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Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

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At least 181 records · Page 10
All sourcesNASA NTRSDOE OSTI
DOE OSTI2025

Exocortex Network for AI-Augmented Human-Led Scientific Expedition

AI advances in science can be viewed along two main directions with a fluid boundary: enhancing efficiency through automation and smart tools to accelerate tasks that humans can already perform; and enabling exploration into uncharted territories and potentially toward AGI. These advances manifest in the AI cognitive core through the development and explainability of foundation models; in the physical embodiment of instruments and facilities; and in the integrated agency of AI workflows exemplified by the science exocortex. To address the role of humans in this evolving landscape, in this Perspective, we suggest a third direction: the development of personalized agents that form human-centered networks, supporting both efficiency and exploration while ensuring that AI remains aligned with human vision.

97 MATHEMATICS AND COMPUTING↗
NASA NTRS1988

Evolution and use of combined mechanical and thermal codes for cryogenic turbopump bearings

Shaft bearing system analysis codes were developed, improved, and used to investigate Space Shuttle Main Engine (SSME) Liquid Oxygen (LOX) turbopump bearing problems, and to support the Marshall Space Flight Center Bearing and Seal Materials Test (BSMT) program. Thermal network modeling uses the SINDA thermal code, and the modeling of bearing quasi-dynamic characteristics uses the SHABERTH bearing/shaft code. These codes are solved concurrently for a bearing/shaft system using software developed for this purpose. Simulation of the SSME LOX turbopump turbine and pump end bearings and the MSFC BSMT operating in liquid nitrogen (LN sub 2) and LOX was done. The thermal network models include the bearing components, bearing carriers, shaft, housing, frictional heat, and viscous fluid energy. A cage model was included to account for heat generation between the cage and rolling elements. Since most bearing surfaces operate at temperatures well above the coolant saturation temperature, and move at high speed relative to the coolant, forced convection boiling is the dominant mechanism for heat removal. Improved modeling of forced convection film boiling was incorporated to take into account the local vapor generation at the high temperature surfaces. Rearing preloads in the pump and tester are provided by preload springs. As bearing operating clearances and contact angles change due to thermal effects and loading, the bearing preload changes with these varying conditions. These characteristics were modeled and are included in the overall system models. Results from these models indicate an operational limit which, if exceeded, predicts a thermal excursion.

Cody, Joe C.↗
NASA NTRS2010

Orion Active Thermal Control System Dynamic Modeling Using Simulink/MATLAB

This paper presents dynamic modeling of the crew exploration vehicle (Orion) active thermal control system (ATCS) using Simulink (Simulink, developed by The MathWorks). The model includes major components in ATCS, such as heat exchangers and radiator panels. The mathematical models of the heat exchanger and radiator are described first. Four different orbits were used to validate the radiator model. The current model results were compared with an independent Thermal Desktop (TD) (Thermal Desktop, PC/CAD-based thermal model builder, developed in Cullimore & Ring (C&R) Technologies) model results and showed good agreement for all orbits. In addition, the Orion ATCS performance was presented for three orbits and the current model results were compared with three sets of solutions- FloCAD (FloCAD, PC/CAD-based thermal/fluid model builder, developed in C&R Technologies) model results, SINDA/FLUINT (SINDA/FLUINT, a generalized thermal/fluid network-style solver ) model results, and independent Simulink model results. For each case, the fluid temperatures at every component on both the crew module and service module sides were plotted and compared. The overall agreement is reasonable for all orbits, with similar behavior and trends for the system. Some discrepancies exist because the control algorithm might vary from model to model. Finally, the ATCS performance for a 45-hr nominal mission timeline was simulated to demonstrate the capability of the model. The results show that the ATCS performs as expected and approximately 2.3 lb water was consumed in the sublimator within the 45 hr timeline before Orion docked at the International Space Station.

