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DOE OSTI2021

Characterizing manufacturing wastewater in the United States for the purpose of analyzing energy requirements for reuse

This paper seeks to inform an improved understanding of the energy tradeoff associated with on-site manufacturing water reuse in the United States from a lifecycle perspective, in part by developing an analytical framework for understanding when this tradeoff for reuse is beneficial. We survey the literature to assess the current state of reuse and its motives and barriers in the United States, before synthesizing information from publicly available EPA data on contaminants in US manufacturing wastewaters and technologies for treating them. Using the available data, we derive a set of “ubiquitous contaminants” among the top ten in terms of mass discharged in more than half of US manufacturing subsectors (NAICS 31–33) according to EPA permit data. We also present information on proven treatment trains and their energy requirements. We then compare water quality requirements for specific contaminants in reclaimed water to those characteristic of wastewater streams currently being discharged from manufacturing plants into surface waters to highlight sectors with reuse opportunities that could require little cost to realize, such as primary metals and, to a lesser extent, petroleum and coal products. We conclude by highlighting data limitations that need to be rectified before applying the framework more broadly and discussing how these data gaps could be filled. Better understanding the relationship between energy and water in the context of on-site manufacturing water reuse would allow manufacturers to improve resiliency by reducing regulatory, physical, and reputational risks while lessening their footprint on local watersheds.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗

DOE OSTI2019

Using the American Time-Use Survey for Travel-Behavior Research

This presentation, by Venu Garikapati of the National Renewable Energy Laboratory, provides information about the American Time Use Survey (ATUS), which was conducted by the U.S. Bureau of Labor Statistics and the U.S. Census Bureau. The presentation will also cover examples on utilizing time use data for understanding travel patterns.

25 ENERGY STORAGE↗

DOE OSTI2023

Software Bill of Materials (SBOM) Sharing Lifecycle Report

As Software Bill of Materials (SBOM) adoption efforts mature, SBOM sharing continues to occur, but no single solution or set of solutions have become ubiquitous. The purpose of this report is to enumerate and describe the different parties and phases of the SBOM sharing lifecycle and assist readers in choosing suitable SBOM sharing solutions based on the amount of time, resources, subject-matter expertise, effort, and access to tooling that is available to the reader to implement a phase of the SBOM sharing lifecycle. The SBOM sharing lifecycle consists of the Discovery, Access, and Transport of an SBOM and this report details these individual phases and how an SBOM goes from author to the consumer. This report also details how potential enrichment activities may be performed on an SBOM to create a new product before or after it has been shared. The concept of a sophistication classification for SBOM sharing solutions is concurrently introduced with a focus on the inclusion or lack of certain features and effort associated with their implementation. Examples of low, medium, and high-sophistication solutions are provided; however, these examples and associated categorizations should not be seen as a qualitative judgment meant to push the reader towards a particular adoption strategy since sharing solutions are chosen based on the unique needs of the user. This report does recommend the SBOM community consider how to make current and future sharing solutions interoperable with each other as well as more automated methods to facilitate sharing and broader SBOM adoption. This report also highlights an SBOM sharing survey results obtained from interviews with stakeholders to understand the current SBOM sharing landscape. The categorized results of the survey suggest that SBOMs are currently transported directly to the receiver through email or similar informal communication mechanisms or alternatively the SBOM resides on a repository available to consumers. In addition to these transport methods, this report captures industry efforts to create private sharing solutions and services that can store and transport enrichment data and may use higher sophistication features that are cloud-based or using distributed ledger technologies.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2018

Neutron Residual Stress Mapping for Spent Nuclear Fuel Storage Canister Weldment

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗

DOE OSTI2019

Fracture Toughness Evaluation for Sandia Mock-up Stainless Steel Canister Weldment Using Spiral Notch Torsion Fracture Toughness Test

