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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 1,477 records · Page 82
All sourcesNASA NTRSDOE OSTI
DOE OSTI2026

A System Level Comparative Study of SecondarySide Rectifiers for UAV Wireless Power Transfer in High-Altitude Airborne Energy Platforms

Wireless power transfer (WPT) systems for unmanned aerial vehicles (UAVs) must satisfy stringent design requirements to ensure reliable operation under constraints related to efficiency, thermal performance, altitude, weight, and reliability, particularly in airborne and high-altitude environments. Thermal management becomes increasingly challenging at higher altitudes due to reduced air density, while reliability is strongly affected by the number and complexity of active components. The weight and compactness of secondary-side rectifiers directly impact UAV flight endurance and system integration, influencing energy consumption and payload allocation. These factors collectively define the practical feasibility of UAV WPT systems and guide rectifier topology selection. This paper presents a comparative and experimentally validated evaluation framework for secondary-side rectifiers under UAV-specific constraints. Three representative rectifier architectures are analyzed using component-based assessment of weight, thermal behavior, and reliability, together with experimental efficiency measurements obtained from a 1 kW WPT prototype. The results show that the single-switch active rectifier achieves the highest peak efficiency of approximately 93.2% and an overall efficiency of 91%, while also offering advantages in weight and reliability. The findings demonstrate how constraint-oriented analysis, combined with experimental validation, enables informed selection of secondary-side rectifiers for UAV wireless power transfer applications, including emerging airborne and high-altitude platforms.

Asa, Erdem [ORNL] (ORCID:0000000190884812)↗
DOE OSTI2022

(110)-Oriented GaAs Devices and Spalling as a Platform for Low-Cost III-V Photovoltaics

Here, we demonstrate the growth of GaAs solar cells by hydride vapor phase epitaxy (HVPE) on epi-ready and previously spalled (110) GaAs wafers as an advance towards a potentially low-cost (110)-based device platform. Controlled spalling offers a fracture-based path to substrate cost amortization, enabling device exfoliation and substrate reuse, but the faceted surface generated during the spalling of (100)-oriented GaAs presents hurdles to direct regrowth of subsequent devices. Spalling of (110) substrates instead eliminates faceting by aligning the substrate surface with the predominant crystal cleavage plane. III-V epitaxy of solar cells is significantly less developed on (110)-oriented substrates, however. Here, we develop (110)-based GaAs solar cells grown by HVPE on epi-ready substrates, demonstrating equal performance to devices grown on the more standard (100) orientation. We also characterize the surface of spalled, (110)-oriented substrates, revealing flat terraces separated by steps with sub-micron-scale height on average. Finally, we present an initial device grown on a previously spalled surface without additional surface re-preparation with nearly 16% efficiency under a simulated AM1.5G spectrum. Together, these results provide preliminary evidence of a potentially low-cost path to enable terrestrial III-V photovoltaics via the (110) substrate orientation.

14 SOLAR ENERGY↗
DOE OSTI2021

Performance Portability of Molecular Docking Miniapp On Leadership Computing Platforms

Rapidly changing computer architectures, such as those found at high-performance computing (HPC) facilities, present the need for mini-applications (miniapps) that capture essential algorithms used in large applications to test program performance and portability, aiding transitions to new systems. The COVID-19 pandemic has fueled a flurry of activity in computational drug discovery, including the use of supercomputers and GPU acceleration for massive virtual screens for therapeutics. Recent work targeting COVID-19 at the Oak Ridge Leadership Computing Facility (OLCF) used the GPU-accelerated program AutoDock-GPU to screen billions of compounds on the Summit supercomputer. In this paper we present the development of a new miniapp, miniAutoDock-GPU, that can be used to evaluate the performance and portability of GPU-accelerated protein-ligand docking programs on different computer architectures. These tests are especially relevant as facilities transition from petascale systems and prepare for upcoming exascale systems that will use a variety of GPU vendors. The key calculations, namely, the Lamarckian genetic algorithm combined with a local search using a Solis-Wets based random optimization algorithm, are implemented. We developed versions of the miniapp using several different programming models for GPU acceleration, including a version using the CUDA runtime API for NVIDIA GPUs, and the Kokkos middle-ware API which is facilitated by C++ template libraries. A third version, currently in progress, uses the HIP programming model. These efforts will help facilitate the transition to exascale systems for this important emerging HPC application, as well as its use on a wide range of heterogeneous platforms.

