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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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DOE OSTI2022

Production Agency Quality Assurance Management Execution Strategy [Thesis]

There are multiple federal directives that Los Alamos National Laboratory (LANL) must follow for the proper implementation of quality assurance, strategic planning, and execution of manufacturing and surveillance operations. Recent assessments identified that manufacturing in all processing areas is dynamic due to influencing scope changes, design modifications, funding adjustments, and staff attrition. In response, this project was started to develop a comprehensive PAQ execution strategy to support the success of LANL’s manufacturing mission. This project’s method for developing an updated architecture was to create an integrated, flexible, reliable, and agile strategic process. The execution plan has five deliverables: 1) an integrated schedule view of PAQ work scope, 2) a resource management plan that aligns with the required scope, 3) a metrics monitoring dashboard, 4) a project management plan for sustaining NAP 401.1A implementation, and 5) a risk management plan. The research design is a mixed-method approach focused on data collection for each deliverable. The methodology entails qualitative and quantitative research, metrics monitoring, data analysis, and the creation of a dashboard. The qualitative methods used include literature reviews and interviews. The quantitative methods include resource, financial, schedule and survey data analysis. Preliminary and final results were peer reviewed by subject matter experts both within the ALDWP organization and deployed support.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗

DOE OSTI2020

Quantum computing based hybrid solution strategies for large-scale discrete-continuous optimization problems

Quantum computing (QC) has gained popularity due to its unique capabilities that are quite different from that of classical computers in terms of speed and methods of operations. This paper proposes hybrid models and methods that effectively leverage the complementary strengths of deterministic algorithms and QC techniques to overcome combinatorial complexity for solving large-scale mixed-integer programming problems. Four applications, namely the molecular conformation problem, job-shop scheduling problem, manufacturing cell formation problem, and the vehicle routing problem, are specifically addressed. Large-scale instances of these application problems across multiple scales ranging from molecular design to logistics optimization are computationally challenging for deterministic optimization algorithms on classical computers. To address the computational challenges, hybrid QC-based algorithms are proposed and extensive computational experimental results are presented to demonstrate their applicability and efficiency. The proposed QC-based solution strategies enjoy high computational efficiency in terms of solution quality and computation time, by utilizing the unique features of both classical and quantum computers.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2020

Extracting and Generating PV Soiling Profiles for Analysis, Forecasting, and Cleaning Optimization

The identification and prediction of the daily soiling profiles of a photovoltaic site is essential to plan the optimal cleaning schedule. In this article, we analyze and propose various methods to extract and generate photovoltaic soiling profiles, in order to improve the analysis and the forecast of the losses. New soiling rate extraction methods are proposed to reflect the seasonal variability of the soiling rates and, for this reason, are found to identify the most convenient cleaning day with the highest accuracy for the investigated sites. Also, we present an approach that could be used to predict future soiling losses through the implementation of stochastic weather generation algorithms whose ability to identify in advance the best cleaning schedule is also successfully tested. The methods presented in this article can optimize the operation and maintenance schedule and could make it possible, in the future, to predict soiling losses through analysis based only on environmental parameters, such as rainfall and particulate matter, without the need of long-term soiling data.

14 SOLAR ENERGY↗

DOE OSTI2025

Dynamic Charging Rendezvous and Motion Planning for a Multi-AGV Team Including a Mobile Charging Host

Teams of automated battery-powered electric vehicles have the potential to execute complex mission tasks in off-road environments for agriculture, military, and other applications. Limited onboard energy reserves hinder their adoption in large-scale resource-constrained environments, where recharging is a necessity. It may be infeasible to install a network of static charging stations in off-road environments. For this reason, dedicated mobile host vehicles with charging capabilities are proposed as a means to increase range and capabilities of the multivehicle team. Here, in this study, we consider an ad hoc planning framework, where results from a high-confidence trajectory planner are leveraged to plan charging rendezvous between a host and other worker vehicles in a receding horizon fashion to provide high confidence that energy reserves will not be prematurely exhausted. The core problem is posed so as to minimize the impact of recharging on the mission in terms of task delays, overall energy utilization, and costs of fast charging. Through extensive Monte Carlo simulations of an off-road mission, we show a decrease in task delays without substantial increases in energy needs by updating the charging rendezvous plan during the mission. However, if updates are made too often, model mismatch may cause unnecessary cycling and mission failure.

