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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 289 records · Page 16
All sourcesNASA NTRSDOE OSTI
DOE OSTI2023

Empirical Study of the Effect of Thermal Loading on the Heating Efficiency of Variable-Speed Air Source Heat Pumps

Heating buildings with air source heat pumps (ASHPs) has the potential to save energy compared to utilizing conventional heat sources. Accurate understanding of the efficiency of ASHPs is important to maximize the energy savings. While it is well understood that, in general, ASHP efficiency decreases with decreasing outdoor temperature, it is not well understood how the ASHP efficiency changes with different levels of thermal loading, even though it is an important consideration for sizing and controlling ASHPs. The goal of this study was to create an empirical model of the ASHP efficiency as a function of two independent variables–outside temperature and level of thermal loading. Four ductless mini-split ASHPs were evaluated in a cold chamber where the temperature (representing the outdoor temperature) was varied over a wide range. For each temperature, the ASHP performance data were collected at several levels of thermal loading. The data for all four ASHPs were combined and approximated with an analytical function that can be used as a general model for the ASHP steady-state efficiency as a function of the outside temperature and level of thermal loading. To the knowledge of the authors, no such empirical model that is solely based on third-party test data has been published before. While limitations exist, the model can be used to help guide future selection and operation of ASHPs.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2022

Technical Characterization and Benefit Evaluation of 5G-Enabled Grid Data Transport and Applications

This report summarizes the Year 1 work of Pacific Northwest National Laboratory’s (PNNL’s) 5G Fabricated Resource and Asset Management Encompassment for energy infrastructure (Energy FRAME) project funded by the Department of Energy Office of Science’s Advanced Scientific Computing Research Program. 5G is a breakthrough technology that enables a fully mobile and connected society, and a 5G-enabled digital continuum will be one of the critical foundations for a clean energy economy and grid modernization. In collaboration with PNNL’s Advanced Wireless Communication team and Center for Advanced Technology Evaluation team, the project team has been evaluating the system performance of 5G testbeds in the PNNL 5G Innovation Studio, and has formulated a co-simulation test case of power system transmission, distribution, and communication (T&D&C) networks considering 5G technology and high penetration of distributed energy resources. The methodology developed in the 5G Energy FRAME project can be customized to fit different future grid scenarios to evaluate multiple (dynamic) configurations (computing, sensing, communication, environment) for different stakeholders. In summary, our main technical highlights in project Year 1 are as follows: 1) Technical characterization of 5G standalone architectures, 2) Formulation of co-simulation test case of T&D&C networks embedded with 5G, 3) Initial benefit evaluation of 5G communication platform for grid use cases, and 4) Additional extended discussions on edge computing, artificial intelligence and machine learning, and high-performance computing and cloud computing adoptions. In addition, a collection of system performance data is shared through the publicly available weblink, https://www.pnnl.gov/projects/5g-energy-frame/publications

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2025

Virtual Reality for Shoot/No-Shoot Decision Training in Law Enforcement: A Literature Review and Research Agenda

Virtual reality (VR) can materially improve “shoot / no-shoot” (SNS) training by giving officers realistic, repeatable practice making high-stakes decisions under pressure. Traditional tools—live-fire ranges and video simulators—build basics, but they cannot adapt to each officer in real time or fully mirror the complexity of the field. VR closes that gap by creating immersive scenarios that are safer, more flexible, easier to scale across units, and able to capture objective performance data. SNS decisions are not just about marksmanship; they rely on perception, judgment, memory, and the ability to hold fire when a threat is uncertain. Effective training therefore needs realism, decision complexity, and branching outcomes that reflect the true consequences of choices. These elements strengthen recognition of hostile intent while reducing false positives and building the self-control required in ambiguous situations. VR brings specific advantages: dynamic environments, full-body interaction, and the ability to measure performance with precision—enabling targeted feedback and better transfer of learning to the street. At the same time, responsible deployment must address scenario quality (credible environments and behaviors), lawful decision models, and user wellbeing (appropriate stress levels, comfort, and safety). Sandia’s VIPER Lab is positioned to lead this work. The team combines human-performance science, AI/ML, and VR/AR development with a deep equipment bench (e.g., omnidirectional treadmill, eye-tracking, haptics, multiple HMDs). This ecosystem supports building and validating next-generation SNS training that is immersive, measurable, and trustworthy. Bottom line: Investment in VR-enabled SNS training that blends evidence-based design with careful validation and legal safeguards is expected to pay off in safer, more consistent decision-making and improved community trust, delivered through training that is practical to deploy at scale.