Thavappiragasam, Mathialakan↗
DOE OSTI2016

FatMan vs. LittleBoy: Scaling Up Linear Algebraic Operations in Scale-Out Data Platforms

Linear algebraic operations such as matrix manipulations form the kernel of many machine learning and other crucial algorithms. Scaling up as well as scaling out such algorithms are highly desirable to enable efficient processing over millions of data points. To this end, we present a matrix manipulation approach to effectively scale-up each node in a scale-out data parallel platform such as Apache Spark. Specifically, we enable hardware acceleration for matrix multiplications in a distributed Spark setup without user intervention. Our approach supports both dense and sparse distributed matrices, and provides flexible control of acceleration by matrix density. We demonstrate the benefit of our approach for generalized matrix multiplication operations over large matrices with up to four billion elements. To connect the effectiveness of our approach with machine learning applications, we performed Gramian matrix computation via generalized matrix multiplications. Our experiments show that our approach achieves more than 2× performance speed-up, and up to 96.1% computation improvement, compared to a state of the art Spark MLlib for dense matrices.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2021

(110)-Oriented GaAs Devices and Spalling as a Platform for Low-Cost III-V Photovoltaics

We demonstrate the growth of (110) GaAs solar cells by hydride vapor phase epitaxy (HVPE) as an advance towards a (110)-oriented device platform with substrate reuse via spalling. Controlled spalling offers a fracture-based path to substrate amortization, allowing device removal and substrate reuse, but the faceted surface generated in spalling of (100)-GaAs presents hurdles to direct regrowth of subsequent devices. Spalling of (110)-oriented substrates takes advantage of the natural (110) cleavage plane in zinc-blende III-V materials, eliminating this faceting. The vast majority of III-V epitaxy development is on (100)-oriented substrates, but epitaxy is not limited to this orientation. Here, we demonstrate (110)-based GaAs solar cells grown by HVPE with equivalent performance to devices grown on the more standard (100) orientation. We also demonstrate spalling of (110) oriented substrates, revealing surfaces with flat terraces separated by steps. These results provide preliminary evidence of a potentially low-cost path for terrestrial III-V photovoltaics via the (110) substrate orientation.

(110) orientation↗
DOE OSTI2023

Evaluating Robust Entanglement on a Trapped Ion Platform

We present proof of principle results for evaluating an entanglement volumetric benchmark [1] on trapped ion platforms. The benchmark quantifies the robustness of multipartite and bipartite entanglement using stabilizer measurements and witness functions. Each n-qubit graph state is prepared and used to evaluate n state-specific stabilizer strings. These stabilizer measurements are used to evaluate entanglement witness functions. The entanglement benchmark defines families of graph states associated with an initial sub-graph of the hardware qubit connectivity - with all-to-all connectivity, trapped ion systems provide a flexibility in the choice of this initial graph, and this will affect the associated family. In this work, we present results targeting several classes of entangled states: 1) 1-D cluster states, 2) n-qubit GHZ states, and 3) cycle graph states. These states have been found in the literature as standard hardware benchmarks, and have connections to many near-term applications.