Many radioactive materials within the nuclear fuel cycle present a significant hazard. Such materials include spent nuclear fuel, high-level waste, legacy waste and other nuclear materials that have no current outlet than storage. These materials are often held in long-term storage as an interim stage within their lifecycle. Lifecycles can include reuse or disposal. Safety and security of spent nuclear fuel (SNF) interim storage installations are very important, due to a great concentration of fission products, actinides and activation products. Stress corrosion cracking of interim storage containers has been identified as a high priority data gap by the Department of Energy. However, little has been done with regards to the canister weld material properties, including the fracture toughness, and their impact on stress corrosion, until recently from Sandia National Laboratory canister mockup program. Furthermore, because no post-weld heat treatment was required, the associated high tensile residual stress within these canister welds can drive the initiation and growth of stress corrosion cracking (SCC) cracks. Thus, the fracture mechanical property, such as fracture toughness, is an essential information to understand the canister weldment fracture resistance behavior and to support mitigation protocol development to counter SCC. In this work, we carried out fracture toughness evaluation on the received mock-up canister weldments from Sandia, using spiral notch torsion fracture test technology. The details of the weldment fracture toughness evaluation program development at ORNL are presented in this progress report. The objective of this research is to collect fracture toughness data on the mock-up canister weldment from Sandia National Laboratory. Data will be collected under quasi-static fracture loading using the Oak Ridge National Laboratory (ORNL) developed Spiral Notch Torsion Test (SNTT) technology carried out on a biaxial tension/torsion tester. These data will be used to support the ongoing SNF dry storage canister modeling activities and to address regulatory issues associated with canister container integrity and the follow-on SNF system transport reliability. SNTT has been a recent breakthrough in measuring fracture toughness for different materials, including metals, ceramics, concrete and polymers composites. Due to its high geometry constraint and unique loading condition, SNTT can be used to measure the fracture toughness with smaller specimens without concern of size effects. The application of SNTT to brittle materials has been proved to be successful. The micro-cracks induced by original notches in brittle materials could ensure crack growth in SNTT samples. Therefore, no fatigue pre-cracks are needed. The application of SNTT to the ductile material to generate valid toughness data will require a test sample with sufficient crack length to increase the sample fracture tip localized constraint. Fatigue pre-crack growth techniques are employed to introduce sharp crack front into the sample. Previously, only rough calculations were applied to estimate the compliance evolution in the SNTT crack growth process; while accurate quantitative descriptions have never been attempted. This generates an urgent need to understand the crack evolution during the SNTT fracture testing process of ductile materials. The newly developed governing equations for SNTT crack growth estimate are used to control the fatigue crack growth to effectively reaching the target crack length. The detailed SNTT approach and its estimated fracture toughness for the as-received Sandia canister weldment and the associated baseline 304 stainless steel are presented in this report. In the past SNTT technology has also been successfully applied to investigate the fracture behavior of X52 and X80 steels and the X52 welded materials used for hydrogen infrastructures. The current study in supporting Sandia SNF Dry Storage Mock-up Canister Program is the first attempt of applying SNTT to the highly ductile stainless steel and the associated weld materials. The SNTT test results indicate that SNTT method is a reliable test approach with good repeatability in applying to SS304 steel and SS304/308 weld material. The estimate JQ upon fracture for the baseline SS304 steel is at 1618 lb/in. The estimate JQ upon fracture for the SS304/308 weld from HAZ and weld specimens are 720 lb/in and 850 lb/in, respectively.

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗

DOE OSTI2017

Sustainability Index for Comparison of Environmental Remediation Technologies - 17492

