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All sourcesNASA NTRSDOE OSTI
NASA NTRSDate not supplied

Early Information Parameter-Set Analysis for Satellite Close Approaches using Machine Learning

Understanding orbital mechanics is essential in space flight and navigation applications, and leveraging modern force models for flight path projection remains an important aspect in space mission design and operation. However, force models do not capture all the dynamics or perturbations in the space environment and thus are subject to errors in predicting the state vectors. The further out the predicted miss distance between spacecraft is from the time of closest approach (TCA), the larger the propagated errors in the predicted miss distance at TCA is. The dependency on these force models for spacecraft flight state prediction calls for a more reliable method that can quantify, or even reduce, these propagated errors. With recent advances in the field artificial intelligence, specifically in machine and deep learning algorithms, a model that implements these approaches can improve on the modern force model approach. The goal for this work is to provide an early-information decision-making threshold, in order to prioritize risk assessment implementation, given the ongoing increase of space objects. In analyzing the relationship of several parameters from conjunction data messages(CDMs) and solar information, early information becomes viable in miss distance prediction with unsupervised learning techniques, which learn the parameters that are linked together with miss distance and probability of collision (Pc) variables. Another approach implemented for identifying relationships within CDMs is supervised learning, in which a shallow neural network binary classifier learns to distinguish events with Pc values¡108. These parameters detected in the unsupervised process are then applied to a regression neural network, which predicts the miss distance at TCA for a specific event within a given uncertainty bound. For the regression neural network, a Long Short Term Memory (LSTM) neural network is implemented, which yields memory about each time step in an event. Using an LSTM network, the model learns to predict miss distance within 0.2km of the value measured at TCA. Although there is a limited amount of "close miss" data to train a network, the network learns to associate parameters, like large energy dissipation rates with the secondary object, with an elevated Pc

Brianna I. Robertson↗
DOE OSTI2021

INTEGRATE - Inverse Network Transformations for Efficient Generation of Robust Airfoil and Turbine Enhancements

The INTEGRATE (Inverse Network Transformations for Efficient Generation of Robust Airfoil and Turbine Enhancements) project is developing a new inverse-design capability for the aerodynamic design of wind turbine rotors using invertible neural networks. This AI-based design technology can capture complex non-linear aerodynamic effects while being 100 times faster than design approaches based on computational fluid dynamics. This project enables innovation in wind turbine design by accelerating time to market through higher-accuracy early design iterations to reduce the levelized cost of energy. INVERTIBLE NEURAL NETWORKS Researchers are leveraging a specialized invertible neural network (INN) architecture along with the novel dimension-reduction methods and airfoil/blade shape representations developed by collaborators at the National Institute of Standards and Technology (NIST) learns complex relationships between airfoil or blade shapes and their associated aerodynamic and structural properties. This INN architecture will accelerate designs by providing a cost-effective alternative to current industrial aerodynamic design processes, including: - Blade element momentum (BEM) theory models: limited effectiveness for design of offshore rotors with large, flexible blades where nonlinear aerodynamic effects dominate - Direct design using computational fluid dynamics (CFD): cost-prohibitive - Inverse-design models based on deep neural networks (DNNs): attractive alternative to CFD for 2D design problems, but quickly overwhelmed by the increased number of design variables in 3D problems AUTOMATED COMPUTATIONAL FLUID DYNAMICS FOR TRAINING DATA GENERATION - MERCURY FRAMEWORK The INN is trained on data obtained using the University of Marylands (UMD) Mercury Framework, which has with robust automated mesh generation capabilities and advanced turbulence and transition models validated for wind energy applications. Mercury is a multi-mesh paradigm, heterogeneous CPU-GPU framework. The framework incorporates three flow solvers at UMD, 1) OverTURNS, a structured solver on CPUs, 2) HAMSTR, a line based unstructured solver on CPUs, and 3) GARFIELD, a structured solver on GPUs. The framework is based on Python, that is often used to wrap C or Fortran codes for interoperability with other solvers. Communication between multiple solvers is accomplished with a Topology Independent Overset Grid Assembler (TIOGA). NOVEL AIRFOIL SHAPE REPRESENTATIONS USING GRASSMAN SPACES We developed a novel representation of shapes which decouples affine-style deformations from a rich set of data-driven deformations over a submanifold of the Grassmannian. The Grassmannian representation as an analytic generative model, informed by a database of physically relevant airfoils, offers (i) a rich set of novel 2D airfoil deformations not previously captured in the data , (ii) improved low-dimensional parameter domain for inferential statistics informing design/manufacturing, and (iii) consistent 3D blade representation and perturbation over a sequence of nominal shapes. TECHNOLOGY TRANSFER DEMONSTRATION - COUPLING WITH NREL WISDEM Researchers have integrated the inverse-design tool for 2D airfoils (INN-Airfoil) into WISDEM (Wind Plant Integrated Systems Design and Engineering Model), a multidisciplinary design and optimization framework for assessing the cost of energy, as part of tech-transfer demonstration. The integration of INN-Airfoil into WISDEM allows for the design of airfoils along with the blades that meet the dynamic design constraints on cost of energy, annual energy production, and the capital costs. Through preliminary studies, researchers have shown that the coupled INN-Airfoil + WISDEM approach reduces the cost of energy by around 1% compared to the conventional design approach. This page will serve as a place to easily access all the publications from this work and the repositories for the software developed and released through this pr...

