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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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314 records · Page 18
All sourcesNASA NTRSDOE OSTI
NASA NTRS2018

Active and Passive Radiative Transfer Modeling of the Olympic Mountains Experiment

Sensor forward models are an important tool for interpreting remote sensing observations of geophysical phenomena. By implementing a three-dimensional framework, we can simulate and analyze observations from various sensors on disparate platforms. To demonstrate our model framework, we simulate observations from the Olympic Mountains Experiment (OLYMPEX). The use of cloud model simulations allows us to understand sensor response to cloud ice, falling snow, and other processes and features, and the application of model tools to observations allows us to quantify precipitation.MIIST 3D Forward ModelThe Multi-Instrument Inverse Solver Testbed(MIIST) uses the Atmospheric Radiative TransferSimulator (ARTS) for solving the vector radiativetransfer (RT) equation in up to three spatialdimensions within a spherical geometry• Gas absorptiono Line-by-line calculationso Fast transmittance tables• Hydrometeor scattering solverso Discrete ordinateo RT4 (Evans, 1D)o Radar Single Scattering (1D or 3D)o Monte Carlo (3D)Scattering TablesHigh-fidelity hydrometeor scatteringtables are necessary for accurateand consistent forward modeling ofmulti-frequency observations• Requires full Stokes matriceso And absorption vector• Randomly oriented particleso Discrete Dipole Approximationo Characteristic Basis Function Method(coming soon)• Horizontally-oriented plateso Invariant Imbedding T-matrix MethodCloud Resolving SimulationsCloud resolving simulations (e.g.,NU-WRF) supply output consistentwith ARTS needs• Atmospheric Informationo Temperatureo Pressure / heighto Water vapor• Hydrometeor Profileso ARTS architecture ripe for explicit binmicrophysics• Examples use Morrison 2M schemeThe Olympic Mountains Experiment (OLYMPEX)Validation for GPM of mid-latitudefrontal systems approaching nearcoastalmountains from the ocean• Large collection of ground-based andairborne sensorso Radarso Radiometerso In situ• Contemporaneous with RADEXo Two sets of radar at same frequenciesRadiometer Simulation (3 km NUWRF, 20151203, 15:00)2018.12.14 7Simulate 166 GHz polarizationdifference• Corresponds to the presence of aligned icecrystals• Look at trends for both simulations andobservations• Simulations can tolerate lower resolutiono Larger domainSimulations from Observations: OLYMPEXSimulate sensor response usinggeophysical retrievals as input• Single frequency radar retrievals• Multiple scattering enhancementapparent at W band• Spatially dependent phenomenonModeling Application: 1D Retrievals03 December 2015• DC-8 and ER-2 flightso Focus on APR-3 (DC-8)• Citationo Stacked microphysics legso Qualitative comparisonso Range of frozen habitso Presence of supercooledliquid cloudsResults• Retrievals match probeso Good qualitative match• Bands of increasedreflectivity correspond tolarge Dm and highaggregate fraction• Significant amounts ofsupercooled liquid water

Adams, Ian S.↗
NASA NTRS2020

Application of Risk Informed Decision Making to a Highly Reliable Three-Dimensionally Woven Thermal Protection System for Mars Sample Return

The NASA Risk Informed Decision Making process is used to assess a trade space of three dimensionally woven thermal protection systems for application to the Mars Sample Return Earth Entry Vehicle. Candidate architectures are assessed based on mission assurance, technical development, cost, and schedule risk. Assessment methodology differed between the architectures, utilizing a four-point quantitative scale for mission assurance and technical development and highly tailored PERT techniques for cost and schedule. Risk results are presented, in addition to a review of RIDM effectiveness for this application.

Needels, J.↗
NASA NTRS2021

Generation-based Evolutionary Tool for the Optimization of Constellations (GenETOC)

With the rapid growth in the capabilities of smaller satellites, satellite architectures that replace a single, extremely capable spacecraft with multiple, cheaper ones are gaining in popularity. Unfortunately, the orbit design process for constellations can be significantly more involved, especiallywhen the relative placement of the individual spacecraft within the constellation is not constrained by mission and/or science objectives. Optimizing a satellite constellation in the presence of multiple, competing objectives is a highly complex problem to which many traditional mathematical optimization methods cannot be applied and few tools exist to help mission designers search for promising candidate mission designs. The Generation-based Evolutionary Tool for the Optimization of Constellations (GenETOC) has been created to search for near-optimal constellation design options. GenETOC combines a modified version of the Non-dominated Sorting Genetic Algorithm II (NSGA II) with STK Components libraries (a 3rdparty .NET package created by Analytical Graphics Inc.) to create a framework that enables a mission designer to generate a simulation that models the design problem and obtain a family of potential, near-optimal solutions that can be investigated more in detail. GenETOC was developed in C# using the .NET framework with Windows Presentation Foundation (WPF) serving as the framework from which to create the graphical user interface (GUI). GenETOC user inputs can be categorized into three major data components: definition of the problem (areas of interest, satellite decision parameters, and sensor configurations), definition of performance objectives, and specification of the genetic algorithm (GA) parameters. In the problem definition component, the user is prompted to define the areas of interest against which the performance metrics will be computed, define the sensor parameters and attach them to specific spacecraft, select which satellite orbital parameters will be added to the decision space of the GA, and specify the range of desired values for each optimization parameter. For performance objectives, the user is presented with a list of available coverage and revisit performance based calculation options from which two metrics are chosen to serve as the objective functions that the GA will use to evaluate solutions during the optimization process. Finally, the definition of the GA parameters provides user control over the number of generations (number of optimization iterations), the population size (number of candidate constellations created in each generation), and the adaptive mutation and crossover threshold values (control parameters for how frequently each process occurs during the optimization). GenETOC has been extensively tested to verify the individual components of the optimization process. The GA has been tested against a suite of GA test problems to confirm convergence to the known two and three-dimensional Pareto fronts. The coverage and revisit performance metrics obtained in GenETOC are compared with STK desktop scenarios, confirming the constellations are being appropriately modeled within GenETOC simulations. A walkthrough of a simple, example problem is provided to illustrate the workings of GenETOC and to demonstrate the output available to the mission designer.

