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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 OSTI2016

Public Charging Opportunity for U.S. Cities based on Parking Lot Data

The objective of this study is to evaluate the opportunity for public charging for a subset of US cities by using available public parking lot data. The capacity of the parking lots weighted by the daily parking occupancy rate is used as a proxy for daily parking demand. The city s public charging opportunity is defined as the percentage of parking demand covered by chargers on the off-street parking network. We assess this opportunity under the scenario of optimal deployment of public chargers. We use the maximum coverage model to optimally locate those facilities on the public garage network. We compare the optimal results to the actual placement of chargers. These empirical findings are of great interest to policymakers as those showcase the potential of increasing opportunities for charging under optimal charging location planning.

parking lot data↗
DOE OSTI2020

Metrics and Analytical Frameworks for Valuing Energy Efficiency and Distributed Energy Resources in the Built Environment: Preprint

This paper summarizes efforts to develop new—and enhance existing—analytical frameworks and metrics to quantify the value that grid-interactive efficient homes with solar (GEB-solar homes) can provide. Industry is working to characterize and understand these capabilities and benefits, but existing analytical frameworks for evaluating energy efficiency (EE) are often siloed from those that evaluate distributed energy resources (DERs). Five metrics were adapted from an extensive literature review and applied to case studies of a modeled home in Riverside, California: ramp up/down, cover factor demand/supply, and curtailable load. Eight different technology scenarios were analyzed using a more tightly connected suite of building-to-grid models (BEopt, REopt, ReEDS, and PLEXOS). Additionally, an initial version of the Cambium tool was developed, characterizing the marginal prices and emissions from NREL’s 2018 Standard Scenarios. These grid costs were extended to a time-varying proxy retail rate and applied as part of a new grid alignment metric. In the results, a more integrated combination of GEB-solar technologies led to a higher cover factor demand—the percentage of gross home load covered by on-site solar—however, a benchmark was required to determine what range of cover factor was “best” for given grid conditions. To that end, a grid alignment cost metric was applied to the case study scenarios. The average cost to serve the net load of the home decreased from a median of ~$0.24/kWh to ~$0.10/kWh when the most integrated technology scenario was optimized towards the grid pricing proxy versus the time-of-use (TOU) rate.

41 EE - Solar Energy Technologies Office (EE-4S)↗
DOE OSTI2017

Data-driven optimal charging decision making for connected and automated electric vehicles: A personal usage scenario

This study introduces an optimal charging decision making framework for connected and automated electric vehicles under a personal usage scenario. This framework aims to provide charging strategies, i.e. the choice of charging station and the amount of charged energy, by considering constraints from personal daily itineraries and existing charging infrastructure. A data-driven method is introduced to establish a stochastic energy consumption prediction model with consideration of realistic uncertainties. This is performed by analyzing a large scale electric vehicle data set. A real-time updating method is designed to construct this prediction model from new consecutive data points in an adaptive way for real-world applications. Based on this energy cost prediction framework from real electric vehicle data, multistage optimal charging decision making models are introduced, including a deterministic model for average outcome decision making and a robust model for safest charging strategies. A dynamic programming algorithm is proposed to find the optimal charging strategies. Detailed simulations and case studies demonstrate the performance of the proposed algorithms to find optimal charging strategies. They also show the potential capability of connected and automated electric vehicles to reduce the range anxiety and charging infrastructure dependency.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
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 OSTI2026

Selecting Critical Scenarios of DER Adoption in Distribution Grids Using Bayesian Optimization

We develop a new methodology to select scenarios of DER adoption most critical for distribution grids. Anticipating risks of future voltage and line flow violations due to additional PV adopters is central for utility investment planning but continues to rely on deterministic or ad hoc scenario selection. We propose a highly efficient search framework based on multi-objective Bayesian Optimization. We treat underlying grid stress metrics as computationally expensive black-box functions, approximated via Gaussian Process surrogates and design an acquisition function based on probability of scenarios being Pareto-critical across a collection of line- and bus-based violation objectives. Our approach provides a statistical guarantee and offers an order of magnitude speed-up relative to a conservative exhaustive search. Case studies on realistic feeders with 200-400 buses demonstrate the effectiveness and accuracy of our approach.

