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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 343 records · Page 19
All sourcesNASA NTRSDOE OSTI
DOE OSTI2020

Implications of a regional resource adequacy program on utility integrated resource planning: Study for the Western United States

LBNL collaborated with the Western Interstate Energy Board and the University of Texas, Austin to investigate the implications of a regional resource adequacy (RA) program on utility integrated resource planning. This report is focused on an active policy discussion to develop a novel voluntary program to share capacity resource and improve RA in the Western Interconnection. This report identifies two RA components of IRP that will be highly impacted by a regional RA program: resource capacity accreditation and RA targets. There are at least four resources that will require specific attention for their capacity credit calculation: (1) variable renewable resources, (2) demand-side resources, (3) hydropower, and (4) contracts. It will then be necessary to decide on a RA target reliability metric (e.g., a planning reserve margin) that is at least the minimum requirement in IRPs to ensure consistency in RA requirement calculations. The report finds that load forecasting and transmission expansion analyses will be moderately impacted, but that most of IRP components will not be significantly impacted. The report includes (i) a review of traditional resource adequacy practices in IRP; (ii) a case study of an existing regional RA program that interacts with IRP (Southwest Power Pool) and (iii) an overview of the NWPP regional resource adequacy proposal that is the object of this work. This paper does not (1) advocate for or against a regional RA program for the NWPP, (2) make detailed design recommendations for this program, or (3) assess its benefits and costs.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2018

An L$_0$L$_1$-norm compressive sensing paradigm for the construction of sparse predictive lattice models using mixed integer quadratic programming

First-principles based lattice models allow the modeling of ab initio thermodynamics of crystalline mixtures for applications such as the construction of phase diagrams and the identification of ground state atomic orderings. The recent development of compressive sensing approaches for the construction of lattice models has further enabled the systematic construction of sparse physical models without the need for human intuition other than requiring the compactness of effective cluster interactions. However, conventional compressive sensing based on L1-norm regularization is strictly only applicable to certain classes of optimization problems and is otherwise not guaranteed to generate optimally sparse and transferable results, so that the method can only be applied to some materials science applications. In this paper, we illustrate a more robust L0L1-norm compressive-sensing method that removes the limitations of conventional compressive sensing and generally results in sparser lattice models that are at least as predictive as those obtained from L1-norm compressive sensing. Apart from the theory, a practical implementation based on state-of-the-art mixed-integer quadratic programming (MIQP) is proposed. The robustness of our methodology is illustrated for four different transition-metal oxides with relevance as battery cathode materials: Li2xTi2(1-x)O2, Li2xNi2yO2, MgxCr2O4, and NaxCrO2. This method provides a practical and robust approach for the construction of sparser and more predictive lattice models, improving on the compressive sensing paradigm and making it applicable to a much broader range of applications.

Huang, W↗
DOE OSTI2016

Comparison of State-Funded Technology Maturation Programs.

This study examines the structure and impact of state-funded technology maturation programs that leverage research institutions for economic development throughout the United States. The lessons learned and practices identified from previous experiences will inform Sandia National Laboratories' Government Relations and Technology Partnerships teams as they participate in near-term discussions about the proposed Technology Readiness Gross Receipts Tax Credit and Program, and continue to shape longer-term program and partnership opportunities. This Page Intentionally Left Blank

99 GENERAL AND MISCELLANEOUS↗
DOE OSTI2020

Camera settings and biome influence the accuracy of citizen science approaches to camera trap image classification

