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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 541 records · Page 30
All sourcesNASA NTRSDOE OSTI
DOE OSTI2023

Low-Temperature Geothermal Geospatial Datasets: An Example from Alaska

This project is a component of a broader effort focused on geothermal heating and cooling (GHC) with the aim of illustrating the numerous benefits of incorporating GHC and geothermal heat exchange (GHX) into community energy planning and national decarbonization strategies. To better assist private sector investment, it is currently necessary to define and assess the potential of low-temperature geothermal resources. For shallow GHC/GHX fields, there is no formal compilation of subsurface characteristics shared among industry practitioners that can improve system design and operations. Alaska is specifically noted in this work, because heretofore, it has not received a similar focus in geothermal potential evaluations as the contiguous United States. The methodology consists of leveraging relevant data to generate a baseline geospatial dataset of low-temperature resources (less than 150 degrees C) to compare and analyze information accessible to anyone trying to understand the potential of GHC/GHX and small-scale low-temperature geothermal power in Alaska (e.g., energy modelers, communities, planners, and policymakers). Importantly, this project identifies data related to (1) the evaluation of GHC/GHX in the shallow subsurface, and (2) the evaluation of low-temperature geothermal resource availability. Additionally, data is being compiled to assess repurposing of oil and gas wells to contribute co-produced fluids toward the geothermal direct use and heating and cooling resource potential. In this work we identified new data from three different datasets of isolated geothermal systems in Alaska and bottom-hole temperature data from oil and gas wells that can be leveraged for evaluation of low-temperature geothermal resource potential. The goal of this project is to facilitate future deployment of GHC/GHX analysis and community-led programs and update the low-temperature geothermal resources assessment of Alaska. A better understanding of shallow potential for GHX will improve design and operations of highly efficient GHC systems. The deployment and impact that can be achieved for low-temperature geothermal resources will contribute to decarbonization goals and facilitate widespread electrification by shaving and shifting grid loads.

15 GEOTHERMAL ENERGY↗
DOE OSTI2022

QGLab v0.0.1

A software program for experimenting with holographic teleportation protocol on quantum computers. The code implements the protocol that was proposed in https://arxiv.org/abs/1911.06314. The code is an end-to-end software solution that facilitates conducting the holographic teleportation experiments on state-of-the-art and emergent generations of QPUs supported by the Qiskit and tket SDKs. The code bundles all stages of an experiment as a single configurable workflow allowing faster development and experimentation cycles. Features: 1. Easy switching between Qiskit and tket quantum compilers. 2. Semi-automatic facilities for finding optimal compilation solutions beyond what Qiskit and tket provide by default. 3. Experiment resolution scaling (based on automatic jobs' batching). 4. Automatic experiment scaling over qubits. 5. Automatic readout error mitigation. 6. Automatic reproducibility analysis. 7. Standalone error-mitigation tools (randomized compiling, mitigation with estimation circuits, zero-noise extrapolation)

Shapoval, Illya↗
DOE OSTI2023

Encoding trade-offs and design toolkits in quantum algorithms for discrete optimization: coloring, routing, scheduling, and other problems

Challenging combinatorial optimization problems are ubiquitous in science and engineering. Several quantum methods for optimization have recently been developed, in different settings including both exact and approximate solvers. Addressing this field of research, this manuscript has three distinct purposes. First, we present an intuitive method for synthesizing and analyzing discrete (i.e., integer-based) optimization problems, wherein the problem and corresponding algorithmic primitives are expressed using a discrete quantum intermediate representation (DQIR) that is encoding-independent. This compact representation often allows for more efficient problem compilation, automated analyses of different encoding choices, easier interpretability, more complex runtime procedures, and richer programmability, as compared to previous approaches, which we demonstrate with a number of examples. Second, we perform numerical studies comparing several qubit encodings; the results exhibit a number of preliminary trends that help guide the choice of encoding for a particular set of hardware and a particular problem and algorithm. Our study includes problems related to graph coloring, the traveling salesperson problem, factory/machine scheduling, financial portfolio rebalancing, and integer linear programming. Third, we design low-depth graph-derived partial mixers (GDPMs) up to 16-level quantum variables, demonstrating that compact (binary) encodings are more amenable to QAOA than previously understood. We expect this toolkit of programming abstractions and low-level building blocks to aid in designing quantum algorithms for discrete combinatorial problems.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2017

Preparing NERSC users for Cori, a Cray XC40 system with Intel many integrated cores

