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NASA NTRSDate not supplied

Innovative Features of NASA's Celestial Mapping System to Support Exploration in the Lunar South Pole

Introduction: NASA's Celestial Mapping System (CMS) is developed to address the need for 3D tools for planetary science investigations, mission planning, in-situ operations, in a 3D-first design constructed around a unified view of a planetary globe. At present CMS provides many critical functionalities that include 1) Equipment planning and optimized placement on Lunar surface 2) Line of sight (visibility ) analysis 3) Powerful measurement tools based on 3D terrain with realistic 3D models to represent rovers, astronauts and equipment 4) Visualization of de-rived mapping products (e.g. resource maps), and 5) Data engine for hosting new observations that are not available in other contemporary lunar data tools. CMS is built on the foundation of powerful NASA WorldWind globe engines. In near future, users will be able to simultaneously deploy CMS onto multiple hardware configurations and platforms such as Windows, Linux, iOS and Android. The users will also have the flexibility to update to the latest imagery and terrain datasets as they are being acquired (in real time) before and/or during the exploration mission. CMS is also capable of consumption and analysis of data from locally hosted and external sources. It supports Open Geospatial Consorti-um (OGC) data and file standards, with current integrations of datasets from the Astrogeology Science Center of USGS which include global and local data acquired from NASA (LRO, Clementine, Lunar Orbiter) and JAXA (SELENE/Kaguya) with the capability of integrating more datasets. With development experience in both the end-user application and planetary engine side, CMS is also able to adapt to newer Lunar cartography standards as they develop and become recognized by international geospatial panels. Overcoming Polar Distortions: 3D geospatial applications traditionally suffer from significant distortion of imagery at the poles due to following reasons – 1) distortions in the source imagery 2) Incompatible tessellation algorithm on the poles 3) map projections. In the lunar context, with the focus on the South pole, this is not acceptable. The CMS team is researching ways to address polar distortion of imagery with new tessellation algorithms and by reprojecting the data using projections that are more accurate in polar scenarios. Figure1 shows the potential error introduced by different tessellation methods, represented by the red and green circles for Shoemaker crater. There is ~2 Km difference in the placement of the crater. Line of Sight Analysis and Traverse Planning: We have developed a built-in line of sight analysis (LOS) tool in CMS that analyzes the terrain profile and obstructions and provides the visibility of a given terrain for a remote observer. Figure 2 shows the viewshed analysis on the PSR in Nobile region. The PSR was created with help of HORUS generated images. The yellow pin shows the observer location outside the PSR. The yellow area shows the visible part of PSR. The obstructed area with no visibility for the observer is shown in red. This analysis was ex-tended further to set different heights for various observers and then perform the viewshed analysis. Combining the different visibility profiles can help designing improved traverses within the crater.

Geospatial Mapping↗
NASA NTRSDate not supplied

Innovative Features of NASA's Celestial Mapping System to Support Exploration in the Lunar South Pole

Introduction: NASA's Celestial Mapping System (CMS) is developed to address the need for 3D tools for planetary science investigations, mission planning, in-situ operations, in a 3D-first design constructed around a unified view of a planetary globe. At present CMS provides many critical functionalities that include 1) Equipment planning and optimized placement on Lunar surface 2) Line of sight (visibility ) analysis 3) Powerful measurement tools based on 3D terrain with realistic 3D models to represent rovers, astronauts and equipment 4) Visualization of de-rived mapping products (e.g. resource maps), and 5) Data engine for hosting new observations that are not available in other contemporary lunar data tools. CMS is built on the foundation of powerful NASA WorldWind globe engines. In near future, users will be able to simultaneously deploy CMS onto multiple hardware configurations and platforms such as Windows, Linux, iOS and Android. The users will also have the flexibility to update to the latest imagery and terrain datasets as they are being acquired (in real time) before and/or during the exploration mission. CMS is also capable of consumption and analysis of data from locally hosted and external sources. It supports Open Geospatial Consorti-um (OGC) data and file standards, with current integrations of datasets from the Astrogeology Science Center of USGS which include global and local data acquired from NASA (LRO, Clementine, Lunar Orbiter) and JAXA (SELENE/Kaguya) with the capability of integrating more datasets. With development experience in both the end-user application and planetary engine side, CMS is also able to adapt to newer Lunar cartography standards as they develop and become recognized by international geospatial panels. Overcoming Polar Distortions: 3D geospatial applications traditionally suffer from significant distortion of imagery at the poles due to following reasons – 1) distortions in the source imagery 2) Incompatible tessellation algorithm on the poles 3) map projections. In the lunar context, with the focus on the South pole, this is not acceptable. The CMS team is researching ways to address polar distortion of imagery with new tessellation algorithms and by reprojecting the data using projections that are more accurate in polar scenarios. Figure1 shows the potential error introduced by different tessellation methods, represented by the red and green circles for Shoemaker crater. There is ~2 Km difference in the placement of the crater. Line of Sight Analysis and Traverse Planning: We have developed a built-in line of sight analysis (LOS) tool in CMS that analyzes the terrain profile and obstructions and provides the visibility of a given terrain for a remote observer. Figure 2 shows the viewshed analysis on the PSR in Nobile region. The PSR was created with help of HORUS generated images. The yellow pin shows the observer location outside the PSR. The yellow area shows the visible part of PSR. The obstructed area with no visibility for the observer is shown in red. This analysis was ex-tended further to set different heights for various observers and then perform the viewshed analysis. Combining the different visibility profiles can help designing improved traverses within the crater.