aerodynamics↗
DOE OSTI2023

CoolPINNs: A physics-informed neural network modeling of active cooling in vascular systems

Emerging technologies like hypersonic aircraft, space exploration vehicles, and batteries avail fluid circulation in embedded microvasculatures for efficient thermal regulation. Modeling is vital during the design and operational phases of these engineered systems. However, many challenges exist in developing a modeling framework. What is lacking is an accurate framework that (i) captures sharp jumps in the thermal flux across complex vasculature layouts, (ii) deals with oblique derivatives (involving tangential and normal components), (iii) handles nonlinearity because of radiative heat transfer, (iv) provides a high-speed forecast for real-time monitoring, and (v) facilitates robust inverse modeling. Here, this paper addresses these challenges by availing the power of physics-informed neural networks (PINNs). We develop a fast, reliable, and accurate Scientific Machine Learning (SciML) framework for vascular-based thermal regulation—called CoolPINNs: a PINNs-based modeling framework for active cooling. The proposed mesh-less framework elegantly overcomes all the mentioned challenges. The significance of the reported research is multi-fold. First, the framework is valuable for real-time monitoring of thermal regulatory systems because of rapid forecasting. Second, researchers can address complex thermoregulation designs since the approach is meshless. Finally, the framework facilitates systematic parameter identification and inverse modeling studies, perhaps the most significant utility of the current framework.

97 MATHEMATICS AND COMPUTING↗
NASA NTRS2012

SWOT: The Surface Water and Ocean Topography Mission. Wide- Swath Altimetric Elevation on Earth

The elevation of the surface of the ocean and freshwater bodies on land holds key information on many important processes of the Earth System. The elevation of the ocean surface, called ocean surface topography, has been measured by conventional nadirlooking radar altimeter for the past two decades. The data collected have been used for the study of large-scale circulation and sea level change. However, the spatial resolution of the observations has limited the study to scales larger than about 200 km, leaving the smaller scales containing substantial kinetic energy of ocean circulation that is responsible for the flux of heat, dissolved gas and nutrients between the upper and the deep ocean. This flux is important to the understanding of the ocean's role in regulatingfuture climate change.The elevation of the water bodies on land is a key parameter required for the computation of storage and discharge of freshwater in rivers, lakes, and wetlands. Globally, the spatial and temporal variability of water storage and discharge is poorly known due to the lack of well-sampled observations. In situ networks measuring river flows are declining worldwide due to economic and political reasons. Conventional altimeter observations suffers from the complexity of multiple peaks caused by the reflections from water, vegetation canopy and rough topography, resulting in much less valid data over land than over the ocean. Another major limitation is the large inter track distance preventing good coverage of rivers and other water bodies.This document provides descriptions of a new measurement technique using radar interferometry to obtain wide-swath measurement of water elevation at high resolution over both the ocean and land. Making this type of measurement, which addresses the shortcomings of conventional altimetry in both oceanographic and hydrologic applications, is the objective of a mission concept called Surface Water and Ocean Topography (SWOT), which was recommended by the National Research Council's first decadal survey of NASA's Earth science program. This document provides wide-ranging examples of research opportunities in oceanography and land hydrology that would be enabled by the new type of measurement. Additional applications in many other branches of Earth System science ranging from ocean bathymetry to sea ice dynamics are also discussed. Many of the technical issues in making the measurement are discussed as well. Also presented is a preliminary design of the SWOT Mission concept, which is being jointly developed by NASA and CNES, with contributions from the Canadian Space Agency.