45 MILITARY TECHNOLOGY, WEAPONRY, AND NATIONAL DEF↗
DOE OSTI2021

KBase Silver Case Study: Determining Media Formulation Requirements for Isolation of Microbiome Constituents

KBase has powerful tools for extracting microbial genomes from metagenomes and performing phylogenomic analysis and metabolic modeling. These tools can be used to predict key media ingredients for isolating uncultured members of microbiomes. Essential to this process are high-quality genomes extracted from metagenomic assemblies, and Kbase has a tool for assessing the quality of genomes too. Below we identify growth factors for a myxobacteria ("slime bacteria") yet to be isolated from the rhizosphere of Miscanthus xgiganteus (hybrid of "Silvergrass"), cultivated at the Kellogg Biological Station in Michigan. Data was transferred with Globus from JGI-IMG. This narrative and the "KBase Gold Case Study: Can you find Delftia?" make up the Silver and Gold Narrative Set for teaching metagenomics concepts to students in the BIT 477/577 course at North Carolina State University. This tutorial will guide the user through the process of extracting and annotating high-quality genomes from a metagenomic data, performing phylogenomic analysis, building a metabolic model, and using these to predict nutrient requirments for growth and isolation of corresponding microbes.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2021

IoT Devices and Applications for Wire-Based Hybrid Manufacturing Machine Tools

Hybrid manufacturing machine tools have the potential to be a disruptive technology as they can leverage the benefits of both additive and subtractive manufacturing by incorporating both processes on the same machine while limiting the downsides of the individual processes. Since these machines use two very disparate manufacturing processes and hybrid manufacturing is an emerging technology, it will be useful to monitor data coming from the machine and apply it to improve the manufacturing process, the operation of the machine, and to integrate the machine into the larger digital framework of Industrial Internet of Things (IoT). The present work discusses IoT devices that would be beneficial to add to a hybrid machine tool as well as applications for those devices. The proposed methods discussed in this work have not been experimentally implemented on a hybrid machine tool and so there are no performance data available yet. The hybrid machine tool used as a basis to generate these IoT applications is the Mazak VC-500A/5x AM Hot Wire Deposition, which is a 5-axis machine tool incorporated with a wire feedstock 4kW laser deposition system. Methodologies and applications will be outlined for machine health and process monitoring. Other areas covered include process benchmarking, secure networking options for the proposed IoT framework, and hybrid process improvement. Limitations of these methods and future work for new sensor devices and application areas is also discussed.

Thien, Austen↗
DOE OSTI2024

Performance Modelling and Yearlong Outdoor Degradation Analysis of a GaAs//Si Tandem Module

Silicon-based tandem photovoltaic cells and modules are forecasted to enter mass production around the year 2026. Previous research efforts on tandem photovoltaic technology have focused heavily on increasing cell efficiencies. However, for successful large-scale deployment of tandem modules, reliability and long-term durability will be equally important. Outdoor performance data beyond the one-year mark enable sophisticated degradation analysis and help build confidence in the reliability and durability of this new technology.

degradation rate↗
DOE OSTI2026

Irradiation of Advanced Cladding Specimens in the High Flux Isotope Reactor: Capsule Designs and Test Matrix

