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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 235 records · Page 13
All sourcesNASA NTRSDOE OSTI
DOE OSTI2025

Multiscale and Machine Learning Modeling for Process-informed Microstructure Prediction in Additively Manufactured Materials using MALAMUTE

The Advanced Materials and Manufacturing Technologies (AMMT) program under the Department of Energy Office of Nuclear Energy aims to develop and qualify additively manufactured materials for nuclear applications. One key challenge to this is the microstructural variability observed in the additively manufactured products and their impact on the properties and performance of the material in extreme environments. AMMT is using a combination of high-throughput experimental and modeling techniques to accelerate qualification. Conventionally, in-situ and ex-situ characterizations and testing are performed to correlate different aspects of the additive manufacturing process to the final product and its performance. However, adopting a trial-and-error approach to experimentally evaluate the vast range of process parameters required to capture microstructural variability is cost-prohibitive. Modeling and simulation provide a comparatively inexpensive way to understand and correlate the microstructural evolution to the processing conditions. The modeling and simulation work-packages within the AMMT program aims to use physics-based and machine learning models to develop a digital twin for additive manufacturing that can correlate the process conditions to the final product and establish a process-structure-property-performance (PSPP) correlation. The melting and subsequent solidification that occurs during the additive process is a complex phenomenon that requires multiscale multiphysics analysis. This work package focuses on understanding the role of process variabilities on the unique microstructural characteristics of additively manufactured materials. Microstructural features at the subgrain level, such as compositional micro-heterogeneity and dislocation cells, are of particular interest here since they can influence the creep properties and radiation performance. Idaho National Laboratory’s Multiphysics Object-Oriented Simulation Environment (MOOSE), specifically the MOOSE Application Library for Advanced Manufacturing UTilitiEs (MALAMUTE) software, provides an ideal platform for developing the multiphysics multiscale model to explore the intricacies of the microstructural evolution during the AM processes within a single framework. Furthermore, given that such full-fidelity simulations can be computationally intensive, reduced order models are necessary to explore the PSPP space for additively manufactured materials in an efficient, reliable, and cost-effective way. This work focuses on capturing the microstructural variabilities at the subgrain level that are often missing in the part-scale models. In fiscal year 2025, we significantly advanced upon our work in the last fiscal year, in terms of the predictive capabilities of the physics-based and ML models, by adding the capabilities to capture subgrain-level micro-segregation during solidification using phase-field model and to predict the time-dependent dynamics of the AM process through the MOGPAR model. The alloy solidification model in MOOSE incorporates the thermodynamic properties and free energy relevant to 316 stainless steel. The model demonstrates the Cr and Ni segregation that occurs during solidification, including that the rate of solidification. The microstructural evolution model is connected to the process conditions via the surrogate model developed in this work. This enables predictions of the final microstructure in conjunctions with the manufacturing process. This work supports AMMT's rapid qualification goals by laying the foundation for an efficient and cost-effective model establishing the PSPP correlation for AM. The generated microstructures and predicted micro-segregation can be used by other work packages under AMMT to evaluate the properties and environmental response of the material at the mesoscale. Thus, this work helps to identify the key microstructural features at the subgrain level that are significant in property and performance predictions of additively manufactured components. This work will also provide inputs to the large-scale process variability models to reevaluate and validate assumptions and simplifications made in the part-scale models. Furthermore, through active learning this work can help identify the data need from both modeling and experimental sides for development of a robust digital twin for additive manufacturing and accelerate the AMMT's qualification efforts.