altimetry↗
NASA NTRS2018

Multiple Uplinks Per Antenna (MUPA) Signal Acquisition Schemes

The Deep Space Network (DSN) currently makes use of the technique of Multiple Spacecraft per Antenna (MSPA) where a single antenna is used to track multiple spacecraft downlinks within its beam, such as in the case of multiple spacecraft orbiting Mars at 8.4 GHz (X-band). It is desired to extend this technique to the uplink where a single station is used to send a signal to multiple spacecraft in order to make more efficient use of ground resources. This would be applicable to numerous smallsat constellations being considered for future missions or to future spacecraft at Venus, Mars, or more distant destinations that are all within the half-power beamwidth of a single 34-m diameter antenna. In one scheme, each spacecraft’s command sequences would be time multiplexed onto a single uplink frequency. Each spacecraft would lock onto the uplink signal and would accept only commands intended for it via special identifier codes. Each spacecraft would also emit a downlink signal to the ground that is coherent with the uplink signal but would have its own allocated frequency channel and identifier information. A couple of key challenges associated with using this technique need to be addressed. Because of the single uplink frequency, coherent turnaround for two-way Doppler and ranging would not conform to established ratios, thus the radios employed by the spacecraft would need to be capable of variable turnaround ratios. In addition, because of the different orbits or spacecraft trajectories, the relative Doppler shifts and rates can be large with respect to the common uplink signal whose frequency would lie at the centroid of the frequencies of the expected received signals of the constellation. This would be problematic with standard analog spacecraft radios whose acquisition bandwidths are relatively small (~1.7 kHz) relative to the large frequency offsets (~100 kHz) expected using the single frequency uplink technique. With the advent of software defined radios (SDRs), signal frequency search algorithms can be utilized within the flight software and/or programmable hardware (e.g., FPGAs) that can easily acquire and track signals with large frequency offsets and varying dynamics. Such techniques could include FFT search algorithms, step-and-sweep search algorithms, or onboard frequency steering making use of trajectory vectors uplinked to each member spacecraft. Other challenges include mitigation of potential interference between received signals. We have identified several software defined radios that are in different stages of development and whose key parameters have been tabulated. We have examined each radio’s capabilities with respect to acquiring and tracking signals with large frequency offsets. Such analyses made use of previous studies supplemented with specially designed tests using both simulation tools and/or existing testbeds. We have compared signal acquisition times computed from provided algorithms along with measured values derived from tests using existing hardware and simulation tools for the purpose of conducting tradeoff studies between the various radio designs and software/firmware programming approaches.

Abraham, Douglas S.↗
NASA NTRSDate not supplied

Development of a Ground Multi-Mission Low-Cost Optical Terminal (LCOT) for Free-Space Optical Communications

Once confined to the realm of laboratory experiments and theoretical papers, space-based laser communications (lasercomm) are on the verge of achieving mainstream status. Organizations from Facebook to NASA, and missions from cubesats to Orion are employing lasercomm to achieve gigabit communication speeds at mass and power requirements lower than that of traditional radio frequency (RF) methods. Since first demonstrating free-space optical communications services with Lunar Laser Communications Demonstration (LLCD) in 2013, NASA has invested in developing optical communications technologies and capabilities to enhancing its space communications networks. Along with evolving optical space terminals, NASA is also developing lasercomm grounds stations capable of meeting the rapidly increasing data volume demands of upcoming missions, from low-earth to lunar orbits and beyond and integrating these advanced capabilities into its Near-Space and Deep Space Networks. To meet this emerging need, the Low-Cost Optical Terminal (LCOT) project at NASA’s Goddard Space Flight Center (GSFC) is designing, building and validating a prototype for a flexible, multi-mission, and economical optical ground terminal that could be used as a blueprint for a global network of optical ground stations, capable of supporting a wide variety of missions. To date a major impediment to widespread adoption of laser communication has been the lack of an existing ground network infrastructure. A mission that wishes to take advantage of laser communication not only needs to invest in an optical space terminal, but it must also finance the creation of ground terminals to receive the downlink signal. This