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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 19 records
All sourcesNASA NTRSDOE OSTI
DOE OSTI2026

Compositional Reasoning for Hierarchical State Machines

Harel statecharts and its derivatives are popular graphical languages for specifying discrete control systems via hierarchical state machines. Separately, there has been a long line of work on specifying concurrent systems with process calculi which come equipped with an algebraic theory, the ability reason compositionally about various temporal properties, and strong type systems. While these two approaches to modeling systems are tantalizingly similar, the integrated reasoning principles that exist for process calculi have not been demonstrated in hierarchical state machines. A key issue is that operational theories for process calculi do not behave like control systems, and thus, there is virtually no tool support for modeling control systems with such languages. For a control system designer, bringing the integrated, more scalable reasoning from the process calculi to state-machine languages would enable the specification of more complex systems and a more modular systems development process. Our insight is that we can recover many important results from the process calculi in hierarchical state machines with local scope. We employ a structural operational semantics, which is ubiquitous in process and 𝜆-calculi but uncommon in hierarchical statemachine formalizations, to enable inductive reasoning about behavior. Taking inspiration from the structure of process calculi metatheories, we define a calculus of refinement and equivalence that we prove sound with respect to local notion of (bi)simulation. Furthermore, we prove that the calculus preserves the behavioral properties of reactivity, observational determinism, traces, and linear temporal properties. Our results are mechanized in the Rocq proof assistant.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2026

Boron Coordination in Multicomponent Glasses: Analytical Models and Machine Learning With Uncertainty

Borosilicate glasses are extensively used in a variety of applications from kitchenware to nuclear waste immobilization due to the strong network formed by the Si-O-B bond that makes it resistant to chemical corrosion and gives it a low thermal expansion. Boron, however, exists in both trigonal BO3 and tetrahedral BO4 bonds in glass systems, which impacts the chemical durability and thermal resistance of the glass, amongst other properties. Boron coordination (N4), or the ratio of the amount of BO4 to BO3 within a glass, may aid in predicting these properties but is difficult to derive without experimental data due to the complexity of impacts from varied glass compositions and processing factors. For this reason, compositional models have been developed to predict boron coordination, but the models typically include a limited number of glass components. To help fill this gap in the models, in this work, a diverse multicomponent glass dataset of 809 glasses is compiled from a literature search, and then a number of analytical and machine learning (ML) models are trained on the dataset. Previously developed modified Bernstein and modified Du Stebbins analytical models were fitted to update parameters with the new dataset. Then, partially Bayesian neural networks, Gaussian process regressor, and heteroskedastic deterministic neural networks were evaluated. The ML models examined all have different strategies to overcome the potential for overfitting as a result of a limited training dataset, and return results that account for model uncertainty, which can be valuable for understanding model reliability. For the first time, cooling rate is introduced as an input parameter for ML models, showing consistent improvements in performance and solidifying the importance of including parameters outside of composition alone for N4 prediction. The machine learning models examined here show promise in accurate predictions of boron coordination in borosilicate glasses, all achieving R2 values of 0.91.

boron coordination↗
DOE OSTI2022

Earth System Model Aerosol–Cloud Diagnostics (ESMAC Diags) package, version 1: assessing E3SM aerosol predictions using aircraft, ship, and surface measurements

Abstract. An Earth system model (ESM) aerosol–cloud diagnostics package is developed to facilitate the routine evaluation of aerosols, clouds, and aerosol–cloud interactions simulated by the Energy Exascale Earth System Model (E3SM) from the US Department of Energy (DOE). The first version focuses on comparing simulated aerosol properties with aircraft, ship, and surface measurements, which are mostly measured in situ. The diagnostics currently cover six field campaigns in four geographical regions: eastern North Atlantic (ENA), central US (CUS), northeastern Pacific (NEP), and Southern Ocean (SO). These regions produce frequent liquid- or mixed-phase clouds, with extensive measurements available from the Atmospheric Radiation Measurement (ARM) program and other agencies. Various types of diagnostics and metrics are performed for aerosol number, size distribution, chemical composition, cloud condensation nuclei (CCN) concentration, and various meteorological quantities to assess how well E3SM represents observed aerosol properties across spatial scales. Overall, E3SM qualitatively reproduces the observed aerosol number concentration, size distribution, and chemical composition reasonably well, but it overestimates Aitken-mode aerosols and underestimates accumulation-mode aerosols over the CUS and ENA regions, suggesting that processes related to particle growth or coagulation might be too weak in the model. The current version of E3SM struggles to reproduce the new particle formation events frequently observed over both the CUS and ENA regions, indicating missing processes in current parameterizations. The diagnostics package is coded and organized in a way that can be extended to other field campaign datasets and adapted to higher-resolution model simulations.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2024

