Toward Energy Efficient High-End Computing and AI
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Dissociative Recombination (DR) and Associative Ionization (AI) Cross Section Calculations for the NO+ + e yields N + O (2D+3P, 2P+3P, and 2D+1D) Reaction for Atmospheric Entry Modeling
During entry of a space craft into Earth atmosphere, the flow surrounding the vehicle becomes partially ionized leading to significant cation and free-electron production. Subsequently, electron impact excitation and dissociation form radiating excited state species that contribute to the heat load on the vehicle. While experimental data on selected total cross sections are available, few experiments address the need for accurate AI cross sections for metastable atomic states at the high temperatures realized in atmospheric re-entry. In order to maintain desired safety margins during atmospheric entry of a space vehicle, chemical reaction models need to accurately account for this process. For the present study, we have computed vibrationally resolved cross sections for the DR of NO+ for electron energies between 0.01 to 10 eV and apply microscopic reversibility to obtain the AI cross sections and rate coefficients. For the DR cross section calculations, we use state-of-the-art MRCI potential energy curves. The resulting adiabatic potential curves are transformed to a diabatic representation, which is used in time-dependent wave packet calculations to describe the nuclear motion of the dissociating cation upon collision with an electron. Based on the wave function evolution in time, these calculations provide T-matrix and cross sections for the DR including recombination into the low energy metastable atomic states. The DR and AI rate coefficients are compared with the available experimental data.
Ewa Papajak↗
Adaptive pattern recognition by mini-max neural networks as a part of an intelligent processor
In this decade and progressing into 21st Century, NASA will have missions including Space Station and the Earth related Planet Sciences. To support these missions, a high degree of sophistication in machine automation and an increasing amount of data processing throughput rate are necessary. Meeting these challenges requires intelligent machines, designed to support the necessary automations in a remote space and hazardous environment. There are two approaches to designing these intelligent machines. One of these is the knowledge-based expert system approach, namely AI. The other is a non-rule approach based on parallel and distributed computing for adaptive fault-tolerances, namely Neural or Natural Intelligence (NI). The union of AI and NI is the solution to the problem stated above. The NI segment of this unit extracts features automatically by applying Cauchy simulated annealing to a mini-max cost energy function. The feature discovered by NI can then be passed to the AI system for future processing, and vice versa. This passing increases reliability, for AI can follow the NI formulated algorithm exactly, and can provide the context knowledge base as the constraints of neurocomputing. The mini-max cost function that solves the unknown feature can furthermore give us a top-down architectural design of neural networks by means of Taylor series expansion of the cost function. A typical mini-max cost function consists of the sample variance of each class in the numerator, and separation of the center of each class in the denominator. Thus, when the total cost energy is minimized, the conflicting goals of intraclass clustering and interclass segregation are achieved simultaneously.
Szu, Harold H.↗
Analysis of AIS Data of the Recluse Oil Field, Recluse, Wyoming
Airborne Imaging Spectrometer (AIS) data were flown over the Recluse, Wyoming oil field on September 9, 1984. Processing software was developed at Earth Satellite Corporation (EarthSat) for interactive analysis of the AIS data. EarthSat's AIS processing capabilities include destriping, solar irradiance corrections, residual calculations, geometric resampling, equal energy normalization, interactive spectral classifications and a variety of compressive algorithms to reduce the data to 8-bit format with a minimum of information loss. The in-house photolab facilities of EarthSat can routinely produce high-quality color renditions of the enhanced AIS data. A total of 80 lithologic samples were collected under the AIS flight lines. Correlation (within the atmospheric windows) between the laboratory and the AIS spectra of sample sites was generally poor. Reasonable correlation was only possible in large, freshly plowed fields. Mixed pixels and contrast between the natural and sample's surfaces were believed responsible for the poor correlation. Finally, a drift of approximately three channels was observed in the diffraction grating position within the 1.8 to 2.1 micron quadrant.