Hamilton, Kathleen↗
DOE OSTI2019

GridAPPS-D™ a distribution management platform to develop applications for rural electric utilities

Rural electric utilities, many of which operate with a co-op management approach, have unique operational challenges not shared with their municipal or IOU counterparts. Rural electric utilities typically have fewer customers per feeder mile, and are generally less able to afford the costs, or provide robust support for today’s Advanced Distribution Management System (ADMS) offerings. Additionally, co-ops typically do not need the full suite of ADMS offerings but would benefit from a subset of applications such as OMS, Unbalanced Power-Flow, Volt-VAR optimization and FLISR (Fault Location Isolation and Service Restoration). Their needs, even within these applications, are simpler and more standardized. Lastly, due to their size, their IT capabilities and resources are commonly limited; hence hardware and software needs to be standards-based and easy to maintain with only most expansions of current capabilities. GridAPPS-D™ is an open-source, application development environment that’s part of the Department of Energy’s Grid Modernization Efforts, being led by the Pacific Northwest National Laboratory. The GridAPPS-D project is developing a reference architecture and implementation with the goal of reducing the time and cost to integrate distribution applications for both planning and operations through the use of standardized interfaces and data models. In this paper, we present a view of the reference architecture for GridAPPS-D and highlight its applicability to the rural electric utilities’ needs, including descriptions of applications being developed using this platform, and how they can be applied to other rural electric utilities.

distribution management, GridAPPS-D, rural electri↗
DOE OSTI2024

Performance Characterization and Provenance of Distributed Task-based Workflows on HPC Platforms

Understanding performance and provenance of task-based workflows poses significant challenges, particularly in distributed configurations where resources are shared by multiple applications. Task-based workflow management systems further complicate performance predictability because of their dynamicity that subtly alters task execution order from run to run. In this paper we propose a layered characterization framework for performance and task provenance for Dask.distributed workflows running on high-performance computing (HPC) platforms. It collects data from jobs, the workflow management system, and the operating system to aid in understanding the performance of these workflows. Our approach encompasses three main contributions: first, an extension of Dask.distributed to capture high-fidelity task provenance using Mochi data services; second, the adaptation of the established HPC I/O characterization tool Darshan to gather high-fidelity I/O data, thereby enhancing the granularity of our analysis; and third, a framework to combine and process the collected data and provide helpful insights into performance characterization and reproducibility, alongside our lessons learned.

Dask↗
DOE OSTI2024

HAPPA: A Modular Platform for HPC Application Resilience Analysis with LLMs Embedded

High-performance computing (HPC) systems are increasingly vulnerable to soft errors, which pose significant challenges in maintaining computational accuracy and reliability. Predicting the resilience of HPC applications to these errors is crucial for robust code protection and detailed resilience analysis. In this study, we present HAppA, a modular platform designed for HPC Application Resilience Analysis. Embedding Large Language Models (LLMs), HAppA addresses understanding the context information of long code sequences typical in HPC applications. HAppA implements a novel code representation module that chunks the code into fixed-size segments and aggregates the embeddings of these segments. Three aggregation methods have been explored: MeanPooling, MaxPooling, and LSTM-based techniques. We built a DAtaset for REsilience analysis using Fault Injection (FI), named DARE. Using our DARE dataset, HAppA is trained for regression prediction tasks. Our evaluation results demonstrate the predictive accuracy of HAppA compared to other models, particularly noting that the LSTM-based aggregation method -- HAppA-LSTM -- achieves a mean squared error (MSE) of 0.078 for SDC prediction, surpassing the existing state-of-the-art PARIS model, which recorded an MSE of 0.1172. Additionally, HAppA with the KeyBERT model extracts a list of keywords representing the source code. A comprehensive importance analysis of these keywords further elucidates the code patterns contributing to the error rate. These findings highlight the effectiveness of HAppA in analyzing the resilience of HPC applications and establish a new benchmark for predictive accuracy in resilience.

Jiang, Hailong [Kent State University]↗
DOE OSTI2022

Design and Performance Characterization of RADICAL-Pilot on Leadership-Class Platforms

Many extreme scale scientific applications have workloads comprised of a large number of individual highperformance tasks. The Pilot abstraction decouples workload specification, resource management, and task execution via job placeholders and late-binding. As such, suitable implementations of the Pilot abstraction can support the collective execution of large number of tasks on supercomputers. We introduce RADICAL-Pilot (RP) as a portable, modular and extensible Pilot enabled runtime system. We describe RP's design, architecture and implementation. We characterize its performance and show its ability to scalably execute workloads comprised of tens of thousands heterogeneous tasks on DOE and NSF leadership-class HPC platforms. Specifically, we investigate RP's weak/strong scaling with CPU/GPU, single/multi core, (non)MPI tasks and python functions when using most of ORNL Summit and TACC Frontera. RADICAL-Pilot can be used stand-alone, as well as the runtime for third-party workflow systems.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2025

