SEARCH · Engineering Papers

Results for “programming”

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.

SourceResearch connectionFrom yearThrough year

Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.

At least 415 records · Page 23

All sources NASA NTRS DOE OSTI

DOE OSTI2019

Panel Session 62: Progress on Deep Repository Programs Around the World

This panel session focused on the progress of deep repository programs, worldwide. While efforts on a deep geological repository for SNF/UNF and HLW in the USA were terminated in 2010, several other repository programs continue to make considerable progress and deserve to be recognized. Panelists were invited from national, multi-national, and international repository programs and organizations to report upon the status and future as well as the perceived reasons underlying the success and the failure of components of their respective repository program. This panel provided a forum for interested parties to present, exchange, and take advantage of, as appropriate, repository progress, challenges, concepts/designs and other lessons learned around the world, including site characterization, site selection, research and development, licensing, construction, operation, and public acceptance and participation. Presentations proposed include: 1 - Cigeo (French Deep Geological Repository Project) update 2018 (Daniel Delort, Andra (France)); 2 - Progress on Deep Geological Repository Programs Around the World (Bruce McKirdy, Radioactive Waste Management (United Kingdom)); 3 - Geological disposal facility in Belgium (Didier Leonard, ONDRAF/NIRAS (Belgium))

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗

DOE OSTI2016

Preliminary Design of the TEX-MOX Criticality Experimental Program using Optimization Algorithms Implemented in the IRSN PROMETHEE Tool

In the frame of its missions, IRSN is widely involved in design, evaluation and analyses of critical experiments in order to contribute to computer codes and nuclear data validation. The work described in this paper was carried out in the framework of the PRINCESS IRSN research project and is part of the IRSN/LLNL collaboration on the TEX program in the frame of the NCSP. The need for epithermal and intermediate energy range critical benchmarks is an established international criticality safety data need, underpinned by the subsequent use of data assimilation methods like GLLSM to reduce Nuclear Data uncertainties. The goal of the TEX program is to address this need by executing experiments with NCSP fissile assets, which can be used to create critical assemblies that span a wide range of fission energy spectra, from the thermal energy range (below 0.625 eV), through the intermediate energy range (0.625 eV to 100 keV), to the fast one (above 100 keV). The TEX program involves different materials of interest, such as TEX-Ta experiments using plutonium ZPPR plates proposed by LLNL. This paper presents an overview of the studies performed at IRSN using numerical optimization algorithms for the design of critical experiments involving Mixed U and Pu fuel plates, referred to as TEX-MOX program. This program ultimately aims to perform critical experiments to validate the MOX fuel configurations encountered in the MOX fuel fabrication process. (authors)

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗

DOE OSTI2024

Developing a Supply Chain Security Program

Amid growing concerns over foreign manufacturing for components and devices deployed in critical energy infrastructure, this research from the national labs will highlight best practices for developing and maintaining a supply chain security program. Tools for asset inventory, tips for developing and maintaining software- and hardware-bills-of-materials (SBOMs and HBOMs), recommended contractual language for vendor agreements, and identification of responsibilities will be shared. We discuss the one-time requirements to enable a successful supply chain security program and the best ways to operationalize this program for maximum impact, including development of robust practices for vulnerability tracking, patch management, and workarounds, with understanding of the reliability and uptime requirements for utilities. The recommendations shared are based on a cyber-informed engineering approach to identification of high-consequence impacts and the engineering controls related to supply chain management that can best mitigate these impacts. This approach allows for prioritization of resources. Additionally, we highlight relative up-front and ongoing costs associated with recommended controls. Viewers will leave with an understanding what a supply chain security program is, and what steps, prioritized for resource-constrained organizations, can build a robust program.

14 SOLAR ENERGY↗

DOE OSTI2022

Generating and Analyzing Program Call Graphs using Ontology

Call graph or caller-callee relationships have been used for various kinds of static program analysis, performance analysis and profiling, and for program safety or security analysis such as detecting anomalies of program execution or code injection attacks. However, different tools generate call graphs in different formats, which prevents efficient reuse of call graph results. In this paper, we present an approach of using ontology and resource description framework (RDF) to create knowledge graphs for specifying call graphs to facilitate the construction of full-fledged and complex call graphs of computer programs, realizing more interoperable and scalable program analyses than conventional approaches. We create a formal ontology-based specification of call graph information to capture concepts and properties of both static and dynamic call graphs so different tools can collaboratively contribute to more comprehensive analysis results. Our experiments show that ontology enables merging of call graphs generated from different tools and flexible queries using a standard query interface. Index Terms—Callgraph, ontology, knowl