Wang, Xiao-Yen J.↗
DOE OSTI2026

Heat Transfer Experiments of a 1st Stage Blade Cascade for Supercritical CO2 Oxy-Combustion Turbine Application

The results of internally cooled 1st stage blade (S1B) cascade testing in a supercritical CO2 environment is presented. The turbine blade design has been previously established for the end application of an oxy-combustion turbine operating in the Allam-Fetvedt cycle with turbine inlet conditions of 305 bar and 1150°C. The internally cooled blade features leading edge (LE) region impingement cooling, mid-section ribbed serpentine passages, and a pin-finned trailing edge (TE) region before cooling ejection holes. The geometry for the tested blade cascade has a cooled central blade with un-cooled blades on either side to match flowpath areas of the actual turbine. The flowpath reuses internal components previously employed for mid-section region ribbed serpentine passage experiments that established Nusselt number enhancement ratios over a range of Reynolds numbers from 100,000-400,000. New components include flow conditioning plates upstream and downstream of the blade cascade to adequately represent the flow field and blade external heat transfer coefficient profiles for the actual turbine. The cooled central blade utilizes uniform crystal temperature sensors (UCTS) with six sensors each on the blade pressure and suction surfaces distributed radially and from LE to TE. The post-processed UCTS quantified the maximum wall temperature seen at each installed sensor location. The test procedure consisted of establishing supercritical CO2 cooling flow temperature and flow rate and maintaining it throughout the test. The flow rate aims to match that for the actual in-service turbine blade design and is maintained through an orifice restriction to keep the pressure differential between internal cooling flow and external hot flow nearly constant. For the sCO2 flow path external to the blade, temperatures were ramped throughout the test via control of the test loop’s natural gas burner heater. The maximum temperature seen was 468°C and held constant for a duration of 10 minutes at which the blade metal temperature was predicted to be at its maximum before ramping down. For the turbine blade design for service inlet conditions, external flow path computational fluid dynamics (CFD) results and an internal cooling 1-D thermal and hydraulic flow network model using experimentally validated correlations served as thermal finite element (FE) boundary conditions to predict blade metal temperatures. These predicted temperatures were subsequently utilized in a structural FE model to predict blade life ratings dictated by Haynes 282 creep strength data, having a strong dependence on temperature. The boundary conditions experienced during testing are used in the same workflow and compared to the experimental results, with the goal of validating the analysis methodology and providing insight on the uncertainty in local metal temperature predictions.

20 FOSSIL-FUELED POWER PLANTS↗
NASA NTRS1996

Hydrogeological Interpretation of Candidate Origin Sites for Martian Meteorite ALH84001

Barlow (this meeting) has identified two potential source craters for the martian meteorite ALH84001. The craters are at 11.7 deg S, 243.3 deg W (Mare Tyrrhenum site) and 14.0 deg S, 343.5 deg W (Sinus Sabaeus site). As noted by Barlow, both craters lie in the heavily cratered terrain (HCT) and are adjacent to fluvial valleys, Here I explore the fluvial history of these areas based upon the surrounding valley morphology. The most prominent valley network at the Sabaeus site is Evros Vallis. This wide, flat-floored valley is approximately 600 km long with an average width of 2.5 km and a depth of 220 m. The eroded volume of the entire Evros network is approximately 6 x 10(exp 11) cc. This is typical for networks located in the heavily cratered terrain (e.g. Warrego and Parana Valles). Evros is also an isolated valley system. No similar networks are found in the surrounding terrain. Thus it is unlikely that Evros formed as a result of widespread rainfall. A localized water source, such as discharge of a hydrothermal system or localized melting of snowfall, seems more consistent Previous modeling has demonstrated that only hydrothermal systems associated with high permeability subsurfaces can discharge sufficient water to form a valley network. The bulk of the discharge from such systems is consequently low temperature, slightly heated water Precipitation of calcium carbonate by low temperature fluids is consistent with most interpretations of the geochemistry of ALH84001. Available imagery at the Tyrrhenum site is of lesser quality. While eroded units of the HCT are nearby, there are no comparable well developed valley networks at this site. Erosion is instead manifested predominantly as gullies on slopes. This style of erosion suggests that water was not present at this site for the length of time as at the more integrated Sabaeus site. The superposition of fluidized ejecta blankets suggests however that ground water or ground ice was still present at this locality fairly recently, if the chosen crater is indeed correct.

