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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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All sourcesNASA NTRSDOE OSTI
DOE OSTI2021

Evaluating adaptive and predictive power management strategies for optimizing visualization performance on supercomputers

Power is becoming an increasingly scarce resource on the next generation of supercomputers, and should be used wisely to improve overall performance. One strategy for improving power usage is hardware overprovisioning, i.e., systems with more nodes than can be run at full power simultaneously without exceeding the system-wide power limit. With this study, we compare two strategies for allocating power throughout an overprovisioned system – adaptation and prediction – in the context of visualization workloads. While adaptation has been suitable for workloads with more regular execution behaviors, it may not be as suitable on visualization workloads, since they can have variable execution behaviors. This study considers a total of 104 experiments, which vary the rendering workload, power budget, allocation strategy, and node concurrency, including tests processing data sets up to 1 billion cells and using up to 18,432 cores across 512 nodes. Overall, we find that prediction is a superior strategy for this use case, improving performance up to 27% compared to an adaptive strategy.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2021

The Polar System Analysis Package (Ver. 1 Specifications)

The Polar System Analysis Package (POLeSTAr) is a modern data fusion tool being designed to benchmark,evaluate and compare the polar physics and biogeochemistry of Earth System Models (ESMs). It is being written in Python to make use of libraries including Xarray, Matplotlib, SciPy, NumPy and PyTorch within algorithms developed specifically for polar system modeling. Initial POLeSTAr algorithms will be adapted from the MATLAB Ridgepack package that was designed to analyze the Model for Prediction Across Scales (MPAS) sea ice component of the Energy Exascale Earth System Model (E3SM) and sea ice model CICE within the Community Earth System Model (CESM).

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2015

SU-E-P-36: Evaluation of MLC Positioning Errors in Dynamic IMRT Treatments by Analyzing Dynalog Files

Purpose: To assess the accuracy of MLC positioning in Varian linear accelerator, in dynamic IMRT technique, from the analysis of dynalog files generated by the MLC controller. Methods: In Clinac accelerators (pre-TrueBeam technology), control system has an approximately 50ms delay (one control cycle time). Then, the system compares the measured position to the planned position corresponding to the next control cycle. As it has been confirmed by Varian technical support, this effect causes that measured positions appear in dynalogs one cycle out of phase with respect to the planned positions. Around 9000 dynalogs have been analyzed, coming from the three linear accelerators of our center (one Trilogy and two Clinac 21EX) equipped with a Millennium 120 MLC. In order to compare our results to recent publications, leaf positioning errors (RMS and 95th percentile) are calculated with and without delay effect. Dynalogs have been analyzed using a in-house Matlab software. Results: The RMS errors were 0.341, 0.339 and 0.348mm for each Linac; being the average error 0.343 mm. The 95th percentiles of the error were 0.617, 0.607 and 0.625; with an average of 0.617mm. A recent multi-institution study carried out by Kerns et al. found a mean leaf RMS error of 0.32mm and a 95th percentile error value of 0.64mm.Without delay effect, mean leaf RMS errors obtained were 0.040, 0.042 and 0.038mm for each treatment machine; being the average 0.040mm. The 95th percentile error values obtained were 0.057, 0.058 and 0.054 mm, with an average of 0.056mm. Conclusion: Results obtained for the mean leaf RMS error and the mean 95th percentile were consistent with the multi-institution study. Calculated error statistics with delay effect are significantly larger due to the speed proportional and systematic leaf offset. Consequently it is proposed to correct this effect in dynalogs analysis to determine the MLC performance.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2016

Upgrading of data acquisition software for centralized radiation monitoring system in Malaysian Nuclear Agency

With the growth of technology, many devices and equipments can be connected to the network and internet to enable online data acquisition for real-time data monitoring and control from monitoring devices located at remote sites. Centralized radiation monitoring system (CRMS) is a system that enables area radiation level at various locations in Malaysian Nuclear Agency (Nuklear Malaysia) to be monitored centrally by using a web browser. The Local Area Network (LAN) in Nuclear Malaysia is utilized in CRMS as a communication media for data acquisition of the area radiation levels from radiation detectors. The development of the system involves device configuration, wiring, network and hardware installation, software and web development. This paper describes the software upgrading on the system server that is responsible to acquire and record the area radiation readings from the detectors. The recorded readings are called in a web programming to be displayed on a website. Besides the main feature which is acquiring the area radiation levels in Nuclear Malaysia centrally, the upgrading involves new features such as uniform time interval for data recording and exporting, warning system and dose triggering.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2023