36 - MATERIALS SCIENCE↗
NASA NTRS2012

Technologies Enabling Exploration of Skylights, Lava Tubes and Caves

Robotic exploration of skylights and caves can seek out life, investigate geology and origins, and open the subsurface of other worlds to humankind. However, exploration of these features is a daunting venture. Planetary voids present perilous terrain that requires innovative technologies for access, exploration, and modeling. This research developed technologies for venturing underground and conceived mission architectures for robotic expeditions that explore skylights, lava tubes and caves. The investigation identified effective designs for mobile robot architecture to explore sub-planetary features. Results provide insight into mission architectures, skylight reconnaissance and modeling, robot configuration and operations, and subsurface sensing and modeling. These are developed as key enablers for robotic missions to explore planetary caves. These results are compiled to generate "Spelunker", a prototype mission concept to explore a lunar skylight and cave. The Spelunker mission specifies safe landing on the rim of a skylight, tethered descent of a power and communications hub, and autonomous cave exploration by hybrid driving/hopping robots. A technology roadmap was generated identifying the maturation path for enabling technologies for this and similar missions.

Robot↗
DOE OSTI2021

Measuring Nd(III) Solution Concentration in the Presence of Interfering Er(III) and Cu(II) Ions: A Partial Least Squares Analysis of Ultraviolet–Visible Spectra

Optical spectroscopy is a powerful characterization tool with applications ranging from fundamental studies to real-time process monitoring. However, it can be difficult to apply to complex samples that contain interfering analytes which are common in processing streams. Multivariate (chemometric) analysis has been examined for providing selectivity and accuracy to the analysis of optical spectra and expanding its potential applications. Here we will discuss chemometric modeling with an in-depth comparison to more simplistic analysis approaches and outline how chemometric modeling works while exploring the limits on modeling accuracy. Understanding the limitations of the chemometric model can provide better analytical assessment regarding the accuracy and precision of the analytical result. This will be explored in the context of UV–Vis absorbance of neodymium (Nd 3+ ) in the presence of interferents, erbium (Er 3+ ) and copper (Cu 2+ ) under conditions simulating the liquid–liquid extraction approach used to recycle plutonium (Pu) and uranium (U) in used nuclear fuel worldwide. Finally, the selected chemometric model, partial least squares regression, accurately quantifies Nd 3+ with a low percentage error in the presence of interfering analytes and even under conditions that the training set does not describe.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
NASA NTRS1999

Exploration of Uncertainty in Glacier Modelling

There are procedures and methods for verification of coding algebra and for validations of models and calculations that are in use in the aerospace computational fluid dynamics (CFD) community. These methods would be efficacious if used by the glacier dynamics modelling community. This paper is a presentation of some of those methods, and how they might be applied to uncertainty management supporting code verification and model validation for glacier dynamics. The similarities and differences between their use in CFD analysis and the proposed application of these methods to glacier modelling are discussed. After establishing sources of uncertainty and methods for code verification, the paper looks at a representative sampling of verification and validation efforts that are underway in the glacier modelling community, and establishes a context for these within overall solution quality assessment. Finally, an information architecture and interactive interface is introduced and advocated. This Integrated Cryospheric Exploration (ICE) Environment is proposed for exploring and managing sources of uncertainty in glacier modelling codes and methods, and for supporting scientific numerical exploration and verification. The details and functionality of this Environment are described based on modifications of a system already developed for CFD modelling and analysis.

Thompson, David E.↗
DOE OSTI2025

Vitamin E Acetate Causes Softening of Pulmonary Surfactant Membrane Models

The popularity of electronic cigarettes and vaping products has launched the outbreak of a condition affecting the respiratory system of users, known as electronic-cigarette/vaping-associated lung injury (EVALI). The build-up of vitamin E acetate (VEA), a diluent of some illicit vaping oils, in the bronchoalveolar lavage of patients with EVALI provided circumstantial evidence as a target for investigation. In this work, we provide a fundamental characterization of the interaction of VEA with lung cells and pulmonary surfactant (PS) models to explore the mechanisms by which vaping-related lung injuries may be present. We first confirm the localization and uptake of VEA in pulmonary epithelial cells. Further, as PS is vitally responsible for the biophysical functions of the lungs, we explore the effect of added VEA on three increasingly complex models of PS: dipalmitoylphosphatidylcholine (DPPC), a lipid-only synthetic PS, and the biologically derived extract Curosurf. Using high-resolution techniques of small-angle X-ray scattering, small-angle neutron scattering, neutron spin–echo spectroscopy, and neutron reflectometry, we compare the molecular-scale behaviors of these membranes to the bulk viscoelastic properties of surfactant monolayer films as studied by Langmuir monolayer techniques. While VEA does not obviously alter the structure or organization of PS membranes, a consistent softening of membrane systems—regardless of compositional complexity—provides a biophysical explanation for the respiratory distress associated with EVALI and yields a new perspective on the behavior of the PS system.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2024