Dorta, E.↗

DOE OSTI2025

INTEGRATION OF DATA ANALYTICS WITH SYSTEM HEALTH PROGRAMS

Industry equipment reliability and asset management programs are essential elements that help ensure the safe and economical operation of nuclear power plants. The effectiveness of these programs is addressed in several industry developed and regulatory programs. However, these programs have proven to be labor intensive and expensive. There is an opportunity to significantly enhance the collection, analysis, and use of this information to provide more cost-effective plant operation. Additionally, there is an acute industry need to leverage advanced technology to reduce costs and improve operational effectiveness. The goal of this paper is to provide effective and efficient analytical methods and tools to support risk-informed decisions for the equipment reliability and asset management programs at nuclear power plants. This is accomplished by creating a direct bridge between component health/lifecycle data and decision making (e.g., maintenance scheduling and project prioritization). Here we are supporting typical system engineer decisions regarding maintenance activity scheduling and component ageing management. This is performed in a risk-informed context where herein the term “risk” is broadly constructed to include both plant reliability and economics. This framework combines data analytics tools to analyze equipment reliability data with risk-informed methods designed to support system engineer decisions (e.g., maintenance and replacement schedules, optimal maintenance posture) in a customizable workflow. A challenge is that the structure of this workflow strongly depends on the decision that needs to be made, the type of data available, and the constraints that need to be considered. Current methods are designed to provide specific answers to specific problems; however, these methods might prove to be inadequate even when problem settings slightly change (e.g., different types of requirements, additional dependencies between system reliability and economics). We tackled this challenge by designing framework in a flexible and modular fashion such that the user can assemble and customize his/her own workflow that integrates SSC economic lifecycle models (e.g., maintenance and replacement costs), system reliability models, and optimization methods.

97 - MATHEMATICS AND COMPUTING↗

DOE OSTI2020

Multistage distributionally robust mixed-integer programming with decision-dependent moment-based ambiguity sets

We study multistage distributionally robust mixed-integer programs under endogenous uncertainty, where the probability distribution of stage-wise uncertainty depends on the decisions made in previous stages. We first consider two ambiguity sets defined by decision-dependent bounds on the first and second moments of uncertain parameters and by mean and covariance matrix that exactly match decision-dependent empirical ones, respectively. For both sets, we show that the subproblem in each stage can be recast as a mixed-integer linear program (MILP). Moreover, we extend the general moment-based ambiguity set in to the multistage decision-dependent setting, and derive mixed-integer semidefinite programming (MISDP) reformulations of stage-wise subproblems. We develop methods for attaining lower and upper bounds of the optimal objective value of the multistage MISDPs, and approximate them using a series of MILPs. We deploy the Stochastic Dual Dynamic integer Programming (SDDiP) method for solving the problem under the three ambiguity sets with risk-neutral or risk-averse objective functions, and conduct numerical studies on multistage facility-location instances having diverse sizes under different parameter and uncertainty settings. Furthermore, our results show that the SDDiP quickly finds optimal solutions for moderate-sized instances under the first two ambiguity sets, and also finds good approximate bounds for the multistage MISDPs derived under the third ambiguity set. We also demonstrate the efficacy of incorporating decision-dependent distributional ambiguity in multistage decision-making processes.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2022

Building Load Control Using Distributionally Robust Chance-Constrained Programs with Right-Hand Side Uncertainty and the Risk-Adjustable Variants

Aggregation of heating, ventilation, and air conditioning (HVAC) loads can provide reserves to absorb volatile renewable energy, especially solar photo-voltaic (PV) generation. In this paper, we decide HVAC control schedules under uncertain PV generation, using a distributionally robust chance-constrained (DRCC) building load control model under two typical ambiguity sets: the moment-based and Wasserstein ambiguity sets. We derive mixed integer linear programming (MILP) reformulations for DRCC problems under both sets. Especially, for the Wasserstein ambiguity set, we use the right-hand side (RHS) uncertainty to derive a more compact MILP reformulation than the commonly known MILP reformulations with big-M constants. All the results also apply to general individual chance constraints with RHS uncertainty. Furthermore, we propose an adjustable chance-constrained variant to achieve tradeoff between the operational risk and costs. We derive MILP reformulations under the Wasserstein ambiguity set and second-order conic programming (SOCP) reformulations under the moment-based set. Using real-world data, we conduct computational studies to demonstrate the efficiency of the solution approaches and the effectiveness of the solutions. Summary of Contribution: The problem studied in this paper is motivated by a building load control problem that uses the aggregation of heating, ventilation, and air conditioning (HVAC) loads as flexible reserves to absorb uncertain solar photovoltaic (PV) generation. The problem is formulated as distributionally robust chance-constrained (DRCC) programs with right-hand side (RHS) uncertainty. In addition, we propose a risk-adjustable variant of the DRCC programs, where the risk level, instead of being predetermined, is treated as a decision variable. The paper aims to provide tractable reformulations and solution algorithms for both the (general) DRCC and the (general) adjustable DRCC models with RHS uncertainty.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2021

