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NASA NTRSDate not supplied

Numerical Investigation of Heat Transfer and Fluid Flow within Electrochemical Hydrogen Peroxide Generation Unit

Long-term manned space missions require the onboard production of disinfectants essential for maintaining crew health and supporting life systems. Currently, disinfection aboard the International Space Station (ISS) relies on disposable wetted wipes, which are regularly resupplied from Earth. This approach imposes a significant burden on resupply logistics, storage, and waste management. To address these challenges and support future missions, efforts are underway to develop an in-situ solution that electrochemically generates hydrogen peroxide disinfectant using onboard resources. In collaboration with NASA, Faraday Technology, Inc. has advanced this concept through a series of Small Business Innovation Research (SBIR) projects, resulting in the development of a Peroxide Generation Unit (PGU). The PGU can produce up to 3 wt.% hydrogen peroxide on-demand at a rate of 1 liter per day, providing a sustainable alternative to Earth-dependent supplies. The resulting aqueous hydrogen peroxide (H₂O₂) is an effective disinfectant, safe for crew use, compatible with spacecraft systems, and free from volatiles, off-gassing, or residues. This innovation offers a reliable, efficient solution for onboard disinfection, reducing dependence on Earth-based resupply while ensuring the health and safety of space crews. Generating hydrogen peroxide at the required rate needs high voltages and currents, exceeding 20V and 2A respectively, which leads to significant heat generation from Joule heating. This temperature rise poses a risk to sensitive system components, especially critical and expensive membranes that can degrade under thermal stress. To mitigate this risk, the thermal, fluid, and electrical flows within the system are modeled computationally using the commercial software COMSOL. The numerical simulations are validated against experimental data from both sub-scale and alpha-scale systems. Once verified, the model is employed to identify thermal hotspots, investigate their underlying causes, and explore solutions to prevent them.

Life Support Systems (LSS)↗

NASA NTRSDate not supplied

A Prognostics Framework for Battery Health Monitoring Integrated with Thermal Modeling

Urban Air Mobility (UAM) promises to revolutionize transportation in major cities, offering passenger travel, cargo delivery, and emergency medical services through a network of electric vertical takeoff and landing (eVTOL) aircraft. However, the limited range of current eVTOLs, due to the low specific energy of lithium-ion batteries along with a possibility of thermal runaway conditions poses significant safety concerns, leading to potentially compromising operational safety. To address this critical challenge, researchers are actively evaluating the impact of flight and environmental conditions on onboard lithium-ion battery health. This involves carefully assessing the performance of battery packs under laboratory and operational conditions for developing models to estimate future health using prognostics framework. This study examines the effectiveness of evaluating battery degradation leading to catastrophic failures under varying operational conditions in laboratory. These are captured using physics based models of underlying phenomenons and integrated into the prognostics framework. A fully charged battery undergoes controlled discharge cycles at varying C-rates based on the simulated power draw profile, with current and voltage, temperature data recorded throughout the experiment. The observed data provides valuable insights into how different operating conditions and mission profiles affect battery performance. This information is crucial for developing strategies to optimize battery systems, enhance range, and ultimately ensure the safe and reliable operation of UAM vehicles.

Thermal Modeling↗

NASA NTRSDate not supplied

A Prognostics Framework for Battery Health Monitoring Integrated with Thermal Modeling