Geospatial Mapping↗
DOE OSTI2024

On the Impact of High-Order Harmonic Generation in Electrical Distribution Systems

The modern power grid has seen a rise in the integration of non-linear loads, presenting a significant concern for operators. These loads introduce unwanted harmonics, leading to potential issues such as overheating and improper functioning of circuit breakers. In pursuing a more sustainable grid, the adoption of electric vehicles (EVs) and photovoltaic (PV) systems in residential networks has increased. Understanding and examining the effects of high-order harmonic frequencies beyond $1.5$ kHz is crucial to understanding their impact on the operation and planning of electrical distribution systems under varying nonlinear loading conditions. This study investigates a diverse set of critical power electronic loads within a household modeled using PSCAD/EMTdc, analyzing their unique harmonic spectra. This information is utilized to run the time-series harmonic analysis program in OpenDSS on a modified IEEE 34 bus test system model. The impact of high-order harmonics is quantified using metrics that evaluate total harmonic distortion (THD), transformer harmonic-driven eddy current loss component, and propagation of harmonics from the source to the substation transformer.

Peerzada, Aaqib A. [BATTELLE (PACIFIC NW LAB)]↗
DOE OSTI2020

The AMUSING++ Nearby Galaxy Compilation. I. Full Sample Characterization and Galactic-scale Outflow Selection

We present here AMUSING++: the largest compilation of nearby galaxies observed with the MUSE integral-field spectrograph so far. This collection consists of 635 galaxies from different MUSE projects covering the redshift interval 0.0002 < z < 0.1. The sample and its main properties are characterized and described here. It includes galaxies of almost all morphological types, with a good coverage in its color–magnitude diagram, within the stellar mass range between 10{sup 8} and 10{sup 12} M {sub ⊙}, and with properties resembling those of a diameter-selected sample. The AMUSING++ sample is, therefore, suitable for studying, with unprecendented detail, the properties of nearby galaxies at global and local scales, providing us with more than 50 million individual spectra. We use this compilation to investigate the presence of galactic outflows. We exploit the use of combined emission-line images to explore the shape of the different ionized components and the distribution along classical diagnostic diagrams to disentangle the different ionizing sources across the optical extension of each galaxy. We use the cross-correlation function to estimate the level of symmetry of the emission lines as an indication of the presence of shocks and/or active galactic nuclei. We uncovered a total of 54 outflows, comprising ∼8% of the sample. A large number of the discovered outflows correspond to those driven by active galactic nuclei (∼60%), suggesting some bias in the selection of our sample. No clear evidence was found that outflow host galaxies are highly star-forming, and outflows appear to be found within all galaxies around the star-formation sequence.

