientific users are benchmarks, allowing for well-defined assessment of hardware, software, and algorithms. Many benchmarks exist in the community, from individual niche benchmarks testing specific features, to large-scale benchmark suites for whole procurements. We survey the available HPC benchmarks, summarizing them in table form with key details and concise categorization, also through an interactive website. For categorization, we present a benchmark taxonomy for well-defined characterization of benchmarks.

Benchmarking↗
DOE OSTI2025

OptiBench: An Optimization Benchmark Tool for Renewable Energy Problems

We propose a benchmark framework and visualization tool, OptiBench, for analyzing the performance of state-of-the-art optimization solvers across a variety of optimization problems in renewable energy research. Our framework is designed from the ground up in the Julia programming language and enables analysis at scale on high performance computing (HPC) systems. Our visualization tool allows effortless evaluation of optimization solver performance, robustness, and accuracy through intuitive plots, e.g., performance profiles, heat maps, and distribution plots. We have tested three benchmark suites relevant to the modeling of renewable energy systems, viz., CUTEst, PGLib-OPF, and WaterTAP water treatment optimization problems. We illustrate benchmarking of CUTEst using OptiBench on the National Renewable Energy Laboratory's (NREL) HPC Kestrel. Our findings indicate that MA57 HSL linear solver demonstrated the best overall performance for an experimental IPOPT implementation. Our work is ongoing and we intend to add support for more optimization solvers and benchmark test suites in the future.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2014

Comparison of Homogeneous and Heterogeneous CFD Fuel Models for Phase I of the IAEA CRP on HTR Uncertainties Benchmark

Computational Fluid Dynamics (CFD) evaluation of homogeneous and heterogeneous fuel models was performed as part of the Phase I calculations of the International Atomic Energy Agency (IAEA) Coordinate Research Program (CRP) on High Temperature Reactor (HTR) Uncertainties in Modeling (UAM). This study was focused on the nominal localized stand-alone fuel thermal response, as defined in Ex. I-3 and I-4 of the HTR UAM. The aim of the stand-alone thermal unit-cell simulation is to isolate the effect of material and boundary input uncertainties on a very simplified problem, before propagation of these uncertainties are performed in subsequent coupled neutronics/thermal fluids phases on the benchmark. In many of the previous studies for high temperature gas cooled reactors, the volume-averaged homogeneous mixture model of a single fuel compact has been applied. In the homogeneous model, the Tristructural Isotropic (TRISO) fuel particles in the fuel compact were not modeled directly and an effective thermal conductivity was employed for the thermo-physical properties of the fuel compact. On the contrary, in the heterogeneous model, the uranium carbide (UCO), inner and outer pyrolytic carbon (IPyC/OPyC) and silicon carbide (SiC) layers of the TRISO fuel particles are explicitly modeled. The fuel compact is modeled as a heterogeneous mixture of TRISO fuel kernels embedded in H-451 matrix graphite. In this study, a steady-state and transient CFD simulations were performed with both homogeneous and heterogeneous models to compare the thermal characteristics. The nominal values of the input parameters are used for this CFD analysis. In a future study, the effects of input uncertainties in the material properties and boundary parameters will be investigated and reported.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
DOE OSTI2023

Implementing and Benchmarking the Locally Competitive Algorithm on the Loihi 2 Neuromorphic Processor

Neuromorphic processors have garnered considerable interest in recent years for their potential in enabling energy-efficient and high-speed computing. The Locally Competative Algorithm (LCA) has been utilized for power efficient sparse coding on neuromophic processors, including the first Loihi processor \cite{appletospikes, loihi1}. With the Loihi 2 processor enabling custom neuron models and graded spike communication, more complex implementations of LCA are possible \cite{loihi2}. We present a new implementation of LCA designed for the Loihi 2 processor and perform an initial set of benchmarks comparing it to LCA on CPU and GPU devices. In these experiments LCA on Loihi 2 is faster and orders of magnitude more efficient, while maintaining similar reconstruction quality. We find this performance improvement increases as the LCA parameters are tuned towards greater representation sparsity. Our study highlights the potential of neuromorphic processors, particularly Loihi 2, in enabling intelligent,autonomous, real-time processing on small robots, satellite where there are strict SWaP (small, lightweighr, and low-power) requirement. By demonstrating the superior performance of LCA on Loihi 2 compared to conventional computing device, our study suggests that Loihi 2 could be a valuable tool in advancing these types of applications. Overall, our study highlights the potential of neuromorphic processors for efficient and accurate data processing on resource-constrained devices.