Severe Convective Storms in Limited Instability Organized by Pattern and Distribution

Abstract Severe convection occurring in high-shear, low-CAPE (HSLC) environments is a common cool-season threat in the southeastern United States. Previous studies of HSLC convection document the increased operational challenges that these environments present compared to their high-CAPE counterparts, corresponding to higher false-alarm ratios and lower probability of detection for severe watches and warnings. These environments can exhibit rapid destabilization in the hours prior to convection, sometimes associated with the release of potential instability. Here, we use self-organizing maps (SOMs) to objectively identify environmental patterns accompanying HSLC cool-season severe events and associate them with variations in severe weather frequency and distribution. Large-scale patterns exhibit modest variation within the HSLC subclass, featuring strong surface cyclones accompanied by vigorous upper-tropospheric troughs and northward-extending regions of instability, consistent with prior studies. In most patterns, severe weather occurs immediately ahead of a cold front. Other convective ingredients, such as lower-tropospheric vertical wind shear, near-surface equivalent potential temperature (θ e ) advection, and the release of potential instability, varied more significantly across patterns. No single variable used to train SOMs consistently demonstrated differences in the distribution of severe weather occurrence across patterns. Comparison of SOMs based on upper and lower quartiles of severe occurrence demonstrated that the release of potential instability was most consistently associated with higher-impact events in comparison to other convective ingredients. Overall, we find that previously developed HSLC composite parameters reasonably identify high-impact HSLC events. Significance Statement Even when atmospheric instability is not optimal for severe convective storms, in some situations they can still occur, presenting increased challenges to forecasters. These marginal environments may occur at night or during the cool season, when people are less attuned to severe weather threats. Here, we use a sorting algorithm to classify different weather patterns accompanying such storms, and we distinguish which specific patterns and weather system features are most strongly associated with severe storms. Our goals are to increase situational awareness for forecasters and to improve understanding of the processes leading to severe convection in marginal environments.

Meteorology & Atmospheric Sciences↗
DOE OSTI2022

Different feeding patterns affect meat quality of Tibetan pigs associated with intestinal microbiota alterations

This study aimed to investigate the effects of different feeding patterns on meat quality, gut microbiota and its metabolites of Tibetan pigs. Tibetan pigs with similar body weight were fed the high energy diets ( HEP , 20 pigs) and the regular diets ( RFP , 20 pigs), and free-ranging Tibetan pigs ( FRP , 20 pigs) were selected as the reference. After 6 weeks of experiment, meat quality indexes of semitendinosus muscle ( SM ) and cecal microbiota were measured. The results of meat quality demonstrated that the shear force of pig SM in FRP group was higher than that in HEP and RFP groups ( p < 0.001); the pH-value of SM in HEP pigs was higher at 45 min ( p < 0.05) and lower at 24 h ( p < 0.01) after slaughter than that in FRP and RFP groups; the SM lightness ( L* value) of FRP pigs increased compared with RFP and HEP groups ( p < 0.001), while the SM redness ( a* value) of FRP pigs was higher than that of RFP group ( p < 0.05). The free fatty acid ( FA ) profile exhibited that the total FAs and unsaturated FAs of pig SM in HEP and RFP groups were higher than those in FRP group ( p < 0.05); the RFP pigs had more reasonable FA composition with higher n-3 polyunsaturated FAs ( PUFAs ) and lower n-6/n-3 PUFA ratio than HEP pigs ( p < 0.05). Based on that, we observed that Tibetan pigs fed high energy diets (HEP) had lower microbial α-diversity in cecum ( p < 0.05), and distinct feeding patterns exhibited a different microbial cluster. Simultaneously, the short-chain FA levels in cecum of FRP and RFP pigs were higher compared with HEP pigs ( p < 0.05). A total of 11 genera related to muscle lipid metabolism or meat quality, including Alistipes , Anaerovibrio , Acetitomaculun , etc., were identified under different feeding patterns ( p < 0.05). Spearman correlation analysis demonstrated that alterations of free FAs in SM were affected by the genera Prevotellaceae_NK3B31_group , Prevotellaceae UCG-003 and Christensenellaceae_R-7_group ( p < 0.05). Taken together, distinct feeding patterns affected meat quality of Tibetan pigs related to gut microbiota alterations.

