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DOE OSTI2025

Applying Gaussian Process Machine Learning and Modern Probabilistic Programming to Satellite Data to Infer CO 2 Emissions

Satellite data provides essential insights into the spatiotemporal distribution of CO 2 concentrations. However, many atmospheric inverse models fail to adequately incorporate the spatial and temporal correlations inherent in satellite observations and often lack rigorous methods for estimating parameters like spatial length scales. We introduce an inference model that processes the spatiotemporal covariance in satellite data and estimates hyperparameters such as covariance length scales. Our approach uses the Gaussian process (GP) machine learning (ML) and modern probabilistic programming languages (PPLs) to perform atmospheric inversions of emissions from satellite data. We develop a GP ML inversion system based on modern PPLs and the GEOS-Chem chemical transport model, simulating atmospheric CO 2 concentrations corresponding to the Orbiting Carbon Observatory-2/3 (OCO-2/3) data for July 2020. In our supervised learning framework, we treat the GEOS-Chem simulated data set as the target, with predictors derived by scaling the target with sector-specific factors hidden from the GP machine. Our results show that the GP model, combined with GPU-enabled PPLs, effectively retrieves true emission scaling factors and infers noise levels concealed within the data. This suggests that our method could be applied over larger areas with more complex covariance structures, enabling comprehensive analysis of the spatiotemporal patterns observed in OCO-2/3 and similar satellite data sets.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2025

Challenges in Tracking Waste Reduction Performance Improvement in Manufacturing Plants

The recently released Circularity Gap Report 2023 by the Circle Economy Foundation states that the circularity score for the global economy is declining. The US Environmental Protection Agency (EPA) tracked municipal solid waste from 1960 to 2018 and found that 50% of the waste was destined for landfills. EPA estimates that US industry is responsible for 2.7 Gt of solid nonhazardous waste annually in the US mostly linear economy model. The circular economy framework aims to decouple economic value generation from the extraction of virgin materials from nature. The linear model of material extraction and disposal at the end of life is highly unsustainable. Manufacturing companies are adopting ambitious waste reduction targets to achieve sustainability. Through the Better Plants program, US DOE has established the Waste Reduction Network, which offers technical assistance to partners to achieve their ambitious waste reduction goals. Basic requirements of establishing a target include identifying a baseline, quantifying waste performance, and measuring progress over time. One problem faced by industry is unstandardized metrics for quantifying waste performance that may not be well suited to demonstrate progress. This paper studies traditional methods used to measure waste performance and highlights advantages, disadvantages, and limitations of each method. It also examines the suitability of applying the methods in different manufacturing circumstances. Finally, the paper presents a case study of a large manufacturer that faced inconsistencies in its tracked measurement metric. A solution was proposed and implemented to alter the methodology to enable more accurate waste performance tracking against a baseline.

circular economy↗

DOE OSTI2024

Coupled aero-hydro-geotech real-time hybrid simulation of offshore wind turbine monopile structures

Real-time hybrid simulation (RTHS) divides a structural system into an analytical and experimental substructure. The former is based on a well-established analytical model while the latter consists of a physical model in the laboratory, for which there is not a well-established analytical model. This paper extends real-time hybrid simulation to monopile-type Offshore Wind Turbines (OWTs) to enable the investigation of their behavior considering the response of pile foundations under operational and more severe conditions. The embedded foundation and surrounding soil of the OWT are modeled physically in a soil box in the laboratory while the remaining parts of the system and loading are modeled analytically. The program OpenFAST, developed by the National Renewable Energy Laboratory (NREL), is linked to the RTHS coordinator to determine the hydrodynamic and aerodynamic loads acting on the OWT, along with modeling the dynamics of the electric power generation equipment and associated controller for the OWT. The RTHS framework along with its initial implementation and validation are described in this paper. RTHSs of a 5 MW OWT subjected to operational and more severe conditions are performed to experimentally validate the framework. The framework offers a realistic approach to investigate the behavior of OWT structures supported on monopiles. Furthermore, this approach accounts for the coupled response of the OWT structure with its foundation, while experimentally capturing the nonlinearities of the soil-foundation interaction in real-time.

