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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 703 records · Page 39
All sourcesNASA NTRSDOE OSTI
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 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↗
DOE OSTI2023

Ciel

Compiler optimizations can alter the numerical results of scientific computing applications. When numerical results differ significantly between compilers, optimization levels, and floating-point hardware, these numerical inconsistencies can impact programming productivity. Ciel is a framework that helps programmers identify locations in the source code that are affected by compiler optimizations in CPU and GPU code. Ciel uses a floating-point precision enhancement strategy, guided by a recursive bisection search algorithm with increasing search granularity, to identify the program expressions that induce numerical inconsistencies due to compiler optimizations.

Miao, Wenjun↗
DOE OSTI2023

MLIR loop optimizations for High-Level Synthesis: a case study

High-Level Synthesis (HLS) tools simplify the design of hardware accelerators by automatically generating Verilog/VHDL code starting from a general purpose software programming language. They include a wide range of optimization techniques in the process, most of them performed on a low-level intermediate representation (IR) of the code. Introducing optimizations on a higher level of abstraction could significantly contribute to the automated design process results; for example, polyhedral techniques for the manipulation of loops could have a significant impact on the generated accelerators when applied on a specialized IR. We use loop pipelining as a case study to explore the introduction of compiler-based transformations on top of an existing HLS process. We leverage the Multi-Level Intermediate Representation (MLIR) framework and an external scheduler to implement the required transformations, and couple them with existing HLS tools to evaluate the improvements that loop pipelining brings to the performance of generated accelerators. The proposed approach can be integrated with other high-level transformations on the MLIR representation, combining different techniques to obtain pre-optimized inputs for HLS that do not have to rely on a specific backend tool.

Curzel, Serena↗
DOE OSTI2019

Multi-Stage Stochastic Programming to Joint Economic Dispatch for Energy and Reserve With Uncertain Renewable Energy

To address the uncertain renewable energy in the day-ahead optimal dispatch of energy and reserve, a multi-stage stochastic programming model is established in this paper to minimize the expected total costs. The uncertainties over the multiple stages are characterized by a scenario tree and the optimal dispatch scheme is cast as a decision tree which guarantees the flexibility to decide the reasonable outputs of generation and the adequate reserves accounting for different realizations of renewable energy. Most importantly, to deal with the “Curse of Dimensionality” of stochastic programming, stochastic dual dynamic programming (SDDP) is employed, which decomposes the original problem into several sub-problems according to the stages. Specifically, the SDDP algorithm performs forward pass and backward pass repeatedly until the convergence criterion is satisfied. At each iteration, the original problem is approximated by creating a linear piecewise function. Besides, an improved convergence criterion is adopted to narrow the optimization gaps. The results on the IEEE 118-bus system and real-life provincial power grid show the effectiveness of the proposed model and method.

economic dispatch↗
DOE OSTI2025

Mixed-Integer Linear Programming Formulation with Embedded Machine Learning Surrogates for the Design of Chemical Process Families

In previous work, we introduced process family design. The main idea is to design a platform of common elements, and, allowing us to capture additional cost savings, simultaneously design a family of processes, and reducing both engineering and deployment timelines. We formulate this as an optimization problem, specifically a nonlinear generalized disjunctive program (GDP). We have proposed two approaches for reformulating and solving this problem: one based on full-discretization of the design space and one that uses Machine Learning (ML) surrogates to replace the nonlinear process models. Using ML surrogates to predict required system costs and performance indicators allows us to reformulate the nonlinearities in the GDP generate an efficient MILP formulation. In this work, we apply the ML surrogate approach to two case studies. One case study involves designing a family of carbon capture systems to cover a set of different flue gas flow rates and inlet CO 2 concentrations, where we consider the absorber and stripper as common unit module types. The second case study focuses on a water-desalination process, where we design a family of these processes for a variety of salt concentrations and flow rates. In both of these case studies, we demonstrate a scalable optimization approach that enables the design of multiple processes simultaneously, reducing the time-to-market and overall costs by maximizing the cost savings due to both economies of scale and economies of numbers.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2018

Instrumentation for Hanford Direct-Feed Low-Activity Waste Real-Time, In-Line Process Monitoring - 18339

The direct-feed low-activity waste (DFLAW) process will utilize facilities in the Hanford tank farms to stage tank waste feed, a new facility to pretreat the tank waste feed, and facilities in the Hanford Waste Treatment and Immobilization Plant (WTP) to immobilize the low-activity waste (LAW) and disposition effluents from the vitrification process. Process sampling is used extensively throughout the DFLAW process to qualify waste feeds, control the process, and demonstrate compliance with regulatory and contractual commitments. The U.S. Department of Energy's Office of River Protection (ORP) has started a program to evaluate the potential benefits of replacing some sample collection requirements with realtime, in-line monitoring (RTIM). The program will identify, develop, and evaluate technologies that can reduce the number of samples required for WTP operation. Potential benefits of RTIM include elimination of process holds for sample analysis, a reduction in worker exposure to radiation and chemical hazards, decreased secondary waste volume, reducing the cost for sample collection and analysis, an improvement in product quality, and timely indication of process anomalies. In order to incorporate RTIM, the process control strategy for the overall process, as well as each unit operation, was reviewed and revisions to the strategies proposed. The following items were identified for incorporation of RTIM: - Real-time material balance calculations linked to the process control system; - Use of RTIM instruments to validate the material balance for selected analytes; - Anion analysis for determination of selected semi-volatile species (e.g., chlorine, sulfur, and fluorine) in the recycle stream. Based on the proposed process control strategy, a preliminary list of potential analytical equipment was developed. This was followed by a preliminary survey of instrument technologies currently on the market building on surveys performed for ORP, literature reviews, vendor web sites, and subject matter experts from the Savannah River National Laboratory and other U.S. Department of Energy national laboratories. Each technology was evaluated for measurement capability, operations and maintenance requirements, space requirements, and potential for application as process control instrumentation. The instrument technologies were chosen based on the following criteria: - Ability to perform analyses needed; - Potential to be used or adapted for use as process control instruments; - Potential to be adapted to size and operational constraints of standard WTP sample cabinets; - Operational and maintenance requirements. (authors)