Parpart, Gavin G.↗
DOE OSTI2026

OptiBench: An Optimization Benchmark Tool for Renewable Energy Problems

We propose a benchmark framework and visualization tool, OptiBench, for analyzing the performance of state-of-the-art optimization solvers across a variety of optimization problems in renewable energy research. Our framework is designed from the ground up in the Julia programming language and enables analysis at scale on high performance computing (HPC) systems. Our visualization tool allows effortless evaluation of optimization solver performance, robustness, and accuracy through intuitive plots, e.g., performance profiles, heat maps, and distribution plots. We have tested three benchmark suites relevant to the modeling of renewable energy systems, viz., CUTEst, PGLib-OPF, and WaterTAP water treatment optimization problems. We illustrate benchmarking of CUTEst using OptiBench on the National Laboratory of the Rockies's (NLR) HPC Kestrel. Our findings indicate that MA57 HSL linear solver demonstrated the best overall performance for an experimental IPOPT implementation. Our work is ongoing and we intend to add support for more optimization solvers and benchmark test suites in the future.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2024

So you think you can track?

This work introduces a multi-camera tracking dataset consisting of 234 hours of video data recorded concurrently from 234 overlapping HD cameras covering a 4.2 mile stretch of 8-10 lane interstate highway near Nashville, TN. Video is recorded in cooperation with Tennessee State Department of Transportation and its policies. The video is recorded during a period of high traffic density with 500+ objects typically visible within the scene and typical object longevities of 3-15 minutes. GPS trajectories from 270 vehicle passes through the scene are manually corrected in the video data to provide a set of ground-truth trajectories for recall-oriented tracking metrics, and object detections are provided for each camera in the scene (159 million total before cross-camera fusion). Initial benchmarking of tracking-by-detection algorithms is performed against the GPS trajectories, and a best HOTA of only 9.5% is obtained (best recall 75.9% at IOU 0.1, 47.9 average IDs per ground truth object), indicating the benchmarked trackers do not perform sufficiently well at the long temporal and spatial durations required for traffic scene understanding. Video data, scene information, and vehicle trajectories are made publicly available at i24motion.org.

42 ENGINEERING↗
DOE OSTI2015

Validation of continuous energy sensitivity coefficients calculations in the MORET 5 code - 14269

A continuous-energy sensitivity coefficients calculation capability has been developed in the MORET 5 Monte Carlo code recently. As discussed in this paper, it relies on the differential operator method, an adjoint weighting model (to avoid calculating the fission source derivatives) and a constrained sensitivity coefficient model for sensitivity profiles to distributions such as, for instance, the fission spectrum. For now, the coefficients are provided per medium, per isotope, per reaction on an energy binning defined by the user. The sensitivity coefficients are currently available for the following quantities: total, elastic scattering, total inelastic scattering, fission, total capture and radiative capture cross sections, average neutron produced by fission and fission spectrum. This paper deals with the validation of this new feature in the MORET code on several benchmarks and applications with various energy spectra and geometrical configurations. This validation is performed through the UACSA expert group and the Jezebel ICSBEP benchmarks, but also thanks to calculations performed in the frame of experimental program design in the STACY facility. Comparisons are made using the MCNP6.1 code and the SCALE6.1 package as references. Preliminary results show a pretty good agreement between the different software. (authors)