17 WIND ENERGY↗

DOE OSTI2026

A Measurement of the Pion-Energy Dependence of Muon Neutrino Charged-Current Scattering to Final States With One Charged Pion in NOvA

The study of neutrino oscillations is a main priority for particle physics as the most immediately tractable lever on physics beyond the Standard Model. In particular, more insight into violation of the combined symmetry of charge-conjugation plus parity could yield clues to the origin of matter-antimatter asymmetry, and in some theoretical frameworks the neutrino mass could give insights into dark matter. NOvA is a long-baseline accelerator neutrino experiment with both a near and far detector that seeks to measure several of the parameters of the neutrino mixing matrix, as well as carry out a broad program of additional physics. NOvA has innovated and developed a variety of techniques in the space of neutrino physics, including expanding the use of machine learning techniques in reconstruction. NOvA has also set the stage for the next generation US-based long baseline experiment, DUNE. This dissertation details the creation of a new neutrino interaction vertex reconstruction package for NOvA, which offers enormous improvements in accuracy above the previous vertexer it replaces. This is accomplished using a Convolutional Visual Network trained on large datasets of simulated events. The vertexer is validated thoroughly against data. This dissertation also presents a cross-section measurement for $\nu_\mu + N \rightarrow \mu^- + 1\pi^\pm + X$ (where X does not include additional charged pions) binned in pion kinetic energy, a challenging measurement in general and for NOvA in particular. Measurements like this provide crucial inputs for neutrino interaction models utilized by neutrino oscillation experiments. This measurement in particular is important to the energy ranges relevant for DUNE in a band not well-covered by any other experiment.

Ewart, Erin [Indiana U., Bloomington (main)]↗

DOE OSTI2026

Multi-Semester Mentoring and GPA Trajectories in SPINS: A Longitudinal Program Evaluation of STEM Scholars

This exploratory longitudinal program evaluation examined GPA trajectories among 14 STEM scholars participating in the Scholarly Partnership in Nuclear Security (SPINS) mentoring program at an HBCU. Using de-identified administrative data (37 scholar-semester observations), the study compared baseline and latest term GPAs anchored to each scholar’s first funded semester. A Wilcoxon signed-rank test was used to assess within-student change. Mean GPA increased modestly from 3.46 (SD = 0.30) at baseline to 3.61 (SD = 0.33) at the latest observed semester, with 12 of 14 scholars (85.7%) showing net improvement (median change = +0.12). The Wilcoxon signed-rank test indicated a statistically significant positive shift (W = 18, p = 0.030). However, scholars entered the program with a high baseline GPA, and ceiling effects were evident for several participants. Grounded in Social Cognitive Career Theory, findings suggest that multi-semester participation in SPINS is associated with GPA stability and modest improvement in an already high-performing cohort. Results should be interpreted cautiously given the small sample size and single-group design. The study highlights the value of sustained, culturally responsive mentoring at an HBCU while underscoring the need for larger evaluations with comparison cohorts and broader psychosocial outcomes. An evidence-informed mentoring framework is proposed to strengthen multi-semester support.

42 ENGINEERING↗

DOE OSTI2022

Advanced Visualization of Silent Error Propagation in HPC Applications

Last year, we refactored the major components of the SpotSDC visualization platform and added a visualization component for fault tolerance boundary. Also, we designed a new analysis framework to understand an application resiliency with a significantly less amount of samples compared to previous approaches. We evaluated the visualization platform and analysis framework for multiple critical HPC computation kernels and collected evidence for the effectiveness of the method developed. We published the findings of our research in the IEEE Transactions on Visualization and Computer Graphics (TVCG), the top journal in visualization. Moreover, we submitted a new paper, "Understanding a Program's Resiliency Through Error Propagation," to the PPOPP 2021 conference.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2026

Design Status and Experimental Strategy for Initial Molten Salt Irradiation Experiments in HFIR