The newest NERSC supercomputer Cori is a Cray XC40 system consisting of 2,388 Intel Xeon Haswell nodes and 9,688 Intel Xeon-Phi “Knights Landing” (KNL) nodes. Compared to the Xeon-based clusters NERSC users are familiar with, optimal performance on Cori requires consideration of KNL mode settings; process, thread, and memory affinity; fine-grain parallelization; vectorization; and use of the high-bandwidth MCDRAM memory. This paper describes our efforts preparing NERSC users for KNL through the NERSC Exascale Science Application Program, Web documentation, and user training. We discuss how we configured the Cori system for usability and productivity, addressing programming concerns, batch system configurations, and default KNL cluster and memory modes. Here, system usage data, job completion analysis, programming and running jobs issues, and a few successful user stories on KNL are presented.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2018

Cold Commissioning Simulant Selection for the Hanford Tank Waste Treatment Plant LAW Facility - 18465

Commissioning the Low-Activity Waste (LAW) Facility of the Hanford Tank Waste Treatment and Immobilization Plant (WTP) will require large volumes (∼2,000,000 liters) of a non-radioactive, chemically analogous waste simulant. Cold commissioning will provide for operator training in an integrated facility, demonstration of mechanical process equipment capacity, environmental performance demonstration of emissions control systems, and process control systems shakedown and tuning. Selection of the cold commissioning simulant is following a structured four-phase process to arrive at the preferred composition and recipe. Phases I, II and III are complete. Phase I laid the framework for decision making with preparation of a Project Management Plan, an update to the Simulant Development, Approval, Validation, and Documentation procedure, and a kickoff meeting to gather stakeholder input into the simulant definition and selection criteria. Phase II consisted of a comprehensive literature search and compilation of LAW simulant recipes, followed by initial screening against the selection criteria identified in Phase I. This process identified 72 candidate simulant recipes and down-selected 9 for further study. Phase III studies involved a) statistical comparisons of the similarity of candidate simulant compositions to the tank waste feed vector, b) process modelling of the commissioning sequence with each simulant to estimate glass former and simulant material consumption, offgas and effluent volumes and compositions, and product glass volumes and compositions, and c) evaluation of simulants against the immobilized LAW glass composition algorithm to determine allo able waste loading to ensure compliance with product specifications and process constraints. The results of these studies identified two of the candidate recipes to be carried forward to full-scale testing. Phase IV consists of testing these two recipes at vendor facilities to determine scalability, fabricability, and stability of the simulants when produced at industrial scale. Scalability is the ability to take a recipe from bench scale (1 to 40 liters) batch size to full scale (>35,000 liters) batch size. Fabricability is the ability of a recipe to be prepared in large batches without the need for complex or expensive mechanical equipment, heating, or cooling systems. Stability is the ability of a simulant to withstand long term storage and variations in environmental conditions without unacceptable changes in key characteristics (the set of chemical, physical, and rheological parameters that the simulant is intended to exhibit during cold commissioning tests). Simulants will also be tested for compatibility with other startup material (i.e., frit), tuning simulants, and environmental performance demonstration test spiking solutions to ensure against unexpected situations. Using a structured process is essential when making high impact decisions such as waste simulant selection. Proper planning and a clear explanation of how the decision process will unfold is key to obtaining stakeholder buy-in as more information is gathered. The documentation resulting from this structured selection process will be compiled and summarized in a Cold Commissioning Simulant Definition deliverable due to the Department of Energy 24 months before the start of Cold Commissioning. (authors)

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗
DOE OSTI2021

The Cost of Decarbonization and Energy Upgrade Retrofits for US Homes

Cost is a major barrier when upgrading homes to reduce carbon emissions required to meet DOE’s climate-related goals. This report summarizes a nationwide effort to gather home energy upgrade project cost data along with household energy performance data. The goal was to develop cost benchmarks and to guide future R&D efforts aimed at cost compression and scaling of the residential upgrade market. The cost data were compiled for both total project costs and costs of individual measures. The majority of energy savings were modeled, with some models using measured site data for calibration. The database was analyzed using clustering techniques to find common energy and CO2 reduction approaches. The individual measures were combined into archetypal solutions to determine least-cost approaches to maximizing energy and carbon savings. Several financial analyses were preformed to examine other cost metrics beyond first cost. Project data was obtained for 1,739 projects, from 15 states and 12 energy programs, with a total of 10,512 individual measures. The database includes a wide-array of projects, ranging from single-measure HVAC upgrades to net-zero energy whole home remodels. Projects were predominantly single-family detached dwellings with wood frame construction. Most of the data was obtained from energy programs because they had recorded the necessary information and were willing to share with this study. This sample of convenience can provide broad guidance and national cost benchmarks, but lacks sufficient detail to draw more disaggregated conclusions, such as geographical trends. The majority of data contributions were obtained without compensation from sources where the required data was already in some sort of structured format. We compensated sources to enter data from individual projects into a structured data format for about 500 projects, with an average cost of about $40 per project. The database was highly skewed to lower cost, lower impact projects due to the nature of the sample of convenience. Less than 10% of projects had savings greater than 50%. The cost data for individual measures in the database are being used in other DOE efforts on residential energy use/decarbonization. This data collection effort should continue in order to provide the best-informed guidance for DOE and industry R&D, as well as deployment efforts (including policy and program planning).

