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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 307 records · Page 17

Blanco DECam Bulge Survey (BDBS)

The Blanco DECam Bulge Survey (BDBS) has imaged more than 200 square degrees of the southern Galactic bulge, providing photometry in the ugrizy filters for ~250 million unique stars. The presence of a strong foreground disk population, along with complex reddening and extreme image crowding, has made it difficult to constrain the presence of young and intermediate age stars in the bulge population. We employed an accurate cross-match of BDBS with the latest data release (EDR3) from the Gaia mission, matching more than 140 million sources with BDBS photometry and Gaia EDR3 photometry and astrometry. We relied on Gaia EDR3 astrometry, without any photometric selection, to produce clean BDBS bulge colour-magnitude diagrams (CMDs). Gaia parallaxes were used to filter out bright foreground sources, and a Gaussian mixture model fit to Galactic proper motions could identify stars kinematically consistent with bulge membership. We applied this method to 127 different bulge fields of 1 deg 2 each, with |$\ell$| ≤ 9.5 ° and –9.5 ° ≤ b ≤ –2.5 °. The astrometric cleaning procedure removes the majority of blue stars in each field, especially near the Galactic plane, where the ratio of blue to red stars is .10%, increasing to values ~20% at higher Galactic latitudes. We rule out the presence of a widespread population of stars younger than 2 Gyr. The vast majority of blue stars brighter than the turnoff belong to the foreground population, according to their measured astrometry. We introduce the distance between the observed red giant branch bump and the red clump as a simple age proxy for the dominant population in the field, and we confirm the picture of a predominantly old bulge. Further work is needed to apply the method to estimate ages to fields at higher latitudes, and to model the complex morphology of the Galactic bulge. We also produce transverse kinematic maps, recovering expected patterns related to the presence of the bar and of the X-shaped nature of the bulge.

79 ASTRONOMY AND ASTROPHYSICS↗

Geological and geothermal investigations for HCMM-derived data

An attempt was made to match HCMM- and U2HCMR-derived temperature data over two test sites of very local size to similar data collected in the field at nearly the same times. Results indicate that HCMM investigations using resolutions cells of 500 m or so are best conducted with areally-extensive sites, rather than point observations. The excellent quality day-VIS imagery is particularly useful for lineament studies, as is the DELTA-T imagery. Attempts to register the ground observed temperatures (even for 0.5 sq mile targets) were unsuccessful due to excessive pixel-to-pixel noise on the HCMM data. Several computer models were explored and related to thermal parameter value changes with observed data. Unless quite complex models, with many parameters which can be observed (perhaps not even measured (perhaps not even measured) only under remote sensing conditions (e.g., roughness, wind shear, etc) are used, the model outputs do not match the observed data. Empirical relationship may be most readily studied.

Lyon, R. J. P.↗

Predicting short-range order evolution in WTaCrVHf refractory high-entropy alloys

Short-range order (SRO) in multicomponent concentrated alloys affects their mechanical response. Hence, is paramount to understand how composition modifies the chemical ordering in the system to design materials with optimal properties. Here, in this work, we present a methodology to predict the SRO and thermodynamic properties in chemically complex systems and apply it to the WTaCrVHf quinary alloy. We observe that the addition of Hf significantly modifies the SRO, mainly at intermediate to low temperatures, matching experimental observations.