Gulick, Virginia C.↗
NASA NTRSDate not supplied

Single and Multi-Node Modeling of Direct, Submerged, and Self-Pressurization of A Cryogenic Propellant Tank Using Nodal Tools

The pressurization of cryogenic propellant tanks will always be an important process so long as cryogenic liquids are being considered as fuel sources or used for other in-space applications. Pressure control of the tank ullage is necessary for managing propellant flowrates to an engine or a receiver tank, and modeling of the process is used to predict the pressurant requirements and the amount of propellant boiloff. Direct ullage pressurization is the more traditional approach to tank pressurization, as the physics are straight-forward, and ample test (flight) data have been collected and analyzed over the past several decades. Submerged injection pressurization is an alternate method for tank pressurization and has been shown to reduce pressurant requirements, subcool the propellant, and reduce the risk of ullage collapse. Additionally, the pressurant gas entering the ullage is usually much colder when using the submerged pressurization approach, resulting in reduced propellant boiloff. These benefits are at the expense of vaporizing a small percentage of the propellent. Both tank pressurization methods are viable options for current and future space missions, and it is important to have the capability of analyzing the tank ullage conditions for both approaches. Our previous work has demonstrated the development of a Generalized Fluid System Simulation Program (GFSSP) model, which contains a thermodynamic equilibrium heat and mass transfer subroutine capable of effectively analyzing both direct and submerged pressurization systems [1-2]. This subroutine has most recently been enhanced to include the non-equilibrium effect of pressurant dissolution into the propellant. To date the ullage has always been represented as a single node, and although the simulated single-node temperatures have good comparison with the volume-averaged ullage temperatures computed from test data, the physics of the thermal stratification in the ullage were never captured, and adjustment factors in the model were required. The purpose of this paper is to introduce the development of a multi-node ullage model using GFSSP and to discuss the improvements of the simulated ullage temperature distribution and its resulting effects on ullage heat transfer processes. Test data from the Cryogenic Propellant Storage and Transfer Engineering Developmental Unit (CPST EDU) was used for model validation. For additional comparison, a Thermal Desktop (TD) model was also developed to analyze the CPST EDU direct ullage pressurization tests using both a single node and multi-node approach. The model includes the direct pressurant line, vent line, fill/drain line, and a TD FloCAD Compartment. The TD FloCAD Compartment is employed to represent the liquid and ullage as single volumes inside the tank, to include a liquid/vapor interface, and to generate network level objects such as lumps (analogous to nodes in GFSSP), paths, and ties between the fluid and thermal elements. An established heat load on the model tank was leveraged from a pre-existing higher-fidelity model correlated to CPST EDU test data.

pressurization↗
NASA NTRS2013

Testing and Analytical Modeling for Purging Process of a Cryogenic Line

The purging operations for cryogenic main propulsion systems of upper stage are usually carried out for the following cases: 1) Purging of the Fill/Drain line after completion of propellant loading. This operation allows the removal of residual propellant mass; and 2) Purging of the Feed/Drain line if the mission is scrubbed. The lines would be purged by connections to a ground high-pressure gas storage source. The flowrate of purge gas should be regulated such that the pressure in the line will not exceed the required maximum allowable value. Exceeding the maximum allowable pressure may lead to structural damage in the line. To gain confidence in analytical models of the purge process, a test series was conducted. The test article, a 20-cm incline line, was filled with liquid hydrogen and then purged with gaseous helium (GHe). The influences of GHe flowrates and initial temperatures were evaluated. The Generalized Fluid System Simulation Program, an in-house general-purpose computer program for flow network analysis, was utilized to model and simulate the testing. The test procedures, modeling descriptions, and the results will be presented in the final paper.

Hedayat, A.↗
NASA NTRS2015

Testing and Analytical Modeling for Purging Process of a Cryogenic Line

The purging operations for cryogenic main propulsion systems of upper stage are usually carried out for the following cases: 1) Purging of the Fill/Drain line after completion of propellant loading. This operation allows the removal of residual propellant mass; and 2) Purging of the Feed/Drain line if the mission is scrubbed. The lines would be purged by connections to a ground high-pressure gas storage source. The flow-rate of purge gas should be regulated such that the pressure in the line will not exceed the required maximum allowable value. Exceeding the maximum allowable pressure may lead to structural damage in the line. To gain confidence in analytical models of the purge process, a test series was conducted. The test article, a 20-cm incline line, was filled with liquid hydrogen and then purged with gaseous helium (GHe). The influences of GHe flow-rates and initial temperatures were evaluated. The Generalized Fluid System Simulation Program, an in-house general-purpose computer program for flow network analysis, was utilized to model and simulate the testing. The test procedures, modeling descriptions, and the results will be presented in the final paper.

