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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 649 records · Page 36
All sourcesNASA NTRSDOE OSTI
DOE OSTI2022

The Need for a System Science Approach to Global Magnetospheric Models

This perspective advocates for the need of a combined system science approach to global magnetospheric models and to spacecraft magnetospheric data to answer the question “Do simulations behave in the same manner as the magnetosphere does?” (instead of the standard validation question “How well do simulations reproduce spacecraft data?”). This approach will 1) validate global magnetospheric models statistically, without the need for a direct comparison against spacecraft data, 2) expose the deficiencies of the models, and 3) provide physics support to the system analysis performed on the magnetospheric system.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2020

A hybrid cyt c maturation system enhances the bioelectrical performance of engineered Escherichia coli by improving the rate-limiting step

Bioelectronic devices can use electron flux to enable communication between biotic components and abiotic electrodes. We have modified Escherichia coli to electrically interact with electrodes by expressing the cytochrome c from Shewanella oneidensis MR-1. However, we observe inefficient electrical performance, which we hypothesize is due to the limited compatibility of the E. coli cytochrome c maturation (Ccm) systems with MR-1 cytochrome c. Here we test whether the bioelectronic performance of E. coli can be improved by constructing hybrid Ccm systems containing protein domains from both E. coli and S. oneidensis MR-1. The hybrid CcmH increased cytochrome c expression by increasing the abundance of CymA 60%, while only slightly changing the abundance of the other cytochromes c. Electrochemical measurements showed that the overall current from the hybrid ccm strain increased 121% relative to the wildtype ccm strain, with an electron flux per cell of 12.3 ± 0.3 fA·cell -1 . Additionally, the hybrid ccm strain doubled its electrical response with the addition of exogenous flavin, and quantitative analysis of this demonstrates CymA is the rate-limiting step in this electron conduit. These results demonstrate that this hybrid Ccm system can enhance the bioelectrical performance of the cyt c expressing E. coli, allowing the construction of more efficient bioelectronic devices.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2024

Next-Generation, High-temperature, High-frequency, High-efficiency, High-power-density Traction System

To meet performance and reliability requirements necessary for broader adoption of electric drive vehicles, the Electrical and Electronics Technical Team of the U.S. Drive partnership has established aggressive design goals for next-generation electric vehicle drivetrains. Specifically, the 2025 roadmap stipulates a 100 kW/L power density target and a $\$$2.7/kW cost target for power electronics, in addition to high-voltage operation (i.e., greater than 800 VDC). The additional targets for traction motor and the overall system performance impose further challenges on the power electronics design. For example, many high specific power machines have reduced iron content, and therefore reduced intrinsic filtering, thus requiring the inverter to supply a low-distortion drive current. These machines also typically have a high pole count, thus requiring drive current at a higher electrical frequency. Other motors, such as brush-less dc and switch reluctance machines, require a carefully-shaped, non-sinusoidal drive current (Yang, Shang, Brown, & Krishnamurthy, 2015), (Zhang, Bowman, O'Connel, & Haran, 2018), (Anderson, et al., 2018). Two- and three-level inverter topologies are the conventional framework for the power electronics design of the drivetrain, and some demonstrations have shown recent progress towards addressing cost, power density and efficiency goals (Gurpinar & Ozpineci, 2018), (Zhu, Kim, Chen, Erickson, & Maksimović, 2018), (Deshpande, Chen, Narayanasamy, Sathyanarayanan, & Luo, 2018), (Alizadeh, et al., 2019). However, an unconventional approach may be necessary to take the dramatic leap in power density necessitated by the roadmap—while simultaneously addressing the other system needs. Therefore, this project leverages the flying capacitor multilevel (FCML) topology, together with a scalable, modular approach, to address these needs. This type of hybrid converter has several advantages: lower voltage (i.e., less than 300 V) transistors can be used, energy-dense capacitors process most of the power, and the output current waveform is multilevel and exhibits a frequency multiplying effect—in other words, the output has reduced dv/dt and filtering requirements for the same high voltage dc bus. For example, in an electric vehicle with an 800 V bus, a 10-level FCML could leverage 100 V, commercially available GaN devices switching at 115 kHz to produce a ~1 MHz switching waveform (modulated according to the motor drive requirements) with one ninth of the dv/dt of a two-level converter. Prior work has already demonstrated promising performance and gravimetric power density figures for more electric aircraft applications (Pallo, Foulkes, Modeer, Coday, & Pilawa-Podgurski, 2018). This project leverages lessons learned to achieve the volumetric power density of 100 kW/L by employing advanced liquid cooling, address the 300,000 mile reliability challenge with redundant design, topology failure studies and online health monitoring, and reduce costs to $\$$2.7/kW through the use of low-cost GaN devices, modular converter assemblies, and modest modifications to traditional manufacturing methods. The project involved several hardware designs, each achieving increasing performance. At the conclusion of the project, a volumetric power density of 380 kW/L was achieved, in a 800V dc-ac converter, greatly surpassing even the aggressive target goal.

