Results for “range prediction” — Page 18

DOE OSTI2023

A high-resolution large-eddy simulation framework for wildland fire predictions using TensorFlow

Background: Wildfires are becoming more severe, so we need improved tools to predict them over a wide range of conditions and scales. One approach towards this goal entails the use of coupled fire/atmosphere modelling tools. Although significant progress has been made in advancing their physical fidelity, existing tools have not taken full advantage of emerging programming paradigms and computing architectures to enable high-resolution wildfire simulations. Aims: The aim of this study was to present a new framework that enables landscape-scale wildfire simulations with physical representation of combustion at an affordable cost. Methods: We developed a coupled fire/atmosphere simulation framework using TensorFlow, which enables efficient and scalable computations on Tensor Processing Units. Key Results: Simulation results for a prescribed fire were compared with experimental data. Predicted fire behavior and statistical analysis for fire spread rate, scar area, and intermittency showed overall reasonable agreement. Scalability analysis was performed, showing close to linear scaling. Conclusions: While mesh refinement was shown to have less impact on global quantities, such as fire scar area and spread rate, it benefits predictions of intermittent fire behavior, buoyancy-driven dynamics, and small-scale turbulent motion. Implications: This new simulation framework is efficient in capturing both global quantities and unsteady dynamics of wildfires at high spatial resolutions.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2022

Resolving the structure of phage–bacteria interactions in the context of natural diversity

Microbial communities are shaped by viral predators. Yet, resolving which viruses (phages) and bacteria are interacting is a major challenge in the context of natural levels of microbial diversity. Thus, fundamental features of how phage-bacteria interactions are structured and evolve in the wild remain poorly resolved. Here we use large-scale isolation of environmental marine Vibrio bacteria and their phages to obtain estimates of strain-level phage predator loads, and use all-by-all host range assays to discover how phage and host genomic diversity shape interactions. We show that lytic interactions in environmental interaction networks (as observed in agar overlay) are sparse—with phage predator loads being low for most bacterial strains, and phages being host-strain-specific. Paradoxically, we also find that although overlap in killing is generally rare between tailed phages, recombination is common. Together, these results suggest that recombination during cryptic co-infections is an important mode of phage evolution in microbial communities. In the development of phages for bioengineering and therapeutics it is important to consider that nucleic acids of introduced phages may spread into local phage populations through recombination, and that the likelihood of transfer is not predictable based on lytic host range.

59 BASIC BIOLOGICAL SCIENCES↗

DOE OSTI2025

Enhancing nanoscale charged colloid crystallization near a metastable liquid binodal

Achieving predictive control over crystallization using non-classical nucleation while avoiding kinetic traps would be a step towards designing materials with new functionalities. We address these challenges by inducing the bottom-up assembly of nanocrystals into ordered arrays, or superlattices. Using electrostatics—rather than density—to tune the interactions between particles, we watch self-assembly proceed through a metastable liquid phase. Here, we systematically investigate the phase behaviour as a function of quench conditions in situ and in real time using small-angle X-ray scattering. By fitting to colloid, liquid and superlattice models, we extract the time evolution of each phase and the system phase diagram, which we find to be consistent with short-range attractive interactions. Using the predictive power of the phase diagram, we establish control of the self-assembly rate over three orders of magnitude, and we identify one- and two-step self-assembly regimes, with only the latter implicating the metastable liquid as an intermediate. The presence of the metastable liquid increases the superlattice formation rate relative to the equivalent one-step pathway, and the superlattice order increases with the rate, revealing a generalizable kinetic strategy for promoting and enhancing ordered assembly.

Tanner, Christian P. N. [University of California,↗

DOE OSTI2025

Role of Electron Correlation beyond the Active Space in Achieving Quantitative Predictions of Spin-Phonon Relaxation

