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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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224 records · Page 13
All sourcesNASA NTRSDOE OSTI
DOE OSTI2021

Decentralized Failure-Tolerant Optimization of Electric Vehicle Charging

We present a decentralized failure-tolerant algorithm for optimizing electric vehicle (EV) charging, using charging stations as computing agents. The algorithm is based on the alternating direction method of multipliers (ADMM) and it has the following features: (i) It handles capacity, peak demand, and ancillary services coupling constraints. (ii) It does not require a central agent collecting information and performing coordination (e.g. an aggregator), instead all agents exchange information and computations are carried out in a fully decentralized fashion. (iii) It can withstand the failure of any number of computing agents, as long as the remaining computing agents are in a connected communications network. We construct this algorithm by reformulating the optimal EV charging problem in a decomposable form, amenable to ADMM, and then developing efficient decentralized solution methods for the subproblems dealing with coupling constraints. We conduct numerical experiments on industry-scale synthetic EV charging datasets, with up to 1,152 charging stations, using a high performance computing cluster. The experiments demonstrate that the proposed algorithm can solve the optimal EV charging problem fast enough to permit the integration of EV charging with real-time electricity markets, even in the presence of failures.

42 ENGINEERING↗
DOE OSTI2023

LANL Meteorological Program: 2022 Data Completeness/Quality Report

Los Alamos National Laboratory (LANL) operates seven mesa-top instrumented meteorology towers: Technical Area (TA) 6, TA-49, TA-53, TA-54, TA-63, TA-54B, and TA-16B. An additional instrumented tower is located in Mortandad Canyon (TA-5 MDCN), and there is a rain gauge at North Community (NCOM), located within the town of Los Alamos. The 10 meter (m) towers at TA-63, TA- 54B, and TA-16B were installed in 2021, and will be included in the 2023 data completeness/quality report. A description of the meteorology monitoring network, prior to the installation of the TA-63, TA-54B, and TA-16B is found in Dewart and Boggs (2014). Four of the mesa-top towers (e.g., TA-6, TA-49, TA-53, and TA-54) are instrumented at the 1.2 m, 11.5 m, 23 m, and 46 m levels. In addition, the TA-6 tower is instrumented at the 92 m level. The TA-5 MDCN tower is 10 m in height and is instrumented at 1.2 m and 10 m. Data are collected and averaged every 15 minutes. Range checking is performed on each measurement every 15 minutes; data that are beyond normal ranges are eliminated from the data set and replaced by a code for missing data. In addition, data are reviewed weekly by qualified meteorologists to identify bad data not identified by the range checking technique. The data steward eliminates these data from the data set and replaces them with a code for missing data. The instrument technicians also review that data and schedule instrument replacement, as required. All instruments are calibrated at a frequency that meets the criteria identified in ANSI/ANS-3.11-2015. Data completeness is determined by the number of total 15-minute records available versus the number of possible measurements for the entire year. As a rule, the meteorologists do not attempt to estimate data that are eliminated as bad data. Original datalogger records, including bad data, can be recalled from program archival storage.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2024

LANL Meteorological Program: 2023 Data Completeness/Quality Report

Los Alamos National Laboratory (LANL) operates seven mesa-top instrumented meteorology towers: Technical Area (TA) 6, TA-49, TA-53, TA-54, TA-63, TA-54B, and TA-16B. An additional instrumented tower is located in Mortandad Canyon (TA-5 MDCN), and there is a rain gauge at North Community (NCOM), located within the town of Los Alamos. The 10 meter (m) towers at TA-63, TA-54B, and TA-16B have been in testing since they were installed in 2021, and will be included in a future data completeness report. A description of the meteorology monitoring network, prior to the installation of the TA-63, TA-54B, and TA-16B is found in Dewart and Boggs (2014). Four of the mesa-top towers (e.g., TA-6, TA-49, TA-53, and TA-54) are instrumented at the 1.2 m, 11.5 m, 23 m, and 46 m levels. In addition, the TA-6 tower is instrumented at the 92 m level. The TA-5 MDCN tower is 10 m in height and is instrumented at 1.2 m and 10 m. Data are collected and averaged every 15 minutes. Range checking is performed on each measurement every 15 minutes; data that are beyond normal ranges are eliminated from the data set and replaced by a code for missing data. In addition, data are reviewed weekly by qualified meteorologists to identify bad data not identified by the range checking technique. The data steward eliminates these data from the data set and replaces them with a code for missing data. The instrument technicians also review that data and schedule instrument replacement, as required. All instruments are calibrated at a frequency that meets the criteria identified in ANSI/ANS-3.11-2015. Data completeness is determined by the number of total 15-minute records available versus the number of possible measurements for the entire year. As a rule, the meteorologists do not attempt to estimate data that are eliminated as bad data. Original datalogger records, including bad data, can be recalled from program archival storage.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2025