The Advanced Fuels Campaign (AFC) has initiated the Advanced Reactor Cladding (ARC) irradiation campaign to generate irradiation performance data for candidate fuel cladding concepts. The campaign includes a diverse set of ferritic/martensitic steels, oxide dispersion strengthened (ODS) alloys, FeCrAlbased alloys, coated materials, and welded cladding specimens produced through multiple US Department of Energy (DOE) programs and international collaborations. Three complementary experimental thrusts comprise the campaign: tensile testing (ARC Tensile) to rapidly screen candidate alloys, fracture toughness testing (ARC Fracture) to evaluate irradiation effects on crack resistance, and tubular weld testing (ARC Weld) to quantify irradiation-induced changes in the mechanical performance of end cap welds. This report documents the irradiation campaign design, including the selected materials, specimen types, irradiation matrix, and capsule designs for irradiation within the High Flux Isotope Reactor (HFIR). A total of 14 irradiation capsules were developed to achieve target irradiation temperatures between 300°C and 600°C and doses up to 30 dpa. Thermal analyses were performed using finite element methods to establish capsule geometries capable of achieving the desired specimen temperatures while accommodating differences in specimen geometry and material properties. The resulting capsule designs provide the basis for irradiation of the AFC-ARC experimental matrix and subsequent post-irradiation examination to assess the effects of neutron irradiation on advanced cladding materials.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
DOE OSTI2020

Application and Certification of Comparative Vacuum Monitoring Sensors for Structural Health Monitoring of 737 Wing Box Fittings

Multi-site fatigue damage, hidden cracks in hard-to-reach locations, disbonded joints, erosion, impact, and corrosion are among the major flaws encountered in today's extensive fleet of aging aircraft and space vehicles. The use of in-situ sensors for real-time health monitoring of aircraft structures are a viable option to overcome inspection impediments stemming from accessibility limitations, complex geometries, and the location and depth of hidden damage. Reliable, structural health monitoring systems can automatically process data, assess structural condition, and signal the need for human intervention. Prevention of unexpected flaw growth and structural failure can be improved if on-board health monitoring systems are used to continuously assess structural integrity. Such systems are able to detect incipient damage before catastrophic failures occurs. Condition-based maintenance practices could be substituted for the current time-based maintenance approach. Other advantages of on-board distributed sensor systems are that they can eliminate costly, and potentially damaging, disassembly, improve sensitivity by producing optimum placement of sensors and decrease maintenance costs by eliminating more time- consuming manual inspections. This report presents a Sandia Labs-aviation industry effort to move SHM into routine use for aircraft maintenance. This program addressed formal SHM technology validation and certification issues so that the full spectrum of concerns, including design, deployment, performance and certification were appropriately considered. The Airworthiness Assurance NDI Validation Center (AANC) at Sandia Labs, in conjunction with Boeing, Delta Air Lines, Structural Monitoring Systems Ltd., Anodyne Electronics Manufacturing Corp. and the Federal Aviation Administration (FAA) carried out a certification program to formally introduce Comparative Vacuum Monitoring (CVM) as a structural health monitoring solution to a specific aircraft wing box application. Validation tasks were designed to address the SHM equipment, the health monitoring task, the resolution required, the sensor interrogation procedures, the conditions under which the monitoring will occur, the potential inspector population, adoption of CVM into an airline maintenance program and the document revisions necessary to allow for routine use of CVM as an alternate means of performing periodic structural inspects. To carry out the validation process, knowledge of aircraft maintenance practices was coupled with an unbiased, independent evaluation. Sandia Labs designed, implemented, and analyzed the results from a focused and statistically-relevant experimental effort to quantify the reliability of the CVM system applied to the Boeing 737 Wing Box fitting application. All factors that affect SHM sensitivity were included in this program: flaw size, shape, orientation and location relative to the sensors, as well as operational and environmental variables. Statistical methods were applied to performance data to derive Probability of Detection (POD) values for CVM sensors in a manner that agrees with current nondestructive inspection (NDI) validation requirements and also is acceptable to both the aviation industry and regulatory bodies. This report presents the use of several different statistical methods, some of them adapted from NDI performance assessments and some proposed to address the unique nature of damage detection via SHM systems, and discusses how they can converge to produce a confident quantification of SHM performance An important element in developing SHM validation processes is a clear understanding of the regulatory measures needed to adopt SHM solutions along with the knowledge of the structural and maintenance characteristics that may impact the operational performance of an SHM system. This report describes the major elements of an SHM validation approach and differentiates the SHM elements from those found in NDI validation. The activities conducted in this program demonstrated the feasibility of routine SHM usage in general and CVM in particular for the application selected. They also helped establish an optimum OEM-airline-regulator process and determined how to safely adopt SHM solutions. This formal SHM validation will allow aircraft manufacturers and airlines to confidently make informed decisions about the proper utilization of CVM technology. It will also streamline the regulatory actions and formal certification measures needed to assure the safe application of SHM solutions.

