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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 829 records · Page 46

Supporting the Development of Soft-Error Resilient Message Passing Applications using Simulation

Radiation-induced bit flip faults are of particular concern in extreme-scale high-performance computing systems. This paper presents a simulation-based tool that enables the development of soft-error resilient message passing applications by permitting the investigation of their correctness and performance under various fault conditions. The documented extensions to the Extreme-scale Simulator (xSim) enable the injection of bit flip faults at specific of injection location(s) and fault activation time(s), while supporting a significant degree of configurability of the fault type. Experiments show that the simulation overhead with the new feature is ~2,325% for serial execution and ~1,730% at 128 MPI processes, both with very fine-grain fault injection. Fault injection experiments demonstrate the usefulness of the new feature by injecting bit flips in the input and output matrices of a matrix-matrix multiply application, revealing vulnerability of data structures, masking and error propagation. xSim is the very first simulation-based MPI performance tool that supports both, the injection of process failures and bit flip faults.

Engelmann, Christian↗

Parallel Evolutionary Optimization for Neuromorphic Network Training

One of the key impediments to the success of current neuromorphic computing architectures is the issue of how best to program them. Evolutionary optimization (EO) is one promising programming technique; in particular, its wide applicability makes it especially attractive for neuromorphic architectures, which can have many different characteristics. In this paper, we explore different facets of EO on a spiking neuromorphic computing model called DANNA. We focus on the performance of EO in the design of our DANNA simulator, and on how to structure EO on both multicore and massively parallel computing systems. We evaluate how our parallel methods impact the performance of EO on Titan, the U.S.'s largest open science supercomputer, and BOB, a Beowulf-style cluster of Raspberry Pi's. We also focus on how to improve the EO by evaluating commonality in higher performing neural networks, and present the result of a study that evaluates the EO performed by Titan.

Schuman, Catherine D↗

Offensive Cybersecurity in the NIST Cybersecurity Framework

Government and corporate computer systems are attacked, networks are penetrated by hackers, and enterprises are protected by demilitarized zones. Language that until recently was used to describe security and warfare in military settings has now become common-place in cybersecurity discussions. The concepts of pre-emptive attack, counterattack and offensive defense fit the linguistic cultural thread of security in cyberspace, at least in part due to the taxonomy adopted by cybersecurity as a discipline.

Bulyk, Mykhaylo↗

ABLEPathPlanner library for Umbra

Umbra contains a flexible, modular path planner that is used to simulate complex entity behaviors moving within 3D terrain environments that include buildings, barriers, roads, bridges, fences, and a variety of other terrain features (water, vegetation, slope, etc…). The path planning algorithm is a critical component required to execute these tactical behaviors to provide realistic entity movement and provide efficient system computing performance.

Oppel III, Fred J↗

Parallel Multi-cycle LES of an Optical Pent-roof DISI Engine Under Motored Operating Conditions

The use of Large-eddy Simulations (LES) has increased due to their ability to resolve the turbulent fluctuations of engine flows and capture the resulting cycle-to-cycle variability. One drawback of LES, however, is the requirement to run multiple engine cycles to obtain the necessary cycle statistics for full validation. The standard method to obtain the cycles by running a single simulation through many engine cycles sequentially can take a long time to complete. Recently, a new strategy has been proposed by our research group to reduce the amount of time necessary to simulate the many engine cycles by running individual engine cycle simulations in parallel. With modern large computing systems this has the potential to reduce the amount of time necessary for a full set of simulated engine cycles to finish by up to an order of magnitude. In this paper, the Parallel Perturbation Methodology (PPM) is used to simulate up to 35 engine cycles of an optically accessible, pent-roof Directinjection Spark-ignition (DISI) engine at two different motored engine operating conditions, one throttled and one un-throttled. Comparisons are made against corresponding sequential-cycle simulations to verify the similarity of results using either methodology. Mean results from the PPM approach are very similar to sequential-cycle results with less than 0.5% difference in pressure and a magnitude structure index (MSI) of 0.95. Differences in cycle-to-cycle variability (CCV) predictions are larger, but close to the statistical uncertainty in the measurement for the number of cycles simulated. PPM LES results were also compared against experimental data. Mean quantities such as pressure or mean velocities were typically matched to within 5- 10%. Pressure CCVs were under-predicted, mostly due to the lack of any perturbations in the pressure boundary conditions between cycles. Velocity CCVs for the simulations had the same average magnitude as experiments, but the experimental data showed greater spatial variation in the root-mean-square (RMS). Conversely, circular standard deviation results showed greater repeatability of the flow directionality and swirl vortex positioning than the simulations.