Mulkin, Olivier↗
NASA NTRS2005

Coevolutionary Free Lunches

Recent work on the foundations of optimization has begun to uncover its underlying rich structure. In particular, the "No Free Lunch" (NFL) theorems [WM97] state that any two algorithms are equivalent when their performance is averaged across all possible problems. This highlights the need for exploiting problem-specific knowledge to achieve better than random performance. In this paper we present a general framework covering most search scenarios. In addition to the optimization scenarios addressed in the NFL results, this framework covers multi-armed bandit problems and evolution of multiple co-evolving agents. As a particular instance of the latter, it covers "self-play" problems. In these problems the agents work together to produce a champion, who then engages one or more antagonists in a subsequent multi-player game In contrast to the traditional optimization case where the NFL results hold, we show that in self-play there are free lunches: in coevolution some algorithms have better performance than other algorithms, averaged across all possible problems. However in the typical coevolutionary scenarios encountered in biology, where there is no champion, NFL still holds.

Wolpert, David H.↗
NASA NTRS2005

Coevolutionary Free Lunches

Recent work on the mathematical foundations of optimization has begun to uncover its rich structure. In particular, the "No Free Lunch" (NFL) theorems state that any two algorithms are equivalent when their performance is averaged across all possible problems. This highlights the need for exploiting problem-specific knowledge to achieve better than random performance. In this paper we present a general framework covering more search scenarios. In addition to the optimization scenarios addressed in the NFL results, this framework covers multi-armed bandit problems and evolution of multiple co-evolving players. As a particular instance of the latter, it covers "self-play" problems. In these problems the set of players work together to produce a champion, who then engages one or more antagonists in a subsequent multi-player game. In contrast to the traditional optimization case where the NFL results hold, we show that in self-play there are free lunches: in coevolution some algorithms have better performance than other algorithms, averaged across all possible problems. We consider the implications of these results to biology where there is no champion.

Wolpert, David H.↗
DOE OSTI2024

NBI optimization on SMART and implications for scenario development

Abstract The SMall Aspect Ratio Tokamak (SMART) under commissioning at the University of Seville, Spain, aims to explore confinement properties and possible advantages in confinement for compact/spherical tokamaks operating at negative vs. positive triangularity. This work explores the benefits of auxiliary heating through Neutral Beam Injection (NBI) for SMART scenarios beyond the initial Ohmic phase of operations, in support of the device’s mission. Expected values of electron and ion temperature achievable with NBI heating are first predicted for the current flat-top phase, including modeling to optimize the NBI injection geometry to maximize NBI absorption and minimize losses for a given equilibrium. Simulations are then extended for a selected case to cover the current ramp-up phase. Differences with results obtained for the flat-top phase indicate the importance of determining the plasma evolution over time, as well as self-consistently determining the edge plasma parameters for reliable time-dependent simulations. Initial simulation results indicate the advantage of auxiliary NBI heating to achieve nearly double values of pressure and stored energy compared to Ohmic discharges, thus significantly increasing the device’s performance. The scenarios developed in this work will also contribute to diagnostic development and optimization for SMART, as well as providing test cases for initial predictions of macro- and micro-instabilities.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2024

Optimization-Based Model Reduction Scheme for Renewable Energy Power Plants Using Standardized Testing Scenarios

This paper presents an optimization-based model reduction scheme for renewable energy (RE) power plants consisting of inverter-based resources (IBRs) operating in grid-following (GFL) or grid-forming (GFM) modes. More importantly, the datasets feeding the optimization-based model reduction scheme are generated and re-used through the standardized grid-interactive testing scenarios. Particularly, the proposed scheme makes use of the power plant point of common coupling (PCC) measurements of various quantities specified by standardized tests (e.g., voltage and frequency ride through) as per IEEE 2800, to estimate the parameters of the reduced-order model such that its dynamic performance aligns with the original detailed power plant model. The proposed model reduction approach does not require the parameters of individual IBRs and using standardized test data as input to the formulated optimization problem simplifies the reduced-order modelling scheme. Extensive case studies following standardized test scenarios verified the remarkable accuracy of the proposed approach.

