Predicting biochar properties and pyrolysis life-cycle inventories with compositional modeling
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Agriculture↗
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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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Utilizing machine learning to predict tensile ductility and yield strength of CoNiV-based multi-principal elements alloys
This study explores the use of machine learning (ML) as a computational tool to accelerate the design of multi-principal element alloys (MPEAs) with improved tensile elongation. An ML model was trained using available experimental data from the literature along with theoretically derived features to predict yield strength (YS) and ductility. A subset of ML-predicted compositions—CoNiVFe, CoNiVTi, CoNiVTiFe, and CoCrNiVTi—was synthesized and evaluated through tensile testing. The ML model underpredicted YS by approximately 20–30 % and overpredicted ductility by 60–70 % for Ti-containing alloys. Microstructural analysis revealed that Ti segregation at interdendritic regions contributed to early fracture, leading to discrepancies in ductility predictions. Ti segregation at these regions likely drives the increased YS due to segregation strengthening. In contrast, the CoNiVFe alloy showed good agreement with both experimental YS and elongation, with prediction errors of ∼10.2 % and ∼20.7 %, respectively. Microstructural characterization revealed minimal segregation in this alloy, suggesting that the ML model can reliably predict the properties of alloys with little to no segregation. These findings highlight the capability of ML in predicting YS with good accuracy but underscore its limitations in capturing defect-driven failure mechanisms such as segregation-induced embrittlement.
36 MATERIALS SCIENCE↗
Advances in predictive chemistry enable a multi-scale rational design approach for biofuels with advantaged properties
Process of building predictive gas-phase chemical mechanisms faster and less computationally intensive using state of the art automated chemical tools and linking the predicted chemical reactivity to underlying chemical structure information.
09 BIOMASS FUELS↗
Effects of turbulence-radiation coupling in pressurized oxy-combustion
Concerns over climate change have led to numerous efforts in developing low-carbon energy technologies. Pressurized oxy-combustion (POC) is a promising candidate to reduce carbon emission in power generation. Due to the strong impact of pressure on thermal radiation, heat transfer in POC differs significantly from the situation of conventional atmospheric pressure combustion. Thus thermal radiation in POC needs to be investigated to aid new combustor development. The present computational work is a step in this direction, initiating a systematic analysis of thermal radiation and heat transfer in a pilot-scale POC combustor, which has been developed at Washington University in St. Louis (WUSTL). In a POC process, pulverized coal is burned under elevated pressure and O2-CO2 environment. While most previous computational works on flame radiation focused on atmospheric pressure condition, this work considered a pressurized flame. Specifically, a 15-bar POC combustor of power 50 kW is modeled employing the Ansys FLUENT commercial platform, using both Reynolds-averaged Navier-Stokes (RANS) modeling and large-eddy simulation (LES). A recently published and validated global radiation model is used to predict the radiative property of the flue gas. The discrete ordinates (DO) radiation model is chosen to solve the radiative transport equation. Incident radiation on the walls of the combustor is identified and investigated. It is revealed that for this pilot-scale, pressurized combustor, thermal radiation exhibits similar patterns in the RANS and LES models. However, the magnitudes of radiation are different in both models, presumably because interactions between radiation and turbulence are embedded in the LES model but not in the RANS model. This difference thereby underlines the major goal of this research: investigating the impact of such turbulence-to-radiation coupling on thermal radiation in a pilot-scale POC setting. Most previous studies on turbulence-to-radiation interactions focused on bench-scale, atmospheric pressure flames. This work is the first effort to extend this study to a pilot-scale, pressurized flame.
Li, Lei↗
Los Alamos National Laboratory R&D Intern
This report details the responsibilities, outcomes, and project details of a summer R&D internship at Los Alamos National Laboratory (LANL). LANL is a multidisciplinary laboratory that focuses on current cutting-edge research in many fields such as national security, engineering, materials science, computational modeling, and advanced manufacturing. The goal of the internship project was to work with lab engineers and resources to develop an energy absorbing structure for high-velocity impact applications. The successful development of this technology and methodology would not only positively impact future project funding but also contribute to the laboratory's commitment to solve national security challenges through simultaneous excellence. Such devices would also support efforts surrounding the research and development of energy absorbing structures and would provide new vital information backed by experimentation. Different computational and modeling methods were used to design these structures, in addition to qualitative background information provided by past literature. The resultant designs were successfully tested, and the test results were successfully quantified. From these results, new computational methods were developed through python programming and modeling to predict ideal materialistic properties for these structures given a sufficiently defined application.
