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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 811 records · Page 45
All sourcesNASA NTRSDOE OSTI
DOE OSTI2025

Development of Predictive Model for Accurate Rupture Time from Multi-Axial Creep in Alloy 709 with Physics-Based Simulations

A physics-based model is developed to predict multiaxial creep behavior in Alloy 709 (A709), an advanced austenitic stainless steel intended for high-temperature applications such as Sodium Fast Reactors (SFRs). Compared to conventional stainless steels like 316H, A709 offers superior high-temperature performance; however, comprehensive data on its multiaxial creep response remain limited. To address this gap, a crystal plasticity finite element (CPFE) framework is used to simulate the deformation and failure mechanisms of A709 under multiaxial loading conditions. The model incorporates an extended Hu-Cocks dislocation creep formulation that accounts for precipitation effects, along with the Sham–Needleman model to capture grain boundary cavitation-driven failure. These advanced constitutive models enable a detailed understanding of the interplay between microstructural evolution and macroscopic creep response. Furthermore, the study evaluates the predictive accuracy of various effective stress measures in estimating creep rupture life, leveraging simulated multiaxial creep data. The findings provide critical insights into the applicability of different stress measures for engineering design and life prediction of A709 components operating under complex loading conditions. This work contributes to improving the reliability of high-temperature structural components by advancing predictive modeling capabilities for advanced austenitic steels.

Alloy 709↗
DOE OSTI2019

Biomass Grinding Process Optimization Using Response Surface Methodology and a Hybrid Genetic Algorithm

Biomass could be a key source of renewable energy. Agricultural waste products, such as corn stover, provide a convenient means to replace fossil fuels, such as coal, and a large amount of feedstock is currently available for energy consumption in the U.S. This study has two main objectives: (1) to understand the impact of corn stover moisture content and grinder speed on grind physical properties; and (2) develop response surface models and optimize these models using a hybrid genetic algorithm. The response surface models developed were used to draw surface plots to understand the interaction effects of the corn stover grind moisture content and grinder speed on the grind physical properties and specific energy consumption. The surface plots indicated that a higher corn stover grind moisture content and grinder speed had a positive effect on the bulk and tapped density. The final grind moisture content was highly influenced by the initial moisture content of the corn stover grind. Optimization of the response surface models using the hybrid genetic algorithm indicated that moisture content in the range of 17 to 19% (w.b.) and a grinder speed of 47 to 49 Hz maximized the bulk and tapped density and minimized the geomantic mean particle length. The specific energy consumption was minimized when the grinder speed was about 20 Hz and the corn stover grind moisture content was about 10% (w.b.).

09 BIOMASS FUELS↗
NASA NTRSDate not supplied

A Parallelized Oxidation-Driven Surface Recession Framework in DSMC Code, SPARTA

Spacecrafts rely on ablative thermal protection systems (TPS) made of composites consisting of a carbon-based reinforcement and a polymeric matrix. These materials are designed to withstand high-temperature oxidation and surface recession during re-entry into the Earth's atmosphere. However, ablation occurs due to a complex interplay of thermal, mechanical, and chemical factors, making it challenging to determine the individual impact of each on the TPS's overall degradation. In this study, we have developed an ablation model that can leverage a finite rate carbon oxidation model to predict material recession and surface states more accurately. Stochastic PArallel Rarified-gas Time-accurate Analyzer (SPARTA), a direct-simulation Monte Carlo (DSMC) code, is modified to allow oxidation-driven ablation of implicitly defined carbon surfaces. In SPARTA, implicit surfaces are generated from the grid corner point values via a marching cubes algorithm, therefore creating a new set of surface elements every time ablation is performed. The finite-rate oxidation model developed by Gopalan et. al can perform both gas-surface and pure-surface reactions and is now adapted to tally surface data on a per grid cell basis. The ablation functionality was also adjusted so once the reactions have occurred, the number of reactions leading to CO formation can be converted to corner point reduction values; therefore, carbon removal is directly proportional to surface recession. We also briefly discuss some unique challenges associated with parallelizing this dynamic surface state and geometry. Finally, we analyze the performance of this parallelized implicit chemistry model with simple 2D and 3D benchmark cases by producing surface state statistics, area changes over time, and visualization across a range of surface temperatures and processors with and without load-balancing.

