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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 163 records · Page 9
All sourcesNASA NTRSDOE OSTI
DOE OSTI2023

Designing an Optimal LSST Deep Drilling Program for Cosmology with Type Ia Supernovae

The Vera C. Rubin Observatory's Legacy Survey of Space and Time (LSST) is forecast to collect a large sample of Type Ia supernovae (SNe Ia) expected to be instrumental in unveiling the nature of dark energy. The feat, however, requires accurately measuring the two components of the Hubble diagram, distance modulus and redshift. Distance is estimated from SN Ia parameters extracted from light-curve fits, where the average quality of light curves is primarily driven by survey parameters. An optimal observing strategy is thus critical for measuring cosmological parameters with high accuracy. We present in this paper a three-stage analysis to assess the impact of the deep drilling (DD) strategy parameters on three critical aspects of the survey: redshift completeness, the number of well-measured SNe Ia, and cosmological measurements. We demonstrate that the current DD survey plans (internal LSST simulations) are characterized by a low completeness (z ~ 0.55–0.65), and irregular and low cadences (several days), which dramatically decrease the size of the well-measured SN Ia sample. We propose a method providing the number of visits required to reach higher redshifts. We use the results to design a set of optimized DD surveys for SN Ia cosmology taking full advantage of spectroscopic resources for host galaxy redshift measurements. The most accurate cosmological measurements are achieved with deep rolling surveys characterized by a high cadence (1 day), a rolling strategy (at least two seasons of observation per field), and ultradeep (z ≳ 0.8) and deep (z ≳ 0.6) fields. A deterministic scheduler including a gap recovery mechanism is critical to achieving a high-quality DD survey.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2016

Modeling Bilevel Programs in Pyomo

We describe new capabilities for modeling bilevel programs within the Pyomo modeling software. These capabilities include new modeling components that represent subproblems, modeling transformations for re-expressing models with bilevel structure in other forms, and optimize bilevel programs with meta-solvers that apply transformations and then perform op- timization on the resulting model. We illustrate the breadth of Pyomo's modeling capabilities for bilevel programs, and we describe how Pyomo's meta-solvers can perform local and global optimization of bilevel programs.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2017

Optimizing Biorefinery Design and Operations via Linear Programming Models

The ability to assess and optimize economics of biomass resource utilization for the production of fuels, chemicals and power is essential for the ultimate success of a bioenergy industry. The team of authors, consisting of members from the National Renewable Energy Laboratory (NREL) and the Idaho National Laboratory (INL), has developed simple biorefinery linear programming (LP) models to enable the optimization of theoretical or existing biorefineries. The goal of this analysis is to demonstrate how such models can benefit the developing biorefining industry. It focuses on a theoretical multi-pathway, thermochemical biorefinery configuration and demonstrates how the biorefinery can use LP models for operations planning and optimization in comparable ways to the petroleum refining industry. Using LP modeling tools developed under U.S. Department of Energy's Bioenergy Technologies Office (DOE-BETO) funded efforts, the authors investigate optimization challenges for the theoretical biorefineries such as (1) optimal feedstock slate based on available biomass and prices, (2) breakeven price analysis for available feedstocks, (3) impact analysis for changes in feedstock costs and product prices, (4) optimal biorefinery operations during unit shutdowns / turnarounds, and (5) incentives for increased processing capacity. These biorefinery examples are comparable to crude oil purchasing and operational optimization studies that petroleum refiners perform routinely using LPs and other optimization models. It is important to note that the analyses presented in this article are strictly theoretical and they are not based on current energy market prices. The pricing structure assigned for this demonstrative analysis is consistent with $4 per gallon gasoline, which clearly assumes an economic environment that would favor the construction and operation of biorefineries. The analysis approach and examples provide valuable insights into the usefulness of analysis tools for maximizing the potential benefits of biomass utilization for production of fuels, chemicals and power.

09 BIOMASS FUELS↗
DOE OSTI2020

Quantum Fast Circuit Optimizer (qFactor) v1

Quantum programs are represented as circuits, or sequences of gates. A common problem during quantum compilation to find a circuit that implements a target program. This is an optimizer, it optimizes a circuit's distance to the target by solving for the best fit gates. It is not a standard optimizer, it only optimizes circuits. It is not a compiler or synthesizer, it cannot build circuits, but given a circuit structure, it will solve for the gates. Quantum compilers (qsearch, qfast) use standard numerical optimizers. This has advantages over those general-purpose optimizers since it is domain-specific.

