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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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At least 361 records · Page 20
All sourcesNASA NTRSDOE OSTI
DOE OSTI2025

Modeling and Simulation of Fuel Dispersal During the Loss-of-Coolant Accident

This document is the compilation of the milestone portion to a larger end of project NEUP report. The executive summary of the modeling portion is provided below: In the event of cladding rupture during a postulated LOCA in a pressurized water reactor, fuel particles, along with fission gases, can be expelled into the reactor core from the fractured fuel rod, a phenomenon referred to as fuel dispersal. The initial stage of fuel dispersal is strongly influenced by the high-pressure ejection of fuel fragments, the size and geometry of the ruptured cladding, and the depressurization history of the fuel rod during the postulated LOCA transient. Depending on the location of the burst orifice relative to the quench front, the dispersal event represents an intricate three-phase flow and heat transfer phenomenon, where high-temperature fuel particles carried by the fission gases interact with the coolant within the narrow subchannels of the fuel assemblies, inducing localized phase change. Given the unique multiphysics nature of this phenomena, the current study develops a dedicated computational framework to predict the mass distribution and cooling of dispersing fuel particles, facilitating post-accident assessment and management of the fuel assemblies. Considering the scale of nuclear reactor applications, a continuum three-fluid model is proposed for simulating the transport of solids within the reactor core. With high-temperature fuel fragments within the liquid media, nucleation sites inducing phase changes are dispersed within the flow domain. Coupled with the fact that the transient dispersal event occurs on different time scales than other three-phase flow applications, this study derives a time-averaged three-fluid flow model without losing generality. The assumptions regarding the continuum treatment of the solid phase and the modeling of fuel dispersal behavior are incorporated to simplify the governing equations and derive applicable closure relations. The computational validation of the model was conducted using adiabatic experimental results obtained from ongoing research at Oregon State University, focusing on characterizing fuel dispersal behavior during simulated LOCA conditions. Settlement characteristics of the solids, quantified by the probability distribution of equivalent particles, closely matched the probability density functions reported in experimental studies. The transport of fuel particles within a scaled 5 × 5 lattice of a pressurized-water reactor rod bundle geometry was modeled through a two-fluid Eulerian framework. The required boundary conditions were evaluated from the fuel performance code BISON in a postulated large-break LOCA scenario. The modeling framework considered solid fuel particles as granular matter, interacting with the gaseous dry steam phase and fission gases through the governing interfacial momentum exchange between the participating fluids. The simulation results provided the volume fraction of the solids obtained at the bottom surface of the enclosing tank geometry. Postulated LOCA leading to fuel dispersal phenomena involves the strong coupling between fuel thermomechanics, cladding deformation, thermal-hydraulics, and fuel particle transport. Incorporation of such a strong coupling in numerical simulation is performed by coupling the multiphysics solvers. In the case of fuel dispersal, a strong coupled simulation can be performed by coupling the BISON code for fuel performance, the TRACE code for system-level thermal hydraulics, and fuel particle transport in Multiphysics Object-Oriented Simulation Environment (MOOSE). For such intricate infrastructure, the MOOSE Framework eases the data transfer between codes. The recent version of MOOSE has incorporated the Navier-Stokes module for the fluid flow. An exploratory exercise was done to gain familiarity with finite volume capabilities in the MOOSE framework to incorporate the Spalart-Allmaras (SA) turbulence model. New finite-volume and auxiliary kernels were introduced to assemble the SA transport equation, compute turbulent viscosity, and evaluate wall distance and diagnostic turbulence terms, fully integrated with existing Navier-Stokes modules. A turbulent lid-driven cavity at a Reynolds number of approximately 10,000 is used for verification. MOOSE shows the robust solver convergence and produces the turbulent features. But it underpredicts the velocity profile and turbulent quantities, emphasizing the need to develop improved SA near-wall treatments (e.g., low-Re corrections or wall functions) as a key direction for future work.