42 ENGINEERING↗
DOE OSTI2024

Real Time-Optimal Power Flow-Based Distributed Energy Resource Management System (DERMS)

This project aims to promote lab-proven clean energy technology to commercially scalable versions of the technology, integrate the technology with broader systems, provide extended performance data, and validate the manufacturability and reliability of the technology. The lab-proven technology, RT-OPF DERMS, was developed and validated through previous U.S. Department of Energy-funded efforts, including Advanced Research Projects Agency-Energy funding under the Network Optimized Distributed Energy Systems program and Holy-Cross Energy High Impact Project. In the Advanced Research Projects Agency-Energy Network Optimized Distributed Energy Systems project, the RT-OPF DERMS was developed and implemented in multiple hardware platforms, demonstrating its performance and capabilities in the lab and field environments. The technology was also evaluated and matured via a participation in the U.S. Department of Energy I-Corps program, whose goal is to pair teams of researchers with industry mentors for an intensive 2-month training in which the researchers define technology value propositions, conduct customer discovery interviews, and develop viable market pathways for their technologies. These activities indicate the high technology maturity and Technology Readiness Level of the RT-OPF DERMS.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2024

Marine Algae Industrialization Consortium (MAGIC): Combining biofuel and high-value bioproducts to meet the RFS

The Marine Algae Industrialization Consortium (MAGIC) was formed to address pressing challenges in the commercialization of microalgae as a source of biofuel. The “Marine Algae Industrialization Consortium (MAGIC): Combining biofuel and high-value bioproducts to meet the RFS” project formally addressed two US Department of Energy Bioenergy Technologies Office (BETO) goals: (1) Model the sustainable supply of 1 million metric tonnes ash free dry weight (AFDW) cultivated algal biomass and (2) Demonstrate valuable co-products produced along with biofuel intermediates to increase value of algal biomass by 30%. To achieve these goals, the project demonstrated and validated high-value co-products to drive down the cost of biofuel by increasing the value of algae “co-products” towards increasing the selling price of total algae biomass as one of the key drivers of economics and adoption. This was accomplished through five core, interdependent tasks including: (1) strain selection to identify and deliver strains for mass culture, (2) mass culture using a hybrid cultivation system and following key operating parameters for downstream applications to provide algae feedstock, (3) recovery and conversion to evaluate two alternative methods to separate dry algae biomass into oil and residuals for downstream testing, (4) product assessment to determine biofuel, aquafeed or poultry feed product efficacy using algae biomass fractions as well as to provide critical performance data for valuation and (5) commercialization to use technoeconomic and life cycle assessments (TEA/LCA) as iterative design and assessment tools including consideration of target markets, competitors, and distribution channels to guide product assessment, development and valuation. A total of 46 peer-review publications, many open-access, provide detail of much of the work carried out and the results of the tasks. Additional reports and presentations provide other technical and public engagement material. At a high level, using a variety of approaches, more than 1000 marine microalgae strains were evaluated to ultimately identify the seven winners that were down-selected to be grown in mass culture. Strain selection demonstrated that there were no ‘super strains’ and that each candidate had strengths and limitations for specific products, growth conditions or operational considerations. Mass culture growth of these seven strains at >5000 L / 29 m 2 scale found that four them were suitable for product assessment. More than 250 kg of biomass was produced across hundreds of pond runs along with thousands of cultivation entries on the growth and biomass characteristics as well as environmental parameters. In the process, dozens of standard operating procedures were generated as was custom software to process and analyze cultivation data. Recovery and conversion of algae biomass demonstrated that a hexane solvent based extraction protocol was most effective at recovering oil (biocrude) from algae and four strains were processed to produce oil and lipid extracted algae (residuals) for downstream testing. Membrane-based oil separation was less successful, but may still be applicable to other commercial applications in the future. Product testing demonstrated that algae biocrude is of high quality and hydrotreating