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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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All sourcesNASA NTRSDOE OSTI
DOE OSTI2021

Nonlinear proper orthogonal decomposition for convection-dominated flows

Autoencoder techniques find increasingly common use in reduced order modeling as a means to create a latent space. This reduced order representation offers a modular data-driven modeling approach for nonlinear dynamical systems when integrated with a time series predictive model. In this Letter, we put forth a nonlinear proper orthogonal decomposition (POD) framework, which is an end-to-end Galerkin-free model combining autoencoders with long short-term memory networks for dynamics. By eliminating the projection error due to the truncation of Galerkin models, a key enabler of the proposed nonintrusive approach is the kinematic construction of a nonlinear mapping between the full-rank expansion of the POD coefficients and the latent space where the dynamics evolve. We test our framework for model reduction of a convection-dominated system, which is generally challenging for reduced order models. Our approach not only improves the accuracy, but also significantly reduces the computational cost of training and testing.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2024

Improving the Concrete Crack Detection Process via a Hybrid Visual Transformer Algorithm

Inspections of concrete bridges across the United States represent a significant commitment of resources, given their biannual mandate for many structures. With a notable number of aging bridges, there is an imperative need to enhance the efficiency of these inspections. This study harnessed the power of computer vision to streamline the inspection process. Our experiment examined the efficacy of a state-of-the-art Visual Transformer (ViT) model combined with distinct image enhancement detector algorithms. We benchmarked against a deep learning Convolutional Neural Network (CNN) model. These models were applied to over 20,000 high-quality images from the Concrete Images for Classification dataset. Traditional crack detection methods often fall short due to their heavy reliance on time and resources. This research pioneers bridge inspection by integrating ViT with diverse image enhancement detectors, significantly improving concrete crack detection accuracy. Notably, a custom-built CNN achieves over 99% accuracy with substantially lower training time than ViT, making it an efficient solution for enhancing safety and resource conservation in infrastructure management. These advancements enhance safety by enabling reliable detection and timely maintenance, but they also align with Industry 4.0 objectives, automating manual inspections, reducing costs, and advancing technological integration in public infrastructure management.

42 ENGINEERING↗
DOE OSTI2021

TEMImageNet training library and AtomSegNet deep-learning models for high-precision atom segmentation, localization, denoising, and deblurring of atomic-resolution images

Abstract Atom segmentation and localization, noise reduction and deblurring of atomic-resolution scanning transmission electron microscopy (STEM) images with high precision and robustness is a challenging task. Although several conventional algorithms, such has thresholding, edge detection and clustering, can achieve reasonable performance in some predefined sceneries, they tend to fail when interferences from the background are strong and unpredictable. Particularly, for atomic-resolution STEM images, so far there is no well-established algorithm that is robust enough to segment or detect all atomic columns when there is large thickness variation in a recorded image. Herein, we report the development of a training library and a deep learning method that can perform robust and precise atom segmentation, localization, denoising, and super-resolution processing of experimental images. Despite using simulated images as training datasets, the deep-learning model can self-adapt to experimental STEM images and shows outstanding performance in atom detection and localization in challenging contrast conditions and the precision consistently outperforms the state-of-the-art two-dimensional Gaussian fit method. Taking a step further, we have deployed our deep-learning models to a desktop app with a graphical user interface and the app is free and open-source. We have also built a TEM ImageNet project website for easy browsing and downloading of the training data.

25 ENERGY STORAGE↗
DOE OSTI2022

Developing the S 32 ( p , d ) S * 31 ( p ) ( γ ) reaction to probe the P 30 ( p , γ ) S 31 reaction rate in classical novae

Background: The 30 P(p,γ) 31 S reaction rate is one of the largest remaining sources of uncertainty in the final abundances of nuclei created in a classical nova involving a ONe white dwarf. The reaction rate directly influences silicon isotopic ratios, which are used as identifiers of presolar grains with nova origins. Additionally, the uncertainty in the 30 P(p,γ) 31 S reaction rate has been found to limit the use of nova nuclear thermometers based on observations of elemental ratios in nova ejecta. Purpose: Reduce uncertainties in the nuclear data for proton-unbound states in 31 S, which act as resonances for the 30 P(p,γ) 31 S reaction at classical nova temperatures, and develop a technique for high efficiency, high-resolution reaction-decay coincidence measurements. Methods: Here, the 32 S(p,d) 31 S reaction was used to populate the states of interest in 31 S. The experiment was performed at the Texas A&M Cyclotron Institute using the LLNL Hyperion array for the detection of charged particles and γ rays. A downstream silicon telescope was used to select reaction deuterons, and a single upstream silicon detector was used to measure protons emitted in the decay of unbound 31 S levels. Results: Several states in 31 S above the proton separation energy were observed to have been populated. Decay protons from the resonant states in 31 S were identified as events in the upstream silicon detectors that came in coincidence with deuterons in the downstream telescope. Protons emitted from these states were measured and branching ratios extracted. Conclusions: While no new reaction rate is derived, spin-parity assignments for several higher-lying proton unbound states have been confirmed. Measured p 0 branching ratios for these levels have been compared to previous measurements with good agreement, and in some cases provided a reduction in uncertainty. The previously identified T = 3/2 state may have been incorrectly assigned a large p 0 branching ratio in a previous measurement. The technique of measuring reaction-decay coincidences with a particle-gamma setup appears promising.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2021