Scientists are increasingly using volunteer efforts of citizen scientists to classify images captured by motion-activated trail cameras. The rising popularity of citizen science reflects its potential to engage the public in conservation science and accelerate processing of the large volume of images generated by trail cameras. While image classification accuracy by citizen scientists can vary across species, the influence of other factors on accuracy is poorly understood. Inaccuracy diminishes the value of citizen science derived data and prompts the need for specific best-practice protocols to decrease error. We compare the accuracy between three programs that use crowdsourced citizen scientists to process images online: Snapshot Serengeti, Wildwatch Kenya, and AmazonCam Tambopata. We hypothesized that habitat type and camera settings would influence accuracy. To evaluate these factors, each photograph was circulated to multiple volunteers. All volunteer classifications were aggregated to a single best answer for each photograph using a plurality algorithm. Subsequently, a subset of these images underwent expert review and were compared to the citizen scientist results. Classification errors were categorized by the nature of the error (e.g., false species or false empty), and reason for the false classification (e.g., misidentification). Our results show that Snapshot Serengeti had the highest accuracy (97.9%), followed by AmazonCam Tambopata (93.5%), then Wildwatch Kenya (83.4%). Error type was influenced by habitat, with false empty images more prevalent in open-grassy habitat (27%) compared to woodlands (10%). For medium to large animal surveys across all habitat types, our results suggest that to significantly improve accuracy in crowdsourced projects, researchers should use a trail camera set up protocol with a burst of three consecutive photographs, a short field of view, and determine camera sensitivity settings based on in situ testing. Accuracy level comparisons such as this study can improve reliability of future citizen science projects, and subsequently encourage the increased use of such data.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2020

Precision inclusive Higgs physics at e+e- colliders with tracking detectors and without calorimetry

A primary goal of a future e + e - collider program will be the precision measurement of Higgs boson properties. For practical reasons it is of interest to determine the minimal set of detector specifications required to reach this and other scientific goals. Here we investigate the precision obtainable for the e + e - Zh μ + μ - X inclusive cross section and the Higgs boson mass using the di-muon recoil method, considering a detector that has only an inner tracking system within a solenoidal magnetic field, surrounded by many nuclear interaction lengths of absorbing material, and an outer muon identification system. We find that the sensitivity achievable in these measurements with such a tracking detector is only marginally reduced compared to that expected for a general purpose detector with additional electromagnetic and hadronic calorimeter systems. The difference results mainly from multi-photon backgrounds that are not as easily rejected with tracking detectors. We also comment on the prospects for an analogous measurement of the e + e - → Zh → e + e - X inclusive cross section. Finally, we study searches for light scalars utilizing the di-muon recoil method, estimating the projected reach with a tracking or general purpose detector.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2019

Computational Simulation of an Additively Manufactured Marine Component

Early work in finite-element analysis of additive manufacturing (AM) components was not able to include distortion and residual stress of the entire build, yielding uncertainty in the computational model predictions. To address this, current versions of recently developed computational tools are being used to understand their role in validation at the Naval Surface Warfare Center, Carderock Division, for laser powder-bed fusion (L-PBF) AM. A 316L stainless steel L-PBF build containing multiple parts, which exhibited the same local part failure over multiple AM fabrication runs, was selected for computational analysis. Single-part simulations analyzed optimization of computational speed and prediction accuracy. Full-build simulations exposed the smallest re-coater tolerances early in part fabrication as a result of part shrinkage and subsequent cupping. This cupping was predicted to change the powder spreading and result in the non-consolidation of the support structures, which paralleled the failures seen in the physical builds. Part redesign of the conventionally produced part to align with AM fabrication requirements was recommended to alleviate the build failures. This project is part of a larger program focused on validation of the computational tools to determine best practices for insertion of AM simulation into the greater Naval enterprise.

36 MATERIALS SCIENCE↗
DOE OSTI2022

Qubit Assignment Using Time Reversal

As quantum computers with large numbers of qubits become increasingly available, experiments executed on a given device may not utilize all available qubits. In this case, the outcome of executing a quantum program will depend on the ability to efficiently select a subset of high-performing physical qubits. For any given quantum program and device there are many ways to assign physical qubits for execution of the program, and assignments will differ in performance due to the variability in quality across qubits and entangling operations on a single device. Evaluating the performance of each assignment using fidelity estimation introduces significant experimental overhead and will be infeasible for many applications, while relying on standard device benchmarks provides incomplete information about the performance of any specific program. Furthermore, the number of possible assignments grows combinatorially in the number of qubits on the device and in the program, motivating the use of heuristic optimization techniques. We demonstrate a practical solution to the problem of qubit assignment by using simulated annealing with a cost function based on the Loschmidt echo, a diagnostic that measures the reversibility of a quantum process. We provide theoretical justification for this choice of cost function by demonstrating that the optimal qubit assignment coincides with the optimal qubit assignment based on state fidelity in the weak error limit, and we provide experimental justification using diagnostics performed on Google’s superconducting qubit devices. We then establish the performance of simulated annealing for qubit assignment using classical simulations of noisy devices as well as optimization experiments performed on a quantum processor. Our results demonstrate that the use of Loschmidt echoes and simulated annealing provides a scalable and flexible approach to optimizing qubit assignment on near-term hardware.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2020

