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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 721 records · Page 40
All sourcesNASA NTRSDOE OSTI
DOE OSTI2018

Optimal blending management of biomass resources used for biochemical conversion

This research develops an optimization model to describe the tradeoff among blend components in the least-cost biomass blend, based on resource availability, quality requirements, and logistics cost for a biochemical conversion. A mixed-integer linear programming model is developed to determine the least-cost blend from a set of candidate feedstocks. A case study – based on a biorefinery located in western Kansas that uses three-pass corn stover, two-pass corn stover, switchgrass, miscanthus, and municipal solid waste fractions to meet biochemical conversion specifications and feedstock demand – shows that the delivered cost of an optimal blend that meets carbohydrate and ash specifications is 12.12% higher than the delivered cost of optimal blend that meets a carbohydrate specification only. The results indicate that a least-cost blend that meets both carbohydrate and ash specifications consists of miscanthus (48.2%) and switchgrass (29.4%) whereas the least-cost blend meeting carbohydrate specification only comprises three-pass corn stover (55.4%) and two-pass corn stover (20.4%). Here an optimal blend uses a low-cost municipal solid waste fraction in all cases, implying that blending could be a potential strategy to reduce delivered feedstock cost.

09 BIOMASS FUELS↗
DOE OSTI2016

Contingency Contractor Optimization Phase 3 Sustainment Platform Requirements - Contingency Contractor Optimization Tool - Prototype

Sandia National Laboratories (Sandia) is in Phase 3 Sustainment of development of a prototype tool, currently referred to as the Contingency Contractor Optimization Tool - Prototype (CCOTP), under the direction of OSD Program Support. CCOT-P is intended to help provide senior Department of Defense (DoD) leaders with comprehensive insight into the global availability, readiness and capabilities of the Total Force Mix. The CCOT-P will allow senior decision makers to quickly and accurately assess the impacts, risks and mitigating strategies for proposed changes to force/capabilities assignments, apportionments and allocations options, focusing specifically on contingency contractor planning. During Phase 2 of the program, conducted during fiscal year 2012, Sandia developed an electronic storyboard prototype of the Contingency Contractor Optimization Tool that can be used for communication with senior decision makers and other Operational Contract Support (OCS) stakeholders. Phase 3 used feedback from demonstrations of the electronic storyboard prototype to develop an engineering prototype for planners to evaluate. Sandia worked with the DoD and Joint Chiefs of Staff strategic planning community to get feedback and input to ensure that the engineering prototype was developed to closely align with future planning needs. The intended deployment environment was also a key consideration as this prototype was developed. Initial release of the engineering prototype was done on servers at Sandia in the middle of Phase 3. In 2013, the tool was installed on a production pilot server managed by the OUSD(AT&L) eBusiness Center. The purpose of this document is to specify the CCOT-P engineering prototype platform requirements as of May 2016. Sandia developed the CCOT-P engineering prototype using common technologies to minimize the likelihood of deployment issues. CCOT-P engineering prototype was architected and designed to be as independent as possible of the major deployment components such as the server hardware, the server operating system, the database, and the web server. This document describes the platform requirements, the architecture, and the implementation details of the CCOT-P engineering prototype.

42 ENGINEERING↗
DOE OSTI2016

Operational Excellence through Schedule Optimization and Production Simulation of Application Specific Integrated Circuits.

Upcoming weapon programs require an aggressive increase in Application Specific Integrated Circuit (ASIC) production at Sandia National Laboratories (SNL). SNL has developed unique modeling and optimization tools that have been instrumental in improving ASIC production productivity and efficiency, identifying optimal operational and tactical execution plans under resource constraints, and providing confidence in successful mission execution. With ten products and unprecedented levels of demand, a single set of shared resources, highly variable processes, and the need for external supplier task synchronization, scheduling is an integral part of successful manufacturing. The scheduler uses an iterative multi-objective genetic algorithm and a multi-dimensional performance evaluator. Schedule feasibility is assessed using a discrete event simulation (DES) that incorporates operational uncertainty, variability, and resource availability. The tools provide rapid scenario assessments and responses to variances in the operational environment, and have been used to inform major equipment investments and workforce planning decisions in multiple SNL facilities.

