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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 379 records · Page 21
All sourcesNASA NTRSDOE OSTI
DOE OSTI2016

Discrete Time McKean–Vlasov Control Problem: A Dynamic Programming Approach

We consider the stochastic optimal control problem of nonlinear mean-field systems in discrete time. We reformulate the problem into a deterministic control problem with marginal distribution as controlled state variable, and prove that dynamic programming principle holds in its general form. We apply our method for solving explicitly the mean-variance portfolio selection and the multivariate linear-quadratic McKean–Vlasov control problem.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2014

Adiabatic quantum optimization for associative memory recall

Hopfield networks are a variant of associative memory that recall patterns stored in the couplings of an Ising model. Stored memories are conventionally accessed as fixed points in the network dynamics that correspond to energetic minima of the spin state. We show that memories stored in a Hopfield network may also be recalled by energy minimization using adiabatic quantum optimization (AQO). Numerical simulations of the underlying quantum dynamics allow us to quantify AQO recall accuracy with respect to the number of stored memories and noise in the input key. We investigate AQO performance with respect to how memories are stored in the Ising model according to different learning rules. Our results demonstrate that AQO recall accuracy varies strongly with learning rule, a behavior that is attributed to differences in energy landscapes. Consequently, learning rules offer a family of methods for programming adiabatic quantum optimization that we expect to be useful for characterizing AQO performance.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2022

Data-Driven Generic Turbines for Distributed Wind Modeling, Optimization, and Economic Studies

As distributed energy resources (DER) become less expensive and more popular, utilities, project developers, and customers have an increasing need to model the performance of existing and proposed DER systems. Distributed wind has been shown to have widespread economic potential but is often represented by a simplified model in - or excluded from - DER modeling tools and studies. There is often no economic imperative to extend models and studies to give full consideration to distributed wind. We present a set of data-driven generic turbines derived from 16 years of annual distributed wind market survey data. The proposed methodology can be used to derive generic turbines from separate or updated data sets. Finally, a mixed-integer linear programming approach to optimal distributed wind project sizing is used to demonstrate the generic turbine models. Combined, these models and methods can reduce barriers to considering distributed wind in modeling tools and studies.

Reiman, Andrew P.↗
DOE OSTI2023

A game-theoretic approach to nuclear fuel cycle transition analysis under uncertainty

We present a novel methodology for optimizing nuclear fuel cycle transitions that incorporates a game-theoretic approach and captures interactions among multiple decision makers. The methodology is demonstrated using a two-person sequential game with uncertainty, where the two players represent a policy maker and an electric utility company, though the method generalizes to any number and type of individual decision making entities. Coupled with a sophisticated nuclear fuel cycle simulator, rich transition scenarios may be analyzed to identify robust transition strategies. These strategies explicitly treat uncertainties using a stochastic programming approach, devising optimal near-term hedging strategies that simultaneously consider all possible states of the world, maintaining flexibility to allow for intelligent recourse decisions once uncertainties are resolved. In the demonstration game, reactor technology and fuel cycle scheme adopted by the electric utility are shown to depend on both the policy maker’s decisions and the distributions over uncertain technological and economic outcomes.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
DOE OSTI2022

Dispatch Optimization, System Design and Cost Benefit Analysis of a Nuclear Reactor with Molten Salt Thermal Storage

Variable renewable energy availability has increased the volatility in energy prices in most markets. Nuclear power plants, with a large ratio of capital to variable costs, have historically operated as base load energy suppliers but the need for more flexible operation is increasing. We simulate the techno-economic performance of a 950 MWt nuclear power plant, based on the Westinghouse lead-cooled fast reactor, coupled with molten salt thermal storage as a method for flexible energy dispatch. We use the System Advisor Model to model the nuclear reactor thermal power input and power cycle operating modes. We combine this robust engineering model with a mixed-integer linear program model for optimized dispatch scheduling. We then simulate the coupled nuclear and thermal storage system under different market scenarios with varying price volatility. We find that the coupled plant outperforms the base plant under markets where energy price peaks fluctuate by a factor of two or more about the mean price. We show that a calculated power purchase agreement price for the plant improves by up to 10% when operating under California energy market conditions. Sensitivity analysis on the thermal storage cost shows that the optimal design remains unchanged even when doubling costs.

