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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 OSTI2023

Transition Core Modeling for Extended Enrichment & Accident-Tolerant Fuels Using Polaris/PARCS

Commercial light water reactor (LWR) operators and fuel vendors are currently interested in increasing the low-enriched uranium (LEU) fuel enrichments from the current limit of 5.0 $^w/_o$ $^{235}U$ up to 10 $^ w/_o$ $^{235}U$ (referred to as "LEU+") in their current fleets; they are also interested in using accident-tolerant fuel (ATF) with both LEU and LEU+ fuel. This report aims to identify modeling challenges and accuracy concerns in transition core analysis using the SCALE Polaris lattice physics code and U.S. Nuclear Regulatory Commission core simulator PARCS. At the time this study was started, no publicly available LEU+ core designs existed for boiling water reactor (BWR) or pressurized water reactor (PWR) systems. Therefore, fuel lattices were shuffled within a multi-assembly model to mimic neutronically challenging lattice combinations seen in transition cores, such as a fresh LEU+ lattice next to depleted LEU lattices. In addition to multi-assembly models, whole-core BWR transition core calculations were performed for ATF and LEU+ fuel using an existing Hatch-1 Cycle 3 core model. A whole-core BWR model was chosen due to the more heterogeneous core designs compared to those for a PWR core. Since the original core is an old checkerboard core design and no core or fuel design optimization was performed for the modeled fuel types, these core calculations were intended only to provide: (1) Comparisons of core characteristics of interest, such as the pin power distributions and peaking factors, Doppler temperature coefficients (DTCs), and control blade worths (CBWs) under challenging core designs, (2) Identification of reactor physics challenges in modeling LEU+ and ATF cores, and (3) A stress test for the Polaris/PARCS two-step modeling approach, including characterization of the relative accuracy for predicting characteristics of interest such as pin power distributions.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
DOE OSTI2020

An Assessment of the Influence of Uncertainty in Temporally Evolving Streamflow Forecasts on Riverine Inundation Modeling

Continental-scale river forecasting platforms forecast streamflow at reaches that can be used as boundary conditions to drive a local-scale flood inundation model. Uncertainty accumulated during this process stems not only from any part of the forecasting chain but can also be caused by the daily variations in weather forcing that keeps evolving as the event advances. This work aims to examine the influence of the evolving forecast streamflow on predicting the maximum inundation for extreme floods. A diagnostic case study was made on the basis of a hindcast of Hurricane Matthew striking the eastern U.S. in 2016. The U.S. National Water Model was one-way coupled to a hydrodynamic inundation model through a developed automated workflow. Although the river forcing has significantly mismatched hydrographs versus observations, the simulated peak water surface elevations and maximum extents were validated to be comparable with the observations, which indicates that the inundation model may not be sensitive to the inherited uncertainty from the weather forcing. Moreover, the uncertainty of the forecast streamflow time series caused only one order of magnitude fewer variations in inundation prediction; this dampening effect may become clearer for extreme events with large areas inundated. In addition, the forecast total volume of stream discharge appears to be an important metric for assessing the performance of river forcing for inundation mapping, as a linear correlation between the total volume and the accuracy of the predicted peak water surface elevation and maximum extent was found, with the coefficients of determination all above 0.8. Extra best-practice experience of running similar operational tasks demonstrated the tradeoff between the cost and accuracy gain.

flood, national water model, RIFT↗
DOE OSTI2021

Heat Transfer and Pressure Drop Characteristics of Water Flow Boiling in Internally Enhanced Tubes

Flow boiling is a critical process for many thermal conversion processes such as HVAC&R, water heating, power generation and desalination applications. The major goal of this paper is to enhance the in-tube heat transfer for flow boiling of water and further improve the efficiency of the boiler. This paper presents an experimental investigation of the heat transfer and pressure drop characteristics for water under forced convection and boiling conditions inside both smooth and enhanced tubes. The results in the smooth copper tube are compared with several correlations and used as a baseline. The two-phase heat transfer and pressure drop performance of the water flow boiling in the internally enhanced tube is investigated, and the influence of internal surface pattern on the thermal characteristics is discussed. The relations of the heat transfer coefficient with mass flux, heat flux and vapor quality are analyzed according to the results.

