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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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NASA NTRS2025

Assembly of catalytic complexes from randomized oligonucleotides

The early evolution of life relied on catalytic RNAs (ribozymes) for central functions. To test whether early catalysts could have assembled from multiple short nucleic acid fragments in random sequence environments, we performed an in vitro selection from a short RNA library in the presence of 256 different DNA 20-nucleotide oligomers. High-throughput sequencing and biochemical analysis showed that most of the selected 1331 RNA sequences required at least one DNA for activity. Representatives for four of six RNA clusters that depended on DNA cofactors were active even when the 256 DNAs were replaced by completely random DNA 20-nucleotide oligomers. The formation of these catalytic complexes and the recruitment of oligonucleotide cofactors from completely random libraries demonstrate an important principle for the emergence of the earliest oligonucleotide catalysts.

Xu Han↗
NASA NTRS2026

Methods for Validation of Random Uncertainty Estimates and Their Applications to Ozone Profiles From Limb-Viewing Satellite Instruments

For satellite measurements of atmospheric composition, the random uncertainty estimates provided by retrieval algorithms might be imperfect due to various approximations used in the retrievals or the presence of unknown error sources. This paper presents an overview of the methods used for the validation of random uncertainty estimates. All methods discussed in this study are categorized, and assumptions and limitations of each method are discussed. This overview evaluates these methods in application to ozone profile measurements from limb and occultation satellite instruments and provides practical illustrations of random uncertainty validation.

Viktoria F Sofieva↗
NASA NTRS2026

Benchmarking Bayesian Optimization Frameworks and Acquisition Strategies for Materials Discovery and Autonomous Laboratories

Bayesian optimization (BO) can accelerate materials discovery by guiding expensive experiments toward the most promising processing conditions. We systematically compare five BO surrogate and framework combinations (Gaussian processes in Ax, Gaussian processes and Monte-Carlo neural networks in BayBE, random forests in Lolopy, and tree-structured Parzen (TPE) estimators in Hyperopt) on three benchmarks that mimic common materials design tasks (a discrete solid-electrolyte composition space, a hybrid discrete/continuous laminate-composite design problem solved with micromechanics modeling, and the continuous Ishigami analytic function which is a standard optimization benchmark). Each BO surrogate is paired with posterior mean, probability of improvement, and expected improvement acquisition functions and run for 100 trials from randomized initial samples with uniform random search providing a control. Across five random seeds per setting, BayBE’s Gaussian-process surrogate with expected improvement consistently reached ≥95 % of the known optimum in the fewest evaluations, while Lolopy’s random forest matched or exceeded GP performance on purely categorical or mixed spaces at a higher computational cost. Posterior mean alone often stagnated at local optima, underscoring the need for exploration, whereas probability and expected improvement balanced exploration and exploitation leading to better optimization in fewer trials. Execution times ranged from milliseconds for TPE to minutes for neural-network and random-forest surrogates. These results establish baseline expectations for BO in automated materials laboratories and highlight expected improvement with Gaussian processes as a reliable first choice, with random forests offering a strong alternative when categorical variables dominate. The benchmark suite and code are released to facilitate future surrogate, acquisition, and constraint-handling research in data-driven materials optimization.

Bayesian optimization↗
NASA NTRS1995

A Probabilistic Model of a Porous Heat Exchanger

This paper presents a probabilistic one-dimensional finite element model for heat transfer processes in porous heat exchangers. The Galerkin approach is used to develop the finite element matrices. Some of the submatrices are asymmetric due to the presence of the flow term. The Neumann expansion is used to write the temperature distribution as a series of random variables, and the expectation operator is applied to obtain the mean and deviation statistics. To demonstrate the feasibility of the formulation, a one-dimensional model of heat transfer phenomenon in superfluid flow through a porous media is considered. Results of this formulation agree well with the Monte-Carlo simulations and the analytical solutions. Although the numerical experiments are confined to parametric random variables, a formulation is presented to account for the random spatial variations.