The development and deployment of molten salt reactor (MSR) technologies require experimental capabilities that can evaluate molten fuel salt behavior and structural material performance under representative irradiation conditions. Although modeling and separate effects testing provide important insight, there remains a critical lack of in-pile data that capture the coupled effects of neutron irradiation, temperature, salt chemistry, and time. Informed by lessons learned from historical MSR programs and recent international irradiation efforts, this report presents a structured approach to addressing existing gaps in molten salt irradiation testing using the High Flux Isotope Reactor (HFIR). A phased irradiation strategy is presented that incrementally increases experimental complexity while managing cost, risk, and facility constraints. The framework progresses from passive, nonfueled static capsule experiments to fueled, instrumented, and ultimately circulating molten salt systems, providing a pathway for materials screening, mechanistic understanding, and qualification-relevant testing. The report defines a near-term Phase 1 passive capsule concept, associated irradiation conditions, and a conceptual post-irradiation examination strategy. Ongoing modeling, out-of-pile testing, and safety analysis activities are described to support continued capability maturation and to enable future phases of molten salt irradiation testing in support of MSR technology deployment.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗

DOE OSTI2022

Sockeye Heat Pipe Analysis Code Verification and Validation

Some of the most promising microreactor designs currently under development utilize heat pipe technology to transfer heat from the reactor core to the secondary side heat exchanger, due to the technology’s compactness, efficiency, passivity, and reliability. Sockeye is an engineering-scale heat pipe tool developed under the Nuclear Energy Advanced Modeling and Simulation Program to be used for the design and safety analysis of microreactors. Sockeye’s core capability lies in a 1D, two-phase, compressible flow model, used to simulate the working fluid inside the heat pipe. Sockeye is built on the Multiphysics Object-Oriented Simulation Environment framework, which allows for seamless multiphysics coupling with other Nuclear Energy Advanced Modeling and Simulation tools and can thus be used in a full-scale simulation of a microreactor assembly, which can include hundreds of heat pipes. This paper presents some initial verification and validation assessments performed for Sockeye. We demonstrate good agreement between Sockeye’s numerical results and steady-state analytic solutions for velocity and pressure drop, with differences attributable to the underlying assumptions made by the analytic solutions. We also show that Sockeye reproduces analytic predictions of key operational limits, such as the capillary limit and the sonic limit. Finally, we compare Sockeye results to experimental data for the SAFE-30 heat pipe module test.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗

DOE OSTI2026

Extreme-scale EV charging infrastructure planning for last-mile delivery using high-performance parallel computing

Here, this paper addresses stochastic charger location and allocation problems under queue congestion for last-mile delivery using electric vehicles (EVs). The objective is to decide where to open charging stations and how many chargers of each type to install, subject to budgetary and waiting-time constraints. We formulate the problem as a mixed-integer non-linear program, where each station-charger pair is modeled as a multiserver queue with stochastic arrivals and service times to capture the notion of waiting in fleet operations. The model is extremely large, with billions of variables and constraints for a typical metropolitan area; even loading the model in solver memory is difficult, let alone solving it. To address this challenge, we develop a Lagrangian-based dual decomposition framework that decomposes the problem by station and leverages parallelization on high-performance computing systems, where the subproblems are solved by using a cutting plane method and their solutions are collected at the master level. We also develop a three-step rounding heuristic to transform the fractional subproblem solutions into feasible integral solutions. Computational experiments on data from the Chicago metropolitan area with hundreds of thousands of households and thousands of candidate stations show that our approach produces high-quality solutions in cases where existing exact methods cannot even load the model in memory. We also analyze various policy scenarios, demonstrating that combining existing depots with newly built stations under multiagency collaboration substantially reduces costs and congestion. These findings offer a scalable and efficient framework for developing sustainable large-scale EV charging networks.

