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NASA NTRSDate not supplied

The NASA Open Science Data Repository: Biomedical Data, Analysis Tools, and Informatic Collaborations

Increased biomedical risks and challenges associated with deep space missions require knowledge discovery, health countermeasures, and biomedical support capabilities. Maximally open-access and reusable data is needed by developers, scientists, and engineers to develop these systems. The NASA Open Science Data Repository (OSDR) is a maximally open access and FAIR database (ie., findable, accessible, interoperable, and reusable), and meets various scientific, technical, and operational needs. It offers users and submitters the ability to upload, download, search, share, analyze, cite, and visualize data across ‘omics, physiological, phenotypic, payload, hardware, behavioral, bioimaging, video, and environmental monitoring telemetry datasets. OSDR is an expanded database, based upon the successes of NASA GeneLab. OSDR has >460 studies with datasets covering model organisms to non-NASA human astronauts. There are ~12 datasets from the Inspiration 4 (I4) mission, spanning metagenomics, comprehensive metabolic panels, clonal hematopoiesis, spatial transcriptomics, proteomics, and cytokine panels. In the interest of data privacy, two I4 datasets with raw files relating to the epitranscriptome, and a new request feature is live in OSDR (with a backend review process established) developed from industry norms. OSDR is collecting and curating biomedical human data from a new sub-orbital research flight and is open to more space life science/biomedical submissions from the international and commercial sectors. OSDR also recently began a collaboration with the European Space Agency (ESA) to collect and curate >200 terabytes of human and model organism data. The OSDR submission portal is designed to ingest and curate ~25 ‘omics and ~50 physiological-phenotypic-imaging assay data types. Tools available for OSDR users include: 1) an Environmental Data Application to compare radiation, CO2, relative humidity, temperature, and other telemetry across missions and subjects, 2) the RadLab database, a collaboration between NASA, ESA, the German and Italian Space Agencies, and the Bulgarian Academy of Sciences, and 3) a Multi-study visualization tool which enables users to look across and combine ‘omics datasets. There are ~600 volunteer OSDR Analysis Working Group (AWG) members providing feedback on scientific data/metadata standards and collaborating to mine-reuse OSDR in research. OSDR/GeneLab has enabled ~60 publications reusing data as of October 2023.

space biology↗
NASA NTRSDate not supplied

Camera Setup for Unsteady Pressure Sensitive Paint at NASA Ames Research Center

The unsteady Pressure-Sensitive Paint Development Team at NASA Ames Research Center is conducting a Launch Vehicle Demo Test in the Unitary Plan Wind Tunnel’s 11-ft Transonic Wind Tunnel in the first half of 2024 to showcase improvements made to unsteady Pressure-Sensitive Paint technology. The unsteady Pressure-Sensitive Paint setup has upgraded from a four-camera system to an eight-camera system, prompting analysis to maximize the system performance and overall data quality. Additional analysis was performed to maximize the system resolving power when subject to test constraints such as model size and model tunnel positions. These analyses have uncovered new opportunities to improve the unsteady Pressure-Sensitive Paint technology and will allow the system to collect higher quality data with simple changes to the setup procedure.

Camera Calibration↗
NASA NTRSDate not supplied

Flight Performance Comparison of Bank-Angle Steering and Alpha-Beta Steering for Mars Entry Systems

Alpha-beta steering is an alternative hypersonic steering scheme to state-of-the-art bank-angle steering systems for guided Mars entry vehicles. This study uses opti- mal control to generate steering commands for a large robotic Mars entry vehicle using either a bank-angle or alpha-beta steering system to provide a more equal comparison of these two hypersonic steering options, not dependent on guidance design or the inclusion of conventional bank reversals. Objectives maximizing terminal altitude or minimizing control effort, while also reaching a desired target point are considered for both steering options in nominal and dispersed cases. Re- sults indicate both steering options have similar performance, although alpha-beta steering is shown to have improved targeting performance, particularly for the lati- tude/crossrange. Alpha-beta steering is also able to reach higher terminal altitudes by about 0.25 km, relative to the vehicle using bank-angle steering. The difference in altitude maximization capability grows as the allowable vehicle rates decrease.