Zhu, Yanbin↗
DOE OSTI2022

MetaText: Compositional Generalization in Deep Language Models

Compositional generalization, the ability to infer and reason using novel combinations of previously encountered entities and structures, is a trait of great utility across a variety of deep learning tasks; it implies a certain ability to reason using consistent and therefore interpretable rules, to understand the constituent parts of its input at multiple levels of granularity, to be robust to spurious differences between semantically equivalent inputs, and more. We seek to understand the degree to which existing natural language models achieve or fall short of compositional generalization across a variety of tasks, whether there are distinct types of compositional generalization in practice, and to identify avenues of intervention through which we can improve models’ compositional generalization ability. After exploring these questions using an array of models, tasks, and potential interventions we see that large, pretrained language models have encountered sufficient training data to account for a variety of fine-grained compositional behavior but still struggle to reason at the level of phrases or larger language structures. Non-intrusive, data-based interventions in the form of augmenting individual sequences with compressed versions of themselves or deriving new examples from induced grammars prove insufficient to encourage greater levels of compositional reasoning, indicating that future work might benefit most from focusing on changes to a model’s inductive bias at the architectural or loss level, or by integrating compositionality-boosting data interventions into the large-scale pretraining process itself.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2022

Printable Fiber Reinforced Cement Composites – Feasibility Study

Additive manufacturing is enabling the manufacturability of structures with previously unattainable complexity or functionality, and there is growing interest in additive manufacturing of “printed” concrete structures. The focus of this Phase 1 Technical Collaboration (TC) project was to evaluate feasibility of printing hybrid cement composite structures reinforced with textile carbon fibers (tCF). This project leverages other (non-IACMI) projects on cement formulation and printing process development, as well as on the production process for tCF. This project’s primary focus was to explore cement composite mix design with textile carbon fibers to be manufactured by MonteFibre (TC partner) and evaluate suitable fiber-matrix interface or sizing for cement composites working with Michelman (TC Partner). This project supports IACMI’s goal of reducing the cost and embodied energy of carbon fiber composites. Cost is one of the fundamental challenges to carbon fiber reinforced cement composites. Cement is an extremely inexpensive material (approximately $\$$0.05/lb). Adding 1 wt% of conventional carbon fiber to cement quadruples its cost. Therefore, the need to use low-cost carbon fiber and ensure that the additional cost of the carbon fiber has a greater cost benefit to the final product. This was the first preliminary evaluation to integrate tCF reinforcement in cement composites, and such potential tCF utilization should significantly reduce materials cost. Cement composite production is energy and emissions intensive, thus by strengthening it less material will be required. Hence, the embodied energy and production time of the resulting structures will be reduced. Additionally, integrating these new materials into additive processes can enable selective use of the material in high load or stress areas. It is noteworthy that past work in this field of fiber reinforced cement composites did not consider the optimization of fiber-matrix interface using suitable sizing. Carbon fiber reinforcement offers potential added benefits of thermal conductivity (which affects cure rate) and flow behavior that could provide opportunities for site specific utilization of carbon fiber on hybrid cement structures (e.g. use the fiber reinforcement on outer surfaces to enhance strength and modulus and then infiltrating the internal structures with conventional concrete). MonteFibre was the industry lead and planned on supplying the tCF for this project. However, during the short Phase-1 duration of this project, MonteFibre was unable to produce tCF for this project due to manufacturing plant being off-line throughout the course of the project. The project team decided to pursue an alternate option which involved demonstrating printable concrete with steel fibers by the ORNL lead, Dr. Brian Post. The University of Tennessee collaboration team focused on evaluating the suitable chemical sizing for carbon fibers working with Michelman and also developed methods for material characterization of cement-based composites to evaluate the material response for compression, shear, flexure, and tension. The two milestones for University of Tennessee, Knoxville were realized related to identification of one sizing suitable for carbon fiber reinforced cement composite and developing data associated with mechanical behavior of unreinforced (neat) and carbon fiber reinforced cement composites. ORNL could not complete the task of carbon fiber reinforced printed cement composites due to the reasons mentioned earlier, but was able to replace tCF with steel fibers to demonstrate the feasibility of printing with fiber reinforced cement composites. The Project team reviewed possible sizing chemistry available in collaboration with Michelman for use on carbon fiber reinforcement in cement composites and concrete applications. Our initial goal was to identify a sizing most promising for formulation with textile carbon fibers (tCF) to deliver excellent mechanical properties in composite material state. Since tCF was not available for this project as originally envisioned, the team continued this task to identify a suitable sizing for carbon fiber applications by applying such sizing to lower cost carbon fibers currently available commercially from Zoltek called Panex fibers. At a future time this can be optimized for textile carbon fibers from Montefibre. The bulk of previous work on carbon fiber reinforced cement has neglected the importance of fiber-matrix adhesion on mechanical properties of the cement composite and identifying this missing link was an important accomplishment for future research. Tensile behavior of fiber reinforced concrete is important to evaluate in order to realize the dream of concrete products that do not need reinforcing steel. Important sample preparation and testing procedures were addressed in this study and it was concluded that substantial improvements in tensile behavior, without compromising compressive strength, and improved ductility can result from the use of carbon fiber reinforcement.