Yang, Cheng-Min↗
DOE OSTI2020

CONTROLLED END GAS AUTO IGNITION WITH EXHAUST GAS RECIRCULATION ON A STOICHIOMETRIC, SPARK IGNITED, NATURAL GAS ENGINE

The goal of this study is to address fundamental limitations to achieving diesel-like efficiencies in heavy duty on-highway natural gas (NG) engines. Engine knock and misfire are barriers to pathways leading to higher efficiency engines. This study explores enabling technologies for development of high efficiency stoichiometric, spark ignited, natural gas engines. These include design strategies for fast and stable combustion and higher dilution tolerance. Additionally, advanced control methodologies are implemented to maintain stable operation between knock and misfire limits. To implement controlled end- gas autoignition (C-EGAI) strategies a Combustion Intensity Metric (CIM) is used for ignition control with the use of a Woodward large engine control module (LECM). Tests were conducted using a single cylinder, variable compression ratio, cooperative fuel research (CFR) engine with baseline conditions of 900 RPM, engine load of 800 kPa indicated mean effective pressure (IMEP), and stoichiometric air/fuel ratio. Exhaust gas recirculation (EGR) tests were performed using a custom EGR system that simulates a high pressure EGR loop and can provide a range of EGR rates from 0 to 40%. The experimental measurements included the variance of EGR rate, compression ratio, engine speed, IMEP, and CIM. These five variables were optimized through a Modified Box- Benken design Surface Response Method (RSM), with brake efficiency as the merit function. A positive linear correlation between CIM and f-EGAI was identified. Consequently, CIM was used as the feedback control parameter for C-EGAI. As such, implementation of C-EGAI effectively allowed for the utilization of high EGR rates and CRs, controlling combustion between a narrower gap between knock and lean limits. The change from fixed to parametric ignition timing with CIM targeted select values of f-EGAI with an average coefficient of variance (COV) of peak pressure of 5.4. The RSM efficiency optimization concluded with operational conditions of 1080 RPM, 1150 kPa IMEP, 10.55:1 compression ratio, and 17.8% EGR rate with a brake efficiency of 21.3%. At this optimized point of peak performance, a f-EGAI for C-EGAI was observed at 34.1% heat release due to auto ignition, a knock onset crank angle value of 10.3° aTDC and ignition timing of - 24.7° aTDC. This work has demonstrated that combustion at a fixed f-EGAI can be maintained through advanced ignition control of CIM without experiencing heavy knocking events.

Cooperative Fuel Research Engine, Auto Ignition, E↗
DOE OSTI2023

Quantification of Energy Savings and Demand Reduction for a Heat Pump Integrated with Thermal Energy Storage (Final Report)

To mitigate the variation in demand on the electric grid, thermal energy storage (TES) is an alternative to electric batteries or installing new peaking power plants. Stakeholders and policy makers across the United States have expressed interests in promoting TES, as demonstrated by the US Department of Energy’s Grid-Interactive Efficient Buildings program and the efforts of various state legislatures. However, the cost value provided by TES are unclear. If reliable cost benefits were determined, stakeholders would have a clearer picture of the financial returns that can be gained from their investment in TES. The study in this report is conducted by ORNL with collaboration with Emerson the Helix Innovation Center. In the first part of this report, EnergyPlus was used to perform whole-building simulations for two residential buildings in Indianapolis and Atlanta. The HVAC system in both buildings were equipped with phase change material TES. The TES tank was charged in off-peak hours and discharged in peak hours to perform load shifting. First, the economic value implied by existing time-of-use (TOU) rates offered by utility companies was analyzed via whole-building simulation. Second, existing demand reduction (DR) incentives sourced from 3 different electrical grid administrators (i.e., California, Texas, and New England region) were surveyed to determine their implied value. The study suggests that the traditional value analysis that focuses on ROI for the building owner significantly undervalues TES technology making economic viability difficult. A more comprehensive value analysis that includes peak demand management and deferred capital for peaking power plants shows that TES should be economically viable but here the value is greater for the utility and requires large market penetration and aggregation to fully realize the benefits. Therefore, to facilitate commercialization, new business models are needed that include a broader range of stakeholders and distribute the value of TES proportionally. In the 2 nd part of this project, the benefits of a novel phase change material (PCM) integrated heat pump configuration were evaluated via detailed component based simulation. A one-dimensional PCM heat exchanger model which discretizes the PCM tank and refrigerant tubes into small control volumes is developed. Each control volume can have different PCM temperatures, PCM properties, and heat transfer coefficients. The PCM tank is charged by a wrapped tank condenser and discharged by an internal refrigerant coil. The PCM heat exchanger model is integrated into DOE/ORNL Heat Pump Design Model for heat pump system simulation. To demonstrate the performance of the PCM integrated heat pump, a case study in Chicago was performed. A Time-of-Use utility structure-based control strategy is implemented to schedule the PCM tank charging and discharging mode switching. Compared with a conventional electric heat pump, the PCM integrated heat pump shows superior performance on load shifting and utility cost reduction. As a result, the proposed system demonstrates 24.6% utility saving for cooling application and 25.8% utility saving for heating application.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2021

