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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 109 records · Page 6
All sourcesNASA NTRSDOE OSTI
DOE OSTI2024

Paths Forward: Approaches to Achieve Plug and Process Load Efficiency and Control in Commercial Buildings: Preprint

To accomplish net-zero carbon in the built environment by 2050, we must equitably decarbonize commercial buildings, which includes reducing plug and process loads (PPLs). PPLs are plug-in or hardwired electric and gas loads that are not directly associated with major building end uses like lighting and heating, ventilating, and air conditioning. PPLs account for a growing portion of U.S. commercial building energy consumption. Although commercial building PPL strategies and technologies are available today, they have not been adopted at a level sufficient to achieve significant savings and load flexibility across the building stock. In our "Pathways to Plug and Process Load Efficiency and Control" study, we investigated why these technologies and strategies have not seen widespread adoption and identified five behavior and technology pathways to increase PPL reduction in commercial buildings. In this paper, we expand beyond identifying the pathways and discuss approaches for achieving them. We discuss the importance of collecting and sharing data and case studies on PPL energy consumption and savings from control technology implementation, including code-required measures, for increasing adoption. Centralizing case studies and data, engaging industry organizations, and promoting awareness of PPL efficiency benefits to relevant groups are also key approaches. Additionally, funding, incentives, and rebate programs play important roles in driving PPL efficiency and control adoption. Finally, we discuss integrating PPL efficiency into broader company goals, such as environmental, social and governance (ESG) strategies and green building certifications, to further drive adoption.

adoption pathways↗
DOE OSTI2022

Neural Lyapunov Differentiable Predictive Control

We present a learning-based predictive control methodology using the differentiable programming framework with sufficient Lyapunov-based guarantees. The neural Lyapunov differentiable predictive control (NLDPC) learns the policy by constructing a computational graph encompassing the system dynamics, state, and input constraints, and the necessary Lyapunov certification constraints, and thereafter using the automatic differentiation to update the neural policy parameters. In conjunction, our approach jointly learns a Lyapunov function that certifies the regions of state-space with stable dynamics. We also provide an initial condition sampling-based statistical guarantee for the training of NLDPC. Our offline training approach provides a computationally efficient and scalable alternative to classical model predictive control solutions, which now directly comes with Lyapunov based guarantees as shown in this paper. We substantiate the advantages of the proposed approach with simulations to stabilize the double integrator model and on an example of controlling an aircraft model.

Mukherjee, Sayak↗
DOE OSTI2022

An Introduction to Los Alamos National Laboratory [Slides]

The Weapon Systems Engineering Division (W) provides the system engineering and program management necessary to sustain the safety, reliability, and security of the Los Alamos National Laboratory’s assets in the active United States nuclear stockpile - the B61, W76, W78 and W88. The Division generates key certification data for Annual Assessment supporting the Laboratory Director's letter to the President on the health of those warheads. This role demands ongoing surveillance of the active stockpile and evaluation of the potential impact of any issues through design, engineering, fabrication, testing using state-of-the-art computational simulation tools and engineering test facilities. The Division works in close liaison with the several production facilities across the nuclear security complex as well as with the customers in the US Navy and Air Force.

98 NUCLEAR DISARMAMENT, SAFEGUARDS, AND PHYSICAL P↗
DOE OSTI2025

Assessment of Microstructure Prediction Capabilities for Powder Bed Fusion Stainless Steel 316

The Advanced Materials and Manufacturing Technologies program aims to accelerate the development, qualification, demonstration, and deployment of advanced materials and manufacturing technologies to enable reliable and economical nuclear energy. However, the characteristic process-structure-property relationships of additive manufacturing (AM) materials pose challenges for the qualification and certification of AM nuclear components. In particular, component-scale variations in microstructure and properties can be driven by localized changes in melt pool dynamics due to how process parameters interact with different part geometries. Computational modeling tools can play a crucial role in predicting and controlling this variability. This report presents final results on process modeling tools designed to predict microstructure variability in additively manufactured stainless steel 316 parts. It details the software packages and physical modeling approaches employed to simulate an AM component within an automated process modeling workflow. Results are demonstrated through comparisons between predicted microstructures and experimental measurements across various representative processing conditions. The report concludes by discussing identified challenges and future opportunities for connecting the developed simulation workflow with mechanics simulations for prediction of part performance.