36 MATERIALS SCIENCE↗
DOE OSTI2023

U-rich U-Mo Solidus, Liquidus, Enthalpy, and Thermal Diffusivity

Several material properties have been studied as a function of composition as part of the USHPRR effort to use U-10Mo for LEU monolithic fuel foils, with special attention to areas where simulation outcomes can be improved. The liquidus and solidus phase boundaries are important for engineering design of processing parameters and for predicting the degree of microsegregation within initial castings. The numerous phase diagram constructions available in the literature differ significantly in their solid/liquid boundary definitions, therefore, an experimental strategy was employed in order to resolve some of the discord. That strategy focuses not on proving any singular point, but instead to provide data with highly characterized uncertainty. The data presented here is the most consistent with the U-rich liquidus and solidus boundaries provided by H. Okamoto (2012), Berche et al. (2011), and Mardon et al. (1959). The recently suggested change in the peritectic reaction temperature (to 1302 K) suggested by Berchè and adopted by Okamoto is also supported, but with lower confidence. The enthalpy of fusion is a basic characteristic of the solidification reaction property for which is required for both research-oriented predictions of behavior and development of applied design strategies for casting engineering. It has been determined that an admixture model for the heat of fusion between U BCC and Mo BCC is a reasonable assumption within the composition region of interest, though limitations of the measurement accuracy impacts the ability to support any trends more detailed than this baseline behavior. A limited number of thermal diffusivity measurements were also performed into the gamma region to provide additional information for solidification modeling and simulations as well as a check on the component properties of thermal conductivity, density, and heat capacity.

36 MATERIALS SCIENCE↗
DOE OSTI2023

Experimental determinations of carbon and hydrogen isotope fractionations and methane clumped isotope compositions associated with ethane pyrolysis from 550 to 600°C