DSMC↗
DOE OSTI2018

Overview of IAEA MODARIA II Program Working Group 1 on Assessment and Decision Making of Existing Exposure Situations for NORM and Nuclear Legacy Sites - 18308

The International Atomic Energy Agency (IAEA) launched the Modelling and Data for Radiological Impact Assessments (MODARIA) II Programme in October. As part of the IAEA effort to enhance the capabilities of its Member States in assessing radiological impacts of radionuclides released to the environment from sites that were not operated in accordance with current standards, and to ensure protection of the public and the environment, the MODARIA II Programme aims to: (1) test the performance of models developed for predicting the transfer of radionuclides in the environment and assessing radiological impacts to the public and the environment; (2) develop and improve models for particular environments and, where appropriate, to agree on data sets that are generally applicable for use in testing environmental transfer models; and (3) provide an international forum for the exchange of experiences, ideas and research information. The Programme will run for 3 years from 2016 to 2019. The Programme comprises seven working groups covering a wide range of topics (https://wwwns.iaea.org/projects/modaria/modaria2.asp?s=8 and l=129). MODARIA II Working Group 1 (WG1) is focused on dose assessment and decision making regarding existing exposure situations due to naturally occurring radioactive material (NORM) and nuclear legacy sites. WG1 was formed as a combination of two MODARIA I WGs: WG1 focused on application of decision-aiding tools in remediation; and WG3 focused on risk assessment for NORM and radioactively contaminated legacy sites (https://wwwns.iaea.org/projects/modaria/default.asp?s=8 and l=129). It was recognized that the decision-making needs will inform the scope of risk assessments, and the risk insights and structured decision analyses will aid in informed decision-making. Therefore, MODARIA II WG1 is focused on developing methods and toolsets for risk-informed decision-making for NORM and nuclear legacy sites, including demonstration of decision-making processes and tools through case studies. MODARIA II WG1 has 91 registered participants from 33 Member States and IAEA (as of November 2017), some 30 of whom are actively engaged in various collaborative research and development activities within WG1. Members of WG1 participated in the First Technical Meeting of MODARIA II, which was held in Vienna, Austria from 31 October to 4 November 2016. The group then held its First Interim Meeting in Brussels, Belgium from 26 to 30 June 2017, which included a field trip to the legacy Tessenderlo Chemie (TCH) Phosphate Processing Site. At this meeting, the TCH Site and the Pridneprovsky Chemical Plant (PChP) in Ukraine were selected for case studies. The Los Alamos National Laboratory (LANL) Material Disposal Area B in the USA and the Beaverlodge Uranium Mill and Mill Site in northern Canada were selected as examples for sharing of good practices and lessons learned in risk-informed decision-making for environmental remediation and land conveyance. At the Second Technical Meeting held in Vienna on 30 October to 3 November 2017, an additional NORM site for tin processing in Brazil was added to the case studies, with another NORM site in Yunnan, China included for consideration at the Second Interim Meeting of WG1. The Second Interim Meeting will be held in in early to mid-May, 2018. The group has also decided to prepare an Interim Report by October 2018 to document the WG1 discussions and analyses for the period of 2016-2018, which will be finalized at the conclusion of MODARIA II in October 2019. Initial assignments have been discussed for IM1 participants to collect data, conduct modelling and analyses, and prepare draft sections for the Interim Report. This paper will provide an overview of the work plans for WG1. It will also highlight some near-term activities that the group is currently working on. (authors)

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

Mass Spectrometry Sample Submission Portal

Each step in the scientific process generates contextual information about the data that is important to consider when performing data integration, developing models of biological process, or training AI models. We will develop a flexible, template-driven tool that will log biological samples, capture metadata about those samples, and track the type(s) of analysis being performed by researchers providing samples for analysis by mass spectrometry.