mission design↗
NASA NTRS2021

Discrete anisotropic radiative transfer modelling of solar-induced chlorophyll fluorescence: Structural impacts in geometrically explicit vegetation canopies

Solar-induced fluorescence (SIF) is a subtle but informative optical signal of vegetation photosynthesis. Remotely sensed SIF integrates environmental, physiological and structural changes that alter photosynthesis at leaf, plant and canopy scales. Radiative transfer models are ideally suited to investigate the complex sources of variability in the SIF signal to guide the interpretation of SIF retrievals from airborne and space-borne platforms. Here, we coupled the Fluspect-Cx model of leaf optical properties and chlorophyll-a fluorescence with the Discrete Anisotropic Radiative Transfer (DART) model to upscale SIF from individual leaves to three-dimensional (3D) structurally explicit canopies. For one-dimensional homogeneous (turbid-like) canopies, DART-SIF was nearly identical to SIF simulated in two existing models, SCOPE and mSCOPE (RMSE <0.221 W./sq.m.μm.sr). DART simulations in geometrically explicit 3D canopies offered four important insights regarding the influence of vegetation structure on the multi-angular top-of-canopy SIF signal. First, changes in the 3D canopy architecture of maize crops, represented by leaf density (leaf area index), and plant clumping (canopy closure) had a larger impact on SIF than the modelled photosynthetic efficiency distinction between sun-adapted and shade-adapted foliage. Second, clumping of leaves at the crop and stand levels was identified as one of the key driving factors of multi-angular anisotropy of red and far-red SIF (686 and 740 nm) for both maize and eucalyptus canopies. Third, non-photosynthetic woody material had a significant impact on top-of-canopy SIF in modelled 3D forest stands. Wood shadowing decreased the photosynthetically active radiation absorbed by green leaves, and consequently the SIF emissions, by 10% in sparse and 17% in dense eucalyptus stands. The wood obstruction (blocking) effect, quantified as a relative difference of SIF escape probabilities from canopies with and without wood in the nadir viewing direction, decreased far-red SIF by 4–6% but it had a smaller and sometimes positive influence (by less than 2%) on red SIF. Fourth, DART 3D radiative budget profiles revealed that the majority of the SIF signal from a dense eucalyptus stand originated from the top 25% of the simulated canopy. Interestingly, the introduction of bark-covered woody elements did not alter the simulated balance and omnidirectional escape factor of red SIF in this upper canopy part but did raise significantly both of them in case of far-red SIF. These results demonstrate the importance of 3D radiative transfer and radiative budget simulations for investigating SIF interactions in structurally complex plant canopies and for a better understanding of spatiotemporal and multi-angular remote sensing SIF observations.

Zbyněk Malenovský↗
NASA NTRS2022

Monte-Carlo Analysis of Minimum Thermocouple Depths using Icarus

Icarus is a three-dimensional, unstructured, finite-volume material response solver developed at NASA Ames Research Center and has been verified against other NASA material response tools like FIAT, which have a long history of successfully designing thermal protection system (TPS). Icarus solves a set of conservation equations for mass and energy and uses Darcy’s Law in place of momentum conservation. An ecosystem of material response tools has been built around a general-purposed Icarus library that in addition to the typical material response analysis also supports TPS sizing (1-D and multi-dimensional), uncertainty quantification, and has been successfully integrated into a multi-physics architecture built around US3D. In this paper, a brief overview of Icarus and its capabilities will be presented using an illustrative Monte Carlo analysis of the one-dimensional, in-depth material response of a representative Dragonfly trajectory.