Daniel L Engel↗
NASA NTRSDate not supplied

Assessment of High Resolution Commercial Satellite Geolocation Accuracy

Commercial companies such as Maxar, PlanetScope, and BlackSky have launched many satellites with revisit times ranging from two hours to one day and have built large archives of high resolution (1-3 m) Earth observing data. The high temporal resolution and global coverage of these satellites makes commercial satellite images ideal for scientists studying rapidly changing processes such as flooding and fires. A key step in assessing changes in these images is co-registration of various images to each other. High geolocation accuracy (at most 0.5 pixels of offset) allows for easy co-registration of images across different times and sensors. Here, we assess the latest PlanetScope imagery and evaluate it for geolocation accuracy. For our assessment, we compare the target image to a reference image with known geolocation accuracy (WorldView imagery) and determine the offset between these images by shifting them to maximize their Pearson Cross-Correlation (PCC) value. Offsets required to maximize the PCC give the geolocation accuracy of the target image relative to the reference image. Previously, our global assessment of Planet data revealed large variability in geolocation accuracy from one continent to another. The measured root mean squared errors (RMSEs) range from 5.4 m in North America to 14.9 m in Africa. We have updated this previous assessment to include both their newest SuperDove series as well as a more robust assessment of the temporal stability of PlanetScope's geolocation accuracy.

Alana G. Semple↗
NASA NTRSDate not supplied

A Science-Focused Artificial Intelligence (AI) Responding in Real-Time to New Information: Capability Demonstration for Ocean World Missions

Introduction: Artificial intelligence (AI) has long been considered a potential mechanism to explore increasingly challenging environments, including those with extreme temperatures and pressures, limited communication capabilities, or those with demanding terrain. We posit that missions in extreme environments could deploy an onboard AI focused on science observations and goals in order to augment a traditional concept(s) of operations (ConOps). An onboard AI capability could perform functions such as data analysis in order to make high-level decisions, including prioritized data transmission for analysis by ground-based teams or autonomously-guided follow-on analyses that maximize science return. Such a capability would empower missions to respond to scientific data of interest in real-time; a mission could make observations and perform a preliminary analysis to alert ground-based scientists to an observation of interest, enabling an informed, rapid response from Earth-based teams. Enceladus Case Study for Onboard AI: We are developing an onboard AI capability for real-time telemetry response that formulates and carries-out informed decisions in service to established mission goals, enabling increased science return of a mission. We focus our AI development for use on a constellation of SmallSats orbiting Enceladus. Our Enceladus case study tests autonomous decision-making capabilities in scenarios with complex orbital dynamics, plume ejecta, extreme cold environments, power restrictions, and a requirement to maximize science return for a potential positive detection of life, while critically evaluating the potential for false positives. Telemetry includes simulated scientific data, spacecraft onboard operational data (e.g., position, velocity, and rotation), and engineering hardware performance data. Enceladus SmallSat Constellation. Our constellation includes eight SmallSat spacecraft in an 8:35 resonant orbit-based formation, leveraging Saturn’s gravitational forces to maintain stable orbits with global coverage around Enceladus. To our knowledge, we simulate the first stable configuration of multiple spacecraft in closed orbits around Enceladus, using a full ephemeris force model (Russell and Lara, 2009). Each spacecraft’s orbit will precess, causing an eastward ground track shift (from an orbiter’s perspective) of each spacecraft for each orbit. However, all spacecraft return to their original positions relative to Enceladus after eight Enceladus revolutions around Saturn. We model communication pathways between SmallSats to understand how information would need to be transmitted across the constellation to enable AI-driven decision-making and resource allocation across the fleet. Capability Demonstration. Our simulated capability demonstration inputs position, velocity, and rotation telemetry from our Enceladus-focused constellation simulations, and mass spectrometry data collected from abiotic and biotic laboratory-analog ocean world experiments (Theiling et al., 2018; Theiling, 2021; Da Poian et al., 2023). Data from these experiments are used to simulate MS measurements and different scenarios of science observations for onboard analysis performed on each of the eight spacecraft. For these demonstrations, we integrate 24 machine learning (ML) algorithms into an onboard intelligence as a ‘knowledge base’, including algorithms evaluating data quality and those predicting (with % confidence) gas composition, ocean aqueous chemistry, and whether the sample was influenced by microbial life. The onboard AI capability is designed to use the knowledge base to come to a consensus-based decision in the interpretation of the observed data in order to request additional action outside of a pre-defined ConOps. Requested actions could include e.g., prioritized downlink to Earth (for analysis by ground-based teams) or follow-on analyses performed across the constellation. The spacecraft’s intelligent onboard planner must then determine whether sufficient resources (e.g., time, power, etc.) are available and weigh the request with mission priorities. In our simulation, the constellation is able to identify potential biosignatures using onboard ML algorithms, evaluate the confidence of that prediction, and perform follow-on analyses across the fleet to confirm the detection, in order to best prepare a transmission of these data to Earth-based teams.