U-10Mo Monolithic Fuel Qualification Plan

The Material Management and Minimization (M3) Program’s primary objective within the U.S. Department of Energy/National Nuclear Security Administration is to achieve permanent threat reduction by minimizing and when possible, eliminating weapon-usable nuclear material around the world. Under M3’s Office of Conversion, the U.S. High Performance Research Reactor (USHPRR) Project, is pursuing a fuel qualification and licensing effort focused on converting high-performance research reactors in the United States from using highly enriched uranium (HEU) fuel to using low-enriched uranium (LEU) fuel. The body of this document is focused on defining the activities required for generic qualification of high density, LEU fuel that then allows reactor conversions to proceed for the four high-performance research reactors and the critical assembly that will operate using the U-10Mo monolithic fuel design (see Subsection 1.3): MITR, MURR, NBSR, ATR, and ATRC. Fuel qualification in this report means generic fuel acceptance of the U-10Mo monolithic fuel form by the NRC for use in reactor conversions in the United States, which allows the fuel to be used in subsequent reactor specific licensing requests. The U 10Mo Monolithic Fuel Research, Development, and Qualification Plan utilizes the functions and requirements of the USHPRR Project that was established in the Functions and Requirements Document (F&RD) [1] and expands on these requirements to ensure that planned tests have traceable results that will ensure the requirement has been met. The methods by which data will be collected to show that these requirements have been met are described in this document.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
DOE OSTI2023

Warming of the Willamette River, 1850–present: the effects of climate change and river system alterations

Abstract. Using archival research methods, we recovered and combined data from multiple sources to produce a unique, 140-year record of daily water temperature (Tw) in the lower Willamette River, Oregon (1881–1890, 1941–present). Additional daily weather and river flow records from the 1850s onwards are used to develop and validate a statistical regression model of Tw for 1850–2020. The model simulates the time-lagged response of Tw to air temperature and river flow and is calibrated for three distinct time periods: the late 19th, mid-20th, and early 21st centuries. Results show that Tw has trended upwards at 1.1 ∘C per century since the mid-19th century, with the largest shift in January and February (1.3 ∘C per century) and the smallest in May and June (∼ 0.8 ∘C per century). The duration that the river exceeds the ecologically important threshold of 20 ∘C has increased by about 20 d since the 1800s, to about 60 d yr−1. Moreover, cold-water days below 2 ∘C have virtually disappeared, and the river no longer freezes. Since 1900, changes are primarily correlated with increases in air temperature (Tw increase of 0.81 ± 0.25 ∘C) but also occur due to alterations in the river system such as depth increases from reservoirs (0.34 ± 0.12 ∘C). Managed release of water affects Tw seasonally, with an average reduction of up to 0.56 ∘C estimated for September. River system changes have decreased variability (σ) in daily minimum Tw by 0.44 ∘C, increased thermal memory, reduced interannual variability, and reduced the response to short-term meteorological forcing (e.g., heat waves). These changes fundamentally alter the response of Tw to climate change, posing additional stressors on fauna.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2024

The effects of exercise training interventions on depression in hemodialysis patients