79 ASTRONOMY AND ASTROPHYSICS↗
NASA NTRS2014

Human Exploration Science Office (KX) Overview

The Human Exploration Science Office supports human spaceflight, conducts research, and develops technology in the areas of space orbital debris, hypervelocity impact technology, image science and analysis, remote sensing, imagery integration, and human and robotic exploration science. NASA's Orbital Debris Program Office (ODPO) resides in the Human Exploration Science Office. ODPO provides leadership in orbital debris research and the development of national and international space policy on orbital debris. The office is recognized internationally for its measurement and modeling of the debris environment. It takes the lead in developing technical consensus across U.S. agencies and other space agencies on debris mitigation measures to protect users of the orbital environment. The Hypervelocity Impact Technology (HVIT) project evaluates the risks to spacecraft posed by micrometeoroid and orbital debris (MMOD). HVIT facilities at JSC and White Sands Test Facility (WSTF) use light gas guns, diagnostic tools, and high-speed imagery to quantify the response of spacecraft materials to MMOD impacts. Impact tests, with debris environment data provided by ODPO, are used by HVIT to predict risks to NASA and commercial spacecraft. HVIT directly serves NASA crew safety with MMOD risk assessments for each crewed mission and research into advanced shielding design for future missions. The Image Science and Analysis Group (ISAG) supports the International Space Station (ISS) and commercial spaceflight through the design of imagery acquisition schemes (ground- and vehicle-based) and imagery analyses for vehicle performance assessments and mission anomaly resolution. ISAG assists the Multi-Purpose Crew Vehicle (MPCV) Program in the development of camera systems for the Orion spacecraft that will serve as data sources for flight test objectives that lead to crewed missions. The multi-center Imagery Integration Team is led by the Human Exploration Science Office and provides expertise in the application of engineering imagery to spaceflight. The team links NASA programs and private industry with imagery capabilities developed and honed through decades of human spaceflight, including imagery integration, imaging assets, imagery data management, and photogrammetric analysis. The team is currently supporting several NASA programs, including commercial demonstration missions. The Earth Science and Remote Sensing Team is responsible for integrating the scientific use of Earth-observation assets onboard the ISS, which consist of externally mounted sensors and crew photography capabilities. This team facilitates collaboration on remote sensing and participates in research with academic organizations and other Government agencies, not only in conjunction with ISS science, but also for planetary exploration and regional environmental/geological studies. Human exploration science focuses on science strategies for future human exploration missions to the Moon, Mars, asteroids, and beyond. This function provides communication and coordination between the science community and mission planners. ARES scientists support the operation of robotic missions (i.e., Mars Exploration Rovers and the Mars Science Laboratory), contribute to the interpretation of returned mission data, and translate robotic mission technologies and techniques to human spaceflight.

Calhoun, Tracy A.↗
DOE OSTI2025

Understanding latent timescales in neural ordinary differential equation models of advection-dominated dynamical systems

The neural ordinary differential equation (ODE) framework has shown considerable promise in recent years in developing highly accelerated surrogate models for complex physical systems characterized by partial differential equations (PDEs). For PDE-based systems, state-of-the-art neural ODE strategies leverage a two-step procedure to achieve this acceleration: a nonlinear dimensionality reduction step provided by an autoencoder, and a time integration step provided by a neural-network based model for the resultant latent space dynamics (the neural ODE). This work explores the applicability of such autoencoder-based neural ODE strategies for PDEs in which advection terms play a critical role. More specifically, alongside predictive demonstrations, physical insight into the sources of model acceleration (i.e., how the neural ODE achieves its acceleration) is the scope of the current study. Such investigations are performed by quantifying the effects of both autoencoder and neural ODE components on latent system time-scales using eigenvalue analysis of dynamical system Jacobians. To this end, the sensitivity of various critical training parameters – de-coupled versus end-to-end training, latent space dimensionality, and the role of training trajectory length, for example – to both model accuracy and the discovered latent system timescales is quantified. Furthermore, this work specifically uncovers the key role played by the training trajectory length (the number of rollout steps in the loss function during training) on the latent system timescales: larger trajectory lengths correlate with an increase in limiting neural ODE time-scales, and optimal neural ODEs are found to recover the largest time-scales of the full-order (ground-truth) system. Demonstrations are performed across fundamentally different unsteady fluid dynamics configurations influenced by advection: (1) the Kuramoto–Sivashinsky equations (2) Hydrogen-Air channel detonations (the compressible reacting Navier–Stokes equations with detailed chemistry), and (3) 2D Atmospheric flow.

Advection-dominated dynamical systems↗
DOE OSTI2024

Spin-Controllable Dynamics in Defect-Engineered Carbon Nanotubes as Single Photon Emitters: Data-Driven Modeling and Computations