O P Agrawal↗
NASA NTRS1992

The thermal engine of Venus: Implications from the impact crater distribution

Questions concerning the state of Venus' volcanological activity are currently under investigation. These questions were raised by the imaging radar and altimetric data obtained by the Magellan spacecraft, which began mapping Venus in Sept. 1990. Clues about the answers to these questions are provided by the nature and distribution of impact craters on the surface of Venus. Statistical tests show that the distribution of craters on Venus cannot be distinguished from a completely spatially random population. Further, the majority of craters look relatively pristine; only about 5 percent of the observed craters appear to be embayed or flooded by lavas. The simplest interpretation of these observations is a model in which the impact craters lie on a surface that has been undisturbed by volcanological and tectonic processes since the time the surface was formed. Counting up the number of craters and estimating the rate at which meteoroids strike the surface then gives an age for the venusian surface of 500 million years; in this model the volcanic activity since that time has been essentially nil. This scenario is described as a 'production' model, or perhaps more appropriately, a 'catastrophic' model.

Phillips, Roger J.↗
NASA NTRSDate not supplied

Dynamic Analysis of Reynolds Number Effects on Trailing Edge Transonic Vortex Shedding and Its Impact on Turbine Blade Aerodynamic Performance

In this work, we will discuss observations from images acquired from a time-resolved, high-speed self-aligned focusing Schlieren campaign that was performed at the CW-22 facility at NASA Glenn Research Center to understand the dynamic behavior of thick trailing-edge high-pressure turbine blades simulating a ceramic matrix composite (CMC) construction at high inlet turbulence conditions. For the CMC-9 blade, which has a trailing edge thickness of 9% of the axial chord, we identified a regime where an excessive total pressure loss (loss anomaly) is observed only for a narrow range of Reynolds numbers at a fixed exit Mach number of 0.74. The loss anomaly is qualitatively explained by our images, which were taken at 10 distinct blade Reynolds numbers spanning a factor of 6. The images show a significant increase in energy related to the oscillations due to transonic vortex shedding at the Reynolds numbers related to the high loss conditions. This increased energy leads to the formation of strong acoustic waves that turn into shock waves at the highest loss conditions. From our observations stemming from Spectral POD analysis of the high-speed images, we see the acoustic waves produced by the trailing edge vortex shedding exist in all conditions tested; but the shedding frequency has a very slight trend upwards as the Reynolds number is increased, varying about 6% in the range tested. Considering this variation of shedding frequency as a function of Reynolds number, which is well-established for other bluff-body flows, we stipulate there may be a potential feedback mechanism involving an acoustic information transfer path across neighboring blades that may explain why only a narrow range of Reynolds numbers displays strong, shock-forming vortex shedding. We consider a few feedback paths and examine the timing based on the mean flow field from a high-resolution LES simulation. It appears that all feedback mechanisms are viable, presenting an integer number of delay cycles with respect to disturbances generated at the trailing edge. Most noteworthy, however, is the acoustic/shock-boundary layer interaction mechanism at the blade geometric throat. Based on our analysis, this mechanism likely plays an important role in realistic, curved turbine blade passages.

Aerodynamics↗
NASA NTRSDate not supplied

Technology Demonstrator of an Americium-Fuelled Radioisotope Stirling Generator

Americium-241 is used as the fuel of choice in European Radioisotope Power Systems. The University of Leicester have developed a number of systems using this fuel type, including a 200 Wth heat source. To maximise the electrical output that can be generated from the heat source, the University of Leicester and NASA Glenn Research Center have collaborated on a radioisotope Stirling generator concept, building on the long heritage of dynamic power conversion developments at NASA GRC. Here we present the design of a technology demonstrator of the Americium-fuelled Radioisotope Stirling Generator (Am-RSG).