Purpose Depression considerably influences the clinical outcomes, treatment compliance, quality of life, and mortality of hemodialysis patients. Exercise plays a beneficial role in depressive patients, but its quantitative effects remain elusive. This study aimed to summarize the effects of exercise training on depression in patients with end-stage renal disease undergoing hemodialysis. Methods The PUBMED, EMBASE, and Cochrane Library databases were systematically searched from inception to April 2023 to identify published articles reporting the effect of exercise training on the depression level of patients with End-Stage Renal Disease undergoing hemodialysis. Data were extracted from the included studies using predefined data fields by two independent researchers. The Cochrane Handbook for Systematic Reviews of Interventions and Joanna Briggs Institute Critical Appraisal Checklist for Quasi-Experimental Studies were employed for quality evaluation. Results A total of 22 studies enrolling 1,059 patients who participated in exercise interventions were included. Hemodialysis patients exhibited superior outcomes with intradialytic exercise (SMD = −0.80, 95% CI: −1.10 to −0.49) and lower levels of depression following aerobic exercise (SMD = −0.93, 95%CI: −1.32 to −0.55) compared to combined exercise (c − 0.85, 95% CI: −1.29 to −0.41) and resistance exercise (SMD = −0.40, 95%CI: −0.96 to 0.17). Regarding exercise duration, patients manifested lower depression levels when engaging in exercise activities for a duration exceeding 6 months (SMD = −0.92, 95% CI: −1.67 to −0.17). Concerning the duration of a single exercise session, the most significant improvement was noted when the exercise duration exceeded 60 min (SMD = −1.47, 95% CI: −1.87 to −1.06). Conclusion Our study determined that exercise can alleviate depression symptoms in hemodialysis patients. This study established the varying impacts of different exercise parameters on the reduction of depression levels in hemodialysis patients and is anticipated to lay a theoretical reference for clinicians and nurses to devise tailored exercise strategies for interventions in patients with depression. Systematic review registration https://www.crd.york.ac.uk/prospero/ , This study was registered in the International Prospective Register of Systematic Reviews (PROSPERO) database, with registration number CRD42023434181.

Yu, Huihui↗
DOE OSTI2023

Energy Optimization of Light and Heavy-Duty Vehicle Cohorts of Mixed Connectivity, Automation and Propulsion System Capabilities via Meshed V2V-V2I and Expanded Data Sharing (Final Scientific and Technical Report)

Vehicle connectivity and automated driving technologies individually have the potential to decrease energy consumption and/or increase safety on light, medium or heavy duty vehicles to varying degrees depending on the traffic infrastructure and specific driving scenarios. Due to advances in sensing, perception and computing power, research and development emphasis in the mobility sector has shifted away from connectivity. Prior research has shown that driving automation with the absence of connectivity can in certain circumstances increase energy consumption. The effectiveness of synergizing connectivity and driving automation technologies is the focus of this work, specifically applied to vehicle cohorts of mixed composition, light and heavy duty, and powertrains ranging from all electric to conventional internal combustion engine. The project team is led by Michigan Technological University (MTU) and partnered with AVL Mobility Technologies Inc. (AVL), Borg Warner (BW), Traffic Technology Services (TTS), American Center for Mobility (ACM) and Navistar (NAV). The main thrusts for the team are to develop a micro-traffic simulation environment with specific VD&PT system attributes and CAV capabilities, 2) field a vehicle test fleet of mixed classification, propulsion and CAV capacity, 3) develop artificial intelligence (AI) and machine learning (ML) based multi-agent optimization methods for various traffic infrastructures, 4) integrate the virtual environment and the optimization methods then deploy the system as a CAV hardware in the loop (HiL) for the vehicle test fleet and 5) conduct closed track and public road testing to validate simulation and demonstrated energy and mobility improvements at multiple scales. For a cohort of mixed vehicles, the team will demonstrate a reduction of energy consumption of 10-50% at intersection, arterial roadway and limited access highway scenarios through connectivity and automation in simulation and at a closed test track. The energy reduction objectives of the project are summarized in Table 1, indicating the infrastructure and over what distances are relevant considered. Single scenario energy reductions are not relevant and thus, the research team took the approach to vary parameters associated with the infrastructure, vehicle cohort composition and dynamic behavior to generate energy consumption distributions for both unconnected and connected scenarios.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2021

HIBP Applications to Advance Understanding of Plasma Transport Physics (2011-2017) (and subsequently titled) Development of Beam Measurements to Advance Understanding of Transients and Improve Validation (2017-2020) (Final Technical Report)