36 MATERIALS SCIENCE↗

Spectral Ratio Imaging with Hyperion Satellite Data for Geological Mapping

Since the advent of LANDSAT I in 1972, many different multispectral satellites have been orbited by the U.S. and other countries. These satellites have varied from 4 spectral bands in LANDSAT I to 14 spectral bands in the ASTER sensor aboard the TERRA space platform. Hyperion is a relatively new hyperspectral sensor with over 220 spectral bands. The huge increase in the number of spectral bands offers a substantial challenge to computers and analysts alike when it comes to the task of mapping features on the basis of chemical composition, especially if little or no ground truth is available beforehand from the area being mapped. One approach is the theoretical approach of the modeler, where all extraneous information (atmospheric attenuation, sensor electronic gain and offset, etc.) is subtracted off and divided out, and laboratory (or field) spectra of materials are used as training sets to map features in the scene of similar composition. This approach is very difficult to keep accurate because of variations in the atmosphere, solar illumination, and sensor electronic gain and offset that are not always perfectly recorded or accounted for. For instance, to apply laboratory or field spectra of materials as data sets from the theoretical approach, the header information of the files must reflect the correct, up-to-date sensor electronic gain and offset and the analyst must pick the exact atmospheric model that is appropriate for the day of data collection in order for classification procedures to accurately match pixels in the scene with the laboratory or field spectrum of a desired target on the basis of the hyperspectral data. The modeling process is so complex that it is difficult to tell when it is operating well or determine how to fix it when it is incorrect. Recently RSI has announced that the latest version of their ENVI software package is not performing atmospheric corrections correctly with the FLAASH atmospheric model. It took a long time to determine that it was wrong, and may take an equally long time (or longer) to fix.

Vincent, Robert K.↗

AgMIP Local, National and Global Assessments of Food System Challenges in A Changing Climate

Food systems connect a diverse network of stakeholders around the world, with complex dynamics governing production, processing, transportation, trade and consumption of food products. Extreme conditions can disrupt multiple components of the food system, with systemic connections providing structure that can both buffer and exacerbate risks. Climate change is shifting current practices in agricultural production and the broader food system, encouraging shifts toward more diverse value chains capable of drawing from multiple food producing regions to reduce the chance that an entire value chain be subject to simultaneous impacts. The IPCC Working Group I report assessed that heat waves, fires, floods and severe storms are becoming more pronounced and widespread with each degree of global warming, increasing food system risk. Food system disruptions can also come from outside the climate system, including from viral outbreaks, geopolitical conflict and socioeconomic changes. This presentation will highlight how the Agricultural Model Intercomparison and Improvement Project (AgMIP) develops modeling approaches that connect climate, biophysical and socioeconomic models in order to capture complex responses of the food system and potential interventions by diverse stakeholders in the public and private sectors. Models are applied on a range of scales that match decision contexts. These include households in specific agricultural production regions (e.g., within a country), national-level policymakers considering policies, sustainability and development priorities for a country’s agricultural sector, and global food market models that balance production and consumption around the world with competition for land, water, energy and sustainability priorities. Models are capable of capturing many important responses, but further development is needed to represent key systemic risks and the possibility for additional interventions beyond the farm gate. Improved food system models will provide important insights into our society’s ability to cope with climate change, as well as our ability to identify agricultural adaptation and mitigation opportunities to reduce overall risk.

Food systems↗

Efficient proactive vehicle relocation for on-demand mobility service with recurrent neural networks

One major challenge for on-demand mobility service (OMS) providers is to seamlessly match empty vehicles with trip requests so that the total vacant mileage is minimized. In this work, we develop an innovative data-driven approach for devising efficient vehicle relocation policy for OMS that (1) proactively relocates vehicles before the demand is observed and (2) reduces the inequality among drivers' income so that the proactive relocation policy is fair and is likely to be followed by drivers. Our approach represents the fusion of optimization and machine learning methods, which comprises three steps: First, we formulate the optimal proactive relocation as an optimal/stable matching problems and solve for global optimal solutions based on historical data. Second, the optimal solutions are then grouped and fed to train the deep learning models which consist of fully connected layers and long short-term memory networks. Low rank approximation is introduced to reduce the model complexity and improve the training performances. Finally, we use the trained model to predict the relocation policy which can be implemented in real time. We conduct comprehensive numerical experiments and sensitivity analyses to demonstrate the performances of the proposed method using New York City taxi data. Here, the results suggest that our method will reduce empty mileage per trip by 54-70% under optimal matching strategy, and a 25-32% reduction can also be achieved by following stable matching strategy. We also validate that the predicted relocation policies are robust in the presence of uncertain passenger demand level and passenger trip-requesting behavior.