I.C. Engines↗

Absorption at San Antonio with 3-Wavelength Photo Acoustic Soot Spectrometer, Tricolor Absorption Photometer (ASAPT) Field Campaign Report

Fine particulate matter (PM2.5; particulate matter with an aerodynamic diameter of 2.5μm and smaller) samples were collected at the University of Texas, San Antonio (UTSA) campus (29.57901, -98.62889) from May 15-30. An additional measurement at the UTSA site included aerosol absorption coefficient of PM1 (particulate matter with an aerodynamic diameter of 1.0μm and smaller) using a tricolor absorption photometer (TAP) (Brechtel Mfg. Inc., Hayward, California). The sampling site is approximately 15 miles northwest of downtown San Antonio and in close proximity to major highway 10 and is just south of a forested area. The setup of the sampler and TAP and collection of PM2.5 samples were completed by Baylor University’s graduate student, Subin Yoon. Three three-wavelength photoacoustic soot spectrometers (PASS-3s) were shipped to San Antonio for testing and possible data collection. R. Subramanian went to San Antonio to lead this effort with assistance from Yoon. Each instrument had a critical flaw that inhibited its use for this campaign. Two instruments had low laser power for each wavelength; in addition, there were power and overheating issues with the one instrument that was installed on a test basis. The third instrument had computer issues that impeded any testing. In short, these three instruments would require a major overhaul in terms of lasers and computer systems to be brought back to field capability. As a result, the filter-based optical measurements were added to the campaign as an alternate measurement that would provide mass absorption efficiency at a single wavelength, but no scattering measurement. The TAP instrument was successfully installed and collected data for San Antonio, but full use of the data would have required a co-located scattering measurement. This campaign was co-located with a Texas Commission on Environmental Quality (TCEQ) ozone field study. That TCEQ field study was a collaboration of the University of Houston (PI James Flynn), Rice University (Co-PI Robert Griffen), and Baylor University (Co-PIs Rebecca Sheesley and Sascha Usenko).

aerosols, tricolor absorption photometer, three-wa↗

Fault location in High Voltage Multi-terminal dc Networks Using Ensemble Learning

Precise location of faults for large distance power transmission networks is essential for faster repair and restoration process. High Voltage direct current (HVdc) networks using modular multi-level converter (MMC) technology has found its prominence for interconnected multi-terminal networks. This allows for large distance bulk power transmission at lower costs. However, they cope with the challenge of dc faults. Fast and efficient methods to isolate the network under dc faults have been widely studied and investigated. After successful isolation, it is essential to precisely locate the fault. The post-fault voltage and current signatures are a function of multiple factors and thus accurately locating faults on a multi-terminal network is challenging. In this paper, we discuss a novel data-driven ensemble learning based approach for accurate fault location. Here we utilize the eXtreme Gradient Boosting (XGB) method for accurate fault location. The sensitivity of the proposed algorithm to measurement noise, fault location, resistance and current limiting inductance are performed on a radial three-terminal MTdc network designed in Power System Computer Aided Design (PSCAD)/Electromagnetic Transients including dc (EMTdc).