Single-molecule magnets (SMMs) are promising candidates for molecular-scale data storage and processing due to their strong magnetic anisotropy and long spin relaxation times. However, as temperature rises, interactions between electronic states and lattice vibrations accelerate spin relaxation, significantly limiting their practical applications. Recently, ab initio simulations have made it possible to advance our understanding of phonon-induced magnetic relaxation, but significant deviations from experiments have often been observed. The description of molecules’ electronic structure has been mostly based on complete active space self-consistent field (CASSCF) calculations, and the impact of electron correlation beyond the active space remains largely unexplored. In this study, we provide the first systematic investigation of spin-phonon relaxation in SMMs with post-CASSCF multiconfigurational methods, specifically CAS followed by second-order perturbation theory and multiconfiguration pair-density functional theory. Taking Co(II)- and Dy(III)-based SMMs as case studies, we analyze how electron correlation influences spin-phonon relaxation rates across a range of temperatures, comparing theoretical predictions with experimental observations. Our findings demonstrate that post-CASSCF treatments make it possible to achieve quantitative predictions for Co(II)-based SMMs. For Dy(III)-based systems, however, accurate predictions require consideration of additional effects, underscoring the urgent necessity of further advancing the study of the effects of electronic correlation in these complex systems.

Energy↗

DOE OSTI2021

Advanced Fuels Campaign Execution Plan

The Advanced Fuels Campaign (AFC) Execution Plan outlines the strategy, mission, scope, near-term and long-term goals, structure, and organization associated with nuclear fuels and materials research, development, and demonstration activities within the Department of Energy’s (DOE) Nuclear Fuel Cycle and Supply Chain (NFCSC) program. NFCSC has been given responsibility to identify and mature advanced fuel technologies for the DOE using a science-based approach, focused on developing a fundamental understanding of nuclear fuels and materials to drive development of integrated nuclear fuel and materials technology. This science-based approach combines theory, experiments, and multiscale modeling and simulation to achieve a predictive understanding of relevant behaviors ranging from fuel fabrication processes (and their resulting fuel microstructures) through fuel/cladding performance under irradiation (in contrast to more empirical, observation-based approaches frequently used in fuel performance modeling and fuel qualification). The traditional scope of AFC includes the evaluation and development of multiple fuel forms to support two fuel cycle options: once-through and full recycle. The word “fuel” is used generically to include conventional fuels, transmutation targets, and any associated cladding or duct materials. The once-through fuel cycle addresses advanced light water reactor fuels with enhanced performance, extended burnup, and reduced waste generation. In fiscal year (FY) 2012, AFC’s scope expanded to include research, development, and demonstration (RD&D) for light water reactor (LWR) fuels with enhanced accident tolerance. Fuel fabrication activities include the development of innovative methods to enhance process efficiencies, reduce waste, and improve control over as-fabricated fuel microstructural properties to achieve desired in-reactor performance. Using modern modeling and simulation approaches, the objective is to predict fresh fuel properties given the feedstock characteristics and fabrication process parameters. The performance-related activities include small-scale, in-reactor, and out-of-reactor phenomenological testing (distinct from, but synergistic with, integral prototypic testing) and extensive, quantitative characterization (focusing on characterization of fuel and cladding materials at the scale of microstructure) both before and after testing. Larger-scale, prototypic experiments are conducted in concert with phenomenological testing to drive a Fuel Development and Qualification program, incorporating a fundamental understanding of fuel behavior performance characteristics. Then, using the tools developed under the productive science-based approach, fuels will be optimized to meet specific performance requirements, thereby minimizing the need to repeatedly perform large-scale, integral experiments over a wide parametric range as a means of experimental exploration. Two significant initiatives are underway within AFC. First, a gap analysis completed in early FY 2019 identified critical irradiation testing needs that are lacking within the national light water reactor (LWR) fuels testbed since the shutdown of the Halden Reactor in 2018. The identified gaps are for instrumented, prototypic testing of LWR fuels, especially under boiling water reactor conditions, ramp conditions, and conditions leading to fuel failure; these needs exist for supporting current LWR fuels and their possible extension to higher burnups, but are especially urgent relative to near-term development and qualification of accident-tolerant fuels. Recommendations that resulted from the Halden Gap Analysis focused on enhancements at Advanced Test Reactor (ATR) and Transient Reactor Test Facility (TREAT) to fill gaps in testing capabilities relative to these needs. Second, a concerted effort to develop and demonstrate a systematic approach to accelerating the development, testing, and qualification of new fuel systems has been initiated. This is highlighted by a test strategy that combines the considerable advances in multiscale, mechanistic fuel modeling of recent years with a MiniFuel separate effects test program in the High Flux Isotope Reactor (HFIR) and a Fission Accelerated Steady-state Testing (FAST) semi-integral accelerated test program in ATR. This approach is being tested/demonstrated using the metallic fuel system, but if successful it is expected to be extensible to multiple fuel types and diverse applications. This document includes an overview of the NFCSC program, a definition of science-based development of nuclear fuels, near-term goals for Advanced LWR fuels (ALFs), and longer-term goals for Advanced Reactor Fuels (ARFs) RD&D. This includes the activities that will be conducted to achieve success toward the grand challenge, as well as the goals and milestones to be achieved over the next few decades of research and development. Long-term goals are based on the DOE Office of Nuclear Energ