Yallamilli, Ram S. [Purdue University]↗
DOE OSTI2013

Greenhouse Gas Mitigation Options in ISEEM Global Energy Model: 2010-2050 Scenario Analysis for Least-Cost Carbon Reduction in Iron and Steel Sector

The goal of the modeling work carried out in this project was to quantify long-term scenarios for the future emission reduction potentials in the iron and steel sector. The main focus of the project is to examine the impacts of carbon reduction options in the U.S. iron and steel sector under a set of selected scenarios. In order to advance the understanding of carbon emission reduction potential on the national and global scales, and to evaluate the regional impacts of potential U.S. mitigation strategies (e.g., commodity and carbon trading), we also included and examined the carbon reduction scenarios in China’s and India’s iron and steel sectors in this project. For this purpose, a new bottom-up energy modeling framework, the Industrial Sector Energy Efficiency Modeling (ISEEM), (Karali et al. 2012) was used to provide detailed annual projections starting from 2010 through 2050. We used the ISEEM modeling framework to carry out detailed analysis, on a country-by-country basis, for the U.S., China’s, and India’s iron and steel sectors. The ISEEM model applicable to iron and steel section, called ISEEM-IS, is developed to estimate and evaluate carbon emissions scenarios under several alternative mitigation options - including policies (e.g., carbon caps), commodity trading, and carbon trading. The projections will help us to better understand emission reduction potentials with technological and economic implications. The database for input of ISEEM-IS model consists of data and information compiled from various resources such as World Steel Association (WSA), the U.S. Geological Survey (USGS), China Steel Year Books, India Bureau of Mines (IBM), Energy Information Administration (EIA), and recent LBNL studies on bottom-up techno-economic analysis of energy efficiency measures in the iron and steel sector of the U.S., China, and India, including long-term steel production in China. In the ISEEM-IS model, production technology and manufacturing details are represented, in addition to the extensive data compiled from recent studies on bottom-up representation of efficiency measures for the sector. We also defined various mitigation scenarios including long-term production trends to project country-specific production, energy use, trading, carbon emissions, and costs of mitigation. Such analyses can provide useful information to assist policy-makers when considering and shaping future emissions mitigation strategies and policies. The technical objective is to analyze the costs of production and CO 2 emission reduction in the U.S, China, and India’s iron and steel sectors under different emission reduction scenarios, using the ISEEM-IS as a cost optimization model. The scenarios included in this project correspond to various CO 2 emission reduction targets for the iron and steel sector under different strategies such as simple CO 2 emission caps (e.g., specific reduction goals), emission reduction via commodity trading, and emission reduction via carbon trading.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2025

Integrated operation scenarios: Chapter 6 of the special issue: on the path to tokamak burning plasma operation

Here we report the progress of the development and optimization of operational scenarios for ITER and beyond, focusing upon baseline, hybrid, and steady-state scenarios since 2007. This includes advancements made by the integrated operation scenarios (IOS) topical group of the international tokamak physical activity as well as contributions from the broader tokamak community. The key area of research involves developing IOSs that encompass tokamak physics, operation, and technology by utilizing integrated modeling and control strategies. This requires leveraging available actuators to simultaneously control plasma position and shape, MHD activities that could lead to disruptions, transport, plasma-wall interaction and power exhaust, fuel cycle, fusion burn, and tritium breeding. The control extends from the plasma initiation phase, through the current ramp-up, flattop, start and end of the fusion burn, and current ramp-down, to the plasma termination phase. A review of the currently developed scenarios and modeling is provided in terms of (i) optimizing plasma initiation in ITER, (ii) preparing for the low activation phase to fully commission all tokamak systems and establish and validate physics and scenario conditions in preparation for deuterim-tritium (DT) operation, (iii) developing and preparing baseline and hybrid scenarios to demonstrate the feasibility of achieving these regimes within device constraints, (iv) exploring steady-state scenarios to meet ITER’s steady-state goals, (v) evaluating and preparing actuators for ITER, (vi) developing integrated control solutions using shared actuators. The most notable achievements include; (i) the development of ITER demonstration discharges by matching various dimensionless parameters, (ii) the development of scenarios in an ITER-like tungsten environment and DT operation, and (iii) the development of scenarios in superconducting tokamaks, enabling long-pulse operations with similar coil constraints to ITER. Along with these significant achievements, outstanding issues and recommendations for further research and development are provided. Importantly, this study goes beyond simply updating the ITER Physics Basis; it carries profound implications for the broader field of burning plasma research, offering valuable insights and guidance for the next generation of fusion experiments and devices.