Thermal Modeling↗

NASA NTRSDate not supplied

Natural Fiber Simulations and Analysis for Composites

As the U.S. government and the aviation industry strive to achieve net-zero carbon emissions by 2050, the Sustainable Manufacturing of Aircraft (SUMAC) project, supported by the Convergent Aeronautics Solutions (CAS) program of the National Aeronautics and Space Administration (NASA), aims to develop sustainably derived thermoplastic composites and manufacturing technologies for future aviation. A multidisciplinary team of experts drives this initiative through a collective effort. The team’s key focus areas are sustainably derived thermoplastic resins, natural fiber reinforcement, composite manufacturing, structural health monitoring, computational materials modeling, and systems analysis. This presentation will give an overview of the SUMAC project. Fiber-scale microscopy analysis characterized fiber cross sections finding that hemp and flax fibers are very deformable and non-circular (circularities of 0.2 were observed), and diameters range from submicron to 20 μm. Fiber shapes range from flattened, with aspect ratios Rlong/Rshort up to 5, to round, Rlong/Rshort =1. And Fiber core perimeters, which likely drives deformability, were calculated Pcore=7 μm. Explicit-fiber yarn simulations, parameterized from single-fiber measurements, resolved and identified slack as a cause for yarn strain-hardening tensile behavior. The bonded network model of PLA, PHA and PA11 resins captures tensile behavior, and can model an infused weave. Computational models of the weave unit cells matched the pattern (including plain, twill and triaxial braid), yarn ply (1to 2), and yarn twist from optical microscopy.

thermoplastic↗

NASA NTRSDate not supplied

Natural Fiber Simulations and Analysis for Composites

thermoplastic↗

NASA NTRS2026

Application of Energy-efficient Electromagnetic Melt-Processing for the Upcycling of Recycled Polyphenylene Sulfide into Multifunctional Segregated Nanocomposites

Polyphenylene sulfide (PPS) is widely used in structural and functional composites because of its thermal stability, chemical resistance, and mechanical strength. As circular manufacturing becomes increasingly important, extending the service life of recycled PPS (rPPS) is essential. However, conventional high-temperature reprocessing accelerates thermo-oxidative degradation, reducing recycled composite performance. This study proposes a rapid and potentially energy-saving upcycling strategy for rPPS using electromagnetic (EM) melt-processing to form segregated carbon nanotube (CNT) networks and produce EM-responsive nanocomposites. The aim was to determine whether CNT-assisted EM heating could reduce polymer degradation while improving multifunctional properties at ultralow filler loadings. rPPS micropellets were coated with CNTs by ball milling to create conductive shells, then compacted into green bodies (GBs) and selectively melted by rapid EM irradiation. Structural, electrical, mechanical, rheological, and electromagnetic interference (EMI) shielding properties were evaluated. Electrical percolation occurred at an ultralow CNT loading of 0.08 wt%, with conductivity reaching (1.24 ± 0.74) × 10 -5 S⋅m -1 at 0.1 wt%. At this concentration, tensile strength and modulus increased by 72% and 99%, respectively. At ~ 0.7 mm thickness, X-band EMI shielding effectiveness reached 6 dB for GBs and 3 dB after EM processing. This shows that EM melt-processing upcycles rPPS into high-performance multifunctional nanocomposites with minimum thermal degradation.

recycled↗

NASA NTRS2022

Cryogenic Spray Quenching of A Simulated Propellant Storage Tank Wall With Heat Transfer Enhancement By A Thin-Film Coating and Flow Pulsing in Microgravity

Human space exploration to the Moon, Mars, and possibly asteroids is NASA’s biggest challenge for the new millennium. One of the critical elements to this mission is the effective, sufficient, and reliable supply of cryogenic propellant fluids. Future lower-earth-orbiting (LEO) propellant fuel depots and human-carrying orbital transfer spacecraft flying to the moon and Mars will have to utilize the high thrust and high efficiency of liquid cryogenic chemical propulsion or nuclear thermal propulsion. Efficient in-space tank-to-tank propellant transfer (propellant fuel depot to orbital transfer spacecraft) of cryogenic propellants is an enabling technology for the planned Crewed Mars Surface Mission. The transfer of cryogenic propellants in space, however, has yet to be accomplished, solely due to the unavailability of cryogenic quenching heat transfer data during chilldown (quenching) and filling of the propellant receiver tank in reduced gravity and microgravity as liquid propellant cannot be stored in a required liquid state until the tank is quenched down to the liquid temperature. Therefore, highly energy efficient thermal-fluid management breakthrough concepts to conserve and minimize the cryogen consumption during propellant transfer have become the focus of research and engineering development, especially for the deep-space mission to Mars. In this paper, we introduce such concepts and demonstrate their feasibility for cryogenic storage tank chilldown in parabolic flights under a simulated space microgravity condition. In order to maximize the storage tank chilldown efficiency for the least amount of cryogen consumption, the technology adopted included cryogenic spray cooling, Teflon thin-film coating of the simulated tank surface, and spray flow pulsing. The completed flight experiments successfully demonstrated that spray cooling is the most efficient cooling method for the tank chilldown in microgravity. In microgravity, Teflon coating alone can improve the efficiency up to 72% and the efficiency can be improved up to 59% by flow pulsing alone. However, Teflon coating together with flow pulsing was found to substantially enhance the chilldown efficiency in microgravity for up to 113%.