42 ENGINEERING↗
DOE OSTI2017

Polyhedral approximation in mixed-integer convex optimization

Generalizing both mixed-integer linear optimization and convex optimization, mixed-integer convex optimization possesses broad modeling power but has seen relatively few advances in general-purpose solvers in recent years. Here, we intend to provide a broadly accessible introduction to our recent work in developing algorithms and software for this problem class. Our approach is based on constructing polyhedral outer approximations of the convex constraints, resulting in a global solution by solving a finite number of mixed-integer linear and continuous convex subproblems. The key advance we present is to strengthen the polyhedral approximations by constructing them in a higher-dimensional space. In order to automate this extended formulation we rely on the algebraic modeling technique of disciplined convex programming (DCP), and for generality and ease of implementation we use conic representations of the convex constraints. Although our framework requires a manual translation of existing models into DCP form, after performing this transformation on the MINLPLIB2 benchmark library we were able to solve a number of unsolved instances and on many other instances achieve superior performance compared with state-of-the-art solvers like Bonmin, SCIP, and Artelys Knitro.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2014

SU-E-T-329: Tissue-Equivalent Phantom Materials for Neutron Dosimetry in Proton Therapy

Purpose: To characterize tissue equivalence of phantom materials in terms of secondary neutron production and dose deposition from neutrons produced in radiation therapy phantom materials in the context of proton therapy using Monte Carlo simulations and measurements. Methods: In order to study the influence of material choice on neutron production in therapeutic proton beams, Monte Carlo simulations using the Geant4 and MCNPX-PoliMi transport codes were performed to generate the neutron fields produced by protons of 155 and 200 MeV. A simple irradiation geometry was used to investigate the effect of different materials. The proton beams were stopped in slab phantoms to study the production of secondary neutrons. The investigated materials were water, Lucite, and tissue-equivalent phantom materials (CIRS Inc., Norfolk, VA). Neutron energy spectra and absorbed dose by neutrons and their secondary particles were scored. In addition, simulations were performed for reference tissues (ICRP/ICRU) to assess tissue equivalence with respect to neutron generation and transport. In order to benchmark the simulation results, measurements were performed with a system developed at the University of Michigan; organic liquid scintillators were used to detect the neutron emissions from the irradiation of tissue-equivalent materials. Additionally, the MPPost code was used to calculate the scintillator response from the MCNPX-PoliMi output. Results: The simulated energy spectra and depth dose curves of the neutrons produced in different phantom materials showed similar shape. The differences of spectra and fluences between all studied materials and reference tissues were well within the achievable precision of neutron dosimetry. The shape of the simulated detector response of the liquid scintillators agreed well with measurements on the proton beamline. Conclusion: Based on Geant4 and MCNPX-PoliMi simulations, the investigated materials appear to be suitable to study the production of neutrons in proton therapy. MC simulations were verified with neutron measurements in therapeutic proton beams. This work was funded in part by the ANDANTE grant of the European Commission in the 7th Framework Program.

07 ISOTOPE AND RADIATION SOURCES↗
DOE OSTI2024

Bragg Spot Finder (BSF): a new machine-learning-aided approach to deal with spot finding for rapidly filtering diffraction pattern images

Macromolecular crystallography contributes significantly to understanding diseases and, more importantly, how to treat them by providing atomic resolution 3D structures of proteins. This is achieved by collecting X-ray diffraction images of protein crystals from important biological pathways. Spotfinders are used to detect the presence of crystals with usable data, and the spots from such crystals are the primary data used to solve the relevant structures. Having fast and accurate spot finding is essential, but recent advances in synchrotron beamlines used to generate X-ray diffraction images have brought us to the limits of what the best existing spotfinders can do. This bottleneck must be removed so spotfinder software can keep pace with the X-ray beamline hardware improvements and be able to see the weak or diffuse spots required to solve the most challenging problems encountered when working with diffraction images. In this paper, we first present Bragg Spot Detection (BSD), a large benchmark Bragg spot image dataset that contains 304 images with more than 66 000 spots. We then discuss the open source extensible U-Net-based spotfinder Bragg Spot Finder (BSF), with image pre-processing, a U-Net segmentation backbone, and post-processing that includes artifact removal and watershed segmentation. Finally, we perform experiments on the BSD benchmark and obtain results that are (in terms of accuracy) comparable to or better than those obtained with two popular spotfinder software packages ( Dozor and DIALS ), demonstrating that this is an appropriate framework to support future extensions and improvements.

