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At least 343 records · Page 19

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DOE OSTI2020

CheKiPEUQ Intro 2: Harnessing Uncertainties from Data Sets, Bayesian Design of Experiments in Chemical Kinetics**

When choosing experimental conditions, Bayesian statistical tools can predict the experimental choices which will yield the highest information gain. Experimental choices could be temperature, pressure, reaction time, number of measurements, reactor volume, etc.. Three example analyses are presented here, each using the software Chemical Kinetics Parameter Estimation and Uncertainty Quantification (CheKiPEUQ). Information gain is a measure of reduction of uncertainty in a model's parameters. The three chemical system examples presented each illustrate Bayesian Design of Experiments using information gain. In the first chemical example, temperature selection impacts the information gain for the free energy of reaction in a two-component equilibrium reaction. In the second example, temperature and pressure are explored for a competitive adsorption Langmuir replacement reaction system. Finally, the third example is a catalytic membrane reactor which is a culmination of the previous examples. The catalytic membrane reactor has a complex and nonlinear response in the observables which is solved by numerical evaluation. In the three examples, the experimental conditions are treated as design variables for maximizing information gain.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2021

The Co-Transport of PFAS and Cr(VI) in porous media

PFAS and Cr are present at some sites as co-contaminants. Here, the objective of this research was to investigate the co-transport behavior of per- and polyfluoroalkyl substances (PFAS) and hexavalent chromium (Cr(VI)) in porous media. Miscible-displacement experiments were conducted using two soils and an aquifer sediment with different geochemical properties. Perfluorooctane sulfonic acid (PFOS) and perfluorooctanoic acid (PFOA) were employed as model PFAS. The retardation of PFOS was decreased in the presence of Cr(VI). Conversely, the transport and retardation of PFOA was not affected by the presence of Cr(VI). The reduction of PFOS retardation caused by Cr(VI) is likely due to sorption competition for both organic-carbon and inorganic (metal-oxides and clay minerals) domains. The relative contributions of the three soil constituents to PFOS sorption and the potential for competition between PFOS and Cr(VI) is a function of the geochemical composition of the porous media (i.e., organic carbon, metal-oxides and clay minerals). The PFAS had minimal impact on the retention and transport of Cr(VI). To our knowledge, the results presented herein represent the first reported data for PFOS and Cr(VI) co-transport in porous media. The results of this study indicate that the presence of Cr(VI) has the potential to increase the migration potential of PFOS in soil and groundwater, which should be considered when characterizing electroplating facilities, leather tanning facilities, and other co-contaminated sites.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2025

Primary material supply configurations and domestic recycling for cost-effective battery material production in the US

Battery cathode active material costs hinge on regionally concentrated, price-volatile metal supply. Here. we construct a regional facility-level cost model based on over 80 global lithium, cobalt, and nickel mines, refineries, and battery-grade material plants. Our model yields aggregated lithium, nickel, manganese, and cobalt production material costs from 392 region-based supply configurations for five different cathode active materials. Focusing on the United States, all-domestic supply is 9–34% costlier than global average, increasing by cobalt content, while these shortfalls can be overcome by selective low-cost material imports. Furthermore, we analyze costs of two U.S.-based recycling facilities from primary data and techno-economic modelling and compare resulting cathode active material-level costs to primary supply. Although it is still significantly higher on cathode active material cost-level, rising end-of-life flows and lowered black-mass prices will, however, make secondary supply cost-competitive to domestic and foreign primary supply cost floors. Facility-level benchmarks reveal targeted import, scaling, and production cost optimization as levers for a resilient, cost-effective U.S. battery-material supply chain.

Energy↗

DOE OSTI2026

CaloTrilogy: Toward a Breakthrough in One-Step, End-to-End, Physics-Guided Shower Generation for Modern Calorimeters

High-precision calorimeter simulation at current and future colliders imposes rapidly growing computational demands, motivating the development of machine-learning surrogates for traditional Monte Carlo tools such as Geant4. Flow matching and diffusion-based generative models have become leading approaches for high-dimensional fast simulation because of their sample quality, but typically require ${\cal O}(100)$ function evaluations at inference and often rely on auxiliary networks to constrain global observables, compromising streamlined end-to-end generation. We introduce a unified framework that improves the balance between speed, shower quality, and physics fidelity. The method combines: (i) an average velocity field integrator that enables sampling in one or a few evaluations; (ii) a learned generative prior in shower space, constructed from data rather than random noise; and (iii) physics-guided loss terms that impose inductive biases on key observables during training. These elements are training time regularizers, preserving end-to-end inference with no additional cost. With only one or a few evaluation steps, the model achieves shower quality competitive with state-of-the-art flow and diffusion approaches, tested on several public high granularity calorimeter datasets. The results demonstrate inter-layer shower structure consistent with the underlying physics, providing a strong candidate for future fast simulation workflows.

