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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 sources NASA NTRS DOE OSTI

NASA NTRS1989

Automated thermal mapping techniques using chromatic image analysis

Thermal imaging techniques are introduced using a chromatic image analysis system and temperature sensitive coatings. These techniques are used for thermal mapping and surface heat transfer measurements on aerothermodynamic test models in hypersonic wind tunnels. Measurements are made on complex vehicle configurations in a timely manner and at minimal expense. The image analysis system uses separate wavelength filtered images to analyze surface spectral intensity data. The system was initially developed for quantitative surface temperature mapping using two-color thermographic phosphors but was found useful in interpreting phase change paint and liquid crystal data as well.

Buck, Gregory M.↗

NASA NTRS1989

Automated thermal mapping techniques using chromatic image analysis

Buck, Gregory M.↗

NASA NTRS2013

Composite Design and Manufacturing Development for Human Spacecrafts

The Structural Engineering Division at the NASA Johnson Space Center (JSC) has begun work on lightweight, multi‐functional pressurized composite structures. The first candidate vehicle for technology development is the Multi‐Mission Space Exploration Vehicle (MMSEV) cabin, known as the Gen 2B cabin, which has been built at JSC by the Robotics Division. Of the habitable MMSEV vehicle prototypes designed to date, this is the first one specifically analyzed and tested to hold internal pressure and the only one made out of composite materials. This design uses a laminate base with zoned reinforcement and external stringers, intended to demonstrate certain capabilities, and to prepare for the next cabin design, which will be a composite sandwich panel construction with multi‐functional capabilities. As part of this advanced development process, a number of new technologies were used to assist in the design and manufacturing process. One of the methods, new to JSC, was to build the Gen 2B cabin with Out of Autoclave technology to permit the creation of larger parts with fewer joints. An 8‐ply pre‐preg layup was constructed to form the cabin body. Prior to lay‐up, a design optimization software called FiberSIM was used to create each ply pattern. This software is integrated with Pro/Engineer to allow for customized draping of each fabric ply over the complex tool surface. Slits and darts are made in the software model to create an optimal design that maintains proper fiber placement and orientation. The flat pattern of each ply is then exported and sent to an automated cutting table where the patterns are cut out of graphite material. Additionally, to assist in lay‐up, a laser projection system (LPT) is used to project outlines of each ply directly onto the tool face for accurate fiber placement and ply build‐up. Finally, as part of the OoA process, a large oven was procured to post‐cure each part. After manufacturing complete, the cabin underwent modal and pressure testing (currently in progress at date of writing) and will go on to be outfitted and used for further ops usage.

Litteken, Douglas↗

NASA NTRS1999

Modeling Laser Altimeter Return Waveforms Over Complex Vegetation Using High-Resolution Elevation Data

The upcoming generation of laser altimeters record the interaction of emitted laser radiation with terrestrial surfaces in the form of a digitized waveform. We model these laser altimeter return waveforms as the sum of the reflections from individual surfaces within laser footprints, accounting for instrument-specific properties. We compare over 1000 modeled and recorded waveform pairs using the Pearson correlation. We show that we reliably synthesize the vertical structure information for vegetation canopies contained in a medium-large diameter laser footprint from a high-resolution elevation data set.

Blair, J. Bryan↗

DOE OSTI2021

Perspectives on Machine Learning-assisted Plasma Medicine: Towards Automated Plasma Treatment

Cold atmospheric plasmas (CAPs) have shown great promise for medical applications through their synergistic chemical, electrical, and thermal effects, which can induce therapeutic outcomes. However, safe and reproducible plasma treatment of complex biological surfaces poses a major hurdle to the widespread adoption of CAPs for medical applications. Predictive modeling of the mutual interactions between the plasma and biological surfaces and, thus, systematic approaches to quantify and predict plasma treatment outcomes remain largely elusive due to the lack of mechanistic understanding of plasma-surface interactions that can span across vastly different length-and time-scales. In addition, real-time sensing capabilities in biomedical CAP devices are often limited, which can be detrimental to plasma treatment due to the intrinsic plasma and surface variability during the treatment, as well as sensitivity to external perturbations. All of these challenges can make reproducible and effective plasma treatment of biological surfaces difficult to realize, which is further compounded by errors due to human operation of hand-held CAP devices. Machine learning and data-driven approaches can be particularly useful in addressing these challenges in three major ways: (i) data-driven modeling of hard-to-model plasma-surface interactions and plasma treatment outcomes; (ii) learning data analytics for plasma and surface diagnostics in real-time; and (iii) developing predictive controllers that enable reliable and effective CAP treatments. Furthermore, this paper discusses the promise of machine learning to accelerate plasma medicine research in these areas, toward machine learning-assisted and automated CAP treatment of complex biological surfaces.