Impacts of Topography‐Driven Water Redistribution on Terrestrial Water Storage Change in California Through Ecosystem Responses

Abstract Lateral subsurface flow plays an essential role in sustaining the terrestrial ecosystem, but it is not explicitly represented in most Earth System Models. In this study, we implemented an explicit lateral saturated flow model into the E3SM land model (ELM). The model explicitly describes lateral flow in the saturated zone by representing, for each model grid, an idealized hillslope consisting of five hydrologically connected soil columns. We conducted three model experiments driven by 0.125° atmospheric forcing data during 1980–2015 over California using models of the default ELM, a modified version of ELM to enhance infiltration, and the model with the lateral saturated flow model. The simulated runoff, evapotranspiration, and terrestrial water storage anomaly (TWSA) from the three simulations were evaluated against available observations, and the model explicitly representing lateral flow performs best. The new model produces greater gridcell‐averaged evapotranspiration especially over the mountainous regions with moderate relief and seasonally dry climates. Most importantly, it improves the modeled seasonal variations, interannual variabilities, and the recent decadal decline of TWSA. Many of these improvements can be attributed to the enhanced ecosystem resilience to droughts as demonstrated by transpiration increases caused by lateral flow. Model sensitivity experiments suggest that subsurface runoff is most sensitive to the ratio between horizontal and vertical saturated hydraulic conductivity, followed by hillslope planforms (convergent, divergent, and uniform), number of columns, and lower boundary conditions. Future work should effectively characterize hillslopes in global models and explore the long‐term influences of lateral water movement on modeled biogeochemical cycle.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2024

A Multidisciplinary Approach to Integrated Energy Systems: Advanced Nuclear Plants with Thermal Storage for Dynamic and Flexible Operation in Diverse Markets

Energy supply, distribution, and demand are continuously evolving in accordance with the integration of new power generation sources. In the United States, there is a progressive shift toward incorporating fluctuating energy sources such as wind and solar into the energy mix, alongside the distributed generation. Simultaneously, we are seeing a revolution in consumer technologies such as electric vehicles, energy devices for both residential and commercial use, and industrial processes—all contributing to a fundamental change in energy consumption dynamics. These scenarios have led to huge demand for enhanced flexibility from nuclear power plants (NPPs), requiring them to operate with unprecedented adaptability. Furthermore, advanced NPPs (A-NPPs) could potentially apply to scenarios in which power generation flexibility is prioritized over its current and conventional baseload generation capacity for traditional demand patterns. The present work covers several configurations for coupling nuclear energy production with thermal energy storage (TES). Previous work evaluated systems entailing a simple nuclear-TES configuration that used steam as a heat source for the TES of three different advanced reactors (ARs), but its heat diversion ratio (HDR) for charging was limited. The present work proposes a new configuration that features full decoupling of the nuclear reactor and the power cycle, using reactor coolant (i.e., gas) as a heat source for the TES and offering a theoretically unlimited HDR. This configuration is, however, more complex, as both high- and low-temperature TES (HT-TES and LT-TES) systems are required in order to cover the extended temperature range. Although this configuration imposes certain limitations on the discharge system configuration, it also presents the opportunity to design a more efficient system. For this case in particular, a steam reheat cycle is advantageous. Conventional TES systems take advantage of nominal efficiency when discharging, whereas the reheat cycle in the fully decoupled TES system affords a significant efficiency increase over conventional cases. Material selection, component/equipment sizing, and costing analyses were performed, followed by economic dispatch and size optimization. Dynamic models were developed for the decoupled system in order to generate insights into aspects of off design operation (e.g., drops in cycle efficiency), and these models enable exploration of potential mitigation strategies for addressing such drops. The models also highlight the need to design fail-safes that ensure continuous operational stability and minimize the impact of power ramping on the reactor parameters.