Quantum technologies, such as quantum computing and sensing, require efficient single-photon emission (SPE) sources that operate at room temperature in telecom wavelengths. While several materials can serve as SPE sources, no single platform meets all the criteria for efficiency, ambient operation, and scalability. Single-walled carbon nanotubes (SWCNTs) with covalently attached molecules offer a promising solution. Their SPE can be easily tuned via modifications of the SWCNT's diameter, chirality, and bonded molecules, enabling emission across near-IR to telecom wavelengths at ambient conditions. However, to fully realize the potential of SWCNTs and unlock their quantum capabilities, a deeper understanding of how structural defects from molecular adducts affect their emission and competing photoexcited processes is essential. To address this gap in our knowledge, this project combined quantum chemistry calculations with data-driven methods of cheminformatics (QSAR) and machine learning (ML). The developed computational approaches have provided several design strategies for covalent functionalization of SWCNTs to improve their optical response. The collaboration with Los Alamos National Lab (LANL) enabled direct comparison of computational and experimental data, facilitating method validation. This partnership was enhanced through access to LANL's Center for Integrated Nanotechnologies (CINT) utilizing User Facility Program and summer internships, which provided three NDSU graduate students with hands-on experience at LANL. The outcomes of this project included (1) Advancing the current stage of computational methods in accurate modeling of non-adiabatic spin-dependent photoexcited dynamics and its applicability to nanosystems consisting of thousands of atoms, realized as open-access codes linked to existing DFT-based software; (2) Establishing the relationship between the structure of adducts and SWCNTs and intrinsic excitonic and spin properties of defect states for guiding novel synthetic strategies and experimental probes of chemically functionalized SWCNTs as near-IR emitting materials; (3) Generating virtual libraries of hypothetical functionalized SWCNTs for virtual screening of their chemical structures and optical properties, leveraging new functionalities of SWCNTs; (4) Offering a unique experience for NDSU graduate students that prepared them for future scientific careers related to materials modeling and big data processing. These results were summarized in 12 published journal papers and 3 recently submitted papers. One of a key finding is that the position of defect sites on the SWCNT surface primarily drives the emission redshift (up to 100 meV), while the polarity of the defect-inducing molecules has a much smaller effect (~10 meV). However, the electron-donating or withdrawing properties of a molecule influence selecting reactivity of defect sites. These insights important for optimizing synthetic protocols for desired emissions in SWCNTs. We also revealed that the interaction between two defects at various positions on the SWCNT enhances the redshift and optical activity of states, favoring strong near-IR emission. This suggests that manipulations in defect concentrations is a promising strategy for controlling efficient emission. Mostly important, the defect position was found controllable by the spin states of photoexcited intermediates: Excited aromatic molecules form ortho defects with SWCNTs at their singlet states in the presence of oxygen, while oxygen-free conditions favor para defects via the triplet-state mechanism. Additionally, a heat-activated [2+2] cycloaddition reaction facilitates divalent defect formation with fewer bonding positions that narrows emission bands. These groundbreaking findings have been experimentally validated and significantly advance our understanding of defect chemistry in SWCNTs. Using a novel encoding technique and 3D-MoRSE descriptors, we developed highly accurate ML/QSAR models to predict both the 3D structure and optical properties of SWCNTs with chemical defects. This model enabled the creation of a virtual library of 125,556 structures, providing new insights into the relationship between SWCNT-defect structure and emission.

77 NANOSCIENCE AND NANOTECHNOLOGY↗
NASA NTRS1991

Some practical universal noiseless coding techniques, part 3, module PSl14,K+

The algorithmic definitions, performance characterizations, and application notes for a high-performance adaptive noiseless coding module are provided. Subsets of these algorithms are currently under development in custom very large scale integration (VLSI) at three NASA centers. The generality of coding algorithms recently reported is extended. The module incorporates a powerful adaptive noiseless coder for Standard Data Sources (i.e., sources whose symbols can be represented by uncorrelated non-negative integers, where smaller integers are more likely than the larger ones). Coders can be specified to provide performance close to the data entropy over any desired dynamic range (of entropy) above 0.75 bit/sample. This is accomplished by adaptively choosing the best of many efficient variable-length coding options to use on each short block of data (e.g., 16 samples) All code options used for entropies above 1.5 bits/sample are 'Huffman Equivalent', but they require no table lookups to implement. The coding can be performed directly on data that have been preprocessed to exhibit the characteristics of a standard source. Alternatively, a built-in predictive preprocessor can be used where applicable. This built-in preprocessor includes the familiar 1-D predictor followed by a function that maps the prediction error sequences into the desired standard form. Additionally, an external prediction can be substituted if desired. A broad range of issues dealing with the interface between the coding module and the data systems it might serve are further addressed. These issues include: multidimensional prediction, archival access, sensor noise, rate control, code rate improvements outside the module, and the optimality of certain internal code options.

Rice, Robert F.↗
DOE OSTI2022

Design and Optimization of Structured Multi-Functional Trapping Catalysts for Conversion of Hydrocarbons and NOx from Diesel and Advanced Combustion Engines

