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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 OSTI2020

Methods for R&D Portfolio Analysis and Evaluation (Workshop Report)

The Workshop on Methods for R&D Portfolio Analysis and Evaluation convened on 17–18 July 2019 at the National Renewable Energy Laboratory in Golden, Colorado, and examined strengths and weaknesses of the various methodologies applicable to R&D portfolio modeling, analysis, and decision support, given pragmatic constraints such as data availability, uncertainties in estimating the impact of R&D spending, and practical operational overheads. Participants employed their deep expertise in approaches such as stochastic optimization, real options, Monte-Carlo analysis, Bayesian networks, decision theory, complex systems analysis, deep uncertainty, and technology-evolution modeling to critique the initial example models developed by the project’s core team and to conduct thought experiments grounded in real-life technology models, progress data, expert elicitation, and portfolio information. This engagement of participants’ methodological expertise with the practical requirements of real-life portfolio decision support yielded ideas for improved approaches, alternative methodological hypotheses, and hybridization of methodologies that are well-grounded theoretically, computationally sound, and realistically executable given data availability and other practical constraints.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2020

Methods for R&D Portfolio Analysis and Evaluation (Workshop Report)

The Workshop on Methods for R&D Portfolio Analysis and Evaluation convened on 17-18 July 2019 at the National Renewable Energy Laboratory in Golden, Colorado, and examined strengths and weaknesses of the various methodologies applicable to R&D portfolio modeling, analysis, and decision support, given pragmatic constraints such as data availability, uncertainties in estimating the impact of R&D spending, and practical operational overheads. Participants employed their deep expertise in approaches such as stochastic optimization, real options, Monte-Carlo analysis, Bayesian networks, decision theory, complex systems analysis, deep uncertainty, and technology-evolution modeling to critique the initial example models developed by the project’s core team and to conduct thought experiments grounded in real-life technology models, progress data, expert elicitation, and portfolio information. This engagement of participants’ methodological expertise with the practical requirements of real-life portfolio decision support yielded ideas for improved approaches, alternative methodological hypotheses, and hybridization of methodologies that are well-grounded theoretically, computationally sound, and realistically executable given data availability and other practical constraints.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗
DOE OSTI2022

Bioproducts from high-protein algal biomass: An economic and environmental sustainability review and risk analysis

This report has High-protein algal biomass as an important bio-commodity that has the potential to provide a new source of sustainable protein products. Herein is a critical review that identifies 1) the most relevant sustainability findings related to the processing of proteinaceous algal biomass to higher value protein products and 2) the potential pathways to improve life cycle assessment (LCA) and techno-economic analysis (TEA) metrics, including life-cycle carbon dioxide equivalent (CO 2 eq), life cycle energy, and minimum selling price (MSP) of these products. The critical review of the literature revealed a large variation in model input parameters relating to these metrics. Therefore, a Monte Carlo analysis was conducted to assess the risk associated with these input variations. To understand the uncertainties that propagate into high-protein algae to products’ systems, we reviewed more than 20 state-of-the-art unit operations for algal biomass processing., including cell disruption, protein solubilization, protein precipitation and purification, and protein concentration. We evaluated displacement of proteinaceous products by algal-bioproducts, including ruminant feed, aquaculture feed, protein tablets, and biopolymers and biopolyesters, with prices in the market ranging from 1.9 to 120 $\$$ kg -1 protein. This review realized that the MSP of ruminant and non-ruminant feed ranges from 0.65±0.56 to 2.9±1.1 $\$$ kg -1 protein, and bioplastics’ MSP ranges from 0.97 to 7.0 $\$$ kg -1 protein. Regarding LCA metrics, there is limited research on life cycle energy in proteinaceous biomass concentration and bioproduct systems, reported at 32.7 MJ kg protein -1 , for animal feed displacement. Animal feed emissions in the literature report negative fluxes, representing environmental benefits, as low as -3.7 kgCO 2 eq kg -1 protein and positive fluxes, i.e., global warming potential, as high as 12.8 kgCO 2 eq kg -1 protein. There is limited research on bioplastics life cycle emissions reported at 0.6 kgCO 2 eq kg -1 protein. In general, the studies to date of algae-derived protein bioproducts showed similar life cycle emissions to soybean meals, nylon, polymers, and polystyrenes. Our risk analysis realized that more than 50% of scenarios can result in negative-net life cycle CO 2 eq emissions. This review and risk analysis assess and demonstrate the scenarios that improve economic and environmental sustainability metrics in high-protein algal bioproducts systems.