33 ADVANCED PROPULSION SYSTEMS↗
DOE OSTI2024

Performance Analysis of an Optimization Algorithm for Metamaterial Design on the Integrated High-Performance Computing and Quantum Systems

Optimizing metamaterials with complex geometries is a big challenge. Although an active learning algorithm, combining machine learning (ML), quantum computing, and optical simulation, has emerged as an efficient optimization tool, it still faces difficulties in optimizing complex structures that have potentially high performance. In this work, we comprehensively analyze the performance of an optimization algorithm for metamaterial design on the integrated HPC and quantum systems. We demonstrate significant time advantages through message-passing interface (MPI) parallelization on the high-performance computing (HPC) system showing approximately 54% faster ML tasks and 67 times faster optical simulation against serial workloads. Furthermore, we analyze the performance of a quantum algorithm designed for optimization, which runs with various quantum simulators on a local computer or HPC-quantum system. Results showcase ~24 times speedup when executing the optimization algorithm on the HPC-quantum hybrid system. This study paves a way to optimize complex metamaterials using the integrated HPC-quantum system.

Kim, Seongmin↗
DOE OSTI2021

The Applicability of Unit Systems to High-Performance Computing Applications

Dimensional analysis is a key technique used to verify the soundness of scientific models. Most experts agree that engineering and scientific software would be made more reliable by integrating dimensional analysis in their type system. We explored how High Performance Computing (HPC) applications could integrate compile-time dimensional analysis. We started by investigating various implementation of unit systems for C++. Eventually, selecting the latest (and most advanced) one to apply to our test codes. We worked with code of increasing complexity, from a projectile trajectory calculation to the proxy-application Lulesh. This included our code, Springs-3D, which focuses on demonstrating language features while performing simple physic computations. Finally, our main contribution is a source-code analysis which extracts constraints on the dimension of all variables, functions, and constants in an application. This resulting system of equations is solved using the dimensions of a few of these objects. This analysis has the potential to greatly reduce the time spent performing dimensional analysis when refactoring application to use a representation of units.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2021

Characterizing Machine Learning I/O Workloads on Leadership Scale HPC Systems

High performance computing (HPC) is no longer solely limited to traditional workloads such as simulation and modeling. With the increase in the popularity of machine learning (ML) and deep learning (DL) technologies, we are observing that an increasing number of HPC users are incorporating ML methods into their workflow and scientific discovery processes, across a wide spectrum of science domains such as biology, earth science, and physics. This gives rise to a diverse set of I/O patterns than the traditional checkpoint/restart-based HPC I/O behavior. The details of the I/O characteristics of such ML I/O workloads have not been studied extensively for large-scale leadership HPC systems. This paper aims to fill that gap by providing an in-depth analysis to gain an understanding of the I/O behavior of ML I/O workloads using darshan - an I/O characterization tool designed for lightweight tracing and profiling. We study the darshan logs of more than 23, 000 HPC ML I/O jobs over a time period of one year running on Summit - the second-fastest supercomputer in the world. This paper provides a systematic I/O characterization of ML I/O jobs running on a leadership scale supercomputer to understand how the I/O behavior differs across science domains and the scale of workloads, and analyze the usage of parallel file system and burst buffer by ML I/O workloads.

