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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 1,297 records · Page 72

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NASA NTRS2008

Extraction of Thermal Performance Values from Samples in the Lunar Dust Adhesion Bell Jar

A simulation chamber has been developed to test the performance of thermal control surfaces under dusty lunar conditions. The lunar dust adhesion bell jar (LDAB) is a diffusion pumped vacuum chamber (10(exp -8) Torr) built to test material samples less than about 7 cm in diameter. The LDAB has the following lunar dust simulant processing capabilities: heating and cooling while stirring in order to degas and remove adsorbed water; RF air-plasma for activating the dust and for organic contaminant removal; RF H/He-plasma to simulate solar wind; dust sieving system for controlling particle sizes; and a controlled means of introducing the activated dust to the samples under study. The LDAB is also fitted with an in situ Xe arc lamp solar simulator, and a cold box that can reach 30 K. Samples of thermal control surfaces (2.5 cm diameter) are introduced into the chamber for calorimetric evaluation using thermocouple instrumentation. The object of this paper is to present a thermal model of the samples under test conditions and to outline the procedure to extract the absorptance, emittance, and thermal efficiency from the pristine and sub-monolayer dust covered samples.

Gaier, James R.↗

NASA NTRS2008

Extraction of Thermal Performance Values from Samples in the Lunar Dust Adhesion Bell Jar

A simulation chamber has been developed to test the performance of thermal control surfaces under dusty lunar conditions. The lunar dust adhesion bell jar (LDAB) is a diffusion pumped vacuum chamber (10-8 Torr) built to test material samples less than about 7 cm in diameter. The LDAB has the following lunar dust stimulant processing capabilities: heating and cooling while stirring in order to degas and remove absorbed water; RF air-plasma for activating the dust and for organic contaminant removal; RF H/He-plasma to simulate solar wind; dust sieving system for controlling particle sizes; and a controlled means of introducing the activated dust to the samples under study. The LDAB is also fitted with an in situ Xe arc lamp solar simulator, and a cold box that can reach 30 K. Samples of thermal control surfaces (2.5 cm diameter) are introduced into the chamber for calorimetric evaluation using thermocouple instrumentation. The object of this paper is to present a thermal model of the samples under test conditions, and to outline the procedure to extract the absorptance, emittance, and thermal efficiency from the pristine and sub-monolayer dust covered samples

Gaier, James R.↗

NASA NTRS2010

Extraction of Thermal Performance Values from Samples in the Lunar Dust Adhesion Bell Jar

Gaier, James R.↗

NASA NTRS2015

Test and Analysis Correlation for a Y-Joint Specimen for a Composite Cryotank

The Composite Cryotank Technology Demonstration (CCTD) project under NASA's Game Changing Development Program (GCDP) developed space technologies using advanced composite materials. Under CCTD, NASA funded the Boeing Company to design and test a number of element-level joint specimens as a precursor to a 2.4-m diameter composite cryotank. Preliminary analyses indicated that the y-joint in the cryotank had low margins of safety; hence the y-joint was considered to be a critical design region. The y-joint design includes a softening strip wedge to reduce localized shear stresses at the skirt/dome interface. In this paper, NASA-developed analytical models will be correlated with the experimental results of a series of positive-peel y-joint specimens from Boeing tests. Initial analytical models over-predicted the experimental strain gage readings in the far-field region by approximately 10%. The over-prediction was attributed to uncertainty in the elastic properties of the laminate and a mismatch between the thermal expansion of the strain gages and the laminate. The elastic properties of the analytical model were adjusted to account for the strain gage differences. The experimental strain gages also indicated a large non-linear effect in the softening strip region that was not predicted by the analytical model. This non-linear effect was attributed to delamination initiating in the softening strip region at below 20% of the failure load for the specimen. Because the specimen was contained in a thermally insulated box during cryogenic testing to failure, delamination initiation and progression was not visualized during the test. Several possible failure initiation locations were investigated, and a most likely failure scenario was determined that correlated well with the experimental data. The most likely failure scenario corresponded to damage initiating in the softening strip and delamination extending to the grips at final failure.