generated numerous fractions of high quality composition for fuel and lubricate based applications. Aquafeed studies performed at a variety of scales showed that both whole and defatted (lipid extracted algae) microalgae were suitable as a feed ingredient, but that the specifics of the fed animal and biochemical composition of the algae are critical factors when determining formulation. Similarly, poultry studies on whole and defatted microalgae generally showed positive outcomes on animal growth and health, with some microalgae providing enhanced nutritional composition of the animal product. Economic and life cycle assessments covered a wide range of possible commercialization and sustainability scenarios. Replacement value, improved product value added, consumer values marketing added valuation and improved animal health were considered as alternatives for microalgae valuation. Using the open pond system, algae productivity was identified as the key driver of commercialization economics, but combination of co-products (e.g. animal feed) with biofuel production substantially increased the total selling price of algae. Modeled microalgae selling price exceeded $\$$1500/tonne and could generate competitive biofuel selling prices below $\$$5 gallon gas equivalents using realistic algal productivities. Short (process scale) and longer (decadal trends) sustainability assessments show that marine microalgae can enhance the sustainability of energy production and lead to other realized benefits in water, fertilizer and land use for other sectors (e.g. agriculture). This project successfully demonstrated all of the components of an end-to-end process from mass microalgae cultivation and dewatering, to recovery and conversion of algae biomass components, to final product demonstration and process valuation; the combined results provide a framework for future commercialization of algae based biofuels.

09 BIOMASS FUELS↗
DOE OSTI2023

Front-End Engineering and Design: Project Tundra Carbon Capture System (Final Report)

As the next phase of Project Tundra, Minnkota Power Cooperative (MPC) completed a front-end engineering and design (FEED) study to install a post-combustion CO 2 capture system (CCS) at the MPC operated (Square Butte Electric Cooperative-owned) Milton R. Young Station (MRYS) Unit 2 (MRY2), a 477-MW power plant fueled by North Dakota lignite. The project team completed the FEED with Fluor’s Econamine FG Plus℠ (EFG+) technology and is taking the next steps leading up to start of construction. Team members included FEED technical lead Fluor Enterprises (Fluor); owner’s engineer and balance of plant (BOP) engineer Burns & McDonnell (BMcD); leading carbon capture consultants (David Greeson Consulting, Hunt International Energy Services); environmental consultants (AECOM, Agora Environmental), other BOP engineering consultants (Golder Associates, Nels); cost-share funding agency, the North Dakota Industrial Commission (NDIC); and the Energy & Environmental Research Center (EERC). Project Tundra’s goal is to implement carbon capture, utilization, and storage (CCUS) in North Dakota, while preserving the use of lignite. Future options could lead to revitalizing legacy oil fields and creating a new CO 2 enhanced oil recovery (EOR) industry. The topic of this report is the FEED study for the carbon dioxide capture portion of Project Tundra. Building on the findings of a pre-FEED study for MRY2, the key deliverables contained in this FEED study are: a) design, costing, and performance data needed to commence project financing activities; b) engineering and material balances required to file for all project permits; and c) a final project schedule. Based on the results of the previous pre-FEED study, MPC and its team evaluated two options to provide the large amount of steam needed to operate the CCS: 1) installation of new natural gas package boilers and a new pipeline to supply natural gas to the facility and 2) extraction of steam from the existing MRYS steam turbine generator. The FEED study was performed using the package boilers option, as it was deemed to have the lowest technical risk, overall cost, and cost uncertainty. A key feature of the design, however, was the mixing the natural gas boilers’ flue gas with the MRY2 flue gas. This increased the size of the CCS system. This design also enabled the tie-in of Unit 1 flue gas for times when Unit 2 is offline. The CCS was designed to capture a combined 12,978 short tons per day (STPD) of CO 2 from the flue gases produced by the existing MRY Unit 2 along with flue gas produced by the new boiler package within the CCS. The 12,978 STPD CO 2 capture is achieved by normally processing 100% of the total available MRY Unit 2 flue gas (considering full load operation) and 100% of the boiler package flue gas, then removing 90% of the CO 2 available from the processed flue gas streams.