Methane clumped isotope compositions signify the relative natural abundances of rare, doubly substituted isotopic species of methane ( 13 CH 3 D and 12 CH 2 D 2 ) and have emerged as a new isotopic tool to trace the sources, sinks, and lifecycles of methane in the environment. Such measurements can identify equilibration (or reequilibration) temperatures if found to be in isotopic equilibrium or non-equilibrium processes (e.g., kinetically controlled reactions or mixing) if not in isotopic equilibrium. Naturally occurring thermogenic methane—formed by the thermally activated breakdown of larger organic molecules—has been found to have clumped isotope compositions consistent with equilibrium at reasonable gas formation temperatures in some settings and non-equilibrium processes occurring during either formation, migration, storage, or extraction in others. To explore the potential controls on the isotopic composition of thermogenic methane, we conducted isothermal time-series ethane pyrolysis experiments at 550 and 600 °C to measure methane and ethane 13 C/ 12 C and D/H fractionations and methane clumped isotope compositions (resolved 13 CH 3 D and 12 CH 2 D 2 ). We explore the effects of modifying the initial clumped isotope composition of ethane and the addition of water vapor to pyrolysis experiments. We observe that ethane and methane 13 C/ 12 C are controlled by kinetic isotope effects and Rayleigh distillation processes. In contrast, ethane and methane D/H and methane clumped isotope compositions appear to be controlled by a combination of these processes and hydrogen isotope exchange. The hydrogen isotope exchange processes lead to isotopic equilibrium as reaction completion is approached for both D/H (ethane/methane) and methane clumped isotope compositions. Here, we develop a chemical model based on a mass balance approach that accounts for inheritance vs. hydrogen-abstraction formation pathways for singly and doubly substituted isotopologues of ethane and methane that is compared to the experimental data. The model allows the determination of carbon and hydrogen kinetic isotope effects associated with ethane cracking and hydrogen abstraction reactions that, where applicable, we compare to prior theoretical constraints. From the comparison of the model to the experimental data, we infer that the kinetically controlled ethane and methane bulk isotope compositions and methane clumped isotope compositions are controlled by kinetic isotope effects (both primary and secondary) associated with both C–C bond and C–H bond cleavage reactions. Specifically, the methane clumped isotope compositions likely result from a combination of clumped isotope effects associated with ethane breakdown and/or assembly of methane isotopologues (expressed in terms of γ-factor parameters ≠ 1) and combinatorial effects that arise probabilistically. We discuss our experimental results in the context of recent pyrolysis experiments and observations of naturally occurring thermogenic methane. We consider a proposal consistent with observations from nature that the hydrogen isotope exchange reactions that promote equilibration of methane isotopic molecules at or near formation temperature may be facilitated by free radicals generated by pyrolysis reactions. In this framework, isotope exchange effectively ceases when pyrolysis effectively ceases locking in compositions that can be consistent with peak formation temperatures.

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

Evaluating High-Halide Waste Form Options for Salt-Based Nuclear Waste Simulants

In this study, waste forms were being explored for an electrochemical salt simulant, , referred to as ERV3, which is a high-LiCl/KCl salt containing simulated fission products (i.e., Sr, Cs, Nd) and Na to represent bond sodium from Experimental Breeder Reactor-II metallic fast reactor fuel. The goal was to find glassy systems that could be used to immobilize the salt in a single-step process. The envisionment of this process would be find a frit glass that could be added to the salt waste, heat treated, poured into waste canisters, and then stored for disposal. To perform this study, a literature review was conducted, the most promising seven systems were fabricated without the salt, and then mixed with salt simulant and heat treated under different processes. The criteria that were used to screen potential compositions included demonstrated alkali incorporation, could be melted at reasonably low temperature ( T ≤ 1000°C), and if the compositions had some demonstrated data for waste-form-related properties, that was a benefit. High marks were given for compositions that showed amorphous nature after slow cooling of samples containing ERV3.

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗
DOE OSTI2021

Transformative Materials for High-Efficiency Thermochemical Production of Solar Fuels (Final Report)

Metal oxide-based two-step solar thermochemical (STC) H 2 O and CO 2 -splitting cycles are a promising route to convert solar thermal energy into fuels. The metal oxide materials are reduced at high temperatures (Step 1), and then at low (but still elevated) temperatures, the reduced oxide is used to split H 2 O or CO 2 (Step 2). However, current applications of these cycles are limited by the efficiency of the metal oxide materials. A lower temperature for reduction is desirable, but that brings a concomitant reduction in the driving force for gas splitting. So, designing novel, high-efficiency materials is challenging. Here, we devised and performed a joint computational-experimental project, combined with materials design strategies and high-throughput approaches with the goal to quickly discover and demonstrate novel thermochemical materials with superior properties. Our approach involves an active feedback loop between experimental and computational research. We have use our previously developed materials design map, to efficiently screen results from high-throughput first- principles computation to predict new compositions and subsequently to experimentally study the properties of novel, predicted materials. Experimental measurements of the thermodynamic properties of selected materials serve as a critical benchmark, not only in evaluating the STCH properties of synthesized compounds, but also serving as a validation dataset for computational approaches. We experimentally explored a set of predicted ABO3 perovskites and focus on obtaining high quality thermodynamic properties for validation of computational prediction of enthalpy and entropy of reduction. These thermodynamic quantities play a major role in designing materials with reduced temperatures of reduction but sufficient gas-splitting rates. Finding good validation between experimental and computational data for enthalpies of reduction, we turn to promising ABO3 materials modified by A and B site substitutions, opening an enormous combinatorial space of materials. We use our high-throughput approach to “tune in” the desired solar thermochemical (STC) properties for STC. This vast composition space can only be reasonably explored using the high-throughput approaches, both computational and experimental, of this proposal. Several promising novel oxide materials have been predicted, synthesized, and verified using our materials design approach.

