AI-Enhanced System Level Design and Optimization of Inertial Fusion Energy (IFE) Power Plants using the Integrated Process Model (IPM)
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Nuclear science and engineering↗
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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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Research on battery-operated electric road vehicles
Mathematical analysis of battery-operated electric vehicles is presented. Attention is focused on assessing the influence of the battery on the mechanical and dynamical characteristics of dc electric motors with series and parallel excitation, as well as on evaluating the influence of the excitation mode and speed control system on the performance of the battery. The superiority of series excitation over parallel excitation with respect to vehicle performance is demonstrated. It is also shown that pulsed control of the electric motor, as compared to potentiometric control, provides a more effective use of the battery and decreases the cost of recharging.
Varpetian, V. S.↗
A Prognostics Framework for Battery Health Monitoring Integrated with Thermal Modeling
Urban Air Mobility (UAM) promises to revolutionize transportation in major cities, offering passenger travel, cargo delivery, and emergency medical services through a network of electric vertical takeoff and landing (eVTOL) aircraft. However, the limited range of current eVTOLs, due to the low specific energy of lithium-ion batteries along with a possibility of thermal runaway conditions poses significant safety concerns, leading to potentially compromising operational safety. To address this critical challenge, researchers are actively evaluating the impact of flight and environmental conditions on onboard lithium-ion battery health. This involves carefully assessing the performance of battery packs under laboratory and operational conditions for developing models to estimate future health using prognostics framework. This study examines the effectiveness of evaluating battery degradation leading to catastrophic failures under varying operational conditions in laboratory. These are captured using physics based models of underlying phenomenons and integrated into the prognostics framework. A fully charged battery undergoes controlled discharge cycles at varying C-rates based on the simulated power draw profile, with current and voltage, temperature data recorded throughout the experiment. The observed data provides valuable insights into how different operating conditions and mission profiles affect battery performance. This information is crucial for developing strategies to optimize battery systems, enhance range, and ultimately ensure the safe and reliable operation of UAM vehicles.
Thermal Modeling↗
A Prognostics Framework for Battery Health Monitoring Integrated with Thermal Modeling
Urban Air Mobility (UAM) promises to revolutionize transportation in major cities, offering passenger travel, cargo delivery, and emergency medical services through a network of electric vertical takeoff and landing (eVTOL) aircraft. However, the limited range of current eVTOLs, due to the low specific energy of lithium-ion batteries along with a possibility of thermal runaway conditions poses significant safety concerns, leading to potentially compromising operational safety. To address this critical challenge, researchers are actively evaluating the impact of flight and environmental conditions on onboard lithium-ion battery health. This involves carefully assessing the performance of battery packs under laboratory and operational conditions for developing models to estimate future health using prognostics framework. This study examines the effectiveness of evaluating battery degradation leading to catastrophic failures under varying operational conditions in laboratory. These are captured using physics based models of underlying phenomenons and integrated into the prognostics framework. A fully charged battery undergoes controlled discharge cycles at varying C-rates based on the simulated power draw profile, with current and voltage, temperature data recorded throughout the experiment. The observed data provides valuable insights into how different operating conditions and mission profiles affect battery performance. This information is crucial for developing strategies to optimize battery systems, enhance range, and ultimately ensure the safe and reliable operation of UAM vehicles.
Thermal Modeling↗
CFD-Assisted Nodal Modeling of Sloshing in a Cryogenic Propellant Tank
During autogenous pressurization, tank sloshing causes a significant increase in pressurant consumption to maintain constant ullage pressure during draining of the tank. This increase in pressurant consumption is caused by a significant increase in condensation at the liquid-vapor interface. Sloshing strongly affects the liquid side heat transfer coefficient and thereby the condensation rate. Traditionally, sloshing is modeled by CFD code using the VOF (Volume of Fluid) method to track the liquid vapor interface during sloshing. CFD calculations require a very fine grid to accurately compute the heat and mass transfer at the interface. Therefore, computations are time consuming and prohibit performing many parametric studies often needed during the design of a new system. This paper describes an alternative approach by developing a CFD-assisted nodal model to predict system parameters more economically with reasonable accuracy. In this approach, a multi-node model of tank pressurization was developed using GFSSP. A multi-node model was needed to account for stratification. The model computes heat and mass transfer at the interface to calculate the condensation rate. The liquid side heat transfer is computed using the parameters of sloshing dynamics such as frequency, wave amplitude, and interface area. The parameters of sloshing dynamics are computed by the CFD code LOCI-Stream. The model predictions were compared with test data for several cases.