Energy constraints↗

DOE OSTI2023

A Secure and Adaptive Hierarchical Multi-Timescale Framework for Resilient Load Restoration Using a Community Microgrid

Distribution system integrated community microgrids (CMGs) can partake in restoring loads during extended duration outages. At such times, the CMGs are challenged with limited resource availability, absence of robust grid support, and heightened demand-supply uncertainty. Here, this paper proposes a secure and adaptive three-stage hierarchical multi-timescale framework for scheduling and real-time (RT) dispatch of CMGs with hybrid PV systems to address these challenges. The framework enables the CMG to dynamically expand its boundary to support the neighboring grid sections and is adaptive to the changing forecast error impacts. The first stage solves a stochastic extended duration scheduling (EDS) problem to obtain referral plans for optimal resource rationing. The intermediate near-real-time (NRT) scheduling stage updates the EDS schedule closer to the dispatch time using new obtained forecasts, followed by the RT dispatch stage. To make the decisions more secure and robust against forecast errors, a novel concept called delayed recourse is designed. The approach is evaluated via numerical simulations on a modified IEEE 123-bus system and validated using OpenDSS and hardware-in-loop simulations. The results show superior performance in maximizing load supply and continuous secure distribution network operation under different operating scenarios.

24 POWER TRANSMISSION AND DISTRIBUTION↗

DOE OSTI2020

Probabilistic Forecasting of Generators Startups and Shutdowns in the MISO System Based on Random Forest

Solving security constrained unit commitment (SCUC) problems to plan an economical generation schedule for day-head electricity market has been an important research topic in recent years. Mixed integer programming method (MIP), the-state-of-art approach for solving SCUC problem, is known computationally hard when the number of binary status variables is large. In this paper, a machine learning-based algorithm - random forest (RF), was applied to forecast the startups (SU) and shutdowns (SD) hours of generators, based on historical hourly system condition observations in the Midcontinent Independent System Operator (MISO) system. The main purpose is to reduce the number of binary status variables, by fixing the SU/SD hours to a narrow range of high confidence. This would significantly reduce the size of the decision space, and therefore speed up SCUC solutions with reduced uncertainty.

Lin, Xinming↗

DOE OSTI2022

GradientGraph

Under this SBIR Phase II, Reservoir Labs has developed G2 Analytics, a new technology that allows network operators to analyze bottleneck and flow performance with high precision. G2 delivers a new analytical approach and framework to resolve a variety of key problems found in modern communication networks, including: traffic engineering, routing, flow scheduling, network design, capacity planning, resiliency analysis, network slicing, or service level agreement (SLA) management, among others. G2 leverages the bottleneck structure of congestion-controlled communication networks, a recent mathematical discovery by the Reservoir team [RL19b, RL20a, RL20b, RL21a]. Bottleneck structures reveal how perturbations on flows and links propagate through the network, providing an analytical framework to measure (qualitatively and quantitatively) the ripple effects induced as they traverse the network. Leveraging the mathematics of bottleneck structures, Reservoir Labs is developing the G2 technology to provide network operators with a framework to design, optimize and troubleshoot network performance. This delivery includes the G2 software stack.

Yellamraju, Sruthi↗

DOE OSTI2025

SNoGloDe: A Structured Nonlinear Global Decomposition Solver

Large-scale optimization problems often require decomposition strategies and customized algorithms to achieve optimal solutions within a reasonable time. Building on the work of Cao and Zavala (2019) for solving nonlinear two-stage stochastic programs to global optimality, we implement and extend their approach. We generalize to optimization problems reformulated with a block-angular constraint structure (e.g., temporal decomposition). Our framework, written in Python using Pyomo, is highly customizable and enables parallel execution of the decomposition. SNoGloDe allows tailored branching strategies, lower bounding problems, and candidate generators to leverage problem-specific knowledge. To demonstrate effectiveness, we compare SNoGloDe’s performance with Gurobi on a temporally decomposed produced water case study.

algorithms↗

DOE OSTI2020

Mixed-Material Scintillators (MMSS Quarterly Report FY20Q2)