01 COAL, LIGNITE, AND PEAT↗
DOE OSTI2022

Flexible machine-learning interatomic potential for simulating structural disordering behavior of Li 7 La 3 Zr 2 O 12 solid electrolytes

Batteries based on solid-state electrolytes, including Li 7 La 3 Zr 2 O 12 (LLZO), promise improved safety and increased energy density; however, atomic disorder at grain boundaries and phase boundaries can severely deteriorate their performance. Machine-learning (ML) interatomic potentials offer a uniquely compelling solution for simulating chemical processes, rare events, and phase transitions associated with these complex interfaces by mixing high scalability with quantum-level accuracy, provided that they can be trained to properly address atomic disorder. To this end, we report the construction and validation of an ML potential that is specifically designed to simulate crystalline, disordered, and amorphous LLZO systems across a wide range of conditions. The ML model is based on a neural network algorithm and is trained using ab initio data. Performance tests prove that the developed ML potential can predict accurate structural and vibrational characteristics, elastic properties, and Li diffusivity of LLZO comparable to ab initio simulations. As a demonstration of its applicability to larger systems, we show that the potential can correctly capture grain boundary effects on diffusivity, as well as the thermal transition behavior of LLZO. Here these examples show that the ML potential enables simulations of transitions between well-defined and disordered structures with quantum-level accuracy at speeds thousands of times faster than ab initio methods.

25 ENERGY STORAGE↗
DOE OSTI2024

Platform for Remote Deployment and Training for Enhanced Building Operation Practices (Building Re-Tuning and On-going Commissioning)

While a building’s energy usage is driven largely by its design and use, building operator behavior has a strong influence on its energy consumption. This project developed and piloted a specific, data-driven coaching methodology to help operators understand how they can adjust operations and/or affect no/low-cost repairs or upgrades to their specific building HVAC systems to reduce energy consumption. Named BuildingCoach, the operational optimization method used is based on the Building Re-tuning approach developed by the Pacific Northwest National Laboratory. A building operations analytics market has matured over the past decade, though its potential to affect energy-saving changes has not been fully realized. Training operators to understand the methods for operational optimization with the explicit approach of using building-system performance data is hypothesized to create a more effective, longer lasting result in building energy efficiency, and this strategy is the fundamental premise of this project. With the support of an Industry Advisory Board, the project succeeded in developing materials and recruiting for and delivering three pilot cohorts. Deliverables included twenty-two self-paced training modules (accessed via a Learning Management System) and a web-based platform that includes access to real-time building system data and a repository for building system documentation. The project set out to have 100 participants from 50 buildings in three pilot cohorts. In the end, there were 28 participants from 17 buildings, i.e., a significant shortfall. The first two pilot cohorts had only two buildings in each, and this was partially due to difficulties in deploying the Building Operator Coaching Solution (“the BOCS”), which is technology that extracts the data from the controls network and presents it as prescribed for coaching. In the third cohort, the project team deployed the BOCS successfully to 13 buildings, the methodology was piloted as intended, and numerous opportunities for optimization were identified. The BuildingCoach business plan charts a path to an economically sustainable effort. However, even with a licensing model captured in the final version of the business plan, the scalability is still limited to keeping less than 1,000 buildings affected by 2033. Even so, there are unexplored paths to greater scalability that are being considered. CUNY BPL is working to perpetuate and grow the use of BuildingCoach. As of this writing, about twenty buildings have either been connected or will be connected with operators coached / to be coached in the NYC municipal portfolio, twelve buildings across four campuses in NY State will use BuildingCoach, a NY upstate county wishes for six or seven buildings to participate with the support of funding from NYSERDA, and others have also expressed interest. In the decades to come, there will be an increasing percentage of large and mid-sized buildings that incorporate automated system optimization (ASO), and the building operators’ role will shift to spend more time on maintenance and monitoring. Meanwhile, programs such as BuildingCoach will play a critical role in optimizing operations. And, regardless of the emergence of ASO, operators will still need to understand how their systems operate so that they can monitor them properly. Within that context, BuildingCoach is an important step towards operators’ understanding of efficient building system operations.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2021