21 SPECIFIC NUCLEAR REACTORS AND ASSOCIATED PLANTS↗
NASA NTRS1994

High power diode laser Master Oscillator-Power Amplifier (MOPA)

High power multiple quantum well AlGaAs diode laser master oscillator - power amplifier (MOPA) systems were examined both experimentally and theoretically. For two pass operation, it was found that powers in excess of 0.3 W per 100 micrometers of facet length were achievable while maintaining diffraction-limited beam quality. Internal electrical-to-optical conversion efficiencies as high as 25 percent were observed at an internal amplifier gain of 9 dB. Theoretical modeling of multiple quantum well amplifiers was done using appropriate rate equations and a heuristic model of the carrier density dependent gain. The model gave a qualitative agreement with the experimental results. In addition, the model allowed exploration of a wider design space for the amplifiers. The model predicted that internal electrical-to-optical conversion efficiencies in excess of 50 percent should be achievable with careful system design. The model predicted that no global optimum design exists, but gain, efficiency, and optical confinement (coupling efficiency) can be mutually adjusted to meet a specific system requirement. A three quantum well, low optical confinement amplifier was fabricated using molecular beam epitaxial growth. Coherent beam combining of two high power amplifiers injected from a common master oscillator was also examined. Coherent beam combining with an efficiency of 93 percent resulted in a single beam having diffraction-limited characteristics. This beam combining efficiency is a world record result for such a system. Interferometric observations of the output of the amplifier indicated that spatial mode matching was a significant factor in the less than perfect beam combining. Finally, the system issues of arrays of amplifiers in a coherent beam combining system were investigated. Based upon experimentally observed parameters coherent beam combining could result in a megawatt-scale coherent beam with a 10 percent electrical-to-optical conversion efficiency.

Andrews, John R.↗
NASA NTRS2013

Raster-Based Approach to Solar Pressure Modeling

An algorithm has been developed to take advantage of the graphics processing hardware in modern computers to efficiently compute high-fidelity solar pressure forces and torques on spacecraft, taking into account the possibility of self-shading due to the articulation of spacecraft components such as solar arrays. The process is easily extended to compute other results that depend on three-dimensional attitude analysis, such as solar array power generation or free molecular flow drag. The impact of photons upon a spacecraft introduces small forces and moments. The magnitude and direction of the forces depend on the material properties of the spacecraft components being illuminated. The parts of the components being lit depends on the orientation of the craft with respect to the Sun, as well as the gimbal angles for any significant moving external parts (solar arrays, typically). Some components may shield others from the Sun. The purpose of this innovation is to enable high-fidelity computation of solar pressure and power generation effects of illuminated portions of spacecraft, taking self-shading from spacecraft attitude and movable components into account. The key idea in this innovation is to compute results dependent upon complicated geometry by using an image to break the problem into thousands or millions of sub-problems with simple geometry, and then the results from the simpler problems are combined to give high-fidelity results for the full geometry. This process is performed by constructing a 3D model of a spacecraft using an appropriate computer language (OpenGL), and running that model on a modern computer's 3D accelerated video processor. This quickly and accurately generates a view of the model (as shown on a computer screen) that takes rotation and articulation of spacecraft components into account. When this view is interpreted as the spacecraft as seen by the Sun, then only the portions of the craft visible in the view are illuminated. The view as shown on the computer screen is composed of up to millions of pixels. Each of those pixels is associated with a small illuminated area of the spacecraft. For each pixel, it is possible to compute its position, angle (surface normal) from the view direction, and the spacecraft material (and therefore, optical coefficients) associated with that area. With this information, the area associated with each pixel can be modeled as a simple flat plate for calculating solar pressure. The vector sum of these individual flat plate models is a high-fidelity approximation of the solar pressure forces and torques on the whole vehicle. In addition to using optical coefficients associated with each spacecraft material to calculate solar pressure, a power generation coefficient is added for computing solar array power generation from the sum of the illuminated areas. Similarly, other area-based calculations, such as free molecular flow drag, are also enabled. Because the model rendering is separated from other calculations, it is relatively easy to add a new model to explore a new vehicle or mission configuration. Adding a new model is performed by adding OpenGL code, but a future version might read a mesh file exported from a computer-aided design (CAD) system to enable very rapid turnaround for new designs