45 MILITARY TECHNOLOGY, WEAPONRY, AND NATIONAL DEF↗
DOE OSTI2017

Roadmap to control HBV and HDV epidemics in China

Hepatitis B virus (HBV) is endemic in China. Almost 10% of HBV infected individuals are also infected with hepatitis D virus (HDV) which has a 5–10 times higher mortality rate than HBV mono-infection. The aim of this manuscript is to devise strategies that can not only control HBV infections but also HDV infections in China under the current health care budget in an optimal manner. Furthermore, using a mathematical model, an annual budget of 10 billion dollars was optimally allocated among five interventions namely, testing and HBV adult vaccination, treatment for mono-infected and dually-infected individuals, second line treatment for HBV mono-infections, and awareness programs. As a result, we determine that the optimal strategy is to test and treat both infections as early as possible while applying awareness programs at full intensity. Under this strategy, an additional 19.8 million HBV, 1.9 million HDV infections and 0.25 million lives will be saved over the next 10 years at a cost-savings of 79 billion dollars than performing no intervention. Introduction of second line treatment does not add a significant economic burden yet prevents 1.4 million new HBV infections and 15,000 new HDV infections. In conclusion, test and treatment programs are highly efficient in reducing HBV and HDV prevalence in the population. Under the current health budget in China, not only test and treat programs but awareness programs and second line treatment can also be implemented that minimizes prevalence and mortality, and maximizes economic benefits.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2022

Model-Based Framework to Optimize Charger Station Deployment for Battery Electric Vehicles

The development of battery electric vehicles (BEVs) is accelerating due to their environmental advantages over gasoline and diesel-powered vehicles, including a decrease in air pollution and an increase in energy efficiency. The deployment of charging infrastructure will need to increase to keep pace with demand, especially for large commercial vehicles for which few public chargers currently exist. In this paper, a new flexible framework is proposed for optimizing the placement of charging stations for BEVs, within which different physical models and optimization techniques may be used. Furthermore, a set of metrics is suggested to help enforce complex constraints and facilitate direct comparison between different optimization techniques. Unlike many existing charger placement techniques, the proposed method directly considers the historical driving patterns on a vehicle-by-vehicle basis, using transparent models to assess impacts of candidate charger placements, thus improving the explainability of the results. In the developed framework, modeled BEVs are first generated along the road network to mimic historical traffic data and are simulated traveling along a given route according to a simplified vehicle model. During the simulation, the charger placement problem is initially relaxed to allow vehicles to charge at any node along the road network, and vehicle states are tracked to assess areas of high charging demand. Charging stations are then placed based on the results of the relaxed simulation, and suggested placements are evaluated via road network simulation with fixed charger locations. This proposed framework is applied to a sample problem of placing charging stations along five major highway corridors for Class 8 over-the-road electric trucks. A novel mixed integer programming (MIP) formulation is proposed to optimize charger placements based upon the expected charging demand. Constraints were imposed on the final placement results to limit expected wait times at each station and ensure a minimum threshold of trucking routes are viable for BEVs. The results demonstrate the flexibility and potential effectiveness of the developed model-based framework for scalable charger station deployment.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗
DOE OSTI2018

Cooperative and Integrated Vehicle and Intersection Control for Energy Efficiency (CIVIC-E²)

Recent advances in connected vehicle technologies enable vehicles and signal controllers to cooperate and improve the traffic management at intersections. This paper explores the opportunity for cooperative and integrated vehicle and intersection control for energy efficiency (CIVIC-E 2 ) to contribute to a more sustainable transportation system. We propose a two-level approach that jointly optimizes the traffic signal timing and vehicles' approach speed, with the objective being to minimize total energy consumption for all vehicles passing through an isolated intersection. More specifically, at the intersection level, a dynamic programming algorithm is designed to find the optimal signal timing by explicitly considering the arrival time and energy profile of each vehicle. At the vehicle level, a model predictive control strategy is adopted to ensure that vehicles pass through the intersection in a timely fashion. Our simulation study has shown that the proposed CIVIC-E 2 system can significantly improve intersection performance under various traffic conditions. Furthermore, compared with conventional fixed-time and actuated signal control strategies, the proposed algorithm can reduce energy consumption and queue length by up to 31% and 95%, respectively.