09 BIOMASS FUELS↗
DOE OSTI2021

A probabilistic economic and environmental impact assessment of a cyanobacteria-based biorefinery

Microbial based biofuels represent a potential promising solution as an environmentally favorable transportation fuel. Cyanobacteria have many of the same advantages as microalgae: ability for rapid growth in otherwise non-arable regions, suitability for genetic engineering, and simple nutritional needs. Additionally, cyanobacteria can be engineered to secrete valuable co-products that can be harvested independent from the produced biomass. However, little work has been done to identify the processes and the economic and environmental impacts associated with a large-scale cyanobacteria-to-fuels facility. The present study is a concurrent techno-economic and life cycle assessment of a facility that generates fuels and methyl laurate, an oleochemical, from the cyanobacterial species Synechocystis sp. PCC 6803. Here, the biorefinery model includes all aspects of cultivation, separation of the secreted methyl laurate, biomass harvesting and fuel processing via hydrothermal liquefaction (HTL) of the dewatered biomass. The assessments leverage Monte Carlo analysis (MCA) to address uncertainty and variability inherent in the most significant input parameters, replacing them with probabilistic functions. For the facility configuration producing both fuels and the oleochemical co-product, the MCA average minimum fuel selling price (MFSP) is $\$2.47$ per decimeter (dm 3 ) or $\$9.34$ per gallon of gasoline equivalent (gge) with the corresponding average global warming potential determined to be 118 g CO 2 -eq-MJ -1 . The case producing only fuels results in an MCA average MFSP of $\$2.01$-(dm 3 ) -1 ($\$7.60$-gge -1 ) and an average environmental impact of 100 g CO 2 -eq-MJ -1 . These results are compared to static optimistic and conservative scenario analysis estimates, illustrating the over- and under-estimation of outcomes associated with non-stochastic methods. Suggested facility improvements include increases in pond productivity of both the biomass and methyl laurate oil production, as well as improvements to carbon utilization and bio-crude yield from HTL processing.

09 BIOMASS FUELS↗
DOE OSTI2024

Embedding Neural Thermal Scattering (NeTS) Modules in SERPENT for Higher Fidelity Advanced Reactor Analysis

When a neutron born in fission thermalizes to the order of $k$ $B$ $T$, it’s de-Broglie wavelength and energy approach the order of inter-atomic spacing and elementary lattice oscillations, respectively. $S$($a,β,t$) or the scattering law, uuantify these temperature-dependent crystallographic contributions to total cross section (or reaction rate). In a Monte Carlo analysis, cumulative distribution functions (CDFs) of $S$($a,β,t$) are loaded to memory from “A Compact ENDF” (ACE) files for stochastically selecting thermal scattered neutron trajectories. In this work, novel neural thermal scattering (NeTS) modules for $S$($a,β,t$) CDFs are designed, trained, serialized and embedded within SERPENT using Python’s limited C-API for on-the-fly deployment of crystalline graphite $S$($a,β,t$) sampling. Torchscript tracing and Numba just-in-time (JIT) compilation streamline neural inference on NVIDIA GPUs with CUDA libraries. Demonstrations of bare sphere thermalization of fast and thermal sources show excellent agreement between embedded NeTS in SERPENT and MCNP. With an explicit model of the reactor, NeTS can predict on-the-fly changes in TREAT neutron spectra as a function of local temperature, which can serve to improve transient and accident predictions in a multiphysics analysis framework. This framework can be further extended to account on-the-fly for changes in local graphitic microstructure to scattering cross sections, and outlines a novel coupling of modern machine learning with state-of-the-art reactor physics methods.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2022

Sensitivity Study of Multiscale and Phenomenological Elasto-Viscoplastic Grade 91 Material Models for Component-Scale Response