Wright, Theodore W. II↗
DOE OSTI2024

Multiscale and Machine Learning Modeling for Process-informed Microstructure Prediction in Additively Manufactured Materials Using MALAMUTE

Advanced Materials and Manufacturing Technologies (AMMT) program under the Department of Energy Office of Nuclear Energy, aims to develop and qualify additively-manufactured materials for nuclear applications. The key challenges to these efforts are the microstructural variabilities observed on the AM products and their impact on the properties and performance of the material in extreme environments. AMMT is using a combination of high-through-put experimental and modeling techniques to accelerate the qualification efforts. Conventionally, in-situ and ex-situ characterizations and testing are performed to correlate different aspects of the AM process to the final product and its performance. However, adopting a trial-and-error approach to experimentally evaluate the vast range of process parameters required to capture the microstructural variabilities is cost-prohibitive. Modeling and simulation provide a comparatively inexpensive way to understand and correlate the microstructural evolution to the processing conditions. The modeling and simulation work-packages within the AMMT program aims to use physics-based and machine learning modeling capabilities to develop a digital twin for AM that can correlate the process conditions to the final product and establish a process-structure-property-performance (PSPP) correlation for AM materials. The melting and subsequent solidification that occurs during the AM process is a complex phenomenon that requires multiscale multiphysics analysis. Idaho National Laboratory’s (INL) Multiphysics Object-Oriented Simulation Environment (MOOSE), specifically the MOOSE Application Library for Advanced Manufacturing UTilitiEs (MALAMUTE) software, provides an ideal platform for developing the multiphysics multiscale model to explore the intricacies of the microstructural evolution during the AM processes within a single framework. Furthermore, given that such full-fidelity simulations can be computationally intensive, reduced order models are necessary to explore the PSPP space for AM materials in an efficient, reliable, and cost-effective way. This work package focuses on understanding the role of process variabilities on the various microstructural characteristics of the AM materials. Microstructures unique to AM materials, such as compositional micro-heterogeneity and dislocation cells, are of particular interest here since they can influence the creep properties and radiation performance. In fiscal year (FY) 24, we significantly advanced upon our work in the last fiscal year, both on physics-based and ML models. The alloy solidification model available in MOOSE has been extended to incorporate the thermodynamic properties and free energy relevant to 316SS. The model demonstrates the Cr segregation that occurs during solidifcation. It is demonstrated that rate of solidification and solute segregation is primarily influence by the cooling rate dictating the level of freezing. This work captures the microstructural variabilities at the subgrain level that are often missing in the part-scale models. With an aim to connect the microstructural evolution model to realistic process conditions, a reduced order model is developed for predicting the thermal conditions around meltpool from high-fidelity process simulations. Furthermore, machine learning approach is used to accelerate the temperature prediction during the AM process. In the following years, MALAMUTE will be used to connect different aspects of the models and quantitatively predict the microstructural evolution. The developed ML-based surrogate model will consider the process conditions as the input to predict the microstructural features in a cost-effective way. The generated microstructures can be used by other work packages under AMMT to evaluate the properties and environmental response of the material at the mesoscale. Thus, this work help identify the key microstructural features at the subgrain level that are significant in property/performance prediction of the AM products. This work will provide inputs to the large-scale process variability models to reevaluate and validate assumptions/simplifications made in the part-scale models. Furthermore, through active learning this work will help identify the data need from both modeling and experimental sides for development of a robust digital twin for AM.