36 MATERIALS SCIENCE↗
DOE OSTI2020

Solar Industry Update (Q2/Q3 2020) [Slides]

Each quarter, the National Renewable Energy Laboratory (NREL) conducts the Quarterly Solar Industry Update, a presentation of technical trends within the solar industry, to the solar office staff. Each presentation focuses on global and U.S. supply and demand, module and system price, investment trends and business models, and updates on U.S. Government programs supporting the solar industry.

14 SOLAR ENERGY↗
DOE OSTI2021

Modeling Savings for ENERGY STAR Smart Home Energy Management Systems

The objective of this study was to develop a repeatable and defensible methodology to analyze the energy savings for Home Energy Management Systems (HEMS) that meets the minimum requirements for certification under ENERGY STAR ® Smart Home Energy Management System (SHEMS) Version 1. Mandatory connected loads include a smart thermostat, two smart lights, and one smart power strip or smart outlet. Control strategies must include feedback to occupants through an in-home display, user programming, occupancy sensor-based controls, and responsiveness to utility signals such as demand response programs. Several occupant behavior patterns were selected to quantify the range of energy savings potential for a HEMS with this basic functionality. A literature review was conducted to establish realistic room-by-room occupancy levels and usage patterns for connected devices. A series of event-driven hourly profiles were created, followed by adjustments based on application of HEMS control strategies to thermostats, interior lighting, and plug load schedules. EnergyPlus modeling was performed using these hourly schedules in three locations (Boston, Houston, and Phoenix) to examine climate dependence of energy savings. Total site energy savings ranged from 4.3 to 27.1 MBtu/year (7%-35%), and utility bill savings ranged from $\$$123 to $\$$670/year (6%-29%). The highest predicted savings was realized by occupants that were not energy conscious prior to HEMS installation, but highly engaged with the HEMS controls once the system was installed. The smart thermostat accounted for most of the savings, followed by the smart power strip. Smart lighting did not save a significant amount of energy in our analysis, based on an assumption that efficient LEDs with no standby power would normally be installed anyway.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2023

Assessment of Process Modeling Tools for Determining Variability in Additively Manufactured Parts

The Advanced Materials and Manufacturing Technologies (AMMT) program aims to accelerate the development, qualification, demonstration, and deployment of advanced materials and manufacturing technologies to enable reliable and economical nuclear energy However, the unique aspects of additive manufacturing (AM) materials in terms of their processing history, microstructure, and properties, are a major barrier for qualification and certification of nuclear components. Much of this challenge may be attributed to component scale variations in microstructure and properties that are driven by local influences of process conditions and geometry on thermal history, melt pool dynamics, and corresponding microstructure evolution. Computational modeling tools may be helpful in this regard to aid in predicting and controlling this level of variability. The purpose of this report is to review the current state-of-the-art for process modeling with regards to metal AM. For this purpose, we consider specifically the case study of laser powder bed fusion (LPBF) processing of SS316, a family of alloys that are both commonly used in nuclear energy applications and suitable for AM processing. The report first introduces the necessary components of a process modeling workflow, followed by a review of the current status of each. At the end, application of these modeling tools to understanding variability in AM process given their current state are considered, and recommendations for future development are proposed

36 MATERIALS SCIENCE↗
DOE OSTI2024

Preliminary Results on Process Modeling Tools for Determining Variability in Additively Manufactured Stainless Steel 316 Parts

The Advanced Materials and Manufacturing Technologies program aims to accelerate the development, qualification, demonstration, and deployment of advanced materials and manufacturing technologies to enable reliable and economical nuclear energy. However, the distinct characteristics of additive manufacturing (AM) materials, stemming from their unique processing history, microstructure, and properties, pose significant challenges for the qualification and certification of nuclear components. These challenges primarily arise from component-scale variations in microstructure and properties influenced by local process conditions and geometry, which affect thermal history, melt pool dynamics, and microstructure evolution. Computational modeling tools can play a crucial role in predicting and controlling this variability. This report presents preliminary results on process modeling tools designed to predict microstructure variability in additively manufactured stainless steel 316 parts. It details the software packages and physical modeling approaches employed to simulate an AM component within an automated process modeling workflow. Initial results are demonstrated through comparisons between predicted microstructures and experimental measurements across various representative processing conditions. The report concludes by discussing the challenges inherent in process modeling of AM components and outlines a plan for future development needs.