Analysis of Benefits Associated With Projects and Technologies Supported by the Clean Transportation Program

The California Energy Commission's Clean Transportation Program (CTP) supports a wide range of alternative, low-carbon fuel and vehicle projects. This report improves upon the 2014 Alternative and Renewable Fuel and Vehicle Technology Program (ARFVTP) Benefits Report(the former name of the Clean Transportation Program), which focused on two components of benefit calculation: expected benefits and market transformation benefits. The "expected benefits" are defined as benefits that accrue because of the direct displacement of petroleum-based fuels or vehicle technologies. The "market transformation benefits" accrue because of CTP funding shifting the underlying market dynamics and accelerating the adoption of alternative fuel vehicles. This report documents the updated methods used in the benefits analysis in 2014 and applies them for this 2021 Clean Transportation Program Benefits Report. The project team used data collected from CTP projects funded from 2009 to the third quarter of 2021 to estimate the benefits between 2021 and 2030. CTP projects valued at $\$898.3 million$ were assessed (out of $\$1.04 billion$ funded) to estimate expected benefits of 249 million gallons per year petroleum reduction and 2.79 million metric tons per year of carbon dioxide equivalent greenhouse gas (GHG) reduction in 2030. Market transformation benefits are additive to the expected benefits and were estimated with high and low ranges for the 315 relevant projects evaluated. The market transformation benefits' GHG reductions are estimated as 2.2 million to 6.2 million metric tons of carbon dioxide equivalent per year and the petroleum reductions as 145.3 million to 671.5 million gasoline gallon equivalents per year in 2030. Combining both benefit types, the CTP projects can make significant progress toward meeting California's long-term GHG and petroleum fuel use reduction goals.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗

DOE OSTI2025

Applying Social Science Methods to Assess and Improve Program Outcomes: A Case Study on the Gap Region Outreach Project

This paper outlines a replicable framework for assessing gaps, reach, and the impact of technical assistance (TA) programs, using the Energy to Communities (E2C) Gap Region Outreach Project as a case study. By applying qualitative social science methods, this framework provides tools for effectively engaging with underrepresented regions, identifying barriers to participation, and tailoring solutions to local needs. The study highlights the importance of leveraging stakeholder insights to address disparities in program engagement and participation, offering actionable recommendations to enhance program responsiveness and optimize resource allocation. This approach serves as a guide for practitioners seeking to expand the reach and effectiveness of federally funded TA programs while ensuring alignment with community priorities and capacity needs.

99 GENERAL AND MISCELLANEOUS↗

DOE OSTI2021

Quantum Chemistry Common Driver and Databases (QCDB) and Quantum Chemistry Engine (QCEngine): Automation and interoperability among computational chemistry programs

We report that community efforts in the computational molecular sciences (CMS) are evolving toward modular, open, and interoperable interfaces that work with existing community codes to provide more functionality and composability than could be achieved with a single program. The Quantum Chemistry Common Driver and Databases (QCDB) project provides such capability through an application programming interface (API) that facilitates interoperability across multiple quantum chemistry software packages. In tandem with the Molecular Sciences Software Institute and their Quantum Chemistry Archive ecosystem, the unique functionalities of several CMS programs are integrated, including CFOUR, GAMESS, NWChem, OpenMM, Psi4, Qcore, TeraChem, and Turbomole, to provide common computational functions, i.e., energy, gradient, and Hessian computations as well as molecular properties such as atomic charges and vibrational frequency analysis. Both standard users and power users benefit from adopting these APIs as they lower the language barrier of input styles and enable a standard layout of variables and data. These designs allow end-to-end interoperable programming of complex computations and provide best practices options by default.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2026

Measuring impacts of California agri-environmental programs using field-scale satellite data