Cryogenic Tank Sloshing↗
Electromagnetic Melt Processing: A Pathway to New Additive Manufacturing Technologies for Functional High-Performance Thermoplastics
In this study, we apply the electromagnetic (EM) melt processing of thermoplastics on an innovative EM field-driven powder bed fusion additive manufacturing (AM) concept for high-performance functional parts: Selective Microwave Melting/Sintering (SMM/SMS). This technique leverages the EM susceptibility of carbon nanotube-coated polymer micro-pellets to achieve rapid, localized heating and powder fusion. Thus, selective microwave melting (SMM) was used to fabricate multilayer specimens made of recycled polyphenylene sulfide (rPPS) and carbon nanotubes (CNTs). The resulting SMM specimens exhibited very good interlayer integrity, localized fusion at pellet boundaries, and tolerable residual porosity, indicating effective fusion and acceptable consolidation. CNT-rich interphases were retained after irradiation, generating anisotropic electrically active network pathways and enabling conductivity enhancement at low filler content. At only 1.0 wt% CNT, the specimens exhibited electrical conductivity approximately three orders of magnitude higher than neat rPPS. Dynamic mechanical analysis showed improved viscoelastic response relative to neat rPPS, while tensile testing confirmed that the SMM-processed specimens retained practical mechanical integrity despite localized voids. These results demonstrate that SMM can effectively consolidate EM susceptible thermoplastic powder beds while preserving their segregated conductive networks. This may become a scalable route for producing multifunctional thermoplastic parts with low filler loadings, tunable anisotropy, and structured materials and parts. Overall, the findings suggest that EM field-based AM can help overcome key limitations of conventional thermoplastic processing by enabling scalable, energy-efficient fabrication of nanostructured composites and expanding AM to a broader range of resins, including high-performance thermoplastics with customized functional properties.
Powder bed fusion↗
2,5‐Dimercapto‐1,3,4‐Thiadiazole (DMCT)‐Based Polymers for Rechargeable Metal–Sulfur Batteries
Organosulfur materials are a sustainable alternative to the present-day layered oxide cathodes in lithium-based batteries. One such organosulfur material that was intensely explored from the 1990s to early 2010s is 2,5-dimercapto-1,3,4-thiadiazole (DMCT). However, research interest declined as the electrode reactions with DMCT were assumed to be too sluggish to be practical. Armed with the advances in metal–sulfur batteries, we revisit DMCT-based materials in the form of poly[tetrathio-2,5-(1,3,4-thiadiazole)], referred to as pDMCT-S. With an appropriate choice of electrode design and electrolyte, pDMCT-S cathode paired with a Li-metal anode shows a capacity of 715 mA h g −1 and a Coulombic efficiency of 97.7% at a C/10 rate, thus quelling the concerns of sluggish reactions. Surprisingly, pDMCT-S shows significantly improved long-term cyclability compared to a sulfur cathode. Investigations into the origin of the stability reveals that the discharge product Li-DMCT in its mesomeric form can strongly bind to polysulfides, preventing their dissolution into the electrolyte and shuttling. This unique mechanism solves a critical problem faced by sulfur cathodes. Encouragingly, this mechanism results in a stable performance of pDMCT-S with Na-metal cells as well. In conclusion, this study opens the potential for exploring other organic materials that have inherent polysulfide sequestering capabilities, enabling long-life metal–sulfur batteries.