spray↗

NASA NTRS2026

Using Satellite Surface Temperature Data to Monitor Urban Heat Island

Exposure to heat is a growing concern nationwide. Temperatures can be elevated in cities compared to surrounding rural areas, referred to as an “urban heat island” (UHI) effect, which is intensified during heat waves. The lack of dense networks of air temperature measurements results in few studies on urban heat. Now, a vast amount of high spatial and temporal satellite data on land surface temperature is available. We identified satellite datasets with the longest surface temperature records but different spatial and temporal resolutions: Landsat (1985-current, biweekly at 60 m and 100 m spatial resolution) and the Moderate Resolution Imaging Spectroradiometer (MODIS) data from the Terra and Aqua satellites (2000-current, daily at 1 km spatial resolution). We investigated how satellites with different spatial and temporal resolutions detect UHI effects differently. We hypothesized that 1) a dataset’s spatial resolution impacts the precision of detected UHIs spatially, since high spatial resolution Landsat data better captures spatial variability in temperature, and 2) daily surface temperature data can detect temporal patterns of UHIs and heatwave frequencies. We analyzed Landsat and MODIS satellite data in the Washington, D.C. and Baltimore region. We found that Landsat describes higher spatial variability of the UHI effect than MODIS data. However, MODIS data shows more consistent seasonal surface temperature patterns than Landsat when compared to in situ air temperature measurements. MODIS data was also able to consistently measure the frequency of heat waves. This study demonstrates the value of NASA satellite data for urban heat and climate change studies.

landsat↗

NASA NTRS2017

Novel Polyimide Battery Separator Imbibed with Room-Temperature Ionic Liquids

The journey to Mars will require advancements in many existing technologies, including space power and energy storage systems. According to the 2015 NASA Technology Roadmaps, energy storage is a critical technology area to develop for both terrestrial as well as future long-term space missions. Currently, batteries represent one of the major areas in need of advancement, both in terms of energy density as well as safety. Recently, concerns regarding the fire safety of commercial lithium-ion batteries have prompted efforts to produce nonflammable battery components, namely the electrolyte and separator. Commercial lithium-ion batteries utilize polyolefin separators imbibed with a lithium salt dissolved in cyclic carbonates. This separator/electrolyte combination imparts good ionic conductivities in the range of 10(exp -2) to 10(exp -3) S/cm. However, the cyclic carbonates and polyolefin separator are inherently flammable. Room-temperature ionic liquids (RTILs) appear to be a safer alternative to cyclic carbonates. They offer good ionic conductivities, similar to those observed in cyclic carbonates, but are inherently nonvolatile and nonflammable giving them a safety advantage. Many promising RTILs for battery electrolytes are not compatible with commercial polyolefin separator materials. Polyimide aerogels possess an open-porous, fibrillar network architecture which offers a high degree of porosity (typically greater than 85 porous), required for lithium ion conduction, as well as good mechanical properties. Furthermore, these materials are compatible with all tested RTILs. By creating a polyimide gel and imbibing the gel with a RTIL containing a lithium salt instead of super critically drying them to form aerogels, a nonflammable separator/electrolyte system with conductivities in the range of 1x10(exp -3) S/cm has been demonstrated.