Many advanced nuclear reactor concepts currently being developed are targeting higher operating temperatures relative to the current fleet of light water nuclear reactors, for efficiency gains and other operational considerations. The design of high temperature structural components with reliable long-term operational performance will depend on material models that accurately capture the inelastic deformation mechanisms active in these environments. In this work, we perform a detailed parameter sensitivity analysis of two unified elasto-viscoplastic Grade 91 material models capable of capturing long term high temperature creep deformation. The first model is a phenomelogical material model from the Nuclear Engineering Material Library (NEML) developed at Argonne National Lab. The NEML model parameters and their uncertainty were fit to a range of Grade 91 experimental data using Bayesian Markov Chain Monte Carlo analysis. The second model is a LAROMance data-driven surrogate material model developed at Los Alamos National Lab. The LAROMance model is fit to a large database of responses produced by a mechanistic crystal plasticity based polycrystal model. Parameters for the LAROMance surrogate material model reflect the pedigree of the Grade 91 microstructure. Both material models have been integrated into the Grizzly code, based on the open-source MOOSE multiphysics simulation framework, to simulate both the progression of aging mechanisms and the effects of that aging on nuclear power plant structures. Grizzly is used analyze a three-dimensional Grade 91 piping system to compare the long-term inelastic response predicted by these two fundamentally different models and assess the sensitivity of the material model input parameters on this quantity of interest.

42 ENGINEERING↗
DOE OSTI2021

Local structure analysis of low-temperature neutron pair distribution function coupled with molecular dynamics simulations of CH 4 and CO 2 hydrates from 2 to 210 K

CH 4 hydrates occur naturally and are an abundant potential fuel source with a corresponding risk of potent greenhouse gas release, due to their stability conditions at low temperature and high pressure. Byproduct CO 2 can be exchanged with CH 4 in these natural deposits and can potentially stabilize the hydrates at higher temperatures. CH 4 , CO 2 , and mixed CH 4 -CO 2 hydrates are studied with in situ neutron pair distribution function experiments from 2 to 210 K to investigate the impact of varying the CH 4 -CO 2 guest composition in the gas hydrate structure. These experiments combined with Reverse Monte Carlo analysis allow for the characterization of intermolecular CH 4 and CO 2 interactions with the water molecules which form the hydrate lattice and how they impact the local structure of the lattice itself. Results indicate that when CH 4 and CO 2 co-occupy the hydrate, the host is more strongly distorted than in the pure CH 4 and CO 2 hydrates, but this becomes less defined with increasing temperature. The presence of CO 2 in mixed hydrate increases the stability range and creates a barrier for CH 4 to completely leave the structure.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2025

Initial Uncertainty Analysis of Carbon Tetrachloride Contamination and Remediation in the Ringold A and Lower Mud Units at the Central Plateau

The long-term effectiveness of groundwater cleanup at the Hanford Site Central Plateau depends on predictive models that can capture key uncertainties in contaminant fate and transport. Carbon tetrachloride (CCl 4 ), a persistent and toxic compound, presents particular challenges due to variability in degradation rates, uncertainty in initial plume distribution, and subsurface heterogeneity. These uncertainties directly influence plume persistence, migration pathways, and remedy performance, and thus must be systematically evaluated to support long-term remediation planning. To address these gaps, a large-scale Monte Carlo analysis was conducted using the Plateau to River (P2R) model framework. The modeling approach parameterized three primary uncertainty factors: (1) degradation rate, (2) initial plume distribution, and (3) hydraulic conductivity. Degradation was represented as a first-order process, with half-lives ranging from 70 to 700 years. Initial plume distributions were created using a geostatistical simulation method (sgsim), which generates many equally plausible versions of how contaminants might be distributed underground. From this, 100 different scenarios were mapped onto the P2R grid. Variability in hydraulic conductivity was represented in a similar way, with 100 scenarios each for the Ringold Lower Mud and Ringold A units (layers 6 and 7), based on fitted exponential variograms and conditioned to well data. In total, more than 1000 realizations were simulated to assess plume behavior under uncertainty. Results demonstrate that degradation kinetics exert the strongest control over plume persistence: Shorter half-lives produced rapid mass reduction, while longer half-lives yielded persistent plumes with limited attenuation. A nonlinear response was observed, with steep mass reductions at half-lives greater than 200 years and near-linear declines beyond this threshold, reflecting interactions between degradation and pumping. The initial plume distribution strongly influenced early transport patterns, with broader sources generating larger plume footprints, although pump-and-treat operations constrained plume migration to managed areas. By comparison, hydraulic conductivity variability in the Ringold units had only a secondary influence, modifying spreading behavior without altering the dominant migration pathways governed by source configuration and hydraulic controls. Overall, the analysis highlights that uncertainty in degradation rate and initial plume configuration are the primary drivers of variability in plume predictions, while conductivity heterogeneity plays a limited role. These findings underscore the need for improved site-specific data on degradation processes and source characterization to enhance the reliability of long-term performance assessments and to better inform remedial decision-making at the Central Plateau.