This is the Final Technical Report for the DoE Measurement Innovation (previously Diagnostic Development) grant DE-SC0006077 titled HIBP Applications to Advance Understanding of Plasma Transport Physics (2011-2017), and subsequently titled Development of Beam Measurements to Advance Understanding of Transients and Improve Validation (2017-2020). The grant was funded by the Office of Fusion Energy Sciences (FES) for the period 1 May 2011–17 July 2020. Work performed through the grant has resulted in diagnostic innovations and new measurement capabilities that will improve understanding of transport, magnetic equilibrium, and electric fields in magnetically confined plasmas. We have developed novel hardware and advanced techniques to expand beam-based diagnostic capabilities, and extend measurements to new operating regimes. Some have been realized using a Heavy Ion Beam Probe (HIBP) having traditional features, while other applications have joined our innovations with subsets of advantageous HIBP features. This work has established smaller, more economical detection systems and, in doing so, may enable substantial extension of scenarios in which beam-based diagnostics are deployed. Key Measurement Innovations include: Computer Models and Simulations of HIBP Applications-Computer modeling and simulating of HIBP applications is central to predicting interaction of beam particles with plasmas, designing diagnostic systems, and anticipating measurement characteristics. We have extended the capability and improved measurement fidelity of the HIBP diagnostic on the Madison Symmetric Torus (MST) reversed field pinch (RFP); simulated feasibility of HIBP operation in the Helically Symmetric eXperiment (HSX) optimized stellarator; and investigated the plausibility of HIBP measurements in the ASDEX Upgrade tokamak. We have also developed a new technique that uses ion optics to model a finite phase-space beam. It yields more realistic (than traditional method) estimates of sample volume characteristics, which influence measurement resolution and sensitivity. Extension of Diagnostic Capabilities and Measurements on the MST RFP-Capabilities have been extended using the first and only HIBP installation on an RFP. Challenges associated with operation of the diagnostic on MST include the three-dimensional nature of particle trajectories, temporal topology and amplitude changes in the plasma equilibrium, and strong particle and radiative emission from the plasma. We have addressed these challenges through development of detailed and higher precision diagnostic simulations, calibrated subtraction of noise to resolve secondary ion signals, and implementation of hardware that enables higher fidelity measurement of plasma fluctuations. Development of New Hardware and Measurement Techniques - We have advanced beam-based measurement capabilities via development of new detectors and techniques to determine the poloidal magnetic flux ψ in the plasma, and enable placement of detectors close to the plasma and thus allow a smaller and less expensive diagnostic. We have demonstrated measurement of ψ, an ability that was made possible by development of highly effective noise reduction techniques. We also modeled the effect of non-ideal and instrumentation effects by developing a virtual (simulated) diagnostic. Application of beam-based diagnostics on various devices and multiple magnetic configurations (e.g. stellarator, tokamak, and reversed field pinch) enables investigation of critical physics issues and measurements resulting in the broad parameter space data needed to test and validate theory & modeling.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2022

SSR COST – COmposite Structural Technologies for advanced reactor deployment (Final Scientific/Technical Report)

Moltex is designing a Generation 4 or Advanced nuclear reactor, the molten salt fast spectrum SSR-W. The design intent is to reduce the cost and complexity of the nuclear systems by relying on inherent safety characteristics, instead of engineered systems. One such characteristic as a natural tendency for power to decrease as temperature increases. Another characteristic that improves natural safety is the fact that the working fluids (molten salts) are at atmospheric pressure at operating temperatures. This significantly reduces the need for pressure boundary components and systems. Moltex is highly motivated to develop simple and economic reactors to reduce the carbon intensity and improve energy security of global electricity production. The COST project investigated the opportunities for reducing construction cost of advanced reactors with the adoption of Rigid Elastic Composite Civil works (RECC). The construction design and methods used in currently operational light water power plants would generally be suboptimal for the SSR-W since there is no requirement for civil structures to withstand high pressure accidents such as hydrogen combustion or steam explosion. In parallel to downselecting the most promising RECC-type technology, accident scenarios representing the largest credible challenge to the SSR-W civil and construction design were modelled. The capability of an SSR-W reactor constructed with and without the selected construction technology was evaluated and the consequential licensing challenges identified. A construction schedule and cost estimate of the SSR-W with and without the selected RECC construction technology was developed. Once the above data was collected, then Moltex was able to review the safety performance, license-ability and cost/schedule benefit of applying the preferred construction technology. The CAPEX/NPV benefit was quantified, and the licensing risks and opportunities were documented. Finally, the CAPEX/NPV benefit of the advanced construction technology was modest due to the suitability of traditional stick-built construction for the SSR-W design. However, previously unidentified benefits of the advanced construction technology were revealed during the studies that could credibly de-risk certain aspects of the design to be licensed. Since licensing (duration and delays) are very significant in new build project costs, the cost benefits may be significant. Further studies on the risk reduction of the construction technology are foreseen.

21 SPECIFIC NUCLEAR REACTORS AND ASSOCIATED PLANTS↗
DOE OSTI2026

ResStock Measure Documentation: Residential Two-Stage Geothermal Heat Pump (4.0 COP, 20.5 EER) With Envelope Improvements and Advanced Air Sealing