Optimizing multigrid reduction-in-time and Parareal coarse-grid operators for linear advection

Parallel-in-time methods, such as multigrid reduction-in-time (MGRIT) and Parareal, provide an attractive option for increasing concurrency when simulating time-dependent partial differential equations (PDEs) in modern high-performance computing environments. While these techniques have been very successful for parabolic equations, it has often been observed that their performance suffers dramatically when applied to advection-dominated problems or purely hyperbolic PDEs using standard rediscretization approaches on coarse grids. In this paper, we apply MGRIT or Parareal to the constant-coefficient linear advection equation, appealing to existing convergence theory to provide insight into the typically nonscalable or even divergent behavior of these solvers for this problem. To overcome these failings, we replace rediscretization on coarse grids with improved coarse-grid operators that are computed by applying optimization techniques to approximately minimize error estimates from the convergence theory. Therefore, one of our main findings is that, in order to obtain fast convergence as for parabolic problems, coarse-grid operators should take into account the behavior of the hyperbolic problem by tracking the characteristic curves. Our approach is tested for schemes of various orders using explicit or implicit Runge–Kutta methods combined with upwind-finite-difference spatial discretizations. In all cases, we obtain scalable convergence in just a handful of iterations, with parallel tests also showing significant speed-ups over sequential time-stepping.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2022

An indirect approach to optimize the reaction rates of thermal NO formation for diesel engines

With stringent emission regulations, it has become more important for modern diesel engine manufacturers to accurately predict engine-out nitrogen oxide (NO x ) emissions across a wide range of operating conditions. Thermal NO is the major source of engine-out NO x in modern diesel engines. For thermal NO formation, several earlier studies have recommended the forward and reverse reaction rate coefficients of the rate-limiting reaction (O + N 2 ⇌ NO + N). However, due to deficiencies in sub-models and inadequacies of reduced chemical mechanisms to represent diesel combustion, these recommended values more often than not need to be adjusted in reduced order combustion models to accurately predict engine-out NO x . Hence, in this work a systematic and computationally efficient approach has been proposed to streamline the process of determining the optimum reaction rate coefficients. Here, to develop the optimization approach, four different production diesel engines with different operating conditions in terms of speed, load, and exhaust-gas recirculation have been considered. Numerical simulations have been performed using a detailed zero-dimensional velocity-composition-frequency transported probability density function (0D-VCF-tPDF) model that uses hundreds of notional particles to capture in-cylinder stratification. Four different combinations of hydrocarbon and NO x chemical mechanisms were used to represent chemistry. It was found that for the rate-limiting reaction, the pre-exponent factors (A f1 , A r1 ) and activation energies (E A,f1 , E A,r1 ) of the forward and reverse reaction rates follow a linear band in A f1 - E A,f1 and A r1 - E A,r1 space where predicted engine-out NO x match the measured values closely. By encompassing such bands from different engines and considering constraints on activation energies, a reduced search domain of pre-exponent factors and activation energies was constructed that is expected to be applicable to any diesel engine. Eventually, computationally efficient three-line and one-line search approaches were proposed to determine the optimum values of the pre-exponent factors and activation energies that led to a minimum error between measured and predicted engine-out NO x . Finally, these three-line and one-line NO x optimization approaches were applied to a fifth production diesel engine for which the 0D-VCF-tPDF model showed a very good predictive performance in terms of predicting peak pressure, 50% burn rate, and engine-out NO x when compared to measured and 3D-CFD values.