33 ADVANCED PROPULSION SYSTEMS↗

Cross-aldol condensation on missing linker sites of metal–organic framework UiO-66

Missing linkers induce coordinatively unsaturated Lewis acid-base pair sites in metal–organic frameworks such as UiO-66 (Zr). The Lewis acid-base pair is induced by replacing the benzene dicarboxylic acid linker with formic acid, which subsequently is thermally decomposed. The concentration of coordinatively unsaturated Zr 4+ cations was titrated with acetone. In parallel to their concentration, the catalytic activity increased while the selectivity for aldol condensation between furfural and acetone remained constant. The missing linkers generate a complex ensemble consisting of an (at least partly) accessible Zr 4+ cation surrounded by oxygen, catalyzing the cross-aldol condensation. As a result, density functional theory calculations, matching the experimental findings provide a more detailed understanding of the elementary steps during the condensation reaction.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

Nanoparticle Tracking to Probe Transport in Porous Media

From the granular and fractured subsurface environment to highly engineered polymer membranes used in pharmaceutical purification, porous materials are ubiquitous in nature and industrial applications. In particular, porous media are used extensively in processes including water treatment, pharmaceutical sterilization, food/beverage processing and heterogeneous catalysis, where hindered mass transport is either essential to the process or a necessary, but undesirable limitation. Unfortunately, there are currently no universal models capable of predicting mass transport based on a description of the porous material because real porous materials are complex, and because many coupled dynamic mechanisms (e.g. adsorption, steric effects, hydrodynamic effects, electrostatic interactions, etc.) give rise to the observed macroscopic transport phenomena. While classical techniques, like nuclear magnetic resonance and dynamic light scattering, provide useful information about mass transport in porous media at the ensemble level, they provide limited insight into the microscopic mechanisms that give rise to complex phenomena such as anomalous diffusion, hindered pore-space accessibility, and unexpected retention under flow, among many others. To address this issue, we have developed refractive index matching imaging systems, combined with single-particle tracking methods, allowing the direct visualization of single particle motion within a variety of porous materials. Here, we summarize our recent efforts to advance the understanding of nanoparticle transport in porous media using single-particle tracking methods in both fundamental and applied scenarios. First, we describe the basic principles for two-dimensional and three-dimensional single-particle tracking in porous materials. Then, we describe concrete examples of nanoparticle transport in porous materials from two perspectives: (1) understanding fundamental elementary particle transport processes in porous media, including pore accessibility and cavity escape, which limit transport in porous media; (2) facilitating applications in industrial processes, e.g. by understanding the mechanisms of particle fouling and remobilization in filtration membranes. Finally, we provide an outlook of opportunities associated with investigating other types of mass transport in confined environments using single-particle tracking methods, including electrophoretic and self-propelled motion.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

Neural network-based classification and regression of magnetohydrodynamic modes in tokamaks

We present a machine learning-based magnetohydrodynamic (MHD) classifier and regressor that utilizes real or complex-valued 3D magnetic sensor array data to determine neoclassical tearing mode (NTM) onset times in tokamaks with millisecond accuracy. The input dataset consists of poloidal profiles of complex Fourier amplitudes with an n = 1 toroidal mode number from 144 human-labeled ITER Baseline Scenario discharges in the DIII-D tokamak, spanning both tearing-dominated and sawtooth-dominated regimes. Since m, n = 2,1 NTMs frequently emerge alongside sawteeth at the same frequency in this scenario, the focus is on isolating the m = 1 and m = 2 components of the n = 1 MHD mode near the tearing onset. To improve model regularization and prediction stability, singular value decomposition was applied to balance the sawtooth and tearing datasets. The enriched datasets facilitated training neural networks that learn the key distinguishing features of sawtooth and tearing modes in the poloidal profiles of their magnetic amplitude and phase. When the modes occur independently, the networks achieve perfect classification due to the modes’ distinct characteristics and low measurement noise. In the more experimentally relevant case where both modes coexist, the networks maintain exceptional performance across key metrics. Tests on synthetic data with known ground truth demonstrate the superior accuracy of the neural network trained on complex-valued input compared to models using real amplitude, phase, or pseudo-complex data, achieving both a mean time delay and standard deviation below 1 ms. Notably, standard linear regression methods fitting the dominant singular modes to the data closely match the neural network’s performance. Applying these methods across a broad range of H-mode scenarios will enable future studies to systematically identify dominant NTM triggers as scenario-specific variables, paving the way for more effective tearing mode avoidance strategies in future fusion reactor designs.