Capacity allocation↗

DOE OSTI2024

SEAS Communication Engine: An Extensible, Flexible Wrapper for Co-Simulation Agents

When modeling and analyzing the power grid and other large scale systems, researchers often express scenarios as optimization problems and feed them into advanced software solvers. In order to allow multiple solvers to communicate with each other and share data from different domains, the National Renewable Energy Laboratory (NREL) and associated Department of Energy (DOE) labs have developed a software framework called the Hierarchical Engine for Large-scale Infrastructure Co-Simulation (HELICS). HELICS allows cosimulation via a collection of client libraries for different languages that can be called from the appropriate optimization software. However, these client libraries do not provide a higher level of abstraction beyond reading and writing data off of the shared HELICS bus. In this paper, we describe a new software library called the SEAS Communication Engine that exposes a higher-level API for running cosimulation problems. The SEAS Engine provides a class-based abstraction on top of the Python HELICS client, in order to allow users to implement their domain-specific cosimulations without needing to interact with core HELICS primitives. This will make adoption of HELICS and cosimulation in general easier, by exposing a simpler API. In the second part of the paper, we validate our library on a collection of different simulation examples, including the canonical IEEE 13 Bus Feeder. Lastly, we demonstrate using the SEAS Engine to directly call domain-specific code written in the Julia programming language. Our hope is that this will serve as a template for easily calling software in different programming languages via the SEAS Engine, thereby avoiding code duplication and complexity.

co-simulation↗

DOE OSTI2020

FLIMJ: An open-source ImageJ toolkit for fluorescence lifetime image data analysis

In the field of fluorescence microscopy, there is continued demand for dynamic technologies that can exploit the complete information from every pixel of an image. One imaging technique with proven ability for yielding additional information from fluorescence imaging is Fluorescence Lifetime Imaging Microscopy (FLIM). FLIM allows for the measurement of how long a fluorophore stays in an excited energy state, and this measurement is affected by changes in its chemical microenvironment, such as proximity to other fluorophores, pH, and hydrophobic regions. This ability to provide information about the microenvironment has made FLIM a powerful tool for cellular imaging studies ranging from metabolic measurement to measuring distances between proteins. The increased use of FLIM has necessitated the development of computational tools for integrating FLIM analysis with image and data processing. To address this need, we have created FLIMJ, an ImageJ plugin and toolkit that allows for easy use and development of extensible image analysis workflows with FLIM data. Built on the FLIMLib decay curve fitting library and the ImageJ Ops framework, FLIMJ offers FLIM fitting routines with seamless integration with many other ImageJ components, and the ability to be extended to create complex FLIM analysis workflows. Building on ImageJ Ops also enables FLIMJ’s routines to be used with Jupyter notebooks and integrate naturally with science-friendly programming in, e.g., Python and Groovy. We show the extensibility of FLIMJ in two analysis scenarios: lifetime-based image segmentation and image colocalization. We also validate the fitting routines by comparing them against industry FLIM analysis standards.

47 OTHER INSTRUMENTATION↗

DOE OSTI2022

Quantifying the Known Unknown: Including Marine Sources of Greenhouse Gases in Climate Modeling

Researchers have recently estimated that Arctic submarine permafrost currently traps 60 billion tons of methane and contains 560 billion tons of organic carbon in seafloor sediments and soil, a giant pool of carbon with potentially large feedbacks on the climate system. Unlike terrestrial permafrost, the submarine permafrost system has remained a “known unknown” because of the difficulty in acquiring samples and measurements. Consequently, this potentially large carbon stock never yet considered in global climate models or policy discussions, represents a real wildcard in our understanding of Earth’s climate. This report summarizes our group’s effort at developing a numerical modeling framework designed to produce a first-of-its-kind estimate of Arctic methane gas releases from the marine sediments to the water column, and potentially to the atmosphere, where positive climate feedback may occur. Newly developed modeling capability supported by the Laboratory Directed Research and Development (LDRD) program at Sandia National Laboratories now gives us the ability to probabilistically map gas distribution and quantity in the seabed by using a hybrid approach of geospatial machine learning, and predictive numerical thermodynamic ensemble modeling. The novelty in this approach is its ability to produce maps of useful data in regions that are only sparsely sampled, a common challenge in the Arctic, and a major obstacle to progress in the past. By applying this model to the circum-Arctic continental shelves and integrating the flux of free gas from in situ methanogenesis and dissociating gas hydrates from the sediment column under climate forcing, we can provide the most reliable estimate of a spatially and temporally varying source term for greenhouse gas flux that can be used by global oceanographic circulation and Earth system models (such as DOE’s E3SM). The result will allow us to finally tackle the wildcard of the submarine permafrost carbon system, and better inform us about the severity of future national security threats that sustained climate change poses.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2023

