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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 1,279 records · Page 71
All sourcesNASA NTRSDOE OSTI
DOE OSTI2018

A Rainbow Coverage Path Planning for a Patrolling Mobile Robot With Circular Sensing Range

In this paper, we proposed a coverage path (CP) planning approach for a mobile robot moving in a dynamically changing environment. Our family of algorithms begins by finding the first path around the center of interior obstacles/disks of a given target region (TR) through the Graham scan algorithm. The first path is then used as a foundational path to generate a collision free, first order differentiable, and observable CP through a complete set of input and output transformation algorithms which together form the rainbow CP planning approach. The last algorithm of the rainbow CP planning approach finds a sufficient number of observation points on the CP needed to observe the TR. The novelty of our rainbow CP planning approach is that it partitions the TR into different shapes with different properties needed to obtain complete coverage while achieving first order path differentiability. The main technical contributions of the proposed approach is to provide a holistic solution that segments any TR, uses triangulation to determine the line of sights and observation points, and computes the collision-free CP within a quadratic runtime. The proposed method can be readily generalized to address problems of higher dimensions, and it is scalable with respect to the size of TR and the number of robots. Computer simulations are used to illustrate the effectiveness and correctness of the proposed approach.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2014

Energy Performance Testing of Asetek's RackCDU System at NREL's High Performance Computing Data Center

In this study, we report on the first tests of Asetek's RackCDU direct-to-chip liquid cooling system for servers at NREL's ESIF data center. The system was simple to install on the existing servers and integrated directly into the data center's existing hydronics system. The focus of this study was to explore the total cooling energy savings and potential for waste-heat recovery of this warm-water liquid cooling system. RackCDU captured up to 64% of server heat into the liquid stream at an outlet temperature of 89 degrees F, and 48% at outlet temperatures approaching 100 degrees F. This system was designed to capture heat from the CPUs only, indicating a potential for increased heat capture if memory cooling was included. Reduced temperatures inside the servers caused all fans to reduce power to the lowest possible BIOS setting, indicating further energy savings potential if additional fan control is included. Preliminary studies manually reducing fan speed (and even removing fans) validated this potential savings but could not be optimized for these working servers. The Asetek direct-to-chip liquid cooling system has been in operation with users for 16 months with no necessary maintenance and no leaks.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2022

SAFE-OCC: A novelty detection framework for Convolutional Neural Network sensors and its application in process control

Herein we present a novelty detection framework for Convolutional Neural Network (CNN) sensors that we call Sensor-Activated Feature Extraction One-Class Classification (SAFE-OCC). We show that this framework enables the safe use of computer vision sensors in process control architectures. Emergent control applications use CNN models to map visual data to a state signal that can be interpreted by the controller. Incorporating such sensors introduces a significant system operation vulnerability because CNN sensors can exhibit high prediction errors when exposed to novel (abnormal) visual data. Unfortunately, identifying such novelties in real-time is nontrivial. To address this issue, the SAFE-OCC framework leverages the convolutional blocks of the CNN to create an effective feature space to conduct novelty detection using a desired one-class classification technique. This approach engenders a feature space that directly corresponds to that used by the CNN sensor and avoids the need to derive an independent latent space. We demonstrate the effectiveness of SAFE-OCC via simulated control environments.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2024

Visualizing an Exascale Data Center Digital Twin: Considerations, Challenges and Opportunities

Digital twins are an excellent tool to model, visualize, and simulate complex systems, to understand and optimize their operation. In this work, we present the technical challenges of real-time visualization of a digital twin of the Frontier supercomputer.We show the initial prototype and current state of the twin and highlight technical design challenges of visualizing such a large High Performance Computing (HPC) system. The goal is to understand the use of augmented reality as a primary way to extract information and collaborate on digital twins of complex systems. This leverages the spatio-temporal aspect of a 3D representation of a digital twin, with the ability to view historical and real-time telemetry, triggering simulations of a system state and viewing the results, which can be augmented via dashboards for details. Finally, we discuss considerations and opportunities for augmented reality of digital twins of large-scale, parallel computers.