60 APPLIED LIFE SCIENCES↗

NASA NTRS1993

The phase composition of Triton's polar caps

Triton's polar caps are modeled as permanent nitrogen deposits hundreds of meters thick. Complex temperature variations on Triton's surface induce reversible transitions between the cubic and hexagonal phases of solid nitrogen, often with two coexisting propagating transition fronts. Subsurface temperature distributions are calculated using a two-dimensional thermal model with phase changes. The phase changes fracture the upper nitrogen layer, increasing its reflectivity and thus offering an explanation for the surprisingly high southern polar cap albedo (approximately 0.8) seen during the Voyager 2 flyby. The model has other implications for the phase transition phenomena on Triton, such as a plausible mechanism for the origin of geyser-like plume vent areas and a mechanism of energy transport toward them.

Duxbury, N. S.↗

NASA NTRS1981

Climate sensitivity of a one-dimensional radiative-convective model with cloud feedback

The potential complexity of the feedback between global mean cloud amount and global mean surface temperature when variations of the vertical cloud distribution are included is illustrated. This is done by studying the behavior of a one-dimensional radiative-convective model with two types of cloud variation: (1) variable cloud cover with constant optical thickness and (2) variable optical thickness with constant cloud cover. The variable parameter is calculated on the assumption that a correlation exists between cloud amount and precipitation or the vertical flux convergence of latent heat. Since the vertical latent heat flux is taken to be a fraction of the total heat flux, modeled by convective adjustment, the sensitivity of the results to two different critical lapse rates is examined. These are a constant 6.5 K/km lapse rate and a temperature-dependent, moist adiabatic lapse rate. The effects of the vertical structure of climate perturbations on the nature of the cloud feedback are also examined. The model results reveal that changes in the vertical cloud distribution and mean cloud optical thickness can be as important to climate variations as are changes in the total cloud cover.

Wang, W.-C.↗

DOE OSTI2020

Characterizing the Small-Scale Dynamical, Ice Microphysical, and Residual Aerosol Properties of Mid-Latitude Cold Clouds: A Pilot Study (Final Report)

Our understanding of cirrus clouds has been advancing rapidly due to improvements in measurements made inside clouds, computer cloud models, and laboratory experimentation. This advancement in knowledge has made it clear that cirrus are complicated systems where the intricate interactions among dynamic air motions, solar and terrestrial radiation, ice crystal growth, and water and air chemistry can substantially influence cirrus structure and evolution. Measurements made inside clouds have shown that cirrus crystals are often complex with shapes that are often unclassifiable, especially at small sizes. Laboratory measurements have revealed that the ice crystal surface is complex, even at small sizes. Recent laboratory and satellite studies show that this surface complexity modifies the radiative properties of cirrus clouds in important ways. Moreover, observations and computer cloud modeling work show that the larger and smaller scale properties of cirrus are sensitive to the interplay between small-scale air motions and the aerosol articles upon which cirrus ice crystals form. Taken together, these studies indicate that accurately accounting for the radiative properties of cirrus clouds requires knowing the air motions and aerosol properties that influence the number concentration and crystal size, along with an understanding of the surface properties of vapor grown ice. Although this prior work has been illuminating, there have been few studies that provide detailed shape and surface complexity measurements for the small ice crystals that often compose cirrus clouds.