08 HYDROGEN↗
DOE OSTI2024

LENS: Learning Enabled Network Synthesis

RTRC and UMD have developed novel machine learning based methods under the ARPA-E DIFFERENTIATE program for rapid acceleration of hypothesis generation in complex architecture design spaces involving both discrete choices of component inclusion and interconnection and continuous parametric decisions. The project named Learning Enabled Network Synthesis (LENS) further demonstrated the developed methods on challenging electrical power converter design problems by identifying the most suitable circuit topologies and simultaneously selecting the most appropriate components to achieve optimized design of power converter with improved performances. We demonstrated that LENS could enable exploration of very large design space of circuit topologies and components by addressing the limitations of conventional design process in non-linear, high switching speed, multi-dimensional power converter design and optimization. The key innovation developed in LENS is the seamless integration of statistical learning and logical reasoning techniques and building on the individual strengths of these techniques for rapid hypothesis discovery. The main component of LENS comprises of: 1) Graph Reasoning Engine (GRE) to enforce composition rules that rapidly reject all discrete architectures that are composed incorrectly and generates an adaptive database of feasible designs which can be used by ML modules, 2) Graph Generative Learning module which is a deep neural network based generative model for graph architectures which can enable design space exploration beyond the dataset generated by the GRE, 3) Graph Reduced Order Model (ROM) for graph domains for accelerating computation of output metrics, and 4) Active learning and Rule Discovery module for sample efficient learning and extracting logical rules from the learned ML models which will be integrated in the GRE to enhance the filtering effectiveness. LENS approach can be applied to any design domains where designs can be represented as multi-attribute graphs. The LENS team integrated the various technical innovations listed above into an optimization pipeline and exercised the optimization pipeline on the converter design problem. The LENS project demonstrated that the developed AI/ML technologies can be used to generate novel converter circuits >45x faster than experts on chosen use-cases. This can enable faster design space exploration and identification of new designs which are not considered by experts due to the increasing design space complexity. This has significant potential impact on the public and energy needs of the country. It is currently estimated that 30% of all electrical powers generated passes through power converters. The future estimate is that 80% of all power generated would be passing through converters. LENS fills a critical gap in this space since by accelerating the design process the designers would be able to generate more efficient converters which can lead to significant energy savings for the country.

42 ENGINEERING↗
DOE OSTI2026

High-Speed Layup and Forming of Automotive Composite Components

This Project is focused on the design and manufacture of automotive components that meet functional and environmental requirements of an existing automotive application at a cost of ≤ $\$$11.00 per kilogram weight reduction. This project fosters the development of composite material technologies suitable for high volume automotive processes and run rates as well as industry workforce development with these technologies. Current automotive manufacturing involves utilizing steel or aluminum in sheet form which is rapidly stamped into components at rates up to 3600 per hour. The metallic sheets are available in many different thicknesses, strength levels, and manufacturing rates are reasonable independent of part size. While composite materials are available for use in automotive applications, the material cost, labor to manufacture and the processing of the waste far exceed the cost compared to metallic designs. Typical composite layer by layer layup procedures don’t meet the desired 60 second layup time that current automotive processes require and are also restricted by part size. Due to these factors, composites have not yet made advances into today’s high volume automotive applications. Industry partners DURA, BASF, Ford, and IACMI core innovation partner MSU collaborated to develop a manufacturing process technology that is capable of manufacturing composite blanks at high volume and independent of part size. IACMI core innovation partner Purdue provided FEA analysis and cost modelling. The objective of this project was to demonstrate a composite sheet layup and consolidation process that can be commercialized for high volume requirements, identify potential layup equipment suppliers, and develop a process of 60 second layup, forming, and trimming of a continuous fiber automotive component for the mainstream market.