36 MATERIALS SCIENCE↗
DOE OSTI2023

US Department of Energy Better Plants Program’s Approach to Manufacturing Workforce Development

Multiple studies suggest that by 2030, millions of jobs may go unfilled because of a significant skills gap between the current manufacturing workforce and the workforce needed to support the shift toward advanced and sustainable manufacturing. According to US manufacturers, finding the right talent is more difficult now than in previous years. Developing a well-trained workforce will play a critical role in transforming the manufacturing sector into a net-zero contributor of greenhouse gas emissions by 2050. To help reduce energy, water, waste, and greenhouse gas emissions as well as build a strong, sustainable manufacturing workforce, the US Department of Energy created the voluntary Better Plants program in 2011. This study discusses the program’s various workforce development activities, including in-plant trainings, virtual in-plant trainings, and in-person bootcamps. The target audiences, training objectives, and topics designed to upskill workers for a low-carbon manufacturing future are described. Feedback that was collected (e.g., new training topics, certifications, hands-on activities) while working with more than 270 US manufacturers for the past 12 years on workforce development are discussed. Finally, some strategies are proposed to address the feedback.

Guo, Wei↗
DOE OSTI2022

ALDWP Operational Highlights 2021

The Weapons Pit production manufacturing team completed pit build 21-1 assembly operations on December 23, 2020. This build utilized all essential flow sheet steps and included the second -01 long tube cut off extension and welding activity. This is the best build produced in the MC4597-01 pit program to date. Final out-of-line operations, non-destructive evaluation, and radiography was completed during the week of January 4. Depending on the results from high energy radiography, this pit will be considered to be a candidate for CERT-02 and shipment to Lawrence Livermore National Laboratory in support of pit certification. It is notable that this build was completed while concurrently making Plutonium (Pu) metal, producing additional castings, machining Pu parts for Build 22, completing disassembly of two pits, supporting Nightshade B Pu target characterization, and precision machining of Jasper certification base plates.

42 ENGINEERING↗
DOE OSTI2023

National Solar Jobs Accelerator (Final Technical Report (FTR))

The aptitudes and experiences gained through military service—such as dynamic leadership, teamwork and critical thinking skills, technical specialization, and a mission-completion work ethic—make veterans exceptional candidates for a wide range of solar energy careers. The solar industry offers a highly collaborative and purpose-driven work environment that resonates with service members and veterans looking to rise to their next challenge, and solar employers are eager to tap into this valuable talent pool. From October 2019 - February 2023, The National Solar Jobs Accelerator (publicly the Solar Ready Vets Network TM (SRV Network; SRVN)) enhanced and streamlined options for military service members and veterans to pursue solar training, certification, and employment, while advancing solar employers’ efforts and capacity to invest in military talent as part of a long-term workforce development strategy. The SRVN was led by the Interstate Renewable Energy Council (IREC) in partnership with the Solar Energy Industries Association (SEIA), the US Chamber of Commerce Foundation’s Hiring Our Heroes program (HOH) and the North American Board of Certified Energy Practitioners (NABCEP). Through several direct-impact and indirect, high-impact capacity building initiatives aligned with six key objectives, the SRV Network strengthened solar career pathways, and promoted increased representation of military talent across all levels and sectors of the solar workforce. A work-based learning Corporate Fellowship model connected transitioning service members with on-the-job experience in leadership roles with solar employers nationwide. The project advanced broader veteran recruitment and talent development by expanding GI Bill eligibility and streamlining veterans’ pathways for solar training and credentialing, supported direct connections to jobs with top solar employers, and led coordination among key education and industry partners to advance registered apprenticeships aligned with solar career pathways. To ensure that the project best served the needs of all stakeholders, an Advisory Committee of military-connected solar professionals, solar employers, and training providers met biannually to guide project activities and sustainability plans. The project team engaged the broader “SRV Network” (comprised of over 2,000 veterans, employers and training organizations) through regular newsletters and targeted outreach to share resources, hiring fairs, webinars, and other opportunities for engagement. The work done under this award builds on the previous iterations of the Department of Energy’s Solar Ready Vets ® program. As the solar industry continues to grow rapidly over the next decade, the military community will continue to be a highly valuable source of talent. The relationships established and work accomplished through this project will have an enduring positive impact well beyond the funding period.