Polyimide↗

NASA NTRSDate not supplied

Laser Forming of Sheet Metal for In-Space Manufacturing Applications

As the in-space economy matures, the migration of typically Earth-bound manufacturing methods, such as metal forming and joining, up to space will be required to meet the increasing demand for refueling and supply depots, habitats, laboratories, lunar surface structures, and sites for deep-space exploration shipbuilding. Only in this way can the magnitude of individual structures begin to exceed what can be launched, deployed, docked, or inflated and allow true economies of scale to be unlocked in space. However, typical terrestrial methods of forming and shaping sheet metal require massive machinery with the ability to deflect reaction forces to a large mass in a fixed reference frame (Earth). To both limit up-mass required for manufacture and to mitigate the issues of reaction forces in the relative reference frames of space, a non-contact, laser-based method of forming metal sheet is proposed. NASA Marshall Space Flight Center (MSFC), in cooperation with DARPA and the University of Florida (UF), have begun investigation of non-contact methods of laser forming sheet metal in thermal vacuum (TVAC) conditions to great success. To date, several grades of aluminum, stainless steel, and titanium coupons have been formed via laser-induced internal stresses in these metals at both ambient atmosphere and vacuum conditions. Temperatures in these tests ranged from -120 to 60 C, and though the total energy required to induce strain does vary with workpiece temperature and environmental pressure, the method remains predictable and controllable for the materials investigated. It is believed that this method will revolutionize manufacturing in-space as it not only continues to build upon the capabilities of the laser (sensing, marking, cutting, drilling, joining, and now forming) as an all-in-one tool, but also reduces the need to counteract forming forces as would be required in mechanical bending. In theory, all required stress to induce bending is generated through thermal gradient and strain within the workpiece, meaning that the need for reaction control and fuel expense on-orbit will be limited. Archimedes once stated, “Give me a lever long enough and a fulcrum on which to place it, and I shall move the world”. With a laser as our lever and our workpiece its own fulcrum, we can move the world’s manufacturing into space.

Laser↗

NASA NTRS2026

Multi-Agent Swarm State of the Art Report

The Next-Generation Multi-Agent Swarm (NGS) Study conducted by NASA’s Ames Research Center for NASA’s Space Technology Mission Directorate (STMD) will develop a comprehensive understanding of emerging multi-agent swarm capabilities. The study aims to identify existing swarm capabilities and asses their potential for persistent lunar space situational awareness, surface monitoring, and distributed autonomy demonstrations. A key objective is to inform the design of a next-generation multi-agent swarm that can perform autonomous distributed remote sensing, position, navigation, and timing (PNT) services, automated deployment that leverages autonomy, edge computing, and interoperable networking to enable cooperative operations without the need for immediate human operation. This study will address specific shortfalls identified by STMD, including intelligent multi-agent constellations, autonomy, edge computation, position, navigation, and timing for small spacecraft, small spacecraft propulsion, and space situational awareness (1625, 1438, 1433, 1557, 1431, 1430, 1589). The NASA Ames Mission Design Center (MDC) will provide subject matter expertise to support systems engineering trades, while experts in autonomy and spacecraft swarms in NASA’s Intelligent Systems Division will lead the study and focus on identifying emerging next-generation swarm capabilities. The study objectives include: capturing the current state-of-the-art for multi-agent swarm capabilities, evaluating technologies and creating technology roadmaps, and developing at least one new technology demonstration mission concept. This initial NGS study report surveys the current state of the art in technology areas relevant for the next-generation multi-agent swarm design. Our primary focus is on surveying relevant deployed space systems1, supplemented with selective analysis of relevant proposed missions and technology developments that have yet to fly.

agent↗

NASA NTRS2026

Highly Porous Polyimide Gel for Use as Battery Separator with Room Temperature Ionic Liquid Electrolytes