Americium-241↗
NASA NTRSDate not supplied

Li-ion Battery Aging with Hybrid Physics-Informed Neural Networks and Fleet-wide Data

In this work, we propose a hybrid model for Li-ion battery discharge and aging prediction that leverages fleet-wide data to predict future capacity drops.The model is built upon an hybrid approach merging physics-based and empirical equations, as well as neural network models in a recurrent neural network cell. The hybrid physics-informed neural network can predict voltage discharge cycles given the loading profile, and estimate the used capacity of the battery under random-loading conditions by tracking aging parameters connected to the residual capacity of the battery. By merging information on the battery aging parameters with existing fleet-wide aging data, the model can predict the future residual capacity of the battery that is being monitored, and therefore enable predictions of voltage discharge curves far ahead in the battery life cycle. We validated the approach using the NASA Prognostics Data Repository Battery data-set, which contains experimental data on Li-ion batteries discharged at random loading conditions in a controlled environment. The approach also allows the identification of discrepancies between the battery aging trend and the trend observed at the fleet level, so that batteries behaving differently from the rest of the fleet can be subject to closer monitoring and further testing to refine predictions.

PINN↗
NASA NTRS1992

Tenth Workshop for Computational Fluid Dynamic Applications in Rocket Propulsion, Part 1

Experimental and computational fluid dynamic activities in rocket propulsion were discussed. The workshop was an open meeting of government, industry, and academia. A broad number of topics were discussed including computational fluid dynamic methodology, liquid and solid rocket propulsion, turbomachinery, combustion, heat transfer, and grid generation.

R.W. Williams↗
NASA NTRS1993

Nickel-hydrogen CPV battery update

The multicell common pressure vessel (CPV) nickel hydrogen battery manufactured by Johnson Controls Battery Group, Inc. has completed full flight qualification, including random vibration at 19.5 g for two minutes in each axis, electrical characterization in a thermal vacuum chamber, and mass-spectroscopy vessel leak detection. A first launch is scheduled for late in 1992 or early 1993 by the Naval Research Laboratory (NRL). Specifics of the launch date are not available at this time due to the classified nature of the program. Release of orbital data for the battery is anticipated following the launch.

Jones, Kenneth R.↗
NASA NTRS1958

Survey of Supersonic Inlets for High Mach Number Applications

The various inlet design philosophies are assessed on the basis of recent experimental results obtained at Mach numbers up to 5. The basic compression systems (external, internal, and combined external-plus-internal) are compared for both Mach 4.0 turbojet and hypersonic-cruise ramjet engines. The inlet that appears best suited for the Mach 4.0 turbojet application is that with combined external-plus-internal compression. Because of the severe cooling and weight problems associated with variable geometry at hypersonic flight speeds, the fixed-geometry self-starting characteristics of external-compression systems may be the overriding factor in the selection of an inlet for the hypersonic-cruise ramjet.

James F Connors↗
NASA NTRS1958

High Mach Number, Low-Cowl-Drag, External-Compression Inlet With Subsonic Dump Diffuser

A zero-cowl-drag, all-external-compression inlet with an abrupt area discontinuity or subsonic dump diffuser has been proposed and demonstrated for high Mach number application. Isentropic compression was focused at the lip of a cylindrical cowl and limited by the requirements for shock attachment. Theoretical calculations of the performance of such an inlet showed that it may be suited for application at Mach numbers of 4 and above. In an experimental investigation at Mach 3.85, a 4.75-inch-diameter inlet attained a total-pressure recovery of 0.41 at a corresponding mass-flow ratio of 0.92. Subsonic diffusion over an area ratio of about 3.5 to 1.0 was accomplished in 1.25 inlet diameters. Essentially no difference in performance was obtained in a comparison of the dump and a conventional subsonic diffuser. On a range basis, the over-all performance of this inlet was at least comparable with that of the best on-design isentropic external-compression inlet previously reported. A variable Mach number configuration utilizing a telescoping spike with a subsonic dump diffuser is also included.