14 SOLAR ENERGY↗
DOE OSTI2022

Long-term trends in connected thermostat performance

Internet-connected thermostats (CTs) control heating and cooling systems in about 30% of North American homes, and capture half of annual thermostat sales. In 2017 the U.S. Environmental Protection Agency created a program to certify the performance of ENERGY STAR® Connected Thermostats. To demonstrate compliance with energy-saving criteria, thermostat vendors must submit performance summaries for a representative sample of up to 1250 homes. Vendors must then re-submit results from a new, representative sample every six months in order to maintain their ENERGY STAR certification. This procedure has created a unique record of each thermostat’s long-term performance in response to changes in weather, customer demographics, building stock, and algorithms. Our analysis covers submissions from 13 different vendors, who submitted data up to 7 times over four years. We found that these semi-annual samples generated relatively stable trends for comfort temperatures and Heating, Ventilating and Air Conditioning (HVAC) runtimes over the study period. However, some vendors achieved consistently more energy-conserving comfort temperatures and shorter HVAC runtimes than others. The most recent submission runtimes averaged 700 hours for heating and 1,300 hours for cooling, but some vendors achieved runtimes as much as 17% below the mean. This implies lower energy consumption is due in large part to more successful algorithms and control strategies used by those vendors.

Meier, Alan↗
DOE OSTI2024

A Hybrid Heavy Duty Diesel Power System for Off-Road Applications—Concept Validation

A multiyear power system R&D program was completed with the objective of developing an off-road hybrid heavy duty diesel engine with front end accessory drive-integrated energy storage. This system was validated to deliver 10.5–25.6% reduction in fuel consumption over current Tier 4 Final-based 18L diesel engines, over various off-road machine application cycles. The power system consisted of a downsized heavy-duty diesel 13L engine containing advanced combustion technologies, capable of elevated peak cylinder pressures and thermal efficiencies, thermal barrier coatings, exhaust waste heat recovery via SuperTurbo™ turbocompounding, and hybrid energy assisting and recovery through both mechanical and electrical systems. Following the concept definition, design, and analysis phases of the program, the final phase focused on building and validating the performance and efficiency in laboratory tests. While aspects of the system such as start/stop and reduced off-road cooling package energy losses were only analytically evaluated, the main 13L concept engine with full hybrid system was successfully built and tested in steady-state and in transient certification and real-world application cycles. Extensive simulations in Caterpillar's DYNASTY™ software environment utilized the validation test data to assess performance more fully and confidently over varied cycles and strategies. An average fuel consumption reduction of 17.9% was realized, and the majority (~13%) of the benefit stemmed from the core concept 13L engine. In conclusion, a total cost of ownership analysis provides context to commercial viability and where adoption focus should be placed.

33 ADVANCED PROPULSION SYSTEMS↗
DOE OSTI2024

Cyber-Informed Engineering Adoption in University Engineering Programs: An Overview of CIE Integration Successes at Nine U.S. Educational Institutions

This report examines the adoption of Cyber-Informed Engineering (CIE) in university engineering programs, driven by the need to protect critical energy infrastructure from adversarial threats. CIE equips current and future engineers and technicians with the necessary mindset, skills, and competencies to enhance the resilience of engineered systems against cyber attacks. This report highlights nine academic partners who are incorporating CIE into their curricula through various approaches, including lectures, courses, and certificates.

42 ENGINEERING↗
DOE OSTI2022

Tensor Network Quantum Virtual Machine for Simulating Quantum Circuits at Exascale

The numerical simulation of quantum circuits is an indispensable tool for development, verification, and validation of hybrid quantum-classical algorithms intended for near-term quantum co-processors. The emergence of exascale high-performance computing (HPC) platforms presents new opportunities for pushing the boundaries of quantum circuit simulation. Here, we present a modernized version of the Tensor Network Quantum Virtual Machine (TNQVM) that serves as the quantum circuit simulation backend in the eXtreme-scale ACCelerator (XACC) framework. The new version is based on the scalable tensor network processing library ExaTN (Exascale Tensor Networks). It provides multiple configurable quantum circuit simulators that perform either an exact quantum circuit simulation via the full tensor network contraction or an approximate simulation via a suitably chosen tensor factorization scheme. Upon necessity, stochastic noise modeling from real quantum processors is incorporated into the simulations by modeling quantum channels with Kraus tensors. By combining the portable XACC quantum programming frontend and the scalable ExaTN numerical processing backend, we introduce an end-to-end virtual quantum development environment that can scale from laptops to future exascale platforms. We report initial benchmarks of our framework, which include a demonstration of the distributed execution, incorporation of quantum decoherence models, and simulation of the random quantum circuits used for the certification of quantum supremacy on Google’s Sycamore superconducting architecture.