Hedayat, A.↗
NASA NTRS2002

A Novel Approach for Modeling Chemical Reaction in Generalized Fluid System Simulation Program

The Generalized Fluid System Simulation Program (GFSSP) is a computer code developed at NASA Marshall Space Flight Center for analyzing steady state and transient flow rates, pressures, temperatures, and concentrations in a complex flow network. The code, which performs system level simulation, can handle compressible and incompressible flows as well as phase change and mixture thermodynamics. Thermodynamic and thermophysical property programs, GASP, WASP and GASPAK provide the necessary data for fluids such as helium, methane, neon, nitrogen, carbon monoxide, oxygen, argon, carbon dioxide, fluorine, hydrogen, water, a hydrogen, isobutane, butane, deuterium, ethane, ethylene, hydrogen sulfide, krypton, propane, xenon, several refrigerants, nitrogen trifluoride and ammonia. The program which was developed out of need for an easy to use system level simulation tool for complex flow networks, has been used for the following purposes to name a few: Space Shuttle Main Engine (SSME) High Pressure Oxidizer Turbopump Secondary Flow Circuits, Axial Thrust Balance of the Fastrac Engine Turbopump, Pressurized Propellant Feed System for the Propulsion Test Article at Stennis Space Center, X-34 Main Propulsion System, X-33 Reaction Control System and Thermal Protection System, and International Space Station Environmental Control and Life Support System design. There has been an increasing demand for implementing a combustion simulation capability into GFSSP in order to increase its system level simulation capability of a liquid rocket propulsion system starting from the propellant tanks up to the thruster nozzle for spacecraft as well as launch vehicles. The present work was undertaken for addressing this need. The chemical equilibrium equations derived from the second law of thermodynamics and the energy conservation equation derived from the first law of thermodynamics are solved simultaneously by a Newton-Raphson method. The numerical scheme was implemented as a User Subroutine in GFSSP.

Sozen, Mehmet↗
DOE OSTI2025

xGFabric: Coupling Sensor Networks and HPC Facilities with Private 5G Wireless Networks for Real-Time Digital Agriculture

Advanced scientific applications require coupling distributed sensor networks with centralized high-performance computing facilities. Citrus Under Protective Screening (CUPS) exemplifies this need in digital agriculture, where citrus research facilities are instrumented with numerous sensors monitoring environmental conditions and detecting protective screening damage. CUPS demands access to computational fluid dynamics codes for modeling environmental conditions and guiding real-time interventions like water application or robotic repairs. These computing domains have contrasting properties: sensor networks provide low-performance, limited-capacity, unreliable data access, while high-performance facilities offer enormous computing power through high-latency batch processing. Private 5G networks present novel capabilities addressing this challenge by providing low latency, high throughput, and reliability necessary for near-real-time coupling of edge sensor networks with HPC simulations. This work presents xGFabric, an end-to-end system coupling sensor networks with HPC facilities through Private 5G networks. The prototype connects remote sensors via 5G network slicing to HPC systems, enabling real-time digital agriculture simulation.

Digital Agriculture↗
NASA NTRS2008

NASA Tech Briefs, April 2008

Topics covered include: Gas Sensors Based on Coated and Doped Carbon Nanotubes; Tactile Robotic Topographical Mapping Without Force or Contact Sensors; Thin-Film Magnetic-Field-Response Fluid-Level Sensor for Non-Viscous Fluids; Progress in Development of Improved Ion-Channel Biosensors; Simulating Operation of a Complex Sensor Network; Using Transponders on the Moon to Increase Accuracy of GPS; Controller for Driving a Piezoelectric Actuator at Resonance; Coaxial Electric Heaters; Dual-Input AND Gate From Single-Channel Thin-Film FET; High-Density, High-Bandwidth, Multilevel Holographic Memory; Fabrication of Gate-Electrode Integrated Carbon-Nanotube Bundle Field Emitters; Hydroxide-Assisted Bonding of Ultra-Low-Expansion Glass; Photochemically Synthesized Polyimides; Optimized Carbonate and Ester-Based Li-Ion Electrolytes; Compact 6-DOF Stage for Optical Adjustments; Ultrasonic/Sonic Impacting Penetrators; Miniature, Lightweight, One-Time-Opening Valve; Supplier Management System; Improved CLARAty Functional-Layer/Decision-Layer Interface; JAVA Stereo Display Toolkit; Remote-Sensing Time Series Analysis, a Vegetation Monitoring Tool; PyPele Rewritten To Use MPI; Data Assimilation Cycling for Weather Analysis; Hydrocyclone/Filter for Concentrating Biomarkers from Soil; Activating STAT3 Alpha for Promoting Healing of Neurons; and Probing a Spray Using Frequency-Analyzed Light Scattering.