The goal of this work is to develop energy efficiency, demand flexibility, and other retrofit end-use load shapes (electricity, gas, propane, or fuel oil) that cover a majority of the high-impact, market-ready (or nearly market-ready) measures. "Measures" refers to retrofits that can be applied to buildings during modeling. An "end-use savings shape" is the difference in energy consumption between a baseline building and a building with an energy efficiency, demand flexibility, or other retrofit measure applied. It results in a time-series profile that is broken down by end use and fuel (electricity or on-site gas, propane, or fuel oil use) at each time step. ResStock is a highly granular, physics-based, bottom-up model that uses multiple data sources, statistical sampling methods, and advanced building energy simulations to estimate the annual subhourly energy consumption of the residential building stock across the United States. The baseline model intends to represent the U.S. residential building stock as it existed in 2018. Technical documentation for the inputs and assumptions in the baseline building stock model is available in Reyna et al. (2025). Calibration and validation of the baseline model results are available in the final technical report of the End-Use Load Profiles project (Wilson, et al. 2022). This document focuses on a single end-use savings shape measure: Residential Two-Stage Geothermal Heat Pump (GHP) (4.0 COP, 20.5 EER) With Envelope Improvements. This measure combines a two-stage GHP with envelope improvements as a single package. As this package is a combination of two other measures, this document focused on documenting the results associated with this combination of technologies, with individual measure documents for two-stage GHPs and envelope improvements providing the information on the details of these measures. When the two technologies are combined, envelope improvements can modestly reduce energy consumption by a further 10%-15%, but also reduce the required size of the ground heat exchanger and heat pump by approximately 33% on average across all sites. The cost of installing envelope improvements in these homes is likely to be more than paid for by the reduction in equipment and drilling costs in these buildings for the majority of the stock.

15 GEOTHERMAL ENERGY↗
DOE OSTI2020

Recent land cover changes in the Southwestern US lead to an increase in surface temperature

The increase in large-scale land cover change (LCC) in recent decades, particularly in response to climate-driven disturbances, has potential to impact local and regional changes in climate due to modification of carbon sources and sinks, albedo, surface roughness and energy fluxes. Using observational data, we predict the impact of two of the most extensive LCCs occurring in the Southwestern US: drought-induced tree mortality and shrub encroachment into grasslands, on surface temperature. We developed a new energy balance method that extracts the biophysical responses to environmental conditions, to predict how structural changes in albedo, surface roughness and canopy conductance following LCC will alter surface temperature. This method allows us not only to explain the observed differences in surface temperature between two non-adjacent study sites with different environmental conditions, but also to separate the contribution of biophysical and non-biophysical properties to surface temperature. Our results suggest that changes in biophysical properties due to shrub encroachment and tree mortality in the Southwestern US (independent of changes in other environmental properties) can potentially lead to an increase in midday surface temperature (11AM to 2PM) of 1 to 2 degrees Celsius, comparable to changes in surface temperature following deforestation. Although the average surface temperature increase in response to both shrub encroachment and tree mortality is similar, the biophysical properties driving the temperature change are different in each scenario. Change in aerodynamic conductance following tree mortality is the largest contributor to heating, while reduced albedo and canopy conductance drive the increase in surface temperature following shrub encroachment. Finally, we also show how this increase in daytime surface temperature could be further intensified with future climate, especially with the expected reduction in soil water availability in the Southwest.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2021

Data-Driven Reduced-Order Modeling of Convective Heat Transfer in Porous Media

This work presents a data-driven Reduced-Order Model (ROM) for parametric convective heat transfer problems in porous media. The intrusive Proper Orthogonal Decomposition aided Reduced-Basis (POD-RB) technique is employed to reduce the porous medium formulation of the incompressible Reynolds-Averaged Navier–Stokes (RANS) equations coupled with heat transfer. Instead of resolving the exact flow configuration with high fidelity, the porous medium formulation solves a homogenized flow in which the fluid-structure interactions are captured via volumetric flow resistances with nonlinear, semi-empirical friction correlations. A supremizer approach is implemented for the stabilization of the reduced fluid dynamics equations. The reduced nonlinear flow resistances are treated using the Discrete Empirical Interpolation Method (DEIM), while the turbulent eddy viscosity and diffusivity are approximated by adopting a Radial Basis Function (RBF) interpolation-based approach. The proposed method is tested using a 2D numerical model of the Molten Salt Fast Reactor (MSFR), which involves the simulation of both clean and porous medium regions in the same domain. For the steady-state example, five model parameters are considered to be uncertain: the magnitude of the pumping force, the external coolant temperature, the heat transfer coefficient, the thermal expansion coefficient, and the Prandtl number. For transient scenarios, on the other hand, the coastdown-time of the pump is the only uncertain parameter. The results indicate that the POD-RB-ROMs are suitable for the reduction of similar problems. The relative L2 errors are below 3.34% for every field of interest for all cases analyzed, while the speedup factors vary between 54 (transient) and 40,000 (steady-state).