The Roman View of Strong Gravitational Lenses

Galaxy–galaxy strong gravitational lenses can constrain dark matter models and the Lambda cold dark matter cosmological paradigm at subgalactic scales. Currently, there is a dearth of images of these rare systems with high signal-to-noise ratio (SNR) and angular resolution. The Nancy Grace Roman Space Telescope (hereafter Roman), scheduled for launch in late 2026, will play a transformative role in strong-lensing science with its planned wide-field surveys. With its remarkable 0.281 square degree field of view and diffraction-limited angular resolution of ~0$^{''}_.$1, Roman is uniquely suited to characterizing dark matter substructure from a robust population of strong lenses. We present a yield simulation of detectable strong lenses in Roman’s planned High Latitude Wide Area Survey (HLWAS). We simulate a population of galaxy–galaxy strong lenses across cosmic time with cold dark matter subhalo populations, select those detectable in the HLWAS, and generate simulated images accounting for realistic Wide Field Instrument detector effects. For a fiducial case of single 146 s exposures, we predict around 160,000 detectable strong lenses in the HLWAS, of which about 500 will have sufficient SNR to be amenable to detailed substructure characterization. We investigate the effect of variation of the point-spread function across Roman’s field of view on detecting individual subhalos and the suppression of the subhalo mass function at low masses. Our simulation products are available to support strong-lens science with Roman, such as training neural networks and validating dark matter substructure analysis pipelines.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2022

DFSynthesizer: Dataflow-based Synthesis of Spiking Neural Networks to Neuromorphic Hardware

Spiking Neural Networks (SNNs) are an emerging computation model that uses event-driven activation and bio-inspired learning algorithms. SNN-based machine learning programs are typically executed on tile-based neuromorphic hardware platforms, where each tile consists of a computation unit called a crossbar, which maps neurons and synapses of the program. However, synthesizing such programs on an off-the-shelf neuromorphic hardware is challenging. This is because of the inherent resource and latency limitations of the hardware, which impact both model performance, e.g., accuracy, and hardware performance, e.g., throughput. We propose DFSynthesizer, an end-to-end framework for synthesizing SNN-based machine learning programs to neuromorphic hardware. The proposed framework works in four steps. First, it analyzes a machine learning program and generates SNN workload using representative data. Second, it partitions the SNN workload and generates clusters that fit on crossbars of the target neuromorphic hardware. Third, it exploits the rich semantics of the Synchronous Dataflow Graph (SDFG) to represent a clustered SNN program, allowing for performance analysis in terms of key hardware constraints such as number of crossbars, dimension of each crossbar, buffer space on tiles, and tile communication bandwidth. Finally, it uses a novel scheduling algorithm to execute clusters on crossbars of the hardware, guaranteeing hardware performance. We evaluate DFSynthesizer with 10 commonly used machine learning programs. Our results demonstrate that DFSynthesizer provides a much tighter performance guarantee compared to current mapping approaches.

Computer Science↗
DOE OSTI2025

Dynamic Charging Rendezvous and Motion Planning for a Multi-AGV Team Including a Mobile Charging Host

Teams of automated battery-powered electric vehicles have the potential to execute complex mission tasks in off-road environments for agriculture, military, and other applications. Limited onboard energy reserves hinder their adoption in large-scale resource-constrained environments, where recharging is a necessity. It may be infeasible to install a network of static charging stations in off-road environments. For this reason, dedicated mobile host vehicles with charging capabilities are proposed as a means to increase range and capabilities of the multivehicle team. Here, in this study, we consider an ad hoc planning framework, where results from a high-confidence trajectory planner are leveraged to plan charging rendezvous between a host and other worker vehicles in a receding horizon fashion to provide high confidence that energy reserves will not be prematurely exhausted. The core problem is posed so as to minimize the impact of recharging on the mission in terms of task delays, overall energy utilization, and costs of fast charging. Through extensive Monte Carlo simulations of an off-road mission, we show a decrease in task delays without substantial increases in energy needs by updating the charging rendezvous plan during the mission. However, if updates are made too often, model mismatch may cause unnecessary cycling and mission failure.