42 ENGINEERING↗
DOE OSTI2021

Interfacial characteristics of P91 steel - Inconel 740H bimetallic structure fabricated using wire-arc additive manufacturing

Although bimetallic structures can offer unique solutions to engineering problems with varying functionality, joining two dissimilar alloys using additive manufacturing involves challenges such as cracking and segregation. In this work, bimetallic structures of Inconel 740H superalloy and P91 steel are processed using wire-arc additive manufacturing (WAAM) for the first time. P91 steel is directly deposited over 740H with a cooling time of 20 minutes in between the two depositions. During microstructure characterization, a large gradient zone with coarse grains spanning for ~2200 μm is observed. Moreover, the microhardness of the gradient zone is the least in comparison with the P91 and 740H deposits. No deleterious phase formation is found; however, intergranular cracks are observed along the gradient zone. In order to deduce the reason for cracking, non-equilibrium solidification simulations are performed for the mixed compositions of P91 and 740H in the gradient zone. Here, the reasons for crack formation in the gradient zone are the sudden change in the volumetric coefficient of thermal expansion due to the formation of MC carbides as well as the development of local strains from the thermal residual stresses. Therefore, altering the deposition sequence and introducing gradient layers with mixed composition between the P91 and 740H deposits will be beneficial in achieving defect-free builds. Through the integration of experiments and simulation, this work gains valuable insights into the processability of dissimilar alloy manufacturing using the WAAM technique.

20 FOSSIL-FUELED POWER PLANTS↗
DOE OSTI2023

A Review of the Mechanical Properties of Materials Used in Nb 3 Sn Magnets for Particle Accelerators

Superconducting magnets experience significant thermo-mechanical loads throughout their life cycle. These are introduced by the electro-magnetic forces during powering, but also by the prestress applied in many magnet designs. Further to this, the large thermal excursion that components of different materials experience can generate significant internal forces. The loads are also experienced by the superconducting coils, whose critical current can decrease as a consequence of the applied strain. It is then crucial to predict the overall mechanical behavior and conservatively design a magnet, avoiding failure of the mechanical components and of the superconducting coils. Finite Element Analysis (FEA) is generally used to perform these tasks, but its results rely heavily on the material properties and models used. This is in particular true for the coil composite, which is simplified to allow reasonable model sizes in full magnet models. Here we present the state-of-art knowledge of the mechanical properties of the materials mostly used in superconducting magnet construction. We review elastic and plastic properties at room and cryogenic temperature, thermal contraction, and summarize the state-of-art failure criteria for these materials. Finally, the paper summarizes the present understanding of the mechanical behavior and limits of Nb3Sn coils. For the first time, an orthotropic failure criteria is proposed.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
DOE OSTI2022

The CALPHAD approach for HEAs: Challenges and opportunities

Phases are key microstructural features that determine material properties. The most intuitive way of representing phase stability in a material is by its phase diagram. The vastness of the composition space is the most compelling reason for investigating high-entropy alloys (HEAs), but it also creates major challenges in controlling phases and microstructures of HEAs due to lack of experimental phase diagrams in highly concentrated multicomponent space. The CALPHAD (Calculation of Phase Diagram) approach, although it needs improvements, is the only viable way to calculate multicomponent phase diagrams. The two major property databases in CALPHAD, Gibbs energy and mobility, when coupled, can provide not only the equilibrium phase information, but also information about diffusion and kinetics. In this article, we will review the use of the CALPHAD approach in understanding phase stability and diffusion in high-entropy alloys. Finally, we will discuss the notable trend of coupling the high-throughput CALPHAD approach with machine learning in HEA design.