33 ADVANCED PROPULSION SYSTEMS↗
DOE OSTI2024

Performance Improvements of the Griffin Solvers in FY24

The Griffin code is a MOOSE-based reactor physics application jointly developed by Idaho National Laboratory and Argonne National Laboratory under the Department of Energy Office of Nuclear Energy Nuclear Energy Advanced Modeling and Simulation Program. This fiscal year, we have made significant efforts to improve the performance of transport solver options and cross-section generation for the efficient use of Griffin in advanced reactor applications. For the HFEM-PN solver, the residual evaluations of HFEM kernels were optimized by utilizing the pre- computed averaged cross sections for individual elements. Numerical integration involving the evaluation of basis functions at quadrature points was bypassed by facilitating precomputed element mass matrices for response matrices. Red-black iterations were improved by introducing a new generalized minimum residual based solver. The memory usage of response matrix storage was significantly reduced by applying basis function rotations on interfaces and calculating volumetric odd-parity moments on the fly. Additionally, the adjoint flux and transient calculation capabilities of the HFEM-PN solver were successfully implemented and verified using the TWIGL benchmark problem. For the DFEM-SN solver, memory footprint and computation time were significantly reduced by not treating angular flux vectors as the MOOSE nonlinear system vectors. Specifically for IQS, scalar adjoint weighting was introduced to further eliminate angular adjoint flux storage in the MOOSE auxiliary system. It was demonstrated through the three-dimensional Advanced Burner Test Reactor core problem that the memory usage for transient calculations with the IQS method was reduced by over 7.5× compared to before the optimizations. For the self-shielding application programming interface, a new double-heterogeneity treatment method, named the Bell Function-Based Analytic Two-Region Slowing Down Method, was developed to efficiently flux-volume homogenize TRISO particles with the matrix. Additionally, optimizations were made to hyper- fine group (HFG) slowing down calculations by pretabulating collision probability coefficients and grouping isotopes, significantly reducing the computational time for calculating scattering sources per HFG. Lastly, the pin power reconstruction module was extended to account for temporal behavior in a microreactor analysis problem, specifically for a control drum transient. Verification tests for each of these improvements demonstrated significant performance enhancements and memory reduction.

22 - GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2016

FOCUSing on Innovative Solar Technologies

Many of ARPA-E’s technology programs seek to break down silos and build new technological communities around a specific energy challenge. In this video, ARPA-E’s Deputy Director for Technology Eric Rohlfing, discusses how the Full-Spectrum Optimized Conversion and Utilization of Sunlight (FOCUS) program is bringing together the photovoltaic (PV) and concentrated solar power (CSP) communities to develop hybrid solar energy systems. This video features interviews with innovators from the FOCUS project team made up by Arizona State University and the University of Arizona, and showcases how the FOCUS program is combining.

14 SOLAR ENERGY↗
DOE OSTI2018

A New Model to Determine Installed System Cost and LCOE for ARPA-E's MOSAIC Micro-Concentrator PV Program: Preprint

The US Department of Energy's Advanced Research Projects Agency-Energy (ARPA-E) started a three-year program in December 2015 called Microscale Optimized Solar-cell Arrays with Integrated Concentration (MOSAIC). It consists of 11 teams from across the United States. Its goal is to have project teams leverage expertise from conventional flat-plate photovoltaic (PV) system configuration and concentrated photovoltaic (CPV) module technology to produce a new class of modules for residential, commercial, and utility-scale markets. The intent is to increase performance and facilitate cost-effective deployment of solar power systems across a wide range of geographical locations in part by concentrating both direct and diffuse components of the sun's irradiance. This paper describes a collaboration of ARPA-E and the National Renewable Energy Laboratory (NREL) to develop a bottom-up system cost model for MOSAIC research teams. The model includes an estimate of regional, all-in, CPV installed cost, and levelized cost of energy (LCOE) by using customized inputs parameters, such as module efficiency, cost, and physical dimensions. This techno-economic cost model can help researchers analyze tradeoffs among different design parameters and then prioritize their distinct research opportunities. Moreover, it establishes a standardized cost benchmarking for MOSAIC micro-CPV systems and a normalized comparison against traditional CPV and today's highly competitive flat-plate PV systems. The model is a valuable design tool within the program, helping validate the various technical approaches and compare future deployed system competitiveness versus incumbent technologies in the PV industry.