This project aims to invent, model, and prototype mixed-material scintillator systems (MMSSs), a new class of radiation detectors that use heterogenous internal structures of different scintillating materials to detect additional properties of radiation. These internal structures can be produced using additive manufacture (3D printing) of scintillator, a currently emerging application of additive manufacture technology. MMSSs combine the low cost and complexity of conventional scintillation detectors with capabilities currently only available in more expensive and complex detectors. By identifying promising MMSS designs, this project will enable a new class of detectors to meet DNN’s mission needs for SNM detection. This quarter, we delivered one the MMSS Particle-ID Modeling report, a major deliverable of this project. This report describes our results on simulations of the particle-ID (PID) class of detectors. We found that these detectors outperform competing approaches for neutron-gamma discrimination, neutron source pointing, and neutron spectroscopy. Further highlights of the report are described below. During this quarter, We alerted HQ to a change in schedule for the remaining simulations planned for this project: we plan to put off further simulation and reporting of the position-resolving (PR) detector class until July. This change in schedule has allowed us to go into additional detail in the PID work that is both very promising and motivates our upcoming proposal. The other active task in this project is prototyping 3D-printed scintillators with the aim of realizing the designs we’ve simulated. On that front, we had three focuses: measuring the light output, increasing the size of printed parts, and combining blue and green scintillators. We succeeded in measuring the light output, showing an output of 30% of commercial standard. This result leaves room for improvement but is within striking distance of where we need to be. We were on track to demonstrate a key requirement for increasing the size of parts and also to show combined green/blue prints before the laboratory moved to a Minimum Safe (MinSafe) Operations posture as a result of local Shelter In Place orders due to Covid-19.

42 ENGINEERING↗

DOE OSTI2023

Final Report for ARPA-E LOCOMOTIVES Advanced Locomotive Technology and Rail Infrastructure Optimization System (ALTRIOS) Project

The Advanced Locomotive Technology and Rail Infrastructure Optimization System (ALTRIOS) is a unique, fully integrated, open-source software tool used to evaluate strategies for cost-effectively deploying advanced locomotive technologies and associated infrastructure. ALTRIOS simulates freight-demand-driven train scheduling, mainline meet-pass planning, locomotive dynamics, train dynamics, energy conversion efficiencies, and energy storage dynamics of line-haul train operations. Because new locomotives represent a significant long-term capital investment and new technologies must be thoroughly demonstrated before deployment, this tool provides guidance on the risk/reward trade-offs and operation integration of different technology rollout strategies. An open, integrated simulation tool is valuable for identifying future research needs and making decisions on technology development, routes, and train selection. This final report details the ALTRIOS software architecture, major modules and components, and data validation process. It demonstrates the software's utility through a 30-year rollout case study targeting high penetration of advanced powertrain technologies by 2050 for two BNSF Railway routes: loaded taconite ore trains from Hibbing, Minnesota, to Superior, Wisconsin, and mixed-freight trains from Superior to Minneapolis, Minnesota.

33 ADVANCED PROPULSION SYSTEMS↗

DOE OSTI2020

Current Status of the Synroc Waste Treatment Facility - 20263

Construction of the Synroc Waste Treatment Facility is well underway at ANSTO. This plant has been designed to treat intermediate level liquid waste arising from the production of Mo-99 within the newly constructed ANSTO Nuclear Medicine (ANM) precinct [1]. The facility utilises ANSTO Synroc technology that has been tailored to the chemical, physical, and radiological properties of the waste. The result is a highly durable wasteform with a significant reduction in the final volume of the treated waste. This paper presents the construction status of the Synroc Waste Treatment Facility and the status of the associated technology maturation plan. Construction of the building is scheduled for completion in late 2020 followed by process installation and commissioning. A key component of the technology maturation plan has been the construction of an Inactive Engineering Demonstrator [2]. This has aided in the mitigation of risks with respect to technology selection, process integration, and process nuclearisation. Furthermore, the demonstration facility has provided an environment for the design and development of the instrumentation and control philosophy resulting in a seamlessly integrated plant control system. Successful demonstration of the technology by the inactive engineering demonstrator has significantly reduced the associated risks. Details of the Synroc Waste Treatment Facility project progress will also be presented. (authors)