adds significant additional cost. Missions that do decide to incur the cost of financing a network of ground terminals end up building highly specialized optical receivers that are operable as receivers for that specific mission only. Significant Non-Recurring Engineering (NRE) cost is invested to build highly specialized one-of-a-kind ground terminals that go into storage after that particular mission is over. This is not an economical approach and does nothing to grow the number of optical ground stations available to future missions. In essence each mission that wants to take advantage of the benefits of lasercom has to start from scratch to provide a ground terminal network to support it. As long as this is the case, the cost for using laser communications will be too high for most missions to consider. LCOT intends to close this gap in technology by designing and developing a standard optical ground terminal design that is flexible enough to serve as a receiver for a wide range of future missions – a ground terminal that can be quickly reconfigured to receive downlinks at different wavelengths using different signal formats. Not only does LCOT have the industry-building objectives of utilizing commercial-off-the-shelf (COTS) components to the maximum extent possible, but also spurs the commercial development of other necessary lasercomm components not currently offered by industry. Finally, LCOT will give NASA scientists and engineers a facility where they can gain real-world experience with optical communications. It will give engineers a cost-effective way to try out new concepts and processes by providing the infrastructure for such testing. In this way it is hoped LCOT will serve as a stimulus for innovation in optical communications and speed its widespread adoption by future missions. The LCOT is comprised of five subsystems: Free-Space Optical, Transceiver, Amplifier, Monitor and Control, and Observatory Infrastructure. In August 2021, the LCOT team installed a 70 cm telescope, developed by Planewave Instruments that was optimized for optical communications. Free-Space Optical subsystem comprises of the telescope and its associated hardware, including a transmitter optical assembly, wide field cameras, two optical benches, and an adaptive optics subsystem. The transmit optical assembly, a unique concept design, is a cluster of four functionally independent transmit subassemblies located on the receive telescope. In addition to receiving optical signals and directing the expanded beam with high precision to the space terminal, it also performs tracking functions. The transmit optical assembly will support operations from Low Earth Orbit (LEO) through lunar and will be used as a template for industry manufacturing. The Optical Infrastructure subsystem is responsible for providing environmentally controlled shelters for LCOT equipment and various other systems. To maintain the safety and proper functionality of the telescope, a 16 ft Astrohaven clamshell dome procured which provides all-sky coverage without the need to rotate the dome. Additionally, Atmospheric Monitoring Assembly (AMA) will be part of optical infrastructure subsystem to ensure accurate performance of the LCOT. Like existing optical ground stations, LCOT will measure standard weather station parameters, infrared all sky image of cloud cover, and cloud height. LCOT, however, adds requirements for measuring night time seeing and, in the future, daytime seeing. Unlike other optical ground terminals, the LCOT is transceiver agnostic; user transceivers may be duplex transceivers, standalone receivers, or standalone transmitters with or without acquisition beacon functionality. As such, the LCOT project accommodates testing with external customer transceivers in a flexible manor, further complimenting its intended multi-mission goals. Another unique component of LCOT is the use of a new amplifier technology – the Very Large Mode Area (VLMA) amplifiers. This new technology allows LCOT to avoid the issues faced by previous laser communications ground terminals, gives users more flexibility and modular capability, and is capable of reaching an order of magnitude higher peak power than traditional High Power Optical Amplifiers (HPOA). One drawback of the VLMA HPOA approach is that the amplified light is output into free-space. The solution developed by LCOT is an optics train that couples the output of the VLMA amplifier into a short fiber for transport to the transmit telescopes with high efficiency. Like many of the LCOT components, a set of detailed manufacturing drawings have been created for the optics train to allow any machine shop with a multi-axis Computer Numerical Control (CNC) machine to fabricate the piece parts from commonly available materials. In line with the goals of LCOT, the monitor and control functions are developed as a modular and flexible system with the ability to support future hardware or algorithm changes, minimizing disruptions. A main priority of development in the Monitor and Control Subsystem (MCS) is the safety monitor system.