Ocelot: An Interactive, Efficient Distributed Compression-As-a-Service Platform With Optimized Data Compression Techniques

Large volumes of data generated by scientific simulations, genome sequencing, and other applications need to be moved among clusters for data collection/analysis. Data compression techniques have effectively reduced data storage and transfer costs. However, users' requirements on interactively controlling both data quality and compression ratios are non-trivial to fulfill. Here, we propose a novel Compression-as-a-Service (CaaS) platform called Ocelot with four important contributions: (1) It offers real-time visualization, interactive compression, and transfer of scientific datasets. (2) It incorporates new strategies for compressing diverse types of datasets more effectively than traditional methods. (3) It provides an effective method for estimating the compression ratio and execution time of compression tasks. (4) Experiments on multiple real-world datasets on geographically distributed computers show that Ocelot can significantly improve data transfer efficiency with a performance gain of more than 10x in computing clusters with relatively slow networks.

compression as a service (CaaS)↗
DOE OSTI2020

Development of a Platform at the Matter in Extreme Conditions End Station for Characterization of Matter Heated by Intense Laser-Accelerated Protons

High intensity short-pulse lasers have made possible the generation of energetic proton beams, unlocking numerous applications in high energy density science. One such application is uniform and isochoric heating of materials to the warm dense matter (WDM) state. We have developed a new experimental platform to simultaneously create and probe warm dense matter at the Matter in Extreme Conditions (MEC) end station at the Linac Coherent Light Source (LCLS). The short pulse optical laser (delivering up to 1 J in 45 fs) and the ultra-bright LCLS x-ray laser with tunable frequency respectively deliver high power required to heat materials to WDM and precision-timed high-resolution x-rays to probe them. The laser-accelerated proton beam driven from a flat 1.5 μm Cu foil was first measured then directed to a secondary sample of Al or polypropylene, typically 300-400 μm away. The time evolution of the sample electron temperature was measured using streaked optical pyrometry, where we observed a peak temperature of 0.9 0.15 eV on the rear surface of an Al sample heated by the proton beam. Here, the simulations using the hybrid-PIC code LSP and the rad-hydro code HELIOS show that a measured proton beam can heat Al to approximately 4 eV and polypropylene to 1 eV if instead focused by a hemispherical Cu target. Through additional LSP simulations, we anticipate creating hotter warm dense matter states (20 eV) by increasing the laser energy to 10 J and keeping the other laser parameters fixed.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2020

The Manufacturing Data and Machine Learning Platform: Enabling Real-time Monitoring and Control of Scientific Experiments via IoT

IoT devices and sensor networks present new opportunities for measuring, monitoring, and guiding scientific experiments. Sensors, cameras, and instruments can be combined to provide previously unachievable insights into the state of ongoing experiments. However, IoT devices can vary greatly in the type, volume, and velocity of data they generate, making it challenging to fully realize this potential. Indeed, synergizing diverse IoT data streams in near-real time can require the use of machine learning (ML). In addition, new tools and technologies are required to facilitate the collection, aggregation, and manipulation of sensor data in order to simplify the application of ML models and in turn, fully realize the utility of IoT devices in laboratories. Here we will demonstrate how the use of the Argonne-developed Manufacturing Data and Machine Learning (MDML) platform can analyze and use IoT devices in a manufacturing experiment. MDML is designed to standardize the research and operational environment for advanced data analytics and AI-enabled automated process optimization by providing the infrastructure to integrate AI in cyber-physical systems for in situ analysis. We will show that MDML is capable of processing diverse IoT data streams, using multiple computing resources, and integrating ML models to guide an experiment.