machine learning↗

Review of NASA's Planned Mars Program

The exploration of Mars has long been a prime scientific objective of the U.S. planetary exploration program. Yet no U.S. spacecraft has successfully made measurements at Mars since the Viking missions of the late 1970s. Mars Observer, which was designed to conduct global observations from orbit, failed just before orbit insertion in 1993. The Russian spacecraft Phobos 2 did succeed in making some observations of the planet in 1989, but it was designed primarily to observe Phobos, the innermost satellite of Mars; the spacecraft failed 2 months after insertion into Mars orbit during the complex maneuvers required to rendezvous with the martian satellite. In fall 1996 NASA plans to launch Mars Pathfinder for a landing on the martian surface in mid-1997. This spacecraft is one of the first two missions in NASA's Discovery program that inaugurates a new style of planetary exploration in which missions are low-cost (less than $150 million) and have very focused science objectives. As can be seen in the comparative data presented in Box 1, this mission is considerably smaller in terms of cost, mass, and scope than NASA's previous Mars missions. NASA's FY 1995 budget initiated a continuing Mars exploration program, called Mars Surveyor, that involves multiple launches of spacecraft as small as or smaller than Mars Pathfinder to Mars over the next several launch opportunities, which recur roughly every 26 months. The first mission in the program, Mars Global Surveyor, set for launch late in 1996, is intended to accomplish many of the objectives of the failed Mars Observer. Like the Discovery program, Mars Surveyor is a continuing series of low-cost missions, each of which has highly focused science objectives. See Box 1 for comparative details of those Surveyor missions currently defined. Around the same time that the Mars Surveyor series was chosen as the centerpiece of NASA's solar system exploration program, the Committee on Planetary and Lunar Exploration (COMPLEX) designated Mars as one of four scientific targets for emphasis in future studies. It was against this background that the Space Studies Board charged COMPLEX to review whether the Mars Pathfinder and Surveyor programs, as presently conceived, satisfy the highest priorities for understanding Mars as provided in its report, An Integrated Strategy for the Planetary Sciences: 1995-2010. The present document is COMPLEX's assessment of the scientific potential of NASA's new approach to Mars exploration. This assessment considers how well the scientific objectives of the Mars Surveyor program match those of the Integrated Strategy; it also addresses some advantages and disadvantages of the smaller-faster-cheaper approach to the exploration of Mars. The capabilities of the various instruments are not discussed in detail since the Mars Observer instruments, all of which are scheduled for reflight, have already been assessed by COMPLEX2 and later instruments are, in general, not yet well defined.

Source record↗

Maneuvering Rotorcraft Noise Prediction: A New Code for a New Problem

This paper presents the unique aspects of the development of an entirely new maneuver noise prediction code called PSU-WOPWOP. The main focus of the code is the aeroacoustic aspects of the maneuver noise problem, when the aeromechanical input data are provided (namely aircraft and blade motion, blade airloads). The PSU-WOPWOP noise prediction capability was developed for rotors in steady and transient maneuvering flight. Featuring an object-oriented design, the code allows great flexibility for complex rotor configuration and motion (including multiple rotors and full aircraft motion). The relative locations and number of hinges, flexures, and body motions can be arbitrarily specified to match the any specific rotorcraft. An analysis of algorithm efficiency is performed for maneuver noise prediction along with a description of the tradeoffs made specifically for the maneuvering noise problem. Noise predictions for the main rotor of a rotorcraft in steady descent, transient (arrested) descent, hover and a mild "pop-up" maneuver are demonstrated.