Mason, Brian H.↗

NASA NTRS2022

Microgreens Root Zone/Shoot Zone Partitioned Planting Box

To enable sustainable food production in future human exploration missions, plant growth is being studied by the Space Crop Production Team at KSC. Microgreens are good candidates for food supplements and contain specific nutrients that are lacking in the prepackaged diet, including vitamin C and vitamin K. Because they are densely sown, typical growth methods do not allow the ability to distinguish between the levels of evaporation from the rooting substrate and transpiration from the leaves. With larger plants, the root and shoot zone can be separated to distinguish these fluxes and accurate transpiration measurements of plant canopies are feasible. Furthermore, separation of the root and shoot zone may also be beneficial when harvesting microgreens in microgravity as it may reduce microbial contamination of the edible biomass by the roots, which have high microbial loads. Using a root and shoot separator box when harvesting may help with microbial contamination, but these tiny plants are challenging to handle in microgravity, so harvest management remains an open question. The innovation proposed here is a microgreen root/shoot partitioned planting box, which offers a solution to these challenges with accurate gas exchange measurements and a safe microgreen harvest in low gravity environments. Being able to measure transpiration of a microgreen canopy will be important for modeling plant growth in reduced gravity environments, so the first objective was to develop a planting unit with a seal between the root zone and the shoot zone. With an unsuitable harvesting technique, freshly harvested microgreens may add debris to the cabin, so the second objective was to test different harvesting techniques and management approaches associated with this innovation. These two objectives were pursued in parallel since many goals were the same: develop a planting unit that 1) separates the shoots from the roots, 2) allows acceptable germination rate, and 3) allows for seedlings to emerge and develop. What differed was the need to have a seal, which was only applicable for our gas exchange goal, and the need to have an embedded harvesting mechanism and bagging method which only applied to our harvesting goal. Testing of the various harvesting mechanisms and bagging methods was performed during a series of parabolic flights. All parabolic flight procedures took place inside a rented secondary containment chamber (e.g., glovebox) that was developed by the University of Louisville specifically for experiments involving fluids and other materials that may become airborne during reduced gravity flight. Three different harvesting methods and two different bagging collection methods were tested for microgreens. A third bagging method was initially tested but found to be unsuitable. Human factors were also taken into consideration, to identify which harvest and bagging collection methods would be easiest to use with favorable results in microgravity. Three parabolic flight tests were performed in total, one in November 2021 and two in December 2021.

Gioia Massa↗

NASA NTRS2012

Hybrid Wing-Body (HWB) Pressurized Fuselage Modeling, Analysis, and Design for Weight Reduction

This paper describes the interim progress for an in-house study that is directed toward innovative structural analysis and design of next-generation advanced aircraft concepts, such as the Hybrid Wing-Body (HWB) and the Advanced Mobility Concept-X flight vehicles, for structural weight reduction and associated performance enhancement. Unlike the conventional, skin-stringer-frame construction for a cylindrical fuselage, the box-type pressurized fuselage panels in the HWB undergo significant deformation of the outer aerodynamic surfaces, which must be minimized without significant structural weight penalty. Simple beam and orthotropic plate theory is first considered for sizing, analytical verification, and possible equivalent-plate analysis with appropriate simplification. By designing advanced composite stiffened-shell configurations, significant weight reduction may be possible compared with the sandwich and ribbed-shell structural concepts that have been studied previously. The study involves independent analysis of the advanced composite structural concepts that are presently being developed by The Boeing Company for pressurized HWB flight vehicles. High-fidelity parametric finite-element models of test coupons, panels, and multibay fuselage sections, were developed for conducting design studies and identifying critical areas of potential failure. Interim results are discussed to assess the overall weight/strength advantages.