Machine Learning↗
DOE OSTI2017

Effect of Second-Order and Fully Nonlinear Wave Kinematics on a Tension-Leg-Platform Wind Turbine in Extreme Wave Conditions

In this study, we assess the impact of different wave kinematics models on the dynamic response of a tension-leg-platform wind turbine. Aero-hydro-elastic simulations of the floating wind turbine are carried out employing linear, second-order, and fully nonlinear kinematics using the Morison equation for the hydrodynamic forcing. The wave kinematics are computed from either theoretical or measured signals of free-surface elevation. The numerical results from each model are compared to results from wave basin tests on a scaled prototype. The comparison shows that sub and superharmonic responses can be introduced by second-order and fully nonlinear wave kinematics. The response at the wave frequency range is better reproduced when kinematics are generated from the measured surface elevation. In the future, the numerical response may be further improved by replacing the global, constant damping coefficients in the model by a more detailed, customizable definition of the user-defined numerical damping.

17 WIND ENERGY↗
DOE OSTI2011

Making a Hybrid Microfluidic Platform Compatible for In Situ Imaging by Vacuum-Based Techniques

A self-contained microfluidic-based device was designed and fabricated for in situ imaging of aqueous surfaces using vacuum techniques. The device is a hybrid between a microfluidic PDMS block and external accessories, all portable on a small platform (10 cm-8 cm). The key feature is that a small aperture with a diameter of 2-3 micrometers is opened to the vacuum, which serves as a detection window for in situ imaging of aqueous surfaces. Vacuum compatibility and temperature drop due to water vaporization are the two most important challenges in this invention. Theoretical calculations and fabrication strategies are presented from multiple design aspects. In addition, results from the time-of-flight secondary ion mass spectrometry (ToF-SIMS) of aqueous surfaces are presented.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2012

TOPAS: An innovative proton Monte Carlo platform for research and clinical applications

Purpose: While Monte Carlo particle transport has proven useful in many areas (treatment head design, dose calculation, shielding design, and imaging studies) and has been particularly important for proton therapy (due to the conformal dose distributions and a finite beam range in the patient), the available general purpose Monte Carlo codes in proton therapy have been overly complex for most clinical medical physicists. The learning process has large costs not only in time but also in reliability. To address this issue, we developed an innovative proton Monte Carlo platform and tested the tool in a variety of proton therapy applications. Methods: Our approach was to take one of the already-established general purpose Monte Carlo codes and wrap and extend it to create a specialized user-friendly tool for proton therapy. The resulting tool, TOol for PArticle Simulation (TOPAS), should make Monte Carlo simulation more readily available for research and clinical physicists. TOPAS can model a passive scattering or scanning beam treatment head, model a patient geometry based on computed tomography (CT) images, score dose, fluence, etc., save and restart a phase space, provides advanced graphics, and is fully four-dimensional (4D) to handle variations in beam delivery and patient geometry during treatment. A custom-designed TOPAS parameter control system was placed at the heart of the code to meet requirements for ease of use, reliability, and repeatability without sacrificing flexibility. Results: We built and tested the TOPAS code. We have shown that the TOPAS parameter system provides easy yet flexible control over all key simulation areas such as geometry setup, particle source setup, scoring setup, etc. Through design consistency, we have insured that user experience gained in configuring one component, scorer or filter applies equally well to configuring any other component, scorer or filter. We have incorporated key lessons from safety management, proactively removing possible sources of user error such as line-ordering mistakes. We have modeled proton therapy treatment examples including the UCSF eye treatment head, the MGH stereotactic alignment in radiosurgery treatment head and the MGH gantry treatment heads in passive scattering and scanning modes, and we have demonstrated dose calculation based on patient-specific CT data. Initial validation results show agreement with measured data and demonstrate the capabilities of TOPAS in simulating beam delivery in 3D and 4D. Conclusions: We have demonstrated TOPAS accuracy and usability in a variety of proton therapy setups. As we are preparing to make this tool freely available for researchers in medical physics, we anticipate widespread use of this tool in the growing proton therapy community.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2013

Evaluation of clinical IMRT treatment planning using the GATE Monte Carlo simulation platform for absolute and relative dose calculations