54 ENVIRONMENTAL SCIENCES↗

NASA NTRS1986

Assimilation of scatterometer winds into surface pressure fields using a variational method

A variational formulation was used to assimilate Seasat-A scatterometer (SASS) surface wind measurements near and during a severe storm in the North Atlantic into conventional National Meteorological Center sea level pressure fields. An estimate of the relative vorticity at every point on a grid was calculated using each of these two data sets. A solution to a modified geostrophic stream function is found subject to the constraints that (1) the relative vorticities calculated from the data agree as closely as possible with the relative vorticities from the variational solution, and that (2) the average kinetic energy is a minimum. Results are obtained which support the idea that averaged satellite data can be treated as synoptic data. Direct substitution rather than a time-weighted insertion made from SASS winds generally resulted in more accurate pressure analyses. In addition, this relatively simple model provides surface pressure fields which agree extremely well with surface truth and the results of other investigators who required additional sources of input data into more complex models. It will be possible to obtain improved wind field maps from future scatterometer pressure fields in mid-latitudes.

Harlan, J., Jr.↗

DOE OSTI2026

Two-phase flow numerical analysis of electrode geometry for alkaline water electrolyzers

Hydrogen is a promising component of a future energy-secure and efficient economy, but its competitiveness depends on reducing production costs. One strategy is to operate alkaline water electrolyzers at higher current densities to increase output. However, this intensifies performance losses due to gas bubble accumulation, which blocks transport pathways and deactivates electrochemically active surfaces. Enhancing bubble evacuation through electrode design is therefore essential. Previous studies have explored various approaches — such as modifying surface morphology, applying sonication or pressure modulation, and introducing surfactants — but these efforts have addressed a limited range of conditions due to the complexity of two-phase flow and electrode geometries. Experiments have also largely been focused on either cell level improvements, which lack the information necessary to isolate each contributing factor, or on modified geometries that are not relevant to practical cell operation. From a modeling perspective, conventional Eulerian multiphase models do not track the complex gas–liquid interfacial dynamics and often neglect surface tension and contact angle effects, reducing their predictive accuracy. To provide insights on the effects of different electrode geometries on the performance of alklaine water electrolyzers this work employs an immersed boundary volume-of-fluid method to simulate bubble behavior in 3D porous electrodes. Multiple base electrode geometries, typically used in practice, with varying porosity are evaluated under a constant surface gas generation rate. Simulation data is analyzed to quantify electrode gas coverage, bubble size dynamics and other relevant metrics. Results show that porosity strongly influences bubble accumulation on electrode surfaces, with higher porosity reducing gas coverage, and its not strictly dependent on the electrode geometry. However, the electrode’s base geometry significantly affects gas accumulation at the separator gap, independent of porosity. A foam electrode geometry resulted in the lowest gas coverage of all electrodes with a median volumetric gas coverage of 11%, but at the cost of a 70% reduction in active area compared with the largest surface area electrode, while gyroid electrodes showed the best trade-off between gas coverage, particularly at the separator surface, and electrochemically active area. In conclusion, the results highlight the need for holistic electrode design strategies.

Alkaline↗

DOE OSTI2025

A neural master equation framework for multiscale modeling of molecular processes: application to atomic-scale plasma processes

Plasma-surface interactions (PSI) play a crucial role in microelectronics fabrication; however, their multiscale nature and array of complex, often unknown interactions make computational modeling of PSIs extremely difficult. To this end, we propose a general neural master equation (NME) framework that uses master equations to describe the dynamics of a molecular process, wherein neural networks learned from atomistic simulations represent unknown transitions between different system states. By leveraging the physics-based structure of master equations and data-driven state transitions, the NME framework promotes generalizability and physics interpretability, and can bridge disparate length and time scales. The framework is demonstrated for multiscale modeling of Si atomic layer etching and reactive ion etching, where the learned NME-based surface kinetic models exhibit good predictive and extrapolative capabilities for predicting experimentally relevant observables as a function of process parameters. The NME-based surface kinetic models obey physical constraints, which are violated in models based on neural ordinary differential equations. The proposed NME framework for multiscale modeling of molecular processes can pave the way for the discovery of new chemistries and materials in atomic-scale plasma processes.