97 MATHEMATICS AND COMPUTING↗
NASA NTRS1994

Bias properties of extragalactic distance indicators. 3: Analysis of Tully-Fisher distances for the Mathewson-Ford-Buchhorn sample of 1355 galaxies

The observational selection bias properties of the large Mathewson-Ford-Buchhorn (MFB) sample of axies are demonstrated by showing that the apparent Hubble constant incorrectly increases outward when determined using Tully-Fisher (TF) photometric distances that are uncorreted for bias. It is further shown that the value of H(sub 0) so determined is also multivlaued at a given redshift when it is calculated by the TF method using galaxies with differenct line widths. The method of removing this unphysical contradiction is developed following the model of the bias set out in Paper II. The model developed further here shows that the appropriate TF magnitude of a galaxy that is drawn from a flux-limited catalog not only is a function of line width but, even in the most idealistic cases, requires a triple-entry correction depending on line width, apparent magnitude, and catalog limit. Using the distance-limited subset of the data, it is shown that the mean intrinsic dispersion of a bias-free TF relation is high. The dispersion depends on line width, decreasing from sigma(M) = 0.7 mag for galaxies with rotational velocities less than 100 km s(exp-1) to sigma(M) = 0.4 mag for galaxies with rotational velocities greater than 250 km s(exp-1). These dispersions are so large that the random errors of the bias-free TF distances are too gross to detect any peculiar motions of individual galaxies, but taken together the data show again the offset of 500 km s(exp-1) fond both by Dressler & Faber and by MFB for galaxies in the direction of the putative Great Attractor but described now in a different way. The maximum amplitude of the bulk streaming motion at the Local Group is approximately 500 km s(exp-1) but the perturbation dies out, approaching the Machian frame defined by the CMB at a distance of approximately 80 Mpc (v is approximately 4000 km s(exp -1)). This decay to zero perturbation at v is approximately 4000 km s(exp -1) argues against existing models with a single attraction at approximately 4500 km s(exp -1) (the Great Attactor model) pulling the local region. Rather, the cause of the perturbation appears to be the well-known clumpy mass distribution within 4000 km s(exp -1) in the busy directions of Hydra, Centaurus, Antila and Dorado, as postulated earlier (Tammann & Sandage 1985).

Federspiel, Martin↗
DOE OSTI2024

Interpreting strain tensor data to characterize and monitor reservoirs for CO2 storage and other applications

Recent advances in instrumentation have made it feasible to measure the transient strain tensor caused by small changes in fluid volume or pressure in the subsurface and this has opened the door to new opportunities for characterization and monitoring. We have deployed strainmeters and then conducted injection well tests in an underlying reservoir at 530m depth. The resulting data indicated that the horizontal strain at shallow strainmeters (30 to 40m depth) was tensile and the vertical strain was compressive. The radial strain was less than the horizontal strain, and the strain rates decreased from 100 nanostrain/day to roughly 10 n/d over a few days. The signal at two strainmeters at shallow depth were consistent, although the magnitude of the horizontal strains were different reflecting the different radial directions from the well. The signal at a deep strainmeter deployed at reservoir depth was much different, with tensile vertical strains and compressive horizontal strains. These data can be interpreted by inverting poroelastic forward models developed using numerical and analytical methods. The average horizontal strain in the caprock resembles the transient pressure in the underlying reservoir and classic type-curve methods from transient well testing can be used for preliminary interpretations of strain data. We have developed closed-form analytical solutions to a pressurized poroelastic inclusion and inhomogeneity in a half-space. This model is fast and can be inverted to estimate reservoir stiffness and geometry. Numerical models developed using finite element methods allow more details of the subsurface to be included in the inversion, but they require much longer run times and this makes inversion cumbersome using standard methods. We have developed an inversion approach that uses a proxy model created using machine learning to do most of the forward calculations. The proxy model is periodically updated and refined using the finite element model to ensure accuracy. This approach significantly reduces the computational requirements and makes it feasible to use Bayesian inversion with large numerical models. We have shown that the strain tensor in the caprock is sensitive to pressure in the reservoir, boundaries in the reservoir, and pressure in the caprock caused by leaks. These results indicate that coupling strain tensor data with inversion has the potential to help evaluate reservoirs during initial characterization, and to monitor them during the CO2 injection and storage process.