Younis, Ed↗
DOE OSTI2011

Material Performance of Fully-Ceramic Micro-Encapsulated Fuel under Selected LWR Design Basis Scenarios (Final Report)

The extension to LWRs of the use of Deep-Burn coated particle fuel envisaged for HTRs has been investigated. TRISO coated fuel particles are used in Fully-Ceramic Microencapsulated (FCM) fuel within a SiC matrix rather than the graphite of HTRs. TRISO particles are well characterized for uranium-fueled HTRs. However, operating conditions of LWRs are different from those of HTRs (temperature, neutron energy spectrum, fast fluence levels, power density). Furthermore, the time scales of transient core behavior during accidents are usually much shorter and thus more severe in LWRs. The PASTA code was updated for analysis of stresses in coated particle FCM fuel. The code extensions enable the automatic use of neutronic data (burnup, fast fluence as a function of irradiation time) obtained using the DRAGON neutronics code. An input option for automatic evaluation of temperature rise during anticipated transients was also added. A new thermal model for FCM was incorporated into the code; so-were updated correlations (for pyrocarbon coating layers) suitable to estimating dimensional changes at the high fluence levels attained in LWR DB fuel. Analyses of the FCM fuel using the updated PASTA code under nominal and accident conditions show: (1) Stress levels in SiC-coatings are low for low fission gas release (FGR) fractions of several percent, as based on data of fission gas diffusion in UO 2 kernels. However, the high burnup level of LWR-DB fuel implies that the FGR fraction is more likely to be in the range of 50-100%, similar to Inert Matrix Fuels (IMFs). For this range the predicted stresses and failure fractions of the SiC coating are high for the reference particle design (500 μm kernel diameter, 100 μm buffer, 35 μm IPyC, 35 μm SiC, 40 μm OPyC). A conservative case, assuming 100% FGR, 900K fuel temperature and 705 MWd/kg (77% FIMA) fuel burnup, results in a 8.0 x 10 -2 failure probability. For a 'best-estimate' FGR fraction of 50% and a more modest burnup target level of 500 MWd/kg ,the failure probability drops below 2.0 x 10 -5 , the typical performance of TRISO fuel made under the German HTR research program. An optimization study on particle design shows improved performance if the buffer size is increased from 100 to 120 μm while reducing the OPyC layer. The presence of the latter layer does not provide much benefit at high burnup levels (and fast fluence levels). Normally the shrinkage of the OPyC would result in a beneficial compressive force on the SiC coating. However, at high fluence levels the shrinkage is expected to turn into swelling, resulting in the opposite effect. However, this situation is different when the SiC-matrix, in which the particles are embedded, is also considered: the OPyC swelling can result in a beneficial compressive force on the SiC coating since outward displacement of the OPyC outer surface is inhibited by the presence of the also-swelling SiC matrix. Taking some credit for this effect by adopting a 5 μm SiC-matrix layer, the optimized particle (100 μm buffer and 10 μm OPyC), gives a failure probability of 1.9 x 10 -4 for conservative conditions. During a LOCA transient, assuming core re-flood in 30 seconds, the temperature of the coated particle can be expected to be about 200K higher than nominal temperature (900K). For this event the particle failure fraction for a conservative case is 1.0 x 10 -2 , for the optimized particle design. For a FGR-fraction of 50% this value reduces to 6.4 x 10 -4 .

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
DOE OSTI2022

Optimization control technology for building energy conservation

A simulation processor generates and stores a simulation model based on conditions associated with a physical structure, such as a building. A neural network processor implements a neural network, having an input layer coupled to receive sensor data from the structure and having an output layer coupled to supply control signals to the at least one electrically operable environmental control device. The neural network is trained using the simulation model. A particle swarm optimization processor programmed to receive the simulation results and perform particle swarm optimization, ascertains optimal parameters for controlling the at least one electrically operable environmental control device and supplies these optimal parameters to the neural network processor. The neural network processor uses the optimal parameters supplied by the particle swarm optimization processor to further train the neural network.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2021

Development and Evaluation of Velocity Predictive Optimal Energy Management Strategies in Intelligent and Connected Hybrid Electric Vehicles