14 SOLAR ENERGY↗
DOE OSTI2014

Efficient algorithm for locating and sizing series compensation devices in large power transmission grids: I. Model implementation

We explore optimization methods for planning the placement, sizing and operations of Flexible Alternating Current Transmission System (FACTS) devices installed to relieve transmission grid congestion. We limit our selection of FACTS devices to Series Compensation (SC) devices that can be represented by modification of the inductance of transmission lines. Our master optimization problem minimizes the l 1 norm of the inductance modification subject to the usual line thermal-limit constraints. We develop heuristics that reduce this non-convex optimization to a succession of Linear Programs (LP) which are accelerated further using cutting plane methods. The algorithm solves an instance of the MatPower Polish Grid model (3299 lines and 2746 nodes) in 40 seconds per iteration on a standard laptop—a speed up that allows the sizing and placement of a family of SC devices to correct a large set of anticipated congestions. We observe that our algorithm finds feasible solutions that are always sparse, i.e., SC devices are placed on only a few lines. In a companion manuscript, we demonstrate our approach on realistically-sized networks that suffer congestion from a range of causes including generator retirement. In this manuscript, we focus on the development of our approach, investigate its structure on a small test system subject to congestion from uniform load growth, and demonstrate computational efficiency on a realistically-sized network.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2017

Characterizing and analyzing ramping events in wind power, solar power, load, and netload

Here, one of the biggest concerns associated with integrating a large amount of renewable energy into the power grid is the ability to handle large ramps in the renewable power output. For the sake of system reliability and economics, it is essential for power system operators to better understand the ramping features of renewable, load, and netload. An optimized swinging door algorithm (OpSDA) is used and extended to accurately and efficiently detect ramping events. For wind power ramps detection, a process of merging 'bumps' (that have a different changing direction) into adjacent ramping segments is included to improve the performance of the OpSDA method. For solar ramps detection, ramping events that occur in both clear-sky and measured (or forecasted) solar power are removed to account for the diurnal pattern of solar generation. Ramping features are extracted and extensively compared between load and netload under different renewable penetration levels (9.77%, 15.85%, and 51.38%). Comparison results show that (i) netload ramp events with shorter durations and smaller magnitudes occur more frequently when renewable penetration level increases, and the total number of ramping events also increases; and (ii) different ramping characteristics are observed in load and netload even with a low renewable penetration level.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2014