33 ADVANCED PROPULSION SYSTEMS↗
DOE OSTI2021

The design of a photoionization front experiment using the Z-Machine as a driving source and estimated measurements

Radiation-driven heat fronts are present in the early universe during reionization, the circumstellar medium of supernovae, and in high-energy-density physics experiments. Dedicated experiments to observe and diagnose the behavior of these types of heat fronts can improve our understanding of these phenomena. A simulation study of photoionization fronts using the HELIOS-CR radiation hydrodynamics code provides an experimental design for the Z-Machine at Sandia National Laboratory using a measurement-calibrated input radiation flux to drive the photoionization front. The simulations use detailed atomic physics and non-diffusive radiation transport in 1D to determine an optimal gas pressure of 0.75 atm for an experiment in N gas as well as the effects of increasing the thickness of the window that seals the gas cell. Post-processing of these simulations demonstrates that ratios of atomic rate coefficients place the heat front in a physics regime where photoionization dominates the energy deposition. Here, to see the sensitivity of the simulations to changes in the model and spatial grid, this analysis performed resolution, atomic model detail, and radiation transport angular grid studies showing less than 10% deviation from the nominal model for increased complexity, when possible. An effort to emulate 3D geometric effects on the radiation flux using an artificial attenuation scheme has shown that, even for conservative estimates of the flux, simulations still produce a photoionization front. Estimations of a streaked, visible spectroscopy measurement using SPECT3D showed that line emission measurements are present early in time and that later in time thermal emission should become dominant.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2021

Evaluating Effects of Shear Processing on 2D Crystalline Materials in 3D Metal Matrices: Atomistic Understanding of High Shear Deformation of Copper Graphene Composites

Composites made of copper and graphene demonstrate high strength, lubricity and enhanced electrical and thermal conductivity compared pure copper. However, manufacturing these composites at bulk volumes for industrial applications has been a big challenge. Shear deformation assisted processing is an effective method for manufacturing materials such as copper-graphene composites demonstrating ultra-fine grain structures and compositional homogeneity. Nevertheless, microstructural evolution of the composites and their property development under such conditions is not clearly understood currently. To rectify this gap in literature, high strain shear deformation of copper coated graphene foils was performed using a tribometer pin in this study. Changes in microstructure of the composite as well as the constituent components under shear deformation was correlated to process conditions. A sharp increase in the coefficient of friction attributed to rupture and smearing of graphene layer into copper substrate was observed during the shear processing. The coefficient of friction of the sheared copper/graphene composite was lower than that of pure copper, suggesting that partially worn graphene is effectively lubricious at the macroscale. A multimodal characterization of the processed region further revealed a shear deformation-induced ultrafine two-phase grain-structure consisting of copper and graphitic domains. Shear deformation reduced the copper grain size from around 50 – 100 µm to ~200 nm on an average and ~2 – 5 nm in some locations. The semicrystalline graphene films were observed to fracture into 10 – 50 µm size flakes. Oxygen enrichment was observed in the processed region. Finally, graphitic domain were identified for the first time in the copper matrix and not just at the grain boundaries providing evidence for a metastable composite microstructure as a result of solid phase processing at room temperature.