HEPA Filter Age Evaluation Report

In 2020, PNNL completed a literature search for high-efficiency particulate air (HEPA) filter age information. HEPA filters are used in many operations to remove particulate matter from effluent exhaust streams. They are thought to degrade over time both during proper storage and during normal operational service; however, the rate at which the filters degrade remains unknown. This brings into question if age is an adequate indicator of HEPA filter performance. Data from six previous reports were obtained from the literature search and combined to create a data set of 1600 operating filters. Filter usage was identified from multiple facilities. The various types of filters (e.g., axial flow, self-contained, and standard 24 x 24 x 11.5 inches; and both separator and separatorless) reported were constructed to the requirements Section FC (HEPA Filters) or Section FK (Special HEPA Filters) of the American Society of Mechanical Engineers AG-1 code. Filters were presumed to have continuously met the operational criteria and in particular passed both annual efficiency tests and annual DP measurements. The environmental conditions within the exhaust system were also assumed adequate for long-term filter operation. The data, by the nature of the reports, excludes rejected filters from quality assurance evaluations, intake, or installation testing. The collective data set shows over half of were operating past the current 10-year Department of Energy (DOE) limit. Data was evaluated for age lifetime using a linear trendline, survival function, probability functions, and failure rate; financial impacts were also addressed. This report supports the notion that HEPA filters can operate safely and efficiently under proper maintenance well past the 10-year lifetime established by DOE. Using the results of the four age evaluation approaches, they collectively point to the reasonableness of an operating HEPA filter lifetime of 20 years. The results are not necessarily definitive, but nevertheless, they are promising. The analysis provides reasonable assurance that when implemented using a graded approach with well-defined performance and operational requirements, extending the service life for HEPA filters beyond 10 years is low risk.

42 ENGINEERING↗
DOE OSTI2022

Photovoltaics module reliability for the terawatt age

Photovoltaics (PV), or solar electricity generation, has become the cheapest form of energy in many locations worldwide and, combined with energy storage, has the potential to satisfy most of our electricity needs. PV has grown at an annual compounded growth rate of approximately 30% in the last three decades. Solar energy systems will continue their impressive growth in distributed energy, microgrids, and utility scale, as efforts are made for dependable electricity in an age with increasing extreme weather. However, within this remarkable success lies a new challenge. The growth curve, combined with rapid product innovation and scale up, means that the majority of PV systems are new, without the three years of performance data that have been required in the past to estimate product lifetime. PV reliability has to address this challenge. In this review we present a brief summary of PV reliability starting with brief historical synopsis, detailing some of the technological challenges and present a framework required for long lifetime.