Murdoch, Larry↗
NASA NTRS2009

A Multiscale Modeling System: Developments, Applications, and Critical Issues

The foremost challenge in parameterizing convective clouds and cloud systems in large-scale models are the many coupled dynamical and physical processes that interact over a wide range of scales, from microphysical scales to the synoptic and planetary scales. This makes the comprehension and representation of convective clouds and cloud systems one of the most complex scientific problems in Earth science. During the past decade, the Global Energy and Water Cycle Experiment (GEWEX) Cloud System Study (GCSS) has pioneered the use of single-column models (SCMs) and cloud-resolving models (CRMs) for the evaluation of the cloud and radiation parameterizations in general circulation models (GCMs; e.g., GEWEX Cloud System Science Team 1993). These activities have uncovered many systematic biases in the radiation, cloud and convection parameterizations of GCMs and have led to the development of new schemes (e.g., Zhang 2002; Pincus et al, 2003; Zhang and Wu 2003; Wu et al. 2003; Liang and Wu 2005; Wu and Liang 2005, and others). Comparisons between SCMs and CRMs using the same large-scale forcing derived from field campaigns have demonstrated that CRMs are superior to SCMs in the prediction of temperature and moisture tendencies (e.g., Das et al. 1999; Randall et al 2003b; Xie et al. 2005).

Tao, Wei-Kuo↗
DOE OSTI2021

Predicting nepheline precipitation in waste glasses using ternary submixture model and machine learning

Nepheline precipitation in nuclear waste glasses during vitrification can be detrimental due to its negative effect on chemical durability. Developing models to accurately predict nepheline precipitation from compositions is important to increase waste loading since existing models can be overly conservative. In this study, an expanded dataset containing 955 glasses was compiled from literature data, where 355 glasses are for high-level waste (HLW). Previously developed submixture models were refitted using the new dataset, where a misclassification rate of 7.8% was achieved. Nine machine learning (ML) algorithms (e.g., k-nearest neighbor, Gaussian process regression, artificial neural network, support vector machine, decision tree, etc.) were applied to evaluate their ability of predicting nepheline precipitation from compositions. Model accuracy, precision, recall/sensitivity, and F1 score were systemically compared between different ML algorithms and modeling protocols. Good model prediction with an accuracy ~0.9 (misclassification rate of ~10%) was observed with different algorithms under certain protocol. This study evaluated various ML models to predict nepheline precipitations in waste glasses, highlighting the importance of data preparation, modeling protocol, and their effect on model stability and reproducibility. The results provide insights into applying ML to predict glass properties and suggest areas for future research on modeling nepheline precipitations.