36 MATERIALS SCIENCE↗
Software reliability models for fault-tolerant avionics computers and related topics
Software reliability research is briefly described. General research topics are reliability growth models, quality of software reliability prediction, the complete monotonicity property of reliability growth, conceptual modelling of software failure behavior, assurance of ultrahigh reliability, and analysis techniques for fault-tolerant systems.
Miller, Douglas R.↗
Cryogenic propellant prestart conditioning for NLS
An overview is presented of a candidate National Launch System (NLS) passive cryogenic propellant prestart conditioning system that offers a stable propellant thermal environment and minimum system complexity. A 2D, multinode model utilizing real fluid properties was developed. This model predicts flow recirculation due to thermal gradients by assuming vertical cold and warm opposing flow streams produced by density differential.
Gaynor, T. L.↗
Coupled land surface/hydrologic/atmospheric models
The topics covered include the following: prototype land cover characteristics data base for the conterminous United States; surface evapotranspiration effects on cumulus convection and implications for mesoscale models; the use of complex treatment of surface hydrology and thermodynamics within a mesoscale model and some related issues; initialization of soil-water content for regional-scale atmospheric prediction models; impact of surface properties on dryline and MCS evolution; a numerical simulation of heavy precipitation over the complex topography of California; representing mesoscale fluxes induced by landscape discontinuities in global climate models; emphasizing the role of subgrid-scale heterogeneity in surface-air interaction; and problems with modeling and measuring biosphere-atmosphere exchanges of energy, water, and carbon on large scales.
Pielke, Roger↗
Optomechanical Alignment of the Grating Wheel Mechanism for a Ground-based, Cryogenic, Near-Infrared Astronomy Instrument
We describe the population, optomechanical alignment, and alignment verification of near-infrared gratings on the grating wheel mechanism (GWM) for the Infrared Multi- Object Spectrometer (IRMOS). IRMOS is a cryogenic (80 K) facility instrument for the Mayall Telescope (3.8 m) at Kitt Peak National Observatory and a MEMS spectrometer concept demonstrator for NASA's James Webb Space Telescope. The IRMOS optics, bench, and mechanisms are predominantly made of Al 6061 -T651. The GWM consists of 13 planar diffraction gratings and one flat imaging mirror (58 x 57 mm), each mounted at a unique compound angle on a 31.8 cm diameter gear. The Al 6061 grating substrates are stress relieved for enhanced cryogenic performance and the optical surface is replicated from an off-the-shelf master. The imaging mirror is diamond turned and post-polished. The grating mechanism spans a projected diameter of approximately 48cm when fully assembled, utilizes several flexure designs throughout the system to accommodate thermal gradient situations, and is controlled using custom software with an off-the-shelf controller. Each optic is aligned in six degrees of freedom relative to the GWM coordinate system, which is defined relative to an optical alignment cube mounted at the center of the gear. The tip/tilt (Rx, Ry) orientation of a given grating is measured using the zero-order return from an autocollimating theodolite. Each optic's mount includes a one-piece shim located between the optic and the gear. The shim is machined to fine align each optic. We also describe alignment verification, where grating diffractive properties are compared to model predictions.
Gutkowski, Sharon M.↗
Optical Probe of the Superconducting Normal Mixed State in a Magnetic Penetration Thermometer
Using ultraviolet photon pulses, we have probed the internal behavior of a molybdenum-gold Magnetic Penetration Thermometer (MPT) that we designed for x-ray microcalorimetry. In this low-temperature detector, the diamagnetic response of a superconducting MoAu bilayer is used to sense temperature changes in response to absorbed photons. We have previously described an approximate model that explains the high responsivity of the detector to temperature changes as a consequence of a Meissner transition of the molybdenum-gold film in the magnetic field applied by the superconducting circuit used to bias the detector. We compare measurements of MPT heat capacity and thermal conductance, derived from UV photon pulse data, to our model predictions for the thermodynamic properties of the sensor and for the electron cooling obtained by quasiparticle recombination. Our data on electron cooling power is also relevant to the operation of other superconducting detectors, such as Microwave Kinetic Inductance Detectors.