Paul, Arnab↗
DOE OSTI2020

Noisy Intermediate-Scale Quantum Applications on a Pathfinder System

Work performed under this one-year LDRD was concerned with estimating resource requirements for small quantum test beds that are expected to be available in the near future. This work represents a preliminary demonstration of our ability to leverage quantum hardware for solving small quantum simulation problems in areas of interest to the DOE. The algorithms enabling such studies are hybrid quantum-classical variational algorithms, in particular the widely-used variational quantum eigensolver (VQE). Employing this hybrid algorithm, in which the quantum computer complements the classical one, we implemented an end-to-end application-level toolchain that allows the user to specify a molecule of interest and compute the ground state energy using the VQE approach. We found significant limitations attributable to the classical portion of the hybrid system, including a greater than greater-than-quartic power scaling of the classical memory requirements with the system size. Current VQE approaches would require an exascale machine for solving any molecule with size greater than 150 nuclei. Our findings include several improvements that we implemented into the VQE toolchain, including a new classical optimizer that is decades old but hadn't been considered before in the VQE ecosystem. Our findings suggest limitations to variational hybrid approaches to simulation that further motivate the need for a gate-based fault-tolerant quantum processor that can implement larger problems using the fully digital quantum phase estimation algorithm.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2021

Neuromorphic Graph Algorithms: Cycle Detection, Odd Cycle Detection, and Max Flow

Neuromorphic computing is poised to become a promising computing paradigm in the post Moore’s law era due to its extremely low power usage and inherent parallelism. Spiking neural networks are the traditional use case for neuromorphic systems, and have proven to be highly effective at machine learning tasks such as control problems. More recently, neuromorphic systems have been applied outside of the arena of machine learning, primarily in the field of graph algorithms. Neuromorphic systems have been shown to perform graph algorithms faster and with lower power consumption than their traditional (GPU/CPU) counterparts, and are hence an attractive option for a co-processing unit in future high performance computing systems, where graph algorithms play a critical role. In this paper, we present a neuromorphic implementation of cycle detection, odd cycle detection, and the Ford-Fulkerson max-flow algorithm. We further evaluate the performance of these implementations using the NEST neuromorphic simulator by using spike counts and simulation time as proxies for energy consumption and run time. In addition to gains inherent in neuromorphic systems, we show that within the neuromorphic implementations early stopping criteria can be implemented to further improve performance.

Kay, Bill↗
DOE OSTI2022

Narrowband microwave-photonic notch filters using Brillouin-based signal transduction in silicon

Abstract The growing demand for bandwidth makes photonic systems a leading candidate for future telecommunication and radar technologies. Integrated photonic systems offer ultra-wideband performance within a small footprint, which can naturally interface with fiber-optic networks for signal transmission. However, it remains challenging to realize narrowband (∼MHz) filters needed for high-performance communications systems using integrated photonics. In this paper, we demonstrate all-silicon microwave-photonic notch filters with 50× higher spectral resolution than previously realized in silicon photonics. This enhanced performance is achieved by utilizing optomechanical interactions to access long-lived phonons, greatly extending available coherence times in silicon. We use a multi-port Brillouin-based optomechanical system to demonstrate ultra-narrowband (2.7 MHz) notch filters with high rejection (57 dB) and frequency tunability over a wide spectral band (6 GHz) within a microwave-photonic link. We accomplish this with an all-silicon waveguide system, using CMOS-compatible fabrication techniques.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2023

Estimating the Performance Loss Rate of Photovoltaic Systems Using Time Series Change Point Analysis