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗
DOE OSTI2020

A Dynamic Trajectory Fit to Multisensor Fireball Observations

Meteorites with known orbital origins are key to our understanding of solar system formation and the source of life on Earth. Fireball networks have been developed globally in a unified effort to record and ultimately retrieve these cosmic samples. However, the accuracy of the determined orbit and the likelihood of meteorite recovery depend directly on the accuracy of the chosen meteoroid triangulation method. There are three leading techniques for meteoroid triangulation discussed in the literature: the method of planes, the straight-line least-squares method, and the multiparameter fit method. Here we describe an alternative method to meteoroid triangulation, called the dynamic trajectory fit. This approach uses the meteoroid’s 3D dynamic equations of motion to fit a realistic trajectory directly to multisensor line-of-sight observations. This method has the ability to resolve fragmentation events, fit systematic observatory timing offsets, and determine mass estimates of the meteoroid along its observable trajectory. Through a comprehensive Monte Carlo analysis of over 100,000 trajectory simulations, we find this new method to more accurately estimate meteoroid trajectories of slow entry events (<25 km s{sup −1}) and events observed from low convergence angles (<10°) compared to existing meteoroid triangulation techniques. Additionally, we triangulate an observed fireball event with visible fragmentation using the various triangulation methods to show that the proposed dynamic trajectory fit implementing fragmentation to best match the captured multisensor line-of-sight data.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2022

Representing Socio‐Economic Uncertainty in Human System Models

Abstract Socio‐economic development pathways and their implications for the environment are highly uncertain, and energy transitions will involve complex interactions among sectors. Here, traditional Monte Carlo analysis is paired with scenario discovery techniques to provide a richer portrait of these complexities. Modeled uncertain input variables include costs of advanced energy technologies, energy efficiency trends, fossil fuel resource availability, elasticities of substitution for labor, capital, and energy across economic sectors, population growth, and labor and capital productivity. The sampled values are simulated through a multi‐sector, multi‐region, recursively dynamic model of the world economy to explore a range of possible future outcomes. We find that many patterns of energy and technology development are possible for various long‐term environmental pathways and that sectoral output for most sectors is little affected through 2050 by the long‐term temperature target, but with tight constraints on emissions, emission intensities must fall much more rapidly. Scenario discovery techniques are applied to the large uncertainty ensembles to explore if there are prevailing storylines behind outcomes of interest. An illustrative investigation focused on different levels of economic growth shows many combinations of pathways and no single storyline emerging for a given economic outcome. This method can be extended to other outcomes of interest, exploring the nature of scenarios with both tail and median outcomes. Sampling from a Monte Carlo generated ensemble provides a rich set of scenarios to investigate, and potentially aids in avoiding heuristic biases in less structured scenario approaches.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗
DOE OSTI2020

Risk Model for EM Decision Support Toolsets

The Government Office of Accountability (GAO) has published several reports identifying the need for the Department of Energy (DOE) Office of Environmental Management (EM) to address mounting costs for DoE's cleanup program. DOEEM could greatly benefit from independent decision tool-sets/models that would allow them to evaluate options and inform business decision at the enterprise level considering site-specific life cycle costs and system plans. Program goals: Develop a tool-set that enables EM to independently evaluate alternatives, assess outcomes from different contracting strategies, and inform critical decisions for the enterprise. Project goals: Adaption of a Risk Model for integration with complimentary tool-sets for project level decision making. Operational Events: Discrete event model that evaluates operational variables (e.g. capacity, throughput, maintenance constraints) and identify bottlenecks. Identifying and Bounding Project Risk: Identify risks that impact confidence in meeting goals (e.g. production). Life cycle Cost: Evaluate impacts of staffing levels, inventory, and capital investments on life cycle costs. Methods and approach: Monte Carlo Analysis is being executed to generate results: Input derives from Risk Register Data; Tied to Projected and Target Schedules; Incorporates float duration within the model. Assumes associated risk mitigation actions being completed within a timeline of five fiscal years. Metrics include: Confidence in Meeting Production Goal; Confidence in Safety Standards; Confidence in Continuous Operation; Other Metrics can be added as appropriate regarding specific site needs. The adapted risk model can be used as a stand alone decision tool or can be integrated complementary tool-sets (i.e. process and cost models) for project-specific decisions. These support tool-sets can then be integrated with others for site- and complex- level evaluations. Future work includes designing an adaptable and modular framework that would allow integration of multiple tool-sets for holistic and/or targeted evaluation of alternative strategies for decision making that could lead to risk and cost reduction across the enterprise.