laser communication↗
NASA NTRSDate not supplied

Development of a Ground Multi-mission Low Cost Optical Terminal(LCOT) for Free-Space Optical Communication

Once confined to the realm of laboratory experiments and theoretical papers, space-based laser communications (lasercomm) are on the verge of achieving mainstream status. Organizations from Facebook to NASA, and missions from cubesats to Orion are employing lasercomm to achieve gigabit communication speeds at mass and power requirements lower than that of traditional radio frequency (RF) methods. Since first demonstrating free-space optical communications services with Lunar Laser Communications Demonstration (LLCD) in 2013, NASA has invested in developing optical communications technologies and capabilities to enhancing its space communications networks. Along with evolving optical space terminals, NASA is also developing lasercomm grounds stations capable of meeting the rapidly increasing data volume demands of upcoming missions, from low-earth to lunar orbits and beyond and integrating these advanced capabilities into its Near-Space and Deep Space Networks. To meet this emerging need, the Low-Cost Optical Terminal (LCOT) project at NASA’s Goddard Space Flight Center (GSFC) is designing, building and validating a prototype for a flexible, multi-mission, and economical optical ground terminal that could be used as a blueprint for a global network of optical ground stations, capable of supporting a wide variety of missions. To date a major impediment to widespread adoption of laser communication has been the lack of an existing ground network infrastructure. A mission that wishes to take advantage of laser communication not only needs to invest in an optical space terminal, but it must also finance the creation of ground terminals to receive the downlink signal. This adds significant additional cost. Missions that do decide to incur the cost of financing a network of ground terminals end up building highly specialized optical receivers that are operable as receivers for that specific mission only. Significant Non-Recurring Engineering (NRE) cost is invested to build highly specialized one-of-a-kind ground terminals that go into storage after that particular mission is over. This is not an economical approach and does nothing to grow the number of optical ground stations available to future missions. In essence each mission that wants to take advantage of the benefits of lasercom has to start from scratch to provide a ground terminal network to support it. As long as this is the case, the cost for using laser communications will be too high for most missions to consider. LCOT intends to close this gap in technology by designing and developing a standard optical ground terminal design that is flexible enough to serve as a receiver for a wide range of future missions – a ground terminal that can be quickly reconfigured to receive downlinks at different wavelengths using different signal formats. Not only does LCOT have the industry-building objectives of utilizing commercial-off-the-shelf (COTS) components to the maximum extent possible, but also spurs the commercial development of other necessary lasercomm components not currently offered by industry. Finally, LCOT will give NASA scientists and engineers a facility where they can gain real-world experience with optical communications. It will give engineers a cost-effective way to try out new concepts and processes by providing the infrastructure for such testing. In this way it is hoped LCOT will serve as a stimulus for innovation in optical communications and speed its widespread adoption by future missions. The LCOT is comprised of five subsystems: Free-Space Optical, Transceiver, Amplifier, Monitor and Control, and Observatory Infrastructure. In August 2021, the LCOT team installed a 70 cm telescope, developed by Planewave Instruments that was optimized for optical communications. Free-Space Optical subsystem comprises of the telescope and its associated hardware, including a transmitter optical assembly, wide field cameras, two optical benches, and an adaptive optics subsystem. The transmit optical assembly, a unique concept design, is a cluster of four functionally independent transmit subassemblies located on the receive telescope. In addition to receiving optical signals and directing the expanded beam with high precision to the space terminal, it also performs tracking functions. The transmit optical assembly will support operations from Low Earth Orbit (LEO) through lunar and will be used as a template for industry manufacturing. The Optical Infrastructure subsystem is responsible for providing environmentally controlled shelters for LCOT equipment and various other systems. To maintain the safety and proper functionality of the telescope, a 16 ft Astrohaven clamshell dome procured which provides all-sky coverage without the need to rotate the dome. Additionally, Atmospheric Monitoring Assembly (AMA) will be part of optical infrastructure subsystem to ensure accurate performance of the LCOT. Like existing optical ground stations, LCOT will measure standard weather station parameters, infrared all sky image of cloud cover, and cloud height. LCOT, however, adds requirements for measuring night time seeing and, in the future, daytime seeing. Unlike other optical ground terminals, the LCOT is transceiver agnostic; user transceivers may be duplex transceivers, standalone receivers, or standalone transmitters with or without acquisition beacon functionality. As such, the LCOT project accommodates testing with external customer transceivers in a flexible manor, further complimenting its intended multi-mission goals. Another unique component of LCOT is the use of a new amplifier technology – the Very Large Mode Area (VLMA) amplifiers. This new technology allows LCOT to avoid the issues faced by previous laser communications ground terminals, gives users more flexibility and modular capability, and is capable of reaching an order of magnitude higher peak power than traditional High Power Optical Amplifiers (HPOA). One drawback of the VLMA HPOA approach is that the amplified light is output into free-space. The solution developed by LCOT is an optics train that couples the output of the VLMA amplifier into a short fiber for transport to the transmit telescopes with high efficiency. Like many of the LCOT components, a set of detailed manufacturing drawings have been created for the optics train to allow any machine shop with a multi-axis Computer Numerical Control (CNC) machine to fabricate the piece parts from commonly available materials. In line with the goals of LCOT, the monitor and control functions are developed as a modular and flexible system with the ability to support future hardware or algorithm changes, minimizing disruptions. A main priority of development in the Monitor and Control Subsystem (MCS) is the safety monitor system.