The accurate quantification of the performance loss rate of photovoltaic systems is critical for project economics. Following the current research activities in the photovoltaic performance and reliability field, this work presents a comparative assessment between common change point methods for performance loss rate estimation of fielded photovoltaic installations. An extensive testing campaign was thus performed to evaluate time series analysis approaches for performance loss rate evaluation of photovoltaic systems. Historical electrical data from eleven photovoltaic systems installed in Nicosia, Cyprus, and the locations’ meteorological measurements over a period of 8 years were used for this investigation. The application of change point detection algorithms on the constructed monthly photovoltaic performance ratio series revealed that the obtained trend might not always be linear. Specifically, thin film photovoltaic systems showed nonlinear behavior, while nonlinearities were also detected for some crystalline silicon photovoltaic systems. When applying several change point techniques, different numbers and locations of changes were detected, resulting in different performance loss rate values (varying by up to 0.85%/year even for the same number of change points). The results highlighted the importance of the application of nonlinear techniques and the need to extract a robust nonlinear model for detecting significant changes in time series data and estimating accurately the performance loss rate of photovoltaic installations.

photovoltaics↗
DOE OSTI2023

Systems, devices, and methods for performing augmented reality responsive to monitoring user behavior

Systems, devices, and methods are described for performing augmented reality (AR) to assist user performing a task in an environment. An AR device may be configured to capture real-time data. An AR engine may be configured to monitor user behavior from the real-time data responsive to feature extraction from the real-time data, compare the user behavior to pre-defined work procedures, and generate augmented reality objects to be output by the AR device.

Yoon, SuJong↗
DOE OSTI2023

Analysis of Tasks in Autonomous Systems Using the EMRALD Dynamic Risk Assessment Tool

An autonomous system refers to the system that has the power and ability for self-governance in the performance of system functions. Autonomous systems have been actively pursued in a variety of domains such as automotive, aviation, maritime, medicine, and nuclear fields. As an unmanned concept employing the highest automation level, the autonomous system basically performs most of the work in normal operations or emergency situations. However, despite advances in technology, many researchers have noted these systems still require human actions. The nature of human actions on autonomous systems is different than the human actions that are considered in existing systems. Nevertheless, only a few studies have been conducted on 1) characterizing the different types of errors and risks associated with human actions interacting with autonomous systems and 2) how to evaluate human actions in the autonomous operations. As a starting point, this study aims to investigate differences of tasks in autonomous operation compared to those in existing nuclear power plant operation using the Event Modeling Risk Assessment Using Linked Diagram (EMRALD) software. In this paper, insights aspect of human error and time are derived out and discussed based on the output of the EMRALD models.

99 GENERAL AND MISCELLANEOUS↗
DOE OSTI2020

The Locus Algorithm IV: Performance metrics of a grid computing system used to create catalogues of optimised pointings

This paper discusses the requirements for and performance metrics of the the Grid Computing system used to implement the Locus Algorithm to identify optimum pointings for differential photometry of 61,662,376 stars and 23,779 quasars. Initial operational tests indicated a need for a software system to analyse the data and a High Performance Computing system to run that software in a scalable manner. Practical assessments of the performance of the software in a serial computing environment were used to provide a benchmark against which the performance metrics of the HPC solution could be compared, as well as to indicate any bottlenecks in performance. These performance metrics indicated a distinct split in the performance dictated more by differences in the input data than by differences in the design of the systems used. This indicates a need for experimental analysis of system performance, and suggests that algorithmic complexity analyses may lead to incorrect or naive conclusions, especially in systems with high data I/O overhead such as grid computing. Further, it implies that systems which reduce or eliminate this bottleneck such as in-memory processing could lead to a substantial increase in performance.

Creaner, Oisín↗
DOE OSTI2020

Scalable and Actionable Performance Measures for Traffic Signal Systems using Probe Vehicle Trajectory Data