Material Response↗
NASA NTRSDate not supplied

Monte-Carlo Analysis of Minimal Thermocouple Depths using Icarus

Icarus is a three-dimensional, unstructured, finite-volume material response solver developed at NASA Ames Research Center \cite{Schulz_2017} and has been verified against other NASA material response tools like FIAT, which have a long history of successfully designing thermal protection system (TPS). Icarus solves a set of conservation equations for mass and energy and uses Darcy’s Law in place of momentum conservation. An ecosystem of material response tools has been built around a general-purposed Icarus library that in addition to the typical material response analysis also supports TPS sizing (1-D and multi-dimensional), uncertainty quantification, and has been successfully integrated into a multi-physics architecture built around US3D \cite{Schroeder_2021}. In this paper, a brief overview of Icarus and its capabilities will be presented using an illustrative Monte Carlo analysis of the one-dimensional, in-depth material response of a representative Dragonfly trajectory.

Material Response↗
NASA NTRS2025

Photophoretic Propulsion Enabling Mesosphere Exploration NIAC Phase I Final Report

This Phase I report presents a comprehensive study on photophoretic flyers—innovative, ultralight, solar-powered vehicles that harness photophoretic forces generated via Knudsen pumping to achieve sustained flight in the mesosphere (50–80 km altitude). By integrating advanced materials such as nanocardboard— characterized by its extremely low areal density (~1 g/m²) and high bending stiffness—with ultrathin light-absorbing coatings, the project converts incident solar radiation directly into a directed thrust. Extensive experimental investigations, coupled with high-fidelity computational fluid dynamics (CFD) simulations using ANSYS Fluent, validate the concept across various three-dimensional geometries, including spherical, conical, and rocket-shaped configurations. These simulations bridge the gap between free-molecular and continuum flow regimes, demonstrating that optimized designs can generate lift forces sufficient to support kilogram-scale payloads even in low-pressure environments. At the heart of this innovation is the use of Knudsen pumping, where temperature gradients across porous surfaces induce directional gas flow, creating a modest overpressure that provides lift. The report introduces an analytical framework that interpolates between the well-known low-Reynolds number drag regime and the high-Reynolds number momentum theory. This model accurately predicts lift based on design parameters such as microchannel dimensions, porous wall geometry, areal density, and nozzle exit area. For instance, simulations indicate that 10-meter-scale structures with carefully engineered porous walls can achieve the necessary pressure differential to support scientifically significant payloads (~1 kg). The study also explores a hybrid propulsion approach that combines solar buoyancy with photophoretic lift. Initially, solar heating creates a buoyant force that elevates the flyer to mesospheric altitudes. Once in the optimal pressure range, the photophoretic mechanism—powered by Knudsen pumping—takes over as the primary source of lift, ensuring stable, long-duration flight. This dual-mode operation not only facilitates the deployment of photophoretic flyers but also broadens the potential applications for mesospheric exploration. In addition to propulsion, the report investigates the integration of photophoretic thrusters for trajectory control of existing research balloons in the upper stratosphere. By dynamically adjusting the nozzle orientation and controlling the flow-through velocity, these thrusters provide precise maneuverability, enabling the flyers to counteract atmospheric disturbances and adjust their flight paths in real time. For example, a photophoretic thruster approximately 7.5 by 7.5 meters in size could be unfolded below a payload gondola of a 60 million-cubic-foot zero-pressure balloon. Such a thruster can provide horizontal speed control of approximately 1 m/s using only sunlight and no moving parts (except those needed to track the Sun and control the jet direction). Importantly, photophoretic thrusters operate more efficiently at higher altitudes, which is complementary to known trajectory control techniques, such as propellers and tethered wings, which are more effective at lower altitudes. Finally, the report identifies three scientific research thrusts where mesospheric aircraft technology can have a profound impact: atmospheric tides, characterization of gravity waves, and investigation of mesospheric instabilities. Overall, the findings of this Phase I project represent a significant advancement in photophoretic propulsion technology. By demonstrating that large-scale, ultralight structures can be powered solely by solar radiation—via carefully engineered Knudsen pumping—this work lays a robust foundation for scalable, near-space flight architectures. Future refinements in material fabrication, structural optimization, and integrated trajectory control are expected to further enhance performance, paving the way for operational demonstrations that could revolutionize atmospheric science, remote sensing, and communication networks.

Knudsen Pump↗
DOE OSTI2022

Modeling Combustion Reaction ODEs with Neural Networks

The chemistry of combustion reactions is complex as it involves many species and reactions. In practice, such a system is often modeled computationally using an empirically derived chemical mechanism. Given the large range of reaction rates, the system is then time evolved using a stiff ODE solver. However, even when reduced chemical mechanisms are employed, the system can become computationally expensive for two-dimensional or three-dimensional systems. Such systems can also face problems with instability. As such, it is desirable to find a cheaper, stable alternative to solving the reaction system. Neural networks offer the potential to learn these reaction ODEs and time evolve a combustion reaction in a more cost-efficient manner than stiff ODE solvers. In this study, a variety of neural networks are trained on zero-dimensional Cantera simulations of methane combustion with varying initial conditions. Several predictive approaches as well as several neural network architectures (artificial neural network with dropout, ResNet, and Neural ODE) are compared in their ability to predict combustion trajectories. Promising models are then identified.

combustion kinetics↗