In this study, a thorough and definitive evaluation of Predictive Optimal Energy Management Strategy (POEMS) applications in connected vehicles using 10 to 20 s predicted velocity is conducted for a Hybrid Electric Vehicle (HEV). The presented methodology includes synchronous datasets gathered in Fort Collins, Colorado using a test vehicle equipped with sensors to measure ego vehicle position and motion and that of surrounding objects as well as receive Infrastructure to Vehicle (I2V) information. These datasets are utilized to compare the effect of different signal categories on prediction fidelity for different prediction horizons within a POEMS framework. Multiple artificial intelligence (AI) and machine learning (ML) algorithms use the collected data to output future vehicle velocity prediction models. The effects of different combinations of signals and different models on prediction fidelity in various prediction windows are explored. All of these combinations are ultimately addressed where the rubber meets the road: fuel economy (FE) enabled from POEMS. FE optimization is performed using Model Predictive Control (MPC) with a Dynamic Programming (DP) optimizer. FE improvements from MPC control at various prediction time horizons are compared to that of full-cycle DP. All FE results are determined using high-fidelity simulations of an Autonomie 2010 Toyota Prius model. The full-cycle DP POEMS provides the theoretical upper limit on fuel economy (FE) improvement achievable with POEMS but is not currently practical for real-world implementation. Perfect prediction MPC (PP-MPC) represents the upper limit of FE improvement practically achievable with POEMS. Real-Prediction MPC (RP-MPC) can provide nearly equivalent FE improvement when used with high-fidelity predictions. Constant-Velocity MPC (CV-MPC) uses a constant speed prediction and serves as a “null” POEMS. Results showed that RP-MPC, enabled by high-fidelity ego future speed prediction, led to significant FE improvement over baseline nearly matching that of PP-MPC.

33 ADVANCED PROPULSION SYSTEMS↗
DOE OSTI2017

Optimal Allocation of Static Var Compensator via Mixed Integer Conic Programming

Shunt FACTS devices, such as, a Static Var Compensator (SVC), are capable of providing local reactive power compensation. They are widely used in the network to reduce the real power loss and improve the voltage profile. This paper proposes a planning model based on mixed integer conic programming (MICP) to optimally allocate SVCs in the transmission network considering load uncertainty. The load uncertainties are represented by a number of scenarios. Reformulation and linearization techniques are utilized to transform the original non-convex model into a convex second order cone programming (SOCP) model. Numerical case studies based on the IEEE 30-bus system demonstrate the effectiveness of the proposed planning model.

Zhang, Xiaohu↗
DOE OSTI2023

Applications of the Dulmage–Mendelsohn decomposition for debugging nonlinear optimization problems

Nonlinear modeling and optimization is a valuable tool for aiding decisions by engineering practitioners, but programming an optimization problem based on a complex electrical, mechanical, or chemical process is a time-consuming and error-prone activity. Therefore, there is a need for model analysis and debugging tools that can detect and diagnose modeling errors. One such tool is the Dulmage–Mendelsohn decomposition, which identifies structurally under- and over-determined subsets in systems of equations and variables by partitioning the bipartite graph of the system. This work provides the necessary background to understand the Dulmage–Mendelsohn decomposition and its application to the analysis of nonlinear optimization problems, demonstrates its use in diagnosing a variety of modeling errors, and introduces software implementations for analyzing nonlinear optimization problems in the Pyomo and JuMP algebraic modeling languages.

42 ENGINEERING↗
DOE OSTI2023

PRO-X Transportation Global Landscape

The Proliferation Resistance Optimization (PRO-X) program is actively supporting the design of new-build nuclear systems by identifying intrinsic characteristics or design choices to minimize the potential for diversion or production of weapons-usable nuclear material. The PRO-X program looks to optimize the safety, security, and performance of the fuel cycle infrastructure for a wide array of reactor types, including research reactors, small modular reactors (SMRs), and large advanced reactors (ARs).

45 MILITARY TECHNOLOGY, WEAPONRY, AND NATIONAL DEF↗
DOE OSTI2012

La Hague Legacy Waste Recovery Program: Scope, Progress and Issues -12080

A significant inventory of process waste of varying natures and quantities has been generated during the thirty years of operation of UP2 400 facility on the site of La Hague, France. The retrieval, packaging and final storage of such an inventory has never been achieved before in France and thus requires the design and qualification of new processes, equipment, and waste packages. Following AREVA strategic decisions and French safety authority requirements, the legacy waste program has begun around the year 2000 and is scheduled to be completed around the year 2025. It is under the responsibility of AREVA Site Value Development Project teams. For each category of waste to be recovered, AREVA teams conducted detailed investigations, defined recovery modes, treatment processes, as well as final waste package forms, which they subsequently submitted to French safety and waste management authorities. A Task force initiative was subsequently launched to optimize the program cost and scenario, and lead to an optimization of about 15% of the entire program. The qualification of processes and waste packages required a significant amount of research and development which is now well under way for processes, and scheduled to be completed in 2015. Preparation work has begun on several installations to clear space for the construction of future retrieval facilities, scheduled to begin in the coming three years. La Hague Legacy waste retrieval program represents a significant challenge in the sense that it covers a significant variety and quantity of waste needing recovery and reconditioning, with tight financial objectives and a binding recovery schedule. During the past five years, AREVA SVD successfully conducted design, research, development, and qualification activities which lead to the definition of qualified processes and waste packages for each retrieval program. Preparation work and supplier consultations are now on-going, in order to meet our objectives of beginning retrieval operations in compliance with our commitments to the safety authorities, in 2015 and 2016. (author)

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