36 MATERIALS SCIENCE↗
DOE OSTI2015

Trinity Benchmarks on the Intel Xeon Phi (Knights Corner)

This report documents the early experiences with porting and performance analysis of the Tri-Lab Trinity benchmark applications on Intel Xeon Phi (Knights Corner) (KNC) processor. KNC, the second generation of the Intel Many Integrated Core (MIC) architectures, uses a large number of small P54C-x86 cores with wide vector units and is deployed as PCI bus attached process accelerators. Sandia has experimental test beds of small InifiniBand clusters and workstations to investigate the performance of the MIC architecture. On these experimental test beds the programming models that may be investigated are "offload", "symmetric" and "native". Among these program usage models our primary interest is in the so called "native" mode, because the planned Trinity system to be deployed in 2016 using the next generation MIC processor architecture called Knights Landing would be self-hosted. Trinity / NERSC-8 benchmark programs cover a variety of scientific disciplines and they were used to guide the procurement of these systems. Architectures such as the Intel MIC are well suited to study evolving processor architectures and a usage model commonly referred to as MPI + X that facilitates migration of our applications to use both coarse grain and fine grain parallelism. Our focus with the applications selected is on the efficacy of algorithms in these applications to take advantage of features like: large number of cores, wide vector units, higher-bandwidth and deeper memory sub-system. This is a first step towards understanding applications, algorithms and programming environments for Trinity and future exascale computing systems.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2025

A comparative analysis of YOLOv8 and U-Net image segmentation approaches for transmission electron micrographs of polycrystalline thin films

Metallic thin films offer a platform to experimentally study the dynamics of microstructural evolution, but the required transmission electron microscopy (TEM)-based imaging generates complex images that are challenging to segment and quantify. This work provides a comparative analysis of a new YOLOv8 model and an established U-Net model for bright-field TEM images of polycrystals, employing a framework leveraging physical observables to evaluate performance against two hand-traced benchmark datasets. This methodology obviates the comparison of large, diversely structured, and manually labeled datasets that are required to assess performance on a per-image/per-pixel basis. It is found that the YOLOv8 model, adapted for real-time instance segmentation, has up to 43× faster inferencing (NVIDIA GeForce RTX 4090) compared to U-Net and reconstructs hand-traced grain size distributions (GSDs) with excellent fidelity, finding mean diameter within 3% for grains near an optimal magnification; for grains that deviate from the optimal pixel-diameter, the size of small- (large)-diameter grains is systematically over- (under)-estimated. This is partially mitigated by including scale-aware augmentations during training. Moreover, when the bias is corrected post-inference by a rigid shift in distribution, the YOLOv8 model reproduces ground truth GSDs with exceptional fidelity, with statistical tests indicating <5% probability that the distributions are distinct. Based on ground truth data, calibration curves pertaining to this shift can be constructed for a given model. This issue is not present in the U-Net model’s results, indicating that for quantitative measurements where the true size of objects is of interest, special procedures must be implemented for YOLO-based models.

36 MATERIALS SCIENCE↗
DOE OSTI2018

Real-time data analysis for medical diagnosis using FPGA-accelerated neural networks

Real-time analysis of patient data during medical procedures can provide vital diagnostic feedback that significantly improves chances of success. With sensors becoming increasingly fast, frameworks such as Deep Neural Networks are required to perform calculations within the strict timing constraints for real-time operation. However, traditional computing platforms responsible for running these algorithms incur a large overhead due to communication protocols, memory accesses, and static (often generic) architectures. In this work, we implement a low-latency Multi-Layer Perceptron (MLP) processor using Field Programmable Gate Arrays (FPGAs). Unlike CPUs and Graphics Processing Units (GPUs), our FPGA-based design can directly interface sensors, storage devices, display devices and even actuators, thus reducing the delays of data movement between ports and compute pipelines. Moreover, the compute pipelines themselves are tailored specifically to the application, improving resource utilization and reducing idle cycles. We demonstrate the effectiveness of our approach using mass-spectrometry data sets for real-time cancer detection. We demonstrate that correct parameter sizing, based on the application, can reduce latency by 20% on average. Furthermore, we show that in an application with tightly coupled data-path and latency constraints, having a large amount of computing resources can actually reduce performance. Using mass-spectrometry benchmarks, we show that our proposed FPGA design outperforms both CPU and GPU implementations, with an average speedup of 144x and 21x, respectively. In our work, we demonstrate the importance of application-specific optimizations in order to minimize latency and maximize resource utilization for MLP inference. By directly interfacing and processing sensor data with ultra-low latency, FPGAs can perform real-time analysis during procedures and provide diagnostic feedback that can be critical to achieving higher percentages of successful patient outcomes.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2017