Mukhopadhyay, Vivek↗

NASA NTRSDate not supplied

Remote Sensing of Lineage Functional Types for Modeling and Monitoring Biodiversity

Hyperspectral remote sensing has the potential to continuously scale plant function and plant diversity information from landscape to global extents. Numerous studies have indicated that VSWIR (400-2500 nm) reflectance properties of vegetation capture evolutionarily conserved biochemical, structural, and other functional attributes of plant species. Spectral properties conserved in plants provide the opportunity to both 1) aggregate species into lineages with improved classification accuracy and 2) link those lineages directly to plant traits. Full realization of this goal will enable parameterization of Land Surface Models (LSMs) with remotely sensed information, e.g., canopy nitrogen, and better representations of biodiversity and functional diversity in biogeographic studies. In this study, we use hyperspectral AVIRIS data from the 2013 HyspIRI campaign over the Southern Sierra Nevada, California flight box to investigate the potential for incorporating evolutionary thinking into landcover classification. We link the airborne hyperspectral data with vegetation plot data from roughly 1372 surveys and a phylogeny representing 1361 species. We aggregate species into lineages ranging from species level groups down to similar number of Plant Functional Types as often used in LSMs. We assessed the ability of Random Forest and Partial Least Squares Discriminant Analysis to discriminate across these different phylogenetic scales and determine the optimal number of lineages to classify. Although there are some temporal and spatial differences in our training data, our best approaches achieved moderate classification accuracy (Kappa > 0.65). Given an optimal number of lineages, we explored approaches to improve classifications including machine learning and unmixing approaches. This work suggests that lineage-based methods may be a promising way to leverage the huge amounts of data that will come from high resolution and high return interval hyperspectral data planned for the Surface Biology and Geology mission with sparsely sampled existing ground-based ecological data.

Hyperspectral↗

DOE OSTI2024

Using Machine Learning to Predict Cloud Turbulent Entrainment–Mixing Processes

Different turbulent entrainment–mixing mechanisms between clouds and environment are essential to cloud–related processes; however, accurate representation of entrainment–mixing in weather/climate models still poses a challenge. This study exploits the use of machine learning (ML) to address this challenge. Four ML (Light Gradient Boosting Machine [LGB], eXtreme Gradient Boosting, Random Forest, and Support Vector Regression) are examined and compared. It is found that LGB performs best, and thus is selected to understand the impact of entrainment–mixing on microphysics using simulation data from Explicit Mixing Parcel Model. Compared with traditional parameterizations, the trained LGB provides more accurate microphysical properties (number concentration and cloud droplet spectral dispersion). The partial dependences of predicted microphysics on features exhibit a strong alignment with physical mechanisms and expectations, as determined by the interpreting method, thus overcoming the limitations of the “black box” scheme. The underlying mechanisms are that the smaller number concentration and larger spectral dispersion correspond to more inhomogeneous entrainment–mixing. Specifically, number concentration after entrainment–mixing is positively correlated with adiabatic number concentration and liquid water content affected by entrainment–mixing, and inversely correlated with adiabatic volume mean radius. Spectral dispersion after entrainment–mixing is negatively correlated with liquid water content affected by entrainment–mixing, turbulent dissipation rate and relative humidity of entrained air. Sensitivity analysis further suggests that number concentration is mainly determined by cloud microphysical properties whereas spectral dispersion is influenced by both cloud microphysical properties and environmental variables. The results indicate that the LGB scheme has the potential to enhance the representation of entrainment–mixing in weather/climate models.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2015

Effect of quantum dot size and size distribution on the intersublevel transitions and absorption coefficients of III-V semiconductor quantum dot

The intersublevel absorption peak energy and absorption coefficient of non-uniform quantum dot (QD) ensembles are calculated analytically. The effect of size variations and size distribution of QDs on their energy states is analyzed. The dots are considered as a quantum box with finite potential at the barriers and the size distribution described by a Gaussian function. The influence of the aspect ratio (base to height ratio) of the QDs on the optical transitions is studied. Our model predicts the dot size (height and base) accurately to determine the absorption peaks and corresponding absorption coefficient. We also compute the absorption coefficient of the QD with different size distributions to verify the results calculated using this model with the reported experimental and other theoretical results.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗

DOE OSTI2021

Sequential Learning of Active Subspaces

This study shows that in recent years, active subspace methods (ASMs) have become a popular means of performing subspace sensitivity analysis on black-box functions. Naively applied, however, ASMs require gradient evaluations of the target function. In the event of noisy, expensive, or stochastic simulators, evaluating gradients via finite differencing may be infeasible. In such cases, often a surrogate model is employed, on which finite differencing is performed. When the surrogate model is a Gaussian process (GP), we show that the ASM estimator is available in closed form, rendering the finite-difference approximation unnecessary. We use our closed-form solution to develop acquisition functions focused on sequential learning tailored to sensitivity analysis on top of ASMs. We also show that the traditional ASM estimator may be viewed as a method of moments estimator for a certain class of GPs. We demonstrate how uncertainty on GP hyperparameters may be propagated to uncertainty on the sensitivity analysis, allowing model-based confidence intervals on the active subspace. Our methodological developments are illustrated on several examples.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2013

TURBULENCE IN THE OUTER REGIONS OF PROTOPLANETARY DISKS. II. STRONG ACCRETION DRIVEN BY A VERTICAL MAGNETIC FIELD

We carry out a series of local, vertically stratified shearing box simulations of protoplanetary disks that include ambipolar diffusion and a net vertical magnetic field. The ambipolar diffusion profiles we employ correspond to 30 AU and 100 AU in a minimum mass solar nebula (MMSN) disk model, which consists of a far-ultraviolet-ionized surface layer and low-ionization disk interior. These simulations serve as a follow-up to Simon et al., in which we found that without a net vertical field, the turbulent stresses that result from the magnetorotational instability (MRI) are too weak to account for observed accretion rates. The simulations in this work show a very strong dependence of the accretion stresses on the strength of the background vertical field; as the field strength increases, the stress amplitude increases. For a net vertical field strength (quantified by β{sub 0}, the ratio of gas to magnetic pressure at the disk mid-plane) of β{sub 0} = 10{sup 4} and β{sub 0} = 10{sup 5}, we find accretion rates M-dot ∼10{sup -8}-10{sup –7} M{sub ☉} yr{sup –1}. These accretion rates agree with observational constraints, suggesting a vertical magnetic field strength of ∼60-200 μG and 10-30 μG at 30 AU and 100 AU, respectively, in a MMSN disk. Furthermore, the stress has a non-negligible component due to a magnetic wind. For sufficiently strong vertical field strengths, MRI turbulence is quenched, and the flow becomes largely laminar, with accretion proceeding through large-scale correlations in the radial and toroidal field components as well as through the magnetic wind. In all simulations, the presence of a low-ionization region near the disk mid-plane, which we call the ambipolar damping zone, results in reduced stresses there.

79 ASTRONOMY AND ASTROPHYSICS↗

DOE OSTI2023

Theoretical framework for new magnetic materials for quantum computing and information storage. Final report for the Award No. DE-SC0018910

The focus of this grant was on molecular magnetic materials for information storage and quantum computing. We have been developing robust, first-principle methods for computing relevant electronic and magnetic properties of molecular building blocks (SMMs) of novel magnetic materials and quantum computers. These tools enable theoretical modeling of SMMs’ behavior, facilitating the interpretation of experimental studies and aiding the design of novel magnetic materials. Our strategy is based on the spin-flip (SF) approach, which extends the hierarchy of black-box single-reference methods to strongly correlated systems. Specifically, we developed general scalable algorithms and computer codes for calculating molecular properties, with an emphasis on spin-related properties, such as zero-field splittings, hyperfine couplings, and g-tensors. While our primary focus was on SF wave functions and SF-TDDFT, the underlying theory and computer codes were formulated using reduced density matrices, such that these tools are applicable to a broader class of methods. To extend the scope of applicability of wave-function-based SF methods to larger systems, we developed reduced-scaling approaches for the equation-of-motion coupled-cluster (EOM-CC) methods and continue developing libtensor (our open-source general tensor contraction library for many-body methods). We carried out extensive benchmarks and also carried out several applications.