Maiterth, Matthias↗
DOE OSTI2018

The experimental nuclear reaction data (EXFOR): Extended computer database and Web retrieval system

The EXchange FORmat (EXFOR) experimental nuclear reaction database and the associated Web interface provide access to the wealth of low- and intermediate-energy nuclear reaction physics data. This resource is based on numerical data sets and bibliographical information of ~22,000 experiments since the beginning of nuclear science. The principles of the computer database organization, its extended contents and Web applications development are described. New capabilities for the data sets uploads, renormalization, covariance matrix, and inverse reaction calculations are presented in this paper. The EXFOR database, updated monthly, provides an essential support for nuclear data evaluation, application development, and research activities. Finally, it is publicly available at the websites of the International Atomic Energy Agency Nuclear Data Section, http://www-nds.iaea.org/exfor, the U.S. National Nuclear Data Center, http://www.nndc.bnl.gov/exfor, and the mirror sites in China, India and Russian Federation.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2015

The dark side of photovoltaic — 3D simulation of glare assessing risk and discomfort

Photovoltaic (PV) systems form an important force in the implementation of renewable energies, but as we all know, the force has always its dark side. Besides efficiency considerations and discussions about architectures of power distribution networks, the increasing numbers of installations of PV systems for implementing renewable energies have secondary effects. PV systems can generate glare due to optical reflections and hence might be a serious concern. On the one hand, glare could affect safety, e.g. regarding traffic. On the other hand, glare is a constant source of discomfort in vicinities of PV systems. Hence, assessment of glare is decisive for the success of renewable energies near municipalities and traffic zones for the success of solar power. Several courts decided on the change of PV systems and even on their de-installation because of glare effects. Thus, location-based assessments are required to limit potential reflections and to avoid risks for public infrastructure or discomfort of residents. The question arises on how to calculate reflections accurately according to the environment's topography. Our approach is founded in a 3D-based simulation methodology to calculate and visualize reflections based on the geometry of the environment of PV systems. This computational model is implemented by an interactive tool for simulation and visualization. Hence, project planners receive flexible assistance for adjusting the parameters of solar panels amid the planning process and in particular before the installation of a PV system. - Highlights: • Solar panels cause glare that impacts neighborhoods and traffic infrastructures. • Glare might cause disability and discomfort. • 3D environment for the calculation of glare • Interactive tool to simulate and visualize reflections • Impact assessment of solar power plant farms.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2023

A dynamic kinetic model captures cell-free metabolism for improved butanol production

Cell-free systems are useful tools for prototyping metabolic pathways and optimizing the production of various bioproducts. Mechanistically-based kinetic models are uniquely suited to analyze dynamic experimental data collected from cell-free systems and provide vital qualitative insight. However, to date, dynamic kinetic models have not been applied with rigorous biological constraints or trained on adequate experimental data to the degree that they would give high confidence in predictions and broadly demonstrate the potential for widespread use of such kinetic models. In this work, we construct a large-scale dynamic model of cell-free metabolism with the goal of understanding and optimizing butanol production in a cell-free system. Using a combination of parameterization methods, the resultant model captures experimental metabolite measurements across two experimental conditions for nine metabolites at timepoints between 0 and 24 hours. Here, we present analysis of the model predictions, provide recommendations for butanol optimization, and identify the aldehyde/alcohol dehydrogenase as the primary bottleneck in butanol production. Sensitivity analysis further reveals the extent to which various parameters are constrained, and our approach for probing valid parameter ranges can be applied to other modeling efforts.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2016

Performance Analysis, Modeling and Scaling of HPC Applications and Tools

E cient use of supercomputers at DOE centers is vital for maximizing system throughput, mini- mizing energy costs and enabling science breakthroughs faster. This requires complementary e orts along several directions to optimize the performance of scienti c simulation codes and the under- lying runtimes and software stacks. This in turn requires providing scalable performance analysis tools and modeling techniques that can provide feedback to physicists and computer scientists developing the simulation codes and runtimes respectively. The PAMS project is using time allocations on supercomputers at ALCF, NERSC and OLCF to further the goals described above by performing research along the following fronts: 1. Scaling Study of HPC applications; 2. Evaluation of Programming Models; 3. Hardening of Performance Tools; 4. Performance Modeling of Irregular Codes; and 5. Statistical Analysis of Historical Performance Data. We are a team of computer and computational scientists funded by both DOE/NNSA and DOE/ ASCR programs such as ECRP, XStack (Traleika Glacier, PIPER), ExaOSR (ARGO), SDMAV II (MONA) and PSAAP II (XPACC). This allocation will enable us to study big data issues when analyzing performance on leadership computing class systems and to assist the HPC community in making the most e ective use of these resources.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2018

Quantum Computing

In recent years Quantum Computing has attracted a great deal of attention in the scientific and technical communities. Interest in the field has expanded to include the popular press and various funding agencies. We discuss the origins of the idea of using quantum systems for computing. We then give an overview in recent developments in quantum hardware and software, as well as some potential applications for high energy physics.