Brentner, Kenneth S.↗

DEVELOPMENT OF A LUMPED-PARAMETER THERMAL MODEL FOR ELECTRO-HYDRAULIC ACTUATORS

This paper describes a thermal-hydraulic modeling method of a closed-circuit electro-hydraulic actuator (EHA). Despite the high energy efficiency of EHAs, it is always important to assess their cooling requirements, to guarantee that the operating temperature remains within an acceptable range. The method proposed in this paper is based on a lumped parameter approach and simplifies some of the complex heat transfer processes by introducing empirical correction factors. The model is validated by means of temperature measurements on an EHA architecture developed by the author. The good match between the simulation results and experiments confirms the capability of the proposed methodology to predict the temperature performance of the reference EHA under different drive cycles. The paper presents a detailed analysis of the power losses and passive heat dissipation for the reference system. Based on this analysis, the cooling requirements of the EHA are studied.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗

Li 4 Ru 2 OCl 10 ·10H 2 O: crystal structure, magnetic properties and bonding interactions in ruthenium-oxo complexes

The results of the structural determination, magnetic characterization, and theoretical calculations of a new ruthenium-oxo complex, Li 4 [Ru 2 OCl 10 ]·10H 2 O, are presented. Single crystals were grown using solvent methods and the crystal structure was characterized by single crystal X-ray diffraction. Li 4 [Ru 2 OCl 10 ]·10H 2 O crystallizes into a low-symmetry triclinic structure ($P\bar1$) due to the much smaller Li + cation compared to K + cation in the tetragonal complex K 4 [Ru 2 OCl 10 ]·H 2 O. The X-ray photoelectron spectra confirm only the single valent Ru 4+ in Li 4 [Ru 2 OCl 10 ]·10H 2 O even though two distinct Ru sites exist in the crystal structure. Magnetic measurements reveal the diamagnetic property of Li 4 [Ru 2 OCl 10 ]·10H 2 O with unpaired electrons existing on Ru 4+ . Furthermore, the molecular orbital analysis matches well with the observed UV and magnetic measurements.

36 MATERIALS SCIENCE↗

Modeling the formation of Sedan Crater using the FLAG and HOSS codes

Numerical modeling of explosion crater formation requires accounting for complex physical processes. Numerical validation of explosion cratering is an important step in modeling and requires experimental data for comparison. Models using discrete elements and continuum models have both benefits and drawbacks to their approaches. In this work, we consider both an arbitrary Lagrangian–Eulerian (ALE) hydrocode and a finite discrete element method (FDEM) approach to modeling the formation of the Sedan crater, the largest human-made crater in the United States. The Sedan crater formed from an underground nuclear detonation in the Nevada desert as part of Project Plowshare. Our models show that the continuum approach of the hydrocode matched well compared to early test time prior to the mound rupture and subsequent fireball venting, when most of the alluvium exhibited fluid behavior. Our FDEM approach matched the final crater dimensions well, after material had settled back into the crater, when material strength and solid mechanics play key roles. Our work shows how leveraging the benefits of multiple numerical approaches can lead to better understanding of complex physical problems, especially problems with limited experimental data. By using a continuum approach to early-time hydrodynamics and an FDEM approach to later-time solid mechanics, we can better understand the different physical regimes of explosion crater formation.

36 MATERIALS SCIENCE↗

AnisONet: A deep neural operator-based anisotropic permeability upscaler from pore to Darcy scale

Directional permeability variations, which govern directional fluid flow in porous media with anisotropy, are important to accurately predict flow behavior, reactive transport, and fluid–solid interactions for various processes such as enhanced geothermal systems, energy storage devices, and biological systems. However, the intricate architecture of porous media makes it difficult to predict directional permeabilities. In this work, we present a novel machine learning (ML) framework, AnisONet, built upon an integration of a convolutional neural network, Swin transformer, and the deep operator network architecture, designed to predict anisotropic permeability and upscale predictions to larger spatial domains. First, AnisONet was evaluated with three classes of two-dimensional (2D) porous media, including synthetic circular and elliptical grains and natural sandstone grains from micro-computed tomography images. A lattice Boltzmann model (LBM) was used to calculate directional permeabilities at every 10° angle, producing 19 data points per image of porous media. AnisONet is then trained to predict permeability as a function of rotation angle. AnisONet showed strong predictive capability of directional permeability. Second, we tested our model for five upscaling cases with a large image size in the finite-element method (FEM) for 2D Darcy flow with various permeability tensor construction methods. Overall, upscaled permeability tensors in FEM simulations produce a reasonably good match with LBM results, highlighting the importance of selecting appropriate tensor formation strategies for accurate permeability upscaling. AnisONet, as a directional permeability estimator, could be further developed for more complex geometries, with the potential to develop a foundational ML model for various applications in porous media.