Dykstra, J. D.↗
Measurement of Insulation Compaction in the Cryogenic Fuel Tanks at Kennedy Space Center by Fast/Thermal Neutron Techniques
The liquid hydrogen and oxygen cryogenic storage tanks at John F. Kennedy Space Center (KSC) use expanded perlite as thermal insulation. Th ere is evidence that some of the perlite has compacted over time, com promising the thermal performance and possibly also structural integr ity of the tanks. Therefore an Non-destructive Testing (NDT) method for measuring the perlite density or void fraction is urgently needed. Methods based on neutrons are good candidates because they can readil y penetrate through the 1.75 cm outer steel shell and through the ent ire 120 cm thickness of the perlite zone. Neutrons interact with the nuclei of materials to produce characteristic gamma rays which are the n detected. The gamma ray signal strength is proportional to the atom ic number density. Consequently, if the perlite is compacted then the count rates in the individual peaks in the gamma ray spectrum will i ncrease. Perlite is a feldspathic volcanic rock made up of the major elements Si, AI, Na, K and 0 along with some water. With commercially available portable neutron generators it is possible to produce simul taneously fluxes of neutrons in two energy ranges: fast (14 MeV) and thermal (25 meV). Fast neutrons produce gamma rays by inelastic scatt ering which is sensitive to Fe and O. Thermal neutrons produce gamma rays by radiative capture in prompt gamma neutron activation (PGNA) and this is sensitive to Si, AI, Na, Kand H. Thus the two energy ranges produce complementary information. The R&D program has three phases: numerical simulations of neutron and gamma ray transport with MCNP s oftware, evaluation of the system in the laboratory on test articles and finally mapping of the perlite density in the cryogenic tanks at KSC. The preliminary MCNP calculations have shown that the fast/therma l neutron NDT method is capable of distinguishing between expanded an d compacted perlite with excellent statistics.
Livingston, R. A.↗
Cross-polar cap potential drop and the energy coupling function
A numerical model is formulated for correlations between the cross polar cap potential, the solar wind, and the AI index and compared with satellite data. The conductivity model was devised from empirically defined relationships between the Hall and Pedersen conductivities and the measured magnitude of the horizontal magnetic disturbance near College, AK. An instantaneous conductivity distribution was generated from data gathered at, e.g., six IMS meridian chain stations. A strong correlation appeared between an energy coupling function (ECF) and the polar cap potential drop. The ECF was derived from the chain station data, implying that potential drop estimates can be made from ground-based magnetometer data.
Ahn, B.-H.↗
Li-Ion Batteries for Space Applications: High Specific Energy and Wide-Operating Temperature
Compared to the conventional Ni-Co oxides (with or without AI additions), the NMC (1/3:1/3:1/3) cathode provides marginal improvement in specific capacity. However, some of the formulations based on the solid solutions of layered Li2Mn03 and LiM02 (M = Mn0.5Ni0.5} have shown capacities as high as 250 mAh/g, combined with high cell voltages (4.5 V) and with the likelihood of enhanced thermal stability. Multi-component electrolytes with low EC-proportions and selected co-solvents provide significant improvement in the low temperature performance, down to -60 C, combined with the non-flammable attribute from the co-solvents. The NMC cathode shows good compatibility with the carbonate-based low temperature electrolytes. Impressive performances have been realized at low temperatures of <= 30 C. Electrolytes with high salt concentration and high EC content fare well at room temperatures, while the formulations with low EC content and low salt concentration are preferred at low temperatures. DPA studies reveal increased SEI growth on the electrodes, especially anode, upon irradiation. Performance of low temperature electrolytes in prototype cells corroborate the findings from laboratory cells.
storage↗
Multilayer Relaxation and Surface Energies of Metallic Surfaces
The perpendicular and parallel multilayer relaxations of fcc (210) surfaces are studied using equivalent crystal theory (ECT). A comparison with experimental and theoretical results is made for AI(210). The effect of uncertainties in the input parameters on the magnitudes and ordering of surface relaxations for this semiempirical method is estimated. A new measure of surface roughness is proposed. Predictions for the multilayer relaxations and surface energies of the (210) face of Cu and Ni are also included.