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗
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 OSTI2025

Planning Amidst Uncertainty: Identifying Core CCS Infrastructure Robust to Storage Uncertainty

Carbon Capture and Storage (CCS) is a critical technology for reducing anthropogenic CO2 emissions, but its large-scale deployment is complicated by uncertainties in geological storage performance. These uncertainties pose significant financial and operational risks, as underperforming storage sites can lead to costly infrastructure modifications, inefficient pipeline routing, and economic shortfalls. To address this challenge, we propose a novel optimization workflow that is based on mixed-integer linear programming and explicitly integrates probabilistic modeling of storage uncertainty into CCS infrastructure design. This workflow generates multiple infrastructure scenarios by sampling storage capacity distributions, optimally solving each scenario using a mixed-integer linear programming model, and aggregating results into a heatmap to identify core infrastructure components that have a low likelihood of underperforming. A risk index parameter is introduced to balance trade-offs between cost, CO2 processing capacity, and risk of underperformance, allowing stakeholders to quantify and mitigate uncertainty in CCS planning. Applying this workflow to a CCS dataset from the US Department of Energy’s Carbon Utilization and Storage Partnership project reveals key insights into infrastructure resilience. Reducing the risk index from 15% to 0% is observed to lead to an 83.7% reduction in CO2 processing capacity and a 77.1% decrease in project profit, quantifying the trade-off between risk tolerance and project performance. Furthermore, our results highlight critical breakpoints, where small adjustments in the risk index produce disproportionate shifts in infrastructure performance, providing actionable guidance for decision-makers. Unlike prior approaches that aimed to cheaply repair underperforming infrastructure, our workflow constructs robust CCS networks from the ground up, ensuring cost-effective infrastructure under storage uncertainty. These findings demonstrate the practical relevance of incorporating uncertainty-aware optimization into CCS planning, equipping decision-makers with a tool to make informed project planning decisions.

Olson, Daniel↗
DOE OSTI2017

Optimal design and dispatch of a system of diesel generators, photovoltaics and batteries for remote locations

Renewable energy technologies, specifically, solar photovoltaic cells, combined with battery storage and diesel generators, form a hybrid system capable of independently powering remote locations, i.e., those isolated from larger grids. If sized correctly, hybrid systems reduce fuel consumption compared to diesel generator-only alternatives. We present an optimization model for establishing a hybrid power design and dispatch strategy for remote locations, such as a military forward operating base, that models the acquisition of different power technologies as integer variables and their operation using nonlinear expressions. Our cost-minimizing, nonconvex, mixed-integer, nonlinear program contains a detailed battery model. Due to its complexities, we present linearizations, which include exact and convex under-estimation techniques, and a heuristic, which determines an initial feasible solution to serve as a “warm start” for the solver. We determine, in a few hours at most, solutions within 5% of optimality for a candidate set of technologies; these solutions closely resemble those from the nonlinear model. Lastly, our instances contain real data spanning a yearly horizon at hour fidelity and demonstrate that a hybrid system could reduce fuel consumption by as much as 50% compared to a generator-only solution.

14 SOLAR ENERGY↗
DOE OSTI2015

A program-level management system for the life cycle environmental and economic assessment of complex building projects

Climate change has become one of the most significant environmental issues, of which about 40% come from the building sector. In particular, complex building projects with various functions have increased, which should be managed from a program-level perspective. Therefore, this study aimed to develop a program-level management system for the life-cycle environmental and economic assessment of complex building projects. The developed system consists of three parts: (i) input part: database server and input data; (ii) analysis part: life cycle assessment and life cycle cost; and (iii) result part: microscopic analysis and macroscopic analysis. To analyze the applicability of the developed system, this study selected ‘U’ University, a complex building project consisting of research facility and residential facility. Through value engineering with experts, a total of 137 design alternatives were established. Based on these alternatives, the macroscopic analysis results were as follows: (i) at the program-level, the life-cycle environmental and economic cost in ‘U’ University were reduced by 6.22% and 2.11%, respectively; (ii) at the project-level, the life-cycle environmental and economic cost in research facility were reduced 6.01% and 1.87%, respectively; and those in residential facility, 12.01% and 3.83%, respective; and (iii) for the mechanical work at the work-type-level, the initial cost was increased 2.9%; but the operation and maintenance phase was reduced by 20.0%. As a result, the developed system can allow the facility managers to establish the operation and maintenance strategies for the environmental and economic aspects from a program-level perspective. - Highlights: • A program-level management system for complex building projects was developed. • Life-cycle environmental and economic assessment can be conducted using the system. • The design alternatives can be analyzed from the microscopic perspective. • The system can be used to establish the optimal O&M strategy at the program-level. • It can be applied to any other country or sector in the global environment.

54 ENVIRONMENTAL SCIENCES↗