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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 721 records · Page 40
All sourcesNASA NTRSDOE OSTI
DOE OSTI2017

ASSESSMENT OF MULTIPHASE CFD WITH ZERO CLOSURE MODEL FOR BOILING WATER REACTOR FUEL ASSEMBLIES

Extension of existing boiling closures to the high void fraction conditions of BWRs is one of challenges for M-CFD applications. This paper presents the assessment of an Eulerian M-CFD model with Zero closure model that has been specifically designed for two-phase flow and heat transfer applications in BWR fuel assemblies. The baseline Zero closure aims at including the simplest and most robust representation of the key physical mechanisms, rather than aiming at absolute accuracy of the predictions. The capabilities of M-CFD with Zero were evaluated against the international OECD/NRC BFBT benchmark data through the comparative analyses of local void fraction, sub-channel void fraction error and exit quality. INL high performance computing system has been employed to demonstrate the scalability of the M-CFD application to nuclear reactor fuel assemblies. The M-CFD simulation results show a good agreement with the experimental data for both quantitative and qualitative results, with the highest uncertainty being observed in the prediction of the local void fraction profile at low void fraction condition. This paper presents a recent view of the best practices for M-CFD of BWR fuel assemblies to provide a useful reference to identify and overcome common challenges in the applications of M-CFD tools. We provide recommendations for geometric modeling, mesh generation and solver configurations to optimize the methodology application.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2016

Machine learning based job status prediction in scientific clusters

Large high-performance computing systems are built with increasing number of components with more CPU cores, more memory, and more storage space. At the same time, scientific applications have been growing in complexity. Together, they are leading to more frequent unsuccessful job statuses on HPC systems. From measured job statuses, 23.4% of CPU time was spent to the unsuccessful jobs. Here, we set out to study whether these unsuccessful job statuses could be anticipated from known job characteristics. To explore this possibility, we have developed a job status prediction method for the execution of jobs on scientific clusters. The Random Forests algorithm was applied to extract and characterize the patterns of unsuccessful job statuses. Experimental results show that our method can predict the unsuccessful job statuses from the monitored ongoing job executions in 99.8% the cases with 83.6% recall and 94.8% precision. Lastly, this prediction accuracy can be sufficiently high that it can be used to mitigation procedures of predicted failures.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2025

Hybrid Quantum Networks with Discrete Polarizations and Continuous Quadrature Variables

Final Scientific/Technical Report for DOE Project DE-SC0022069. Quantum communication and computation systems have evolved along two largely independent paradigms: discrete-variable (DV) systems that encode information in qubits such as photon polarizations or photon-number states, and continuous-variable (CV) systems that encode information in optical field quadratures. Each approach offers distinct advantages—DVs provide low error rates and compatibility with single-photon platforms, while CVs support deterministic operations and efficient quantum state manipulation. A fundamental challenge for building a scalable Quantum Internet lies in interfacing these two regimes into a unified hybrid architecture that can coherently distribute and process quantum information across heterogeneous quantum nodes. This project aims to develop and demonstrate a hybrid optical quantum network that seamlessly integrates DV and CV systems. Specifically, we investigate a new class of hybrid entanglement between the discrete polarizations of single photons and the continuous quadrature variables of optical cat states, overcoming incompatibilities in existing DV–CV demonstrations. Using this new entanglement resource, the team investigates a multi-node hybrid quantum local area network (Q-LAN) testbed capable of DV–CV entanglement generation, swapping, and distribution across fiber links.

74 ATOMIC AND MOLECULAR PHYSICS↗
DOE OSTI2024

Enhanced read resolution in reconfigurable memristive synapses for Spiking Neural Networks