25 ENERGY STORAGE↗
DOE OSTI2020

Scenario Grouping and Decomposition Algorithms for Chance-Constrained Programs

A lower bound for a finite-scenario-based chance-constrained program is the quantile value corresponding to the sorted optimal objective values of scenario subproblems. This quantile bound can be improved by grouping subsets of scenarios at the expense of solving larger subproblems. The quality of the bound depends on how the scenarios are grouped. In this paper, we formulate a mixed-integer bilevel program that optimally groups scenarios to tighten the quantile bounds. For general chance-constrained programs, we propose a branch-and-cut algorithm to optimize the bilevel program, and for chance-constrained linear programs, a mixed-integer linear-programming reformulation is derived. Here, we also propose several heuristics for grouping similar or dissimilar scenarios. Our computational results demonstrate that optimal grouping bounds are much tighter than heuristic bounds, resulting in smaller root-node gaps and better performance of scenario decomposition for solving chance-constrained 0-1 programs. Also, the optimal grouping bounds can be greatly strengthened using larger group size.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2015

Comparison of Calibration of Sensors Used for the Quantification of Nuclear Energy Rate Deposition

This present work deals with a collaborative program called GAMMA-MAJOR 'Development and qualification of a deterministic scheme for the evaluation of GAMMA heating in MTR reactors with exploitation as example MARIA reactor and Jules Horowitz Reactor' between the National Centre for Nuclear Research of Poland, the French Atomic Energy and Alternative Energies Commission and Aix Marseille University. One of main objectives of this program is to optimize the nuclear heating quantification thanks to calculation validated from experimental measurements of radiation energy deposition carried out in irradiation reactors. The quantification of the nuclear heating is a key data especially for the thermal, mechanical design and sizing of irradiation experimental devices in specific irradiated conditions and locations. The determination of this data is usually performed by differential calorimeters and gamma thermometers such as used in the experimental multi-sensors device called CARMEN 'Calorimetric en Reacteur et Mesures des Emissions Nucleaires'. In the framework of the GAMMA-MAJOR program a new calorimeter was designed for the nuclear energy deposition quantification. It corresponds to a single-cell calorimeter and it is called KAROLINA. This calorimeter was recently tested during an irradiation campaign inside MARIA reactor in Poland. This new single-cell calorimeter differs from previous CALMOS or CARMEN type differential calorimeters according to three main points: its geometry, its preliminary out-of-pile calibration, and its in-pile measurement method. The differential calorimeter, which is made of two identical cells containing heaters, has a calibration method based on the use of steady thermal states reached by simulating the nuclear energy deposition into the calorimeter sample by Joule effect; whereas the single-cell calorimeter, which has no heater, is calibrated by using the transient thermal response of the sensor (heating and cooling steps). The paper will concern these two kinds of calorimetric sensors. It will focus in particular on studies on their out-of-pile calibrations. Firstly, the characteristics of the sensor designs will be detailed (such as geometry, dimension, material sample, assembly, instrumentation). Then the out-of-pile calibration methods will be described. Furthermore numerical results obtained thanks to 2D axisymmetrical thermal simulations (Finite Element Method, CAST3M) and experimental results will be presented for each sensor. A comparison of the two different thermal sensor behaviours will be realized. To conclude a discussion of the advantages and the drawbacks of each sensor will be performed especially regarding measurement methods. (authors)

21 SPECIFIC NUCLEAR REACTORS AND ASSOCIATED PLANTS↗
DOE OSTI2023

Optimized thermodynamic properties of REE aqueous species (REE 3+ and REEOH 2+ ) and experimental database for modeling the solubility of REE phosphate minerals (monazite, xenotime, and rhabdophane) from 25 to 300 °C