36 MATERIALS SCIENCE↗

DOE OSTI2016

A UNIFIED FRAMEWORK FOR THE ORBITAL STRUCTURE OF BARS AND TRIAXIAL ELLIPSOIDS

We examine a large random sample of orbits in two self-consistent simulations of N-body bars. Orbits in these bars are classified both visually and with a new automated orbit classification method based on frequency analysis. The well-known prograde x1 orbit family originates from the same parent orbit as the box orbits in stationary and rotating triaxial ellipsoids. However, only a small fraction of bar orbits (∼4%) have predominately prograde motion like their periodic parent orbit. Most bar orbits arising from the x1 orbit have little net angular momentum in the bar frame, making them equivalent to box orbits in rotating triaxial potentials. In these simulations a small fraction of bar orbits (∼7%) are long-axis tubes that behave exactly like those in triaxial ellipsoids: they are tipped about the intermediate axis owing to the Coriolis force, with the sense of tipping determined by the sign of their angular momentum about the long axis. No orbits parented by prograde periodic x2 orbits are found in the pure bar model, but a tiny population (∼2%) of short-axis tube orbits parented by retrograde x4 orbits are found. When a central point mass representing a supermassive black hole (SMBH) is grown adiabatically at the center of the bar, those orbits that lie in the immediate vicinity of the SMBH are transformed into precessing Keplerian orbits that belong to the same major families (short-axis tubes, long-axis tubes and boxes) occupying the bar at larger radii. During the growth of an SMBH, the inflow of mass and outward transport of angular momentum transform some x1 and long-axis tube orbits into prograde short-axis tubes. This study has important implications for future attempts to constrain the masses of SMBHs in barred galaxies using orbit-based methods like the Schwarzschild orbit superposition scheme and for understanding the observed features in barred galaxies.

79 ASTRONOMY AND ASTROPHYSICS↗

DOE OSTI2024

Neural Networks for Prediction of Complex Chemistry in Water Treatment Process Optimization

Water chemistry plays a critical role in the design and operation of water treatment processes. Detailed chemistry modeling tools use a combination of advanced thermodynamic models and extensive databases to predict phase equilibria and reaction phenomena. The complexity and formulation of these models preclude their direct integration in equation-oriented modeling platforms, making it difficult to use their capabilities for rigorous water treatment process optimization. Neural networks (NN) can provide a pathway for integrating the predictive capability of chemistry software into equation-oriented models and enable optimization of complex water treatment processes across a broad range of conditions and process designs. Herein, we assess how NN architecture and training data impact their accuracy and use in equation-oriented water treatment models. We generate training data using PhreeqC software and determine how data generation and sample size impact the accuracy of trained NNs. The effect of NN architecture on optimization is evaluated by optimizing hypothetical black-box desalination processes using a range of feed compositions from USGS brackish water data set, tracking the number of successful optimizations, and testing the impact of initial guess on the final solution. Our results clearly demonstrate that data generation and architecture impact NN accuracy and viability for use in equation-oriented optimization problems.

Dudchenko, Alexander V↗

NASA NTRS1976

Automated fully-stressed design with NASTRAN

An automated strength sizing capability is described. The technique determines the distribution of material among the elements of a structural model. The sizing is based on either a fully stressed design or a scaled feasible fully stressed design. Results obtained from the application of the strength sizing to the structural sizing of a composite material wing box using material strength allowables are presented. These results demonstrate the rapid convergence of the structural sizes to a usable design.

Wallerstein, D. V.↗

NASA NTRS1981

On variable hydrostatic transmission for road vehicles, powered by supply of fluid at constant pressure

Various hydrostatic power transmission systems for automotive applications with power supply at constant pressure and unrestricted flow and with a Volvo Flygmotor variable displacement motor as the principal unit were investigated. Two most promising concepts were analyzed in detail and their main components optimized for minimum power loss at the EPA Urban Driving Cycle. The best fuel consumption is less than 10 lit. per 100 kM for a 1542 kG vehicle with a hydrostatic motor and a two speed gear box in series (braking power not recovered). Realistic system pressure affects the fuel consumption just slightly, but the package volume/weight drastically. Back pressure increases losses significantly. Special attention was paid to description of the behavior and modeling of the losses of variable displacement hydrostatic machines.