astrobiology↗
NASA NTRS2023

Estimating the Subsolar Magnetopause Position from Soft X-Ray Images Using a Low-Pass Image Filter

The Lunar Environment heliospheric X-ray Imager (LEXI) and Solar wind Magnetosphere Ionosphere Link Explorer (SMILE) missions will image the Earth’s dayside magnetopause and cusps in soft X-rays after their respective launches in the near future, to specify global magnetic reconnection modes for varying solar wind conditions. To support the success of these scientific missions, it is critical to develop techniques that extract the magnetopause locations from the observed soft X-ray images. In this research, we introduce a new geometric equation that calculates the subsolar magnetopause position ( R s ) from a satellite position, the look direction of the instrument, and the angle at which the X-ray emission is maximized. Two assumptions are used in this method: (1) The look direction where soft X-ray emissions are maximized lies tangent to the magnetopause, and (2) the magnetopause surface near the subsolar point is almost spherical and thus R s is nearly equal to the radius of the magnetopause curvature. We create synthetic soft X-ray images by using the Open Geospace General Circulation Model (OpenGGCM) global magnetohydrodynamic model, the galactic background, the instrument point spread function, and Poisson noise. We then apply the fast Fourier transform and Gaussian low-pass filters to the synthetic images to remove noise and obtain accurate look angles for the soft X-ray peaks. From the filtered images, we calculate R 2 and its accuracy for different LEXI locations, look directions, and solar wind densities by using the OpenGGCM subsolar magnetopause location as ground truth. Our method estimates R s with an accuracy of <0.3 R E when the solar wind density exceeds >10 cm -3 . The accuracy improves for greater solar wind densities and during southward interplanetary magnetic fields. The method captures the magnetopause motion during southward interplanetary magnetic field turnings. Consequently, the technique will enable quantitative analysis of the magnetopause motion and help reveal the dayside reconnection modes for dynamic solar wind conditions. This technique will support the LEXI and SMILE missions in achieving their scientific objectives.

Hyangpyo Kim↗
NASA NTRSDate not supplied

Overview of Rotor Hover Performance Capabilities at Low Reynolds Number for Mars Exploration

The Evolutionary aLgorithm for Iterative Studies of Aeromechanics (ELISA) software was developed in support of the Rotorcraft Optimization for the Advancement of Mars eXploration (ROAMX) project. ELISA was developed to enable aerodynamic rotor hover optimization for low Reynolds number flows in the Mars atmosphere. ELISA comprises two modules. The first module is dedicated to airfoil optimization and allows for the creation of multi-objective Pareto optimal (PO) airfoil sets with the airfoil performance evaluation performed using OVERFLOW. The second module is dedicated to rotor hover performance optimization and generates multi-objective PO rotor sets with the rotor performance evaluation performed using the comprehensive analysis code CAMRAD II. This paper presents recent updates to the ELISA optimization toolset. The airfoil module now includes variation in section Reynolds number, alongside simultaneous maximization of section lift and minimization of section drag. Consequently, the rotor optimization module can query PO airfoil sets (as a function of section lift, drag, and Reynolds number) and generate PO C81 decks tailored to specific Reynolds numbers, eliminating the need for adequate initial chord guesses and allowing for arbitrary rotor solidities to be studied. Furthermore, the rotor optimization has been extended to incorporate a third dimension, alongside maximization of blade loading and minimization of rotor power. This enables optimization across a relevant density range on Mars, presenting the lowest power rotor hover geometry, for each attainable blade loading, for each density. The goal of this work is to present the relevance of recent updates to the ELISA optimization toolset, by showing full rotor hover optimization using unconventional airfoils across a practical Mars density range, and by presenting various optimizations for changing blade numbers with unconstrained solidity in the Mars atmosphere.