Oxides of nitrogen in the form of nitric oxide (NO) and nitrogen dioxide (NO 2 ) commonly referred to as NOx, is one of the two chemical precursors that lead to ground-level ozone, a ubiquitous air pollutant in urban areas. A major source of NOx is generated by equipment and vehicles powered by diesel engines, which have a combustion exhaust that contains NOx in the presence of excess O 2 . Vehicular emission control catalysts are ineffective in eliminating CO, hydrocarbons, and NOx during engine cold-start when exhaust temperatures are below 200°C. The objective of the project was to develop and demonstrate a multi-functional, catalyzed trap that enables vehicles with advanced combustion strategies to meet Tier 3 emissions standards while achieving the 150 °C challenge for sustained co-oxidation of HCs and CO and ≥90% NO trapping and release during warmup. Specifically, the multi-functional Lean HC+NOx (LHCNT) was developed for application in the exhaust aftertreatment of conventional diesel engines and engines having low temperature combustion (LTC) regimes. Activities included the design and synthesis of adsorbents and catalysts, screening and evaluation. Passive NOx absorbers (PNA), hydrocarbon (HC) traps, and oxidation catalysts (OC) were evaluated for use in series or as integrated devices. Predictive tools were developed utilizing the characterization and analysis of these materials, and an emission system was designed and optimized utilizing the catalyst systems. Microkinetic models were developed for the PNA for the simple NO-only feed and complex feed containing CO, H 2 , and model hydrocarbons (ethylene and dodecane). A first-principles, mechanistic-based model of the PNA was developed which utilized molecular-scale estimates (density functional theory) of energy barriers, mechanistic-based kinetics and realistic treatments of the flow and transport processes. Two new oxidation catalysts were developed (PdCu alloy, mixed copper-ceria-cobalt oxide), both of which significantly lessened the detrimental inhibition by CO on hydrocarbon and NO oxidation. A method for lessening the detrimental impact of CO on PNA activity was developed that involves use of an oxidation catalyst upstream of the PNA. The SwRI Ectolab TM burner system was applied to evaluate the baseline PNA material and confirmed performance comparable to the benchflow PNA studies using simulated exhaust. Spatially-resolved mass spectrometry (SpaciMS) was used to measure the transient spatial profiles of reacting species spanning the length of a three-function LHCNT containing PNA, HCT, and OC. The findings from this study provide diesel vehicle and catalyst companies valuable information to develop more cost effective emission control catalysts which helps to expand the use of more fuel efficient diesel power. The fundamental modeling and experimental tools and findings from this project can be applied to catalyst technologies used in the energy and chemical industries. The project led to 14 publications in the peer-reviewed literature with 2 additional currently under review. Finally, the project also led to training of several doctoral students who were placed in research jobs in industry and academia. Specifically, Mugdha Ambast (UH) has joined Cummins, Kevin Gu (UVa) has joined GM, and Abhay Gupta (UH) is to join Caterpillar.

02 PETROLEUM↗
DOE OSTI2022

Design and Optimization of Structured Multi-Functional Trapping Catalysts for Conversion of Hydrocarbons and NOx from Diesel and Advanced Combustion Engines

Oxides of nitrogen in the form of nitric oxide (NO) and nitrogen dioxide (NO 2 ) commonly referred to as NOx, is one of the two chemical precursors that lead to ground-level ozone, a ubiquitous air pollutant in urban areas. A major source of NOx is generated by equipment and vehicles powered by diesel engines, which have a combustion exhaust that contains NOx in the presence of excess O 2 . Vehicular emission control catalysts are ineffective in eliminating CO, hydrocarbons, and NO x during engine cold-start when exhaust temperatures are below 200°C. The objective of the project was to develop and demonstrate a multi-functional, catalyzed trap that enables vehicles with advanced combustion strategies to meet Tier 3 emissions standards while achieving the 150 °C challenge for sustained co-oxidation of HCs and CO and ≥90% NO trapping and release during warmup. Specifically, the multi-functional Lean HC+NOx (LHCNT) was developed for application in the exhaust aftertreatment of conventional diesel engines and engines having low temperature combustion (LTC) regimes. Activities included the design and synthesis of adsorbents and catalysts, screening and evaluation. Passive NOx absorbers (PNA), hydrocarbon (HC) traps, and oxidation catalysts (OC) were evaluated for use in series or as integrated devices. Predictive tools were developed utilizing the characterization and analysis of these materials, and an emission system was designed and optimized utilizing the catalyst systems. Microkinetic models were developed for the PNA for the simple NO-only feed and complex feed containing CO, H 2 , and model hydrocarbons (ethylene and dodecane). A first-principles, mechanistic-based model of the PNA was developed which utilized molecular-scale estimates (density functional theory) of energy barriers, mechanistic-based kinetics and realistic treatments of the flow and transport processes. Two new oxidation catalysts were developed (PdCu alloy, mixed copper-ceria-cobalt oxide), both of which significantly lessened the detrimental inhibition by CO on hydrocarbon and NO oxidation. A method for lessening the detrimental impact of CO on PNA activity was developed that involves use of an oxidation catalyst upstream of the PNA. The SwRI Ectolab TM burner system was applied to evaluate the baseline PNA material and confirmed performance comparable to the benchflow PNA studies using simulated exhaust. Spatially-resolved mass spectrometry (SpaciMS) was used to measure the transient spatial profiles of reacting species spanning the length of a three-function LHCNT containing PNA, HCT, and OC. The findings from this study provide diesel vehicle and catalyst companies valuable information to develop more cost effective emission control catalysts which helps to expand the use of more fuel efficient diesel power. The fundamental modeling and experimental tools and findings from this project can be applied to catalyst technologies used in the energy and chemical industries. The project led to 14 publications in the peer-reviewed literature with 2 additional currently under review. Finally, the project also led to training of several doctoral students who were placed in research jobs in industry and academia. Specifically, Mugdha Ambast (UH) has joined Cummins, Kevin Gu (UVa) has joined GM, and Abhay Gupta (UH) is to join Caterpillar.