ITER↗
DOE OSTI2011

Muon Acceleration-RLA and FFAG

Various acceleration schemes for muons are presented. The overall goal of the acceleration systems: large acceptance acceleration to 25 GeV and 'beam shaping' can be accomplished by various fixed field accelerators at different stages. They involve three superconducting linacs: a single pass linear Pre-accelerator followed by a pair of multi-pass Recirculating Linear Accelerators (RLA) and finally a non-scaling FFAG ring. The present baseline acceleration scenario has been optimized to take maximum advantage of appropriate acceleration scheme at a given stage. The solenoid based Pre-accelerator offers very large acceptance and facilitates correction of energy gain across the bunch and significant longitudinal compression trough induced synchrotron motion. However, far off-crest acceleration reduces the effective acceleration gradient and adds complexity through the requirement of individual RF phase control for each cavity. The RLAs offer very efficient usage of high gradient superconducting RF and ability to adjust path-length after each linac pass through individual return arcs with uniformly periodic FODO optics suitable for chromatic compensation of emittace dilution with sextupoles. However, they require spreaders/recombiners switchyards at both linac ends and significant total length of the arcs. The non-scaling Fixed Field Alternating Gradient (FFAG) ring combines compactness with very large chromatic acceptance (twice the injection energy) and it allows for large number of passes through the RF (at least eight, possibly as high as 15).

43 PARTICLE ACCELERATORS↗
DOE OSTI2026

An overview of 3D field optimization for control of transport and edge instabilities on KSTAR

An international team from several laboratories and universities has made key advances over the last few years in the control of plasma transport and edge instabilities with applied 3D fields in the KSTAR tokamak to optimize long pulse operation scenarios. This overview begins with the optimization of both core and edge resonant magnetic perturbations (RMP) to improve fast ion confinement to avoid excessive limiter heat loads due to fast ion losses and successful modeling of the experimental results. Integrated and advanced plasma control techniques with machine learning (ML) and adaptive control were then used to optimize the 3D field spectrum in real-time to control edge localized modes (ELMs) while avoiding core locked modes that could disrupt the plasma. Accelerating the offline model of 3D fields with a surrogate ML model can optimize ELM suppression in the edge while limiting the impact of the applied RMP fields deeper in the plasma core in real-time. In addition, the impact of the 3D fields on the divertor heat load has been modeled and compared with experimental measurements. An analysis of a multi-machine database including KSTAR has been performed to better understand the metrics for the observed RMP thresholds for ELM suppression and the resulting plasma performance. Predictive modeling of the operational space for ELM suppression and density pumpout due to RMP has shown the importance of magnetic islands in the plasma edge and their impact on plasma turbulence. This research has culminated in the development of successful long pulse operational scenarios on KSTAR while attempting to overcome challenges of the new tungsten divertor.

3D fields↗
DOE OSTI2015

Noncoplanar VMAT for nasopharyngeal tumors: Plan quality versus treatment time

Purpose: The authors investigated the potential of optimized noncoplanar irradiation trajectories for volumetric modulated arc therapy (VMAT) treatments of nasopharyngeal patients and studied the trade-off between treatment plan quality and delivery time in radiation therapy. Methods: For three nasopharyngeal patients, the authors generated treatment plans for nine different delivery scenarios using dedicated optimization methods. They compared these scenarios according to dose characteristics, number of beam directions, and estimated delivery times. In particular, the authors generated the following treatment plans: (1) a 4π plan, which is a not sequenced, fluence optimized plan that uses beam directions from approximately 1400 noncoplanar directions and marks a theoretical upper limit of the treatment plan quality, (2) a coplanar 2π plan with 72 coplanar beam directions as pendant to the noncoplanar 4π plan, (3) a coplanar VMAT plan, (4) a coplanar step and shoot (SnS) plan, (5) a beam angle optimized (BAO) coplanar SnS IMRT plan, (6) a noncoplanar BAO SnS plan, (7) a VMAT plan with rotated treatment couch, (8) a noncoplanar VMAT plan with an optimized great circle around the patient, and (9) a noncoplanar BAO VMAT plan with an arbitrary trajectory around the patient. Results: VMAT using optimized noncoplanar irradiation trajectories reduced the mean and maximum doses in organs at risk compared to coplanar VMAT plans by 19% on average while the target coverage remains constant. A coplanar BAO SnS plan was superior to coplanar SnS or VMAT; however, noncoplanar plans like a noncoplanar BAO SnS plan or noncoplanar VMAT yielded a better plan quality than the best coplanar 2π plan. The treatment plan quality of VMAT plans depended on the length of the trajectory. The delivery times of noncoplanar VMAT plans were estimated to be 6.5 min in average; 1.6 min longer than a coplanar plan but on average 2.8 min faster than a noncoplanar SnS plan with comparable treatment plan quality. Conclusions: The authors’ study reconfirms the dosimetric benefits of noncoplanar irradiation of nasopharyngeal tumors. Both SnS using optimized noncoplanar beam ensembles and VMAT using an optimized, arbitrary, noncoplanar trajectory enabled dose reductions in organs at risk compared to coplanar SnS and VMAT. Using great circles or simple couch rotations to implement noncoplanar VMAT, however, was not sufficient to yield meaningful improvements in treatment plan quality. The authors estimate that noncoplanar VMAT using arbitrary optimized irradiation trajectories comes at an increased delivery time compared to coplanar VMAT yet at a decreased delivery time compared to noncoplanar SnS IMRT.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2026