Amundson, James↗
DOE OSTI2024

Quantum/AI Topology-Aware Latency-Adaptive HPC Workflow Scheduling Optimization

The growing demand for more powerful high-performance computing (HPC) systems has led to a steady rise in energy consumption by supercomputing worldwide. This study is focused on comparing our Application-Topology Mapper (ATMapper) to the popular Simple Linux Utility for Resource Management (SLURM) for the purpose of exploring methods that can further optimize job-scheduling within HPC systems. ATMapper is an Artificial-Intelligence based approach to job-scheduling that is currently being enhanced with quantum annealing (QA) to generate optimal schedules faster. We are applying QA to speedup our ATMapper process to achieve higher computing efficiency, thereby reducing HPC energy consumption. Here, we examine how four job-scheduling approaches perform in processor node assignment when using an example network architecture of 4 interconnected nodes. Using a specialized script, we are assessing the schedule of a computation flow with 11 interdependent tasks. The data movements among nodes were tracked to count for the number of interactions (network hops) between nodes needed to complete the tasks. The total number of hops and the job completion time were then used to quantify the efficiency of the different mapping approaches. In addition to SLURM, we also compare our ATMapper to the QA-enabled LBNL TIGER and the D-Wave Distributed Computing processor assignment approaches. The preliminary results showed that our topology-aware, latency-adaptive ATMapper is significantly more efficient when compared to the other scheduling approaches due to its load-imbalance network allocation. The scheduler displayed a computing efficiency of 53% by performing significantly fewer network hops than its alternatives. By reducing the number of hops, ATMapper was able to perform all 11 tasks by using only 3 nodes out of given 4. This research indicates the potential to use QA/AI for HPC job-scheduling. Later, we will test a SLURM simulator program to draw further comparisons on the effectiveness of ATMapper's scheduling approach. The results of this comparison will serve as a baseline for later improving SLURM's performance using a QA-enhanced ATMapper approach.

Caraveo, Braulio [University of Huston - Clear Lak↗
DOE OSTI2020

Process Heating Assessments Using DOE’s Manufacturing Energy Assessment Software for Utility Reduction (MEASUR) Tool Suite

Process heating is the most energy-intensive manufacturing process for most sectors of industry. To quantify energy savings from various energy conservation measures, the Department of Energy (DOE) sponsored the development of the Process Heating Assessment and Survey Tool (PHAST) and similar tools for other industrial systems in the early 2000s. It has been used extensively in the Save Energy Now Program’s Energy Savings Assessments and the Better Plants Program’s In-Plant Trainings. Since the initial development of the legacy tools, both computer operating systems and software development have evolved significantly. Thus, DOE has invested in the modernization of PHAST and other legacy software tools to create the Manufacturing Energy Assessment Software for Utility Reduction (MEASUR) tool suite. MEASUR offers a collection of software tools that can aid manufacturing facilities in improving the efficiency of energy systems and equipment (specifically pumps, fans, steam, and process heating) and in conducting “Energy Treasure Hunts”. Eventually, the tool will also add compressed air and process cooling systems. The Process Heating Assessment (PHA) module of MEASUR is an upgrade of the PHAST tool. PHA provides the means to model fuel-fired, steam-based, and electric process heating systems, covering process heating for most industrial plants in manufacturing sector. It also includes several key upgrades, including the ability to consider multi-component charge loads and account for several different areas of energy losses. The new tool includes a comprehensive flue gas calculator to quantify available heat and heat loss for various gaseous, liquid, and solid fuels and new heat loss calculators. It generates a report and a dynamic Sankey diagram to show the energy consumption in various areas of energy use. MEASUR has significantly improved the user experience by adopting a modern software design. This paper details the structure and workflow of PHA and presents a real-world case study to demonstrate energy savings quantification and MEASUR’s outstanding reporting capabilities.