Rare earth elements (REE) are critical elements found in monazite, xenotime, and hydrated REE phosphates which typically form in hydrothermal mineral deposits. Accurate predictions of the solubility of these REE phosphates and the speciation of REE in aqueous fluids are both key to understanding the controls on the transport, fractionation, and deposition of REE in natural systems. Previous monazite and xenotime solubility experiments indicate the presence of large discrepancies between experimentally derived solubility constants versus calculated solubilities by combining different data sources for the thermodynamic properties of minerals and aqueous species at hydrothermal conditions. In this study, these discrepancies were resolved by using the program GEMSFITS to optimize the standard partial molal Gibbs energy of formation (Δ f G° 298 ) of REE aqueous species (REE 3+ and REE hydroxyl complexes) at 298.15 K and 1 bar while keeping the thermodynamic properties fixed for the REE phosphates. A comprehensive experimental database was compiled using solubility data available between 25 and 300 °C. The latter permits conducting thermodynamic parameter optimization of Δ f G° 298 for REE aqueous species. Optimal matching of the rhabdophane solubility data between 25 and 100 °C requires modifying the Δ f G° 298 values of REE 3+ by 1–6 kJ/mol, whereas matching of the monazite solubility data between 100 and 300 °C requires modifying the Δ f G° 298 values of both REE 3+ and REEOH 2+ by ~15–31 kJ/mol and ~2–10 kJ/mol, respectively. For xenotime, adjustments of Δ f G° 298 values by 1–26 kJ/mol are only necessary for the REE 3+ species. The optimizations indicate that the solubility of monazite in acidic solutions is controlled by the light (L)REE 3+ species at <150 °C and the LREEOH 2+ species at >150 °C, whereas the solubility of xenotime is controlled by the heavy (H)REE 3+ species between 25 and 300 °C. Based on the optimization results, we conclude that the revised Helgeson-Kirkham-Flowers equation of state does not reliably predict the thermodynamic properties of REE 3+ , REEOH 2+ , and likely other REE hydroxyl species at hydrothermal conditions. We therefore provide an experimental database (ThermoExp_REE) as a basic framework for future updates, extensions with other ligands, and optimizations as new experimental REE data become available. As a result, the optimized thermodynamic properties of aqueous species and minerals are available open access to accurately predict the solubility of REE phosphates in fluid-rock systems.

58 GEOSCIENCES↗
DOE OSTI2011

Final Technical Report on STTR Project DE-FG02-06ER86281 Particle Tracking in Matter-Dominated Beam Lines (G4beamline)

This project has been for software development of the G4beamline [1] program, which is a particle-tracking simulation program based on the Geant4 toolkit [2], optimized for beam lines. This program can perform more realistic simulations than most alternatives, while being significantly easier to use by physicists. This project has fostered the general acceptance of G4beamline within the muon community, and has assisted in expanding its role outside that community. During this project, the G4beamline user community has grown from about a half-dozen users to more than 200 users around the world. This project also validated our business decision to keep G4beamline an open-source program, judging that an STTR project would provide more development resources than would marketing and selling the program. G4beamline is freely available to the physics community, and has been well validated against experiments and other codes within its domain. Muons, Inc. continues to support and develop the program, and a major part of the company's continued success and growth is directly related to our expertise in applying this program to interesting applications.

43 PARTICLE ACCELERATORS↗
DOE OSTI2013

Optimal planning for the sustainable utilization of municipal solid waste

Highlights: • An optimization approach for the sustainable management of municipal solid waste is proposed. • The proposed model optimizes the entire supply chain network of a distributed system. • A case study for the sustainable waste management in the central-west part of Mexico is presented. • Results shows different interesting solutions for the case study presented. - Abstract: The increasing generation of municipal solid waste (MSW) is a major problem particularly for large urban areas with insufficient landfill capacities and inefficient waste management systems. Several options associated to the supply chain for implementing a MSW management system are available, however to determine the optimal solution several technical, economic, environmental and social aspects must be considered. Therefore, this paper proposes a mathematical programming model for the optimal planning of the supply chain associated to the MSW management system to maximize the economic benefit while accounting for technical and environmental issues. The optimization model simultaneously selects the processing technologies and their location, the distribution of wastes from cities as well as the distribution of products to markets. The problem was formulated as a multi-objective mixed-integer linear programing problem to maximize the profit of the supply chain and the amount of recycled wastes, where the results are showed through Pareto curves that tradeoff economic and environmental aspects. The proposed approach is applied to a case study for the west-central part of Mexico to consider the integration of MSW from several cities to yield useful products. The results show that an integrated utilization of MSW can provide economic, environmental and social benefits.

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗
DOE OSTI2024

Research Highlight - Dispatch Optimization, System Design and Cost Benefit Analysis of a Nuclear Reactor with Molten Salt Thermal Storage

We highlight our work from our previous publication titled "Dispatch Optimization, System Design, and Cost Benefit Analysis of a Nuclear Reactor with Molten Salt Thermal Storage" published in MDPI Energies 2022 (doi.org/10.3390/en15103599). Variable renewable energy availability has increased the volatility in energy prices in most markets. Nuclear power plants, with a large ratio of capital to variable costs, have historically operated as base load energy suppliers but the need for more flexible operation is increasing. We simulate the techno-economic performance of a 950 MWt nuclear power plant, based on the Westinghouse lead-cooled fast reactor, coupled with molten salt thermal storage as a method for flexible energy dispatch. We use the System Advisor Model to model the nuclear reactor thermal power input and power cycle operating modes. We combine this robust engineering model with a mixed-integer linear program model for optimized dispatch scheduling. We then simulate the coupled nuclear and thermal storage system under different market scenarios with varying price volatility. We find that the coupled plant outperforms the base plant under markets where energy price peaks fluctuate by a factor of two or more about the mean price. We show that a calculated power purchase agreement price for the plant improves by up to 10% when operating under California energy market conditions. Sensitivity analysis on the thermal storage cost shows that the optimal design remains unchanged even when doubling costs.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2020