Source record↗
NASA NTRS2015

Standardized Modular Power Interfaces for Future Space Explorations Missions

Earlier studies show that future human explorations missions are composed of multi-vehicle assemblies with interconnected electric power systems. Some vehicles are often intended to serve as flexible multi-purpose or multi-mission platforms. This drives the need for power architectures that can be reconfigured to support this level of flexibility. Power system developmental costs can be reduced, program wide, by utilizing a common set of modular building blocks. Further, there are mission operational and logistics cost benefits of using a common set of modular spares. These benefits are the goals of the Advanced Exploration Systems (AES) Modular Power System (AMPS) project. A common set of modular blocks requires a substantial level of standardization in terms of the Electrical, Data System, and Mechanical interfaces. The AMPS project is developing a set of proposed interface standards that will provide useful guidance for modular hardware developers but not needlessly constrain technology options, or limit future growth in capability. In 2015 the AMPS project focused on standardizing the interfaces between the elements of spacecraft power distribution and energy storage. The development of the modular power standard starts with establishing mission assumptions and ground rules to define design application space. The standards are defined in terms of AMPS objectives including Commonality, Reliability-Availability, Flexibility-Configurability and Supportability-Reusability. The proposed standards are aimed at assembly and sub-assembly level building blocks. AMPS plans to adopt existing standards for spacecraft command and data, software, network interfaces, and electrical power interfaces where applicable. Other standards including structural encapsulation, heat transfer, and fluid transfer, are governed by launch and spacecraft environments and bound by practical limitations of weight and volume. Developing these mechanical interface standards is more difficult but an essential part of defining physical building blocks of modular power. This presentation describes the AMPS projects progress towards standardized modular power interfaces.

Spacecraft Power↗
DOE OSTI2024

Generative Physics-Informed Neural Network Solving Multi-Scale and Multi-Phase Plasma Chemical Flow Field

Low-temperature plasmas (LTPs) are non-equilibrium systems with near-room-temperature gas and highly energetic electrons. This makes them ideal for delicate applications in biomedicine and semiconductor manufacturing, enabling processes like wound healing, sterilization, etching, and plasma-enhanced chemical vapor deposition without thermal damage. However, LTPs involve complex chemistries, with hundreds of species and thousands of reactions, complicating their diagnosis, prediction, and control. Conventional diagnostics, such as Fourier-transform infrared spectroscopy (FTIR), laser-induced fluorescence (LIF), and optical emission spectroscopy (OES), offer limited species detection, while mass spectrometry (MS) struggles with low-sensitivity species. Additionally, LTP simulations face multi-scale challenges, as macroscopic fluid dynamics and microscopic particle collisions operate on vastly different timescales. To address these issues, we developed an artificial intelligence (AI) based diagnostic system: a generative physics-informed neural network (PINN-Gen) that can predict spatially resolved species concentrations and temperatures in LTPs by integrating experimental data from planar LIF with microscopic plasma chemical kinetics and macroscopic fluid mechanics, including plasma-liquid interactions at the interface between two phases. PINN-Gen solves no equations but checks the errors of physical laws by substituting the output from neural network, and the comparison with the experimental results. Thus, it naturally avoids the multi-scale difficulty of numerical simulations and predicts the results of conventionally unsolvable multi-scale and multi-phase problems. The real-time prediction will be robust due to the physical information used in the training of such a neural network, and only very limited input of condition required due to its generative feature.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2026

Fracture Intersections under Stress: Laboratory Data and Code [Data set]