Jiang, Cheng [Edinburgh U.]↗

DOE OSTI2023

Defining a compact dry cooler design to reduce LCOE contribution in a CSP facility

Concentrating solar power (CSP), when coupled with a supercritical carbon dioxide (sCO 2 ) power cycle and sensible heat storage, presents a renewable and clean alternative for utility-scale power generation. However, in order to be competitive in the current and future markets, CSP facilities must limit their levelized cost of electricity (LCOE) by minimizing capital costs and reducing operating costs over the lifetime of the plant. Targeting this goal, this study investigates the LCOE impact of the power cycle pre-cooler. This study considers a compact dry cooler with micro-channel technology on the CO 2 side and formed fin geometry on the air side, using directly-coupled centrifugal fans and a transition duct to improve air distribution across the fins as well as protect the fins from contaminants which may cause blockage, soiling, fouling, and damage. In an effort to better understand the dry cooler impact on LCOE, a sensitivity study was conducted using various combinations of end-to-end approach temperatures, air-side pressure drop values, CO 2 -side pressure drop values, fan types, cooler turndown control schemes, cooler module sizes, and design-point ambient temperatures. Furthemore, off-design cycle performance data was calculated for each dry cooler design using NPSS simulation software; cycle performance data were then input to System Advisor Model (SAM) along with the associated capital costs for LCOE prediction of a 100 MW system over a 30 year plant lifetime. Results of this study show the LCOE is most sensitive to air-side performance, followed by heat transfer effectiveness and capital cost. It was found that a power cycle with a mid- to high-performance dry cooler will produce the most competitive power-production costs. Designing at the extreme ends for approach temperature (or effectiveness), design-point ambient temperature, and compactness (footprint) produce higher LCOE values; mid-range values for these parameters balance performance, operating costs, and associated capital cost to optimize LCOE.

14 SOLAR ENERGY↗

DOE OSTI2023

Permissiveness and competition within and between Neurospora crassa syncytia

A multinucleate syncytium is a common growth form in filamentous fungi. Comprehensive functions of the syncytial state remain unknown, but it likely allows for a wide range of adaptations to enable filamentous fungi to coordinate growth, reproduction, responses to the environment, and to distribute nuclear and cytoplasmic elements across a colony. Indeed, the underlying mechanistic details of how syncytia regulate cellular and molecular processes spatiotemporally across a colony are largely unexplored. Here, we implemented a strategy to analyze the relative fitness of different nuclear populations in syncytia of Neurospora crassa , including nuclei with loss-of-function mutations in essential genes, based on production of multinucleate asexual spores using flow cytometry of pairings between strains with differentially fluorescently tagged nuclear histones. The distribution of homokaryotic and heterokaryotic asexual spores in pairings was assessed between different auxotrophic and morphological mutants, as well as with strains that were defective in somatic cell fusion or were heterokaryon incompatible. Mutant nuclei were compartmentalized into both homokaryotic and heterokaryotic asexual spores, a type of bet hedging for maintenance and evolution of mutational events, despite disadvantages to the syncytium. However, in pairings between strains that were blocked in somatic cell fusion or were heterokaryon incompatible, we observed a “winner-takes-all” phenotype, where asexual spores originating from paired strains were predominantly one genotype. These data indicate that syncytial fungal cells are permissive and tolerate a wide array of nuclear functionality, but that cells/colonies that are unable to cooperate via syncytia formation actively compete for resources.

59 BASIC BIOLOGICAL SCIENCES↗

DOE OSTI2022

Tree crown damage and its effects on forest carbon cycling in a tropical forest

Abstract Crown damage can account for over 23% of canopy biomass turnover in tropical forests and is a strong predictor of tree mortality; yet, it is not typically represented in vegetation models. We incorporate crown damage into the Functionally Assembled Terrestrial Ecosystem Simulator (FATES), to evaluate how lags between damage and tree recovery or death alter demographic rates and patterns of carbon turnover. We represent crown damage as a reduction in a tree's crown area and leaf and branch biomass, and allow associated variation in the ratio of aboveground to belowground plant tissue. We compare simulations with crown damage to simulations with equivalent instant increases in mortality and benchmark results against data from Barro Colorado Island (BCI), Panama. In FATES, crown damage causes decreases in growth rates that match observations from BCI. Crown damage leads to increases in carbon starvation mortality in FATES, but only in configurations with high root respiration and decreases in carbon storage following damage. Crown damage also alters competitive dynamics, as plant functional types that can recover from crown damage outcompete those that cannot. This is a first exploration of the trade‐off between the additional complexity of the novel crown damage module and improved predictive capabilities. At BCI, a tropical forest that does not experience high levels of disturbance, both the crown damage simulations and simulations with equivalent increases in mortality does a reasonable job of capturing observations. The crown damage module provides functionality for exploring dynamics in forests with more extreme disturbances such as cyclones and for capturing the synergistic effects of disturbances that overlap in space and time.