Chemical engineering↗

DOE OSTI2020

Potential-pH diagrams considering complex oxide solution phases for understanding aqueous corrosion of multi-principal element alloys

The potential-pH diagram, a graphical representation of the thermodynamically predominant reaction products in aqueous corrosion, is originally proposed for the corrosion of pure metals. The original approach only leads to stoichiometric oxides and hydroxides as the oxidation products. However, numerous experiments show that non-stoichiometric oxide scales are prevalent in the aqueous corrosion of alloys. In the present study, a room temperature potential-pH diagram considering oxide solid solutions, as a generalization of the traditional potential-pH diagram with stoichiometric oxides, is constructed for an FCC single-phase multi-principal element alloy (MPEA) based on the CALculation of PHAse Diagram method. The predominant reaction products, the ions in aqueous solution, and the cation distribution in oxides are predicted. The oxide solid solution is stabilized by the mixing free energy (or mixing entropy) and the stabilizing effect becomes more significant as the temperature increases. Consequently, solid solution oxides are stable in large regions of the potential-pH diagram and the mixing free energy mostly affects the equilibrium composition of the stable oxides, while the shape of stable regions for oxides is mostly determined by the structure of the stable oxides. Agreements are found for Ni 2+ , Fe 2+ , and Mn 2+ between the atomic emission spectroelectrochemistry measurements and thermodynamic calculations, while deviations exist for Cr 3+ and Co 2+ possibly due to surface complexation with species such as Cl - and the oxide dissolution. By incorporating the solution models of oxides, the current work presents a general and more accurate way to analyze the reaction products during aqueous corrosion of MPEAs.

Materials Science↗

DOE OSTI2022

Hardware-Accelerated Ray Tracing of CAD-Based Geometry for Monte Carlo Radiation Transport

Monte Carlo radiation transport (MCRT) methods have been used to simulate radiation environments for many decades by tracking individual particles through a model to accumulate statistical information. MCRT geometry is historically formed using the constructive solid geometry (CSG). Recently, significant work has been performed to support simulations using computer-aided design (CAD)-based tessellated surfaces to support highly complex geometries. Ray tracing acceleration data structures from the rendering and visualization community are applied to accelerate particle tracking in CAD-based models. Despite these efforts, CSG representations provide the superior performance in surface intersection operations during particle flight. Concurrently, pseudo Monte Carlo methods have become prevalent in rendering applications to support more realistic models for scattering media, motivating innovations that are advantageous for MCRT simulations. Finally, the authors’ work extends these innovations by employing Intel’s Embree ray tracing kernel within a geometry toolkit for Monte Carlo to improve the simulation performance using CAD-based models by factors of 1.5 to 2.

42 ENGINEERING↗

NASA NTRS1989

Definition of NASTRAN sets by use of parametric geometry

Many finite element preprocessors describe finite element model geometry with points, lines, surfaces and volumes. One method for describing these basic geometric entities is by use of parametric cubics which are useful for representing complex shapes. The lines, surfaces and volumes may be discretized for follow on finite element analysis. The ability to limit or selectively recover results from the finite element model is extremely important to the analyst. Equally important is the ability to easily apply boundary conditions. Although graphical preprocessors have made these tasks easier, model complexity may not lend itself to easily identify a group of grid points desired for data recovery or application of constraints. A methodology is presented which makes use of the assignment of grid point locations in parametric coordinates. The parametric coordinates provide a convenient ordering of the grid point locations and a method for retrieving the grid point ID's from the parent geometry. The selected grid points may then be used for the generation of the appropriate set and constraint cards.

Baughn, Terry V.↗

DOE OSTI2025

A comparison of surrogate constitutive models for viscoplastic creep simulation of HT-9 steel

Mechanistic microstructure-informed constitutive models for the mechanical response of polycrystals are a cornerstone of computational materials science. However, as these models become increasingly more complex – often involving coupled differential equations describing the effect of specific deformation modes – their associated computational costs can become prohibitive, particularly in optimization or uncertainty quantification tasks that require numerous model evaluations. To address this challenge, surrogate constitutive models that balance accuracy and computational efficiency are highly desirable. Data-driven surrogate models, that learn the constitutive relation directly from data, have emerged as a promising solution. In this work, we develop two local surrogate models for the viscoplastic response of a steel: a piecewise response surface method and a mixture of experts model. These surrogates are designed to adapt to complex material behavior, which may vary with material parameters or operating conditions. The surrogate constitutive models are applied to creep simulations of HT-9 steel, an alloy of considerable interest to the nuclear energy sector due to its high tolerance to radiation damage, using training data generated from viscoplastic self-consistent (VPSC) simulations. In conclusion, we define a set of test metrics to numerically assess the accuracy of our surrogate models for predicting viscoplastic material behavior, and show that the mixture of experts model outperforms the piecewise response surface method in terms of accuracy.