Site remediation is a multi-decade process that incorporations planning from a variety of parties including stakeholders, regulators, scientists and engineers, and community members. When Best Management Practices are integrated during remediation action at sites, there are significant improvements to a variety of metrics including economic, environmental, and societal factors. Green and sustainable remediation tools are Best Management Practices that are a valuable means to reduce or remove environmental footprints after remedial action at sites. Applying the analysis to future feasibility studies, EM takes the next steps towards being a leader in global sustainability. The sustainability index is a quantitative test in response to a recent study published by scientists at the Hanford Site detailing an exit strategy for pump and treat remediation technologies in an effort to move away from active remediation in favor of passive techniques. The sustainability index attempts to quantify the relative sustainability of active and passive remediation strategies by examining a variety of metrics and perspectives from those involved in the decision-making process. Analysis compared 10 metrics encompassing environmental, social, and economic aspects of sustainability for the two types of remediation techniques. Perspectives and values of the stakeholders, regulators, scientists and engineers, and community members involved the decision-making process were also incorporated in this tool. Data were evaluated from active and passive remediation technologies at two locations (Richland, Washington and Mound, Ohio). Data suggest passive remediation technologies performed better in terms of sustainability performance than active technologies. Switching from active remediation to passive remediation techniques has the following impacts: aids in the conservation of local ecosystems, reduces community impacts and improves the community perception of the cleanup, lowers the lifecycle cost of the project, and contributes positively to global sustainability by using less energy and raw materials. (authors)

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗

DOE OSTI2020

DLHub: Simplifying publication, discovery, and use of machine learning models in science

Machine Learning (ML) has become a critical tool enabling new methods of analysis and driving deeper understanding of phenomena across scientific disciplines. There is a growing need for "learning systems" to support various phases in the ML lifecycle. While others have focused on supporting model development, training, and inference, few have focused on the unique challenges inherent in science, such as the need to publish and share models and to serve them on a range of available computing resources. In this paper, we present the Data and Learning Hub for science (DLHub), a learning system designed to support these use cases. Specifically, DLHub enables publication of models, with descriptive metadata, persistent identifiers, and flexible access control. It packages arbitrary models into portable servable containers, and enables low-latency, distributed serving of these models on heterogeneous compute resources. In this work, we show that DLHub supports low-latency model inference comparable to other model serving systems including TensorFlow Serving, SageMaker, and Clipper, and improved performance, by up to 95%, with batching and memoization enabled. We also show that DLHub can scale to concurrently serve models on 500 containers. Finally, we describe five case studies that highlight the use of DLHub for scientific applications.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2015

MODULES FOR EXPERIMENTS IN STELLAR ASTROPHYSICS (MESA): BINARIES, PULSATIONS, AND EXPLOSIONS

We substantially update the capabilities of the open-source software instrument Modules for Experiments in Stellar Astrophysics (MESA). MESA can now simultaneously evolve an interacting pair of differentially rotating stars undergoing transfer and loss of mass and angular momentum, greatly enhancing the prior ability to model binary evolution. New MESA capabilities in fully coupled calculation of nuclear networks with hundreds of isotopes now allow MESA to accurately simulate the advanced burning stages needed to construct supernova progenitor models. Implicit hydrodynamics with shocks can now be treated with MESA, enabling modeling of the entire massive star lifecycle, from pre-main-sequence evolution to the onset of core collapse and nucleosynthesis from the resulting explosion. Coupling of the GYRE non-adiabatic pulsation instrument with MESA allows for new explorations of the instability strips for massive stars while also accelerating the astrophysical use of asteroseismology data. We improve the treatment of mass accretion, giving more accurate and robust near-surface profiles. A new MESA capability to calculate weak reaction rates “on-the-fly” from input nuclear data allows better simulation of accretion induced collapse of massive white dwarfs and the fate of some massive stars. We discuss the ongoing challenge of chemical diffusion in the strongly coupled plasma regime, and exhibit improvements in MESA that now allow for the simulation of radiative levitation of heavy elements in hot stars. We close by noting that the MESA software infrastructure provides bit-for-bit consistency for all results across all the supported platforms, a profound enabling capability for accelerating MESA's development.