Needham, Jessica F.↗

DOE OSTI2025

Hourly Load Profile Dataset for Federal, State, and Municipal Electric Vehicle Fleets in the United States

The electrification of U.S. federal, state, and municipal fleets is accelerating rapidly, driven by an increased availability of competitive electric vehicle (EV) options and supportive policies and targets. The dataset described in this report, accessible at data.nrel.gov/submissions/280, provides a critical foundation for identifying fleet electricity demand, projecting these future demands, and developing actionable strategies to support the widespread electrification of government fleets. The dataset incorporates available fleet data, including 54% of federal agency vehicles approved for analysis (notably, the U.S. Postal Service is absent). Additionally, it includes data from 50,000 state government vehicles and 94,000 local government vehicles. While this represents a small fraction of the 4.4 million vehicles owned by state and local governments reported by the Federal Highway Administration (2022), the framework supports future expansion as more fleet inventory data become available.

33 ADVANCED PROPULSION SYSTEMS↗

DOE OSTI2022

Hanford Reach Fall Chinook Salmon Productivity Analysis, Brood Years 1975–2016

Updating the Hanford Reach fall Chinook salmon productivity analysis to include seven additional years of data (BY 2010–2016), including three years of record escapements (>150,000 adults), reaffirmed the highly productive nature of this population and the positive effect of flow fluctuation constraints as implemented at Priest Rapids Dam by the VBSA and HRFCPP. The addition of several years of high escapements also revealed that competition among juveniles (and not spawning habitat limitations) is likely the mechanism driving density dependence in the Hanford Reach. The addition of the high escapement brood years also provided evidence for the high recruitment capacity of the population. As such, the WDFW may consider increasing the maximum escapement goal from 42,000 adults to 49,000 adults, which would be more in-line with SMSY as estimated from this update. Given the in-river protections that are in place as part of the HRFCPP, ocean conditions appear to be the primary factor controlling adult production. Therefore, the success of any additional management actions taken in freshwater are likely to be dependent on survival in the ocean. As such, the most effective management actions may be those that limit juvenile competition and density dependent growth effects in the Hanford Reach. Limiting competition would be expected to increase growth, resulting in larger smolts that would be less likely to suffer size-dependent mortality in the marine environment, even during years of poor ocean conditions. Increasing terminal harvest rates when high escapements are anticipated (from downstream dam counts) is one management option that could help to curtail juvenile competition.

Harnish, Ryan A.↗

DOE OSTI2020

Energy Storage Analysis

This study presents a comprehensive techno-economic characterization of energy storage and exible low carbon power generation technologies that can shift energy across days, weeks, or months to balance daily, weekly, and seasonal disparities in supply and demand. Energy storage technologies evaluated here include pumped hydropower storage (PHS), adiabatic and diabatic compressed air energy storage (CAES), vanadium redox flow batteries (VRBs), pumped thermal energy storage (P-TES), and renewably produced hydrogen stored in either geologic formations or underground pipes with re-electrification via combustion turbines in combined cycles, stationary proton exchange membrane (PEM) fuel cells, or the novel use of PEM fuel cells designed for heavy-duty vehicles (HDVs) which are expected to have shorter lives but also lower capital costs. We also evaluate flexible low-carbon power generation systems, including ethanol combustion in gas turbines and natural gas combustion with carbon capture and sequestration (CCS). We estimate current costs with literature data, use learning rates to characterize future costs, and develop capacity factors calibrated to an 85% renewables grid to calculate the levelized cost of energy (LCOE) of each technology. Results illustrate that at the 12-hour storage duration, PHS and CAES have the lowest LCOE with current costs, and VRBs become competitive if future costs are achieved. At the 120-hour storage duration, hydrogen systems with geologic storage and natural gas with CCS achieve the lowest LCOE in both current and future capital cost scenarios. In particular, the new configuration of HDV-PEM fuel cells with hydrogen storage in geologic formations evaluated here could lower the LCOE by 22-27% compared to stationary fuel cell systems typically evaluated and might help enable very high (>80%) renewable energy electric power systems. P-TES and hydrogen stored in underground pipes are the least-cost options at the 120-hour storage duration rating that do not require some form of geologic storage. Sensitivity analysis and Monte Carlo analysis illustrate that the general trends seen in this study are valid for a wide range of capacity factors and future cost scenarios. The study also illustrates that coproducing and selling hydrogen to other markets could reduce the LCOE of hydrogen systems by up to 39%.