An agent-based approach to study the diffusion rate and the effect of policies on joint placement of photovoltaic panels and green roof under climate change uncertainty

As two of the highest trending green technologies, photovoltaic panels and green roofs are proven to be effective practices for energy generation and energy saving. The achievable impact from the widespread installation of such technologies is, however, not clearly established. This is mainly because the degree of this impact highly depends on the inherently uncertain environmental and climate factors, as well as the unknown adoption rates of these technologies, which in turn depend on different characteristics of decision makers and interactions among them. To that end, this study aims to investigate the diffusion rate of these green technologies under uncertainties caused by climate change, characteristics of adopters, and their interactions. An integrated framework is developed to capture the interplay between financial and attitudinal aspects, as well as the uncertainties due to both the stochastic nature of system parameters and the interactions among agents involving human beings. Specifically, this framework consists of a integer programming model to optimize the green roof and/or photovoltaic panel installation settings for a given building under climate change uncertainty, and an agent-based model to factor in the role of human behavior and interactions. A case study for the city of Knoxville, TN, is presented to evaluate the effects of different policies on the diffusion rate of the green technologies of interest. The results show that the affordability of green technologies and public awareness are the key drivers of the adoption of these technologies, which highlight the important role of the decision makers in impacting the diffusion rate.

14 SOLAR ENERGY↗
DOE OSTI2011

The Structural Role of Zr within Alkali Borosilicate Glasses for Nuclear Waste Immobilisation

Zirconium is a key constituent element of High Level nuclear Waste (HLW) glasses, occurring both as a fission product and a fuel cladding component. As part of a wider research program aimed at optimizing the solubility of zirconium in HLW glasses, we have investigated the structural chemistry of zirconium in such materials using X-ray Absorption Spectroscopy (XAS). Zirconium K-edge XAS data were acquired from several inactive simulant and simplified waste glass compositions, including a specimen of blended Magnox/UO{sub 2} fuel waste glass. These data demonstrate that zirconium is immobilized as (octahedral) six-fold coordinate ZrO{sub 6} species in these glasses, with a Zr-O contact distance of 2.09 {angstrom}. The next nearest neighbors of the Zr species are Si at 3.42 {angstrom} and possibly Na at 3.44 {angstrom}, no next nearest neighbor Zr could be resolved.

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

Performance Refactoring of Instrumentation, Measurement, and Analysis Technologies for Petascale Computing. The PRIMA Project

The growing number of cores provided by today’s high-­end computing systems present substantial challenges to application developers in their pursuit of parallel efficiency. To find the most effective optimization strategy, application developers need insight into the runtime behavior of their code. The University of Oregon (UO) and the Juelich Supercomputing Centre of Forschungszentrum Juelich (FZJ) develop the performance analysis tools TAU and Scalasca, respectively, which allow high-­performance computing (HPC) users to collect and analyze relevant performance data – even at very large scales. TAU and Scalasca are considered among the most advanced parallel performance systems available, and are used extensively across HPC centers in the U.S., Germany, and around the world. The TAU and Scalasca groups share a heritage of parallel performance tool research and partnership throughout the past fifteen years. Indeed, the close interactions of the two groups resulted in a cross-­fertilization of tool ideas and technologies that pushed TAU and Scalasca to what they are today. It also produced two performance systems with an increasing degree of functional overlap. While each tool has its specific analysis focus, the tools were implementing measurement infrastructures that were substantially similar. Because each tool provides complementary performance analysis, sharing of measurement results is valuable to provide the user with more facets to understand performance behavior. However, each measurement system was producing performance data in different formats, requiring data interoperability tools to be created. A common measurement and instrumentation system was needed to more closely integrate TAU and Scalasca and to avoid the duplication of development and maintenance effort. The PRIMA (Performance Refactoring of Instrumentation, Measurement, and Analysis) project was proposed over three years ago as a joint international effort between UO and FZJ to accomplish these objectives: (1) refactor TAU and Scalasca performance system components for core code sharing and (2) integrate TAU and Scalasca functionality through data interfaces, formats, and utilities. As presented in this report, the project has completed these goals. In addition to shared technical advances, the groups have worked to engage with users through application performance engineering and tools training. In this regard, the project benefits from the close interactions the teams have with national laboratories in the United States and Germany. We have also sought to enhance our interactions through joint tutorials and outreach. UO has become a member of the Virtual Institute of High-­Productivity Supercomputing (VI-­HPS) established by the Helmholtz Association of German Research Centres as a center of excellence, focusing on HPC tools for diagnosing programming errors and optimizing performance. UO and FZJ have conducted several VI-­HPS training activities together within the past three years.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2025