Abstract The synapse is a key element circuit in any memristor-based neuromorphic computing system. A memristor is a two-terminal analog memory device. Memristive synapses suffer from various challenges including high voltage, SET or RESET failure, and READ margin issues that can degrade the distinguishability of stored weights. Enhancing READ resolution is very important to improving the reliability of memristive synapses. Usually, the READ resolution is very small for a memristive synapse with a 4-bit data precision. This work considers a step-by-step analysis to enhance the READ current resolution or the read current difference between two resistance levels for a current-controlled memristor-based synapse. An empirical model is used to characterize the $${\hbox {HfO}}_{2}$$ HfO 2 based memristive device. $$1\textrm{st}$$ 1 st and $$2\textrm{nd}$$ 2 nd stage device of our proposed synapse design can be scaled to enhance the READ current margin up to $$\sim$$ ∼ 4.3 $$\times$$ × and $$\sim$$ ∼ 21%, respectively. Moreover, READ current resolution can be enhanced with run-time adaptation techniques such as READ voltage scaling and body biasing. The READ voltage scaling and body biasing can improve the READ current resolution by about 46% and 15%, respectively. TENNLab’s neuromorphic computing framework is leveraged to evaluate the effect of READ current resolution on classification, control, and reservoir computing applications. Higher READ current resolution shows better accuracy than lower resolution even when facing different levels of read noise.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2018

Performing fully parallel constraint logic programming on a quantum annealer

A quantum annealer exploits quantum effects to solve a particular type of optimization problem. The advantage of this specialized hardware is that it effectively considers all possible solutions in parallel, thereby potentially outperforming classical computing systems. However, despite quantum annealers having recently become commercially available, there are relatively few high-level programming models that target these devices. In this article, we show how to compile a subset of Prolog enhanced with support for constraint logic programming into a two-local Ising-model Hamiltonian suitable for execution on a quantum annealer. In particular, we describe the series of transformations one can apply to convert constraint logic programs expressed in Prolog into an executable form that bears virtually no resemblance to a classical machine model yet that evaluates the specified constraints in a fully parallel manner. We evaluate our efforts on a 1,095-qubit D-Wave 2X quantum annealer and describe the approach's associated capabilities and shortcomings.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2018

Interactive Visualization of Complex Seismic Data and Models Using Bokeh

Visualizing multidimensional data and models becomes more challenging as the volume and resolution of seismic data and models increase. But thanks to the development of powerful and accessible computer systems, a model web browser can be used to visualize complex scientific data and models dynamically. In this paper, we present four examples of seismic model visualization using an open-source Python package Bokeh. One example is a visualization of a surface-wave dispersion data set, another presents a view of three-component seismograms, and two illustrate methods to explore a 3D seismic-velocity model. Unlike other 3D visualization packages, our visualization approach has a minimum requirement on users and is relatively easy to develop, provided you have reasonable programming skills. Finally, utilizing familiar web browsing interfaces, the dynamic tools provide us an effective and efficient approach to explore large data sets and models.

58 GEOSCIENCES↗
DOE OSTI2018

All-Solid-State Synaptic Transistor with Ultralow Conductance for Neuromorphic Computing

Electronic synaptic devices are important building blocks for neuromorphic computational systems that can go beyond the constraints of von Neumann architecture. Although two-terminal memristive devices are demonstrated to be possible candidates, they suffer from several shortcomings related to the filament formation mechanism including nonlinear switching, write noise, and high device conductance, all of which limit the accuracy and energy efficiency. Electrochemical three-terminal transistors, in which the channel conductance can be tuned without filament formation provide an alternative platform for synaptic electronics. In this work, an all-solid-state electrochemical transistor made with Li ion–based solid dielectric and 2D α-phase molybdenum oxide (α-MoO 3 ) nanosheets as the channel is demonstrated. These devices achieve nonvolatile conductance modulation in an ultralow conductance regime (<75 nS) by reversible intercalation of Li ions into the α-MoO 3 lattice. Based on this operating mechanism, the essential functionalities of synapses, such as short- and long-term synaptic plasticity and bidirectional near-linear analog weight update are demonstrated. Simulations using the handwritten digit data sets demonstrate high recognition accuracy (94.1%) of the synaptic transistor arrays. These results provide an insight into the application of 2D oxides for large-scale, energy-efficient neuromorphic computing networks.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
DOE OSTI2015

Design for a Soft Error Resilient Dynamic Task-Based Runtime, In: 2015 IEEE International Parallel and Distributed Processing Symposium

As the scale of modern computing systems grows, failures will happen more frequently. On the way to Exactable a generic, low-overhead, resilient extension becomes a desired aptitude of any programming paradigm. In this paper we explore three additions to a dynamic task-based runtime to build a generic framework providing soft error resilience to task-based programming paradigms. The first recovers the application by re-executing the minimum required sub-DAG, the second takes critical checkpoints of the data flowing between tasks to minimize the necessary re-execution, while the last one takes advantage of algorithmic properties to recover the data without re-execution. These mechanisms have been implemented in the PaRSEC task-based runtime framework. Experimental results validate our approach and quantify the overhead introduced by such mechanisms.