42 ENGINEERING↗
NASA NTRS1998

X-33 Integrated Test facility, Extended Range Simulation

In support of the X-33 Single Stage To Orbit program, NASA Dryden Flight Research Center was selected to provide continuous communications coverage of the X-33 vehicle from launch, through landing at Malmstrom Air Force Base, Montana and Michaels Army Air Field, Utah. An extensive real-time range simulation capability is being developed to ensure successful communications with the autonomous X-33 vehicle. This paper will provide an overview of the various levels of simulation being developed to support the X-33 extended range subsystems. These subsystems include the Flight Termination System, L-Band command uplink subsystem and the S-Band telemetry downlink subsystem. In addition, the radar model developed provides continuous azimuth, elevation and range information based on the flight trajectory. The Dynamic Ground Station Analysis model developed by NASA Goddard Space Flight Center, calculate the received signal strength at each ground station. This model takes into consideration Radio Frequency (RF) link parameters such as frequency, antenna gain, space loss, plasma effects and the vehicle's position and attitude at any point in time during the flight path. All three RF links are then attenuated based on this calculated level and the RF signals are sent into telemetry receivers to emulate remote sites, or the power incident on the vehicle from uplinked signals. The best source received telemetry data is then passed back to the Launch and Mission Control Monitoring System (LMCMS) resident in the Operations Control Center. The LMCMS also provides the range simulation system the uplink command combined with differential GPS corrections. Later stages will require the progressive integration of actual range hardware with this simulation effort, leading to communication between telemetry, uplink and FTS antennas at NASA Dryden Flight Research Center, with vehicle antennas mounted on the Walter C. Williams Research Aircraft Integration Facility (RAIF). Decommutated Pulse Code Modulated (PCM) data is displayed on one of the four monitors that comprise the Range Safety Officer's (RSO) station. Also displayed are instantaneous impact prediction models, and Federal Aviation Administration (FAA) data for notification of other traffic in the area. Aside from initiating the flight termination command and validating communication links, the RSO station with the range simulation will be used to provide both range control and range safety officers training. The training is necessary to perform their respective functions with greater levels of confidence prior to first flight.

Sharma, Ashley↗
NASA NTRSDate not supplied

Towards Energy Scale Calibration and Drift Correction of TES Detectors for Athena X-IFU

The Athena X-Ray Integral Field Unit (X-IFU) comprises a 2376-pixel array of transition edge sensors (TES) read out with time-division multiplexing (TDM). X-IFU will provide spatially resolved, high-resolution spectroscopy (2.5 eV full-width-half-maximum up to 7 keV) over the energy range 0.2 to 12 keV, with an absolute energy scale accuracy of 0.4 eV. The energy scale function maps the optimally filtered pulse height, in arbitrary engineering units, to real calibrated energy. Uncertainties in the calibration can result from imperfect fitting of the energy scale between the known calibration points. Furthermore, temporal changes in the TES operating environment, such as heat-sink temperature, magnetic field and bias voltage, can cause significant variations in the detector gain function over time. If not properly corrected, this can result in degradation of the energy resolution, and systematic errors in the absolute energy scale. The non-linear nature of TES detectors, coupled with the possibility of multiple simultaneously occurring sources of drift, can make effective corrections over the full bandpass of the instrument extremely challenging. Athena X-IFU will employ an on-board calibration source that provides known reference x-ray lines. This provides real-time monitoring of the gain stability of the detector system and information that can be used to correct for gain drifts. For X-IFU the baseline approach is to measure a series of calibration curves under different environmental conditions, which bound the expected drifts the instrument is predicted to see over the course of the mission. Using the information from the in-flight calibration source, these energy scale functions can be interpolated to generate a new corrected energy scale as a function of time. In this paper we discuss progress towards demonstrating that the X-IFU energy scale requirements can be met. We present measurements on ~ 200 pixels in a prototype X-IFU array read out with 8-column x 32-row TDM. We use a rotating target source containing 12 fluorescent targets to generate x-ray lines covering the energy range 4 keV (Sc-Kα) to 12 keV (Br-Kα). We present measurements of the non-linear energy scale function and show how variations in heat-sink temperature, TES bias voltage and magnetic field affect the shape of TES energy scale differently and introduce different residual gain errors over the bandpass. We explore different drift correction algorithms that use either a single or multiple referential lines to track and correct the gain from these various sources of drift. In addition to the pulse-height, the DC ‘baseline’ level of the TES can contain information about its bias conditions. Thus, we test a multi-parameter gain correction algorithm that attempts to incorporate both the pulse height and the additional baseline information into the algorithm.