Magi, M.↗

DOE OSTI2015

Direct comparisons of X-ray scattering and atomistic molecular dynamics simulations for precise acid copolymers and ionomers

Designing acid- and ion-containing polymers for optimal proton, ion, or water transport would benefit profoundly from predictive models or theories that relate polymer structures with ionomer morphologies. Recently, atomistic molecular dynamics (MD) simulations were performed to study the morphologies of precise poly(ethylene-co-acrylic acid) copolymer and ionomer melts. Here, we present the first direct comparisons between scattering profiles, I(q), calculated from these atomistic MD simulations and experimental X-ray data for 11 materials. This set of precise polymers has spacers of exactly 9, 15, or 21 carbons between acid groups and has been partially neutralized with Li, Na, Cs, or Zn. In these polymers, the simulations at 120 °C reveal ionic aggregates with a range of morphologies, from compact, isolated aggregates (type 1) to branched, stringy aggregates (type 2) to branched, stringy aggregates that percolate through the simulation box (type 3). Excellent agreement is found between the simulated and experimental scattering peak positions across all polymer types and aggregate morphologies. The shape of the amorphous halo in the simulated I(q) profile is in excellent agreement with experimental I(q). We found that the modified hard-sphere scattering model fits both the simulation and experimental I(q) data for type 1 aggregate morphologies, and the aggregate sizes and separations are in agreement. Given the stringy structure in types 2 and 3, we develop a scattering model based on cylindrical aggregates. Both the spherical and cylindrical scattering models fit I(q) data from the polymers with type 2 and 3 aggregates equally well, and the extracted aggregate radii and inter- and intra-aggregate spacings are in agreement between simulation and experiment. Furthermore, these dimensions are consistent with real-space analyses of the atomistic MD simulations. By combining simulations and experiments, the ionomer scattering peak can be associated with the average distance between branches of type 2 or 3 aggregates. Furthermore, this direct comparison of X-ray scattering data to the atomistic MD simulations is a substantive step toward providing a comprehensive, predictive model for ionomer morphology, gives substantial support for this atomistic MD model, and provides new credibility to the presence of stringy, branched, and percolated ionic aggregates in precise ionomer melts.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

NASA NTRS2008

On Convergence of Development Costs and Cost Models for Complex Spaceflight Instrument Electronics

Development costs of a few recent spaceflight instrument electrical and electronics subsystems have diverged from respective heritage cost model predictions. The cost models used are Grass Roots, Price-H and Parametric Model. These cost models originated in the military and industry around 1970 and were successfully adopted and patched by NASA on a mission-by-mission basis for years. However, the complexity of new instruments recently changed rapidly by orders of magnitude. This is most obvious in the complexity of representative spaceflight instrument electronics' data system. It is now required to perform intermediate processing of digitized data apart from conventional processing of science phenomenon signals from multiple detectors. This involves on-board instrument formatting of computational operands from row data for example, images), multi-million operations per second on large volumes of data in reconfigurable hardware (in addition to processing on a general purpose imbedded or standalone instrument flight computer), as well as making decisions for on-board system adaptation and resource reconfiguration. The instrument data system is now tasked to perform more functions, such as forming packets and instrument-level data compression of more than one data stream, which are traditionally performed by the spacecraft command and data handling system. It is furthermore required that the electronics box for new complex instruments is developed for one-digit watt power consumption, small size and that it is light-weight, and delivers super-computing capabilities. The conflict between the actual development cost of newer complex instruments and its electronics components' heritage cost model predictions seems to be irreconcilable. This conflict and an approach to its resolution are addressed in this paper by determining the complexity parameters, complexity index, and their use in enhanced cost model.

Kizhner, Semion↗