TU-PIS-Exhibit Hall-01: CT Dose Optimization Technologies II

Partners in Solutions is an exciting new program in which AAPM partners with our vendors to present practical “hands-on” information about the equipment and software systems that we use in our clinics. The imaging topic this year is CT scanner dose optimization capabilities. Note that the sessions are being held in a special purpose room built on the Exhibit Hall Floor, to encourage further interaction with the vendors. Siemens‘ Commitment to the Right Dose in Computed Tomography Presentation Time: 11:15 - 11:45 AM Providing sustainable clinical results at highest patient safety: This is the challenge in medical imaging. Especially for Computed Tomography this means applying not simply the lowest, but the right dose for sound diagnostic imaging. Consequently, Siemens is committed to deliver the right dose in CT. In order to reduce radiation to the right dose, the first step is to provide the right dose technology. Through decades of research and development in CT imaging, Siemens CT has constantly introduced new ideas leading to a comprehensive portfolio of unique CARE technologies to deliver the right dose. For example automated kV adjustment based on patient size and the clinical question with CARE kV and three generations of iterative reconstruction. Based on the right dose technology, the next step is to actually scan at the right dose. For this, it is key to know the right dose targets for every examination. Siemens continuously involves CT experts to push developments further and outline how users can best adapt their procedures to the right dose. For users to know whether they met the right dose targets, it is therefore important to understand and monitor the actual absolute dose values. All scanners are delivered with defined default protocols which automatically use the available right dose technologies. Finally, to deliver the right dose not just in singular cases, but ideally to patients everywhere, organizations need then to manage dose across their institutions. Here Siemens offers the right dose management tools for administrations to analyze and document dose, to train their clinical staff and to continuously optimize the application of the right dose in clinical routine. Siemens CT is not only adhering to the ALARA principle, but is inspired by it. With CARE Right Siemens show their commitment to the right dose in CT. Toshiba Aquilion CT Dose Management Tools Presentation Time: 11:45 - 12:15 PM A practical review of Toshiba’s CT dose management technologies from the physicist’s perspective. This presentation will include an overview of Toshiba’s CT dose reduction technologies and dose safeguards. Content will include answers to frequently asked questions relating to dose management, dose tracking, CTDI measurement, ACR accreditation, ACR QC, and protocol review on Toshiba CT systems. Specific topics will include the following: Toshiba’s mechanism of tube current modulation; Exposure record format and interpretation of the values; Dose reporting and dose safeguard tools; Use of AIDR 3D iterative reconstruction; The configuration of task specific image quality target and its integration with AIDR 3D; Tips for scanning the ACR phantom with wide beam geometry; Measuring CTDI for wide beam geometry; Discussion of daily QC procedures and specifications; Tips for annual QC evaluation; Protocol management tools.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2025

Age at release affects developmental physiology and sex-specific phenotypic diversity of hatchery steelhead trout (Oncorhynchus mykiss)

Most steelhead trout hatcheries increase growth rate during rearing to produce and release yearling smolts for harvest augmentation, but natural steelhead exhibit variable age of smoltification, so this common rearing practice may not be ideal for programs focused on recovering imperiled wild stocks; therefore, it is important to investigate and compare alternative hatchery rearing methods that promote life history diversity. Over six consecutive years, the Winthrop National Fish Hatchery on the Methow River, WA reared and released paired groups of age-1 (S1) and age-2 (S2) steelhead smolts. To understand how the two rearing methods affected developmental ontogeny and life-history, fish were sampled prior to hatchery release for factors associated with smoltification (size, gill Na+/K+ ATPase activity, and a qualitative smolt phenotype) and sexual maturation (sex, pituitary and testis mRNA transcripts, gonadosomatic index, and plasma 11-ketotestosterone). Our objectives were to quantify levels of smoltification and male maturation during hatchery rearing, combine metrics to estimate residualism (failure to migrate upon release), and compare the treatments by sex. Overall, S2 rearing produced 7.8% more smolts and 44-fold (4.4 vs. 0.1%) more precociously mature males than S1 rearing. Conversely, S1 rearing produced 31.6% more residuals than S2 rearing. While the proportion of total male residuals was comparable between treatments, the S1 treatment produced approximately five-fold more female residuals (20.6 vs. 4.2%). Because residuals contribute minimally to adult returns and the number of returning adult females is critical to the success of salmonid supplementation efforts, developing rearing techniques that maximize migration in females is a management priority. Physiological assessments are useful for characterizing and quantifying the effects and risks of different hatchery rearing regimes on steelhead life-history, in addition to providing sex-specific guidance to inform and optimize conservation management goals in supplementation programs.