24 POWER TRANSMISSION AND DISTRIBUTION↗

High-performance computing leadership to enable advances in artificial intelligence and a thriving compute ecosystem

The past three decades have witnessed the widespread adoption of high-performance computing (HPC) as an essential tool in the advancement of science. From accelerating critical research in the wake of a global pandemic to climate modelling and national security, HPC has become integral to the most cutting-edge scientific research around the world and across application domains. The global competition to debut the next fastest supercomputer keeps pushing the field forward. Meanwhile, increasing capabilities are bringing hallmarks of science fiction, such as artificial intelligence (AI), into daily life. However, the tremendous power of new computing systems comes with an increased concern about equitable access to these resources and their impact on supporting a thriving workforce.

Tourassi, Georgia↗

Data-Driven Whole-Genome Clustering to Detect Geospatial, Temporal, and Functional Trends in SARS-CoV-2 Evolution

Current methods for defining SARS-CoV-2 lineages ignore the vast majority of the SARS-CoV-2 genome. We develop and apply an exhaustive vector comparison method that directly compares all known SARS-CoV-2 genome sequences to produce novel lineage classifications. We utilize data-driven models that (i) accurately capture the complex interactions across the set of all known SARS-CoV-2 genomes, (ii) scale to leadership-class computing systems, and (iii) enable tracking how such strains evolve geospatially over time. We show that during the height of the original Omicron surge, countries across Europe, Asia, and the Americas had a spatially asynchronous distribution of Omicron sub-strains. Moreover, neighboring countries were often dominated by either different clusters of the same variant or different variants altogether throughout the pandemic. Analyses of this kind may suggest a different pattern of epidemiological risk than was understood from conventional data, as well as produce actionable insights and transform our ability to prepare for and respond to current and future biological threats.

Jacobson, Daniel↗

Error containment for enabling local checkpoint and recovery

Various embodiments include a parallel processing computer system that detects memory errors as a memory client loads data from memory and disables the memory client from storing data to memory, thereby reducing the likelihood that the memory error propagates to other memory clients. The memory client initiates a stall sequence, while other memory clients continue to execute instructions and the memory continues to service memory load and store operations. When a memory error is detected, a specific bit pattern is stored in conjunction with the data associated with the memory error. When the data is copied from one memory to another memory, the specific bit pattern is also copied, in order to identify the data as having a memory error.

Cherukuri, Naveen↗

Classification based anomaly detection

An example device includes processing circuitry and a memory. The memory includes instructions that cause the device to perform various functions. The functions include receiving datastreams from a plurality of sensors of a high performance computing system, classifying each datastream of the each sensor to one of a plurality of datastream models, selecting an anomaly detection algorithm from a plurality of anomaly detection algorithms for each datastream, determining parameters of the each anomaly detection algorithm, determining an anomaly threshold for each datastream, and generating an indication that the sensor associated with the datastream is acting anomalously.

Serebryakov, Sergey↗

Thermo mechanical analysis of fully ceramic microencapsulated fuel during in-pile operation

A proposed fuel for improved performance in LWRs (Light Water Reactors) is FCM (fully encapsulated micro-ceramic) fuel concept that involves TRISO (Tri-structural-Isotropic) fuel particles embedded in a NITE (Nano Infiltrated Eutectic) silicon carbide matrix. TRISO fuel particles contain a spherical fuel kernel that will likely range from about 500 to in excess of 800 μm in diameter. The kernel and buffer layer are then typically coated with 3 isotropic layers consisting of a dense inner pyrolytic carbon (IPyC), a silicon carbide (SiC) layer, and an outer layer of dense, pyrolytic carbon (OPyC). These layers are typically around 40 μm thick, but can range from 20 to 60 μm. The TRISO particle packing fraction in the NITE-SiC matrix is expected to be 40-45 vol.%. The release of radioactivity into the coolant is dependent on the integrity of the silicon carbide layer of the TRISO particles and the NITE-SiC matrix. BISON, a code under development by Idaho National Laboratory, has been built on the Multi-physics Object Oriented Simulation Environment (MOOSE). MOOSE is a massively parallel, finite element computational system that uses a Jacobian-free, Newton-Krylov (JFNK) method to solve coupled systems and non-linear partial differential equations. A single particle comparison to fuel performance codes such as PARFUME was conducted and demonstrated that BISON is able to simulate the required physics. FCM pellets embedded with homogenous particles were simulated. It was found that the interior matrix temperature was predicted to be considerably lower than for an equivalent UO{sub 2} pellet. The swelling dependence on temperature created a compressive region around the perimeter of the pellet. However, at fluences above 6.10{sup 25} neutrons/m{sup 2}, the swelling of the pyrolytic carbon might cause significant tensile stresses and possible cracks near the surface of the pellet. This suggests that the pyrolytic carbon swelling may be an important factor in the lifetime of this fuel type.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗

XAFS Investigation of Uranium Binding by Seawater-Contacted Amidoxime Adsorbents

Extraction of uranium from seawater constitutes an important avenue for ensuring nuclear fuel availability beyond the coming century. With a predicted 100 years of uranium remaining in terrestrial ores, efficient extraction of the 4.5 billion tons of uranium dissolved in ocean water would enable access to an effectively limitless supply. More near-term benefits include establishment of a backstop technology, preventing runaway price inflation for this critical resource. The current state-of-the-art material for seawater extraction of uranium is a polymeric adsorbent composed of a robust trunk material subsequently surface functionalized by copolymerization of polyacrylonitrile and a hydrophilic comonomer. Treatment of this material with hydroxylamine results in the formation of poly-amidoxime functionalities which have been known to bind strongly with uranium since the early 1980's. Despite more than three decades of investigation, many questions remain unanswered regarding how these adsorbents bind uranium in seawater. The physical characteristics of the polymer prohibit many classical characterization techniques, while most spectroscopic approaches are impeded by the relatively low concentration of uranium compared to the polymer itself and other metals extracted by the adsorbent. The traditional approach has been to investigate small molecule analogues of uranyl - dioxouranium (VI), the form of uranium present in seawater - as bound by amidoxime or amidoxime derivatives, and assert the findings to be representative of the larger polymer system. Computational efforts have also been recently leveraged to a similar end. This approach is not without difficulty, as it has been routinely proposed that the treatment with hydroxylamine and subsequent alkaline conditioning results in formation of a cyclic imide dioxime site, which is truly responsible for uranium binding, as opposed to the 'open chain' amidoxime. Further complications ensue from considering the possibility of cooperative binding interactions from the different hydrophilic comonomers which are known to be necessary for optimizing uranium capacity. Finally, application of different surface polymerization techniques has the potential to directly affect the morphology of the surface polymer by influencing the degree of grafting, extent of polymer crosslinking, and polymer density. The impact of these morphological considerations on the uranyl binding environment cannot be easily replicated with small molecule studies or by computation. Ultimately, the precise binding environment of uranium is unknown, but constitutes critical knowledge for the design and engineering of advanced adsorbent materials capable of selective extraction of uranium from seawater. Analysis of the EXAFS fits reveals three of the four polymer morphologies appear to bind uranium in different chemical environments, none of which were predicted by crystallographic study or computational investigation, challenging numerous long-held assumptions. Both AI-8 and AN/HEA spectra possess features which could be attributable to cooperative binding of uranium by comonomers, but this cannot be definitively determined by EXAFS without further investigation. Unlike AF-1, neither AI-8 or AN/HEA require an adjacent μ{sup 2}-oxo-bridged transition metal to obtain a reasonable fit. The fit for Cyclic AF-1 is refined to almost identical parameters to the fit of the original AF-1 adsorbent, with the only difference is the contracting of the light elements in the second coordination sphere. It is thus proposed that the DMSO and heat treatment of the AF-1 fiber does very little to change the uranium binding site, and any differences in uptake are more readily explained by changes in the morphology of the graft polymer. There is no evidence for cyclic amidoxime functionality contributing significantly to the extraction of uranium from seawater, and efforts to maximize the number of these binding sites should be discontinued. While molecular interactions necessarily occur in adsorption processes, these results clearly reveal physical phenomena at the mesoscale play a critical role in adsorbent behavior, even to the extent that the binding environment differs from those predicted by small molecule studies. In contrast to the current paradigm where chemical functionality is varied, improvements in the performance of polymer-based amidoxime-functionalized adsorbents for extraction of uranium from seawater need also be pursued through interrogation and modulation of bulk morphology and improvements in polymer hydrophilicity. (authors)