Advanced aerospace vehicular concepts require advances in many existing technologies, including space power and energy storage systems. Batteries represent one of the major areas in need of improvement, both in terms of energy density and safety, with growing concerns over the fire safety of commercial lithium-ion batteries. This has prompted efforts to develop nonflammable battery components, namely the electrolyte and separator. Existing commercial lithium-ion batteries utilize polyolefin microporous membranes as separators with an electrolyte consisting of a lithium salt dissolved in a mixture of cyclic carbonate solvents. This separator/electrolyte combination has ionic conductivities in the range of 10 −2 to 10 −3 S/cm. However, the cyclic carbonate solvents are inherently flammable. Room-temperature ionic liquids (RTILs) appear to be a safer alternative. They offer good ionic conductivities and are inherently nonvolatile and nonflammable, giving them a safety advantage. However, many promising RTILs for battery electrolytes are not compatible with commercial polyolefin separator materials. Alternative separator materials, such as polyimides, are non-flammable and are capable of accepting RTILs into their structure. Polyimide gels, with a composition of 4,4′-oxydianiline, 3,3′,4,4′-tetracarboxylic dianhydride and cross-linked with Desmodur N3300A, possess an open-porous, fibrillar network architecture which offers a high degree of porosity (typically greater than 85% porosity) for lithium-ion transport and conduction, as well as good mechanical properties. Furthermore, these polyimide gels are compatible with selected imidazolium-based RTILs. Nonflammable separator/electrolyte systems with room-temperature conductivities in the range of 10 −3 S/cm have been evaluated. It has been demonstrated that 1-ethyl-3-methylimidazolium bis(trifluoromethylsulfonyl)imide was the most promising among six RTILs screened, in terms of both ionic conductivity and constant current cycling.

Polyimide↗

NASA NTRSDate not supplied

Physics-Guided Deep Learning for Complex System Health Management and Decision Making

The landscape of complex engineered systems is rapidly evolving, from smart manufacturing facilities to next-generation transportation vehicles. As these systems become increasingly sophisticated and interconnected, the need for advanced health management systems grows ever more critical. These systems must go beyond simple monitoring, actively predicting potential failures before they occur. This paradigm shift from fixed maintenance schedules to condition-based predictions is key to optimizing system performance, enhancing safety, and paving the way for autonomous decision-making across various industries. Whether in industrial processes, energy systems, or advanced transportation, the ability to anticipate and prevent failures is becoming a cornerstone of operational excellence. To accurately predict the future health of any complex system, knowledge of its current health state and future operational conditions is essential. Recent advancements in data-driven algorithms have generated growing interest in artificial intelligence for industrial applications. However, the limitations of pure data-driven methods, particularly in industries where data acquisition is costly and limited, have become apparent. This has led to a focus on blending physics with data-driven algorithms, mitigating the drawbacks of both approaches while emphasizing their respective advantages. This research proposes a novel framework for integrating physics-based performance models with deep learning algorithms for the prognostics of complex safety-critical systems. In this approach, physics-based models serve as a blueprint, capturing fundamental system behaviors, while deep learning algorithms, leveraging real-world sensor data, fill in gaps and identify subtle patterns indicative of potential problems. This hybrid methodology, utilizing techniques such as Physics-Informed Neural Networks (PINNs), offers a powerful solution for predicting system health. By fusing domain knowledge with data-driven insights, this approach promises more accurate, adaptable, and reliable models for health prediction. The resulting framework is versatile, applicable across various sectors including aerospace, manufacturing, and energy systems, ultimately contributing to safer, more efficient operations in our increasingly complex technological landscape.