79 ASTRONOMY AND ASTROPHYSICS↗

DOE OSTI2016

Advancing cloud lifecycle representation in numerical models using innovative analysis methods that bridge arm observations over a breadth of scales

From its location on the subtropics-midlatitude boundary, the Azores is influenced by both the subtropical high pressure and the midlatitude baroclinic storm regimes, and therefore experiences a wide range of cloud structures, from fair-weather scenes to stratocumulus sheets to deep convective systems. This project combined three types of data sets to study cloud variability in the Azores: a satellite analysis of cloud regimes, a reanalysis characterization of storminess, and a 19-month field campaign that occurred on Graciosa Island. Combined analysis of the three data sets provides a detailed picture of cloud variability and the respective dynamic influences, with emphasis on low clouds that constitute a major uncertainty source in climate model simulations. The satellite cloud regime analysis shows that the Azores cloud distribution is similar to the mean global distribution and can therefore be used to evaluate cloud simulation in global models. Regime analysis of low clouds shows that stratocumulus decks occur under the influence of the Azores high-pressure system, while shallow cumulus clouds are sustained by cold-air outbreaks, as revealed by their preference for post-frontal environments and northwesterly flows. An evaluation of CMIP5 climate model cloud regimes over the Azores shows that all models severely underpredict shallow cumulus clouds, while most models also underpredict the occurrence of stratocumulus cloud decks. It is demonstrated that carefully selected case studies can be related through regime analysis to climatological cloud distributions, and a methodology is suggested utilizing process-resolving model simulations of individual cases to better understand cloud-dynamics interactions and attempt to explain and correct climate model cloud deficiencies.

58 GEOSCIENCES↗

DOE OSTI2015

Biodiesel Basics (Spanish Version); Clean Cities, Energy Efficiency & Renewable Energy (EERE)

This Spanish-language fact sheet provides a brief introduction to biodiesel, including a discussion of biodiesel blends, which blends are best for which vehicles, where to buy biodiesel, how biodiesel compares to diesel fuel in terms of performance, how biodiesel performs in cold weather, whether biodiesel use will plug vehicle filters, how long-term biodiesel use may affect engines, biodiesel fuel standards, and whether biodiesel burns cleaner than diesel fuel. The fact sheet also dismisses the use of vegetable oil as a motor fuel.

33 ADVANCED PROPULSION SYSTEMS↗

DOE OSTI2014

Biodiesel Basics [Fact Sheet]

This fact sheet provides a brief introduction to biodiesel, including a discussion of biodiesel blends, which blends are best for which vehicles, where to buy biodiesel, how biodiesel compares to diesel fuel in terms of performance, how biodiesel performs in cold weather, whether biodiesel use will plug vehicle filters, how long-term biodiesel use may affect engines, biodiesel fuel standards, and whether biodiesel burns cleaner than diesel fuel. The fact sheet also dismisses the use of vegetable oil as a motor fuel.

09 BIOMASS FUELS↗

DOE OSTI2026

Robotics for HVAC applications: A critical review and future perspectives

Recent advances in artificial intelligence (AI), enhanced computational capabilities, and innovations in sensors and hardware have driven the increasing development and application of robots in heating, ventilation, and air conditioning (HVAC) systems. We selected and reviewed 101 studies published between 2005 and 2025, sourced from IEEE Xplore, Scopus, Web of Science, and the ACM Digital Library. To analyze these works, we developed a five-dimensional analytical framework (morphology, sensing, navigation, task execution, and system integration), inspired by the Springer Handbook of Robotics and tailored specifically for robotic applications in HVAC. Based on the reviewed studies, six distinct tasks spanning the entire HVAC lifecycle have been identified. Among the six tasks, inspection and maintenance dominate (59 %), followed by indoor monitoring and auditing (21 %), whereas leakage detection, comfort support, and installation/retrofit remain less explored. To address the identified gaps, this review proposes future research directions including investigating robot-aware HVAC design principles, developing multimodal HVAC sensing and data fusion techniques, enhancing robot training and hardware capabilities, and expanding robotic applications beyond Maintenance and Operations (M&O). The findings from this review inform future robotics research for HVAC applications and ultimately enhance system affordability, energy efficiency, resilience or reliability, and occupant environmental comfort. Moreover, it seeks to inspire researchers to explore the intersections of robotics, computer science, building science, and HVAC engineering fostering advancements in this multidisciplinary field.

