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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 811 records · Page 45

Developing High-Performance Computing Model of Vapor Transition Through Advanced Membranes for Applications in 7AC Liquid Desiccant Air Conditioners

This poster for the 2019 Advanced Manufacturing Office Peer Review covers the high-performance computing model development for applications in Liquid Desiccant Air Conditioners. Conventional compressor-based cooling has undergone incremental changes over the past 100 years. Further improvements in efficiency require evermore complex systems, especially under humid conditions. Microporous membranes offer unique opportunities for efficient humidity control in buildings using an absorbent desiccant solution, but membranes are not designed for this purpose. This project used molecular dynamic (MD) simulations to determine the effect of membrane properties on important metrics for these air-conditioning applications, focusing on the membrane properties at the membrane/liquid/air interface. Improved membranes can enable smaller, more durable, and less costly designs for these membrane air conditioning systems.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗

The Institute for Sustained Performance, Energy, and Resilience

The Green Queue framework is a framework designed to automate the development and deployment of customized architecture- and application-aware power savings recipes for large-scale HPC applications achieving up to 21% and 32% energy savings on HPC production applications run at scale. Additional work focused on the memory sub-system involved our methodology that uses application and machine characterization information to build predictive machine learning models that can accurately quantify phase-level sensitivity to the reduced per core memory bandwidth resulting from changes in the memory bus frequency to reduce the power. We evaluated the predictive capability of the model on real applications and validated them at a fine grain level by looking at 43 individual computational phases or application hotspots as well as the whole application. For more than 91% of the application hotspots, the prediction error is less than 10% (15) . Building from these validated performance and power models collaborations among SUPER team members developed an automated end-to-end system to reduce the complexity of developing and deploying machine learning models for performance, power, and energy. The new framework Automatic Multi-objective Modeling with Machine Learning (AutoMOMML) enabled multi-objective optimizations (power and performance) for HPC workloads.

97 MATHEMATICS AND COMPUTING↗

Exploring cycling induced crystallographic change in NMC with X-ray diffraction computed tomography

This study presents the application of X-ray diffraction computed tomography for the first time to analyze the crystal dimensions of LiNi 0.33 Mn 0.33 Co 0.33 O 2 electrodes cycled to 4.2 and 4.7 V in full cells with graphite as negative electrodes at 1 μm spatial resolution to determine the change in unit cell dimensions as a result of electrochemical cycling. The nature of the technique permits the spatial localization of the diffraction information in 3D and mapping of heterogeneities from the electrode to the particle level. An overall decrease of 0.4% and 0.6% was observed for the unit cell volume after 100 cycles for the electrodes cycled to 4.2 and 4.7 V. Additionally, focused ion beam-scanning electron microscope cross-sections indicate extensive particle cracking as a function of upper cut-off voltage, further confirming that severe cycling stresses exacerbate degradation. Finally, the technique facilitates the detection of parts of the electrode that have inhomogeneous lattice parameters that deviate from the bulk of the sample, further highlighting the effectiveness of the technique as a diagnostic tool, bridging the gap between crystal structure and electrochemical performance.

25 ENERGY STORAGE↗

Co-design Center for Exascale Machine Learning Technologies (ExaLearn)

We report rapid growth in data, computational methods, and computing power is driving a remarkable revolution in what variously is termed machine learning (ML), statistical learning, computational learning, and artificial intelligence. In addition to highly visible successes in machine-based natural language translation, playing the game Go, and self-driving cars, these new technologies also have profound implications for computational and experimental science and engineering, as well as for the exascale computing systems that the Department of Energy (DOE) is developing to support those disciplines. Not only do these learning technologies open up exciting opportunities for scientific discovery on exascale systems, they also appear poised to have important implications for the design and use of exascale computers themselves, including high-performance computing (HPC) for ML and ML for HPC. The overarching goal of the ExaLearn co-design project is to provide exascale ML software for use by Exascale Computing Project (ECP) applications, other ECP co-design centers, and DOE experimental facilities and leadership class computing facilities.

97 MATHEMATICS AND COMPUTING↗

Equation of State Model Quality Study for Ti and Ti64.