Diagnostics↗

NASA NTRS2026

Moon Base Transportation - Deliveries to the Lunar Surface

Development of the Moon Base will enable a home away from home for astronauts who will live and work at humanity’s first lunar outpost. In this effort, NASA’s Moon Transportation Office is responsible for enabling the transformational missions required to deliver habitats, supplies, science payloads, and all other elements needed to cultivate a permanent presence on the Lunar Surface. The Mission Concept (MC) is characterized through evaluation of an end-to-end architecture that can successfully deliver a generalized heavy large-volume payload, in excess of 4000 kg, to a precision landing and touchdown on the lunar surface. The mission architecture utilizes a single launch configuration of a Lunar Lander (LL) with a unique propellant system. The LL has an integral orbital transfer capability and features jettisonable elements. The design circumvents the need for prop transfer on orbit and multiple launch configurations. The launch vehicle (LV) for this work will assume the capability to deliver a payload in excess of 40,000 kg to orbit, affording multiple LV solutions. Considerations for the LL and payload deployment from the fairing are assumed to be handled through compliance with a launch providers’ Interface Requirements Document (IRD). The MC will span from launch at Kennedy Space Center (KSC) to terminal descent and touchdown on the lunar surface, requiring a total ΔV on the order of 6 km/s beyond what is required to get the vehicle stack to a 200 km circular Low Earth Orbit (LEO). Major mission phases include: launch and launch vehicle separation, transfer operations, pre-landing navigation, and lunar descent and touchdown. A Concept of Operations (ConOps) is used as the primary design driver for defining architecture of the vehicles necessary to achieve final payload delivery. Numerous ground rules and assumptions will be provided for each phase of the mission. Concept designs for the LL is presented. An emphasis of the design maximizes a feasible path for maturation, manufacturing, and operation. A self-imposed practical consideration for this effort is the incorporation of legacy designed hardware to minimize expensive, time-intensive, and high-risk hardware development cycles. The propulsion system of the LL adopts a conventional storable bipropellant configuration of monomethyl hydrazine (MMH) and mixed oxides of nitrogen (MON3). This effort will showcase a unique propellant delivery system to minimize the reliance on propellant management devices (PMDs) during descent. Numerous key constraints have been considered, across the multiple segments of the mission. These include the unique aspects of center of gravity (CG) management, thruster plume effects including self-impingement, propulsion system hardware limitations, navigation during multiple mission phases, and landing gear geometry for uneven terrain. These constraints shape the trades necessary for precision landing of heavy cargo and ensure compatibility with broader Moon Base Transportation concepts. The resulting insights inform future transportation strategies for the Moon and beyond; directly contributing to the development of cargo‑delivery standards that will support the long‑term buildup of a sustainable, continuously inhabited Moon Base.

Lunar Habitat↗

NASA NTRS1993

Radio Metric Errors Due to Mismatch and Offset Between a DSN Antenna Beam and the Beam of a Troposphere Calibration Instrument

Two components of the error of a troposphere calibration measurement were quantified by theoretical calculations. The first component is a beam mismatch error, which occurs when the calibration instrument senses a conical volume different from the cylindrical volume sampled by a Deep Space Network (DSN) antenna. The second component is a beam offset error, which occurs if the calibration instrument is not mounted on the axis of the DSN antenna. These two error sources were calculated for both delay (e.g., VLBI) and delay rate (e.g., Doppler) measurements. The beam mismatch error for both delay and delay rate drops rapidly as the beamwidth of the troposphere calibration instrument (e.g., a water vapor radiometer or an infrared Fourier transform spectrometer) is reduced. At a 10-deg elevation angle, the instantaneous beam mismatch error is 1.0 mm for a 6-deg beamwidth and 0.09 mm for a 0.5-deg beam (these are the full angular widths of a circular beam with uniform gain out to a sharp cutoff). Time averaging for 60-100 sec will reduce these errors by factors of 1.2-2.2. At a 20-deg elevation angle, the lower limit for current Doppler observations, the beam-mismatch delay rate error is an Allan standard deviation over 100 sec of 1.1 x 10 -14 with a 4-deg beam and 1.3 x 10 -15 for a 0.5-deg beam. A 50-m beam offset would result in a fairly modest (compared to other expected error sources) delay error (less than or equal to 0.3 mm for 60-sec integrations at any elevation angle is greater than or equal to 6 deg). However, the same offset would cause a large error in delay rate measurements (e.g., an Allan standard deviation of 1.2 x 10 -14 over 100 sec at a 20-deg elevation angle), which would dominate over other known error sources if the beamwidth is 2 deg or smaller. An on-axis location is essential for accurate troposphere calibration of delay rate measurements. A half-power beamwidth (for a beam with a tapered gain profile) of 1.2 deg or smaller is desired for calibration of all types of radio metrics. A water-vapor radiometer calibration beam of this size with very low sidelobes would require a clear aperture antenna with a diameter of at least 1.5 m if the primary water vapor sensing channel were in the 20-22 GHz range.