Rotor↗
NASA NTRS2024

Discounting Water for Optimal Carbon Gain as a Basis of Stomatal Closure

The exchange of carbon dioxide and water vapor between terrestrial ecosystems and the atmosphere is regulated by stomata (small pores in the leaves of plants). Unsurprisingly, environmental factors controlling the opening and closure of stomata has been sought as early as 1800. One approach, popularized in the early 1970s, is a stomatal optimization framework. This framework is based on the hypothesis that plants optimize carbon gain subject to water loss or water availability constraints. This constraint optimization problem was solved in various forms assuming instantaneous adjustments of stomatal aperture to maximize a reward function with no future foresight or legacy effects. Holtzman et al. (2024, https://doi.org/10.1029/2023av001113) offers a novel approach that can diagnose the effective timescale over which the reward function maximization must be time-integrated. The developed method thus optimizes an integrated carbon gain function but adjusted by a discount factor subject to water availability in the root zone. The discount factor considers how the plant values carbon gain to save water and its timescale can be inferred from observations because the model is analytically tractable. The results suggest that the most important climate factor that determines this discount timescale is multi-annual mean of the longest dry period during the growing season. The findings highlight how local climate traits influence the spatial variation in ecosystem-level water use strategies. This sets the stage for expanding such a framework to cases where multiple constraints act in concert while operating at distinct time scales.

stomata↗
NASA NTRS2023

CFD Modeling of Bi-Directional PMD inside Cryogenic Propellant Tanks Onboard Parabolic Flights

Future cryogenic propulsion systems will require efficient methods with which to transfer cryogenic propellants from a depot storage tank to a customer receiver tank to minimize cost and maximize reusability. The Reduced Gravity Cryogenic Transfer project is currently developing advanced cryogenic fluid management technology and developing and validating new numerical models for three phases of transfer: line chilldown, tank chilldown, and tank fill. Additionally, multiple liquid nitrogen (LN 2 ) parabolic flight transfer rigs are being designed by universities and NASA to investigate the gravitational sensitivities that exist in these three technologies. In order to maximize the collection of low-g data during flights, it is required to extract as much (LN 2 as possible from the supply tank, despite variable gravity levels. The purpose of this paper is to present computational fluid dynamics (CFD) volume of fluid simulations of (LN 2 behavior in the supply tank onboard parabolic flights to validate the optimal design of a bi-directional propellant management device (PMD) using the commercial software FLOW-3D. A parametric study is conducted on the effects of gravity level, fill level, pore size, open area, thickness, and type of baffle on PMD performance. Based on results, the PMD as designed exceeds the targeted expulsion efficiency.

Jason Hartwig↗
DOE OSTI2017

Utilization of Model Predictive Control to Balance Power Absorption Against Load Accumulation: Preprint

Wave energy converter (WEC) control strategies have been primarily focused on maximizing power absorption. The use of model predictive control strategies allows for a finite-horizon, multiterm objective function to be solved. This work utilizes a multiterm objective function to maximize power absorption while minimizing the structural loads on the WEC system. Furthermore, a Kalman filter and autoregressive model were used to estimate and forecast the wave exciting force and predict the future dynamics of the WEC. The WEC's power-take-off time-averaged power and structural loads under a perfect forecast assumption in irregular waves were compared against results obtained from the Kalman filter and autoregressive model to evaluate model predictive control performance.

16 TIDAL AND WAVE POWER↗
DOE OSTI2018

A Sequential Game of Defense and Attack on an Interdependent System of Systems

This research studies the defense strategy of an interdependent system in the face of rational attacks. We propose a sequential game between an attacker and a defender on an interdependent system of systems (SoS) to explore the effect of interdependency on the optimal defense strategy. An algorithm of backward induction isdeveloped to obtain the Nash equilibrium of the game.The attacker is the first mover as he applies an attack strategy on constituent systems that maximize his utility. The defender observes and responds by a defense strategy that maximizes her utility. Both players’ utilities are evaluated as the difference between a player’s reward due to SoS functionality (dysfunctionality) and the cost of the action. The sensitivity analysis compares the effects of different parameters on the attacker’s and defender’s strategies such as the effectiveness of defense (attack), the unit cost of defense (attack) and the interdependency level on constituent systems.