Lu, Xiaonan↗
DOE OSTI2020

Investigating the linkage between simulated precipitation climatology and ENSO-related precipitation anomaly based on multi-model and perturbed parameter ensembles

It remains a challenge for climate models to correctly capture the relationship between precipitation and ENSO. This studyexamines the linkage between the simulated precipitation climatology and ENSO-related precipitation anomaly during borealwinter based on the multi-model ensemble from the Atmospheric Model Intercomparison Project Phase 5 (AMIP5) and perturbedparameter ensemble (PPE) with the Beijing Climate Center (BCC) atmospheric model. The AMIP5 models have large biases insimulating the tropical precipitation anomaly during El Niño, such as the shifts of the Inter-Tropical Convergence Zone (ITCZ)and South Pacific Convergence Zone (SPCZ). The inter-model differences show that the precipitation change in response to seasurface temperature (SST) change increases with enhanced precipitation climatology. The ENSO-related precipitation anomalycan also be related to the spatial distribution of the mean-state precipitation. The simulated ITCZ/SPCZ displacements aresignificantly correlated with the spatial precipitation-SST relationship in the mean state. Models with stronger mean-stateprecipitation-SST relationship also produce stronger SPCZ/ITCZ displacements. The mean-state tropical precipitation also hasstrong impacts on the ENSO-related precipitation anomalies over East Asia. In the BCC PPE, the connection between the mean-state precipitation and ENSO-related precipitation anomaly is overall consistent with that in AMIP5. Parameters associated withthe low-cloud and deep convection processes are the most influential ones for the precipitation simulations in BCC. Comparedwith the version in AMIP5, the new BCC model can better simulate the precipitation climatology and the relationship betweenENSO and precipitation over southern China. These results have important implications for model development and model-basedclimate predictions.

Yang, Ben↗
DOE OSTI2024

Source term analysis of FeCrAl accident tolerant fuel using MELCOR

It has been established that incremental improvements in beyond design basis accident performance can be achieved through accident tolerant fuel (ATF). However, they have the potential to recover margin with respect to conventional fuel and therefore enhance plant economics through uprate or cycle length increase. To realize this potential, it is necessary to quantify the reduction in source term due to use of ATF, and correspondingly how this is affected by increasing the cycle length and/or burnup. This requires development of a risk informed analysis methodology for ATF under high burnup conditions, which is being developed within LWRS. To this end, a MELCOR simulation of FeCrAl ATF in a recovered Large Break LOCA (LBLOCA) scenario has been developed, using a model based on the Zion Pressurized Water Reactor (PWR). The new user defined material capability, along with the inclusion of detailed neutronics- and depletion-derived parameters such as core power distribution, decay heat behavior, and fission product inventories, allows a more detailed simulation of FeCrAl clad material properties and behavior than has previously been possible using MELCOR. These detailed FeCrAl results were compared with a zircaloy clad model to investigate the differences between the two clad types and quantify the benefits of FeCrAl ATF with an 18-month cycle. Next, the fuel cycle with ATF was extended to 24 months, to determine whether any additional safety margin provided by FeCrAl ATF could be leveraged to implement high burnup FeCrAl-clad fuel while retaining the same operating and safety limits as current zircaloy-clad fuel. For the particular scenario analyzed, the delay to fuel failure from using ATF was of the same order as the LOCA recovery time, and hence significant in reducing fission product release, with the 24-month FeCrAl core performing better than the 18-month zircaloy core. It is noted that for other transients, the reduction in release due to using FeCrAl may be less significant. Furthermore, the material model developed here can be used in such further studies in support of determining the overall source term.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
DOE OSTI2021

Modelling a compressible packed bed flow-through washing and deacetylation reactor for corn stover pretreatment

The deacetylation process is a key step in the lignocellulosic biomass pretreatment process that currently is done in batch reactors. A flow-through diffuser type reactor is a promising approach to make the process more efficient and economically favorable. A washing step before deacetylation can further reduce chemical usage and increase the process efficiency. However, the highly compacted biomass particles can cause significant pressure-drop for fluid flow through the bed, which in the worst case can cause self-enhancing pressure-drop leading to reactor blocking. Detailed understanding of the fundamentals of flow through a compressible packed bed is necessary to scale up the washing and deacetylation process. This work closely examines the classical Kozeny Carman equation and proposes modifications to accurately predict the pressure-drop at different compaction states. Here, a compressible packed bed model is further developed based on the interaction between solid mechanics and fluid dynamics. Experimental measurements of water flow through a packed bed of corn stover show good agreement with the developed model. Corn stover washing process scale up simulations show that smaller reactor diameter, higher friction lining material, lower process throughput, and higher process temperature is favorable in stabilizing the bed from extensive compression when the bed height is fixed.