Haleh Safavi↗
DOE OSTI2024

GeoThermalCloud: Cloud Fusion of Big Data and Multi-Physics Models using Machine Learning for Discovery, Exploration, and Development of Hidden Geothermal Resources

The primary goals of this project are exploring hidden geothermal resources in the U.S.A. and designing profitable enhanced geothermal systems (EGS). Many processes and parameters control geothermal exploration and energy production from geothermal fields. Diverse datasets (e.g., geology, geochemistry, geophysics, satellite, airborne geophysics) are available to help characterize subsurface geothermal conditions. Sparse and multi-scale characteristics of these datasets prohibit properly leveraging these datasets for geothermal exploration and profitable EGS design. Recent advancements in machine learning (ML) promise to resolve these issues. The tremendous challenges and risks of geothermal exploration and production bring the demand for novel ML methods and tools that can (1) analyze large field datasets, (2) assimilate model simulations (large inputs and outputs), (3) process sparse datasets, (4) perform transfer learning (between sites with different exploratory levels), (5) extract hidden geothermal signatures in the field and simulation data, (6) label geothermal resources and processes, (7) identify high-value data acquisition targets, and (8) guide geothermal exploration and production by selecting optimal exploration, production, and drilling strategies. To address these necessities, ML-based geothermal resources exploration and enhanced geothermal systems (EGS) design tools have been developed. The exploration tool is called GeoThermalCloud and EGS design tool is called GeoDT-ML. GeoThermalCloud (https://github.com/SmartTensors/GeoThermalCloud.jl) utilizes a LANL unsupervised ML platform called SmartTensors (https://tensors.lanl.gov/) to automate data analyses and interpretations by extracting hidden signatures to identify geothermal prospects. Also, it enables the identification of critical measurements needed to identify geothermal resource signatures. Alternatively, GeoDT-ML (https://github.com/SmartTensors/GeoThermalCloud.jl/tree/master/EGS) is an ML-based alternative to GeoDT (https://github.com/GeoDesignTool/GeoDT.git), a fast, simplified multi-physics solver to evaluate EGS project designs in uncertain geologic systems. GeoDT-ML leverages recent advances in deep learning and high-performance computing. It is a faster and simpler version of GeoDT. To make this project a success, we used capabilities of LANL, PNNL, Google, Stanford, and Julia Computing. We analyzed eight datasets of the U.S.A. using GeothermalCloud and demonstrated potential highly prospective geothermal resources and identified key factors defining highly prospective sites. The first data set includes 44 locations in southwest New Mexico and 18 geological, hydrogeological, geophysical, geothermal, geochemical attributes. We defined low- and medium-temperature hydrothermal systems and discovered a new highly prospective site. The second data set analyzed 18 shallow water chemistry attributes at 14,342 locations in the Great Basin. It demarcated modestly, moderately, and highly prospective sites including key attributes for each type of prospectivity. The third data set analyzed Utah FORGE data including satellite (InSAR), geophysical (gravity, seismic), geochemical, and geothermal attributes. Here, we performed prospectivity analysis to identify future drilling locations using geological, geochemical, and geophysical attributes. Maps of temperature at depth and heat flow are constructed based on the available data. Prospectivity maps were generated, and drilling locations were proposed for future geothermal field exploration. The fourth data set analyzed 21 attributes at 120 locations in Tularosa Basin, New Mexico; data comes from past play fairway analyses in this region. ML analyses identified geothermal signatures associated with modestly, moderately, and highly hydrothermal systems. We also defined dominant attributes and spatial distribution of the geothermal signatures. The fifth, sixth, seventh, and eighth datasets include Tohatchi Springs, New Mexico, Hawaii, Brady site, Nevada, and EGS Collab, respectively. Moreover, we coupled GeothermalCloud and magnetotellurics data to pinpoint drilling locations for developing geothermal projects in the Tularosa Basin, New Mexico. GeothermalCloud found potential prospective locations for geothermal resources near White Sands Missile Range and McGregor Range at Fort Bliss. Magnetotellurics data determined the potential depth (~1800m) of geothermal prospects at McGregor Range based on apparent resistivity structures/layers in the subsurface. The McGregor Range consists of three resistivity layers and two resistivity structures. Magnetotellurics data also helps identify that the western portion of the McGregor Range has thick and low-resistivity earth materials. The low resistivity to the west is most likely for a fault system. Assuming temperature is consistent with a geothermal reservoir, the west-central part of the McGregor Range has the highest geothermal potential because of the increase in porosity and associated permeability attributed to the interpreted fault system. Also, we devised a coupling strategy between a process model and GeothermalCloud to characterize hydrogeological conditions and geothermal conditions, respectively. The process model characterizes hydrogeological and geothermal conditions on highly prospective geothermal sites provided by GeothermalCloud. We developed a physics-informed neural network (PINN) version of the Burns equation that can be easily coupled with GeothermalCloud. Furthermore, we performed an optimal design decision maximizing the economic value of an EGS power plant. This study optimized the range of well spacing between injection and production wells maximizing net present value in dollars (NPV). For this task, we used the GeoDT to simulate the Utah FORGE EGS development cycle from the initial well design to the end of production. Next, we accomplished another crucial task, which is predicting permeability of geothermal reservoirs. Predicting permeability of geothermal reservoirs is a non-trivial task because of huge computational runtime of simulation and lack of measurements. To avoid these limitations, we used easy-to-measure chemical concentrations in the subsurface as measurement data and convolutional neural network based ML model of a high-fidelity model. Next, we predicted permeability using Markov chain Monte Carlo simulation. We found that Markov chain Monte Carlo simulation predicts permeability with a high certainty if the prediction zone in the simulation area has chemical concentration data. Finally, we analyzed the DOE funded INGENIOUS and GeoDAWN projects data. For discovering hidden geothermal systems in the Great Basin, the INGENIOUS project accumulated old data, collected new data, and released them in 2022. The dataset includes a total of 24 geological, geophysical, and geochemical attributes. Data resolution and scale significantly vary prohibiting an appropriate usage. To avoid such limitations, we brought all data in the same resolution and scale by applying the inverse distance weighting interpolation technique for predicting data in unsampled locations. Subsequently, we analyzed LiDAR data of the GeoDAWN project. We received data in tiles format. The DOE’s overarching goal is to use ML on LiDAR data for finding favorable geological structures (e.g., step up faults in Brady, Nevada). To serve the purpose, we need to label favorable geologic structures that correspond to LiDAR data. We wrote an algorithm to label the LiDAR data with the favorable geologic structures.