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗

DOE OSTI2025

Electrifying Airport GSE: Monte Carlo Grid Impacts

Airports globally are shifting from ICE-powered to electric Ground Support Equipment (eGSE) to enhance efficiency, reduce operational costs, and improve operator health. Leveraging predictable routes, flat terrain, and low operational speeds, airports provide ideal conditions for electrification. This study evaluates freight GSE electrification at Dallas-Fort Worth International Airport (DFW), USA, using the Agile@ platform, which integrates three analytical methods: Freight Facility Model (FFM), Activity-Structure-Intensity-Fuel (ASIF), and Monte Carlo simulations. Results from 10,000 simulations indicate modest but critical increases in electricity demand and significant variability in GSE energy consumption. These insights emphasize the importance of data-driven scheduling, targeted maintenance, and strategic infrastructure planning. For high-uncertainty scenarios, airports are advised to deploy buffer energy storage systems (battery banks), implement demand-response charging strategies, schedule flexible workforce shifts, and prioritize proactive maintenance-particularly for equipment with higher operational uncertainty, such as tug tractors with trailers. Agile@ thus offers a robust, scalable, and data-driven framework to optimize long-term GSE planning and enhance reliability across diverse airport environments.

Bose, Ranjan [ORNL] (ORCID:0009000791026327)↗

DOE OSTI2023

Power system restoration incorporating diverse distributed energy resources

An example system includes an aggregator configured to receive a service collaboration request and iteratively determine, based on minimum and maximum power values for DERs under its management, an optimized operation schedule. The aggregator may also be configured to iteratively determine, based on the optimized operation schedule, an estimated flexibility range for devices under its management and output an indication thereof. The system may also include a power management unit (PMU) configured to iteratively receive the indication and determine, based on a network model that includes the estimated flexibility range, a reconfiguration plan and an overall optimized operation schedule for the network. The PMU may also be configured to iteratively cause reconfiguration of the network based on the plan. The PMU and aggregator may also be configured to iteratively, at a fast timescale, cause energy resources under their management to modify operation based on the overall optimized operation schedule.

Ding, Fei↗

DOE OSTI2021

G3P3 Phase 3 Project Execution Plan. Issue 1

The National Solar Thermal Test Facility (NSTTF) at Sandia National Laboratories New Mexico (SNL/NM) developed this Project Execution Plan (PEP) to document its process for executing, monitoring, controlling and closing-out Phase 3 of the Gen 3 Particle Pilot Plant G3P3. This plan serves as a resource for stakeholders who wish to be knowledgeable of project objectives and how they will be accomplished. The plan is intended to be used by the development partners, principal investigator, and the federal project director. Project objectives are derived from the mission needs statement, and an integrated project team assists in development of the PEP. This plan is a living document and will be updated throughout the project to describe current and future processes and procedures. The scope of the PEP covers: Cost, schedule, and scope Project reporting Staffing plan Quality assurance plan Environment, safety, security, and health This document is a tailored approach for the Facilities Management and Operations Center (FMOC) to meet the project management principles of DOE Order 413.3B, Program and Project Management for the Acquisition of Capital Assets , and DOE G 413.3-15, DOE Guide for Project Execution Plans. This document will elaborate on content as knowledge of the project is gained or refined.

14 SOLAR ENERGY↗

DOE OSTI2021

Modeling and Optimization Methods for Controlling and Sizing Grid-connected Energy Storage: A Review

Purpose of Review Energy storage is capable of providing a variety of services and solving a multitude of issues in today's rapidly evolving electric power grid. This paper reviews recent research on modeling and optimization methods for optimally controlling and sizing grid-connected battery energy storage systems (BESSs). Open issues and promising research directions are discussed. Recent Findings Recent studies on BESS dispatch, evaluation, and sizing focus on advanced modeling and optimization methods to maximize stacked value streams from multiple services. BESS models have been improved to better represent operational characteristics or capture degradation effects. Different solution methods and optimization techniques have been proposed to improve the benefits and cost-effectiveness of BESSs, using deterministic approaches prevalently but with impressive progress in capturing and addressing uncertainties. Summary Recent progress in BESS scheduling and sizing better supports planning and operational decision-making in different use cases, which is highly important to advance the deployment of BESSs. Additional research is required to properly model the trade-off between short-term benefits and service life with multiple degradation effects explicitly considered in the decision-making process. Advanced methods are to be developed for effectively determining optimal BESS sizes that maximize overall benefits within a varying lifetime considering diversified system, conditions as well as uncertainties at planning and operational stages.