Computer Science↗
DOE OSTI2018

Thermodynamic Modeling and Optimization of the Copper Flash Converting Process Using the Equilibrium Constant Method

Based on the principle of multiphase equilibrium, a mathematical model of the copper flash converting process was established by the equilibrium constant method, and a computational system was developed with the use of MetCal software platform. The mathematical model was validated by comparing simulated outputs, industrial data, and published data. To obtain high-quality blister copper, a low copper content in slag, and increased impurity removal rate, the model was then applied to investigate the effects of the operational parameters [oxygen/feed ratio (R{sub OF}), flux rate (R{sub F}), and converting temperature (T)] on the product weights, compositions, and the distribution behaviors of impurity elements. The optimized results showed that R{sub OF}, R{sub F}, and T should be controlled at approximately 156 Nm{sup 3}/t, within 3.0 pct, and at approximately 1523 K (1250 °C), respectively.

36 MATERIALS SCIENCE↗
DOE OSTI2026

New Time Integrators and Capabilities in SUNDIALS Versions 6.2.0-7.4.0

SUNDIALS is a well-established numerical library that provides robust and efficient time integrators and nonlinear solvers. This article overviews several significant improvements and new features added over the last 3 years to support scientific simulations run on high-performance computing systems. Notably, three new classes of one-step methods have been implemented: low storage Runge–Kutta, symplectic partitioned Runge–Kutta, and operator splitting. In addition, we describe new timestep adaptivity support for multirate methods, adjoint sensitivity analysis capabilities for explicit Runge–Kutta methods, additional options for Anderson acceleration in nonlinear solvers, and improved error handling and logging.

Computer science↗
DOE OSTI2020

Integrating Commercial Ready Autonomous Robotic Platforms to Enhance Worker Safety and Efficiency - 20053

This paper will explore the challenges and triumphs of implementing advanced robotic systems and the supporting technology for the waste management industry. Deployment-ready solutions for autonomous robotic platforms are new to the radioactive waste management industry and have been slow to implement across the complex. Specifically, it will explore Hanford Tank Operations Contractor's implementation of ground-based autonomous robotic platforms to enhance workforce safety and efficiency. The first objective of the project is to demonstrate safe operation of autonomous instrumented robots, followed by a scaling up of capabilities for the platform to meet any additional requests of internal stakeholders. Autonomous robotics platforms offer a fantastic range of capabilities that will aid in decreasing long-term liability of radioactive waste management industry by enhancing clean-up efforts and worker safety. Over the past five years, deployment-ready solutions for autonomous robotic platforms have come a long way. Autonomous robotic platforms are increasingly available to purchase from many vendors, often for prices under $100,000. Additionally, the vendors who produce these products are efficient in accommodating customized solutions to unique challenges, such as those often found in the radioactive waste management industry. However, the newness of this technology to the radioactive waste management industry and has resulted in a slow implementation across the complex. This paper will explore the challenges and triumphs of implementing advanced robotic systems and supporting technology for the waste management industry. Specifically, it will explore Hanford Tank Operations Contractor's implementation of ground based autonomous robotic platforms to enhance workforce safety and efficiency. While the company has utilized robots in many parts of the tank farms mission, this was first attempted in the deployment of an autonomous platform that required no operator with only minor maintenance and created challenges not yet encountered. These challenges included stakeholder buy-in, operational safety, procurement uncertainties, adaption of 'off the shelf products, overcoming the 'Fukushima complexity' perception, security concerns, and the initial criteria down select. Hanford Tank Operations Contractor's management identified the need for a workforce protecting low cost robotics solution in the fall of 2017. The project scope generated was part of the response to the Tank Vapor Assessment Team recommendation to enhance the safety of the workforce by utilizing new technology. Since the projects inception, it has been incorporated into the Tank Stewardship Plan. This project was unique to Hanford Tank Operations Contractor, as the company does not frequently incorporate advanced technology for field deployment quickly. The project was also progressive to the Hanford Tank Operations Contractor as it functions as a partnership project with a local university. The first objective of the project is to demonstrate safe operations of autonomous instrumented robotics, monitoring and collecting data, and the wireless transmission of data to a central computing system. Following the completion of this first objective, the project will scale up capabilities of the platform to meet the requests of additional internal stakeholders throughout the tank farms - including completion of specific in-field tasks. (authors)