36 MATERIALS SCIENCE↗

NASA NTRS2011

Global Precipitation Measurement, Validation, and Applications Integrated Hydrologic Validation to Improve Physical Precipitation Retrievals for GPM

Land surface modeling and data assimilation can provide dynamic land surface state variables necessary to support physical precipitation retrieval algorithms over land. It is well-known that surface emission, particularly over the range of frequencies to be included in the Global Precipitation Measurement Mission (GPM), is sensitive to land surface states, including soil properties, vegetation type and greenness, soil moisture, surface temperature, and snow cover, density, and grain size. In order to investigate the robustness of both the land surface model states and the microwave emissivity and forward radiative transfer models, we have undertaken a multi-site investigation as part of the NASA Precipitation Measurement Missions (PMM) Land Surface Characterization Working Group. Specifically, we will demonstrate the performance of the Land Information System (LIS; http://lis.gsfc.nasa.gov; Peters-Lidard et aI., 2007; Kumar et al., 2006) coupled to the Joint Center for Satellite Data Assimilation (JCSDA's) Community Radiative Transfer Model (CRTM; Weng, 2007; van Deist, 2009). The land surface is characterized by complex physical/chemical constituents and creates temporally and spatially heterogeneous surface properties in response to microwave radiation scattering. The uncertainties in surface microwave emission (both surface radiative temperature and emissivity) and very low polarization ratio are linked to difficulties in rainfall detection using low-frequency passive microwave sensors (e.g.,Kummerow et al. 2001). Therefore, addressing these issues is of utmost importance for the GPM mission. There are many approaches to parameterizing land surface emission and radiative transfer, some of which have been customized for snow (e.g., the Helsinki University of Technology or HUT radiative transfer model;) and soil moisture (e.g., the Land Surface Microwave Emission Model or LSMEM).

Peters-Lidar, Christa D.↗

NASA NTRSDate not supplied

Improved Models for Simulation of Cryogenic Spray Cooling for Propellant Tank Chilldown

This paper presents advances in spray boiling models for cryogenic fluids that exhibit strong variation in thermodynamic properties; it accounts for the interaction of the spray droplets with walls and the complex heat transfer and boiling processes that play a vital role in chilling solid surfaces. The model has been developed by corroborating and validating it with test data in liquid nitrogen obtained at the University of Connecticut. The test data includes detailed visualization and plate temperature measurements for liquid nitrogen sprays impinging on a warm test plate and subsequent transient chilldown of the plate. Simulations of transient plate chilldown in liquid nitrogen were carried out and temperature histories at various locations on the plate were compared with test measurements. The final paper will present sensitivity studies and additional comparisons with test data.

Computational Fluid Dynamics, Cryogenic, Spray Chi↗

NASA NTRSDate not supplied

Improved Models for Simulation of Cryogenic Spray Cooling for Propellant Tank Chilldown

This paper presents advances in spray boiling models for cryogenic fluids that exhibit strong variation in thermodynamic properties; it accounts for the interaction of the spray droplets with walls and the complex heat transfer and boiling processes that play a vital role in chilling solid surfaces. The model has been developed by corroborating and validating it with test data in liquid nitrogen obtained at the University of Connecticut. The test data includes detailed visualization and plate temperature measurements for liquid nitrogen sprays impinging on a warm test plate and subsequent transient chilldown of the plate. Simulations of transient plate chilldown in liquid nitrogen were carried out and temperature histories at various locations on the plate were compared with test measurements to demonstrate the validity of the spray boiling models that have been developed for cryogens.

Boiling↗