Energy constraints↗
DOE OSTI2021

SMC 2021 : Analyzing Resource Utilization and User Behavior on Titan Supercomputer

Resource utilization statistics of submitted jobs on a supercomputer can help us understand how users from various scientific domains use HPC platforms and better design a job scheduler. We explore to generate insight regarding workload distribution and usage pattern domains from job scheduler trace, GPU failure information, and project-specific information collected from Titan supercomputer. Furthermore, we want to know how the scheduler performance varies over time and how the users' scheduling behavior changes following a system failure. These observations have the potential to provide valuable insight, which is helpful to prepare for system failures. These practices will help us develop and apply novel machine learning algorithms in understanding system behavior, requirement, and better scheduling of HPC systems. There are two datasets, RUR and GPU. RUR: This dataset is the job scheduler traces collected from the Titan supercomputerfrom 01/01/2015 to 07/31/2019 (2015.csv - 2019.csv). These were collected usingResource Utilization Report (RUR), a Cray-developed resource-usage data collectionand reporting system. It contains the usage information of its critical resources (CPU,Memory, GPU, and I/O) of each running job on Titan during that period [2]. ProjectAreas: Every job is associated with a project ID. TheProjectAreas.csvdatasetprovides a mapping of the project ID to its domain science. GPU: There have been some hardware-related issues in the GPUs in Titan that caused some GPUs to fail, sometimes irrecoverably during some job runs. This dataset provides information regarding these failures during the execution of the submitted jobs. GPUs on Titan are uniquely identified by a serial number (SN), and they are installed in a location. A GPU can be installed in a location, then removed from that location following a failure, and then re-installed in a different location after fixing the problem. If the failure can't be recovered, the GPU might be removed entirely from Titan. There are two prominent types of failures that resulted in the removal of GPUs from Titan: Double Bit Error (DBE) and Out of the Bus (OTB). The dataset (gc_full.csv) has the following fields: 1. SN : Serial number of a GPU 2. location : The location where it is installed 3. insert : The time when it was inserted into that location 4. remove : The time when it was removed from that location 5. duration : Amount of time the GPU spent in this location 6. out : If the device was taken out entirely w/o a re-installment into a new location. 7. event : If the GPU was taken out entirely, the reason for its removal. To learn more about this dataset, please refer to the git repositoryhttps://github.com/olcf/TitanGPULifeand the related publication [1]. References [1] George Ostrouchov, Don Maxwell, Rizwan A Ashraf, Christian Engelmann, MallikarjunShankar, and James H Rogers. Gpu lifetimes on titan supercomputer: Survival analysisand reliability. InSC20: International Conference for High Performance Computing,Networking, Storage and Analysis, pages 1-14. IEEE, 2020. [2] Feiyi Wang, Sarp Oral, Satyabrata Sen, and Neena Imam. Learning from five-yearresource-utilization data of titan system. In2019 IEEE International Conference onCluster Computing (CLUSTER), pages 1-6. IEEE, 2019.

42 ENGINEERING↗
DOE OSTI2025

Deep Learning–Assisted Multiobjective Optimization of Geological CO 2 Storage Performance under Geomechanical Risks

In geological CO 2 storage, designing the optimal well control strategy for CO 2 injection to maximize CO 2 storage while minimizing the associated geomechanical risks is not trivial. This challenge arises due to pressure buildup, CO 2 plume migration, the highly nonlinear nature of geomechanical responses to rock-fluid interaction, and the high computational cost associated with coupled flow and geomechanics simulations. In this paper, we introduce a novel optimization framework to address these challenges. The optimization problem is formulated as follows: maximize total CO 2 storage while minimizing geomechanical risks by adjusting the injection schedules within bounded constraints. The geomechanical risks are primarily driven by injection-induced pressure build-up, which is characterized by ground displacement and the induced microseismicity. We used the Fourier neural operator (FNO)-based deep learning model to construct surrogate models, replacing the time-consuming coupled flow and geomechanics simulations for evaluating the aforementioned objective functions. The developed surrogate models have been incorporated into a multiobjective optimization framework through a genetic algorithm to reduce the computational burden. The proposed optimization framework reduces the computational cost from approximately 2,400 hours, when using objective function evaluations based on physics-based simulations, to around 20 minutes. A set of Pareto-optimal solutions of the proposed workflow yields nontrivial optimal decisions, reducing the microseismicity potential and the vertical displacement. This Pareto front highlights the optimal trade-offs between CO 2 storage amount, safety, and ground displacement, emphasizing the need for careful optimization and management of injection strategies to achieve a balanced outcome. The novelty of this work is twofold. First, we demonstrate the importance of incorporating the minimization of the geomechanical risks as objective functions into the CO 2 storage optimization workflow to mitigate the potential risk of induced microseismicity and ground displacement. Second, we leverage the FNO-based surrogate models to optimize a real-field CO 2 storage operation.

42 ENGINEERING↗