36 MATERIALS SCIENCE↗
DOE OSTI2022

Fast Characterization of Inducible Regions of Atrial Fibrillation Models With Multi-Fidelity Gaussian Process Classification

Computational models of atrial fibrillation have successfully been used to predict optimal ablation sites. A critical step to assess the effect of an ablation pattern is to pace the model from different, potentially random, locations to determine whether arrhythmias can be induced in the atria. In this work, we propose to use multi-fidelity Gaussian process classification on Riemannian manifolds to efficiently determine the regions in the atria where arrhythmias are inducible. We build a probabilistic classifier that operates directly on the atrial surface. We take advantage of lower resolution models to explore the atrial surface and combine seamlessly with high-resolution models to identify regions of inducibility. We test our methodology in 9 different cases, with different levels of fibrosis and ablation treatments, totalling 1,800 high resolution and 900 low resolution simulations of atrial fibrillation. When trained with 40 samples, our multi-fidelity classifier that combines low and high resolution models, shows a balanced accuracy that is, on average, 5.7% higher than a nearest neighbor classifier. We hope that this new technique will allow faster and more precise clinical applications of computational models for atrial fibrillation. All data and code accompanying this manuscript will be made publicly available at: https://github.com/fsahli/AtrialMFclass.

59 BASIC BIOLOGICAL SCIENCES↗
NASA NTRS1983

Two-dimensional Finite Element Modeling for Modeling Tectonic Stress and Strain

Techniques of finite element analysis in two dimensional plane strain were applied to problems of geophysics and tectonics. More specifically, the flexibility of the finite element method was employed to address problems involving geological complexity and fault interactions. The modeling of effective anisotropy in material elastic properties proved useful in describing the deformation of faulted crustal blocks. The applications of this modeling work to problems of actual tectonics in southern California was explored. Preliminary models show encouraging agreement with measured tectonic strain in this region, and modeling work was done to gain an understanding of the stress state in a locked fault region with future seismic potential.

Lyzenga, G. A.↗
NASA NTRS1985

Optimal symmetric flight studies

Several topics in optimal symmetric flight of airbreathing vehicles are examined. In one study, an approximation scheme designed for onboard real-time energy management of climb-dash is developed and calculations for a high-performance aircraft presented. In another, a vehicle model intermediate in complexity between energy and point-mass models is explored and some quirks in optimal flight characteristics peculiar to the model uncovered. In yet another study, energy-modelling procedures are re-examined with a view to stretching the range of validity of zeroth-order approximation by special choice of state variables. In a final study, time-fuel tradeoffs in cruise-dash are examined for the consequences of nonconvexities appearing in the classical steady cruise-dash model. Two appendices provide retrospective looks at two early publications on energy modelling and related optimal control theory.

Weston, A. R.↗
NASA NTRS1995

Radiation Pressure-Driven Magnetic Disk Winds in Broad Absorption Line Quasi-Stellar Objects

We explore a model in which QSO broad absorption lines (BALS) are formed in a radiation pressure-driven wind emerging from a magnetized accretion disk. The magnetic field threading the disk material is dragged by the flow and is compressed by the radiation pressure until it is dynamically important and strong enough to contribute to the confinement of the BAL clouds. We construct a simple self-similar model for such radiatively driven magnetized disk winds, in order to explore their properties. It is found that solutions exist for which the entire magnetized flow is confined to a thin wedge over the surface of the disk. For reasonable values of the mass-loss rate, a typical magnetic field strength such that the magnetic pressure is comparable to the inferred gas pressure in BAL clouds, and a moderate amount of internal soft X-ray absorption, we find that the opening angle of the flow is approximately 0.1 rad, in good agreement with the observed covering factor of the broad absorption line region.