In the past decade, California has invested over $\$$200 million in direct grants to growers to support the adoption of agricultural practices that save water and/or improve soil health while also reducing greenhouse gas emissions. Ex-post evaluation of agri-environmental outcomes of these grant programs, however, is limited. We use satellite data to monitor changes in field-level consumptive water use and greenness (i.e. normalized difference vegetation index), a proxy for agricultural productivity, for the most frequently funded crop-types (almonds, grapes, and walnuts) in two California Department of Food and Agriculture programs. Nearly 600 fields receiving funding during the 2014–2022 period were analyzed using two causal inference methods. Fields that received grants to both upgrade irrigation systems and install irrigation water management sensors showed reduced consumptive water use and greenness by an average of 3.5% and 4.2%, respectively (significant at the 10% level). In contrast, we find that the adoption of only irrigation water management sensors, which are designed to inform irrigation scheduling and management, resulted in an average increase of 4.1% and 4.8% in consumptive water use and greenness respectively (significant at the 5% level). We find negligible effects for either consumptive water use or greenness when both pump efficiency upgrades and sensors were implemented. We further find that grants for compost addition and cover cropping led to small greenness increases of 1.7% and 2.8% respectively (significant at the 10% level) and had insignificant effects on consumptive water use. Our analysis of five agri-environmental program interventions reveals that several practice outcomes may be at odds with stated program goals of reducing water use while maintaining or improving agricultural productivity.

agriculture↗

DOE OSTI2017

Sequential computation of elementary modes and minimal cut sets in genome-scale metabolic networks using alternate integer linear programming

Elementary (flux) modes (EMs) have served as a valuable tool for investigating structural and functional properties of metabolic networks. Identification of the full set of EMs in genome-scale networks remains challenging due to combinatorial explosion of EMs in complex networks. It is often, however, that only a small subset of relevant EMs needs to be known, for which optimization-based sequential computation is a useful alternative. Most of the currently available methods along this line are based on the iterative use of mixed integer linear programming (MILP), the effectiveness of which significantly deteriorates as the number of iterations builds up. To alleviate the computational burden associated with the MILP implementation, we here present a novel optimization algorithm termed alternate integer linear programming (AILP). Results: Our algorithm was designed to iteratively solve a pair of integer programming (IP) and linear programming (LP) to compute EMs in a sequential manner. In each step, the IP identifies a minimal subset of reactions, the deletion of which disables all previously identified EMs. Thus, a subsequent LP solution subject to this reaction deletion constraint becomes a distinct EM. In cases where no feasible LP solution is available, IP-derived reaction deletion sets represent minimal cut sets (MCSs). Despite the additional computation of MCSs, AILP achieved significant time reduction in computing EMs by orders of magnitude. The proposed AILP algorithm not only offers a computational advantage in the EM analysis of genome-scale networks, but also improves the understanding of the linkage between EMs and MCSs.

59 BASIC BIOLOGICAL SCIENCES↗

DOE OSTI2021

Enabling Pulse-level Programming, Compilation, and Execution in XACC

Noisy gate-model quantum processing units (QPUs) are currently available from vendors over the cloud, and digital quantum programming approaches exist to run low-depth circuits on physical hardware. These digital representations are ultimately lowered to pulse-level instructions by vendor quantum control systems to affect unitary evolution representative of the submitted digital circuit. Vendors are beginning to open this pulse-level control system to the public via specified interfaces. Robust programming methodologies, software frameworks, and backend simulation technologies for this analog model of quantum computation will prove critical to advancing pulse-level control research and development. Prototypical use cases for this include error mitigation, optimal pulse control, and physics-inspired pulse construction. Here we present an extension to the XACC quantum-classical software framework that enables pulse-level programming for superconducting, gate-model quantum computers, and a novel, general, and extensible pulse-level simulation backend for XACC that scales on classical compute clusters via MPI. Our work enables custom backend Hamiltonian definitions and gate-level compilation to available pulses with a focus on performance and scalability. We end with a demonstration of this capability, and show how to use XACC for pertinent pulse-level programming tasks.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2020