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗
DOE OSTI2016

Inspection of Dry Storage Casks Using PGAA and NAA Techniques

In this effort, an assessment of a chloride detection method is performed for inspecting the surfaces of dry cask storage system (DCSS) canisters, with focus on the implementation of a non-destructive nuclear analysis and methodology. Some DCSS canisters (especially those located in coastal environments) will be exposed to marine environments, which can cause atmospheric stress corrosion cracking (SCC). Chloride deposits on canister surfaces provide the corrosive environment necessary for atmospheric SCC with the residual stresses and materials properties conditions. The most susceptible regions on the canister surface are the weld areas, or where cold work has been performed, and may provide the necessary driving force for crack growth. The heat-affected zones (HAZs) near welds are also potentially susceptible to atmospheric SCC due to sensitization of the material caused by chromium depletion during the welding process. Information collected from the field and from laboratory studies has not been able to rule out the possibility of atmospheric chloride induced stress corrosion cracking (CISCC), although no occurrences of atmospheric SCC in DCSS canisters have been detected to-date. A number of researchers have demonstrated that localized corrosion can take place under such situations on materials commonly used for interim storage containers, including 304SS (Cook et al. 2010; Shirai et al. 2011) and 316SS (Tani et al. 2009). In this summary, the preliminary results of a neutron activation analysis (NAA) and prompt gamma activation analysis (PGAA) are performed using MCNP6, a highly sophisticated, multi-particle Monte Carlo simulation code (with the employment of the high performance computing system at the Idaho National Laboratory), and confirm the feasibility of using PGAA and NAA as an environmental inspection methodology for detection and monitoring of small quantities of chlorine on canister surfaces. The specific signature of gamma rays due reactions of sea salt constituents are documented and summarized in this paper. The environmental constituent for passive oxide film breaching is chlorine, which can cause pitting initiation and incubation time that may lead to cracking in stainless steel. Therefore, an assessment of chloride presence at a canister surface is essential before moving to other nondestructive analysis (NDA) methods for detection of pitting and cracking. Two nonlinear acoustic techniques will also be used in our integrated research project (IRP) research study that proves to be useful in the detection of SCC: nonlinear resonant ultrasound spectroscopy (NRUS), and time revisited elastic nonlinear diagnostic (TREND). These two techniques are complementary to marine atmospheric detection techniques. In this presentation only PGAA and NAA are described in detail. (authors)

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗
DOE OSTI2016

Fault Tolerance Assistant (FTA): An Exception Handling Programming Model for MPI Applications

Future high-performance computing systems may face frequent failures with their rapid increase in scale and complexity. Resilience to faults has become a major challenge for large-scale applications running on supercomputers, which demands fault tolerance support for prevalent MPI applications. Among failure scenarios, process failures are one of the most severe issues as they usually lead to termination of applications. However, the widely used MPI implementations do not provide mechanisms for fault tolerance. We propose FTA-MPI (Fault Tolerance Assistant MPI), a programming model that provides support for failure detection, failure notification and recovery. Specifically, FTA-MPI exploits a try/catch model that enables failure localization and transparent recovery of process failures in MPI applications. We demonstrate FTA-MPI with synthetic applications and a molecular dynamics code CoMD, and show that FTA-MPI provides high programmability for users and enables convenient and flexible recovery of process failures.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2021