2,5-dimercapto-1,3,4-thiadiazole↗
Analytical Model for Steady Flow through a Finite Channel with One Porous Wall with Arbitrary Variable Suction or Injection
This paper presents an exact solution of two-dimensional laminar flow through a finite length channel with one porous wall. It improves upon previous solutions by (1) satisfying the no-slip boundary condition at the channel dead end, (2) adding a turbulent term to the porous wall boundary condition, (3) allowing for arbitrary variable suction or injection across the porous wall, and (4) model validation against new cryogenic liquid hydrogen and oxygen experimental data. Of particular interest in the current work is the modeling of cryogenic propellant flow through a porous liquid acquisition device (LAD) screen and channel inside a propellant tank. First, a detailed review of the literature is presented for previously attempted solutions to channel flow with one porous wall. Next, the governing equations, boundary conditions, and model assumptions are used to derive the analytical flow solution and present general model results for pressure and velocity fields within the channel. Then, the model solution is compared with horizontal LAD channel flow data in liquid oxygen as well as vertical LAD channel flow data in an inverted outflow configuration in liquid hydrogen. Model results are used to update the static cryogenic bubble point pressure model with a dynamic bubble point term which factors in enhanced convection and cooling at the screen during propellant outflow. Convective heat transfer at the LAD screen during outflow is also quantified by comparing model and data. The new analytical flow solution with the dynamic bubble point model is shown to compare well with available cryogenic experimental data
Navier Stokes Equations↗
Accelerating SNP Development with Radiation Source Approximations for Far-Field Environment Modeling
The successful development and deployment of space nuclear power and propulsion technology requires a high-fidelity, efficient means of modeling ex-core radiation fields. Monte Carlo particle transport codes provide enable the highest level of fidelity in radiation field modeling but are inefficient for modeling ex-core radiation without modification. This paper details the development of an approach to ex-core radiation field modeling that maintains the fidelity of a Monte Carlo transport approach without sacrificing computational efficiency. This approach is based on recent improvements to an existing Monte Carlo transport acceleration technique known as surface source banking. As the transport of many particles is required to ensure adequate uncertainty in far field transport, analytical reconstruction techniques are employed to represent surface source banks as sampleable series of distributions in particle phase from which an arbitrarily large number of particles can be generated and simulated. All steps of this approach are incorporated into an AMA model to test both the underlying mathematics of the analytical source reconstruction process, as well as the relevance of the technique as a whole towards SNP applications. These tests demonstrated that this approach is capable of accelerating far-field radiation modeling in SNP-relevant scenarios by factors of at least nine over purely eigenvalue-based scenarios without significantly sacrificing simulation accuracy.
Surface Source Banking↗
An Investigation of State-Space Model Fidelity for SSME Data
In previous studies, a variety of unsupervised anomaly detection techniques for anomaly detection were applied to SSME (Space Shuttle Main Engine) data. The observed results indicated that the identification of certain anomalies were specific to the algorithmic method under consideration. This is the reason why one of the follow-on goals of these previous investigations was to build an architecture to support the best capabilities of all algorithms. We appeal to that goal here by investigating a cascade, serial architecture for the best performing and most suitable candidates from previous studies. As a precursor to a formal ROC (Receiver Operating Characteristic) curve analysis for validation of resulting anomaly detection algorithms, our primary focus here is to investigate the model fidelity as measured by variants of the AIC (Akaike Information Criterion) for state-space based models. We show that placing constraints on a state-space model during or after the training of the model introduces a modest level of suboptimality. Furthermore, we compare the fidelity of all candidate models including those embodying the cascade, serial architecture. We make recommendations on the most suitable candidates for application to subsequent anomaly detection studies as measured by AIC-based criteria.
Space Shuttles↗
Numerical Modeling of the Transient Chilldown Process of a Cryogenic Propellant Transfer Line
Before cryogenic fuel depots can be fully realized, efficient methods with which to chill down the spacecraft transfer line and receiver tank are required. This paper presents numerical modeling of the chilldown of a liquid hydrogen tank-to-tank propellant transfer line using the Generalized Fluid System Simulation Program (GFSSP). To compare with data from recently concluded turbulent LH2 chill down experiments, seven different cases were run across a range of inlet liquid temperatures and mass flow rates. Both trickle and pulse chill down methods were simulated. The GFSSP model qualitatively matches external skin mounted temperature readings, but large differences are shown between measured and predicted internal stream temperatures. Discrepancies are attributed to the simplified model correlation used to compute two-phase flow boiling heat transfer. Flow visualization from testing shows that the initial bottoming out of skin mounted sensors corresponds to annular flow, but that considerable time is required for the stream sensor to achieve steady state as the system moves through annular, churn, and bubbly flow. The GFSSP model does adequately well in tracking trends in the data but further work is needed to refine the two-phase flow modeling to better match observed test data.