15 GEOTHERMAL ENERGY↗
NASA NTRSDate not supplied

Investigating Low-Altitude Constellations of Ad-Hoc Lunar PNT System for Distributed Spacecraft Autonomy

In this study, we examine a low-altitude Lunar Position, Navigation, and Timing (LPNT) constellations and the localization performance of Centralized Extended Kalman Filter (CEKF) and Decentralized Extended Kalman Filter (DEKF) algorithms. The primary investigation involves a 100-node swarm operating at a 100 km altitude, in contrast to previous studies that examined a 21-node asset in a frozen-orbit at 5,500 km. The autonomous operation of large-scale swarm is based on two-way Inter-Satellite Link (ISL) measurements, which involve pseudoranges and relative velocities among swarm nodes. We perform a numerical assessment of the two filtering approaches, utilizing ‘fully sampled’ measurements from all available assets as well as ‘two ISL’ measurements where each spacecraft is restricted to only two antennas. This research includes an analysis of CEKF under 2-ISL constraints and evaluates the performance of DEKF in a 100-node swarm, which has not been explored in previous studies. In addition, we examine the impact of increasing the sampling frequency for DEKF, showing that the update cycle can be shortened from a 10-minute interval. A novel approach for ‘2-ISL limited’ DEKF will also be introduced, using a matching formulation that exhaustively enumerates all potential matches. This study provides valuable insights into large-scale distributed swarm operations, considering various filter configurations, sampling frequencies, matching strategies, and scalability of CEKF and DEKF for low-altitude LPNT applications. The Lunar PNT technology plays a key role in providing reliable and robust navigation services on the Moon's surface and the South pole, where the primary Lunar missions are planned. To support upcoming Lunar missions, including small satellites from NASA's Commercial Lunar Payload Services program, the Lunar PNT system must be adaptable to smaller platforms like CubeSats. Driven by the growing involvement of public and private exploration partnerships, the traditional low Earth orbit missions are shifting to beyond geosynchronous orbit [1]. These upcoming missions aim to foster a sustainable and innovative exploration program, in collaboration with commercial and international partners, to facilitate human expansion throughout the solar system and return new knowledge and opportunities to Earth [2]. As part of this trend, there are increasing efforts to utilize science missions in Lunar orbit to develop a non-dedicated and ad-hoc PNT network system. Two traditional approaches, the Deep Space Network (DSN) and the weak signal Global Positioning System (GPS), are established deep-space navigation technologies for missions beyond the geosynchronous orbit. Beginning in 1958, the DSN was developed to communicate with the Explorer 1 spacecraft based on the use of radiometric tracking in spacecraft navigation [3]. The DSN is capable of providing nearly unfettered coverage to spacecraft beyond low-Earth orbit (LEO), however, increased space mission volume has created concerns about future expectations of DSN usage for spacecraft navigation [4]. For cislunar mission applications, the position accuracy using DSN achieves 100 m (3σ) with at least three geometrically diverse ground stations when using radiometric tracking alone [5]. The DSN's dependence on Earth-based ground stations restricts its operational capabilities to periods of Earth visibility. This limitation, coupled with its poor localization performance, renders the DSN unsuitable for future lunar missions that demand continuous tracking and precise positioning. To satisfy the increasing requirements of DSN in Lunar applications, spacecrafts are also required to improve their onboard antenna power and efficiency of the transmission. However, there is an important aggregate cost trade between adding capabilities to every spacecraft and adding to a capacity on the ground that serves multiple spacecraft [6]. A weak GPS system can provide PNT service while the user spacecraft is bound to the Moon, leveraging a single, steerable high gain antenna with the relatively narrow beam which includes all the sources in its field of view [7]. However, the higher the altitude the receiver is above the GPS constellations, the poorer and the weaker are the relative geometry and the received signal powers, respectively, leading to a significant navigation accuracy reduction [8]. The transmitted power becomes weaker with increasing distance from the Earth as well as signals tracked from one of the side lobes of the GPS antenna pattern. As a results, the number of visible satellites and relative geometric condition of the GPS satellites at very high altitude drops dramatically and reduces the navigation solution accuracy. Therefore, the weak GPS system is also not an ideal way to provide PNT service to upcoming Lunar missions when considering its limited geometric condition and the recued navigation accuracy. Another navigation approach on the Moon is being developed, similar to the Global Navigation Satellite System (GNSS) on Earth, aiming to offer navigation service with continuous 24/7 coverage across the entire Lunar surface. For example, lunar communications relay and navigation systems (LCRNS) by NASA and Lunar navigation satellite systems (LNSS) by JAXA are designed to serve as dedicated Position, Navigation, and Timing (PNT) systems for the Moon. However, designing a dedicated LNSS and PNT service involves additional challenges, which are unique to the lunar environment, including limited payload capacity for the CubeSat platform, i.e., the size, weight, and power (SWaP) of the onboard clock, limited lunar ground monitoring stations, and limited financial investment as compared to the legacy Earth-GPS [9]. NASA’s focus on utilizing CubeSat platforms on the Moon leads to an alternative Lunar navigation platform that leverages the existing Lunar science and exploration assets. The small satellites used in Lunar missions can be used to create a low-cost, autonomous, ad-hoc, and on-demand mission-centric Lunar PNT swarm capable of providing PNT services to these low-cost lunar missions [10]. As upcoming Lunar missions will often operate at low-altitude about 30 km to 100 km for scientific