Water and Wastewater Annual Price Escalation Rates for Selected Cities Across the United States: 2023 Edition

This report builds on the 2016 Water and Wastewater Annual Price Escalation Rates for Selected Cities across the United States report prepared by Pacific Northwest National Laboratory (PNNL) for the U.S. Department of Energy’s (DOE) Federal Energy Management Program (FEMP). The 2017 report examined annual water and wastewater price escalation rate trends across the U.S. Annual water and wastewater price escalation rates are an important factor when conducting life-cycle cost analyses (LCCA) of water efficiency measures, which is required for Federal agencies. Following the framework of the 2017 report, PNNL used the American Water Works Association (AWWA) water and wastewater rate surveys to gather historical rate data for water and wastewater utilities in the US, which were used to calculate a sample set of water and wastewater annual price escalation rates (Figure E.1). An annual price escalation rate was calculated for 113 water utilities and 76 wastewater utilities that reported at least two AWWA surveys across the past 13 years (i.e., 2008 to 2021) and at least a five-year range between those two surveys. Statistical trends in the annual price escalation rates are also provided by the seven regions identified in Figure E.1. In the 2017 report, the average annual price escalation for the surveyed sample of water and wastewater rates were 3.0% and 3.2%, respectively. This report also calculated aggregate average annual price escalation rates for the surveyed sample of water and wastewater utilities to be 3.1% and 2.7%, respectively. These escalation rates were based on 87 water utilities and 46 wastewater utilities that reported data in at least two AWWA surveys with at least a five-year range between those two surveys and reported at least one AWWA survey in 2019 or 2021. Among water utilities, the highest average annual price escalation rate was 8.8%, and the highest average annual water de-escalation rate was -2.0%. And for wastewater utilities, the highest average annual wastewater price escalation rate was 10.2%, and the highest average annual wastewater de-escalation rate was -2.3%.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗

DOE OSTI2023

Development and Performance of RFD Crab Cavity Prototypes for HL-LHC AUP

The US will be contributing to the HL-LHC upgrade at CERN with the fabrication and qualification of RFD crabbing cavities in the framework of the HL-LHC Accelerator Upgrade Project (AUP) managed by Fermilab. AUP received Critical Decision 3 (CD-3) approval by DOE in December 2020 launching the project into the production phase. The electro-magnetic design of the cavity was inherited from the LHC Accelerator Research Program (LARP) but needed to be revised to meet new project requirements and to prevent issues encountered during beam tests performed at CERN in the R&D phase. Two prototype cavities were manufactured in industry and cold tested. Challenges specific to the RFD cavity were the stringent interface tolerances, the pole symmetry, and the higher-order-mode impedance spectrum. Chemical pro-cessing and heat treatments were performed initially at FNAL/ANL and are now being transferred to industry for the production phase. HOM dampers are manufactured and validated by JLAB. A summary of cold test results with and without HOM dampers is presented.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗

DOE OSTI2023

Development and Performance of RFD Crab Cavity Prototypes for HL-LHC AUP

The US will be contributing to the HL-LHC upgrade at CERN with the fabrication and qualification of RFD crabbing cavities in the framework of the HL-LHC Accelerator Upgrade Project (AUP) managed by Fermilab. AUP received Critical Decision 3 (CD-3) approval by DOE in December 2020 launching the project into the production phase. The electro-magnetic design of the cavity was inherited from the LHC Accelerator Research Program (LARP) but needed to be revised to meet new project requirements and to prevent issues encountered during beam tests performed at CERN in the R&D phase. Two prototype cavities were manufactured in industry and cold tested. Challenges specific to the RFD cavity were the stringent interface tolerances, the pole symmetry, and the higher-order-mode impedance spectrum. Chemical processing and heat treatments were performed initially at FNAL/ANL and are now being transferred to industry for the production phase. HOM dampers are manufactured and validated by JLAB. A summary of cold test results with and without HOM dampers is presented.