DeKool, Martin↗
DOE OSTI2023

BSM (Bioenergy Scenario Model) 2023 FKA: Biomass Scenario Model [SWR-09-09]

The U.S. Department of Energy's (DOE's) Bioenergy Technologies Office and the National Renewable Energy Laboratory (NREL) developed the BSM (Bioenergy Scenario model) to explore the development of a U.S. biofuels industry. The BSM is a system dynamics model built on the STELLA software platform. The model represents the dynamic interactions of the major sectors of the biofuels industry—feedstock production, feedstock logistics, biomass to biofuels conversion, and biofuels end use, including fuels inventory, dispensing, distribution, fuel use, and the vehicle fleet. The BSM represents contextual aspects of the developing biofuels industry, including investment in new biomass to biofuel conversion technologies, competition from petroleum fuels, vehicle demand for biofuels, and various government policies, using all of these to simulate the development of the industry. The purpose of the BSM is to generate and explore plausible scenarios for the evolution of a biofuels industry in the United States, and as a high-level system model it is not designed for precise, quantitative forecasting. Instead, it is best used to (1) analyze and evaluate alternate policies; (2) generate scenarios; (3) identify high-impact levers and bottlenecks to system evolution; and (4) seed focused discussion among policymakers, analysts, and stakeholders.

Bush, Brian↗
DOE OSTI2024

Computer vision models enable mixed linear modeling to predict arbuscular mycorrhizal fungal colonization using fungal morphology

Abstract The presence of Arbuscular Mycorrhizal Fungi (AMF) in vascular land plant roots is one of the most ancient of symbioses supporting nitrogen and phosphorus exchange for photosynthetically derived carbon. Here we provide a multi-scale modeling approach to predict AMF colonization of a worldwide crop from a Recombinant Inbred Line (RIL) population derived from Sorghum bicolor and S. propinquum . The high-throughput phenotyping methods of fungal structures here rely on a Mask Region-based Convolutional Neural Network (Mask R-CNN) in computer vision for pixel-wise fungal structure segmentations and mixed linear models to explore the relations of AMF colonization, root niche, and fungal structure allocation. Models proposed capture over 95% of the variation in AMF colonization as a function of root niche and relative abundance of fungal structures in each plant. Arbuscule allocation is a significant predictor of AMF colonization among sibling plants. Arbuscules and extraradical hyphae implicated in nutrient exchange predict highest AMF colonization in the top root section. Our work demonstrates that deep learning can be used by the community for the high-throughput phenotyping of AMF in plant roots. Mixed linear modeling provides a framework for testing hypotheses about AMF colonization phenotypes as a function of root niche and fungal structure allocations.

59 BASIC BIOLOGICAL SCIENCES↗
NASA NTRS1995

Approaches for averaging surface parameters and fluxes over heterogeneous terrain

Successful prediction of possible climate change depends on realistic parameterization of land surface processes in climate models. Such parameterizations must take appropriate account of the heterogeneities that are found in most earth surfaces. In this study, different averaging strategies for aggregating patch-scale heterogeneities to scales that are appropriate for mesoscale and climate model grids have been explored. A simple model for estimating area-average 'effective' surface flux parameters is evaluated. The model satisfies the energy balance equation and leads to a set of relationships between local and effective parameters in the governing equations for the surface energy balance. One outcome is that the resulting effective surface temperature is not a simple area-weighted average of component temperatures, but is a function of a specific combination of different resistances of the individual surface elements. A set of heterogeneous surfaces has been simulated to study the effective fluxes obtained using the described model. A comparison with results obtained by other investigators using different averaging methods is also performed.

Chehbouni, A.↗
NASA NTRSDate not supplied

Rapid Development of Instrument Thermal Models: Perspectives and Guidelines from NASA Goddard’s Instrument Design Laboratory

- The design and development of robotic spaceflight instruments is a critical part of NASA’s vision to discover and expand knowledge for the benefit of humanity - For typical flight instrument projects, thermal engineers will develop initial instrument thermal models over weeks or months, then iterate them over a project’s lifespan – In each iteration, the engineer will: - Refine their thermal models and thermal designs in accordance with updates from other subsystems - Perform trade studies - Solve very detailed and complex analysis problems, including worst-cases and contingencies - Pick hardware and plan for testing and integration - However, prior to a project being established, or for proposal development at an early conceptual stage, the luxury of multiple instrument design iterations may be limited or nonexistent – Within a short timeline, how do you complete a thermal model or explore multiple possible instrument configurations? – What are the critical parameters for your model? Which details do you include or leave out?

Kan Yang↗