29 ENERGY PLANNING, POLICY, AND ECONOMY↗
DOE OSTI2016

Rolex: Resilience-oriented language extensions for extreme-scale systems

Future exascale high-performance computing (HPC) systems will be constructed from VLSI devices that will be less reliable than those used today, and faults will become the norm, not the exception. This will pose significant problems for system designers and programmers, who for half-a-century have enjoyed an execution model that assumed correct behavior by the underlying computing system. The mean time to failure (MTTF) of the system scales inversely to the number of components in the system and therefore faults and resultant system level failures will increase, as systems scale in terms of the number of processor cores and memory modules used. However every error detected need not cause catastrophic failure. Many HPC applications are inherently fault resilient. Yet it is the application programmers who have this knowledge but lack mechanisms to convey it to the system. In this paper, we present new Resilience Oriented Language Extensions (Rolex) which facilitate the incorporation of fault resilience as an intrinsic property of the application code. We describe the syntax and semantics of the language extensions as well as the implementation of the supporting compiler infrastructure and runtime system. Furthermore, our experiments show that an approach that leverages the programmer's insight to reason about the context and significance of faults to the application outcome significantly improves the probability that an application runs to a successful conclusion.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2023

FLASH: FPGA-Accelerated Smart Switches with GCN Case Study

Some communication switches, e.g., the Mellanox SHArP and those in the IBM BlueGene clusters, are augmented to process packets at the application level with fixed-function collectives. This approach, however, lacks flexibility, which limits their applicability in diverse and dynamic workloads. Recently, a new type of programmable packet processor, which uses high-level languages, e.g., P4, has emerged as possible candidates. P4-based switches, however, fall short in certain applications, including machine learning, where capabilities not currently supported by P4 are needed. These include more complex calculation, such as sparse computation and fused multiply-accumulate, data-intensive floating point operations, data reuse, and significant memory. The problem addressed here is that such a switch augmentation needs to support: a large amount of state, significant flexible compute capability, and ease of programming, all while maintaining full functionality, including ensuring high throughput, and demonstrating utility. In this work, we propose a programmable look-aside-type accelerator that can be embedded into, or attached to, existing communication switch pipelines and that is capable of processing packets at line-rate. The proposed in-switch accelerator is based on mixing an ISA (subset of RISC-V instructions) with dataflow graphs (found in CGRAs). To augment performance, vector instructions are also supported. To facilitate usability, we have developed a complete toolchain to compile user-provided C/C++ codes to appropriate back-end instructions for configuring the accelerator. While this approach is flexible enough to support various workloads, in this paper, we consider Graph Convolutional Networks (GCNs) as a case study. Experimental results show that this approach considerably improves the performance of distributed GCN applications.

Haghi, Pouya↗
DOE OSTI2021

Sandia Academic Alliance Program Collaboration Report: 2020-2021 Accomplishments

University partnerships play an essential role in sustaining Sandia’s vitality as a national laboratory. The SAA is an element of Sandia’s broader University Partnerships program, which facilitates recruiting and research collaborations with dozens of universities annually. The SAA program has two three-year goals. SAA aims to realize a step increase in hiring results, by growing the total annual inexperienced hires from each out-of-state SAA university. SAA also strives to establish and sustain strategic research partnerships by establishing several federally sponsored collaborations and multi-institutional consortiums in science & technology (S&T) priorities such as autonomy, advanced computing, hypersonics, quantum information science, and data science. The SAA program facilitates access to talent, ideas, and Research & Development facilities through strong university partnerships. Earlier this year, the SAA program and campus executives hosted John Myers, Sandia’s former Senior Director of Human Resources (HR) and Communications, and senior-level staff at Georgia Tech, U of Illinois, Purdue, UNM, and UT Austin. These campus visits provided an opportunity to share the history of the partnerships from the university leadership, tours of research facilities, and discussions of ongoing technical work and potential recruiting opportunities. These visits also provided valuable feedback to HR management that will help Sandia realize a step increase in hiring from SAA schools. The 2020-2021 Collaboration Report is a compilation of accomplishments in 2020 and 2021 from SAA and Sandia’s valued SAA university partners.