43 PARTICLE ACCELERATORS↗

DOE OSTI2024

Challenges in Tracking Waste Reduction Performance Improvement in Manufacturing Plants

The recently released Circularity Gap Report 2023 by Circle Economy Foundation found that the circularity score for the global economy is declining. This indicates that material extraction from virgin sources is climbing over earlier years. As per data released by US Geological Survey, material use in the US economy has increased exponentially with the expanding US economy over last century. EPA tracked municipal solid waste from 1960 to 2018 and found that 50% of the waste is destined for landfills. EPA estimates that US industry is responsible for 2.7 billion ton of solid non-hazardous waste annually in our mostly linear economy model. The circular economy framework aims at decoupling economic value generation from virgin materials extraction from nature. The linear model of material extraction and disposal at end of life is highly unsustainable. Manufacturing companies have realized this and in their commitments to sustainability are adopting ambitious waste reduction targets. Through Better Plants program US Department of Energy has established Waste Reduction Network where it is offering technical assistance to partners to achieve these ambitious waste reduction goals. Basic requirements of establishing a target include identifying a baseline, quantifying waste performance, and measuring progress over time. One problem faced by industry is the unstandardized metrics to quantify waste performance that may not be well suited to demonstrate progress. In this paper, we research methods traditionally used to measure waste performance and highlight advantages, gaps and limitations of each method. Suitability of the methods applicable to different manufacturing circumstances are also examined. Finally, we present a case study of a large manufacturer that faced inconsistencies in their tracked measurement metric. A solution was proposed to alter the methodology to enable more accurate waste performance tracking against a baseline.

Chaudhari, Subodh↗

DOE OSTI2020

Multiscale model predictive control of battery systems for frequency regulation markets using physics-based models

In this work, we propose a multiscale model predictive control (MPC) framework for stationary battery systems that exploits high-fidelity models to trade-off short-term economic incentives provided by energy and frequency regulation (FR) markets and long-term degradation effects. We find that the MPC framework can drastically reduce long-term degradation while properly responding to FR and energy market signals (compared to MPC formulations that use low-fidelity models). Our results also provide evidence that sophisticated battery models can be embedded within closed-loop MPC simulations by using modern nonlinear programming solvers (we provide an efficient and easy-to-use implementation in Julia). We use insights obtained with our simulations to design a low-complexity MPC formulation that matches the behavior obtained with high-fidelity models. This is done by designing a suitable terminal penalty term that implicitly captures long-term degradation. The results suggest that complex degradation behavior can be accounted for in low-complexity MPC formulations by properly designing the cost function. We believe that our proof-of-concept results can be of industrial relevance, as battery vendors are seeking to participate in fast-changing electricity markets while maintaining asset integrity.

42 ENGINEERING↗

DOE OSTI2022

Incorporating energy justice into utility-scale photovoltaic deployment: A policy framework

Utility-scale photovoltaic (PV) installations made up 77 GW (6%) of installed capacity in the United States, as of the end of 2021. This will grow to more than 500 GW by 2050 under a mid-case projection or more than 800 GW if solar costs decline more rapidly. While utility-scale PV is projected to grow rapidly, to date, unlike energy efficiency or distributed PV, utility-scale PV has not been used to provide substantial financial benefits to underserved communities, either through ownership, financing of assets, or direct electricity bill reduction. We assess two mechanisms through which utility-scale PV could benefit underserved communities. We find that while a framework for direct electricity bill reduction can be meaningful to customers, this mechanism falls short of providing restorative justice via wealth creation for minority-owned businesses. In contrast, we find that a framework for procurement of utility-scale PV by public and private entities from PV projects that are financed, owned, and/or developed by minority-owned businesses can provide this restorative justice benefit, and thereby facilitate an equitable energy transition. We conclude with concrete recommendations for new policies and programs to ensure that the benefits of utility-scale PV systems are distributed to underserved communities.

14 SOLAR ENERGY↗