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗

DOE OSTI2024

Advanced Fuels Campaign Execution Plan

The Advanced Fuels Campaign (AFC) Execution Plan outlines the strategy, mission, scope, near-term and long-term goals, structure, and organization associated with nuclear fuels and materials research, development, and demonstration activities within the Department of Energy’s (DOE) Nuclear Fuel Cycle and Supply Chain (NFCSC) program. NFCSC has been given responsibility to identify and mature advanced fuel technologies for the DOE using a science-based approach, focused on developing a fundamental understanding of nuclear fuels and materials to drive development of integrated nuclear fuel and materials technology. This science-based approach combines theory, experiments, and multiscale modeling and simulation to achieve a predictive understanding of relevant behaviors ranging from fuel fabrication processes (and their resulting fuel microstructures) through fuel/cladding performance under irradiation (in contrast to more empirical, observation-based approaches frequently used in fuel performance modeling and fuel qualification). The traditional scope of AFC includes the evaluation and development of multiple fuel forms to support two fuel cycle options: once-through and full recycle. The word “fuel” is used generically to include conventional fuels, transmutation targets, and any associated cladding or duct materials. The once-through fuel cycle addresses advanced light water reactor fuels with enhanced performance, extended burnup, and reduced waste generation. In fiscal year (FY) 2012, AFC’s scope expanded to include research, development, and demonstration (RD&D) for light water reactor (LWR) fuels with enhanced accident tolerance. Fuel fabrication activities include the development of innovative methods to enhance process efficiencies, reduce waste, and improve control over as-fabricated fuel microstructural properties to achieve desired in-reactor performance. Using modern modeling and simulation approaches, the objective is to predict fresh fuel properties given the feedstock characteristics and fabrication process parameters. The performance-related activities include small-scale, in-reactor, and out-of-reactor phenomenological testing (distinct from, but synergistic with, integral prototypic testing) and extensive, quantitative characterization (focusing on characterization of fuel and cladding materials at the scale of microstructure) both before and after testing. Larger-scale, prototypic experiments are conducted in concert with phenomenological testing to drive a Fuel Development and Qualification program, incorporating a fundamental understanding of fuel behavior performance characteristics. Then, using the tools developed under the productive science-based approach, fuels will be optimized to meet specific performance requirements, thereby minimizing the need to repeatedly perform large-scale, integral experiments over a wide parametric range as a means of experimental exploration. Two significant initiatives are underway within AFC. First, a gap analysis completed in early FY 2019 identified critical irradiation testing needs that are lacking within the national light water reactor (LWR) fuels testbed since the shutdown of the Halden Reactor in 2018. The identified gaps are for instrumented, prototypic testing of LWR fuels, especially under boiling water reactor conditions, ramp conditions, and conditions leading to fuel failure; these needs exist for supporting current LWR fuels and their possible extension to higher burnups, but are especially urgent relative to near-term development and qualification of accident-tolerant fuels. Recommendations that resulted from the Halden Gap Analysis focused on enhancements at Advanced Test Reactor (ATR) and Transient Reactor Test Facility (TREAT) to fill gaps in testing capabilities relative to these needs. Second, a concerted effort to develop and demonstrate a systematic approach to accelerating the development, testing, and qualification of new fuel systems has been initiated. This is highlighted by a test strategy that combines the considerable advances in multiscale, mechanistic fuel modeling of recent years with a MiniFuel separate effects test program in the High Flux Isotope Reactor (HFIR) and a Fission Accelerated Steady-state Testing (FAST) semi-integral accelerated test program in ATR. This approach is being tested/demonstrated using the metallic fuel system, but if successful it is expected to be extensible to multiple fuel types and diverse applications. This document includes an overview of the NFCSC program, a definition of science-based development of nuclear fuels, near-term goals for Advanced LWR fuels (ALFs), and longer-term goals for Advanced Reactor Fuels (ARFs) RD&D. This includes the activities that will be conducted to achieve success toward the grand challenge, as well as the goals and milestones to be achieved over the next few decades of research and development. Long-term goals are based on the DOE Office of Nuclear Energ