42 ENGINEERING↗

Chemical beam epitaxy for high efficiency photovoltaic devices

InP-based multijunction tandem solar cells show great promise for the conversion efficiency (eta) and high radiation resistance. InP and its related ternary and quanternary compound semiconductors such as InGaAs and InGaAsP offer desirable combinations for energy bandgap values which are very suitable for multijunction tandem solar cell applications. The monolithically integrated InP/In(0.53)Ga(0.47)As tandem solar cells are expected to reach efficiencies above 30 percent. Wanlass, et.al., have reported AMO efficiencies as high as 20.1% for two terminal cells fabricated using atmospheric-pressure metalorganic vapor phase epitaxy (APMOVPE). The main limitations in their technique are first related to the degradation of the intercell ohmic contact (IOC), in this case the In(0.53)Ga(0.47)As tunnel junction during the growth of the top InP subcell structure, and second to the current matching, often limited by the In(0.53)Ga(0.47)As bottom subcell. Chemical beam epitaxy (CBE) has been shown to allow the growth of high quality materials with reproducible complex compositional and doping profiles. The main advantage of CBE compared to metalorganic chemical vapor deposition (MOCVD), the most popular technique for InP-based photovoltaic device fabrication, is the ability to grow high purity epilayers at much lower temperatures (450 C - 530 C). In a recent report it was shown that cost-wise CBE is a breakthrough technology for photovoltaic (PV) solar energy progress in the energy conversion efficiency of InP-based solar cells fabricated using chemical beam epitaxy. This communication summarizes our recent results on PV devices and demonstrates the strength of this new technology.

Bensaoula, A.↗

Spatiotemporal Variability and Propagation of Equatorial Noise Observed by Cluster

We report a multipoint case study of the electromagnetic equatorial noise observed by the Cluster project. High-resolution data were measured in three close points in space located in the morning sector of the outer plasmasphere. We demonstrate a narrow latitudinal extent of the emissions with a typical width of 2 degrees, centered near the minimum-B equator. Power spectra recorded by the different satellites show a complex structure of emission lines whose relative intensities and positions vary at timescales of 1-2 min and/or at spatial scales of tens of wavelengths. These lines do not match harmonics of the local proton cyclotron frequency, as it would be expected if the waves are generated by energetic ions and observed near the source region. We bring observational evidence that the waves propagate with a significant radial component and thus can propagate from a distant generation region located at different radial distances where ion cyclotron frequencies match the observed fine structure.

Santolik, O.↗

Mitigating Photon Jitter in Optical PPM Communication

A theoretical analysis of photon-arrival jitter in an optical pulse-position-modulation (PPM) communication channel has been performed, and now constitutes the basis of a methodology for designing receivers to compensate so that errors attributable to photon-arrival jitter would be minimized or nearly minimized. Photon-arrival jitter is an uncertainty in the estimated time of arrival of a photon relative to the boundaries of a PPM time slot. Photon-arrival jitter is attributable to two main causes: (1) receiver synchronization error [error in the receiver operation of partitioning time into PPM slots] and (2) random delay between the time of arrival of a photon at a detector and the generation, by the detector circuitry, of a pulse in response to the photon. For channels with sufficiently long time slots, photon-arrival jitter is negligible. However, as durations of PPM time slots are reduced in efforts to increase throughputs of optical PPM communication channels, photon-arrival jitter becomes a significant source of error, leading to significant degradation of performance if not taken into account in design. For the purpose of the analysis, a receiver was assumed to operate in a photon- starved regime, in which photon counts follow a Poisson distribution. The analysis included derivation of exact equations for symbol likelihoods in the presence of photon-arrival jitter. These equations describe what is well known in the art as a matched filter for a channel containing Gaussian noise. These equations would yield an optimum receiver if they could be implemented in practice. Because the exact equations may be too complex to implement in practice, approximations that would yield suboptimal receivers were also derived.

Moision, Bruce↗