11 - NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
DOE OSTI2023

Integration of Biorelevant Pediatric Dissolution Methodology into PBPK Modeling to Predict In Vivo Performance and Bioequivalence of Generic Drugs in Pediatric Populations: a Carbamazepine Case Study

This study investigated the impact of gastro-intestinal fluid volume and bile salt (BS) concentration on the dissolution of carbamazepine (CBZ) immediate release (IR) 100 mg tablets and to integrate these in vitro biorelevant dissolution profiles into physiologically based pharmacokinetic modelling (PBPK) in pediatric and adult populations to determine the biopredictive dissolution profile. Dissolution profiles of CBZ IR tablets (100 mg) were generated in 50–900 mL biorelevant adult fasted state simulated gastric and intestinal fluid (Ad-FaSSGF and Ad-FaSSIF), also in three alternative compositions of biorelevant pediatric FaSSGF and FaSSIF medias at 200 mL. This study found that CBZ dissolution was poorly sensitive to changes in the composition of the biorelevant media, where dissimilar dissolution (F2 = 46.2) was only observed when the BS concentration was changed from 3000 to 89 μM (Ad-FaSSIF vs Ped-FaSSIF 50% 14 BS). PBPK modeling demonstrated the most predictive dissolution volume and media composition to forecast the PK was 500 mL of Ad-FaSSGF/Ad-FaSSIF media for adults and 200 mL Ped-FaSSGF/FaSSIF media for pediatrics. A virtual bioequivalence simulation was conducted by using Ad-FaSSGF and/or Ad-FaSSIF 500 mL or Ped-FaSSGF and/or Ped-FaSSIF 200 mL dissolution data for CBZ 100 mg (reference and generic test) IR product. The CBZ PBPK models showed bioequivalence of the product. This study demonstrates that the integration of biorelevant dissolution data can predict the PK profile of a poorly soluble drug in both populations. Further work using more pediatric drug products is needed to verify biorelevant dissolution data to predict the in vivo performance in pediatrics.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2024

Adsorption of molecular iodine and alkyl iodides from spent-nuclear-fuel-reprocessing off-gas using reduced silver mordenite

Radioactive iodine is a hazardous byproduct of spent-nuclear-fuel reprocessing that must be removed from the off-gas stream before it can be discharged. Reduced silver mordenite (Ag 0 Z) is currently the baseline material for iodine capture in the U.S. Although the performance characteristics and capture mechanisms of I 2 and CH 3 I have been established, similar investigations into long-chain organic iodides have yet to be performed. In this study, thin beds of Ag 0 Z were loaded with I 2 , CH 3 I, C 4 H 9 I, and C 12 H 25 I (5 ppm to 50 ppm) carried in a dry air stream at 150°C. The maximum iodine capacity was 105 ± 5 mg I/g Ag 0 Z for all species. Saturated Ag 0 Z samples were characterized using scanning electron microscopy, X-ray fluorescence, X-ray photoelectron spectroscopy, diffuse reflectance UV–visible spectroscopy, pair distribution function analysis, and thermogravimetric analysis. Further, near-complete Ag utilization and similar physical/chemical properties were observed for all samples. Through a comparison with previous studies and an investigation of aged Ag 0 Z, we propose that iodine species react with Ag + at exchange sites, forming α-AgI within the mordenite channels, and with surface Ag 0 nanoparticles, yielding β-/γ-AgI. The available Ag sites in the interior (Ag + ) and exterior (Ag 0 ) of mordenite determine the adsorption capacity since α-AgI formation is limited by the total pore volume. Potential iodine uptake routes were summarized for aging and non-aging environments. A scalable predictive model was implemented for deep-bed iodine removal, and predictions were in good agreement with experimental data. Sensitivity analysis suggests that iodine uptake kinetics is governed by pore diffusion.

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗
DOE OSTI2025

Experimental test of model predictive control in a variable air volume system

Model predictive control (MPC) has been widely studied as a promising approach for improving energy efficiency and operational flexibility in buildings, yet its real-world performance for commercial variable air volume (VAV) systems remains insufficiently characterized. In particular, the impacts of model mismatch on control robustness, real-time computational burden, and device-level operation are rarely evaluated using long-term field data. Here, this study presents a comprehensive experimental evaluation of MPC applied to a full-scale VAV system in Oak Ridge National Laboratory’s Flexible Research Platform-2 building with constant cooling/heating temperature setpoints and no occupancy. The study offers three key advantages over existing work: (1) it uses a representative building in a full-scale experimental test, capturing realistic system dynamics and complexity; (2) it evaluates a relatively sophisticated MPC formulation using two different optimization solvers (Gurobi and PSO), fully accounting for computational complexity and methodological diversity; and (3) it systematically assesses potential negative impacts on various building devices, benchmark against a well-established baseline, ASHRAE Guideline 36 (G36). To isolate zone- and air-handling-unit–level supervisory control effects, the supply fan was operated with a fixed static pressure setpoint under all strategies, and the trim-and-response static pressure reset in G36 was not enabled. Results show that MPC maintained thermal comfort while improving energy efficiency. Abrupt solar radiation variations degraded performance. Computation times ranged from ∼1 s (Gurobi) to ∼ 70 s (PSO). Compared with G36, MPC achieves 33% energy savings and reduces median reheat coil output by approximately a factor of 5–10 for a representative cooling day under matched weather conditions. However, it increases the maximum discomfort deviation from 0.5 to 1°C and results in a 32% increase in staging frequency. In addition, PSO-based MPC introduced damper oscillations, also affecting actuator longevity.