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗

DOE OSTI2023

Multi-mode quasi-static excitation for systems with nonlinear joints

Finite element models can be used to model and predict the hysteresis and energy dissipation exhibited by nonlinear joints in structures. As a result of the nonlinearity, the frequency and damping of a mode is dependent on excitation amplitude, and when the modes remain uncoupled, quasi-static modal analysis has been shown to efficiently predict this behavior. However, in some cases the modes have been observed to couple such that the frequency and damping of one mode is dependent on the amplitude of other modes. To model the interactions between modes, one must integrate the dynamic equations in time, which is several orders of magnitude more expensive than quasi-static analysis. This work explores an alternative where quasi-static forces are applied in the shapes of two or more modes of vibration simultaneously, and the resulting load–displacement curves are used to deduce the effect of other modes on the effective frequency and damping of the mode in question. This methodology is demonstrated on a simple 2D cantilever beam structure with a single bolted joint which exhibits micro-slip nonlinearity over a range of vibration amplitudes. The predicted frequency and damping are compared with those extracted from a few expensive dynamic simulations of the structure, showing that the quasi-static approach produces reasonable albeit highly conservative bounds on the observed dynamics. This framework is also demonstrated on a 3D structure where dynamic simulations are infeasible.

42 ENGINEERING↗

DOE OSTI2025

Microstructural, Oxidation, and Mechanical Behavior of NbTi-Based Refractory Alloys with 5 to 10 Pct Co, Cr, and Ni Additions

NbTi-based refractory alloys with additions of Co, Cr, and Ni represent an interesting medium-entropy alloy system with potential for protective oxide film formation, high strength, and ductility. This study investigates the microstructural evolution, oxidation behavior, and mechanical properties of NbTi-based alloys containing 5 to 10 at. pct Co, Cr, and Ni. CALPHAD predictions suggest that this composition range can be heat treated to obtain a predominantly body-centered cubic matrix phase. Mechanical properties, including microhardness, yield strength, maximum strength, and specific strength are evaluated through isothermal compression tests conducted between room temperature and 800 °C. The oxidation kinetics of these alloys are assessed through discontinuous oxidation tests. Parabolic oxidation kinetics were observed for NbTi–10Ni and NbTi–5Co, while linear oxidation kinetics were found for NbTi–10Cr and NbTi–10(CoCrNi). Microstructures and oxide layers are characterized using X-ray diffraction, electron backscatter diffraction, energy-dispersive X-ray spectroscopy, and scanning electron microscopy. All alloys exhibit significant mechanical softening between room temperature and 800 °C, with elastic-perfectly plastic flow observed at 800 °C. The addition of 10 pct Cr to NbTi resulted in two BCC phases up to 1050 °C, conflicting with CALPHAD predictions of a single-phase solid solution at this temperature, and resulting in higher flow stress at 800 °C. NbTi–10(CoCrNi) exhibited the lowest flow stress at 800 °C despite having more ‘cocktail effect’ potential and insufficient molar fractions of Co, Cr, or Ni to form a desirable protective oxide film.

36 MATERIALS SCIENCE↗

DOE OSTI2020

Brown watersnakes (Nerodia taxispilota) as bioindicators of mercury contamination in a riverine system