Wu, Di↗

DOE OSTI2020

Nationwide HVAC Energy-Saving Potential Quantification for Office Buildings with Occupant-Centric Controls in Various Climates

The occupant-centric control (OCC) is receiving an increasing attention due to its ability to reduce building heating ventilation and air-conditioning (HVAC) system energy consumptions while not affecting the occupant thermal comfort. This paper aims to investigate and quantify the nationwide energy-saving potential of implementing the occupant-centric HVAC controls in typical office buildings using a whole building simulation software EnergyPlus. First, the medium office and large office from the Department of Energy (DOE) Commercial Prototype Building Models (CBPM) were enhanced to have detailed layouts and dynamic occupancy schedules. Then, a comprehensive simulation plan was created by incorporating the multiple zone-level and system-level occupant-centric building HVAC controls recommended by the updated ASHRAE Standard 90.1 – 2019 and ASHRAE Guideline 36 – 2018. Three control scenarios with different occupancy sensing methods were identified in this simulation plan. A nation-wide parametric analysis which includes two building types, three occupancy sensing scenarios, two building code versions, and 16 U.S. climate zones was carried out. The simulation results of the key control variables and HVAC energy consumption suggest that generally, both the occupancy presence sensor and occupant counting sensor could achieve energy savings for the office buildings in majority of the scenarios. However, compared with the occupancy presence sensor, which could support both the temperature setpoint reset and operational breathing zone airflow rate reset for the unoccupied zones, the occupant counting sensor only brings a marginal benefit. Besides, a higher HVAC energy-saving ratio could be achieved in the heating-dominated zone, since the energy reduction brought with the minimum outdoor airflow rate reset is stronger in the heating mode.

Pang, Zhihong↗

DOE OSTI2020

Modeling evacuation demand during no-notice emergency events: Tour formation behavior

Disastrous events have been drastically increasing – both in frequency and destructive capacity – over the past few years. While advance-notice events have received a great deal of attention in the literature of disaster management, not much attention so far has been given to the no-notice events mainly because of the scarcity of data. As an attempt to address this critical gap, the current study proposes a disaggregate evacuation demand framework to understand evacuees’ travel behavior in case of no-notice emergency events. Here, the proposed framework comprises four main steps of evacuation decision, evacuation planning, tour formation, and activity schedule update. This article is dedicated to the introduction of the framework structure and elaboration on the tour formation step. In this step, we first estimate the total number of intermediate stops, travel time, and distance of the evacuation tours for those who decide to evacuate through a joint modeling structure and then, determine the type of each intermediate stop (if any). It is found that a broad range of factors including evacuees’ demographic profiles, built-environment attributes, and characteristics of the disastrous event plays a significant role in people’s evacuation behavior during no-notice emergency events. The findings of this study can assist responsible agencies in understanding evacuees’ complex behavior, and consequently, in devising effective strategies to alleviate economic damages and casualties resulted by such events.

99 GENERAL AND MISCELLANEOUS↗

DOE OSTI2023

ALTRIOS (Advanced Locomotive Technology and Rail Infrastructure Optimization System) [SWR-22-54]

The Advanced Locomotive Technology and Rail Infrastructure Optimization System (ALTRIOS) is a unique, fully integrated, open-source software tool to evaluate strategies for deploying advanced locomotive technologies and associated infrastructure for cost-effective decarbonization. ALTRIOS simulates freight-demand driven train scheduling, mainline meet-pass planning, locomotive dynamics, train dynamics, energy conversion efficiencies, and energy storage dynamics of line-haul train operations. Because new locomotives represent a significant long-term capital investment and new technologies must be thoroughly demonstrated before deployment, this tool provides guidance on the risk/reward tradeoffs of different technology rollout strategies. An open, integrated simulation tool is invaluable for identifying future research needs and making decisions on technology development, routes, and train selection. ALTRIOS was developed as part of a collaborative effort by a team comprising the National Renewable Energy Laboratory (NREL), University of Illinois Urbana-Champaign (UIUC), Southwest Research Institute (SwRI), and BNSF Railway. Python Package: https://pypi.org/project/altrios/ Rust Crate: https://crates.io/crates/altrios-core

Baker, Chad↗