carbon capture and sequestration↗

DOE OSTI2023

2023 Real-time Optimization Workflow Status Update

Nuclear integrated energy systems are composed of a diverse set of energy generation sources and exist in dynamic and competitive electricity markets. With the inclusion of thermal energy storage, nuclear power systems can store heat for future use through various processes, such as water desalination or hydrogen generation. This heat storage can be managed in such a way as to economically optimize its usage. By combining real-time price data from the day-ahead and real-time markets with predictive and intelligent models, the charging and discharging of the thermal energy storage may be determined and optimized. This research details an approach through models and systems for the real-time optimization (RTO) of capacity allocation. Virtual models of the energy system and its physics phenomenon and component interactions provide intelligence to verification and prediction of operations. An optimization framework can use data generated from both a set of physical assets as well as the virtual models to predict future performance and create optimization and control workflows. Data warehouse technologies can be used to combine data across models, optimization workflows, market price data, and sensor data to intuitively store various types of data and provide integrations to physical control systems as well as user visualizations. Put together, these components can create a system for the RTO of nuclear integrated energy systems. Various virtual models and bench-scale physical demonstrations have been successfully performed and verified using this system. Larger scale testbeds that include thermal energy storage systems have been identified as future opportunities. A gap analysis that details the steps necessary to reach a physical demonstration at this scale is provided, along with conclusions on the current effort and future work.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗

DOE OSTI2022

Proper orthogonal decomposition based reduced-order modeling of flux-Limited gray thermal radiation

Here, in this work, a proper orthogonal decomposition (POD) based reduced-order model (ROM) is developed to solve gray, flux-limited thermal radiation diffusion. We focus on the variable opacity radiation penetration benchmark posed by Olson, Auer, and Hall. The T -3 relationship for opacity in conjunction with high-temperature radiation penetrating an initially cold material produces a strong thermal radiation shock. This class of problems is particularly challenging for standard POD-based reduced-order modeling due to the nonlinearities presented by 1) the T 4 source term, and 2) flux-limited diffusion operator. To address these challenges and develop a cost competitive ROM, we employ a “hyper-reduction” technique through discrete empirical interpolation (DEIM) and allow for adaptive reduced-order projections through principal interval decomposition (PID). Performance of the proposed methodology is quantified by comparing the cost savings and accuracy relative to a full-order computation. Reference solutions and snapshot data are obtained through a full-order calculation performed by the University of Chicago maintained astrophysics code, FLASH. For consistency and potential extensibility, the developed ROM is also implemented in FLASH. We find that in the initialization regime, where the thermal radiation wave is initially created by the warming of the material, this class of problems is highly reducible and suitable for POD-based ROMs. However, the strong convective nature of the wave propagation regime is less reducible and more challenging to create an efficient ROM.

42 ENGINEERING↗

DOE OSTI2021

Measuring cosmological distances using cluster edges as a standard ruler

ABSTRACT The line-of-sight velocity dispersion profile of galaxy clusters exhibits a ‘kink’ corresponding to the spatial extent of orbiting galaxies. Because the spatial extent of a cluster is correlated with the amplitude of the velocity dispersion profile, we can utilize this feature as a gravity-calibrated standard ruler. Specifically, the amplitude of the velocity dispersion data allows us to infer the physical cluster size. Consequently, observations of the angular scale of the ‘kink’ in the profile can be translated into a distance measurement to the cluster. Assuming the relation between cluster radius and cluster velocity dispersion can be calibrated from simulations, we forecast that with existing data from the Sloan Digital Sky Survey we will be able to measure the Hubble constant with 3.0 per cent precision. Implementing our method with data from the Dark Energy Spectroscopic Instrument (DESI) will result in a 1.3 per cent measurement of the Hubble constant. Adding cosmological supernova data improves the uncertainty of the DESI measurement to 0.7 per cent. While these error estimates are statistical only, they provide strong motivation for pursuing the necessary simulation program required to characterize and calibrate the systematic uncertainties impacting our proposed measurement. Whether or not our proposed measurement can in fact result in competitive H0 constraints will depend on what the eventual systematics floor for this method is.