Herein, mercury (Hg) is a contaminant that enters the environment through natural or anthropogenic means. Ecological risk assessments have examined Hg bioaccumulation and effects in many taxa, but little is known about Hg dynamics in reptiles, or their potential use as bioindicator species for monitoring Hg in aquatic systems. Numerous snake species, like North American watersnakes (Nerodia spp.), are piscivorous and are exposed to Hg through their diet. The purpose of this study was to identify factors associated with Hg accumulation in a common watersnake species and compare Hg concentrations of the snakes to those in fish occupying the same habitats. To this end, we sampled brown watersnakes (Nerodia taxispilota) from the Savannah River, a major river system in the southeastern U.S., and compared N. taxispilota Hg accumulation trends to those of bass (Micropterus salmoides), catfish (Ictalurus and Ameiurus spp.), and panfish (Lepomis and Pomoxis spp.) collected from the same reach. Total Hg (THg) in N. taxispilota tail tips ranged from 0.020 to 0.431 mg/kg (wet weight; mean: 0.104 ± 0.008). Snake tail THg was significantly correlated with blood THg, which ranged from 0.003 to 1.140 mg/kg (0.154 ± 0.019). Snake size and site of capture were significantly associated with tail THg. Snake tail THg increased at sites along and downstream of the area of historic Hg pollution, consistent with fish THg. Snake muscle THg was predicted based on tail THg and ranged from 0.095 to 1.160 (0.352 ± 0.022). To gauge Hg biomagnification in N. taxispilota, we compared predicted snake muscle THg concentrations to THg in fish of consumable size. Average biomagnification factors for THg in N. taxispilota were 3.1 (panfish) and 5.4 (catfish), demonstrating N. taxispilota likely biomagnify Hg through their diet. These results reveal N. taxispilota to be an effective bioindicator species for monitoring Hg in aquatic environments.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2025

Combination of searches for singly produced vectorlike top quarks in 𝑝⁢𝑝 collisions at $\sqrt{𝑠}$ =13 TeV with the ATLAS detector

A combination of searches for the single production of vectorlike top quarks (𝑇) is presented. These analyses are based on proton-proton collisions at $\sqrt{𝑠}$ = 13 TeV recorded in 2015–2018 with the ATLAS detector at the Large Hadron Collider, corresponding to an integrated luminosity of 139 fb −1 . The 𝑇 decay modes considered in this combination are into a top quark and either a Standard Model Higgs boson or a 𝑍 boson (𝑇 → 𝐻⁡𝑡 and 𝑇 → 𝑍⁢𝑡). The individual searches used in the combination are differentiated by the number of leptons (𝑒, 𝜇) in the final state. The observed data are found to be in good agreement with the Standard Model background prediction. Interpretations are provided for a range of masses and couplings of the vectorlike top quark for benchmark models and generalized representations in terms of 95% confidence level limits. For a benchmark signal prediction of a vectorlike top quark SU(2) singlet with electroweak coupling, 𝜅, of 0.5, masses below 2.1 TeV are excluded, resulting in the most restrictive limits to date.

Composite models↗

DOE OSTI2024

Transferring predictions of formation energy across lattices of increasing size*

In this study, we show the transferability of graph convolutional neural network (GCNN) predictions of the formation energy of the nickel-platinum solid solution alloy across atomic structures of increasing sizes. The original dataset was generated with the large-scale atomic/molecular massively parallel simulator using the second nearest-neighbor modified embedded-atom method empirical interatomic potential. Geometry optimization was performed on the initially randomly generated face centered cubic crystal structures and the formation energy has been calculated at each step of the geometry optimization, with configurations spanning the whole compositional range. Using data from various steps of the geometry optimization, we first trained our open-source, scalable implementation of GCNN called HydraGNN on a lattice of 256 atoms, which accounts well for the short-range interactions. Using this data, we predicted the formation energy for lattices of 864 atoms and 2048 atoms, which resulted in lower-than-expected accuracy due to the long-range interactions present in these larger lattices. We accounted for the long-range interactions by including a small amount of training data representative for those two larger sizes, whereupon the predictions of HydraGNN scaled linearly with the size of the lattice. Therefore, our strategy ensured scalability while reducing significantly the computational cost of training on larger lattice sizes.