R P Linfield↗

NASA NTRSDate not supplied

Enabling Interoperability in Earth System Digital Twins (ESDT): Integrating Observations, Models, and AI for Actionable Insights Through NASA'S Intelligent Systems Technology Program

NASA’s Intelligent Systems Technology Program (IST) is driving a paradigm shift in Earth science through the development of Earth System Digital Twins (ESDT). These integrated information systems create a dynamic "digital replica" of the Earth by harmonizing continuous, multi-source observations with high-fidelity models and state-of-the-art artificial intelligence (AI) that enable “What now?”, “What next?”, and “What if?” scenario building. These scenarios are reflected in NASA IST’s series of ESDTs, from the Coastal Zone Digital Twin that integrates complex data on the current state of the Chesapeake Bay to the Terrestrial Environmental Rapid-Replication and Assimilation Hydrometeorological (TerraHydro) AI-based ESDT that forecasts water movement across Earth’s surface, to the Agriculture Land Information System (AgLIS) which can be used to assess optimal planting dates and crop yield estimates. By bridging the gap between vast data archives and actionable insights, these projects enable a system-of-systems approach to understanding complex, interacting Earth processes. This poster will highlight recent innovations and future directions from NASA’s ESDT initiatives: Continuous Data Assimilation & Multi-Source Fusion. A core requirement of the ESDT work is the transition from static models to dynamic "living" replicas. This involves creating frameworks for the continual assimilation of near-real-time data from uncoordinated, heterogeneous sources, including satellite observations and airborne assets, and ground-based Internet of Things (IoT) sensors. These systems link design, operational status, and environmental data, ensuring the digital twin accurately reflects the current state of the physical Earth system. High-Fidelity Hybrid Modeling & Computational Acceleration to enable interactive "what-if" explorations, programs are moving beyond traditional, slow physical solvers by developing fast surrogate machine learning models and Deep Generative Models (DGMs). These hybrid approaches use neural networks to emulate complex physics, such as cloud feedback or ocean dynamics, at a fraction of the original computing cost, often leveraging advanced hardware like Graphics Processing Units (GPUs) to achieve the necessary scale. Federated Ecosystems & Interoperable Frameworks rather than building isolated tools, NASA IST is moving toward federated ESDTs and reusable analytic collaborative frameworks. This theme focuses on interoperability standards and common ontologies that allow specialized digital twins to interact and share data. This system-of-systems architecture supports multi-discipline investigations, such as analyzing how upstream watershed changes impact downstream urban flooding or how wildfire emissions affect regional air quality. By leveraging these advancements, ESDTs empower researchers and decision-makers to conduct real-time analysis and run complex hypothetical scenarios, ultimately improving our understanding of Earth’s evolving systems and informing critical real-world applications.