36 MATERIALS SCIENCE↗
DOE OSTI2025

Simulation of a High-Confinement Tidal Energy Array (Abstract)

When the projected area of a current turbine array occupies a substantial fraction of a tidal or river channel cross-sectional area, the confinement associated with the channel boundaries substantially increases turbine efficiency. This effect can be exploited to substantially reduce the levelized cost of energy for current turbine arrays. However, the presence of such an array will affect inflow velocity, water depth, and circulation in the surrounding area. This has implications for array performance and environmental impacts. This project combines existing numerical modeling capability from PNNL with an understanding of confined array performance from UW. The work will 1) Simulate tidal currents and water surface elevation in a regional circulation model that includes the Knik Arm site at appropriate resolution; 2) Incorporate a varying thrust coefficient into this model that reflects the outcomes of tidal hydrodynamic simulations of confined turbine arrays; 3) Use the model to determine the effects of array operation on water levels and inflow conditions as a function of different blockage ratios and control strategies; and 4) Identify the “far field” environmental implications of operating an array of this type. UW will use the outputs from this model to evaluate the implications of “feedback effects” and variable inflow conditions on levelized cost of energy estimates for a confinement exploiting array of cross-flow turbines.

16 TIDAL AND WAVE POWER↗
DOE OSTI2024

Leveraging design of experiments to build chemometric models for the quantification of uranium (VI) and HNO3 by Raman spectroscopy

Partial least squares regression (PLSR) and support vector regression (SVR) models were optimized for the quantification of U(VI) (10–320 g L −1 ) and HNO 3 (0.6–6 M) by Raman spectroscopy with optimized calibration sets chosen by optimal design of experiments. The designed approach effectively minimized the number of samples in the calibration set for PLSR and SVR by selecting sample concentrations with a quadratic process model, despite complex confounding and covarying spectral features in the spectra. The top PLS2 model resulted in percent root mean square errors of prediction for U(VI), HNO 3 , and NO 3 − of 3.7%, 3.6%, and 2.9%, respectively. PLS1 models performed similarly despite modeling an analyte with a majority linear response (i.e., uranyl symmetric stretch) and another with more covarying vibrational modes (i.e., HNO 3 ). Partial least squares (PLS) model loadings and regression coefficients were evaluated to better understand the relationship between weaker Raman bands and covarying spectral features. Support vector machine models outperformed PLS1 models, resulting in percent root mean square error of prediction values for U(VI) and HNO 3 of 1.5% and 3.1%, respectively. The optimal nonlinear SVR model was trained using a similar number of samples (11) compared with the PLSR model, even though PLS is a linear modeling approach. The generic D-optimal design presented in this work provides a robust statistical framework for selecting training set samples in disparate two-factor systems. This approach reinforces Raman spectroscopy for the quantification of species relevant to the nuclear fuel cycle and provides a robust chemometric modeling approach to bolster online monitoring in challenging process environments.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2022

Neutronic design and fuel cycle analysis of a fluoride salt-cooled High Temperature Reactor (FHR)

The Fluoride salt-cooled High temperature Reactor (FHR) is a new Gen IV reactor concept that can operate under near atmospheric pressure circumstances and further enhance the inherent safety. In this study, an FHR core design with 165 megawatts of thermal output (MWth) is proposed. The reactor core employs tri-structural-isotropic (TRISO) particle fuel within prismatic graphite blocks as the basic fuel form, FLiBe (2 {sup 7}LiF-BeF{sub 2}) as the primary coolant, and a three-batch fuel cycle scheme. Sensitivity analyses on various parameters were performed to optimize the cycle length and neutronic parameters. The fuel cycle of this core design was evaluated in detail from 4 aspects: cycle length, power peaking factor (PPF), discharge burnup, and temperature coefficient. It was found that larger fuel channel pitch (FCP) would have a relatively harder neutron spectrum, and yield relatively longer cycle length, lower, better fuel temperature coefficient (FTC), and moderator temperature coefficient (MTC). In addition, burnable poison (BP), Er{sub 2}O{sub 3}, can effectively reduce PPF, hold down the multiplication factor, and more importantly it can improve the MTC. (authors)

21 SPECIFIC NUCLEAR REACTORS AND ASSOCIATED PLANTS↗
DOE OSTI2021

Evaluation of clear-sky and satellite-derived irradiance data for determining the degradation of photovoltaic system performance