Bozzolo, Guillermo↗
Toward detecting California shrubland canopy chemistry with AIS data
Airborne Imaging Spectrometer (AIS)-2 data of coastal sage scrub vegetation were examined for fine spectral features that might be used to predict concentrations of certain canopy chemical constituents. A Fourier notch filter was applied to the AIS data and the TREE and ROCK mode spectra were ratioed to a flat field. Portions of the resulting spectra resemble spectra for plant cellulose and starch in that both show reduced reflectance at 2100 and 2270 nm. The latter are regions of absorption of energy by organic bonds found in starch and cellulose. Whether the relationship is sufficient to predict the concentration of these chemicals from AIS spectra will require testing of the predictive ability of these wavebands with large field sample sizes.
Price, Curtis V.↗
Energy-Efficient Systems Eliminate Icing Danger for UAVs
Ames Research Center engineer Leonard Haslim invented an anti-icing t echnology called an electroexpulsive separation system, which uses m echanical force to shatter potentially dangerous ice buildup on an ai rcraft surface. Temecula, California-based Ice Management Systems (no w known as IMS-ESS) licensed the technology from Ames and has discov ered a niche market for the lightweight, energy-efficient technology: unmanned aerial vehicles (UAVs). IMS-ESS systems now prevent damagi ng ice accumulation on military UAVs, allowing the vehicles to carry out crucial missions year round.
Source record↗
AI-3000K Infrared Line List for Hot CO 2
AI-3000K is a semi-empirical IR line list constructed for hot CO 2 spectra analysis and simulation up to 3000 – 4000 K. Compared to previously published Ames-1000K and UCL-4000, it represents a major upgrade, utilizing a new algorithm for optimization and including the latest improvements in potential energy surface (PES), dipole moment surface (DMS), and room temperature IR line list (Ames-2021 296K). To maximize the success of introducing experimental based accurate line positions, a new PES (X01d) was refined with respect to >800 selected CDSD2019 [Tashkun et al. JQSRT (2019) 228, 124] energy levels in the range of 0 – 24,000 cm -1 , with σ rms = 0.5 – 0.7 cm -1 . Most differences between the X01d PES based levels and CDSD2019 energies are within ± 2 cm -1 . A new DMS is fitted from extrapolated CCSD(T)/aug-cc-pV(T,Q,5)Z dipole calculations, with σ rms = 5.1 × 10 -6 au for 11,155 geometries up to 40,000 cm -1 , denoted Ames-2021–40K. Compared to the best available Ames-2021 DMS and room temperature IR line list [Huang et al J. Phys. Chem. A (2022) 126, 5940], the relative intensity differences are expected to be ∼ 1 ‰. The line position accuracy of ''X01d + Ames-2021–40K'' IR line list is significantly improved by adopting CDSD2019 energy levels up to 24,000 cm -1 (J ≤ 150). The Einstein A21 coefficients for E' < 15,000 cm -1 transitions are replaced by more accurate values from the Ames-2021 296K IR line list. In short, the AI-3000K is the X01d PES and Ames-2021–40K DMS based line list enhanced with the A 21 of Ames-2021 296K line list and CDSD energy levels. It provides continuous coverage from 0 to 20,000 cm -1 for the four most abundant isotopologues: 12 C 16 O 2 (626), 13 C 16 O 2 (636), 16 O 12 C 18 O (628), and 16 O 12 C 17 O (627). The impacts of isotopologue and E'/E'' cutoffs have been examined. Intensity convergence (not accuracy) of AI-3000K line list is quantitatively estimated in 1 cm -1 bins. It is better than 99% in the whole range of 0 – 20,000 (or 10,000) cm -1 at 1000 K (or 2000 K), or better than 90% in the whole range of 0 – 15,000 (or 9000) cm -1 at 2000 K (or 3000 K), respectively. Convergence beyond 3000 K will require a new PES and DMS for E' > 40,000 cm -1 . The AI-3000K and HITEMP based IR simulations are compared to high resolution shock tube experiments for CO 2 -Ar mixture up to 2000 K. With line position accuracy comparable to that of HITEMP, AI-3000K IR line list yields better agreements at more frequencies. Potential sources of discrepancies with experiment are discussed.