Nimbalkar, Sachin U.↗
DOE OSTI2025

A Performance Model of In-Situ Techniques

The computational capacity of High-Performance Computing (HPC) systems increases continuously with the rapid development of central processing units (CPUs) and graphic processing units (GPUs), while the in-/output (IO) subsystem develops relatively slowly and storage capacity is also limited. Data-intensive applications, which are designed to leverage the high computational capacity of HPC resources, typically generate a considerable amount of data for post-processing visualizations and data analytics. The limited IO speed and storage space could lead to constraints in the actual performance of these applications and, therefore, scientific discovery. In-situ techniques, where data is visualized/analysed while still in memory rather than through disk, can contribute to alleviating these problems as they can reduce or even fully avoid data writing/reading through the IO subsystem to/from storage. However, the overall efficiency of insitu techniques crucially depends on the characteristics of both the in-situ tasks and the applications, and the resource distribution among them. Therefore, choosing the right in-situ approach (synchronous, asynchronous, or hybrid) and resource allocation is essential to minimize overhead and maximize the benefits of concurrent execution. In this paper, we present a performance model of in-situ techniques to find the most beneficial in-situ approach and the preferred resource configuration. We verify the high accuracy of our approach with over 6800 measurements and provide use cases with different applications.

Ju, Yi [Max Planck Computing and Data Facility, Ga↗
DOE OSTI2024

Celeritas Midterm SciDAC Report

Celeritas is a new Monte Carlo (MC) code that helps satisfy the increasing demand for high energy physics (HEP) detector simulation, using Graphics Processing Unit (GPU) hardware on high performance computing (HPC) systems to model Large Hadron Collider (LHC) experiments and beyond. This report details the project’s progress midway through its SciDAC funding period, highlighting the first complete implementation of standard electromagnetic (EM) physics on GPUs, initial results for performance and scalability on Leadership Computing Facilities (LCFs), and preliminary integration into the CMS and ATLAS experiments. By integrating HEP domain knowledge with expertise in MC transport, Celeritas has catalyzed a shift in the HEP community’s perception of GPU platforms as the future for HPC simulations.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2018

Predictive analytics tools to adjust and monitor performance metrics for the ATLAS Production System

Having information such as an estimation of the processing time or possibility of system outage (abnormal behaviour) helps to assist to monitor system performance and to predict its next state. The current cyber-infrastructure of the ATLAS Production System presents computing conditions in which contention for resources among high-priority data analyses happens routinely, that might lead to significant workload and data handling interruptions. The lack of the possibility to monitor and to predict the behaviour of the analysis process (its duration) and system's state itself provides motivation for a focus on design of the built-in situational awareness analytic tools.

47 OTHER INSTRUMENTATION↗
DOE OSTI2023

Sensitivity of Dislocation-GB interactions to simulation setups in atomistic models

Dislocation-grain boundary (GB) interactions play an integral role in strengthening of crystalline materials, and are dependent on external loading conditions and atomic arrangements at the grain boundary. While molecular dynamics (MD) simulations can provide critical insights into relationships between these parameters and the exact interaction, the outcomes are sensitive to the computational setup. Here, in this work, we explore the effect of computational setup (system size, GB orientation and boundary conditions) on stress-controlled dislocation-grain boundary (DGB) reactions using MD simulations for a well-studied copper symmetric $\langle$1 1 2$\rangle$ tilt boundary. The study demonstrates that the DGB reactions (pinning, absorption, transmission, etc.) are sensitive to the system size and the orientation of the GB to the applied stress. Additionally, the current work shows that there is a critical system size for the dislocation-grain boundary reaction stresses to converge, one which was found to be much larger than the setups in comparable MD studies. This was attributed to the specific stress states and the influence of surfaces, fixed or free, as these computational setups are modified. Finally, a stress-controlled setup with minimal model artifacts is examined, and the effect of coupled Schmid and non-Schmid stress components on the dislocation-GB reactions are discussed.