AI↗

DOE OSTI2022

WBS 1.2.3.405 - Life Cycle Assessment of Storage Technologies

Recent commitments by the Biden administration have established targets to achieve a net-zero energy system by 2050. Meeting these targets will spur a rapid transition to clean energy technologies and a commensurate need to develop and deploy energy storage technologies at scale. Pumped Storage Hydro (PSH) is expected to be part of this solution because its ability to provide grid flexibility and stability and enable the dispatching of disparate variable renewable energy technologies. Despite PSH being a mature technology with a history of deployment dating back several decades, there is very little information on the greenhouse gas (GHG) implications of PSH as compared to other storage technologies. The objective of this project is to perform a full lifecycle assessment (LCA) of new PSH projects in the U.S. This LCA includes all project phases (resource extraction, construction, operation, maintenance, end-of-life). The functional unit for this study is 1 kWh electricity delivered by system to grid substation connection point and the estimated lifetime for our base case is 80 years. Data used in this study are based on over 30 potential PSH projects that are in preliminary planning phases and are represent a wide range of potential closed-loop PSH systems in terms of location, technology, and capacity. The project approach, data sources, and modeling assumptions have been informed by a technical review committee of stakeholders that include experts from academia, national and international government, industry, and utilities. The GHGs and energy return on investment (EROI) from PSH will be compared to other storage technologies (e.g., stationary battery storage). Results from this project will improve the PSH community's understanding of the environmental impacts and sustainability of new PSH projects and how PSH compares to other storage technologies. The approach used in this project relies on open-source programming. The analysis framework (source code and data) and will be made publicly available at the end of the project. In addition to reporting results for the base case, we will perform rigorous sensitivity analysis to identify the major drivers, understand impacts of different configurations, and future energy markets. Results from this project will be published in a suitable journal.

ENERGY PLANNING, POLICY, AND ECONOMY,HYDRO ENERGY↗

DOE OSTI2025

Optimizing Hydronic Heating for Comfort and Performance in Multifamily Housing

Inefficient control settings in multifamily boilers often lead to substantial energy and cost penalties. To address this, a Fault Detection and Diagnostic (FDD) tool was developed to automate data analysis and identify operational faults such as suboptimal outdoor temperature sensor placement, misconfigured outdoor air reset (OAR) curves, excess boiler cycling, and domestic hot water (DHW) setpoint errors. By comparing pre- and post-implementation periods and applying engineering models, the tool quantifies energy savings and reduces manual analysis time by over 90%. Testing on over 100 monitored sites and a targeted subset of 12 buildings showed an average 11% energy savings from remote optimization; further validation across 19 OAR curve changes confirmed the tool’s accuracy, predicting actual savings within ±5% for most cases. Simple payback can be under three years for many multifamily buildings, though rising hardware, labor, and fuel costs create uncertainties, and decarbonization goals increasingly shift focus to electrification. The FDD tool remains invaluable for optimizing existing boilers, enhancing future electrification measures, and adapting to new technologies by refining building load estimates. In doing so, it supports both near-term efficiency and long-term transitions to low-carbon alternatives, ensuring buildings achieve substantial cost and energy benefits throughout their system lifecycles.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗

DOE OSTI2024

REopt Model Overview and Example Use Cases [Slides]

REopt(R) is a mixed-integer optimization model that minimizes the lifecycle cost of serving energy loads at a site. This work provides and introduction to the model along with its key workflow, techno-economic inputs, key outputs, and key caveats for readers to understand REopt the when, why, how of using this model. This resource also includes helpful links related to REopt model and the data sources it uses during the optimization.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗

DOE OSTI2024

ORNL Package Testing Program Software Quality Assurance Plan

The Oak Ridge National Laboratory (ORNL) Package Testing Program (PTP) uses commercial off-the-shelf (COTS) software in performing data collection of thermal test results for package designs that contain radioactive materials. Specifically, this software is used to collect temperature data from the furnace, packages, and ambient air to prepare and execute the thermal test specified in 10 CFR 71.73, “Thermal Test.” This software quality assurance (SQA) plan sets forth the guidelines, standards, and procedures that shall be used to provide SQA for PTP software applications. This is a living document that will be maintained for the lifecycle of the PTP program. The SQA plan follows the requirements set forth in ORNL Standards Based Management System (SBMS): Information Technology; Subject Area: Software Quality Assurance. When applicable to the requirements as described in ORNL SBMS, Software Quality Assurance, the software shall be listed in the ORNL Software Registration System (SRS). Exemptions to this SBMS are COTS and firmware that are not modified; spreadsheet applications and personal productivity tools that do not have a utility or safety application, research applications, legacy software, system software, vendor-supplied software used to interface with the vendor’s services, software used within the organization to facilitate processing or management of information, and software developed for applications not specific to the US Department of Energy (DOE).

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2017

Advanced Manufacturing for Thermal and Environmental Control Systems: Achieving National Energy Goals

This project was part of a regional initiative in the five counties of Central New York (CNY) that received funding from the U.S. Department of Energy (DOE) and four other federal agencies through the 2012 Advanced Manufacturing Jobs and Innovation Accelerator Challenge (AMJIAC). The CNY initiative was focused on cultivating the emergent regional cluster in “Advanced Manufacturing for Thermal and Environmental Control (AM-TEC).” As one component of the CNY AM-TEC initiative, the DOE-funded project supported five research & development seed projects that strategically targeted: 1) needs and opportunities of CNY AM-TEC companies, and 2) the goal of DOE’s Advanced Manufacturing Office (AMO) to reduce energy consumption by 50% across product life-cycles over 10 years. The project also sought to fulfill the AMO mission of developing and demonstrating new, energy-efficient processing and materials technologies at a scale adequate to prove their value to manufacturers and spur investment. The five seed projects demonstrated technologies and processes that can reduce energy intensity and improve production as well as use less energy throughout their lifecycles. The project was conducted over three years in two 18-month budget periods. During the first budget period, two projects proposed in the original AMJAIC application were successfully completed: Seed Project 1 focused on saving energy in heat transfer processes via development of nano structured surfaces to significantly increase heat flux; Seed Project 2 addressed saving energy in data centers via subzero cooling of the computing processors. Also during the first budget period, a process was developed and executed to select a second round of seed projects via a competitive request for proposals from regional companies and university collaborators. Applicants were encouraged to form industry-academic partnerships to leverage experience and resources of public and private sectors in the CNY region. Proposals were evaluated by a national panel of experts. Three projects were selected for awards and were completed successfully during the second budget period: Seed Project 3 focused on enabling self-powered furnaces to permit residents to shelter in place during power outages; Seed Project 4 addressed development of a novel method of controlling air conditioning systems that could enable flexible load matching in market segments not possible with existing technologies; and Seed Project 5 focused on the creation of smarter occupancy sensors to enable effective highly localized demand based ventilation.

30 DIRECT ENERGY CONVERSION↗

DOE OSTI2024

PHASM: A Toolkit for Creating AI Surrogate Models within Legacy Codebases

PHASM (“Parallel Hardware viA Surrogate Models”) is a software toolkit for creating AI-based surrogate models of scientific code. AI-based surrogate models are widely used for creating fast and inverse simulations. PHASM anticipates an additional future use case: adapting legacy code to modern hardware. While data centers are investing in heterogeneous hardware such as GPUs and FPGAs, many established scientific codebases remain unable to take advantage of the hardware’s higher parallelism without undergoing a costly rewrite. An alternative is to train a AI-based surrogate model to mimic computationally intensive functions in the code, and run the surrogate instead. PHASM formalizes a development lifecycle for such surrogate models, including discovering functions amenable to replacement with a surrogate model, predicting the resulting performance, identifying the function’s space of inputs and outputs, binding the model to the code, and managing model versions. A suite of software tools for facilitating these steps was written and validated against a set of model problems.

97 MATHEMATICS AND COMPUTING↗