ASHRAE guideline 36↗
DOE OSTI2021

Exploring Autoencoder-based Error-bounded Compression for Scientific Data

Error-bounded lossy compression is becoming an indispensable technique for the success of today's scientific projects with vast volumes of data produced during the simulations or instrument data acquisitions. Not only can it significantly reduce data size, but it also can control the compression errors based on user-specified error bounds. Autoencoder (AE) models have been widely used in image compression, but few AE-based compression approaches support error-bounding features, which are highly required by scientific applications. To address this issue, we explore using convolutional autoencoders to improve error-bounded lossy compression for scientific data, with the following three key contributions. (1) We provide an in-depth investigation of the characteristics of various autoencoder models and develop an error-bounded autoencoder-based framework in terms of the SZ model. (2) We optimize the compression quality for main stages in our designed AE-based error-bounded compression framework, fine-tuning the block sizes and latent sizes and also optimizing the compression efficiency of latent vectors. (3) We evaluate our proposed solution using five real-world scientific datasets and comparing them with six other related works. Experiments show that our solution exhibits a very competitive compression quality from among all the compressors in our tests. In absolute terms, it can obtain a much better compression quality (100%similar to 800% improvement in compression ratio with the same data distortion) compared with SZ2.1 and ZFP in cases with a high compression ratio.

Liu, Jinyang↗
DOE OSTI2020

Hydrogen Diffusivity Measurements of YH 1.87 Moderator Material with Incoherent Quasielastic Neutron Scattering

Yttrium hydride is an optimal choice of moderator material for thermal neutron spectrum reactors requiring small core volumes. The Transformation Challenge Reactor (TCR) program at Oak Ridge National Laboratory (ORNL) aims to develop an additively manufactured microreactor core by leveraging recent advances in materials, manufacturing, data analytics, and high-fidelity modeling and simulation. Yttrium hydride was selected as the moderator material for TCR due to its high moderating power, which enables the spatially efficient reactor design and thermal stability that is superior when compared with zirconium hydride. Hydrogen desorption from yttrium hydride is expected at elevated temperatures, posing safety and performance concerns. As a lifetime component in advanced reactors, appropriate strategies to mitigate hydrogen release from yttrium hydride over long-term reactor operation are required. Understanding hydrogen mobility in yttrium hydride provides the scientific basis for developing a hydrogen barrier for hydride moderator and predicting the hydrogen redistribution within the material during various operational conditions. This study investigated the hydrogen motion in YH 1.87 as a function of temperature ranging from 200 to 1,173 K using incoherent quasielastic neutron scattering (IQNS) at the Spallation Neutron Source (SNS). The results are presented and discussed in terms of hydrogen self-diffusion coefficients, activation energy for translational hydrogen motion, and hydrogen jump distances. Based on the IQNS data, YH 1.87 is found to be stable at high temperatures with no translational H motion below 1,023 K within the IQNS detection limits. In the temperature range of 1,073–1,173 K, hydrogen diffusivity in YH 1.87 is $D = 4.57 X 10^{⁻3} [m^2/s] exp (⁻\frac{1.73[eV]}{RT}$). The hydrogen concentration’s dependence on hydrogen diffusivity is also discussed in this report.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2023