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗
DOE OSTI2023

Quantifying uncertainty in analysis of shockless dynamic compression experiments on platinum. I. Inverse Lagrangian analysis

Absolute measurements of solid-material compressibility by magnetically driven shockless dynamic compression experiments to multi-megabar pressures have the potential to greatly improve the accuracy and precision of pressure calibration standards for use in diamond anvil cell experiments. Here, to this end, we apply characteristics-based inverse Lagrangian analysis (ILA) to 11 sets of ramp-compression data on pure platinum (Pt) metal and then reduce the resulting weighted-mean stress–strain curve to the principal isentrope and room-temperature isotherm using simple models for yield stress and Grüneisen parameter. We introduce several improvements to methods for ILA and quasi-isentrope reduction, the latter including calculation of corrections in wave speed instead of stress and pressure to render results largely independent of initial yield stress while enforcing thermodynamic consistency near zero pressure. More importantly, we quantify in detail the propagation of experimental uncertainty through ILA and model uncertainty through quasi-isentrope reduction, considering all potential sources of error except the electrode and window material models used in ILA. Compared to previous approaches, we find larger uncertainty in longitudinal stress. Monte Carlo analysis demonstrates that uncertainty in the yield-stress model constitutes by far the largest contribution to uncertainty in quasi-isentrope reduction corrections. We present a new room-temperature isotherm for Pt up to 444 GPa, with 1-sigma uncertainty at that pressure of just under ±1.2%; the latter is about a factor of three smaller than uncertainty previously reported for multi-megabar ramp-compression experiments on Pt. The result is well represented by a Vinet-form compression curve with (isothermal) bulk modulus K 0 = 270.3 ± 3.8 GPa, pressure derivative K$^{'}_{0}$= 5.66 ± 0.10, and correlation coefficient $R_{K_{0},K^{'}_{0}}$= –0.843.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2023

Techno-economic analysis of advanced small modular nuclear reactors

Here, small modular nuclear reactors (SMRs) represent a robust opportunity to develop low-carbon and reliable power with the potential to meet cost parity with conventional power systems. This study presents a detailed, bottom-up economic evaluation of a 12 × 77 MW e (924 MW e total) light-water SMR (LW-SMR) plant, a 4 × 262 MW e (1,048 MW e ) gas-cooled SMR (GC-SMR) plant, and a 5 × 200 MW e (1,000 MW e total) molten salt SMR (MS-SMR) plant. Cost estimates are derived from equipment costs, labor hours, material inputs, and process-engineering models. The advanced SMRs are compared to natural gas combined cycle plants with and without post-combustion carbon capture and a conventional large nuclear reactor. Overnight capital cost (OCC) and levelized cost of energy (LCOE) estimates are developed. The OCC of the LW-SMR, GC-SMR, and MS-SMR are found to be $\$4,844$/kW, $\$4,355$/kW, and $\$3,985$/kW respectively. The LCOE of the LW-SMR, GC-SMR, and MS-SMR are found to be $\$89.6$/MWh, $\$81.5$/MWh, and $\$80.6$/MWh respectively. A Monte Carlo analysis is performed, for which the OCC and construction time of the LW-SMR is found to have a lower mean and standard deviation than a conventional large reactor. The LW-SMR OCC is found to have a mean of $\$5,233$/kW with a standard deviation of $\$658$/kW and a 90 % probability of remaining between $\$4,254$/kW and $\$6,399$/kW, while the construction duration is found to have a mean of 4.5 years with a standard deviation of 0.8 years and a 90 % probability of remaining between 3.4 and 6.0 years. The economic impact of economies of scale, simplification, modularization, and construction time for SMRs are discussed. Additionally, policy implications for direct SMR capital subsidies and the impact of a carbon tax on natural gas emissions are explored.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2022