AI-3000K↗
NASA POWER: Providing Analysis-Ready, Cloud-Optimized Data for AI /ML Training and Applications in Earth Science
As global demand for sustainable development grows, the integration of Earth Observation (EO) data into decision making frameworks has become a primary objective for the scientific community. The NASA Prediction of Worldwide Energy Resources (POWER) project serves as a bridge between NASA EO data and the specialized needs of the renewable energy, sustainable infrastructure and agroclimatology communities. In this poster presentation we will present an overview of POWER data products and services along with its use in diverse research to decision-making workflows. By providing over 40 years of high-resolution historical, hourly and daily solar and meteorological data, POWER transforms satellite observations and global model reanalysis into actionable, Analysis-Ready Dataset (ARD). Currently, the project delivers over 250 industry-friendly parameters to the users from different NASA datasets like CERES SYN1Deg, MERRA-2, and IMERG alongside downscaled CMIP6 climate model data, fulfilling over 16 million requests from 50,000 unique users monthly. To ensure data quality and traceability, these parameters are rigorously validated against the ground-based observations from the Baseline Surface Radiation Network (BSRN) and the Global Surface Summary of the Day (GSOD) – these results will be discussed in the presentation. A newly introduced web-based PaRameter Uncertainty ViEwer (PRUVE) tool will be presented that provides an online validation platform to the users that benchmarks satellite-based and assimilation data products against these surface measurements. To reduce technical barriers to data adoption, POWER data is accessible through RESTful APIs, ESRI ArcGIS Image Services, a web-based Data Access Viewer tool, allowing users to visualize, validate and apply the dataset. For efficient data delivery POWER data is cloud-optimized into Zarr datastore accessible through NASA managed Amazon S3 ensures high-performance allowing users to integrate EO directly into operational pipelines. These customized services will be presented. Use cases from application will be presented from the energy sector - such as for design of generation systems, performance monitoring of solar power plants, in infrastructure sector- optimizing building energy efficiency and thermal comfort, in agriculture – such as driving crop simulation and yield forecasting models to enable climate resilient farming. Furthermore, the shift toward machine learning (ML) in EO research that has positioned POWER as a key provider for training datasets which will be discussed. Use-cases will be presented to showcase how NASA data is enabling the development of predictive tools for climate variability and resource management. The poster will present POWER’s future plans including technology development to enhance data traceability and reproducibility and improving I/O performance to support the rapid integration of new EO products, ensuring that POWER remains a robust scalable backend for the evolving landscape of AI-driven Earth Science. Additionally, POWER is developing an AI Agent and an MCP-Server to enable industry AI-Agentic workflows.
Neha Khadka↗
Immobile Robots: AI in the New Millennium
A new generation of sensor rich, massively distributed, autonomous systems are being developed that have the potential for profound social, environmental, and economic change. These include networked building energy systems, autonomous space probes, chemical plant control systems, satellite constellations for remote ecosystem monitoring, power grids, biosphere-like life support systems, and reconfigurable traffic systems, to highlight but a few. To achieve high performance, these immobile robots (or immobots) will need to develop sophisticated regulatory and immune systems that accurately and robustly control their complex internal functions. To accomplish this, immobots will exploit a vast nervous system of sensors to model themselves and their environment on a grand scale. They will use these models to dramatically reconfigure themselves in order to survive decades of autonomous operations. Achieving these large scale modeling and configuration tasks will require a tight coupling between the higher level coordination function provided by symbolic reasoning, and the lower level autonomic processes of adaptive estimation and control. To be economically viable they will need to be programmable purely through high level compositional models. Self modeling and self configuration, coordinating autonomic functions through symbolic reasoning, and compositional, model-based programming are the three key elements of a model-based autonomous systems architecture that is taking us into the New Millennium.
Williams, Brian C.↗
CI Controls for Energy and Environment
Computational intelligence (CI) is a rapidly evolving field that utilizes life imitating metaphors for guiding model building including, but not limited to neural networks, fuzzy logic, genetic algorithms, artificial life, and hybrid CI paradigms. Although the boundaries between artificial intelligence (AI) and CI are not distinct, their research communities are separate and distinct. CI researchers tend to focus on processing numerical data from sensors, while the AI community generally relies on symbolic computing to capture human knowledge. In both areas, there is a great deal of interest and activity in hybrid systems that can offset the limitations of individual methods, extend their capabilities, and create new capabilities. Examples of the benefits that can accrue from hybrid systems are contained.