Titanium and the titanium alloy Ti64 (6% aluminum, 4% vanadium and the balance ti- tanium) are materials used in many technologically important applications. To be able to computationally investigate and design these applications, accurate Equations of State (EOS) are needed and in many cases also additional constitutive relations. This report describes what data is available for constructing EOS for these two materials, and also describes some references giving data for stress-strain constitutive models. We also give some suggestions for projects to achieve improved EOS and constitutive models. In an appendix, we present a study of the 'cloud formation' issue observed in the ALEGRA code. This issue was one of the motivating factors for this literature search of available data for constructing improved EOS for Ti and Ti64. However, the study shows that the cloud formation issue is only marginally connected to the quality of the EOS, and, in fact, is a physical behavior of the system in question. We give some suggestions for settings in, and improvements of, the ALEGRA code to address this computational di culty.

97 MATHEMATICS AND COMPUTING↗

Analog Systems for Edge Optimization

Over the past decade, analog computing has the subject of substantial research interest providing a path toward improved computational efficiency in the post-Dennard era. Analog matrix vector multiplication (MVM) accelerators provide a popular approach given the ubiquity of MVM operations in numerous applications. However, historically analog computing systems can struggle with applications requiring high precision due to the inherent susceptibility of these systems to analog non-idealities. Therefore, prior work on analog systems has focused either on applications known to be tolerant of limited precision (e.g., neural network inference), or using expensive techniques to emulate high-precision using many analog MVM operations. In this work, we propose an alternative approach. Motivated by recent advances in inexact nonlinear solvers and optimizers, we explore the potential of co-designing optimization algorithms which can take full advantage of the fundamentally inexact analog MVM operations. To enable these co-designed algorithms we also develop a general mathematical theory of the precision and energy efficiency of analog operations, and a new system architecture for tightly-coupled analog and digital computation. Finally, we examine the applicability of analog computing to a wider class of symmetric positive definite systems and find potential in using analog operations as a sparse approximate inverse preconditioner. With these core innovations, this project provides a path toward effectively implementing optimization algorithms on power-constrained autonomous and semi-autonomous systems.

97 MATHEMATICS AND COMPUTING↗

Compression-based integral curve data reuse framework for flow visualization

Currently, by default, integral curves are repeatedly re-computed in different flow visualization applications, such as FTLE field computation, source-destination queries, etc., leading to unnecessary resource cost. We present a compression-based data reuse framework for integral curves, to greatly reduce their retrieval cost, especially in a resource-limited environment. In our design, a hierarchical and hybrid compression scheme is proposed to balance three objectives, including high compression ratio, controllable error, and low decompression cost. Specifically, we use and combine digitized curve sparse representation, floating-point data compression, and octree space partitioning to adaptively achieve the objectives. Results have shown that our data reuse framework could acquire tens of times acceleration in the resource-limited environment compared to on-the-fly particle tracing, and keep controllable information loss. Moreover, our method could provide fast integral curve retrieval for more complex data, such as unstructured mesh data.

97 MATHEMATICS AND COMPUTING↗

TUNE: Compiler-Directed Automatic Performance Tuning

This project has developed compiler-directed performance tuning technology targeting the Cray XT4 Jaguar system at Oak Ridge, which has multi-core Opteron nodes with SSE-3 SIMD extensions, and the Cray XE6 Hopper system at NERSC. To achieve this goal, we combined compiler technology for model-guided empirical optimization for memory hierarchies with SIMD code generation, which have been developed by the PIs over the past several years. We examined DOE Office of Science applications to identify performance bottlenecks and apply our system to computational kernels that operate on dense arrays. Our goal for this performance-tuning technology has been to yield hand-tuned levels of performance on DOE Office of Science computational kernels, while allowing application programmers to specify their computations at a high level without requiring manual optimization. Overall, we aim to make our technology for SIMD code generation and memory hierarchy optimization a crucial component of high-productivity Petaflops computing through a close collaboration with the scientists in national laboratories.