Learning to Solve Large-Scale Security-Constrained Unit Commitment Problems

Security-constrained unit commitment (SCUC) is a fundamental problem in power systems and electricity markets. In practical settings, SCUC is repeatedly solved via mixed-integer linear programming (MIP), sometimes multiple times per day, with only minor changes in input data. In this work, we propose a number of machine learning techniques to effectively extract information from previously solved instances in order to significantly improve the computational performance of MIP solvers when solving similar instances in the future. Based on statistical data, we predict redundant constraints in the formulation, good initial feasible solutions, and affine subspaces where the optimal solution is likely to lie, leading to a significant reduction in problem size. Computational results on a diverse set of realistic and large-scale instances show that using the proposed techniques, SCUC can be solved on average 4.3 times faster with optimality guarantees and 10.2 times faster without optimality guarantees, with no observed reduction in solution quality. Out-of-distribution experiments provide evidence that the method is somewhat robust against data-set shift. Summary of Contribution. The paper describes a novel computational method, based on a combination of mixed-integer linear programming (MILP) and machine learning (ML), to solve a challenging and fundamental optimization problem in the energy sector. The method advances the state-of-the-art, not only for this particular problem, but also, more generally, in solving discrete optimization problems via ML. We expect that the techniques presented can be readily used by practitioners in the energy sector and adapted, by researchers in other fields, to other challenging operations research problems that are solved routinely.

Machine Learning↗
DOE OSTI2020

Data analysis and modeling pipelines for controlled networked social science experiments

There is large interest in networked social science experiments for understanding human behavior at-scale. Significant effort is required to perform data analytics on experimental outputs and for computational modeling of custom experiments. Moreover, experiments and modeling are often performed in a cycle, enabling iterative experimental refinement and data modeling to uncover interesting insights and to generate/refute hypotheses about social behaviors. The current practice for social analysts is to develop tailor-made computer programs and analytical scripts for experiments and modeling. This often leads to inefficiencies and duplication of effort. In this work, we propose a pipeline framework to take a significant step towards overcoming these challenges. Our contribution is to describe the design and implementation of a software system to automate many of the steps involved in analyzing social science experimental data, building models to capture the behavior of human subjects, and providing data to test hypotheses. The proposed pipeline framework consists of formal models, formal algorithms, and theoretical models as the basis for the design and implementation. We propose a formal data model, such that if an experiment can be described in terms of this model, then our pipeline software can be used to analyze data efficiently. The merits of the proposed pipeline framework is elaborated by several case studies of networked social science experiments.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2018

Final Report- Local Energy Matters: Solar Development in Duluth, Minnesota

The Local Energy Matters project advanced solar deployment in the City of Duluth, MN- a cold-climate community of 86,000. At the beginning of the project, Duluth had 254.57 kW installed solar capacity with an average cost of 5.04 USD/watt installed in 2014. The project worked with cross-sector stakeholders to benchmark the current market, implement best practices for solar deployment and soft cost reduction, develop pilot deployment programs in residential rooftop, community solar, and commercial/industrial sectors, work with the City of Duluth to determine appropriate sites for utility scale developments, and demonstrate solar pus storage. Over the three years of the project, Duluth’s installed residential and commercial solar capacity grew by 344% to 875.9 kW with an additional 702 kW solar garden capacity subscribed by Duluth residents, businesses, and institutions. Installation costs dropped 48% over this timeframe to 4.08 USD/watt installed (exclusive of solar garden construction). This report documents the process used to identify levers for increased solar installation and cost reductions in a nascent cold-climate solar market.