Biondo, Samuel J.↗
The poleward edge of the mid-latitude trough - Its formation, orientation and dynamics
Data from the Advanced Ionospheric Sounder (AIS) deployed at Halley, Antarctica (76-deg S, 27-deg W; L = 4.2) and the Dynamics Explorer-2 spacecraft (DE-2) are used to investigate several aspects of the formation processes and dynamics of the poleward edge of the midlatitude electron density trough. These include a study of the flux and energy of charged particles precipitating into the F-region as a function of Magnetic Local Time. It is found that local energetic electron precipitation is a major source of ionization of the poleward edge in the evening sector, but only after magnetic midnight transport processes become more important. Occasionally a significant increase in the flux of conjugate photoelectrons is colocated with the poleward edge of the trough in the morning sector. The combination of AIS and DE-2 data has allowed identification of significant longitudinal structure on the poleward edge of the trough that may be the result of substorm activity. It is found that the orientation of the poleward edge of the trough and the locus of the plasmapause predicted from the 'tear-drop' model vary in rather a similar manner with local time, though no close physical link between the two features is inferred from this coincidence.
Rodger, A. S.↗
Generalized Airy functions for use in one-dimensional quantum mechanical problems
The solution of the one dimensional, time independent, Schroedinger equation in which the energy minus the potential varies as the nth power of the distance is obtained from proper linear combinations of Bessel functions. The linear combinations called generalized Airy functions, reduce to the usual Airy functions Ai(x) and Bi(x) when n equals 1 and have the same type of simple asymptotic behavior. Expressions for the generalized Airy functions which can be evaluated by the method of generalized Gaussian quadrature are obtained.
Eaves, J. O.↗
Application of expert systems in the Common Module electrical power system
Each Common Module (CM) of the Space Station must be capable of handling a 50 kW electricity supply, 25 kW for transmission and 25 kW for consumption. The power must be handled and managed by on-board systems, a necessity that dovetails with the objectives of Public Law 98-371, which mandates that the Space Station push the state of the art of automation and AI. Expert systems will be needed to handle the large data flow for the power system and to ensure that the system degrades gracefully. Features of the first expert systems expected for the power system, i.e., a dynamic load planner/scheduler and energy storage subsystem management, fault diagnosis/analysis, health status/trend analysis, and orbital replacement advisor expert systems, are described. Finally, growth Space Station expert systems applications are discussed.
Weeks, D. J.↗
Preliminary analysis of Airborne Visible/Infrared Imaging Spectrometer (AVIRIS) for mineralogic mapping at sites in Nevada and Colorado
Airborne Visible/Infrared Imaging Spectrometer (AVIRIS) data for sites in Nevada and Colorado were evaluated to determine their utility for mineralogical mapping in support of geologic investigations. Equal energy normalization is commonly used with imaging spectrometer data to reduce albedo effects. Spectra, profiles, and stacked, color-coded spectra were extracted from the AVIRIS data using an interactive analysis program (QLook) and these derivative data were compared to Airborne Imaging Spectrometer (AIS) results, field and laboratory spectra, and geologic maps. A feature extraction algorithm was used to extract and characterize absorption features from AVIRIS and laboratory spectra, allowing direct comparison of the position and shape of absorption features. Both muscovite and carbonate spectra were identified in the Nevada AVIRIS data by comparison with laboratory and AIS spectra, and an image was made that showed the distribution of these minerals for the entire site. Additional, distinctive spectra were located for an unknown mineral. For the two Colorado sites, the signal-to-noise problem was significantly worse and attempts to extract meaningful spectra were unsuccessful. Problems with the Colorado AVIRIS data were accentuated by the IAR reflectance technique because of moderate vegetation cover. Improved signal-to-noise and alternative calibration procedures will be required to produce satisfactory reflectance spectra from these data. Although the AVIRIS data were useful for mapping strong mineral absorption features and producing mineral maps at the Nevada site, it is clear that significant improvements to the instrument performance are required before AVIRIS will be an operational instrument.
Kruse, Fred A.↗