IDAES-PSE 2.0 Release

The Institute for the Design of Advanced Energy Systems (IDAES) Integrated Platform is a versatile computational environment offering extensive process systems engineering (PSE) capabilities for optimizing the design and operation of complex, interacting technologies and systems. IDAES enables users to efficiently search vast, complex design spaces to discover the lowest cost, most environmentally sustainable solutions while supporting the full process modeling lifecycle, from conceptual design to dynamic optimization and control. The extensible, open platform empowers users to create models of novel processes and rapidly develop custom analyses, workflows, and end-user applications. IDAES-PSE 2.0.0 Release Highlights Removal of deprecated features from IDAES v1 Update to Pyomo v6.5 – this required a number of updates to support the new NL solver writer and to address some changes in Pyomo Creation of new testing suite for backward compatibility, model robustness and verification More general implementation of the Helmholtz EoS. This brings some new features like standard property diagrams, choice of mass or mole basis, and new state variable options Standardizing names in Heat Exchanger models (breaking change from v2.0.0a2): Control Volumes named hot_side and cold_side Ports names hot_side_inlet, hot_side_outlet, cold_side_inlet and cold_side_outlet Config Blocks names hot_side_config and cold_side_config Config arguments for user provided names for each side: hot_side_name and cold_side_name. Updating Keras surrogate tool to use v1.1 of OMLT New prototype API for model initialization (idaes.core.initialization) The new API uses "Model Initializer" objects instead of class methods, allowing for the definition of multiple initialization routines for a single model A number of common, model agnostic initialization routines have also been defined, including initialization from data, block-decomposition and a general hierarchical approach equivalent to the existing method for common unit models New metadata for thermophysical properties – valid_range This can be used to record the range of values over which a property value can be trusted, such as the range of experimental data used to regress parameters A number of new utility functions have been added to check for properties with values outside the valid range and to set bounds based on this metadata Updated construction of balance expressions in Control Volumes to remove unneeded terms In the past, unneeded terms were added as a constant 0 term, however they will now be dropped entirely from the expression This was necessary due to more strict unit checking in the new Pyomo solver writer which no longer ignores 0 terms Updates to metadata for thermophysical properties to better define known properties and units of measurement This results in more strict enforcement of standard naming for thermophysical and reaction properties Users can still define custom properties, but these must be done explicitly using the define_custom_properties() method instead of being implicitly created by add_property() Updated convergence tester utility tool to support definition of benchmark files (JSON format) and comparison of performance to benchmarks Set default iteration limit for IPOPT in IDAES config to 200 iterations Update scaling of example models to work with new Pyomo NL solver writer Improve testing of extensions and examples infrastructure to avoid need for downloading files Updated distillation column to centralize common functionality and remove a number of Pyomo warnings

IDAES↗
DOE OSTI2023

Enabling Real-time Scattering Data Analysis with Scalable Optimization [Slides]

Diffraction experiments produce datasets with rich multidimensional physics information such as microstructure, equations of state, crystal structure, elastoplastic properties, and other key inputs to LANL mission-essential multiphysics models. This information is typically extracted through a process called Rietveld refinement, which involves selecting appropriate models of the instrument, crystal structure, and microstructure, identifying suitable starting values, and then fitting often hundreds of model parameters using a sequence of empirical parameter turnon/off sequences within a non-global gradient-based optimization. Extensive user expertise is required to properly setup a refinement, identify appropriate models, and select initial parameter values close to truth, such that the refinement will yield parameter values that are optimally predictive. This is a very tedious manual process performed far after the beamline campaign has ended. As facilities have become capable of generating larger volumes of data, the limitation in throughput due to Rietveld refinement has led to a dramatic increase in unanalyzed data as opposed to an intended increase in new science. In our FY22 TED, we demonstrated an integrated toolset providing near real-time automated Rietveld analysis. If this toolset can be optimized to provide automated Rietveld analysis in real-time, this could alleviate the bottleneck in unanalyzed diffraction data, aid in decision-making during experiments, and increase efficiency of the facility.

74 ATOMIC AND MOLECULAR PHYSICS↗
DOE OSTI2020

Irradiation enhanced diffusion and diffusional creep in U₃Si₂

U₃Si₂ is a candidate as an advanced fuel for light water reactors (LWRs). Compared to UO₂, there are significant data gaps for the thermophysical and thermomechanical properties of U₃Si₂. Atomic scale simulations can help fill in those gaps, while also providing a more mechanistic understanding of the material properties. Creep is an important property for fuel performance as it influences the pellet-cladding gap, which in turn affects the temperature of the fuel (a key parameter in many other aspects of fuel behavior). In this work, we employed a DFT-informed cluster dynamics framework to predict point defect concentrations in U₃Si₂. The concentrations, diffusivities, and volumes of these defects were used to develop a diffusional creep model based on bulk (Nabarro-Herring) and grain boundary (Coble) diffusion processes. It was found that irradiation enhanced Nabarro-Herring creep is dominant at low temperatures, while Coble creep dominates for high temperatures. The model compares well against available experimental data and has been implemented in BISON. A demonstration case using simple power profiles has been carried out, showing that negligible creep occurs due to the low temperatures experienced by U₃Si₂, a consequence of its high thermal conductivity.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
DOE OSTI2025