Stephen J Smith↗
DOE OSTI2026

ZENN: A thermodynamics-inspired computational framework for heterogeneous data–driven modeling

Traditional entropy-based methods—such as cross-entropy loss in classification problems—have long been essential tools for representing the information uncertainty and physical disorder in data and for developing artificial intelligence algorithms. However, the rapid growth of data across various domains has introduced new challenges, particularly the integration of heterogeneous datasets with intrinsic disparities. To address this, we introduce a zentropy-enhanced neural network (ZENN), extending zentropy theory into the data science domain via intrinsic entropy, enabling more effective learning from heterogeneous data sources. ZENN simultaneously learns both energy and intrinsic entropy components, capturing the underlying structure of multisource data. To support this, we redesign the neural network architecture to better reflect the intrinsic properties and variability inherent in diverse datasets. We demonstrate the effectiveness of ZENN on classification tasks and energy landscape reconstructions, showing its superior generalization capabilities and robustness-particularly in predicting high-order derivatives. In image and text classification tasks, ZENN demonstrates superior generalization by introducing a learnable temperature variable that models latent multisource heterogeneity, allowing it to surpass state-of-the-art models on CIFAR-10/100, BBC News, and AG News. As a practical application in materials science, we employ ZENN to reconstruct the Helmholtz energy landscape of Fe3Pt using data generated from density functional theory and capture key material behaviors, including negative thermal expansion and the critical point in the temperature–pressure space. Overall, this work presents a zentropy-grounded framework for data-driven machine learning, positioning ZENN as a versatile and robust approach for scientific problems involving complex, heterogeneous datasets.

36 MATERIALS SCIENCE↗
DOE OSTI2023

HIGH-LOW FIDELITY THERMAL HYDRAULIC COUPLING USING AI/MACHINE LEARNING ALGORITHMS

The primary goal of the US Department of Energy (DOE) office of Nuclear Energy Integrated Energy Systems (IES) program is to develop the tools and framework for coupling multi-scale and multi-physical thermal and electrical energy usage and storage systems. High- and low-fidelity (high–low) coupling is a key feature of multi-scale, multi-component systems and has been an important focus of research in the nuclear energy community for the past two decades. An essential feature of demonstrating the capability to couple high-fidelity and low-fidelity systems for real-time applications are surrogate/reduced order models (ROM). For the purposes of this study, surrogate models are essentially Blackbox models, typically developed using supervised Machine learning (ML) algorithms. The surrogate models can be used to mimic the response of high-fidelity models to represent large historical datasets and coupled with more general low-fidelity system models distributed as Functional Mock-up Interface (FMI) or Functional Mock-up Units (FMU) modules. The example is demonstrated with Spallation Neutron Source (SNS) First Target Station flow loop data. The flow loop is a liquid mercury loop with a pump, piping, heat exchange, and internal heat generation in the target window. This work elucidates some of the potential benefits and future needs of developing tools for high–low system coupling of energy systems.

Williams, Wesley↗
DOE OSTI2022

Development of a web-based screening tool for ground source heat pump applications

Ground source heat pump (GSHP) technology has great potential to help the nation meet its energy and decarbonization goals, but several barriers hinder the wide application of GSHP. Important barriers include the lack of a coherent toolset for analyzing the technical feasibility and economic viability of the GSHP application. The current design and analysis methods are ineffective and require significant expertise to apply. Although building energy modeling is increasingly important in designing buildings, the tools for GSHP modeling and simulation are lacking. A web-based free-to-use tool is being developed for quick techno-economic analysis of GSHP applications in nearly any building in the United States. This tool is enabled by improvements in the calculation methodology to allow rapid sizing of borehole configurations that provide significant cost savings. The screening tool currently uses US Department of Energy (DOE) prototype building models and an extended g-function library to size ground heat exchangers and simulate the performance of GSHP systems. The team is integrating with DOE's Oak Ridge National Laboratory's AutoBEM program to automatically create a building model based on user inputs. This paper introduces the structure, components, features, and results of the web-based screening tool for GSHP applications. Future directions for further developing the tool are also discussed.

Liu, Xiaobing↗
NASA NTRS1995

Morphology, near-infrared luminosity, and mass of the Galactic bulge from COBE DIRBE observations