German, Péter (ORCID:0000000307285283)↗
DOE OSTI2025

MOOSE ProbML: Parallelizable Probabilistic Machine Learning and Uncertainty Quantification Capabilities

The Multiphysics Object Oriented Simulation Environment (MOOSE) is a widely used open- source finite element software for performing multiphysics multiscale simulations in a massively parallel fashion. Recently, the computational team at Idaho National Laboratory (INL) has implemented Probabilistic Machine Learning (ProbML) capabilities in MOOSE—in a parallelized fashion—and enable active learning with large-scale computational models for tasks such as surrogate model development, scale bridging, forward/inverse uncertainty quantification (UQ), Bayesian optimization, etc. This presentation summarizes these developments in MOOSE along with demonstrations on several real applications relevant to nuclear energy. At the fundamental level, samplers like Monte Carlo/Latin Hypercube, variance reduction, parallelized Markov Chain Monte Carlo (MCMC) support uncertainty propagation in both forward and inverse settings. These samplers can be integrated with the Gaussian processes (GP) suite in MOOSE, which offer several variants like scalar GPs, multi-output GPs, and deep GPs, to enable active learning. These GPs can be tuned using gradient-based optimization methods like Adam and its variants or gradient-free methods like the elliptical slice sampler (a variant of MCMC adept under Gaussian settings) for more complex covariance kernels or likelihoods whose gradient computations can be cumbersome. A variety of batch acquisition functions permit parallelized evaluation of the computational model and support different learning objectives with high efficiency like Bayesian inference, global surrogate development, optimization, etc. Furthermore, libtorch integration supports training, evaluation, and re-training of neural networks and other complex machine learning models in active learning settings. The impacts of these developments are shown on several real applications: (1) nuclear fuel inverse UQ and model inadequacy assessment using the Kennedy O’Hagan framework; (2) uncertainty aware surrogate modeling for additive manufacturing to predict field quantities; (3) nuclear reactor rare events analysis; and (4) complex fluid flow prediction using a global surrogate with quantified prediction uncertainty. Finally, the outlook of MOOSE ProbML is discussed for both outer-loop and inner-loop computations in the broad view to accelerate fuels and materials qualification, address gaps in knowledge and data, and assess new reactor/fuel systems.

11 - NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
DOE OSTI2024

Life-Cycle Assessment Integration into Scalable Open-Source Numerical Models (LiAISON) for Prospective Impact Analysis of Novel Technologies

Decarbonizing the industrial sector is a significant challenge in achieving a net-zero greenhouse gas (GHG) emissions economy by 2050 and the Paris Agreement, i.e., a global climate change mitigation target of achieving a maximum average temperature change potential of 1.5 Degrees Celsius or less by 2100 with respect to pre-industrial levels. In the United States (US), the industrial sector accounts for 23% of total GHG emissions and is home to a number of hard-to-electrify activities. The chemicals subsector has the single largest subsector emissions profile after direct emissions from fossil fuel combustion and leakage from fossil fuel distribution systems. Within the chemicals subsector, many processes depend on hydrogen or ammonia precursors. Decarbonizing these two commodities would contribute significantly to decarbonizing the industrial sector as hydrogen could also be used for low carbon steel production (e.g., hydrogen-based direct reduction of iron) and other industrial applications. Emerging technologies require the application of prospective life cycle assessment (LCA), which can account for technology (foreground) scaling and process improvements via learning-by-doing, among others. In many cases, the future system context (background) in which the technologies are assumed to operate in is equally relevant. Background scenarios generated by integrated assessment models (IAM) can coherently incorporate potential future dynamics of the energy-climate-human-land system. Further, IAM scenarios are harmonized across socioeconomic and climate change mitigation pathways, which facilitates the comparability of prospective LCAs using different IAMs. We introduce an open source prospective LCA framework, the Life-cycle Assessment Integration into Scalable Open-source Numerical models (LiAISON), to analyze the non-linear relationships between technology foreground and the future energy system background across a series of midpoint and resource use metrics. The integration of LCA and IAM data is achieved using prospective environmental Impact assessment (PREMISE). We showcase it by assessing two Power-to-Hydrogen (PtH2) processes, namely Solid Oxide Electrolysis (SOE) and Polymer Electrolyte Membrane Electrolysis (PEME). We compare the technologies to a baseline of hydrogen production via natural gas-based Steam Methane Reforming (SMR) in a US context of multiple energy system and climate change mitigation futures. Besides providing an analysis that specifies the LCA results ranges with temporal and geospatial explicitness across the two technologies, metrics, and impact assessment methods, this research also aims to establish a base framework that can be expanded to use other IAM generated scenarios and US open-source life cycle inventory (LCI) databases. We find that the temporal environmental performance of either technology or their difference to SMR is directly influenced by the underlying background dynamics. Additionally we compare our results by linking two other prospective models with LiAISON - GCAM (Global Change Assessment Model) and ReEDS (Regional Energy Deployment System) to analyze the effect of changing background scenarios using varying predictions in life cycle analysis.