14 SOLAR ENERGY↗
DOE OSTI2016

TITANS Strategic Intern Pipeline: Best Practices and Resource Guide

This guide is a compilation of best practices and resources from three of Sandia's leading strategic intern programs which make up the Technical Internships to Advance National Security (TITANS) program. TITANS consist of the Center for Analysis Systems and Applications (CASA), the Center for Cyber Defenders (CCD), and the Monitoring Systems & Technology Intern Center (MSTIC).

99 GENERAL AND MISCELLANEOUS↗
DOE OSTI2023

Engineering Computational Practices (Rev. 1)

This manual will attempt to motivate the use of an automated build system for the purposes of computational science and engineering. As part of this motivation, the surrounding computational practices of version control, documen tation, compute environment management, and regression testing will also be addressed as applied to the practice of computational engineering. Specifically, this manual intends to motivate the adoption of these traditional software engineering practices for use in research and production engineering simulation projects. This manual is not the first such effort in the greater scientific computing community. In fact, the authors relied heavily on the lesson plans of the Software Carpentry, established to teach computing skills to researchers in 1998. As the intention for this manual is to lay out fundamental practices of engineering computing, it will not attempt to fully teach the underlying concepts and will instead reference the well designed lesson plans of the Software Carpentry. Where possible, this manual will explain to general computing practices and concepts and limit discussion of specific software implementations to examples or vehicles for practice in concrete application. The specific software taught by the Software Carpentry curriculum is an excellent starting point to learn the core concepts of computational engi neering. However, the authors have found that applications to engineering simulation and analysis require translation of these software development concepts into the language and workflows of computational engineers. Adopting these computational tools may require engineers to re-imagine their workflows in some combination of traditional engineer ing and software concepts. It has also been necessary to extend existing software build systems for engineering practices beyond the simple wrap ping of engineering software execution. Where necessary, examples of specific software and their method of extension to engineering simulations will be given, with reference to the User Manual for recommended practical use. Where this manual relies on specific implementation examples, it should be understood that the practicing engineer may find that different software is more amenable to their specific work. It is always the overall collection of computational practices is more important than any specific software implementation. The ability to recognize which concepts are implemented by a software package will make a practicing engineer agile to changing project needs, computing resources, numeric solvers, programming languages, and even available funding.

42 ENGINEERING↗
DOE OSTI2023

Basic Research Needs in Quantum Computing and Networking

Employing quantum mechanical resources in computing, information processing, and networking opens the door to potential exponential advantages over classical counterparts. However, quantifying and realizing such advantages poses extensive scientific and engineering challenges. Department of Energy (DOE) investments have driven steady progress in addressing such challenges. Recently developed quantum algorithms offer asymptotic exponential advantages in speed or accuracy for fundamental scientific problems. These problems include simulating physical systems, solving systems of linear equations, differential equations, and optimization problems. Empirical demonstrations on nascent quantum hardware suggest better performance on contrived computational tasks than classical analogs. However, the requirements for a quantum computer or network to demonstrate an end-to-end rigorously quantifiable performance improvement over classical analogs remains a grand challenge, especially for problems of practical value. In particular, what will be required for quantum technology to ultimately exhibit scalable, rigorous, and transformative performance advantages for practical applications? In July 2023, DOE’s Advanced Scientific Computing Research program in the Office of Science convened the Workshop on Basic Research Needs in Quantum Computing and Networking, where major opportunities and grand challenges were identified. The following five priority research directions (PRDs) were identified as a result of the workshop.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2026

Genesis Mission Data cards

As data-intensive research and artificial intelligence become central to DOE mission science, the need for machine-actionable dataset documentation has grown accordingly. However, many DOE-aligned communities, including the Office of Science, NNSA, and cross-laboratory collaborations, have developed independent metadata practices. This fragmentation creates friction for discovery, federation, and reuse across programs. To address these challenges, this talk introduces the Genesis Data Card: a shared metadata artifact developed in collaboration with a broad DOE community (Jefferson Lab and the National Lab of the Rockies, Oak Ridge, Sandia, Idaho, Berkeley, and Los Alamos). The Genesis Data Card aims to standardize dataset documentation across DOE-aligned initiatives while remaining extensible to discipline-specific needs. This talk will describe the data card template and the supporting code to validate completed data cards, using a companion LinkML schema. I'll walk through the design decisions behind the template, its alignment with existing standards, its treatment of sensitivity and governance metadata, and the phased roadmap toward lifecycle-integrated "xCards" that support autonomous discovery and reuse. The talk closes with current gaps, ongoing work, and how others can contribute datasets and feedback to the shared repository.