The connectivity of natural and induced fractures governs the injection and withdrawal of fluids from subsurface reservoirs. Connectivity depends on intersections that control how fluids mix and move through the entire system. Here, we present data sets from 3D X-ray microscopy measurements of simple fracture networks under stress. 3D printing was used to create prismatic blocks that formed fracture networks composed of 2 orthogonal fractures. The network orientation was either "x" or "+" relative to an applied vertical stress. 3D data sets were collected for normal loads of 25, 100 and 200 Newtons for samples with fracture surfaces with either correlated or uncorrelated asperity distributions. The file contains data from the 12 samples analyzed along with an example code used to extract the intersection geometry. Additional experimental details can be found in the manuscript "Geologic Stress Modulates Fluid Mixing at Fracture Intersections" (10.1038/s43247-026-03525-9)and supplemental information to appear in Communications Earth & Environment in 2026.

02 PETROLEUM↗
DOE OSTI2025

Thermo-hydraulic steam pipe models for district heating simulations: Simplifications to balance accuracy and simulation speed

Steam piping networks are essential for optimizing performance in industrial processes and district heating systems. However, dynamic models that balance thermo-hydraulic accuracy with computational efficiency remain limited. In response, this paper presents a new discretized steam pipe model based on the plug flow approach, capturing key thermo-hydraulic behaviors while simplifying steam phase change processes. Implemented in Modelica, the model accurately calculates temperature and pressure distributions along steam pipelines. To improve computational efficiency for district-scale simulations, five model simplifications are introduced: lumped thermo-hydraulic functions, empirical correlations, fluid state approximations, steady-state dynamics and inclusion of flow derivatives. These simplified models achieve 85%-98% accuracy in predicting pressure drop and condensation losses, including dynamic condensate behavior during pipe warm-up—a factor often overlooked in existing models. The models support diverse network configurations, scaling effectively to systems with multiple distribution pipes and connected building loads. Discrete models provide detailed insights but exhibit a cubic increase in simulation time as the network scales by N connected building O(N 2.42 ). In contrast, lumped models simulate 10–28 times faster than discrete, offering quadratic scaling of simulation time O(N 1.73 ). However, they still require 6 times more computation time than a lossless network, highlighting the inherent computational challenges of modeling compressible fluid flow. In conclusion, the steady-state lumped variant, with its near-linear scalability in computational time O(N 1.01 ), emerges as an efficient solution for preliminary design evaluations and extensive parametric studies.

15 GEOTHERMAL ENERGY↗
DOE OSTI2025

Learning and discovering multiple solutions using physics-informed neural networks with random initialization and deep ensemble

In this work we explore the capability of physics-informed neural networks (PINNs) to discover multiple solutions. Many real-world phenomena governed by nonlinear differential equations (DEs), such as fluid flow, exhibit multiple solutions under the same conditions, yet capturing this solution multiplicity remains a significant challenge. A key difficulty lies in providing appropriate initial conditions or guesses, as widely used time-marching schemes and Newton’s method are highly sensitive to these choices when solving complex computational problems. While machine learning models, particularly PINNs, have shown promise in solving DEs, their ability to capture multiple solutions remains underexplored. In this work, we propose a simple and practical approach using PINNs to learn and discover multiple solutions. We first demonstrate that PINNs, when combined with random initialization and deep ensemble method—originally developed for uncertainty quantification—can effectively uncover multiple solutions to nonlinear ordinary and partial DEs. Although training large ensembles of PINNs may appear computationally demanding, this can be done efficiently using vectorization techniques supported by modern deep learning frameworks, allowing many networks to be trained simultaneously. Our approach highlights the critical role of initialization in shaping solution diversity, addressing an often-overlooked aspect of machine learning for scientific computing. Furthermore, we propose utilizing PINN-generated solutions as initial conditions or initial guesses for conventional numerical solvers to enhance accuracy and efficiency in capturing multiple solutions. Extensive numerical experiments, including the Allen–Cahn equation and cavity flow, where our approach successfully identifies both stable and unstable solutions, validate the effectiveness of our method. These findings establish a general and efficient framework for addressing solution multiplicity in nonlinear DEs.

97 MATHEMATICS AND COMPUTING↗