Knowing the degradation in performance of a photovoltaic (PV) system over time is important for estimating the lifetime energy produced and the financial return. A key parameter for normalizing performance and determining degradation is the plane-of-array (POA) irradiance. Because accurate long-term POA measurements are not always readily available, three methods of providing irradiance data for determining the degradation rate of PV systems were evaluated—a method using irradiance data modeled with the Ineichen clear-sky model and monthly Linke turbidity coefficients, a method using the supplemental clear-sky irradiance data from the National Solar Radiation Data Base (NSRDB), and a method using the NSRDB solar irradiance data for both cloudy and clear-sky conditions (all-sky). The irradiance data from the three methods were evaluated using measured irradiance data from 1998 through 2018 for the seven-station SURFRAD network and for 3-, 5-, and 10-year periods that might be used for evaluating PV system performance. Only the two clear-sky methods for the 10-year periods had less uncertainty with respect to determining PV system degradation than the expected median degradation rate for PV systems of -0.5%/year to -0.6%/year. Shorter periods and the all-sky method had larger uncertainties, making their use questionable for determining the degradation rates of PV systems.

14 SOLAR ENERGY↗
DOE OSTI2021

High Thermoelectric Performance through Crystal Symmetry Enhancement in Triply Doped Diamondoid Compound Cu 2 SnSe 3

The presence of high crystallographic symmetry and nanoscale defects are favorable for thermoelectrics. With proper electronic structures, a highly symmetric crystal tends to possess multiple carrier channels and promote electrical conductivity without sacrificing Seebeck coefficient. In addition, nanoscale defects can effectively scatter acoustic phonons to suppress thermal conductivity. Here, we report that the triple doping of Cu 2 SnSe 3 leads to a high ZT value of 1.6 at 823 K for Cu 1.85 Ag 0.15 (Sn 0.88 Ga 0.1 Na 0.02 )Se 3 , and a decent average ZT (ZT ave ) value of 0.7 is also achieved for Cu 1.85 Ag 0.15 (Sn 0.93 Mg 0.06 Na 0.01 )Se 3 from 475 to 823 K. Our study reveals: (1) Ag doping on Cu sites generates numerous point defects and greatly decreases lattice thermal conductivity. (2) Doping Mg or Ga converts the monoclinic Cu 2 SnSe 3 into a cubic structure. This symmetry enhancing leads to increase in the effective mass from 0.8 m e to 2.6 m e (m e , free electron mass) and the power factor from 4.3 μW/cm –1 K –2 for Cu 2 SnSe 3 to 11.6 μW/cm –1 K –2 . (3) Na doping creates dense dislocation arrays and nanoprecipitates, which strengthens the phonon scattering. (4) Pair distribution function analysis shows localized symmetry breakdwon in the cubic Cu 1.85 Ag 0.15 (Sn 0.88 Ga 0.1 Na 0.02 )Se 3 . Furthermore, the present work provides a standpoint to design promising thermoelectric materials by synergistically manipulating crystal symmetry and nanoscale defects.

36 MATERIALS SCIENCE↗
DOE OSTI2022

Atmospheric Brown Carbon on the Tibetan Plateau: Regional Differences in Chemical Composition and Light Absorption Properties

In this work, fine particulate matter (PM 2.5 ) filter samples were collected from two high-altitude remote sites located in the southern (QOMS) and northern (WLG) regions of the Tibetan Plateau (TP) to explore the regional differences in brown carbon (BrC) properties. Chemical differences in BrC composition representative of these two areas were inferred from molecular-level analysis of the PM 2.5 samples using high-performance liquid chromatography coupled with photodiode array and high-resolution mass spectrometry detectors. The results show that more polar BrC chromophores were abundant in QOMS samples, while contributions from polar and less polar chromophores were comparable in WLG samples. A higher mass absorption coefficient of BrC was observed at QOMS than at WLG. Strong BrC chromophores in QOMS samples were identified as oxygenated aromatics and nitrophenol compounds, while organosulfate compounds were found in the WLG sample. The results of this study indicate regional differences in BrC chromophores and provide insights into their sources and chemical processes, which should be considered for predictive understanding and modeling of the radiative forcing of aerosol in the TP area.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2025