McSpadden, Helen [Thomas Jefferson National Accele↗
DOE OSTI2019

Small Unmanned Aerial Systems (sUAS) on the NNSS White Paper

The Nevada National Security Site (NNSS) prides itself on being the premier outdoor, indoor, and underground national laboratory. It is the preferred location for experiments supporting the National Nuclear Security Administration’s nuclear weapons Stockpile Stewardship Programs, national defense programs, and national security research, development, and training programs, as well as vital programs of other federal agencies. Work at the NNSS supports the Stockpile Stewardship Program, Global and Homeland Security programs, and Environmental Management. The NNSS looks to incorporate safer work practices and continuous improvements. Unmanned Aerial Surveys (UAS) have already been implemented in areas off the NNSS for multiple uses. Using these best practices with systems catered to the NNSS would allow integration of new technology and would allow work at the NNSS to maintain the high level of quality already exhibited. The Management and Operating Contractor (M&O) has shown experience in multiple UAS platforms. Through the coordination and experience developed through these projects, the M&O can further improve the quality and cost effectiveness of services currently rendered and offer new services not currently available today. Several NNSS departments that could implement UAS technology are Emergency Response, Facility Management, Environmental Management, and Training. These departments would be able to improve their capabilities and expand personnel effectiveness with the inclusion of UASs and the information they provide.

61 RADIATION PROTECTION AND DOSIMETRY↗
DOE OSTI2022

COMPOFF: A Compiler Cost model using Machine Learning to predict the Cost of OpenMP Offloading

The HPC industry is inexorably moving towards an era of extremely heterogeneous architectures, with more devices configured on any given HPC platform and potentially more kinds of devices, some of them highly specialized. Writing a separate code suitable for each target system for a given HPC application is not practical. The better solution is to use directive-based parallel programming models such as OpenMP. OpenMP provides a number of options for offloading a piece of code to devices like GPUs. To select the best option from such options during compilation, most modern compilers use analytical models to estimate the cost of executing the original code and the different offloading code variants. Building such an analytical model for compilers is a difficult task that necessitates a lot of effort on the part of a compiler engineer. Recently, machine learning techniques have been successfully applied to build cost models for a variety of compiler optimization problems. In this paper, we present COMPOFF, a cost model which uses the multi-layer perceptrons to statically estimates the Cost of OpenMP OFFloading. We used six different transformations on a parallel code of Wilson Dslash Operator to support GPU offloading, and we predicted their cost of execution on different GPUs using COMPOFF during compile time. Our results show that this model can predict offloading costs with a root mean squared error in prediction of less than 0.5 seconds. Our preliminary findings indicate that this work will make it much easier and faster for scientists and compiler developers to port legacy HPC applications that use OpenMP to new heterogeneous computing environment.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2017

Employing Whisper in selecting benchmarks for traditional validation methods

The Nuclear Criticality Safety Program at Sandia National Laboratories (SNL) uses a collection of benchmarks developed from the International Handbook of Evaluated Criticality Safety Benchmark Experiments for validation of MCNP. In early 2016, an effort began to expand the benchmark collection to cover a broader range of materials and neutron spectra. The program undertook the task of updating current benchmarks to reflect best practices from Los Alamos National Laboratory (LANL) for MCNP calculations. Spring of 2016, LANL shared the Whisper code with SNL in a collaborated effort to investigate application of the code to activities at SNL. Whisper is a statistical package developed to support nuclear criticality safety validation efforts. It measures how well benchmarks correlate to a system or process and calculates a USL. Whisper has begun to develop into an asset for the SNL Nuclear Criticality Safety team to determine and reassure benchmarks selected for validation truly correlate process parameters (i.e., fall within an acceptable Area of Applicability). SNL's use of traditional statistical methods, with the abilities of Whisper to identify correlating benchmarks, has facilitated in ensuring confidence in validation and USL determination. (authors)

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