Fermionic quantum processing with programmable neutral atom arrays

Simulating the properties of many-body fermionic systems is an outstanding computational challenge relevant to material science, quantum chemistry, and particle physics.-5.4pc]Please note that the spelling of the following author names in the manuscript differs from the spelling provided in the article metadata: D. González-Cuadra, D. Bluvstein, M. Kalinowski, R. Kaubruegger, N. Maskara, P. Naldesi, T. V. Zache, A. M. Kaufman, M. D. Lukin, H. Pichler, B. Vermersch, Jun Ye, and P. Zoller. The spelling provided in the manuscript has been retained; please confirm. Although qubit-based quantum computers can potentially tackle this problem more efficiently than classical devices, encoding nonlocal fermionic statistics introduces an overhead in the required resources, limiting their applicability on near-term architectures. In this work, we present a fermionic quantum processor, where fermionic models are locally encoded in a fermionic register and simulated in a hardware-efficient manner using fermionic gates. We consider in particular fermionic atoms in programmable tweezer arrays and develop different protocols to implement nonlocal gates, guaranteeing Fermi statistics at the hardware level. We use this gate set, together with Rydberg-mediated interaction gates, to find efficient circuit decompositions for digital and variational quantum simulation algorithms, illustrated here for molecular energy estimation. Finally, we consider a combined fermion-qubit architecture, where both the motional and internal degrees of freedom of the atoms are harnessed to efficiently implement quantum phase estimation as well as to simulate lattice gauge theory dynamics.

74 ATOMIC AND MOLECULAR PHYSICS↗
DOE OSTI2016

SU-F-R-12: Prediction of TrueBeam Hardware Issues Using Trajectory Log Analysis

Purpose: To predict potential failures of hardware within the Varian TrueBeam linear accelerator in order to proactively replace parts and decrease machine downtime. Methods: Machine downtime is a problem for all radiation oncology departments and vendors. Most often it is the result of unexpected equipment failure, and increased due to lack of in-house clinical engineering support. Preventative maintenance attempts to assuage downtime, but often is ineffective at preemptively preventing many failure modes such as MLC motor failures, the need to tighten a gantry chain, or the replacement of a jaw motor, among other things. To attempt to alleviate downtime, software was developed in house that determines the maximum value of each axis enumerated in the Truebeam trajectory log files. After patient treatments, this data is stored in a SQL database. Microsoft Power BI is used to plot the average maximum error of each day of each machine as a function of time. The results are then correlated with actual faults that occurred at the machine with the help of Varian service engineers. Results: Over the course of six months, 76,312 trajectory logs have been written into the database and plotted in Power BI. Throughout the course of analysis MLC motors have been replaced on three machines due to the early warning of the trajectory log analysis. The service engineers have also been alerted to possible gantry issues on one occasion due to the aforementioned analysis. Conclusion: Analyzing the trajectory log data is a viable and effective early warning system for potential failures of the TrueBeam linear accelerator. With further analysis and tightening of the tolerance values used to determine a possible imminent failure, it should be possible to pinpoint future issues more thoroughly and for more axes of motion.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2025

Polynomial Chaos Surrogate Construction for Random Fields with Parametric Uncertainty

Engineering and applied science rely on computational experiments to rigorously study physical systems. The mathematical models used to probe these systems are highly complex, and sampling-intensive studies often require prohibitively many simulations for acceptable accuracy. Surrogate models provide a means of circumventing the high computational expense of sampling such complex models. In particular, polynomial chaos expansions (PCEs) have been successfully used for uncertainty quantification studies of deterministic models where the dominant source of uncertainty is parametric. We discuss an extension to conventional PCE surrogate modeling to enable surrogate construction for stochastic computational models that have intrinsic noise in addition to parametric uncertainty. We develop a PCE surrogate on a joint space of intrinsic and parametric uncertainty, enabled by Rosenblatt transformations, which are evaluated via kernel density estimation of the associated conditional cumulative distributions. Furthermore, we extend the construction to random field data via the Karhunen–Loève expansion. We then take advantage of closed-form solutions for computing PCE Sobol indices to perform a global sensitivity analysis of the model which quantifies the intrinsic noise contribution to the overall model output variance. Additionally, the resulting joint PCE is generative in the sense that it allows generating random realizations at any input parameter setting that are statistically approximately equivalent to realizations from the underlying stochastic model. The method is demonstrated on a chemical catalysis example model and a synthetic example controlled by a parameter that enables a switch from unimodal to bimodal response distributions.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2015

Software-Defined Solutions for Managing Energy Use in Small to Medium Sized Commercial Buildings

The Project uses state-of-the-art computer science to extend the benefits of Building Automation Systems (BAS) typically found in large buildings (>100,000 square foot) to medium-sized commercial buildings (<50,000 sq ft). The BAS developed in this project, termed OpenBAS, uses an open-source and open software architecture platform, user interface, and plug-and-play control devices to facilitate adoption of energy efficiency strategies in the commercial building sector throughout the United States. At the heart of this “turn key” BAS is the platform with three types of controllers—thermostat, lighting controller, and general controller—that are easily “discovered” by the platform in a plug-and-play fashion. The user interface showcases the platform and provides the control system set-up, system status display and means of automatically mapping the control points in the system.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2019

Memristive Networks

Explore the source record for details and available documents.