decarbonizing↗
DOE OSTI2024

Life-Cycle Assessment Integration into Scalable Open-Source Numerical Models (LiAISON) for Analyzing Emerging Low-Carbon Technologies

Decarbonizing the industrial sector is a significant challenge in achieving a net-zero greenhouse gas (GHG) emissions economy by 2050 and the Paris Agreement, i.e., a global climate change mitigation target of achieving a maximum average temperature change potential of 1.5 degrees Celsius or less by 2100 with respect to pre-industrial levels. In the United States (US), the industrial sector accounts for 23% of total GHG emissions and is home to a number of hard-to-electrify activities. The chemicals subsector has the single largest subsector emissions profile after direct emissions from fossil fuel combustion and leakage from fossil fuel distribution systems. Within the chemicals subsector, many processes depend on hydrogen or ammonia precursors. Decarbonizing these two commodities would contribute significantly to decarbonizing the industrial sector as hydrogen could also be used for low carbon steel production (e.g., hydrogen-based direct reduction of iron) and other industrial applications. Emerging technologies require the application of prospective life cycle assessment (LCA), which can account for technology (foreground) scaling and process improvements via learning-by-doing, among others. In many cases, the future system context (background) in which the technologies are assumed to operate in is equally relevant. Background scenarios generated by integrated assessment models (IAM) can coherently incorporate potential future dynamics of the energy-climate-human-land system. Further, IAM scenarios are harmonized across socioeconomic and climate change mitigation pathways, which facilitates the comparability of prospective LCAs using different IAMs. We introduce an open source prospective LCA framework, the Life-cycle Assessment Integration into Scalable Open-source Numerical models (LiAISON), to analyze the non-linear relationships between technology foreground and the future energy system background across a series of midpoint and resource use metrics. The integration of LCA and IAM data is achieved using prospective environmental Impact assessment (PREMISE). We showcase it by assessing two Power-to-Hydrogen (PtH2) processes, namely Solid Oxide Electrolysis (SOE) and Polymer Electrolyte Membrane Electrolysis (PEME). We compare the technologies to a baseline of hydrogen production via natural gas-based Steam Methane Reforming (SMR) in a US context of multiple energy system and climate change mitigation futures. Besides providing an analysis that specifies the LCA results ranges with temporal and geospatial explicitness across the two technologies, metrics, and impact assessment methods, this research also aims to establish a base framework that can be expanded to use other IAM generated scenarios and US open-source life cycle inventory (LCI) databases. We find that the temporal environmental performance of either technology or their difference to SMR is directly influenced by the underlying background dynamics. Under baseline projections (i.e., no decarbonization goals), neither process reaches parity with the incumbent technology across several environmental metrics. Under the decarbonization scenarios, the underlying sectoral shifts result in declining impacts over time, compared to 2020 levels, except for metal depletion levels, which increase. The background shifts postulate a heavily decarbonized economy and energy system, which help technologies reach parity with SMR between 2040-2050 (RCP2.6) and 2030-2040 (RCP1.9) for global warming. Despite declines across several other metrics over time, neither PtH2 technology break even with SMR by 2100 besides for global warming.

decarbonizing↗
DOE OSTI2024

Life-Cycle Assessment Integration into Scalable Open-Source Numerical Models (LiAISON) for Analyzing Emerging Low-Carbon Technologies