Earth System↗

NASA NTRS2009

Modular Battery Charge Controller

A new approach to masterless, distributed, digital-charge control for batteries requiring charge control has been developed and implemented. This approach is required in battery chemistries that need cell-level charge control for safety and is characterized by the use of one controller per cell, resulting in redundant sensors for critical components, such as voltage, temperature, and current. The charge controllers in a given battery interact in a masterless fashion for the purpose of cell balancing, charge control, and state-of-charge estimation. This makes the battery system invariably fault-tolerant. The solution to the single-fault failure, due to the use of a single charge controller (CC), was solved by implementing one CC per cell and linking them via an isolated communication bus [e.g., controller area network (CAN)] in a masterless fashion so that the failure of one or more CCs will not impact the remaining functional CCs. Each micro-controller-based CC digitizes the cell voltage (V(sub cell)), two cell temperatures, and the voltage across the switch (V); the latter variable is used in conjunction with V(sub cell) to estimate the bypass current for a given bypass resistor. Furthermore, CC1 digitizes the battery current (I1) and battery voltage (V(sub batt) and CC5 digitizes a second battery current (I2). As a result, redundant readings are taken for temperature, battery current, and battery voltage through the summation of the individual cell voltages given that each CC knows the voltage of the other cells. For the purpose of cell balancing, each CC periodically and independently transmits its cell voltage and stores the received cell voltage of the other cells in an array. The position in the array depends on the identifier (ID) of the transmitting CC. After eight cell voltage receptions, the array is checked to see if one or more cells did not transmit. If one or more transmissions are missing, the missing cell(s) is (are) eliminated from cell-balancing calculations. The cell-balancing algorithm is based on the error between the cell s voltage and the other cells and is categorized into four zones of operation. The algorithm is executed every second and, if cell balancing is activated, the error variable is set to a negative low value. The largest error between the cell and the other cells is found and the zone of operation determined. If the error is zero or negative, then the cell is at the lowest voltage and no balancing action is needed. If the error is less than a predetermined negative value, a Cell Bad Flag is set. If the error is positive, then cell balancing is needed, but a hysteretic zone is added to prevent the bypass circuit from triggering repeatedly near zero error. This approach keeps the cells within a predetermined voltage range.

Button, Robert↗

NASA NTRSDate not supplied

NASA POWER: Providing Analysis-Ready, Cloud-Optimized Data for AI /ML Training and Applications in Earth Science

As global demand for sustainable development grows, the integration of Earth Observation (EO) data into decision making frameworks has become a primary objective for the scientific community. The NASA Prediction of Worldwide Energy Resources (POWER) project serves as a bridge between NASA EO data and the specialized needs of the renewable energy, sustainable infrastructure and agroclimatology communities. In this poster presentation we will present an overview of POWER data products and services along with its use in diverse research to decision-making workflows. By providing over 40 years of high-resolution historical, hourly and daily solar and meteorological data, POWER transforms satellite observations and global model reanalysis into actionable, Analysis-Ready Dataset (ARD). Currently, the project delivers over 250 industry-friendly parameters to the users from different NASA datasets like CERES SYN1Deg, MERRA-2, and IMERG alongside downscaled CMIP6 climate model data, fulfilling over 16 million requests from 50,000 unique users monthly. To ensure data quality and traceability, these parameters are rigorously validated against the ground-based observations from the Baseline Surface Radiation Network (BSRN) and the Global Surface Summary of the Day (GSOD) – these results will be discussed in the presentation. A newly introduced web-based PaRameter Uncertainty ViEwer (PRUVE) tool will be presented that provides an online validation platform to the users that benchmarks satellite-based and assimilation data products against these surface measurements. To reduce technical barriers to data adoption, POWER data is accessible through RESTful APIs, ESRI ArcGIS Image Services, a web-based Data Access Viewer tool, allowing users to visualize, validate and apply the dataset. For efficient data delivery POWER data is cloud-optimized into Zarr datastore accessible through NASA managed Amazon S3 ensures high-performance allowing users to integrate EO directly into operational pipelines. These customized services will be presented. Use cases from application will be presented from the energy sector - such as for design of generation systems, performance monitoring of solar power plants, in infrastructure sector- optimizing building energy efficiency and thermal comfort, in agriculture – such as driving crop simulation and yield forecasting models to enable climate resilient farming. Furthermore, the shift toward machine learning (ML) in EO research that has positioned POWER as a key provider for training datasets which will be discussed. Use-cases will be presented to showcase how NASA data is enabling the development of predictive tools for climate variability and resource management. The poster will present POWER’s future plans including technology development to enhance data traceability and reproducibility and improving I/O performance to support the rapid integration of new EO products, ensuring that POWER remains a robust scalable backend for the evolving landscape of AI-driven Earth Science. Additionally, POWER is developing an AI Agent and an MCP-Server to enable industry AI-Agentic workflows.

Neha Khadka↗