Rao, Nageswara↗
DOE OSTI2018

Modeling Framework and Results to Inform Charging Infrastructure Investments

Plug-in electric vehicle (PEV) sales in the United States increased by 40% in 2016, reaching a total stock of over 500,000 vehicles. Still, widespread market adoption of PEVs remains hindered by many factors, including a lack of a convenient and ubiquitous network of charging stations. Understanding the barriers to and benefits of deploying a widespread and effective network of PEV charging stations, also known as electric vehicle supply equipment (EVSE), is particularly important. Such a network would promote PEV consumer acceptance and market growth, enable long-distance travel for BEVs (alleviating the range anxiety concerns of many consumers), and potentially increase the share of electric miles driven by PHEVs. Infrastructure planning must anticipate PEV adoption while remaining cost-effective so low station utilization does not severely undermine the business case for building and operating stations. This project presents an approach for developing a U.S. network of non-residential EVSE that enables broader PEV adoption and maximizes PEV use. This analysis can help inform various public and private stakeholders who are seeking to provide nationwide charging coverage and improve the business case for building stations by maximizing station utilization.

33 ADVANCED PROPULSION SYSTEMS↗
DOE OSTI2018

Thermal Desalination as Cooling for a Supercritical Carbon Dioxide Brayton Cycle

Multi-effect distillation (MED) is commonly used for seawater desalination. MED offers numerous advantages over other existing thermal desalination systems. MED systems are commonly integrated with a steam-Rankine power cycle, for simultaneous production of power and freshwater. Low-pressure steam is extracted from the power cycle to act as a heat source for the MED. The operating temperature of the low-pressure steam must be high enough to drive the MED system. This necessitates an increase in the operating pressure on the power-cycle condenser, resulting in a decrease in power production. Hence, integration of MED with the steam-Rankine cycle is a parasitic load for the power plant. The supercritical carbon dioxide (sCO2) Brayton cycle is being pursued as a new power cycle because of its potential for higher energy efficiency and compact turbomachinery. The sCO2 exiting the low-temperature recuperator is quite hot and is cooled as a sensible-heat fluid before flowing back to the compressor. This heat is sufficiently hot to drive an MED system, rather than being rejected to the environment. Such a thermal integration could lead to simultaneous production of power and potable water without affecting the sCO2 Brayton cycle efficiency. The objective of the present study is to maximize the distillate production from MED without acting as a parasitic load. Optimal feed configuration for the MED is identified for maximizing the distillate concentration. Techno-economic analysis showed the cost of distillate for MED is about 30% lower than distillate produced by reverse osmosis (RO).

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2019

Parameter Co-optimization for Hybrid Electric Vehicles Powertrain System Leveraging V2V/V2I Information

A parameter tuning based co-optimization scheme for the hybrid electric vehicles (HEV) powertrain system is designed to maximize the fuel efficiency. The optimization controlled input parameters are chosen based on sensitivity study of powertrain control parameters. The vehicle to vehicle (V2V) and vehicle to infrastructure information is another optimization input, to have the driving conditions taking in to considerations for maximizing fuel efficiency. The catalyst temperature is considered as an additional constraint as the speed to reach lightoff temperature should not decrease during optimized operation. Neural network is used to develop a simplified yet equivalent model for the optimization problem model. We have achieved an average of 9.22% fuel savings for a random driving cycle on a Toyota Prius test model.

Hong, Wanshi↗
DOE OSTI2019

Cyber Threat Screening Using a Queuing-Based Game-Theoretic Approach

Dynamic and uncertain security environments, such as cyber systems, often involve strategic interactions among multiple decision-making agents. In this paper, we consider a cybersecurity setting where a system administrator (defender) has to screen malicious service requests of an attacker who seeks to exhaust available cyber resources and inconvenience users with normal requests. We propose a novel cyber-threat inspection model, based on Stackelberg games, that unies aspects of Threat Security Games with the Erlang-B queuing framework to provide equilibrium strategies for both the attacker and defender. In our proposed model, the defender seeks to determine the optimal number of inspection nodes required to maximize detection probability of malicious requests, while the attacker maximizes the probability of legitimate requests dropping out of the system. We derive analytical expressions of the equilibrium solutions of the proposed Stackelberg game under realistic assumptions on system observability and payoff structure of the players. A numerical case study is presented, and steps for further research are identified.