Decarbonizing the industrial sector is a significant challenge in achieving a net-zero greenhouse gas (GHG) emissions economy by 2050 and the Paris Agreement, i.e., a global climate change mitigation target of achieving a maximum average temperature change potential of 1.5 degrees C or less by 2100 with respect to pre-industrial levels. In the United States (US), the industrial sector accounts for 23% of total GHG emissions and is home to a number of hard-to-electrify activities. The chemicals subsector has the single largest subsector emissions profile after direct emissions from fossil fuel combustion and leakage from fossil fuel distribution systems. Within the chemicals subsector, many processes depend on hydrogen or ammonia precursors. Decarbonizing these two commodities would contribute significantly to decarbonizing the industrial sector as hydrogen could also be used for low carbon steel production (e.g., hydrogen-based direct reduction of iron) and other industrial applications. Emerging technologies require the application of prospective life cycle assessment (LCA), which can account for technology (foreground) scaling and process improvements via learning-by-doing, among others. In many cases, the future system context (background) in which the technologies are assumed to operate in is equally relevant. Background scenarios generated by integrated assessment models (IAM) can coherently incorporate potential future dynamics of the energy-climate-human-land system. Further, IAM scenarios are harmonized across socioeconomic and climate change mitigation pathways, which facilitates the comparability of prospective LCAs using different IAMs. We introduce an open source prospective LCA framework, the Life-cycle Assessment Integration into Scalable Open-source Numerical models (LiAISON), to analyze the non-linear relationships between technology foreground and the future energy system background across a series of midpoint and resource use metrics The integration of LCA and IAM data is achieved using prospective environmental Impact assessment (PREMISE). We showcase it by assessing two Power-to-Hydrogen (PtH2) processes, namely Solid Oxide Electrolysis (SOE) and Polymer Electrolyte Membrane Electrolysis (PEME). We compare the technologies to a baseline of hydrogen production via natural gas-based Steam Methane Reforming (SMR) in a US context of multiple energy system and climate change mitigation futures. Besides providing an analysis that specifies the LCA results ranges with temporal and geospatial explicitness across the two technologies, metrics, and impact assessment methods, this research also aims to establish a base framework that can be expanded to use other IAM generated scenarios and US open-source life cycle inventory (LCI) databases. We find that the temporal environmental performance of either technology or their difference to SMR is directly influenced by the underlying background dynamics. Under baseline projections (i.e., no decarbonization goals), neither process reaches parity with the incumbent technology across several environmental metrics. Under the decarbonization scenarios, the underlying sectoral shifts result in declining impacts over time, compared to 2020 levels, except for metal depletion levels, which increase. The background shifts postulate a heavily decarbonized economy and energy system, which help technologies reach parity with SMR between 2040-2050 (RCP2.6) and 2030-2040 (RCP1.9) for global warming. Despite declines across several other metrics over time, neither PtH2 technology break even with SMR by 2100 besides for global warming.

decarbonizing↗
DOE OSTI2024

Towards Prospective LCA Using Life-Cycle Assessment Integration into Scalable Open-Source Numerical Models (LiAISON) Framework for Analyzing Emerging Low-Carbon Technologies

Decarbonizing the industrial sector is a significant challenge in achieving a net-zero greenhouse gas (GHG) emissions economy by 2050 and the Paris Agreement, i.e., a global climate change mitigation target of achieving a maximum average temperature change potential of 1.5 Degrees Celsius or less by 2100 with respect to pre-industrial levels. In the United States (US), the industrial sector accounts for 23% of total GHG emissions and is home to a number of hard-to-electrify activities. The chemicals subsector has the single largest subsector emissions profile after direct emissions from fossil fuel combustion and leakage from fossil fuel distribution systems. Within the chemicals subsector, many processes depend on hydrogen or ammonia precursors. Decarbonizing these two commodities would contribute significantly to decarbonizing the industrial sector as hydrogen could also be used for low carbon steel production (e.g., hydrogen-based direct reduction of iron) and other industrial applications. Emerging technologies require the application of prospective life cycle assessment (LCA), which can account for technology (foreground) scaling and process improvements via learning-by-doing, among others. In many cases, the future system context (background) in which the technologies are assumed to operate in is equally relevant. Background scenarios generated by integrated assessment models (IAM) can coherently incorporate potential future dynamics of the energy-climate-human-land system. Further, IAM scenarios are harmonized across socioeconomic and climate change mitigation pathways, which facilitates the comparability of prospective LCAs using different IAMs. We introduce an open source prospective LCA framework, the Life-cycle Assessment Integration into Scalable Open-source Numerical models (LiAISON), to analyze the non-linear relationships between technology foreground and the future energy system background across a series of midpoint and resource use metrics The integration of LCA and IAM data is achieved using prospective environmental Impact assessment (PREMISE). We showcase it by assessing two Power-to-Hydrogen (PtH2) processes, namely Solid Oxide Electrolysis (SOE) and Polymer Electrolyte Membrane Electrolysis (PEME). We compare the technologies to a baseline of hydrogen production via natural gas-based Steam Methane Reforming (SMR) in a US context of multiple energy system and climate change mitigation futures. Besides providing an analysis that specifies the LCA results ranges with temporal and geospatial explicitness across the two technologies, metrics, and impact assessment methods, this research also aims to establish a base framework that can be expanded to use other IAM generated scenarios and US open-source life cycle inventory (LCI) databases. We find that the temporal environmental performance of either technology or their difference to SMR is directly influenced by the underlying background dynamics. Additionally we compare our results by linking two other prospective models with LiAISON - GCAM(Global Change Assessment Model) and ReEDS (Regional Energy Deployment System) to analyze the effect of changing background scenarios using varying predictions in life cycle analysis.

emissions↗