14 SOLAR ENERGY↗
DOE OSTI2025

Field‐Relevant Degradation Mechanisms in Metal Halide Perovskite Modules

Field testing, failure analysis, and understanding of degradation mechanisms are essential to advancing metal halide perovskite (MHP) photovoltaic (PV) technology toward commercialization. Here, we present performance data from up to 1 year of outdoor testing of MHP modules in Golden, Colorado. The module encapsulation architecture and encapsulant materials have a significant impact on module reliability, with modules containing a polyolefin elastomer (POE) in addition to a desiccated polyisobutylene (PIB) edge seal outlasting modules with only a PIB edge seal or PIB blanket. Nondestructive and destructive characterization of the field-tested modules points to module scribes and interfaces as areas of potential mechanical weakness and chemical migration, resulting in shunt pathways and increased series resistance. Finally, indoor accelerated stress testing with light and elevated temperatures is performed, demonstrating failure with similar scribe degradation signatures as compared to the field-tested modules. In conclusion, under both outdoor testing and light and elevated temperature conditions, electrochemical corrosion between the copper electrode and the mobile iodine ions appeared dominant, with a significant progression at the scribes that is speculated to result from an interplay between the initial laser damage and joule heating from enhanced ion diffusion under bias.

14 SOLAR ENERGY↗
DOE OSTI2021

Hydropower Biological Evaluation Toolset Best Practice Guide

Field studies using live fish are necessary for the evaluation of turbine biological performance, but they cannot determine the specific hydraulic conditions or physical stresses experienced by the fish, the locations where deleterious conditions occur, or the specific causes of the biological response. Using the Sensor Fish (SF) sensing technology, this deficiency can be overcome because the SF can be released independently or concurrently with live fish directly into operating infrastructure, and it takes high-frequency measurements of hydraulic conditions such as pressure, acceleration, and rotation acting on a body in situ during downstream passage. The Hydropower Biological Evaluation Tools (HBET; Hou et al. 2018) software package, developed by Pacific Northwest National Laboratory (PNNL), is designed to assemble, organize, and process data collected by the PNNL-developed SF and by live fish. HBET was developed specifically to design SF field studies, process the raw data, and analyze the processed data efficiently and scientifically. Its objectives are to facilitate SF studies focused on characterizing hydraulic conditions and to apply SF data for evaluating the impacts on fish from passage through hydro-structures. HBET allows users to design new studies, analyze data, perform statistical analyses, and evaluate predicted biological responses. It can be used by researchers, turbine designers, hydropower operators, and regulators to evaluate hydro-structures to enhance environmental sustainability in a cost-effective manner.

13 HYDRO ENERGY↗
DOE OSTI2024

Earth System Reanalysis in Support of Climate Model Improvements

Recent climate model developments, established through increased model resolution, have led to substantial improvements in model simulations of the time-evolving, coupled Earth system and its subcomponents. However, regardless of resolution, climate models will always produce climate features and variability that differ from the real world and will be prone to biases. This is due to many remaining uncertainties, such as in parametric and structural model uncertainty, in the initial conditions prescribed, and in the prescribed (scenario) forcing which varies on decadal to centennial timescales. Further model improvements are expected to arise specifically from improved representation of physical processes realized through model-data fusion. This will create an unprecedented opportunity to better exploit a large array of Earth observations, from in situ measurements to weather radars and satellite observations, as the resolved scales of the models approach those of the observations. For this, climate DA will be the central tool to bring models and observations into consistency, by improving initial conditions, inferring uncertain model parameters and structure, and quantifying uncertainty. Generally, there will be advantages and complementarities of adjoint-based smoother approaches, ensemble-based filter approaches, or new ML-inspired approaches. Yet, the ever-increasing model resolution will present growing challenges arising from computational cost, calling for new ways of performing data assimilation and model optimization. Using the complementarity in a hybrid approach, blending tools and concepts from variational, ensemble and ML methods might be what is required in the future. In this context ML could be important to handle non-linear responses, and to better approximate non-Gaussian distributions.

54 ENVIRONMENTAL SCIENCES↗