Cybersecurity, Stackelberg game, Erlang-B loss sys↗
DOE OSTI2020

Numerical Model Development of a Variable-Geometry Attenuator Wave Energy Converter: Preprint

Because the wave energy industry is still in its infancy, an optimal design for wave energy converters (WECs) has yet to be established; more work is needed to explore various cost reduction pathways. The primary cost-reduction pathway considered for this work is the optimization of the geometric profile on an attenuator WEC to maximize power production while, at the same time, minimizing capital expenditures through the use of variable-geometry modules. In this investigation, the variable geometry modules consist of inflatable bags placed on either side of a base central steel cylinder that would be inflated in low moderate sea states to maximize power capture and then deflated in moderate-extreme sea states to minimize wave loading. The numerical model and simulation of the attenuator WEC were developed and completed using WEC-Sim, which is an open-source code that is appropriate for use in evaluating the dynamic response of the different WEC models in operational seas. The power production estimates were obtained from the Wave Energy Prize (WEP) sea states, which are representative of U. S. deployment sites, to calculate the average climate capture width that is used in the WEP ACE calculation. Preliminary capital expenditure costs were obtained assuming the base central steel cylinder mass was equal to the fluid displaced mass, minus the mass of the variable-geometry bags. The additional weight required to offset the additional buoyancy from the variable-geometry bags was assumed to come from the addition of seawater ballast. The variable-geometry attenuator model was found to have a similar power capture efficiency as a fixed-body model, but is expected to have a lower characteristic capital expenditure given its more streamlined profile, which demonstrates that variable-geometry modules may provide a realistic cost-reduction pathway to help design a more cost-competitive WEC.

50 EE - Wind and Water Power Program - Water (EE-4↗
DOE OSTI2020

Vertex Reordering for Real-world Graphs and Applications: An Empirical Evaluation

Vertex reordering is a way to improve locality in graph computations. Given an input (or ``natural'') order, reordering aims to compute an alternate permutation of the vertices that is aimed at maximizing a locality-based objective. Given decades of research on this topic, there are tens of graph reordering schemes, and there are also several linear arrangement ``gap'' measures for treatment as objectives. However, a comprehensive empirical analysis of the efficacy of the ordering schemes against the different gap measures, and against real-world applications is currently lacking. In this study, we present an extensive empirical evaluation of up to 11 ordering schemes, taken from different classes of approaches, on a set of 34 real-world graphs emerging from different application domains. Our study is presented in two parts: a) a thorough comparative evaluation of the different ordering schemes on their effectiveness to optimize different linear arrangement gap measures, relevant to preserving locality; and b) extensive evaluation of the impact of the ordering schemes on two real-world, parallel graph applications, namely, community detection and influence maximization. Our studies show a significant divergence among the ordering schemes (up to $40\times$ between the best and the poor) in their effectiveness to reduce the gap measures; and a wide ranging impact of the ordering schemes on various aspects including application runtime (up to $4\times$), memory and cache use, load balancing, and parallel work and efficiency. The comparative study also help reveal the nuances of a parallel environment (compared to serial) on the ordering schemes and their role in optimizing applications.

Barik, Reet↗
DOE OSTI2020

GOOML: Geothermal Operational Optimization with Machine Learning

Geothermal Operational Optimization with Machine Learning (GOOML) is a project focused on maximizing increased availability and capacity from existing industrial-scale geothermal generation assets. The GOOML project will develop a suite of machine learning-based algorithms that analyze historical production datasets and provide predictive setpoints for geothermal field operations. Historical datasets from New Zealand and the US will provide the input to develop digital geothermal system twins which allow prediction of market conditions, maintenance operations and steamfield optimization. The algorithms will identify key parameters within fields and suggest setpoints for components of the system to maintain optimal generation. Set-points can be instructed to follow mass flow restrictions, generation maximization and optimal field/reservoir balance and give field operators a guide by which generation can be optimized. The datasets that will be used to develop GOOML are sourced from operating geothermal fields in New Zealand and the United States with varying degrees of complexity. This will ensure that most geothermal systems can utilize the GOOML tool to assist in optimizing operations. GOOML aims to achieve a step-change in geothermal operations by developing state-of-the-art machine learning algorithms, comprehensive data analytics, and a first-of-its-kind automated, intelligent geothermal system model.

algorithms↗