Data-Driven Energy Resilience Assessment and Enhancement in Urban Communities: A Case Study in Detroit

This paper presents a data-driven framework for assessing and enhancing energy resilience in urban communities. The resilience assessment is based on two datasets: 1) annual aggregated power outage data and 2) 15-minute interval outage data. High-impact, low-probability (HILP) events are identified within these datasets to evaluate community resilience under extreme conditions. To enhance resilience, an optimization framework utilizing mixed integer linear programming is developed to determine the optimal sizing and placement of solar photovoltaic (PV) systems and battery energy storage systems (BESS). This method offers a cost-effective and practical solution for improving energy resilience in vulnerable communities. Furthermore, a case study of the City of Detroit in Michigan demonstrates the effectiveness of the framework through simulation and validation.

Energy resilience assessment↗
DOE OSTI2022

Sustainable hydrogen manufacturing via renewable-integrated intensified process for refueling stations

The widescale consumer adoption of hydrogen fuel cell electric vehicles (HFCEVs) is currently hindered by the high cost of small-scale hydrogen generation and the lack of extensive hydrogen refueling infrastructure. Natural gas-based hydrogen is cheaper when produced in large volumes but is also associated with high CO 2 emissions. To counter these challenges, we propose a hybrid approach where both natural gas and renewables are integrated in a synergistic manner using a dynamic process intensification technology that can be deployed on-site for meeting local demands of refueling stations. The technology is based on sorption enhanced steam methane reforming (SE-SMR) that utilizes a combination of reaction with in-situ CO 2 adsorption for enhancing process modularity, productivity and efficiency thereby outperforming conventional SMR at small scale. We develop a mixed integer linear programming (MILP)-based optimization framework for simultaneous design and scheduling of the SE-SMR process. The simultaneous optimization provides a synergistic combination whereby the renewables allow sustainable hydrogen manufacturing and the dynamic SE-SMR allows optimal use of the intermittency of the renewables. The U.S. nationwide analysis indicates that for futuristic renewable prices and a hydrogen production capacity of 2 ton/day, hydrogen can be produced at 50% less cost compared to the current cost of small-scale hydrogen generation. Finally, the city-wise analysis with varying hydrogen demand shows that even with just 5% HFCEV market penetration level, hydrogen production cost less than $3/kg can be obtained at small scales across the United States with even cheaper hydrogen for large cities.

08 HYDROGEN↗
DOE OSTI2024

HHL algorithm with mapping function and enhanced sampling for model predictive control in microgrids

Here, this paper presents a refined quantum Harrow Hassidim Lloyd (HHL) algorithm for microgrid control. The first novelty of the developed method is that a mapping shift function enables the original HHL algorithm to handle general linear equations with non-singular and indefinite matrix. Second, a method of Matrix Extension for Amplifying Sampling Probabilities of Intended Solution (ME-ASPI) is proposed to design the reformulated linear algebraic equations, allowing for improved sampling efficiency of the quantum tomography in the refined HHL algorithm. Then, we applied the method to solve the model predictive control (MPC) problem in nonlinear dynamical microgrids. Specifically, with the ME-ASPI method, the refined HHL algorithm can effectively obtain the intended partial optimal control inputs for MPC. The optimization of quadratic programming problem in each time step of MPC is transformed into a linear system problem, which is addressed by the proposed quantum solver through using only partial information, with the time complexity improved from $\mathscr{O}(\mathscr{N}^{2.37286})$ classically to $\mathscr{O}(\mathscr{N}^{2} log \mathscr{N}$ x $p$ log $p)$ in quantum. Numerical examples have validated the effectiveness of the refined HHL algorithm with the proposed mapping function and the ME-ASPI method. By leveraging quantum properties, the proposed method provides a hybrid quantum–classical framework for microgrid control. This generic method can also potentially tackle many other challenges in analyzing and controlling general complex engineered systems.

24 POWER TRANSMISSION AND DISTRIBUTION↗