97 MATHEMATICS AND COMPUTING↗

Predictive Theory of Topological States of Matter (Final Report for DOE Award DE-SC0018945)

This DOE award supported the PI’s research on topological quantum materials between 2018 and 2021. The PI developed predictive theories for new classes of topological insulators and topological/unconventional superconductors and identified their material realizations in collaboration with experimentalists. The PI theorized new topological transport phenomena, including spin superfluidity in moire materials and the superconducting diode effect. Topological transport phenomena have potential applications in cryogenic computing, low-power electronics, high-frequency rectification, infrared detection, energy harvesting and thermoelectric application. By discovering new quantum materials and new functional properties, the PI’s research contributed significantly to the advancement of DOE scientific and technological base, especially in energy frontier research.

36 MATERIALS SCIENCE↗

Editorial: Quantum Information and Quantum Computing for Chemical Systems

Quantum computing has emerged as an exciting inter-disciplinary research topic that cuts across the traditional fields of physics, computer science, and engineering. It is a revolutionary model of computation that has offered new insights into methods for modeling and simulation of chemical systems. Applications of quantum computing to chemistry have demonstrated rapid progress on both theoretical and experimental fronts. Past theoretical efforts have shown how to adapt quantum computation to a variety of problems including electronic structure and molecular dynamics. In addition, the development of quantum algorithms for quantum chemistry has been stimulated by the greater availability of more capable quantum computing devices. Advances in the number and quality of qubits continues to enable remarkable proof-of-concept demonstrations working towards a milestone of quantum computational advantage.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗

Fluctuating Hydrodynamics Methods for Dynamic Coarse-Grained Implicit-Solvent Simulations in LAMMPS

We present a software package integrated with the molecular dynamics software LAMMPS for fluctuating hydrodynamics simulations of fluid-structure interactions subject to thermal fluctuations. The package is motivated to provide dynamic thermostats to extend implicit-solvent coarse-grained (IS-CG) models by incorporating kinetic contributions from the solvent to facilitate their use in a wider range of applications. To capture the thermal and hydrodynamic contributions of the solvent to dynamics, we introduce momentum conserving thermostats and computational methods based on fluctuating hydrodynamics and the stochastic Eulerian Lagrangian method (SELM). SELM couples the coarse-grained microstructure degrees of freedom to continuum stochastic fields to capture both the relaxation of hydrodynamic modes and thermal fluctuations. Features of the SELM software include (i) numerical time-step integrators for SELM fluctuating hydrodynamics in inertial and quasi-steady regimes, (ii) Lees--Edwards-style methods for imposing shear, (iii) a Java-based graphical user interface (GUI) for setting up models and simulations, (iv) standardized XML formats for parametrization and data output, and (v) standardized formats VTK for continuum fields and microstructures. The SELM software package facilitates for pre-established models in LAMMPS easy adoption of the SELM fluctuating hydrodynamics thermostats. We offer here an overview of the SELM software package, computational methods, and applications.

97 MATHEMATICS AND COMPUTING↗

Exploring Advanced Computational Tools and Techniques with Artificial Intelligence and Machine Learning in Operating Nuclear Plants

This report presents the project Idaho National Laboratory conducted for Nuclear Regulatory Commission to explore the advanced computational tools and techniques, such as artificial intelligence (AI) and machine learning (ML), for operating nuclear plants. The report reviews the nuclear data sources, with the focus on the operating experience data, that could be applied by advanced computational tools and techniques. Plant-specific and generic (national and international) data from different sources are described. The report describes the relationships between statistics and AI/ML and then introduces the most widely used AI/ML algorithms in both supervised and unsupervised learning. The report reviews the recent applications of advanced computational tools and techniques in various fields of nuclear industry, such as reactor system design and analysis, plant operation and maintenance, and nuclear safety and risk analysis. Finally, the report presents the insights from the project on the potential applicability of AI/ML techniques in improving advanced computational capabilities, how the advanced tools and techniques could contribute to the understanding of safety and risk, and what information would be needed to provide meaningful insights to decision makers. The report also documents an NRC survey on the current state of commercial nuclear power operations relative to the use of AI and ML tools as well as the role of AI/ML tools in nuclear power operations was published by the NRC as in FRN NRC-2021-0048 in April 2021. A summary of the survey including the survey questions, survey participants, survey responses, and the conclusions and insights derived from the survey is provided in the report. Finally, the report investigates potential applications of using AI/ML in operating NPPs and advanced reactors (both advanced LWRs and advanced NLWRs) to improve nuclear plant safety and efficiency. Three main application fields are defined and discussed: (1) plant safety and security assessments; (2) plant degradation modeling, fault and accident diagnosis and prognosis; and (3) plant operation and maintenance efficiency improvement.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗

Systematic computational study of oxide adsorption properties for applications in photocatalytic CO 2 reduction

While the adsorption properties of transition metal catalysts have been widely studied, leading to the discovery of various scaling relations, descriptors of catalytic activity, and well-established computational models, a similar understanding of semiconductor catalysts has not yet been achieved. In this work, we present a high-throughput density functional theory investigation into the adsorption properties of 5 oxides of interest to the photocatalytic CO 2 reduction reaction: TiO 2 (rutile and anatase), SrTiO 3 , NaTaO 3 , and CeO 2 . Using a systematic approach, we exhaustively identify unique surfaces and construct adsorption structures to undergo geometry optimizations. We then perform a data-driven analysis, which reveals the presence of weak adsorption energy scaling relations, the propensity of adsorbates of interest to interact with oxygen surface sites, and the importance of slab deformation upon adsorption. Our findings are presented in the context of experimental observations and in comparison to previously studied classes of catalysts, such as pure metals and tellurium-containing semiconductors, and reinforce the need for a comprehensive approach to the study of site-specific surface phenomena on semiconductors.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

Increased Reliability for Near-Term Quantum Computers via Low-Level Control (CRADA Final Report)

This project helped narrow the gap between near-term hardware and practical applications of quantum computing through the development and implementation of optimized compilation and error mitigation techniques. These advances were enabled by low-level control available on the Advanced Quantum Testbed, and led to methods that characterized and improved the platform’s performance on quantum algorithms. Results of this work were published in the scientific literature and used to inform internal software development of partner Super.tech.

97 MATHEMATICS AND COMPUTING↗

Blackcomb2: Hardware-Software Co-design for Nonvolatile Memory in Exascale Systems

The impending shift from DRAM and Flash to fast nonvolatile memory (NVM) provides a game-changing opportunity to rethink traditional system architectures; to address their energy inefficiencies; to leverage the new devices for greater performance; and to exploit the new capability of nonvolatility to enhance system resilience, even in the face of larger system scale and degraded reliability of components. Building on the accomplishments of the Blackcomb Project, funded in 2010, we propose to identify, evaluate, and optimize the most promising NVM hardware and software technologies, which are essential to provide the necessary memory capacity, performance, resilience, and energy efficiency in exascale systems. Capacity and energy are the key drivers. Today's pressing application requirements mandate exascale computing; the datasets and concurrency of these applications demand memory capacity that cannot be met by traditional memory technologies. Although charge-based DRAM will endure, experts foresee only modest DRAM capacity gains. And, as moving data between levels of the hierarchy, and storing and accessing data on disk consumes significant energy, a more energy efficient solution is required. We, therefore, posit that exascale systems will need high density, energy-efficient storage technologies, such as nonvolatile memory for access, transformation, and management of exascale data. To address these issues, in this renewal request, we propose continue the work of our vertically- integrated, focussed team, where our co-design is informed by proxies and interactions with DOE co-design centers. In software, we start by assuming that nonvolatility requires new abstractions and new infrastructure, allowing persistent objects, and delivering performance gains for _le systems and robustness through fast checkpointing. In architecture, we evaluate the tradeoffs of performance and energy by employing a scalable and accurate simulation system to analyze realistic proxy applications. Through this co-design process, we rethink the whole memory architecture, from the cell to the array to the device, with ECC, wearout management, and interfaces. Finally, we provide open-source software models of these NVM-based memory systems.

97 MATHEMATICS AND COMPUTING↗

Block segmentation in feature space for realtime object detection in high granularity images

Computer vision has applications in object detection, image recognition and classification, and object tracking. One of the challenges of computer vision is the presence of useful information at multiple distance scales. Filtering techniques may sacrifice details at small scales in order to prioritize the analysis of large-scale features of the image. We present a strategy for coarse-graining multidimensional data while maintaining fine-grained detail for subsequent analysis. The algorithm is based on fixed-size block segmentation in the feature space. We apply this strategy to solve the long-standing challenge of detecting particle trajectories at the Large Hadron Collider in real time.

Computer vision↗