Quantitative Biostratigraphic Analysis and Age Estimates of Middle Cretaceous Sequences in The Baltimore Canyon Trough, Offshore Mid-Atlantic U.S. Margin

ABSTRACT We applied quantitative methods to previously published biostratigraphic data from the Baltimore Canyon Trough (offshore of the Mid-Atlantic U.S.A.) to provide an improved chronostratigraphic framework for Cretaceous sequences. Here, we successfully used graphic correlation of 228 planktonic foraminifera, nannofossil, and palynological events spanning 22 wells to define assemblage and interval zones as well as major paleoenvironmental changes in the Dawson Canyon, Logan Canyon (three sequences), and Missisauga Formations (two sequences, undifferentiated here). Ranking and scaling techniques were not successful because of the of the limited number of usable biostratigraphic markers. The ages of the sequences previously identified using well logs and seismic profiles were temporally constrained based on chronostratigraphically significant biostratigraphic markers that we identified: the late Cenomanian to Turonian DCx sequence (Rotalipora cushmani and Thalmanninella greenhornensis); the early Cenomanian LC1 sequence; the middle and late Albian LC2 sequence (Braarudosphaera africana, Planomalina buxtorfi, and Spinidinium vestitum); the late Aptian LC3 sequence (Cyclonephelium tabulatum); and the early Aptian to Barremian Missisauga sequences (Aptea anaphrissa, Pseudoceratium pelliferum, and Muderongia simplex). These five biostratigraphic associations are correlated with six prominent seismic reflectors and sequence boundaries that can be traced across the basin. Duration of hiatuses associated with these sequence boundaries are uncertain, though our Monte Carlo analysis allows extraction of age estimates from broad and sometimes contradictory ranges and suggests correlation of hiatuses with global sea-level falls. Together, these seismic and biostratigraphic interpretations can be applied (1) to evaluate reservoir continuity and the viability of offshore carbon storage reservoirs in the Baltimore Canyon Trough, (2) to better define the tectonostratigraphic evolution of the basin, and (3) to contribute to the understanding of regional and global variations in Cretaceous sea level.

Paleontology↗
DOE OSTI2021

Exploring new physics with O(keV) electron recoils in direct detection experiments

Motivated by the recent XENON 1T results, we explore various new physics models that can be discovered through searches for electron recoils in O(keV)-threshold direct-detection experiments. First, we consider the absorption of axion-like particles, dark photons, and scalars, either as dark matter relics or being produced directly in the Sun. In the latter case, we find that keV mass bosons produced in the Sun provide an adequate fit to the data but are excluded by stellar cooling constraints. We address this tension by introducing a novel Chameleon-like axion model, which can explain the excess while evading the stellar bounds. We find that absorption of bosonic dark matter provides a viable explanation for the excess only if the dark matter is a dark photon or an axion. In the latter case, photophobic axion couplings are necessary to avoid X-ray constraints. Second, we analyze models of dark matter-electron scattering to determine which models might explain the excess. Standard scattering of dark matter with electrons is generically in conflict with data from lower-threshold experiments. Momentum-dependent interactions with a heavy mediator can fit the data with dark matter mass heavier than a GeV but are generically in tension with collider constraints. Next, we consider dark matter consisting of two (or more) states that have a small mass splitting. The exothermic (down)scattering of the heavier state to the lighter state can fit the data for keV mass splittings. Finally, we consider a subcomponent of dark matter that is accelerated by scattering off cosmic rays, finding that dark matter interacting though an O(100 keV)-mass mediator can fit the data. The cross sections required in this scenario are, however, typically challenged by complementary probes of the light mediator. Throughout our study, we implement an unbinned Monte Carlo analysis and use an improved energy reconstruction of the XENON 1T events.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2021

Melt Pool and Heat Treatment Optimization for the Fabrication of High-Strength and High-Toughness Additively Manufactured 4340 Steel