superconductivity↗
Quantifying the Relative Impact of Model Microphysics Parameterizations and Scattering Models in Simulating Synthetic Radar and Microwave Radiometer Observations
Output from numerical weather models is often used to simulate observations from remote sensing instruments, for purposes ranging from data assimilation, synthetic retrievals of geophysical quantities, and optimization of observing systems. However, when hydrometeors are present, the level of detail provided by the weather model is generally insufficient to fully constrain the input to the radiative transfer model (RTM), and further assumptions must be made by the RTM user in order to produce synthetic observations. Using a hierarchy of models including cloud-resolving, double-moment, bin microphysical, and ice-habit predicting models, along with scattering properties from the OpenSSP, Atmospheric Radiative Transfer Simulator (ARTS) databases, as well as relatively simple geometries (e.g., cylindrical plates and columns), we demonstrate the spread in synthetic observation output and the extent to which it is reduced when microphysics is more strongly constrained by the model. As an intermediate step, an error budget for the RTM simulations was derived and from that we developed and will describe best practices for observation simulation (e.g., optimal number of hydrometeor size bins, truncation of the particle size distribution, angular resolution of scattering phase function). Some statistical comparisons with observations will also be presented.
Munchak, S. Joseph↗
Midwest Food Security & Agriculture II: Leveraging NASA Earth Observations to Analyze and Display Crop Phenology Data and Weather Conditions to Support Expansion of Small Grain Crops in the Midwest
Agriculture in the Midwest is dominated by monoculture systems that strip the soil of nutrients, decrease yields, and worsen water quality. Crop diversification and cultivating small grains is economically and ecologically advantageous, but limited in practice due to a lack of data about small-grain crop performance as well as monoculture-favoring insurance coverage, economic incentives, and sociocultural traditions. Practical Farmers of Iowa (PFI) strives to create more sustainable agriculture and supports Midwestern farmers in adding small grains into their crop rotations. The DEVELOP team partnered with the USDA Agricultural Research Service and the USA National Phenology Network to assist PFI in applying NASA Earth observation data to track small-grain crop performance. This project developed a Google Earth Engine user interface that enables PFI farmers to compare satellite-assessed vegetation production and climatic conditions of their farm with other PFI farmers. The team assessed the crop performance through Normalized Difference Vegetation Index (NDVI) values derived from Landsat 7 Enhanced Thematic Mapper Plus (ETM+) and Landsat 8 Operational Land Imager (OLI). Additionally, the team created a regression model to analyze the predictive properties of vegetation indices and crop yield, ultimately establishing a relationship with mean NDVI from the early growing season. PFI farmers can utilize these products to make informed decisions about their crop production as well as advocate for more expansive insurance policies to protect small-grain crops.
Sophie Barrowman↗
Size-Transferable Prediction of Excited State Properties for Molecular Assemblies with a Machine Learning Exciton Model
Computational modeling of the excited states of molecular aggregates faces significant computational challenges and size heterogeneity. Current machine learning (ML) models, typically trained on specific-sized aggregates, struggle with scalability. We found that the exciton model Hamiltonian of large aggregates can be decomposed into dimer pairs, allowing an ML model trained on dimers to reconstruct Hamiltonians for aggregates of any size. We also proposed a new method to address the phase-correction problem by introducing coupling terms’ approximations. Our model accurately predicted the excitation energies of the trimer and tetramer of perylene and tetracene and estimated S1 oscillator strengths of perylene aggregates. Leveraging our ML model, the optical gaps of nanosized perylene aggregates with up to 50 monomers are analyzed, qualitatively revealing the role of different couplings on their size dependency. Future work will explore transferability across different monomers to predict optical properties in heterogeneous assemblies.
37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
Differentiable modeling and optimization of non-aqueous Li-based battery electrolyte solutions using geometric deep learning
Electrolytes play a critical role in designing next-generation battery systems, by allowing efficient ion transfer, preventing charge transfer, and stabilizing electrode-electrolyte interfaces. In this work, we develop a differentiable geometric deep learning (GDL) model for chemical mixtures, DiffMix, which is applied in guiding robotic experimentation and optimization towards fast charging battery electrolytes. In particular, we extend mixture thermodynamic and transport laws by creating GDL-learnable physical coefficients. We evaluate our model with mixture thermodynamics and ion transport properties, where we show improved prediction accuracy and model robustness of Diff-Mix than its purely data-driven variants. Furthermore, with a robotic experimentation setup, Clio, we improve ionic conductivity of electrolytes by over 18.8% within 10 experimental steps, via differentiable optimization built on DiffMix gradients. By combining GDL, mixture physics laws, and robotic experimentation, DiffMix expands the predictive modeling methods for chemical mixtures and enables efficient optimization in large chemical spaces.