Coupled THM modeling of bentonite heating and hydration in tank tests with a new temperature-dependent water retention model

This study presents a coupled thermo-hydro-mechanical (THM) model for simulating the heating and hydration behavior of bentonite, a buffer material in deep geological repositories (DGRs). The model incorporates a new temperature-dependent soil water retention curve which captures the thermal-induced shift in water retention behavior. It also distinguishes between liquid and gas permeability, modeling intrinsic gas permeability as a function of accessible porosity to improve vapor transport and desaturation predictions. The model was validated against two large-scale tank tests, demonstrating good agreement with measured temperature, relative humidity, and water inflow data. It revealed a complex porosity evolution driven by thermal expansion, vapor movement, vapor condensation, and hydration-induced swelling during heating and hydration processes. The simulation results also suggest that the permeability of the hydration layer plays a critical role in controlling water intake. Clogging of this layer can significantly reduce the volume of water inflow during the hydration phase. Furthermore, while the model effectively captures key THM behavior, further development of the mechanical constitutive law is required to account for possible thermo-elasto-plastic volume changes and microstructural effects. Overall, the model provides a robust tool for evaluating the evolution of bentonite-based barrier material in DGRs.

Guo, Guanlong [Lawrence Berkeley National Laborato↗
DOE OSTI2025

Modeling of multi-component precipitation and crystallization for zero-liquid-discharge desalination

This study proposes novel ZLD treatment trains that integrate multi-component chemical precipitation and multi-effect evaporative crystallization for efficient brine management. The methodology emphasizes sustainability by integrating CO₂ for chemical precipitation and reducing environmental impact, exemplified with two case studies: produced water that includes industrial waste heat utilization by adopting the emerging vacuum air-gapped membrane distillation (VAGMD) technology, and brackish groundwater that is abundant in sulphate which undergoes treatment by low-salt-rejection reverse osmosis (LSRRO) with interstage chemical precipitation. Here, this study is the first of its kind to simultaneously account for reducing risk of mineral scaling and effective recovery of valuable solids when incorporating VAGMD and LSRRO in ZLD treatment trains. Using Reaktoro and WaterTAP, both Python-based, open-source platforms, we model the recovery of high-purity magnesium, calcium, and sodium salts while optimizing energy consumption and operational efficiency in proposed ZLD pathways for case studies of produced water and brackish desalination brine management. Validation with experimental and reference data confirms the reliability of the models used. In both case studies, the optimized ZLD process achieves recovery rates of 97%, 99%, and 90% for Mg, Ca, and Na, with purities exceeding 99%, and brine volume reduced to less than 4% of the initial feed flow.

3D CT scans↗
DOE OSTI2024

Computing the solubility of argon and xenon in molten sodium chloride and potassium chloride salts

Molten salt reactors (MSRs) offer significant advancements in nuclear reactor safety and efficiency by operating at higher temperatures and lower pressures compared to traditional reactors. A critical aspect of MSR operation involves understanding the solubility of fission byproducts, particularly noble gases, in the molten salts used. This study employs molecular dynamics (MD) simulations to compute Henry’s law constants and enthalpies of solvation for argon and xenon in molten sodium chloride (NaCl) and potassium chloride (KCl). We developed a new pairwise potential for the noble gas and salt interactions based on first principles calculations. We then used this potential to calculate Henry’s law constants of the two gases in the molten salts, which were modeled using both a rigid ion model (RIM) and a polarizable ion model (PIM). The solubility calculations, performed using the Widom insertion method, show qualitative agreement with limited experimental data, highlighting the temperature dependence and greater solubility of both gases in KCl compared to NaCl. Additionally, free volume analysis elucidated the role of available space within the molten salts in governing solubility trends. Our findings suggest that PIM trajectories provide more reliable predictions for noble gas solubility than RIM due to their accurate density representation. Furthermore, these results enhance understanding of gas solubility in MSR environments, and the methods can be readily extended to other systems.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2025