Middleton, Mollie A. (ORCID:0009000905577865)↗
DOE OSTI2020

Data-Based Resilience Enhancement Strategies for Electric-Gas Systems Against Sequential Extreme Weather Events

Some extreme weather events, such as the hurricane, pass through an area sequentially and thus are called sequential extreme weather events (SEWEs). This paper proposes a data-based robust optimization (RO) model to enhance the resilience of the integrated electricity and gas system (IEGS) against SEWEs. Specifically, the SEWE strikes the IEGS sequentially. After each attack, the system state is adjusted immediately to minimize the maximized expected system cost caused by the SEWE. The attack-defense procedures are repeated alternatively during the SEWE. Preventive measures, hardening, are made in advance to reduce the impact of sequential attacks. The entire process is formulated as a multi-period RO model. Furthermore, it is proved that the most effective resilience enhancement strategies for this model are the same as those for a two-stage RO model, which can be solved by the nested column-and-constraint generation (C&CG) algorithm. In addition, the property of SEWEs, sequentially endangering limited regions of the IEGS, is incorporated to build a data-based uncertainty set and reduce its conservativeness. Simulation results on two IEGSs validate the effectiveness of the proposed model.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2013

Final Report A Multi-Language Environment For Programmable Code Optimization and Empirical Tuning

This report summarizes our effort and results of building an integrated optimization environment to effectively combine the programmable control and the empirical tuning of source-to-source compiler optimizations within the framework of multiple existing languages, specifically C, C++, and Fortran. The environment contains two main components: the ROSE analysis engine, which is based on the ROSE C/C++/Fortran2003 source-to-source compiler developed by Co-PI Dr.Quinlan et. al at DOE/LLNL, and the POET transformation engine, which is based on an interpreted program transformation language developed by Dr. Yi at University of Texas at San Antonio (UTSA). The ROSE analysis engine performs advanced compiler analysis, identifies profitable code transformations, and then produces output in POET, a language designed to provide programmable control of compiler optimizations to application developers and to support the parameterization of architecture-sensitive optimizations so that their configurations can be empirically tuned later. This POET output can then be ported to different machines together with the user application, where a POET-based search engine empirically reconfigures the parameterized optimizations until satisfactory performance is found. Computational specialists can write POET scripts to directly control the optimization of their code. Application developers can interact with ROSE to obtain optimization feedback as well as provide domain-specific knowledge and high-level optimization strategies. The optimization environment is expected to support different levels of automation and programmer intervention, from fully-automated tuning to semi-automated development and to manual programmable control.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2014

Impact of Residential Mechanical Ventilation on Energy Cost and Humidity Control

The DOE Building America program has been conducting research leading to cost effective high performance homes since the early 1990's. Optimizing whole house mechanical ventilation as part of the program's systems engineered approach to constructing housing has been an important subject of the program's research. Ventilation in residential buildings is one component of an effective, comprehensive strategy for creation and maintenance of a comfortable and healthy indoor air environment. The study described in this white paper is based on building energy modeling with an important focus on the indoor humidity impacts of ventilation. The modeling tools used were EnergyPlus version 7.1 (E+) and EnergyGauge USA (EGUSA). Twelve U.S. cities and five climate zones were represented. A total of 864 simulations (2*2*3*3*12= 864) were run using two building archetypes, two building leakage rates, two building orientations, three ventilation systems, three ventilation rates, and twelve climates.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2023

Optimization with Neural Network Feasibility Surrogates: Formulations and Application to Security-Constrained Optimal Power Flow

In many areas of constrained optimization, representing all possible constraints that give rise to an accurate feasible region can be difficult and computationally prohibitive for online use. Satisfying feasibility constraints becomes more challenging in high-dimensional, non-convex regimes which are common in engineering applications. A prominent example that is explored in the manuscript is the security-constrained optimal power flow (SCOPF) problem, which minimizes power generation costs, while enforcing system feasibility under contingency failures in the transmission network. In its full form, this problem has been modeled as a nonlinear two-stage stochastic programming problem. In this work, we propose a hybrid structure that incorporates and takes advantage of both a high-fidelity physical model and fast machine learning surrogates. Neural network (NN) models have been shown to classify highly non-linear functions and can be trained offline but require large training sets. In this work, we present how model-guided sampling can efficiently create datasets that are highly informative to a NN classifier for non-convex functions. We show how the resultant NN surrogates can be integrated into a non-linear program as smooth, continuous functions to simultaneously optimize the objective function and enforce feasibility using existing non-linear solvers. Overall, this allows us to optimize instances of the SCOPF problem with an order of magnitude CPU improvement over existing methods.

24 POWER TRANSMISSION AND DISTRIBUTION↗