Scalable and actionable performance measures for traffic signal systems provide opportunities for practitioners to measure and improve the transportation network. Historically, traffic signal improvements have relied on scheduled signal retiming based on limited data collection, or on the public to call and alert engineers of an issue. This inefficient method of improving signal timing led to the creation of automated traffic signal performance measures (ATSPMs). These metrics rely on expensive infrastructure, including detection and communications, which has produced barriers for numerous agencies to fully adopt. Recently, third-party data providers have begun to release vehicle trajectory data, which allows for enhanced signal metrics with no investment in physical equipment. The purpose of this study is to demonstrate the use of these data and summarize the scalability of the created metrics. This work builds on previous efforts to quantify signal performance on nine intersections in Michigan, U.S. Ten signalized corridors in Columbus, Ohio, were chosen to scale a performance assessment using crowdsourced trajectory data. A total of 136 intersections were assessed in 2-h intervals using data from all weekdays in 2017. High-level corridor summary metrics including average percent of vehicles stopping (18%–32%), average delay (9.4–20.5 s), and level of travel time reliability (1.23–2.73) were calculated for each corridor direction. Intersection-level metrics were also introduced, which can be used by practitioners to identify problems, improve signal timings, and prioritize future infrastructure investments.

99 GENERAL AND MISCELLANEOUS↗
DOE OSTI2024

High Performance Computing Management: A Sustainable System Software Approach

The demand for high performance computing (HPC) resources continues to grow, driven by the increasing complexity of modeling and simulation, artificial intelligence (AI), and machine learning (ML) workloads [Porter]. The growing energy consumption demand of these HPC systems is a significant concern, both in terms of operational costs and environmental impact. AI hardware accelerators are expected to reach 1.5% of the world’s power consumption by 2029 [Shah].

97 - MATHEMATICS AND COMPUTING↗
DOE OSTI2020

reV (The Renewable Energy Potential Model - Open Source) [SWR-21-59, SWR-20-20 and SWR-17-34]

The Renewable Energy Potential (reV) model is a platform for the detailed assessment of renewable energy resources and their geospatial intersection with grid infrastructure and land use characteristics. The reV model currently supports photovoltaic (PV), concentrating solar power (CSP), and land-based wind turbine technologies. Modules in the reV framework function at different spatial and temporal resolutions, allowing for the assessment of resource potential, technical potential, and supply curves at varying levels of detail. The platform runs on the National Renewable Energy Laboratory’s (NREL’s) high-performance computing system, providing scalable and efficient performance from a single location up to a continent, for a single year or decades of time-series resource data. Coupled with NREL’s System Advisor Model (SAM), reV supports resource assessments from 5-minute to hourly temporal resolutions and supports the analysis of long-term (i.e., year-on-year) variability of renewable generation (e.g., interannual variability and exceedance probabilities).

Maclaurin, Galen↗
DOE OSTI2021

The Renewable Energy Potential (reV) Model: A Geospatial Platform for Technical Potential and Supply Curve Modeling

The Renewable Energy Potential (reV) model is a platform for detailed assessment of renewable energy (RE) resources and their geospatial intersection with grid infrastructure and land use characteristics. The reV model currently supports photovoltaic (PV), concentrating solar power (CSP) and land-based wind turbine technologies. Modules in the reV framework function at different spatial and temporal resolutions, allowing for assessment of resource potential, technical potential and supply curves at varying levels of detail. The platform runs on NREL's High Performance Computing system, providing scalable and efficient performance from a single location all the way up to continental scales, for a single year or decades of time series resource data. Coupled with NREL's System Advisor Model (SAM), reV supports resource assessment from 5-minute to hourly temporal resolution and provides for analysis of long-term (i.e., year-on-year) variability of RE generation (e.g., interannual variability and exceedance probabilities). Technical potential is measured as a function of resource potential and limitations put on developable land area defined by the user. For example, the user can limit development by land ownership, terrain, land use/cover, and urban areas, as well as custom inputs. Technology, grid interconnection and operation costs, based on the latest market data and future projections, are also embedded in the model. The supply curve module is a spatial sorting algorithm based on plant siting, grid interconnection cost, and regional competition, which provides a geographically discrete estimate of levelized cost of electricity (LCOE) and supply (i.e., capacity) for specific renewable technologies. The reV model currently provides broad coverage across North America, South and Central Asia, South America and South Africa to inform national- and international-scale analyses as well as regional infrastructure and deployment planning.

13 HYDRO ENERGY↗