09 BIOMASS FUELS↗
DOE OSTI2024

Applying machine learning and quantum chemistry to predict the glass transition temperatures of polymers

Glass transition temperature (T g ) is important for understanding the physical and mechanical properties of a polymer material because it relates to the thermal energy required to transition between a hard glassy state and a soft rubbery one. Over the years, various models have been developed for predicting this thermal property from molecular structure to aid in designing novel polymers in selected classes. This work builds on those efforts by utilizing both machine learning (ML) and quantum chemistry (QC) techniques to develop models that can predict T g values from the molecular structure under different data availability scenarios and for a wide variety of polymer types. For the ML model, a graph convolutional network (GCN) was used to map topological polymer features; this model was trained against a dataset of more than 7500 T g values and resulted in a root mean square error (RMSE) of 38.1 °C. The QC-based regression model was trained on 83 T g values and produced an RMSE of 34.5 °C. In conclusion, this work demonstrated that while both model techniques produce accurate predictions and are suitable for different data availability scenarios, the QC-based regression model offered a more interpretable model framework with significantly less training data.

36 MATERIALS SCIENCE↗
DOE OSTI2024

Structure Sensitive Reaction Kinetics of Chiral Molecules on Intrinsically Chiral Surfaces

Enantiospecific heterogeneous catalysis utilizes chiral surfaces to resolve enantiomers via structure sensitive surface chemistry. The catalyst design challenge is the identification of chiral surface structures that maximize enantiospecificity. Herein, we develop data driven models for the enantiospecificity of tartaric acid reactions on chiral Cu(hkl) R&S surfaces. Measurements of enantiospecific rate constants were obtained by using curved Cu(hkl) R&S surfaces that enable kinetic measurements on hundreds of chiral surface orientations. One model uses feature vectors derived from generalized coordination numbers to capture the local structure around Cu atoms exposed by the Cu(hkl) R&S surfaces. The second model introduces the use of chiral cubic harmonic functions to capture the symmetry constraints of the face-centered cubic Cu structure. The model using 58 generalized coordination numbers has a fitting error similar to that of the model using only 5 cubic harmonic functions. The two models predict maxima in the enantiospecificity on surfaces with very similar surface orientations. The models developed in this work are applicable for any enantiospecific reaction happening on any chiral material with a cubic lattice structure, opening the way to understanding the surface structure sensitivity of the enantiospecific reaction kinetics.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2024

Diffusioosmotic flow reversals due to ion–ion electrostatic correlations

Existing theories of diffusioosmosis have neglected ion–ion electrostatic correlations, which are important in concentrated electrolytes. Here, we develop a mathematical model to numerically compute the diffusioosmotic mobilities of binary symmetric electrolytes across low to high concentrations in a charged parallel-plate channel. We use the modified Poisson equation to model the ion–ion electrostatic correlations and the Bikerman model to account for the finite size of ions. We report two key findings. First, ion–ion electrostatic correlations can cause a unique reversal in the direction of diffusioosmosis. Such a reversal is not captured by existing theories, occurs at ≈ 0.4 M for a monovalent electrolyte, and at a much lower concentration of ≈ 0.003 M for a divalent electrolyte in a channel with the same surface charge. This highlights that diffusioosmosis of a concentrated electrolyte can be qualitatively different from that of a dilute electrolyte, not just in its magnitude but also its direction. Second, we predict a separate diffusioosmotic flow reversal, which is not due to electrostatic correlations but the competition between the underlying chemiosmosis and electroosmosis. This reversal can be achieved by varying the magnitude of the channel surface charge without changing its sign. However, electrostatic correlations can radically change how this flow reversal depends on the channel surface charge and ion diffusivity between a concentrated and a dilute electrolyte. Furthermore, the mathematical model developed here can be used to design diffusioosmosis of dilute and concentrated electrolytes, which is central to applications such as species mixing and separation, enhanced oil recovery, and reverse electrodialysis.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2020