Nguyen, Thien↗
DOE OSTI2020

Energy Intensity Baselining and Tracking Guidance

Each company joining the U.S. Department of Energy’s (DOE’s) Better Buildings, Better Plants Program (Better Plants) commits to establishing an energy consumption and energy intensity (EI) baseline and to tracking its energy performance over a 10-year period against that baseline. The baseline must reflect a company’s energy consumption over a 12-month period, covering all its U.S.-based operations. Energy consumption is calculated by fuel type in terms of primary energy (also known as source energy). EI is broadly defined as the amount of energy consumed per unit of output produced. For this guidance document and for the program, the term energy performance represents an evaluation of a facility’s capacity to use energy efficiently. Metrics used to assess a facility’s energy performance can include EI, energy consumption, improvements in EI, etc. Establishing an energy baseline and tracking system is a critical first step in effectively managing energy use. Developing a baseline can help a company understand energy use within the corporation and give it a point of comparison to evaluate future efforts to improve energy performance. It can also support efforts to validate a company’s energy management activities, improve comparative analyses when using benchmarks, and help in predicting future energy needs. In addition, a company that normalizes its performance data can determine highly defensible measures of energy savings generated through implemented energy efficiency projects. Establishing a baseline and tracking energy performance is also a requirement for ISO 50001 certification. Although basic energy data can be collected through utility bills, most manufacturers will have to perform additional analyses to develop accurate and robust energy baselines and tracking systems. Energy is consumed in many ways within the manufacturing sector and can come from multiple sources. Energy is sometimes generated and sold to other parties or captured and reused on-site. External events can exert a significant impact on a facility or company’s energy use independent of any purposeful efforts to improve energy efficiency. Operational changes, such as production shifts—which may be inevitable for some companies over the 10-year period covered by the program—can also make a big difference in energy use. Since Better Plants asks companies to account for all their U.S.-based operations, mergers, acquisitions, and divestitures can also have significant implications for a company’s energy metrics. This document aims to demystify the sometimes complex baselining process. It devotes special attention to the task of normalizing and adjusting energy consumption to account for external factors, such as weather and production changes. A key recommendation is that companies use regression analysis to normalize their energy consumption data whenever possible. Regression analysis is a statistical technique that estimates the dependence of a variable (i.e., energy use in the context of Better Plants) on one or more independent variables such as ambient temperature, while controlling for the influence of other variables at the same time. A properly developed regression analysis can provide a reliable estimate of energy savings resulting from energy improvement strategies and projects by accounting for the effects of variables such as annual production levels and weather. DOE has developed a companion Energy Performance Indicator software tool (EnPI) to simplify the baselining process. This tool can run regression models, calculate changes in EI at the facility level, and automatically compile facility-level data into a corporate-wide metric. Note that although the relevant equations used to calculate EI are provided in this document, the EnPI tool will automatically perform most calculations for the user. Additionally, Better Plants Partners (Partners) can call on their Technical Account Manager (TAM) to help them establish a baseline and assist with the necessary calculations to track progress.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2021

Competitiveness Improvement Project 2020 Informational Workshop

These presentations from the Dec. 8, 2020, Competitiveness Improvement Project (CIP) workshop provide an overview of the CIP process, evaluation criteria, certification requirements, and NREL's technical support opportunities for those interested in applying to CIP solicitations.

49 EE - Wind and Water Power Program - Wind (EE-4W↗
DOE OSTI2021

Digital Platform Informed Certification of Components Derived from Advanced Manufacturing Technologies

The Transformational Challenge Reactor is being designed at Oak Ridge National Laboratory to demonstrate the feasibility of constructing a reactor core using advanced manufacturing technology. This technology includes additive manufacturing combined with machine learning, materials science, and data science technologies in an effort to facilitate the expansion of additive manufacturing into advanced nuclear energy systems and other applications requiring a high level of quality assurance. The Transformational Challenge Reactor is employing additive manufacturing and artificial intelligence to deliver a new approach. Beginning in FY21, the focus of the program has shifted away from demonstrating a reactor, and instead, towards delivering on four key thrust areas: (1) artificial intelligence-informed design, (2) advanced materials, (3) integrated sensing and control, and (4) the digital platform. Of these four thrust areas, the most pertinent to this report is the digital platform. The digital platform has the potential to be a key enabler for a paradigm shift in how components, those derived from advanced manufacturing technologies, are certified for use in nuclear applications. This is achieved primarily using machine learning to discover correlations from the abundance of data produced through additive manufacturing and those physical properties critical to the performance of the component.

21 SPECIFIC NUCLEAR REACTORS AND ASSOCIATED PLANTS↗