Purpose: The objective of this study was to evaluate and validate the use of the Geant4 application for emission tomography (GATE) Monte Carlo simulation platform for clinical intensity modulated radiotherapy (IMRT) dosimetry studies. Methods: The first step consisted of modeling a 6 MV photon beam linear accelerator (LINAC), with its corresponding validation carried out using percent depth dose evaluation, transverse profiles, tissue phantom ratio, and output factor on water phantom. The IMRT evaluation was performed by comparing simulation and measurements in terms of absolute and relative doses using IMRT dedicated quality assurance phantoms considering seven different patient datasets. Results: Concerning the LINAC simulated model validation tissue phantom ratios at 20 and 10 cm in water TPR{sub 10}{sup 20} obtained from GATE and measurements were 0.672 {+-} 0.063 and 0.675, respectively. In terms of percent depth dose and transverse profiles, error ranges were, respectively: 1.472%{+-} 0.285% and 4.827%{+-} 1.323% for field size of 4 Multiplication-Sign 4, 5 Multiplication-Sign 5, 10 Multiplication-Sign 10, 15 Multiplication-Sign 15, 20 Multiplication-Sign 20, 25 Multiplication-Sign 25, 30 Multiplication-Sign 30, and 40 Multiplication-Sign 40 cm{sup 2}. Most errors were observed at the edge of radiation fields because of higher dose gradient in these areas. Output factors showed good agreement between simulation and measurements with a maximum error of 1.22%. Finally, for IMRT simulations considering seven patient datasets, GATE provided good results with a relative error of 0.43%{+-} 0.25% on absolute dose between simulated and measured beams (measurements at the isocenter, volume 0.125 cm{sup 3}). Planar dose comparisons were also performed using gamma-index analysis. For the whole set of beams considered the mean gamma-index value was 0.497 {+-} 0.152 and 90.8%{+-} 3.6% of the evaluated dose points satisfied the 5%/ 4 mm criterion. Conclusions: These results show that GATE allows reliable simulation of complex beams in radiotherapy after an accurate LINAC modeling is validated. A simple cross-calibration procedure proposed in this work allows obtaining absolute dose values even in complex fields.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2014

SU-E-T-89: Comprehensive Quality Assurance Phantom for the Small Animal Radiation Research Platform

Purpose: Use of Small Animal Radiation Research Platform (SARRP) systems for conducting state-of-the-art image guided radiotherapy (IGRT) research on small animals has become more common over the past years. The purpose of this work is to develop and test the suitability and performance of a comprehensive quality assurance (QA) phantom for the SARRP. Methods: A QA phantom was developed for carrying out daily, monthly and annual QA tasks including imaging, dosimetry and treatment planning system (TPS) performance evaluation of the SARRP. The QA phantom consists of nine (60×60×5 mm3) KV-energy tissue equivalent solid water slabs that can be employed for annual dosimetry QA with film. Three of the top slabs are replaceable with ones incorporating Mosfets or OSLDs arranged in a quincunx pattern, or a slab drilled to accommodate an ion chamber insert. These top slabs are designed to facilitate routine daily and monthly QA tasks such as output constancy, isocenter congruency test, treatment planning system (TPS) QA, etc. One slab is designed with inserts for image QA. A prototype of the phantom was applied to test the performance of the imaging, planning and treatment delivery systems. Results: Output constancy test results showed daily variations within 3%. For isocenter congruency test, the phantom could be used to detect 0.3 mm deviations of the CBCT isocenter from the radiation isocenter. Using the Mosfet in phantom as target, the difference between TPS calculations and measurements was within 5%. Image-quality parameters could also be assessed in terms of geometric accuracy, CT number accuracy, linearity, noise and image uniformity, etc. Conclusion: The developed phantom can be employed as a simple tool for comprehensive performance evaluation of the SARRP. The study provides a reference for development of a comprehensive quality assurance program for the SARRP, with proposed tolerances and frequency of required tests.

07 ISOTOPE AND RADIATION SOURCES↗