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗

Novel Approaches Toward Scalable Composable Workflows in Hyper-Heterogeneous Computing Environments

The annual Workshop on Workflows in Support of Large-Scale Science (WORKS) is a premier venue for the scientific workflow community to present the latest advances in research and development on the many facets of scientific workflows throughout their life-cycle. The Lightning Talks at WORKS focus on describing a novel tool, scientific workflow, or concept, which are work-in-progress and address emerging technologies and frameworks to foster discussion in the community. This paper summarizes the lightning talks at the 2023 edition of WORKS, covering five topics: leveraging large language models to build and execute workflows; developing a common workflow scheduler interface; scaling uncertainty workflow applications on exascale computing systems; evaluating a transcriptomics workflow for cloud vs. HPC systems; and best practices in migrating legacy workflows to workflow management systems.

Titov, Mikhail↗

Blockchain-based decentralized computing

An exemplary blockchain-based decentralized computing system and method are disclosed for industrial analytics applications. The exemplary system and method leverage blockchain technology to deliver and execute privacy-preserving decentralized predictive analytics, machine learning, and optimization operations for various industrial applications using a set of self-contained analytics block smart contracts that can be readily utilized and in analytics applications to deploy across multiple sites.

Ramanan, Paritosh P.↗

Quantum computing based hybrid deep learning for fault diagnosis in electrical power systems

Quantum computing (QC) and deep learning have shown promise of supporting transformative advances and have recently gained popularity in a wide range of areas. Here, this paper proposes a hybrid QC-based deep learning framework for fault diagnosis of electrical power systems that combine the feature extraction capabilities of conditional restricted Boltzmann machine with an efficient classification of deep networks. Computational challenges stemming from the complexities of such deep learning models are overcome by QC-based training methodologies that effectively leverage the complementary strengths of quantum assisted learning and classical training techniques. The proposed hybrid QC-based deep learning framework is tested on a simulated electrical power system with 30 buses and wide variations of substation and transmission line faults, to demonstrate the framework’s applicability, efficiency, and generalization capabilities. High computational efficiency is enjoyed by the proposed hybrid approach in terms of computational effort required and quality of diagnosis performance over classical training methods. In addition, superior and reliable fault diagnosis performance with faster response time is achieved over state-of-the-art pattern recognition methods based on artificial neural networks (ANN) and decision trees (DT).

42 ENGINEERING↗

Method, system, and computer-readable medium for determining performance characteristics of an object undergoing one or more arbitrary aging conditions

A method, system, and computer-readable medium are described for characterizing performance loss of an object undergoing an arbitrary aging condition. Baseline aging data may be collected from the object for at least one known baseline aging condition over time, determining baseline multiple sigmoid model parameters from the baseline data, and performance loss of the object may be determined over time through multiple sigmoid model parameters associated with the object undergoing the arbitrary aging condition using a differential deviation-from-baseline approach from the baseline multiple sigmoid model parameters. The system may include an object, monitoring hardware configured to sample performance characteristics of the object, and a processor coupled to the monitoring hardware. The processor is configured to determine performance loss for the arbitrary aging condition from a comparison of the performance characteristics of the object deviating from baseline performance characteristics associated with a baseline aging condition.

97 MATHEMATICS AND COMPUTING↗

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↗