observations and mapping purposes, the low-altitude orbital constellations could be employed to create an ad-hoc Lunar PNT system. However, several issues must be addressed, such as the instability of these orbits, which often require maintenance or are only suitable for short-duration missions, operating for fewer than 90 days. Additionally, at an altitude of 100 km, the satellites have a limited period during which they are above the horizon and capable of providing PNT service to users. The implementation of a non-dedicated, ad-hoc Lunar navigation constellation facilitates on-demand PNT services. A preliminary study of ad-hoc Lunar PNT system was conducted using 21 spacecraft in 5,5000 km altitude frozen orbits to test its feasibility and a basic performance of orbital asset localization among ad-hoc Lunar constellations in small satellites format [10]. These swarm assets are designed for autonomous localization with minimal Earth interaction, reducing dependency on bandwidth and ground resources. The design in [10] demonstrated the feasibility of a decentralized PNT approach, specifically employing a DEKF approach for state estimation, which helps minimize onboard operating costs. The DEKF method distributes computation across individual satellites, which lightens the computational load while maintaining accuracy in orbit ephemeris and clock offsets, similar to centralized systems [11]. In a follow-on study [12], each spacecraft was limited to 2 communications antennae, forcing the selection of measurements and scheduling spacecraft activities to perform the measurements. A matching algorithm is implemented to select the best measurements and schedule position estimation updates. The decentralized localization performance is also investigated with increasing levels of network degradation for swarm assets considering the impact of intermittent and permanent communication failure, to demonstrate the robustness and fidelity of the decentralized Lunar PNT service [13]. This study confirmed that the ad-hoc PNT constellations in frozen orbit are highly robust and resilient to communication failures. However, unlike frozen orbit swarm assets, the low-altitude satellites have a limited ground view at an altitude of 100 km, where the ad-hoc Lunar constellation consists of 98 low-altitude satellites, evenly distributed across seven circular polar orbital planes, alongside two satellites in a frozen orbit at an altitude of 5,500 km (Figure 1). Therefore, the number of satellites visible to ground users is significantly limited in low-altitude orbit constellations. As each visibility of a spacecraft remains intact for only a few ticks before it moves out of the field of view, the ground user encounters challenges in maintaining continuous navigation service, resulting in sparse availability and provision of Lunar PNT system. Consequently, service availability is primarily restricted to the Lunar South Pole region (Figure 2). Given these limitations and concerns, the localization performance of low-altitude swarm assets will be assessed in this study. We focus on the investigation of the localization performance of low-altitude swarm assets and ground users near the Lunar South Pole. The overall flow of the Lunar PNT simulation incorporates the DEKF approach of asset localization and the weighted least-squares approach in user localization (Figure 3). The autonomous Lunar PNT simulation is primarily implemented in MATLAB, where the DEKF based on the matching scheduler is implemented with Google’s OR-tools as a model builder and Gurobi optimization tool as a backend solver. The General Mission Analysis Tool (GMAT) is utilized to generate ephemeris data for swarm assets, and accounts for satellite orbital details, mass, and perturbations like solar radiation pressure and drag coefficients. Each ephemeris dataset is produced in the Moon International Celestial Reference Frame (ICRF) inertial coordinate system. For state estimation, the distributed swarm assets rely on two-way Inter-Satellite Link (ISL) measurements, which involve tracking pseudoranges and relative velocities between visible satellites and anchor nodes during each observation. Numerical evaluations of the decentralized localization process are conducted to demonstrate the feasibility of the low-altitude PNT system in providing reliable navigation services. The main approach involves using DEKF and CEKF to localize 100 satellites in low-altitude constellations, where the CEKF is implemented to serve as a baseline for comparing the performance of distributed algorithms. In both cases, we evaluate ‘fully sampled’ measurements from all available assets, and ‘two ISL’ measurements when spacecraft are constrained to have only two antennas. We test four estimation techniques: CEKF fully sampled, CEKF two ISL, DEKF fully sampled, and DEKF two ISL filters. As the DEKF update cycle is comprised of network setup, communication, and computations, a global broadcast network and 2-way ISL network setup will take from 4 to 6 minutes as maximum [12]. In this simulation, the DEKF update cycle is set to 10 minutes, including a 4-minute latency for obtaining and computing the actual measurement updates. We experiment an increased update cycle to demonstrate the feasibility and evaluate the impact on localization performance using various tuning values for measurement noise covariances (Figures 4 and 5). By comparing centralized and decentralized approaches using a matching algorithm, we analyze the influence of cross-correlation factors in the covariance matrix, assuming 100% reliability of all assets and measurements. The increased frequency and the adjustments of tuning parameters reveal distinct error patterns between the two scenarios. The localization accuracy of the swarm assets and ground users is assessed by taking the median error across 100 assets and one ground user (84.9°S, 137.5°E) over 7-day simulation period (Table 1). Since the user localization accuracy is significantly affected by the performance of the swarm assets, it is crucial to maintain high localization accuracy within the swarm. This study will continue to explore decentralized filtering for autonomous LPNT operations, with further investigation of an 'iterative' matching approach which enumerates every valid matching pair, planned for the following month.

Yeji Kim↗