Recommendations for Distributed Energy Resource Patching

While computer systems, software applications, and operational technology (OT)/Industrial Control System (ICS) devices are regularly updated through automated and manual processes, there are several unique challenges associated with distributed energy resource (DER) patching. Millions of DER devices from dozens of vendors have been deployed in home, corporate, and utility network environments that may or may not be internet-connected. These devices make up a growing portion of the electric power critical infrastructure system and are expected to operate for decades. During that operational period, it is anticipated that critical and noncritical firmware patches will be regularly created to improve DER functional capabilities or repair security deficiencies in the equipment. The SunSpec/Sandia DER Cybersecurity Workgroup created a Patching Subgroup to investigate appropriate recommendations for the DER patching, holding fortnightly meetings for more than nine months. The group focused on DER equipment, but the observations and recommendations contained in this report also apply to DERMS tools and other OT equipment used in the end-to-end DER communication environment. The group found there were many standards and guides that discuss firmware lifecycles, patch and asset management, and code-signing implementations, but did not singularly cover the needs of the DER industry. This report collates best practices from these standards organizations and establishes a set of best practices that may be used as a basis for future national or international patching guides or standards.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2026

Augmenting LLM-Based Agents for Improved Performance in Pentesting and Commissioning Operational Technology in Critical Infrastructure

Artificial intelligence (AI), and more specifically large language models (LLMs) have the potential for use in penetration testing (“pentesting”) against devices, networks, and computer systems in information technology (IT). We explore the possibility of extending pentesting from IT systems to operational technology (OT) systems, which are more obscure than IT systems in their protocols and design. A challenge therefore exists when applying pretrained LLMs to OT systems as corpora are likely to underrepresent OT systems in comparison to other more prevalent systems. We evaluate augmentations of LLMs with various methods, especially retrieval augmented generation (RAG), to improve performance of the LLMs in the OT domain. In addition to pentesting, some of the testing of these OT devices may include commissioning to ensure that the newly installed devices work correctly. Our framework may also be applied in such cases.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2023

Is Knowledge about Running Applications Helping Improve Runtime Prediction of HPC Jobs?

High-performance computing systems rely upon scheduling algorithms to achieve high utilization. These schedulers rely upon user estimates of job resource requirements, such as runtime, to determine optimal scheduling of incoming jobs. These user estimates, however, are prone to error. To mitigate this error, significant research has been directed at providing better estimates of job runtime, usually employing machine learning techniques. These techniques are dependent upon the input features selected. Among the possible features is the primary application used by the job. In a survey of more than 20 papers directed at improving runtime prediction, only four included primary application as an input feature. We focus this investigation specifically on the value of adding primary application as an input feature, and find that it does improve model performance, especially for jobs with longer runtimes, though this improvement varies based on the application used. We recommend further research to determine the cause of this variability as well as an optimal strategy for employing a mixture of models both including and not including primary application as a feature.

MATHEMATICS AND COMPUTING↗
DOE OSTI2017

Establishing Malware Attribution and Binary Provenance Using Multicompilation Techniques

Malware is a serious problem for computer systems and costs businesses and customers billions of dollars a year in addition to compromising their private information. Detecting malware is particularly difficult because malware source code can be compiled in many different ways and generate many different digital signatures, which causes problems for most anti-malware programs that rely on static signature detection. Our project uses a convolutional neural network to identify malware programs but these require large amounts of data to be effective. Towards that end, we gather thousands of source code files from publicly available programming contest sites and compile them with several different compilers and flags. Building upon current research, we then transform these binary files into image representations and use them to train a long-term recurrent convolutional neural network that will eventually be used to identify how a malware binary was compiled. This information will include the compiler, version of the compiler and the options used in compilation, information which can be critical in determining where a malware program came from and even who authored it.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2023

Is Knowledge About Running Applications Helping Improve Runtime Prediction of HPC Jobs?

High-performance computing systems rely upon scheduling algorithms to achieve high utilization. These schedulers rely upon user estimates of job resource requirements, such as runtime, to determine optimal scheduling of incoming jobs. These user estimates, however, are prone to error. To mitigate this error, significant research has been directed at providing better estimates of job runtime, usually employing machine learning techniques. These techniques are dependent upon the input features selected. Among the possible features is the primary application used by the job. In a survey of more than 20 papers directed at improving runtime prediction, only four included primary application as an input feature. We focus this investigation specifically on the value of adding primary application as an input feature, and find that it does improve model performance, especially for jobs with longer runtimes, though this improvement varies based on the application used. We recommend further research to determine the cause of this variability as well as an optimal strategy for employing a mixture of models both including and not including primary application as a feature.

feature selection↗