Hydrogen and Electric Charging Infrastructure for Heavy-Duty Trucks: A Nationally Scalable Megaregion Assessment

Decarbonizing regional and long-haul freight is challenging due to the limitations of battery-electric commercial vehicles and infrastructure constraints. Hydrogen fuel cell medium- and heavy-duty vehicles (MHDVs) offer a viable alternative, aligning with the decarbonization goals of the Department of Energy and commercial entities. Historically, alternative fuels like compressed natural gas and liquefied propane gas have faced slow adoption due to barriers like infrastructure availability. To avoid similar issues, effective planning and deploying zero-emission hydrogen fueling infrastructure is crucial. This research develops deployment plans for affordable, accessible, and sustainable hydrogen refueling stations, supporting stakeholders in the decarbonized commercial vehicle freight system. It aims to benefit underserved and rural energy-stressed communities by improving air quality, reducing noise pollution, and enhancing energy resiliency. This research also provides a blueprint for replacing diesel in over-the-road Class 8 freight truck applications with hydrogen fueling solutions. The study focuses on the Texas Triangle Megaregion (I-45, I-35, and I-10), the I-10 corridor between San Antonio, TX, and Los Angeles, CA, and the I-5/CA-99 corridors between Los Angeles, CA, and San Francisco, CA. This area represents a significant portion of U.S. heavy-duty freight movement, carrying ~8.5% of the national freight volume. Using the OR-AGENT (Optimal Regional Architecture Generation for Efficient National Transport) modeling framework, the study conducts an advanced assessment of commercial vehicles, road and freight networks, and energy systems. The framework integrates data on freight mobility, traffic, weather, and energy pathways to deliver a region-specific, optimized vehicles powertrain architectures, infrastructure deployment solutions, operational logistics, and energy pathways. By considering all vehicle origin-destination pairs utilizing these corridors and all feasible fueling station location options, the framework's genetic algorithm identifies the minimum number and optimal locations of hydrogen refueling stations, ensuring no vehicle is stranded. It also determines fuel schedules and quantities at each station. A roadmap for station deployment based on multiple adoption trajectories ensures a strategic rollout of hydrogen refueling infrastructure.

Sujan, Vivek [ORNL] (ORCID:0000000269882342)↗
DOE OSTI2025

Baseline Climate Variables for Earth System Modelling

The Baseline Climate Variables for Earth System Modelling (ESM-BCVs) are defined as a list of 135 variables which have high utility for the evaluation and exploitation of climate simulations. The list reflects the most frequently used elements of the Coupled Model Intercomparison Project Phase 6 (CMIP6) archive. Successive phases of CMIP have supported strong results in science and substantially influence international climate policy formulation. This paper responds to both interest in exploiting CMIP data standards in a broader range of climate modelling activities and a need to achieve greater clarity about the significance and intention of variables in the CMIP Data Request. As Earth system modelling archives grow in scale and complexity, there are emerging problems associated with weak standardisation at the variable collection level. That is, there are good standards covering how specific variables should be archived, but this paper fills a gap in the standardisation of which variables should be archived. The ESM-BCV list is intended as a resource for ESM intercomparison projects (MIPs) developing requests to enable greater consistency among MIPs and as a reference for modelling centres to enhance consistency within MIPs. Provisional planning for the CMIP7 Data Request exploits the ESM-BCVs as a core element. The baseline variable list includes 98 variables which have modest or minor data volume footprints and could be generated systematically when simulations are produced and archived for exploitation by the World Climate Research Programme (WCRP) community. A further 35 variables are classed as “high volume” and are only suitable for production when the resource implications are justified.

Juckes, Martin [University of Oxford (United Kingd↗
DOE OSTI2024

Probing intergalactic intergalactic magnetic fields with LOFAR LoTSS DR2 data

We use Faraday rotation measurements from the latest catalog LoTSS DR2 from LOFAR to probe intergalactic magnetic fields. To identify the extragalactic component of the observed rotation measure (RM) we use two different techniques: residual rotation measure (RRM) and close radio pairs. For the RRM approach, we conclude that, despite smaller measurement errors in the LOFAR data, robust and conservative treatment of the systematic uncertainties in the Galactic contribution to RM results in the constraint on a homogeneous volume-filling magnetic field at the level 2.4 nG, slightly weaker than previous constraints from NVSS data, and does not allow to probe the presence of over-magnetized bubbles predicted by the AGN feedback model of the IllustrisTNG. Analyzing close radio pairs we found that in only 0.5% of our mock realizations of observed data, the expected contribution from the over-magnetized bubbles does not exceed LoTSS DR2 data.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2023