21 SPECIFIC NUCLEAR REACTORS AND ASSOCIATED PLANTS↗
DOE OSTI2025

Literature Review Investigating Historical Plutonium Solubility in SRS Tank Waste

The Savannah River Site (SRS) has designed the Accelerated Basin De-inventory (ABD) program to accelerate the de-inventory of L-Basin and accelerate the Spent Nuclear Fuel (SNF) disposition mission. Similarly, the H-Canyon facility at SRS is reestablishing the 6.3D electrolytic dissolver for dissolving unirradiated stainless-steel (SS) clad Fast Critical Assembly (FCA) fuel. In both discard types (ABD and FCA), plutonium is present and its complex solubility when composited to Concentration, Storage, and Transfer Facility (CSTF) sludge is being investigated as it may have downstream impacts to the liquid waste (LW) organization. This literature review aims to highlight and compile the existing literature on plutonium solubility in waste streams relevant to ABD and FCA discards, as well as discuss some considerations in analyzing solubility data of plutonium. This review serves to help define the analysis methods for future experiments involving plutonium (and other actinides) and in designing appropriate testing conditions surrounding these studies. This review is broken up into five parts and will discuss: (i) The possible effects of testing hold time and temperature on plutonium solubility, (ii) the influence of neutralization rate and particle size of freshly precipitated discards, (iii) the coprecipitation of plutonium with iron and uranium, (iv) predictive solubility modeling and the influence of supernate anions on solubility, and (v) the speciation of plutonium in solutionas a result of supernate anions.

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗
DOE OSTI2014

Proceedings of RIKEN/RBRC Workshop: Scientific Review Committee Meeting: October 30 - November 1, 2013 (Final Report) [Slides]

The thirteenth scientific evaluation of the RIKEN BNL Research Center (RBRC) took place at the Physics Building #510 at Brookhaven National Laboratory. The members of the Scientific Review Committee (SRC), present at the meeting, were: Prof. Sinya Aoki, Prof. Peter Braun-Munzinger, Prof. Kenichi Imai, Prof. Tetsuo Matsui, Prof. Richard Milner (Chair), Prof. Alfred Mueller, Prof. Charles Young Prescott, and Prof. Akira Ukawa. We are pleased that Dr. Hideto En’yo, the Director of the Nishina Institute of RIKEN, Japan, participated in this meeting both in informing the committee of the activities of the RIKEN Nishina Center for Accelerator-Based Science and the role of RBRC and as an observer of this review. In order to illustrate the breadth and scope of the RBRC program, each member of the Center made a presentation on his/her research efforts. This encompassed three major areas of investigation: theoretical, experimental and computational physics. In addition, the committee met privately with the fellows and postdocs to ascertain their opinions and concerns. Although the main purpose of this review is a report to RIKEN management on the health, scientific value, management and future prospects of the Center, the RBRC management felt that a compendium of the scientific presentations are of sufficient quality and interest that they warrant a wider distribution. Therefore we have made this compilation and present it to the community for its information and enlightenment. We thank Brookhaven National Laboratory and the U.S. Department of Energy* for providing the facilities to hold this meeting.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2016

RIKEN/RBRC Scientific Review Committee Meeting April 5-8, 2016. Proceedings of RIKEN BNL Research Center, Volume 126

Considerable progress in lattice gauge computing has been made by the RBRC Computing Group over the last 5 years. The lattice gauge community has recognized some of this work by awarding two Wilson prizes, in 2012 and in 2015. The Computing Group, in collaboration with RIKEN, BNL and the UKQCD collaboration, has focused on both physics simulations and technical developments, in both software and hardware. The physics output has been primarily in the area of fundamental physical quantities, e.g., in K and B mesons, nucleon form factors, g-2 of the muon, electric dipole moments and physics beyond the Standard Model. The software effort has produced new and/or more efficient algorithms, such as the all-mode averaging (AMA), and ensemble generation techniques. This proceedings records the 14th scientific evaluation of the RIKEN BNL Research Center (RBRC). In order to illustrate the breadth and scope of the RBRC program, each member of the Center made a presentation on his/her research efforts. This encompassed three major areas of investigation: theoretical, experimental and computational physics. In addition, the committee met privately with the fellows and postdocs to ascertain their opinions and concerns. Although the main purpose of this review is a report to RIKEN management on the health, scientific value, management and future prospects of the Center, the RBRC management felt that a compendium of the scientific presentations are of sufficient quality and interest that they warrant a wider distribution. Therefore we have made this compilation and present it to the community for its information and enlightenment.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2023