36 MATERIALS SCIENCE↗
DOE OSTI2025

Multimuons in cosmic-ray events as seen in ALICE at the LHC

ALICE is a large experiment at the CERN Large Hadron Collider. Located 52 meters underground, its detectors are suitable to measure muons produced by cosmic-ray interactions in the atmosphere. In this paper, the studies of the cosmic muons registered by ALICE during Run 2 (2015–2018) are described. The analysis is limited to multimuon events defined as events with more than four detected muons (N μ > 4) and in the zenith angle range 0° < θ < 50°. The results are compared with Monte Carlo simulations using three of the main hadronic interaction models describing the air shower development in the atmosphere: QGSJET-II-04, EPOS-LHC, and SIBYLL 2.3d. The interval of the primary cosmic-ray energy involved in the measured muon multiplicity distribution is about 4 × 10 15 < E prim < 6 × 10 16 eV. In this interval none of the three models is able to describe precisely the trend of the composition of cosmic rays as the energy increases. However, QGSJET-II-04 is found to be the only model capable of reproducing reasonably well the muon multiplicity distribution, assuming a heavy composition of the primary cosmic rays over the whole energy range, while SIBYLL 2.3d and EPOS-LHC underpredict the number of muons in a large interval of multiplicity by more than 20% and 30%, respectively. The rate of high muon multiplicity events (N μ > 100) obtained with QGSJET-II-04 and SIBYLL 2.3d is compatible with the data, while EPOS-LHC produces a significantly lower rate (55% of the measured rate). For both QGSJET-II-04 and SIBYLL 2.3d, the rate is close to the data when the composition is assumed to be dominated by heavy elements, an outcome compatible with the average energy E prim ∼ 10 17 eV of these events. This result places significant constraints on more exotic production mechanisms.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2024

Electrolyte and Cutoff Potential Effects on Cycle Life of Li4Ti5O12/LiNi0.9Mn0.1O2 Batteries for Behind-the-Meter Storage Applications

Behind-the-Meter Storage (BTMS) is a stationary battery energy storage system that is connected to the electrical distribution system on the customer's side of the utility's service meter. BTMS systems are used to store electrical energy from the grid as well as inconstant, renewable energy, such as local solar and wind generation. A successful BTMS system will allow the customer to pair their energy generation and storage to optimize electrical consumption from the grid, improving reliability and minimizing cost. For BTMS applications, batteries must be designed and optimized with different set of criteria from other leading segments of the Li-ion battery market, like transportation, due the system being stationary and proximal to the residential or commercial building it's benefitting. BTMS applications prioritize safety, cost (low/no-critical materials), reliability (20-year calendar life), and durability (10,000 cycle life), while having the ability to (minimally) compromise energy density and rate capability. Lithium titanate (Li4Ti5O12-, LTO) is a promising anode candidate for BTMS applications due to its high safety and capacity retention, while maintaining a reasonable 160 mAhg-1 reversable capacity and composition of relatively abundant materials. (1) Specifically, LTO has a high working voltage which helps to prevent Li dendrite formation, improving safety. Furthermore, LTO also has negligible lithiation-based volume change, leading to less mechanical pulverization, or loss of active material, upon cycling. For the cathode, materials with little or no Co are of high interest due to the high cost and low abundance of Co. LiMn2O4 (LMO) has been paired with LTO for BTMS applications in the past due to its safety, low cost (abundancy), and reasonably high operating voltage. (2-4) However, the low capacity of LMO limits energy density and specific energy. While not the highest priority for BTMS applications, increasing energy density will enable deployment in space constrained BTMS applications and decrease total cost. LiNi0.9Mn0.1O2 (LN-MO) is a recently developed material with promise due to its high operating voltage and relatively low price. (5) However, Ni-rich layered oxides, including LNMO, tend to struggle with capacity retention during high-voltage cycling due to mechanical pulverization, irreversible phase transitions, and unstable solid-electrolyte interphase. The study presented here focuses on building an understanding of how electrolyte solvent and varied cutoff potentials will impact the cycle life of LTO/LN-MO cells. Specifically, a comparison is provided between ethylene carbonate (EC), ethyl methyl carbonate (EMC), fluoroethylene carbonate (FEC), and Gen2 electrolyte solvents with 1M Lithium hexafluorophosphate (LiPF6) salt, cycling to two upper termination potentials, 2.6V and 2.7V. Electrochemical testing and diagnostics (e.g., differential capacity analysis, area specific impedance, constant voltage hold, and rate capability) and post-mortem characterization will be used to understand the aging behavior and failure mechanisms of the 8 cell combinations (four electrolytes and two voltage cutoffs). Cells with FEC electrolyte showed a lower initial capacity compared to cells with Gen2, EMC, and EC cycling at both voltages; however, the cells with FEC showed consistent trends in capacity retention with 2.6V and 2.7V termination potentials, while the cells with the other electrolytes showed much higher rates of capacity loss when cycling to the higher voltage. These results indicate that FEC may play a role in improving durability of high-voltage, Ni-rich electrode systems for use in high-cycle applications, such as BTMS.

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