Additively manufactured (AM) components offer superior design flexibility compared to their conventionally manufactured counterparts, and optimizing processing parameters is key to achieving high-quality depositions with desirable and predictable mechanical properties. This study was focused on 4340 steel fabricated using laser powder bed fusion (LPBF), and 42 laser power and scan speed combinations have been systematically investigated to determine an optimized melt pool geometry that would ensure fully-dense parts. The AM material was compared with a wrought 4340 equivalent and studied in two customized heat treated conditions, optimized for strength and toughness, respectively. The microstructures of the as-fabricated and heat treated AM and wrought materials were characterized to assess differences introduced by the layer-by-layer fabrication process and subsequent heat treatment. Tensile properties of both materials were also evaluated and demonstrate that the AM materials offer equal or superior properties compared to the wrought equivalents. Differences in fracture surface morphologies indicate the distinct failure mechanisms associated with the materials’ characteristic microstructures, and the role of inclusions in the failures was studied to elucidate these differences. Complementary to the experimental investigations, the dataset was leveraged to make recommendations for future design of experiments to optimize AM build parameters in other material systems. A statistical Monte Carlo analysis was used to predict the interpolation error produced using reduced datasets and to enable informed processing parameters selection. These findings are discussed to make recommendations for the use of AM materials for high-integrity structural applications.

36 MATERIALS SCIENCE↗
DOE OSTI2021

A Bayesian Approach to the Eagar–Tsai Model for Melt Pool Geometry Prediction with Implications in Additive Manufacturing of Metals

Here, this paper focuses on improving the melt pool geometry predictions and quantifying uncertainties using an adapted version of the Eagar–Tsai (E–T) model that incorporates temperature-dependent properties of the material as well as powder conditions. Additionally, Bayesian inference is employed to predict distributions for the E–T model input parameters of laser absorptivity and powder bed porosity by incorporating experimental results into the analysis. Monte Carlo uncertainty propagation is then used with these parameter distributions to estimate the melt pool depth and associated uncertainty. Our results for the 316L stainless steel suggest that both the absorptivity and powder bed porosity are strongly influenced by the laser power. In contrast, the scanning speed has only a marginal effect on both the absorptivity and powder bed porosity. We constructed a printability map using the Bayesian E–T model based on power-dependent input parameter values to demonstrate the merit of the approach. The Bayesian approach improved the accuracy in predicting the keyhole regions in the laser power-scan speed parameter space for the 316L stainless steel. Although applied to a specific adaptation of the E–T model, the method put forth can be extended to quantify uncertainties in other numerical models as well as in the estimation of unknown parameters.

316L Stainless Steel (SS).↗
DOE OSTI2024

Futures for electrochromic windows on high performance houses in arid, cold climates

This study investigates high performance electrochromic windows used on a passive house and residential dwelling to IECC 2021 (i.e., IECC dwelling). In the lab, the electrochromic film switches transmitted solar heat gain coefficient (SHGC) from 0.09 to 0.7 and visible transmittance from 0.15 to 0.82 with power consumption of 1.23 W/m 2 during switching times less than 3 minutes. We extrapolate these results to a window assembly. Building energy models of the houses were evaluated in Santa Fe, New Mexico. A Monte Carlo analysis for 2020, 2040, 2060, and 2080 was conducted for Shared Socioeconomic Pathways 2-4.5, 3-7.0, and 5-8.5. Cases with and without the electrochromic windows and with and without electricity were used to determine energy use intensity and hours beyond thermal safety thresholds. The passive house showed 1.3-3.1% mean energy savings and the IECC dwelling 4.4-5.1% with electrochromic efficiency benefits growing into the future for both cases. Even so, overall savings decrease into the future for the passive house, due to growth in cooling load being dominant, conversely overall energy savings increase into the future for the IECC dwelling due to heating loads being dominant. For thermal resilience, the passive house exhibited a mean percent decrease of 0.02-0.31% hours in the extreme caution (i.e., > 32.2 °C, ≤ 39.4 °C) range while the IECC dwelling exhibited 0.38-4.38%. The study therefore shows that electrochromic windows will have smaller benefits for the passive house in comparison to the IECC dwelling. The relationship between electrochromic windows is shown to have a complex relationship between house efficiency and climate change by these results.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