36 MATERIALS SCIENCE↗

DOE OSTI2025

Langevin Dynamics modeling of gas-phase ion-ion recombination (Final Technical Report)

A self-consistent trajectory simulation approach to model MN reactions (Fig. 1) which incorporates the probability of electron transfer as a Monte Carlo operator (Fig. 2) was developed and published as Liu et al. J. Chem. Phys. 159, 114111 (2023). The electron transfer probability p ET estimated using the two-state Landau-Zener (LZ) theory was incorporated into classical trajectory simulations to elicit predictions of MN reaction cross-section σ (vacuum) or rate constant β (finite pressure). Electronic structure calculations with multireference configuration interaction (MRCI) and large correlation consistent basis sets were used to derive inputs to the LZ theory. The key advance of our trajectory simulation approach is the incorporation of electron transfer probability and the inclusion of the effect of ion-neutral interactions on MN using a Langevin representation of the effect of neutral gas on ions. For H + – H - and Li + – H(D) - pairs, our approach quantitatively agrees with measured speed-dependent cross-sections for up to ~10 5 m/s. For the ion pair Ne + – Cl - , our predictions of the MN rate constant at ~1 torr are a factor of ~2 – 3 higher than the experimentally measured value. Similarly, for Xe + – F - in the pressure range of ~20000 – 80000 Pa, our predictions of the MN rate constant are ~20% lower but are in excellent qualitative agreement with experimental data. The paradigm of using trajectory simulations to self-consistently model MN reactions is the basis for inclusion of additional non-classical, and static magnetic and electric field effects. Subsequent work, published as Roy et al. focused on modeling recombination rate constant for three ion pairs (rare gas Ar + cation and halide anions): Ar + – Cl - , Ar + – Br - , Ar + – I - , 2) considering spin-orbit couplings in the electronic structure calculations to obtain high-fidelity estimates of the electron transfer probability and incorporated within the classical trajectory simulations to elicit predictions. In addition to calculations of ion-ion recombination rate constants, a classical trajectory simulation technique (published as Roy et al. J. Chem. Phys. 162(9), 094104 (2023)) that uses quaternions to represent orientation of non-spherical particles (ions or aerosol particles) was developed to simulate the recombination of diatomic or more generally, polyatomic molecules. Finally, several other ion pairs such as Ne + – Cl - , Kr + – Cl - , were explored using the developed semi-classical trajectory simulations to understand various challenges in tackling electronic structure calculations. Using empirical approaches to parameterize the electron transfer radius, trajectory simulations were also used to probe the effect of ion number density on MN rate constant.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗

DOE OSTI2023

Point-particle drag, lift, and torque closure models using machine learning: Hierarchical approach and interpretability

Developing deterministic neighborhood-informed point-particle closure models using machine learning has garnered interest recently from the dispersed multiphase flow community. The robustness of neural models for this complex multibody problem is hindered by the availability of particle-resolved data. Here, the present work addresses this unavoidable limitation of data paucity by implementing two strategies: (1) by using a rotation and reflection equivariant neural network and (2) by pursuing a physics-based hierarchical machine learning approach. The resulting machine-learned models are observed to achieve a maximum accuracy of 85% and 96% in the prediction of neighbor-induced force and torque fluctuations, respectively, for a wide range of Reynolds number and volume fraction conditions considered. Furthermore, we pursue force and torque network architectures that provide universal prediction spanning a wide range of Reynolds number (0.25 ≤ Re ≤250) and particle volume fraction (0 ≤ φ ≤0.4). The hierarchical nature of the approach enables improved prediction of quantities such as streamwise torque, by going beyond binary interactions to include trinary interactions.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗

DOE OSTI2021

Leptonic sum rules from flavour models with modular symmetries

Sum rules in the lepton sector provide an extremely valuable tool to classify flavour models in terms of relations between neutrino masses and mixing parameters testable in a plethora of experiments. In this manuscript we identify new leptonic sum rules arising in models with modular symmetries with residual symmetries. These models simultaneously present neutrino mass sum rules, involving masses and Majorana phases, and mixing sum rules, connecting the mixing angles and the Dirac CP-violating phase. The simultaneous appearance of both types of sum rules leads to some non-trivial interplay, for instance, the allowed absolute neutrino mass scale exhibits a dependence on the Dirac CP-violating phase. We derive analytical expressions for these novel sum rules and present their allowed parameter ranges as well as their predictions at upcoming neutrino experiments.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗

DOE OSTI2023

Global QCD analysis and dark photons

We perform a global QCD analysis of high energy scattering data within the JAM Monte Carlo framework, including a coupling to a dark photon that augments the Standard Model (SM) electroweak coupling via kinetic mixing with the hypercharge B boson. We first set limits on the dark photon mass and mixing parameter assuming that the SM is the true theory of Nature, taking into account also the effect on g – 2 of the muon. If instead we entertain the possibility that the dark photon may play a role in deep-inelastic scattering (DIS), we find that the best fit is preferred over the SM at 6.5σ, even after accounting for missing higher order uncertainties. The improvement in χ 2 with the dark photon is stable against all the tests we have applied, with the improvements in the theoretical predictions spread across a wide range of x and Q 2 . The largest improvement corresponds to the fixed target and HERA DIS data, while the best fit yields a value of g – 2 which significantly reduces the disagreement with the latest experimental determination.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗

DOE OSTI2022

Quantifying the anion effect of gas solubility within ionic liquids using the solvation affinity index

The solubility of gas molecules within ionic liquids (ILs) is strongly dictated by the anion effect and the free volume effect, and of these two influences, the anion effect can be challenging to predict. We evaluate a wide range of different gas solutes (O 2 , NH 3 , CH 4 , H 2 S, SO 2 , Ar, H 2 , N 2 , CO 2 ) in different ILs, and we find that the solubility is strongly correlated to one of three different IL properties, which can be directly calculated from the anion electrostatic potential (ESP) surfaces. Thus, the solutes can be grouped into three fundamental types, leading to a general descriptor, the solvation affinity index (SAI), which can identify the affinity between a wide range of gas solutes and IL solvents. The SAI of nine common industrial gases with 47 common anions are generated. Furthermore, this can provide a fast approach for pre-screening IL solvents or even for ILs embedded within porous supports.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2021

Machine learning in materials science: From explainable predictions to autonomous design

The advent of big data and algorithmic developments in the field of machine learning (and artificial intelligence, in general) have greatly impacted the entire spectrum of physical sciences, including materials science. Materials data, measured or computed, combined with various techniques of machine learning have been employed to address a myriad of challenging problems, such as, development of efficient and predictive surrogate models for a range of materials properties, screening and down-selection of novel candidate materials for targeted applications, new methodologies to improve and further expedite molecular and atomistic simulations, with likely many more important developments to come in the foreseeable future. While the applications thus far have provided a glimpse of the true potential data-enabled routes have to offer, it has also become clear that further progress in this direction hinges on our ability to understand, explain and rationalize findings of a machine learning model in light of the domain-knowledge. This focused review provides an overview of the main areas where machine learning has been widely and successfully used in materials science. Subsequently, a brief discussion of several techniques that have been helpful in extracting physically-meaningful insights, causal relationships and design-centric knowledge from materials data is provided. Finally, we identify some of the imminent opportunities and challenges that materials community faces in this exciting and rapidly growing field.

36 MATERIALS SCIENCE↗

DOE OSTI2021

An experimentally validated evaporative phase change heat transfer model for low mass flux applications using R134a in plate heat exchangers

Plate frame heat exchangers are common in liquid-coupled vapor compression systems due to their compactness and ease of maintenance. In some of these systems, the refrigerant within the evaporator can enter the heat exchanger as a subcooled liquid. As a result, the refrigerant passes through three different phases: single-phase liquid, two-phase fluid, and a superheated vapor. Unfortunately, there have been limited prior studies that have identified the best method to predict performance under these circumstances. Furthermore, previous investigations have not evaluated refrigerant evaporation in large industrial sized plate frame heat exchangers under conditions experienced in the present study experimental test facility, especially at mass fluxes below 7 kg m –2 s –1 . In the present investigation, a model was developed to predict the performance of plate and frame heat exchangers when the fluid enters as a subcooled liquid and exits as a superheated vapor. The model used full-sized plate heat exchanger geometry that was discretized into 20 sections to accurately capture local heat transfer and pressure drop effects. The model was validated using an R134a counter flow heat exchanger used in a turbo-compression cooling system test facility that had subcooled liquid entering the evaporator at low mass fluxes. A variety of empirical correlations were evaluated to determine what combination yielded the best predictive capability over the following range of conditions: 5.8 < G < 6.8 kg m –2 s –1 , 2 < q” < 2.8 kW m –2 , and 10 °C < T sat < 15 °C. When the correlations used a fixed control volume length in the heat flux calculation, the most accurate combination resulted in a mean absolute error of 5.5%. Finally, future studies can use the approach described here to optimize heat exchanger size and performance.

42 ENGINEERING↗