A Verified Optimizer for Quantum Circuits

We present VOQC, the first verified optimizer for quantum circuits, written using the Coq proof assistant. Quantum circuits are expressed as programs in a simple, low-level language called SQIR, a small quantum intermediate representation, which is deeply embedded in Coq. Optimizations and other transformations are expressed as Coq functions, which are proved correct with respect to a semantics of SQIR programs. SQIR programs denote complex-valued matrices, as is standard in quantum computation, but we treat matrices symbolically to reason about programs that use an arbitrary number of quantum bits. SQIR’s careful design and our provided automation make it possible to write and verify a broad range of optimizations in VOQC, including full-circuit transformations from cutting-edge optimizers.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2025

Automatically parallelizing batch inference on deep neural networks using Fiats and Fortran 2023 `do concurrent`

This paper introduces novel programming strategies that leverage features of the Fortran 2023 standard of the International Standards Organization (ISO) to automatically parallelize computations on deep neural networks. The paper focuses on the interplay of object-oriented, parallel, and functional programming paradigms in the Fiats deep learning library. We demonstrate how several infrequently used language features play a role in enabling efficient, parallel execution. Specifically, the ability to explicitly declare that a procedure is pure facilitates inference in the context of the language’s loop-parallelism construct `do concurrent`. Also, explicitly prohibiting the overriding of a parent type’s type-bound procedures eliminates the need for dynamic dispatch in performance-critical code. Finally, this paper uses batch inference calculations on a neural network surrogate for atmospheric aerosol dynamics to demonstrate that LLVM Flang compiler’s automatic parallelization of `do concurrent` achieves roughly the same performance and scalability as achieved by OpenMP compiler directives. We also demonstrate that double-precision inference costs 37–72% longer runtime than default-real precision with most values in the range 57-60%.

Rouson, Damian↗
DOE OSTI2016

Development plan for the External Hazards Experimental Group. Light Water Reactor Sustainability Program

This report describes the development plan for a new multi-partner External Hazards Experimental Group (EHEG) coordinated by Idaho National Laboratory (INL) within the Risk-Informed Safety Margin Characterization (RISMC) technical pathway of the Light Water Reactor Sustainability Program. Currently, there is limited data available for development and validation of the tools and methods being developed in the RISMC Toolkit. The EHEG is being developed to obtain high-quality, small- and large-scale experimental data validation of RISMC tools and methods in a timely and cost-effective way. The group of universities and national laboratories that will eventually form the EHEG (which is ultimately expected to include both the initial participants and other universities and national laboratories that have been identified) have the expertise and experimental capabilities needed to both obtain and compile existing data archives and perform additional seismic and flooding experiments. The data developed by EHEG will be stored in databases for use within RISMC. These databases will be used to validate the advanced external hazard tools and methods.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗
DOE OSTI2013

Software design space exploration for exascale combustion co-design

The design of hardware for next-generation exascale computing systems will require a deep understanding of how software optimizations impact hardware design trade-offs. In order to characterize how co-tuning hardware and software parameters affects the performance of combustion simulation codes, we created ExaSAT, a compiler-driven static analysis and performance modeling framework. Our framework can evaluate hundreds of hardware/software configurations in seconds, providing an essential speed advantage over simulators and dynamic analysis techniques during the co-design process. Our analytic performance model shows that advanced code transformations, such as cache blocking and loop fusion, can have a significant impact on choices for cache and memory architecture. Our modeling helped us identify tuned configurations that achieve a 90% reduction in memory traffic, which could significantly improve performance and reduce energy consumption. These techniques will also be useful for the development of advanced programming models and runtimes, which must reason about these optimizations to deliver better performance and energy efficiency.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2014

Compiler generation and autotuning of communication-avoiding operators for geometric multigrid

This paper describes a compiler approach to introducing communication-avoiding optimizations in geometric multigrid (GMG), one of the most popular methods for solving partial differential equations. Communication-avoiding optimizations reduce vertical communication through the memory hierarchy and horizontal communication across processes or threads, usually at the expense of introducing redundant computation. We focus on applying these optimizations to the smooth operator, which successively reduces the error and accounts for the largest fraction of the GMG execution time. Our compiler technology applies both novel and known transformations to derive an implementation comparable to manually-tuned code. To make the approach portable, an underlying autotuning system explores the tradeoff between reduced communication and increased computation, as well as tradeoffs in threading schemes, to automatically identify the best implementation for a particular architecture and at each computation phase. Results show that we are able to quadruple the performance of the smooth operation on the finest grids while attaining performance within 94% of manually-tuned code. Overall we improve the overall multigrid solve time by 2.5× without sacrificing programer productivity.

97 MATHEMATICS AND COMPUTING↗