Machine Learning-Driven Optimization of Building Enclosures for Moisture Durability and Thermal Performance

The design of moisture-durable building enclosures with low embodied carbon often involves an iterative process of selecting the materials for the specific exposure conditions to meet the performance requirements. While hygrothermal simulations are commonly used to evaluate moisture durability, they often require advanced expertise for proper implementation. Machine learning (ML) provides a promising alternative by streamlining the design process and minimizing the reliance on complex simulations. This study presents a machine learning-based approach for predicting moisture durability in residential wall assemblies. The ML model was trained to estimate the mold index and maximum moisture content of various layers under typical exposure conditions. The model achieved a high predictive accuracy, with a coefficient of determination (R²) exceeding 0.90 when compared to traditional hygrothermal simulations on materials that were not part of training the ML model. Building on these results, the ML model was developed into a practical tool for optimizing wall assembly designs. This tool allows users to automatically optimize material selections based on energy, moisture, and carbon performance criteria. By incorporating multi-objective optimization, the tool identifies configurations that minimize embodied carbon while maintaining moisture safety and code-compliant thermal performance. Additionally, it provides insights into how material choices influence assembly durability, energy efficiency, and carbon reduction. The tool will be implemented in the Building Science Advisor (BSA) to enhance its performance and provide more granularity on the results. This research highlights the potential for ML-driven tools to simplify the design of high-performance building enclosures, offering architects and engineers a faster, more efficient way to balance critical performance factors.

Salonvaara, Mikael [ORNL] (ORCID:0000000318991554)↗
DOE OSTI2025

Measurements and interpretations of W ± Z production cross-sections in pp collisions at $\sqrt{s}=13$ TeV with the ATLAS detector

Measurements of integrated and differential cross-sections for W ± Z production in proton-proton collisions are presented. The data collected by the ATLAS detector at the Large Hadron Collider from 2015 to 2018 at a centre-of-mass energy of $\sqrt{s}=13$ TeV are used, corresponding to an integrated luminosity of 140 fb −1 . The W ± Z candidate events are reconstructed using leptonic decay modes of the gauge bosons into electrons or muons. The integrated cross-section per lepton flavour for the production of W ± Z is measured in the detector fiducial region with a relative precision of 4%. The measured value is compared with the Standard Model prediction at a precision of up to next-to-next-to-leading-order in QCD and next-to-leading-order in electroweak. Cross-sections for W + Z and W − Z production and their ratio are presented. The W ± Z production is also measured differentially as functions of various kinematic variables, including new observables sensitive to CP-violation effects. All measurements are compared with state-of-the-art Standard Model predictions from fixed-order calculations or Monte Carlo generators based on next-to-leading-order matrix elements interfaced with parton showers. An effective field theory interpretation of the measurements is performed, considering both CP-conserving and CP-violating dimension-6 operators modifying the W ± Z production. In the absence of observed deviations from the Standard Model, limits on CP-conserving Wilson coefficients are extracted using the transverse mass of the W ± Z system. For CP-violating coefficients a machine learning approach is used to construct an observable with enhanced sensitivity to CP-violation effects.

hadron-hadron scattering↗
DOE OSTI2021

Machine learning application to single channel design of molten salt reactor

This study proposes a robust approach to quickly design a nuclear reactor core and explores the best performing machine learning (ML) technique for predicting feature parameters of the core. Here we implemented the approach into a hypothetical channel of molten salt reactors to demonstrate the applicability of the method. We prepared a Python tool, named Plankton, which couples to a reactor physics code and an optimization tool, and imports ML methods. The tool performs three consecutive phases: reactor database generation, machine learning application, and design optimization. We identified the extra trees method as the best performing estimator. With the estimator, we found nine optimum designs in total, one for each fuel-salt pair, and estimated all the performance metrics of the designs with a <5% prediction error compared to their actual values. U-Pu-NaCl fuel-salt gave promising results with the highest conversion ratio, the most negative feedback coefficient, and the lowest fast flux.

21 SPECIFIC NUCLEAR REACTORS AND ASSOCIATED PLANTS↗