Temperature Sensors↗
Strong Kitaev Interaction in BaCo 2 (AsO 4 ) 2
The inelastic neutron scattering results and their analysis unequivocally point to a dominant Kitaev interaction in the honeycomb-lattice cobaltate BaCo 2 (AsO 4 ) 2 . Our anisotropic-exchange model closely describes all available neutron scattering data in the material’s field-polarized phase. Furthermore, the density-matrix renormalization group results for our model are in close accord with the unusual double-zigzag magnetic order and the low in-plane saturation field of BaCo 2 (AsO 4 ) 2 .
75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
A generative machine learning model for designing metal hydrides applied to hydrogen storage
Developing new metal hydrides is a critical step toward efficient hydrogen storage in carbon-neutral energy systems. However, existing materials databases, such as the Materials Project, contain a limited number of well-characterized hydrides, which constrains the discovery of optimal candidates. This work presents a framework that integrates causal discovery with a lightweight generative machine learning model to generate novel metal hydride candidates that may not exist in current databases. Using a dataset of 450 samples (270 training, 90 validation, and 90 testing), the model generates 1000 candidates. After ranking and filtering, six previously unreported chemical formulas and crystal structures are identified, four of which are validated by density functional theory simulations and show strong potential for future experimental investigation. Overall, the proposed framework provides a scalable and time-efficient approach for expanding hydrogen storage datasets and accelerating materials discovery.
generative model↗
Application of Ibuprofen Sodium Dihydrate for Thermochemical Energy Storage
Thermochemical energy storage (TCES) offers a transformative approach to address grid instability by harnessing reversible chemical reactions for efficient heat storage and release. Here, we introduce pharmaceutical organic salt hydrates as a class of materials with exceptional performance for low-grade waste heat recovery. We demonstrate ibuprofen sodium dihydrate (ISD) as an example organic hydrate exhibiting a dehydration temperature range of 60-110 °C and a remarkable dehydration enthalpy of up to 59.5 kJ/mol of water, ideally suited for capturing industrial and residential waste heat. Using rigorous multimodal characterization, including thermogravimetric analysis, differential scanning calorimetry, in-situ FTIR, in-situ PXRD, and NMR, we demonstrate ISD's superior thermal, chemical, and structural stability over 150 hydration-dehydration cycles, achieving an unprecedented cycling efficiency of ~99.9%. Compared to conventional inorganic salt hydrates like strontium chloride hexahydrate and calcium oxalate monohydrate, ISD showcases enhanced durability without deliquescence or pulverization, even under high-humidity conditions. In-situ analyses confirm the transition from ISD to ibuprofen sodium anhydrous (ISA) proceeds with structural reorganization, thereby combining the dehydration mechanism with phase transitions, resulting in higher energy storage capacity. Microstructural analyses reveal that repeated water intercalation and structural transitions aid in creating significant porosity that enhances water transport kinetics, further improving the hydration/dehydration performance. By combining the phase change and chemical dehydration mechanisms, ISD paves the way for designing a new class of organic salt hydrates, offering tunable properties to meet diverse thermal energy storage demands and supporting sustainable grid resilience.
Thangaraj, Kavin C.↗
Thermoplastic Matrix Composite Design for Cryotanks Using Multiscale Modeling and Bayesian Optimization
Designing lightweight, robust cryogenic storage tanks is critical for future launch vehicles, in-space propellant storage, and hydrogen powered aircraft. This work presents a multiscale modeling and Bayesian optimization framework for the design of thermoplastic matrix composite cryotanks. Molecular dynamics simulations are first used to determine temperature-dependent constituent properties for candidate thermoplastic matrices, which are homogenized to the lamina scale using NASA’s Multiscale Analysis Tool (NASMAT). These lamina properties, in combination with laminate family generation rules, are evaluated in HyperX structural optimization software to identify stacking sequences that meet all cryogenic load requirements. A Bayesian optimization framework is applied, with HyperX in the loop (via the HyperX API) to efficiently search across material and laminate design variables, yielding an optimized cryotank configuration with significant reductions in design cycle time compared to exhaustive search approaches.