Exponential Decay in the Sensitivity Analysis of Nonlinear Dynamic Programming

In this report, we study the sensitivity of discrete-time dynamic programs with nonlinear dynamics and objective to perturbations in the initial conditions and reference parameters. Under uniform controllability and boundedness assumptions for the problem data, we prove that the directional derivative of the optimal state and control at time $k$, $x^*_k$, and $u^*_k$, with respect to the reference signal at time $i$, $d_i$, will have exponential decay in terms of $|k-i|$ with a decay rate $\rho$ independent of the temporal horizon length. The key technical step is to prove that a version of the convexification approach proposed by Verschueren et al. can be applied to the KKT conditions and results in a convex quadratic program with uniformly bounded data. In turn, Riccati techniques can be further employed to obtain the sensitivity result, borne from the observation that the directional derivatives are solutions of quadratic programs with structure similar to the KKT conditions themselves. We validate our findings with numerical experiments on a small nonlinear, nonconvex, dynamic program.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2016

Enhancing Participation in the U.S. Global Change Research Program

The US Global Change Research Program (USGCRP) is a collection of 13 Federal entities charged by law to assist the United States and the world to understand, assess, predict, and respond to human-induced and natural processes of global change. As the understanding of global change has evolved over the past decades and as demand for scientific information on global change has increased, the USGCRP has increasingly focused on research that can inform decisions to cope with current climate variability and change, to reduce the magnitude of future changes, and to prepare for changes projected over coming decades. Overall, the current breadth and depth of research in these agencies is insufficient to meet the country's needs, particularly to support decision makers. This report provides a rationale for evaluating current program membership and capabilities and identifying potential new agencies and departments in the hopes that these changes will enable the program to more effectively inform the public and prepare for the future. It also offers actionable recommendations for adjustments to the methods and procedures that will allow the program to better meet its stated goals.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗

DOE OSTI2015

Jamaica National Net-Billing Pilot Program Evaluation

This technical report discusses the effectiveness of the Jamaica Public Service Company Limited Net-Billing Pilot Program. The National Renewable Energy Laboratory (NREL) collected and analyzed data from a wide range of stakeholders, conducted in-country research, and compared program elements to common interconnection practices to form programmatic recommendations for the Jamaica context. NREL finds that the net-billing pilot program has successfully contributed to the support of the emerging solar market in Jamaica with the interconnection of 80 systems under the program for a total of 1.38 megawatts (MW) at the time of original analysis.

24 POWER TRANSMISSION AND DISTRIBUTION↗

DOE OSTI2016

Germany-US Nuclear Theory Exchange Program for QCD Studies of Hadrons & Nuclei 'GAUSTEQ'

GAUSTEQ was a Germany-U.S. exchange program in nuclear theory whose purpose was to focus research efforts on QCD studies of hadrons and nuclei, centered around the current and future research programs of Jefferson Lab and the Gesellschaft fur Schwerionenforschung (GSI) in Germany. GAUSTEQ provided travel support for theoretical physicists at US institutions conducting collaborative research with physicists in Germany. GSI (with its Darmstadt and Helmholtz Institute Mainz braches) served as the German “hub” for visits of U.S. physicists, while Jefferson Lab served as the corresponding “hub” for visits of German physicists visiting U.S. institutions through the reciprocal GUSTEHP (German-US Theory Exchange in Hadron Physics) program. GAUSTEQ was funded by the Office of Nuclear Physics of the U.S. Department of Energy, under Contract No.DE-SC0006758 and officially managed through Old Dominion University in Norfolk, Virginia. The program ran between 2011 and 2015.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗

DOE OSTI2018

Final Report from The University of Texas at Austin for DEGAS: Dynamic Global Address Space programming environments

The Dynamic, Exascale Global Address Space programming environment (DEGAS) project will develop the next generation of programming models and runtime systems to meet the challenges of Exascale computing. Our approach is to provide an efficient and scalable programming model that can be adapted to application needs through the use of dynamic runtime features and domain-specific languages for computational kernels. We address the following technical challenges: Programmability: Rich set of programming constructs based on a Hierarchical Partitioned Global Address Space (HPGAS) model, demonstrated in UPC++. Scalability: Hierarchical locality control, lightweight communication (extended GASNet), and ef- ficient synchronization mechanisms (Phasers). Performance Portability: Just-in-time specialization (SEJITS) for generating hardware-specific code and scheduling libraries for domain-specific adaptive runtimes (Habanero). Energy Efficiency: Communication-optimal code generation to optimize energy efficiency by re- ducing data movement. Resilience: Containment Domains for flexible, domain-specific resilience, using state capture mechanisms and lightweight, asynchronous recovery mechanisms. Interoperability: Runtime and language interoperability with MPI and OpenMP to encourage broad adoption.

97 MATHEMATICS AND COMPUTING↗