25 - ENERGY STORAGE↗
Uncertainty quantification for molecular property predictions with graph neural architecture search
Graph Neural Networks (GNNs) have emerged as a prominent class of data-driven methods for molecular property prediction. However, a key limitation of typical GNN models is their inability to quantify uncertainties in the predictions. This capability is crucial for ensuring the trustworthy use and deployment of models in downstream tasks. To that end, we introduce AutoGNNUQ, an automated uncertainty quantification (UQ) approach for molecular property prediction. AutoGNNUQ leverages architecture search to generate an ensemble of high-performing GNNs, enabling the estimation of predictive uncertainties. Our approach employs variance decomposition to separate data (aleatoric) and model (epistemic) uncertainties, providing valuable insights for reducing them. In our computational experiments, we demonstrate that AutoGNNUQ outperforms existing UQ methods in terms of both prediction accuracy and UQ performance on multiple benchmark datasets, and generalizes well to out-of-distribution datasets. Additionally, we utilize t-SNE visualization to explore correlations between molecular features and uncertainty, offering insight for dataset improvement. AutoGNNUQ has broad applicability in domains such as drug discovery and materials science, where accurate uncertainty quantification is crucial for decision-making.
37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
Inference of Rock Flow and Mechanical Properties from Injection-Induced Microseismic Events During Geologic CO 2 Storage
Monitoring microseismic activities during CO 2 injection into geologic formations is important for ensuring the safety of the storage operations. The resulting data provide insight into the response of the storage formation to CO 2 injection and can be used to infer the underlying rock flow and mechanical properties. In this paper, assimilation of microseismic data is performed for dynamic characterization of the storage formation by using a stochastic simulation model to forecast the microseismic response of a geologic formation during CO 2 injection. Two modeling approaches are adopted to predict the space-time distribution of the injection-induced microseismicity. The first model is based on pore pressure relaxation assumption, while the second model uses coupled flow and geomechanics simulation to establish the complex physical relation between the storage formation properties and the corresponding microseismic responses during CO 2 injection. The stochastic predictive models in each case are used in ensemble data assimilation frameworks to estimate rock properties from the observed microseismic data. Two data assimilation methods are considered: (i) a new ensemble-based stochastic point process filter (EnPPF) that can directly integrate discrete microseismic events, and (ii) a variant of ensemble smoother, known as the ensemble smoother with multiple data assimilation (ES-MDA), which requires continuous representation of microseismic events for assimilation. The two methods are successfully applied to a geologically realistic model of the Farnsworth Field in Texas, with complex geologic flow units and interacting fault systems.
42 ENGINEERING↗
Physics of the Inner Heliosphere 1-10 Rs: Plasma Diagnostics and Models
While the mechanisms responsible for the solar corona and the high-speed solar wind streams are still unknown, model computations offer means of predicting the properties of such mechanisms in light of the empirical constraints currently available. Modeling and data analysis efforts were aimed at understanding the plasma properties of the acceleration of the solar wind, its filamentary nature, and the conditions needed to account for a rapidly accelerating solar wind, reaching its terminal speed within 10 R(sub s). A sequence of models ranging from steady one-fluid descriptions of the solar wind to multi-fluid time-dependent models were developed. Plasma diagnostics evolved from the analysis of data acquired from Skylab to SOHO, and complemented by ground-based observations.
Habbal, Shadia R.↗
Thermophysical Property Measurements on Salt Mixture
Thermal conductivity, specific heat, viscosity, and density are critical properties for understanding thermal hydraulics of heat transfer fluids (HTFs) like molten salts. These properties are important for design and safe operation of any HTF loop. These characterizations are required to benchmark computational chemical models that are used to predict properties, but existent data are limited. These measurements are extremely difficult to make due to the operating temperature range and corrosive properties of the salt.Density measurements are needed to predict volumetric behavior. Thermal expansion mismatches can cause mechanical failure of structural materials and important components that are in contact with the molten salt. All of these properties are related to heat transfer in HTFs. The heat generated in a reactor must be high enough to derive power; however it should not be so hot that it cannot be safely removed via the HTF at an appropriate rate. It is important to understand the thermal hydraulics required to operate at the desired power generation level and to avoid situations in which thermal shifts can damage components,cause failure of structural materials, and/or lead to accidents. Understanding these thermal properties can aid in component and process design and can contribute to optimized operational and safety parameters.
22 GENERAL STUDIES OF NUCLEAR REACTORS↗