79 ASTRONOMY AND ASTROPHYSICS↗

DOE OSTI2025

Advancing Manufacturing Water Resilience: Addressing Risks through New Approaches and Technologies

Water is indispensable in manufacturing operations, and many manufacturers operate on the assumption that water of sufficient quality and quantity will be available whenever and wherever needed. As such, the importance of water to this sector has been overlooked, despite its critical importance for operations and meeting production demands, due to perceived sufficient availability and low cost of water to manufacturers. However, the situation is changing as water-related risks are markedly increasing due to aging infrastructure, changing climate, and resource extraction, which alter global and regional water cycles and characteristics. These changes are occurring against a backdrop of intensifying competition from other sectors for scarce and/or unevenly distributed water resources and changing trends in water needs. Section 1 of this report explores those risks in the U.S. context, while the remaining sections detail the work needed to advance the resilience of manufacturing water supplies to these changing risks by filling critical data gaps, re-evaluating the value of water to manufacturers, and advancing opportunities for novel technologies and analyses. The intended audience for this report is broad, including manufacturers, decision makers, researchers, and policymakers.

36 MATERIALS SCIENCE↗

DOE OSTI2025

Advancing Manufacturing Water Resilience: Addressing Risks through New Approaches and Technologies

36 MATERIALS SCIENCE↗

DOE OSTI2024

Assessment of Cloud-based Applications for Enabling a Scalable Riskinformed Predictive Maintenance Strategy

The current light-water reactor fleet uses time-based maintenance strategies to achieve high-capacity factors. But to make nuclear more competitive in the energy market, these reactors could utilize emerging artificial intelligence (AI) and cloud computing technologies to achieve a cost-effective, predictive-maintenance strategy. This paper presents discussion and results on the application of cloud computing in the nuclear industry. The technical viability of cloud computing was analyzed using data from a boiling-water reactor’s safety relief valve. The models were hosted on three different systems: a local personal computer, Idaho National Laboratory’s high-performance computer system, and Microsoft Azure. The data were loaded and processed, and two types of models were trained in an A/B fashion. Based on the speed at which these actions were completed, it was determined that cloud computing affords adequate computing resources. Additionally, the computing power can scale with the demanded load. To enable cloud computing in the existing fleet, additional sensors, networks, and other requirements must be implemented to ensure a smooth transition from current maintenance strategies. However, the benefit is that the plants no longer need to manage their own servers, software, cybersecurity, and information technology support staff for in-house data analytics purpose. Many of these features can be offloaded to the cloud provider for a potential cost savings. Demonstrating how AI can improve the maintenance and operation of non-safety-related systems seems the likely path forward for implementing AI and cloud computing resources inside nuclear power plants.

azure↗

DOE OSTI2020

Finite-temperature density matrix embedding theory

We describe a formulation of the density matrix embedding theory at finite temperature. We present a generalization of the ground-state bath orbital construction that embeds a mean-field finite-temperature density matrix up to a given order in the Hamiltonian, or the Hamiltonian up to a given order in the density matrix. In this work, we assess the performance of the finite-temperature density matrix embedding on the one-dimensional Hubbard model both at half-filling and away from it, and the two-dimensional Hubbard model at half-filling, comparing to exact data where available, as well as results from finite-temperature density matrix renormalization group, dynamical mean-field theory, and dynamical cluster approximations. The accuracy of finite-temperature density matrix embedding appears comparable to that of the ground-state theory, with, at most, a modest increase in bath size, and competitive with that of cluster dynamical mean-field theory.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗

DOE OSTI2023

Determining biomolecular structures near room temperature using X-ray crystallography: concepts, methods and future optimization

For roughly two decades, cryocrystallography has been the overwhelmingly dominant method for determining high-resolution biomolecular structures. Competition from single-particle cryo-electron microscopy and micro-electron diffraction, increased interest in functionally relevant information that may be missing or corrupted in structures determined at cryogenic temperature, and interest in time-resolved studies of the biomolecular response to chemical and optical stimuli have driven renewed interest in data collection at room temperature and, more generally, at temperatures from the protein–solvent glass transition near 200 K to ∼350 K. Fischer has recently reviewed practical methods for room-temperature data collection and analysis [Fischer (2021), Q. Rev. Biophys. 54 , e1]. Here, the key advantages and physical principles of, and methods for, crystallographic data collection at noncryogenic temperatures and some factors relevant to interpreting the resulting data are discussed. For room-temperature data collection to realize its potential within the structural biology toolkit, streamlined and standardized methods for delivering crystals prepared in the home laboratory to the synchrotron and for automated handling and data collection, similar to those for cryocrystallography, should be implemented.

59 BASIC BIOLOGICAL SCIENCES↗