Computer Science↗
DOE OSTI2025

ESnet-JLab FPGA Accelerated Transport (control plane) [EJFAT (udplbd2)] v2.0

The ESnet-JLab FPGA Accelerated Transport system is a solution for streaming high-speed scientific measurement data from Data Acquisition Systems (DAQs) to high-performance computing facilties. It is generally compatible with many science workflows, and makes no assumptions about the specifics of any particular experiment. This program (udplbd version 2) implements the control plane for the system. It is responsible for programming network forwarding rules into the data plane (implemented by the hardware designed named udplb, described separately). It also implements the control loop necessary to match up offered workload with available capacity on high-performance compute nodes.

Howard, Derek [Lawrence Berkeley National Laborato↗
DOE OSTI2021

Hypernetwork Science: From Multidimensional Networks to Computational Topology

As data structures and mathematical objects used for complex systems modeling, hypergraphs sit nicely poised between on the one hand the world of network models, and on the other that of higher-order mathematical abstractions from algebra, lattice theory, and topology. They are able to represent complex systems interactions more faithfully than graphs and networks, while also being some of the simplest classes of systems representing topological structures as collections of multidimensional objects connected in a particular pattern. In this paper we discuss the role of (undirected) hypergraphs in the science of complex networks, and provide a mathematical overview of the core concepts needed for hypernetwork modeling, including duality and the relationship to bicolored graphs, quantitative adjacency and incidence, the nature of walks in hypergraphs, and available topological relationships and properties. We close with a brief discussion of two example applications: biomedical databases for disease analysis, and domain-name system (DNS) analysis of cyber data.

Joslyn, Cliff A.↗
DOE OSTI2023

Decentralized digital twins of complex dynamical systems

Abstract In this article, we introduce a decentralized digital twin (DDT) modeling framework and its potential applications in computational science and engineering. The DDT methodology is based on the idea of federated learning, a subfield of machine learning that promotes knowledge exchange without disclosing actual data. Clients can learn an aggregated model cooperatively using this method while maintaining complete client-specific training data. We use a variety of dynamical systems, which are frequently used as prototypes for simulating complex transport processes in spatiotemporal systems, to show the viability of the DDT framework. Our findings suggest that constructing highly accurate decentralized digital twins in complex nonlinear spatiotemporal systems may be made possible by federated machine learning.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2025

Workflows for Science: A comprehensive guide for ensemble workflow tools usage with applications on OLCF systems

The growing demand for robust computational and workflow environments for scientific applications and user communities at the Oak Ridge Leadership Computing Facility (OLCF) has prompted collaboration with ensemble tools development teams and facility users to produce this technical paper. We connect science applications to the RADICAL-Pilot (RP) workflow tool to execute ensemble instantiations using the Frontier supercomputer. The documented installation, usage, and execution demonstrates how RP streamlines scientific workflows at OLCF. We outline the specific steps OLCF users can follow to integrate this tool with their applications and advance their research. This document stands as a comprehensive guide to OLCF users of ensemble workflow tools with examples on real applications using the Frontier supercomputer.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2019

Storage Systems and Input/Output: Organizing, Storing, and Accessing Data for Scientific Discovery. Report for the DOE ASCR Workshop on Storage Systems and I/O. [Summary Brief]

In September, 2018, the Department of Energy, Office of Science, Advanced Scientific Computing Research Program convened a workshop to identify key challenges and define research directions that will advance the field of storage systems and I/O over the next 5-7 years.The workshop concluded that addressing these combined challenges and opportunities requires tools and techniques that greatly extend traditional approaches and require new research directions. Key research opportunities were identified. This summary brief highlights three areas: The Impact of Storage and I/O, Research Opportunities, and, A new and productive Storage and I/O Environment. The full report from the workshop can be viewed at DOI: 10.2172/1491994.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2024

A Unified Data Infrastructure for Biological and Environmental Research: A Report from the BER Advisory Committee

The Biological and Environmental Research (BER) program within the U.S. Department of Energy (DOE) Office of Science supports large-scale data generation efforts across its two divisions: Biological Systems Science and Earth and Environmental Systems Sciences. These efforts include user facilities in atmospheric radiation measurements, genomics, metabolomics, proteomics, compute, and imaging. In addition, BER supports the development of plant-based fuels; research in biosystems design, environmental microbiomes, and atmospheric systems; energy flux monitoring; climate-based ecosystem experiments; pathogen biopreparedness; and modeling of climate, urban interfaces, and interactions between people and energy resources. For data access, BER supports community data services at its user facilities, along with specialized data initiatives for Earth and environmental science, climate modeling, genomic and microbial analysis, and multisector dynamics modeling.

54 ENVIRONMENTAL SCIENCES↗