36 MATERIALS SCIENCE↗
DOE OSTI2022

Understanding the Impact of Data Staging for Coupled Scientific Workflows

We report the rate of data generated by cutting-edge experimental science facilities and large-scale simulations enabled by current high-performance computing (HPC) systems has continued to grow at a far greater pace than the development of the network and storage capabilities on which these systems rely. To cope with this challenge, scientist are moving toward the creation of autonomous experiments and HPC simulations using machine learning. However, efficiently moving, storing, and processing large amounts of data away from the point of origin presents an incredible challenge. In-memory computing, in situ analysis, data staging, and data streaming are recognized viable alternatives to traditional file-based methods for transferring data between coupled workflows. However, the performance trade-offs and limitations for these methods are not fully understood when used in HPC applications. This article presents a comprehensive performance assessment of the current solutions for data staging when applied to applications that are not necessary I/O intensive which makes them not ideal candidates for these methods. Our study is based on experiments running at scale on Oak Ridge National Laboratory's Summit supercomputer using applications and simulations that cover typical computational motifs and patterns. We investigated the usability and cost/benefit trade-offs of staging algorithms for HPC applications under different scenarios and highlight opportunities for optimizing the dataflow between coupled simulation workflows.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2022

Management and Storage of Scientific Data

Scientific discoveries rely heavily on efficient access, search, and management of massive data sets. Data management technologies have, for decades, provided foundational capabilities for scientific computing. Just as storage, input/output (I/O), and data management have been fundamental to simulation-based science for many years, so too are capable data-management technologies key to the success of today’s scientific workflows utilizing data intensive and machine learning (ML) techniques. The Department of Energy, Office of Science, Advanced Scientific Computing Research (ASCR) program has invested broadly in data-management research focused on high-performance computing (HPC) systems, from parallel file systems that store data to application software that makes these systems more productive. Still, advances in technology combined with growing diversity of supported science strongly motivate continued investment in this area. In January 2022, ASCR convened a workshop to identify priority research directions in the area of data management for high-performance and scientific computing. Attendees were challenged to identify promising approaches that would support the breadth of the DOE mission, including the explosion of artificial intelligence (AI) uses and the growing needs of experimental and observational science. Technological and science drivers were identified and considered as they relate to key aspects of data management such as interfaces, architectural design, and FAIR principles (Findable, Accessible, Interoperable, and Reusable). The thoughts of the workshop participants were distilled into a set of four priority research directions with the potential for high impact on DOE science. These research directions are summarized in the following pages.

96 KNOWLEDGE MANAGEMENT AND PRESERVATION↗
DOE OSTI2016

An asynchronous traversal engine for graph-based rich metadata management

Rich metadata in high-performance computing (HPC) systems contains extended information about users, jobs, data files, and their relationships. Property graphs are a promising data model to represent heterogeneous rich metadata flexibly. Specifically, a property graph can use vertices to represent different entities and edges to record the relationships between vertices with unique annotations. The high-volume HPC use case, with millions of entities and relationships, naturally requires an out-of-core distributed property graph database, which must support live updates (to ingest production information in real time), low-latency point queries (for frequent metadata operations such as permission checking), and large-scale traversals (for provenance data mining). Among these needs, large-scale property graph traversals are particularly challenging for distributed graph storage systems. Most existing graph systems implement a "level synchronous" breadth-first search algorithm that relies on global synchronization in each traversal step. This performs well in many problem domains; but a rich metadata management system is characterized by imbalanced graphs, long traversal lengths, and concurrent workloads, each of which has the potential to introduce or exacerbate stragglers (i.e., abnormally slow steps or servers in a graph traversal) that lead to low overall throughput for synchronous traversal algorithms. Previous research indicated that the straggler problem can be mitigated by using asynchronous traversal algorithms, and many graph-processing frameworks have successfully demonstrated this approach. Such systems require the graph to be loaded into a separate batch-processing framework instead of being iteratively accessed, however. In this work, we investigate a general asynchronous graph traversal engine that can operate atop a rich metadata graph in its native format. We outline a traversal-aware query language and key optimizations (traversal-affiliate caching and execution merging) necessary for efficient performance. We further explore the effect of different graph partitioning strategies on the traversal performance for both synchronous and asynchronous traversal engines. Our experiments show that the asynchronous graph traversal engine is more efficient than its synchronous counterpart in the case of HPC rich metadata processing, where more servers are involved and larger traversals are needed. Furthermore, the asynchronous traversal engine is more adaptive to different graph partitioning strategies.

96 KNOWLEDGE MANAGEMENT AND PRESERVATION↗