thermoplastics↗
Development, Characterization, and Validation of Elevated-Temperature Constitutive Models: Deformation and Damage
This report provides a brief review of experimentally observed hereditary and nonhereditary material behavior along with background information on standard as well as advanced internal state variable constitutive modeling at elevated temperature. A description of exploratory, characterization, and validation testing is presented along with a detailed outline of what constitutes “sufficient” data content (i.e., quality and quantity) for developing or enhancing, characterizing, and validating a particular sophisticated nonlinear time- and history-dependent (hereditary) class of constitutive models known as GVIPS (generalized viscoplasticity with potential structure). The tests described are necessary to reveal a material’s behavior in the reversible (or viscoelastic) and irreversible (or viscoplastic) regimes, for the identification of both deformation and damage model parameters. Results presented are primarily for metallic materials. In all cases, both uniaxial and multiaxial tests are described, and the linkage between the specific tests and the parameters within the model that can be characterized from the results of these tests are also discussed. Discussion is also provided relative to the role information management must play relative to material data collection, analysis, maintenance, and dissemination. The need for such an information system is particularly important as both analyst and designer move toward utilizations of sophisticated, nonlinear time- and history dependent (hereditary) constitutive models. Lastly, the concept of state space and its utility in understanding and establishing constitutive models is addressed throughout. The intent behind this document is to help both the modeler and experimentalist understand each other’s specific points of view and provide guidance for both model development and characterization. Emphasis has been placed on providing the mechanician with information regarding how tests are performed and what issues to be aware of when interpreting results. It is hoped that experimentalists will take away a new perspective on the types of information that modelers and analysts are looking for from them.
Constitutive Modeling↗
Audible Noise Modeling of Hydrogen Release Sonic Hazards in Rail Maintenance Facilities
This study implemented validated literature models to predict audible noise due to pressurized gaseous hydrogen releases through a thermally-activated pressure relief device (TPRD) and attached vent stack. A literature survey discovered limited hydrogen-specific noise prediction models validated by experiments. However, empirical noise prediction models for air flowing through pipes and valves were identified. These empirical models were used to predict noise levels and compared against hydrogen noise data reported in two studies: one experimental study of noise from hydrogen leaking through a pipe and another which modeled hydrogen flowing through a solenoid valve during a fuel cell vehicle refueling. The valve flow model was then applied to predict noise for hydrogen releases through a TPRD. Results show that hydrogen releases through a TPRD can produce harmful noise levels varying from 134 to 150 dB. However, further model validation and additional experimental data are needed to improve prediction confidence and accuracy.
08 HYDROGEN↗
Complex-Concentrated Anion Doping Enables Ultra-Stable Lattice Oxygen and Structural Integrity in Lithium-Rich Layered Oxide Cathodes
Lithium- and manganese-rich layered oxides (LMR) stand out as next-generation lithium-ion cathode chemistries, which harness both transition-metal and lattice-oxygen redox processes to deliver exceptional capacity and energy density. However, their full potential is hindered by intrinsic oxygen instability and structural degradation, resulting in pronounced voltage fade and capacity decay. Here, we present a complex-concentrated anion-doping paradigm in which multiple anions, F, Br, and S, are incorporated into the oxygen sublattice to enhance oxygen-redox and structural stability. X-ray absorption spectroscopy and aberration-corrected scanning transmission electron microscopy confirm ultra-stable local oxygen coordination environments during long-term cycling, with detrimental phase transformations and oxygen-loss-induced cavitation dramatically inhibited. Notably, we show that the characteristic LiTM6 transition metal (TM) honeycomb ordering is preserved even after electrochemical cycling. Concurrently, this strategy yields an unprecedented volume change of only 0.63% upon charging to 4.8 V vs. Li+/Li, achieving the first zero-strain LMR cathode. The resulting LMR cathode delivers ultralow voltage fade (1 mV per cycle during the first 100 cycles and becomes negligible in subsequent cycles) and outstanding energy retention (93% after 200 cycles) in a pouch cell configuration. Our complex-concentrated anion-doping concept establishes a broadly applicable strategy for resolving chemo-mechanical failure mechanisms in ceramic intercalation electrodes for next-generation energy storage.
Li-ion batteries↗