Results of the GABLS3 diurnal-cycle benchmark for wind energy applications

We present results of the GABLS3 model intercomparison benchmark revisited for wind energy applications. The case consists of a diurnal cycle, measured at the 200-m tall Cabauw tower in the Netherlands, including a nocturnal low-level jet. The benchmark includes a sensitivity analysis of WRF simulations using two input meteorological databases and five planetary boundary-layer schemes. A reference set of mesoscale tendencies is used to drive microscale simulations using RANS k-ϵ and LES turbulence models. The validation is based on rotor-based quantities of interest. Cycle-integrated mean absolute errors are used to quantify model performance. The results of the benchmark are used to discuss input uncertainties from mesoscale modelling, different meso-micro coupling strategies (online vs offline) and consistency between RANS and LES codes when dealing with boundary-layer mean flow quantities. Altogether, all the microscale simulations produce a consistent coupling with mesoscale forcings.

17 WIND ENERGY↗
DOE OSTI2017

Development of the Advanced D1S for Shutdown Dose Rate Calculations in Fusion Reactors

The assessment of the shutdown dose rate due to neutron activation is fundamental for shielding design, materials selection, licensing and planning of maintenance operations in high performances fusion devices. The shutdown dose rate calculations require the combined use of radiation transports and inventory codes. The typical approach consists in a two steps procedure. A pure neutron transport simulation is performed in a first step to calculate the spatial distribution of neutron spectra. The results are used as input for an activation calculation that provides the decay gamma source used in a second step for a pure gamma transport simulation (rigorous-two step, R2S). The Direct 1-Step (D1S) approach has been developed more than ten years ago by ENEA and the ITER team: this technique relies on a coupled neutron-decay gamma transport in a single simulation. It is based on the use of a modified version of the MCNP5 Monte Carlo code with specially tailored nuclear cross-section data for transport and FISPACT inventory code for temporal behaviour. The 'Advanced-D1S' is an improved version of D1S in which additional computational features have been introduced, such as the mesh tally capability and continuous time evolution assessment of the dose rate in single run dose. It has been validated through measurements at JET tokamak and comparison with R2S codes and a future benchmark experiment during next high performance deuterium-tritium campaign is in preparation at JET. It has also been extensively used for ITER calculations and recently applied to DEMO assessment. Present limitations and in progress code improvement strategy are described and discussed in this paper.

61 RADIATION PROTECTION AND DOSIMETRY↗
DOE OSTI2022

Qubit Assignment Using Time Reversal

As quantum computers with large numbers of qubits become increasingly available, experiments executed on a given device may not utilize all available qubits. In this case, the outcome of executing a quantum program will depend on the ability to efficiently select a subset of high-performing physical qubits. For any given quantum program and device there are many ways to assign physical qubits for execution of the program, and assignments will differ in performance due to the variability in quality across qubits and entangling operations on a single device. Evaluating the performance of each assignment using fidelity estimation introduces significant experimental overhead and will be infeasible for many applications, while relying on standard device benchmarks provides incomplete information about the performance of any specific program. Furthermore, the number of possible assignments grows combinatorially in the number of qubits on the device and in the program, motivating the use of heuristic optimization techniques. We demonstrate a practical solution to the problem of qubit assignment by using simulated annealing with a cost function based on the Loschmidt echo, a diagnostic that measures the reversibility of a quantum process. We provide theoretical justification for this choice of cost function by demonstrating that the optimal qubit assignment coincides with the optimal qubit assignment based on state fidelity in the weak error limit, and we provide experimental justification using diagnostics performed on Google’s superconducting qubit devices. We then establish the performance of simulated annealing for qubit assignment using classical simulations of noisy devices as well as optimization experiments performed on a quantum processor. Our results demonstrate that the use of Loschmidt echoes and simulated annealing provides a scalable and flexible approach to optimizing qubit assignment on near-term hardware.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