Automated bidirectional coupling of multiscale models of aerosol dosimetry: Validation with subject-specific deposition data

Assessing the toxicity of airborne particulate matter or the efficacy of inhaled drug depends upon accurate estimates of deposited fraction of inhaled materials. In silico approaches can provide important insights into site- or airway-specific deposition of inhaled aerosols in the respiratory system. In this study, we improved on our recently developed 3D/1D model that simulate aerosol transport and deposition in the whole lung over multiple breath cycles. A subject-specific multiscale lung model of a healthy male subject using computational fluid-particle dynamics (CFPD) in a 3D model of the oral cavity through the large bronchial airways entering each lobe was bidirectionally coupled with a recently improved Multiple Path Particle Dosimetry (MPPD) model to predict aerosol deposition over the entire respiratory tract over multiple breaths for four conditions matching experimental aerosol exposures in the same subject from which the model was developed. These include two particle sizes (1 and 2.9 μm) and two subject-specific breathing rates of ~300 ml/s (slow breathing) and ~750 ml/s (fast breathing) at a target tidal volume of 1 L. In silico predictions of retained fraction were 0.31 and 0.29 for 1 μm and 0.66 and 0.62 for 2.9 μm during slow and fast breathing, respectively, and compared well with experimental data (1 μm: 0.31 ± 0.01 (slow) and 0.27 ± 0.01 (fast), 2.9 μm: 0.63 ± 0.03 (slow) and 0.68 ± 0.02 (fast)). Furthermore, these results provide a great deal of confidence in the validity and reliability of our approach.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2023

Kinetic model-based group contribution method for derived cetane number prediction of oxygenated fuel components and blends

A four-step autoignition model was used to derive an expression for the ignition delay (ID) measured in ignition quality testers (IQT) with the derived cetane number (DCN) determined from this ID using the ASTM D6890 standard correlation. The model predicts DCN for individual compounds and blends as a function of each compound's global initiation and net chain branching rate constants. Expressions for these values were determined assuming they could be related to the functional groups present in each compound. Measurement data for 125 compounds and 94 binary and ternary blends (including oxygenates: alcohols, aldehydes, esters, ethers, and ketones), from literature and from measurements performed in our lab, were used to obtain the dependence of the measured ignition delay on each functional group. The new kinetic model-based group contribution method was able to predict the ignition delay of both pure compounds and blends, with an average DCN error of 4.4 (19%) and 2.8 (11%), respectively. Here, the blend model was also used to develop an ID mixing rule by incorporating existing IQT ignition delay data for each compound. Use of the mixing rule gave an average DCN error of 3.6 (16%) for blends. Both the blend model and mixing rule were found to be superior compared to standard linear by volume fraction or mole fraction mixing rules commonly used to estimate the DCN of mixtures.

42 ENGINEERING↗
DOE OSTI2025

Towards a new generation of solid total-energy detectors for neutron-capture time-of-flight experiments with intense neutron beams

Challenging neutron-capture cross-section measurements of small cross sections and samples with a very limited number of atoms require high-flux time-of-flight facilities. In turn, such facilities need innovative detection setups that are fast, have low sensitivity to neutrons, can quickly recover from the so-called γ-flash, and offer the highest possible detection sensitivity. In this paper, we present several steps towards such advanced systems. Specifically, we describe the performance of a high-sensitivity experimental setup at CERN n_TOF EAR2. It consists of nine sTED detector modules in a compact cylindrical configuration, two conventional used large-volume C 6 D 6 detectors, and one LaCl 3 (Ce) detector. The performance of these detection systems is compared using 93 Nb(n, γ) data. We also developed a detailed G EANT small4 Monte Carlo model of the experimental EAR2 setup, which allows for a better understanding of the detector features, including their efficiency determination. This Monte Carlo model has been used for further optimization, thus leading to a new conceptual design of a γ detector array, STAR, based on a deuterated-stilbene crystal array. Finally, the suitability of deuterated-stilbene crystals for the future STAR array is investigated experimentally utilizing a small stilbene-d12 prototype. The results suggest a similar or superior performance of STAR with respect to other setups based on liquid-scintillators, and allow for additional features such as neutron-gamma discrimination and a higher level of customization capability.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