Near-infrared images of the Galactic bulge at 1.25, 2.2, 3.5, and 4.9 microns obtained by the Diffuse Infrared Background Experiment (DIRBE) onboard the Cosmic Background Explorer (COBE) satellite are used to characterize its morphology and to determine its infrared luminosity and mass. Earlier analysis of the DIRBE observations (Weiland et al. 1994) provided supporting evidence for the claim made by Blitz & Spergel (1991) that the bulge is bar-shaped with its near end in the first Galactic quadrant. Adopting various triaxial analytical functions to represent the volume emissivity of the source, we confirm the barlike nature of the bulge and show that triaxial Gaussian-type functions provide a better fit to the data than other classes of functions, including an axisymmetric spheroid. The introduction of a `boxy' geometry, such as the one used by Kent, Dame, & Fazio (1991) improves the fit to the data. Our results show that the bar is rotated in the plane with its near side in the first Galactic quadrant creating an angle of 20 deg +/- 10 deg between its major axis and the line of sight to the Galactic center. Typical axis ratios of the bar are (1:0.33 +/- 0.11:0.23 +/- 0.08), resembling the geometry of prolate spheroids. There is no statistically significant evidence for an out-of-plane tilt of the bar at 2.2 microns, and marginal evidence for a tilt of approximately equal 2 deg at 4.9 microns. The introduction of a roll around the intrinsic major axis of the bulge improves the `boxy' appearance of some functions. A simple integration of the observed projected intensity of the bulge gives a bulge luminosity of 1.2 x 10(exp 9), 4.1 x 10(exp 8), 2.3 x 10(exp 8), and 4.3 x 10(exp 7) solar luminosity, respectively, at 1.25, 2.2, 3.5, and 4.9 microns wavelength for a Galactocentric distance of 8.5 kpc. The 2.2 microns luminosity function of the bulge population in the direction of Baade's window yields a bolometric luminosity of L(sub bol) = 5.3 x 10(exp 9) solar luminosity. Stellar evolutionary models relate this luminosity to the number of main-sequence progenitor stars that currently populate the red giant branch. Combined with the recent determination of the main-sequence turnoff mass for the bulge by the Hubble Space Telescope (Holtzman et al. 1993) we derive a photometrically determined bulge mass of approximately equal to 1.3 x 10(exp 10) solar mass for a Salpeter initial mass function extended down to 0.1 solar mass.

Dwek, E.↗
NASA NTRS2015

Characterization of Flap Edge Noise Radiation from a High-Fidelity Airframe Model

The results of an experimental study of the noise generated by a baseline high-fidelity airframe model are presented. The test campaign was conducted in the open-jet test section of the NASA Langley 14- by 22-foot Subsonic Tunnel on an 18%-scale, semi-span Gulfstream airframe model incorporating a trailing edge flap and main landing gear. Unsteady surface pressure measurements were obtained from a series of sensors positioned along the two flap edges, and far field acoustic measurements were obtained using a 97-microphone phased array that viewed the pressure side of the airframe. The DAMAS array deconvolution method was employed to determine the locations and strengths of relevant noise sources in the vicinity of the flap edges and the landing gear. A Coherent Output Power (COP) spectral method was used to couple the unsteady surface pressures measured along the flap edges with the phased array output. The results indicate that outboard flap edge noise is dominated by the flap bulb seal cavity with very strong COP coherence over an approximate model-scale frequency range of 1 to 5 kHz observed between the array output and those unsteady pressure sensors nearest the aft end of the cavity. An examination of experimental COP spectra for the inboard flap proved inconclusive, most likely due to a combination of coherence loss caused by decorrelation of acoustic waves propagating through the thick wind tunnel shear layer and contamination of the spectra by tunnel background noise at lower frequencies. Directivity measurements obtained from integration of DAMAS pressure-squared values over defined geometric zones around the model show that the baseline flap and landing gear are only moderately directional as a function of polar emission angle.

Humphreys, William M., Jr.↗
DOE OSTI2026

Revealing EDL-driven reduction mechanisms in binary, ternary, and quaternary fluorinated electrolytes via an integrated MD–DFT–ML framework

Accurately predicting solid electrolyte interphase (SEI) formation requires explicitly resolving the electric double layer (EDL) structure, which deviates significantly from that of the bulk electrolyte. Although an established molecular dynamics (MD) and Density Functional Theory (DFT) framework can model SEI formation by evaluating reduction reactions of local clusters in the EDL, it suffers from a combinatorial computational bottleneck. To overcome this limitation, we introduce a machine-learning-accelerated simulation workflow (MD–DFT–ML), integrating a gradient-boosted regression model trained on EDL composition data to efficiently predict reduction potentials. We apply this framework to seven fluorinated electrolytes comprising fluorinated anions, a fluorinated ester solvent, two types of diluent (ion-solvating ester vs. non-solvating ether), and an FEC additive. The analysis shows that the EDL selectively accumulates cation-binding species; consequently, the non–cation-binding ether diluent rarely enters the EDL and makes minimal contributions to SEI formation. DFT calculations on statistically representative EDL clusters provide reduction potentials and fluorine-release pathways, while the ML model, which substantially reduces the DFT workload, predicts cluster reduction energies with a mean absolute error of 0.1 eV. The combined MD–DFT–ML approach also quantifies contributions from different sources to LiF formation in the SEI. This methodology establishes a generalizable route for multiscale modeling electrolyte and interphase design for next-generation electrochemical energy-storage systems.

DFT-MD-ML workflow↗