Simulating competition in the US bioeconomy to produce hard‐to‐electrify transportation fuels using limited biomass resources

This study presents a novel bioeconomy optimization framework, BiOpt, designed to address critical questions regarding the strategic use of limited US biomass resources for biofuel production. By integrating detailed techno-economic analyses, life cycle assessments, and resource assessment data, BiOpt optimizes resource distributions across competing technologies to maximize economic performance and/or minimize greenhouse gas emissions. Using feedstock scenarios from the 2023 Billion Ton Study, the analysis explores optimal biomass allocations across sustainable aviation fuel, diesel, and marine biofuel conversion pathways given varying production targets and policy incentives. Results demonstrate distinct feedstock preferences and pathway utilizations when prioritizing economic returns vs. emissions reductions. For instance, fats, oils, and greases were highly favored in cost-optimized scenarios, while low-carbon feedstocks such as wet waste dominated greenhouse gas-minimized strategies. The findings underscore the pivotal role of policy incentives and technological advances in shaping biofuel supply chains and provide actionable insights for scaling sustainable biofuel production to decarbonize hard-to-electrify sectors. This framework offers a robust tool for policymakers and stakeholders to evaluate biofuel strategies that balance energy output, economic viability, and environmental impact.

09 BIOMASS FUELS↗
DOE OSTI2025

Assimilating partial observation to enhance feedback control of stochastic dynamical systems

Here, in this paper, we present a novel methodology to tackle feedback optimal control problems in scenarios where the exact state of the controlled process is unknown. It integrates data assimilation techniques and optimal control solvers to manage partial observation of the state process, a common occurrence in practical scenarios. Traditional stochastic optimal control methods assume full state observation, which is often not feasible in real-world fluid dynamics control problems. Our approach underscores the significance of utilizing observational data to inform control policy design. Specifically, we introduce a kernel learning backward stochastic differential equation (SDE) filter to enhance data assimilation efficiency and propose a sample-wise stochastic optimization method within the stochastic maximum principle framework. We demonstrate the efficacy and accuracy of our method in the control of advection-diffusion-reaction flow problem and the Dubins airplane maneuvering problem with model uncertainty.

data driven↗
DOE OSTI2016

Contingency Contractor Optimization Phase 3 Sustainment Software Design Document - Contingency Contractor Optimization Tool - Prototype

This document describes the final software design of the Contingency Contractor Optimization Tool - Prototype. Its purpose is to provide the overall architecture of the software and the logic behind this architecture. Documentation for the individual classes is provided in the application Javadoc. The Contingency Contractor Optimization project is intended to address Department of Defense mandates by delivering a centralized strategic planning tool that allows senior decision makers to quickly and accurately assess the impacts, risks, and mitigation strategies associated with utilizing contract support. The Contingency Contractor Optimization Tool - Prototype was developed in Phase 3 of the OSD ATL Contingency Contractor Optimization project to support strategic planning for contingency contractors. The planning tool uses a model to optimize the Total Force mix by minimizing the combined total costs for selected mission scenarios. The model optimizes the match of personnel types (military, DoD civilian, and contractors) and capabilities to meet mission requirements as effectively as possible, based on risk, cost, and other requirements.

97 MATHEMATICS AND COMPUTING↗