Thermoeconomic cost optimization of superconducting magnets for proton therapy gantries

A compact gantry delivering 70-220 MeV protons with fixed field in the superconducting magnets could reduce the cost and improve the adoption of proton therapy. While a number of magnet and cryogenics designs have been proposed, the combined capital and operating costs of state-of-the-art superconducting materials have not been analyzed. In response, we develop a thermoeconomic model of a multi-stage, conduction cooled gantry lattice and analyze the cryocooler operating cost, cryocooler capital cost and conductor capital cost for Nb-Ti, Nb 3 Sn, REBCO and Bi-2223 over a continuous range of magnet temperatures, and a differential evolution algorithm is used to identify the optimal combination of thermal intercept temperatures. Although Nb3Sn yields the lowest Net Present Value (NPV) of $111.7k at a magnet temperature of 9.4 K, the optimized Bi-2223 design at 12.8 K approaches the realm of commercial feasibility by offering improved thermal stability and forgoing the need for costly conductor heat treatment and magnet quench training. Furthermore, it was found that Nb3Sn was more cost effective than Nb-Ti and that REBCO was not economically viable for the parameters of this investigation. Overall, the thermoeconomic model developed herein can optimize conductor choices, magnet temperatures and thermal staging which has value for any conduction-cooled superconducting magnet.

BSCCO↗
DOE OSTI2021

Flow Assisted Evaporative Cooling for Electric Motor

This paper examines a novel concept of flow assisted latent heat driven two-phase evaporative cooling (EC) confined in-between slot liner and active-winding of electric motor. Wicking micro-structure enhanced PDMS liner axially sucks coolant in the form of thin film between the PDMS liner and active-winding and eventually enables thin film evaporation on the outer surface of the active-winding. Therefore, EC based thermal management eliminates contact resistance between the winding and slot-liner and, enhances the heat extraction from the winding without compromising the electro-magnetic performance. Two-way coupled electro-magnetic (EM) – computational fluid dynamics/heat transfer (CFD/HT) and EM - lumped parameter thermal network (LPTN) models have been developed to assess the electro-thermal performance of the EC under steady and transient conditions. Taking a case study of a 125 kW jacket cooled BMW i3 motor and dielectric coolant FC-84, EC is shown to be capable of handling a maximum steady state rms current density of 26 A/mm2 at a evaporative heat transfer coefficient of 5,000 W/m2.K, which is about 78.7% higher compared to the traditional jacket cooling (JC). In case of EC, a maximum steady state and transient rms current density of 30 A/mm2 (106.2% higher compared to the JC), and 40.8 A/mm2 have been realized by using high thermal conductivity epoxy (1.9 W/m.K) impregnation material. Thermal performance of the EC is also assessed and compared with JC over a dynamic drive cycle. Lastly, a motorette testing has been performed to demonstrate the applicability of the proposed EC method and to validate the developed modeling framework.

42 ENGINEERING↗
DOE OSTI2025

Effects cascade debris and helium bubbles on the strength of aged plutonium

The radioactive decay of aging Pu is dominated by α-decay. This persistent α-decay produces crystalline defects in the form of dislocation loops and helium bubbles that evolve with time. Comparable defects are produced in other metallic alloys when subject to neutron irradiation, and these defects are known to modify the plastic deformation of irradiated materials. Models have been developed for these irradiated materials and validated against experimental confirmations of yield strength and the concomitant microstructural evolution. In this paper, we deploy those previously developed models and apply their mechanics to plutonium aging.

Chemical elements↗