James F Connors↗
NASA NTRSDate not supplied

Dynamic Analysis of Reynolds Number Effects on Trailing Edge Transonic Vortex Shedding and Its Impact on Turbine Blade Aerodynamic Performance

Time-resolved, high-speed self-aligned focusing schlieren images were acquired in the NASA Glenn Research Center Transonic Turbine Blade Cascade facility to help understand the aerodynamic behavior of high-pressure, thick trailing edge turbine blades. The trailing edge thickness of 9% of axial chord tested represents simulated ceramic matrix composite fabrication constraints, which was verified previously to possess a high-loss flow regime at high inlet turbulence conditions over a narrow range of Reynolds numbers and at a fixed design exit Mach number of 0.74. Our high-speed images, which were acquired at 10 distinct Reynolds numbers, show a significant increase in energy from flow oscillations due to transonic vortex shedding at Reynolds numbers corresponding to the high loss conditions. For those conditions, strong acoustic waves turn into shock waves. Spectral Proper Orthogonal Decomposition of the high-speed images shows acoustic waves from trailing edge vortex shedding at all conditions, with increased spectral energy at the high-loss conditions and slightly increasing frequency (about 6%) as a function of Reynolds number. Analysis of potential feedback timing is performed using velocity fields from a previous LES simulation, considering different feedback mechanisms. Most noteworthy is the acoustic/shock-boundary layer interaction mechanism on the suction surface at the blade geometric throat, which likely plays an important role in realistic curved blade passages.

Trailing Edge↗
NASA NTRS1995

Extensional Flow Convecting a Reactant Undergoing a First Order Homogeneous Reaction and Diffusional Mass Transfer From a Sphere at Low to Intermediate Peclet and Damkohler Numbers

Forced convective diffusion-reaction is considered for viscous axisymmetric extensional convecting velocity in the neighborhood of a sphere. For Peclet numbers in the range 0.1 ≤ Pe ≤ 500 and for Damkohler numbers increasing with increasing Pe but in the overall range 0.02 ≤ Da ≤ 10, average and local Sherwood numbers have been computed. By introducing the eigenfunction expansion c(r,Θ) = Σ c n (r)P n (cosΘ) into the forced convective diffusion equation for the concentration of a chemical species undergoing a first order homogeneous reaction and by using properties of the Legendre functions P n (cosΘ), the variable coefficient PDE can be reduced to a system of N+1 second order ODEs for the radial functions C n (r), n=0,1,2, ... ,N. The adaptive grid algorithm of Pereyra and Lentini can be used to solve the corresponding 2(N+ 1) first order differential equations as a two-point boundary value problem on 1 ≤ r ≤ r •• . Convergence of the expansion for a specific value of N can thus be established and provides "spectral" behavior as well as the full concentration field c(r,Θ).

N Y Shah↗
NASA NTRSDate not supplied

Americium-Fuelled Radioisotope Stirling Generator Elegant Breadboard

High power solutions for spacecraft are becoming increasingly sought after in the new era of space exploration, with energy intensive in-situ lunar surface exploration becoming a key part of the roadmap for major space agencies, as well as commercial companies. Nuclear power provides the most feasible solution to achieve continued operation through the long lunar night, and in extended periods in Permanently Shadowed Regions (PSRs). Americium-241 (241Am) is used as the fuel of choice in European Radioisotope Power Systems (RPS). The University of Leicester have developed a number of systems using this fuel type, including a 200 Wth heat source. Building on existing international partnerships, the University of Leicester is continuing to work with NASA Glenn Research Center (GRC) to maximise the electrical output that can be generated from the heat source by progressing the radioisotope-Stirling generator concept to elegant breadboard level, building on the long heritage of dynamic power conversion developments at NASA Glenn. Presented in the following conference paper is the next iteration of the Americium-fuelled Radioisotope Stirling Generator (Am-RSG) Elegant Breadboard, following on from a successful technology demonstration design conducted at NASA Glenn in early 2025. The design laid-out in this paper is a more flight-like version of the technology demonstrator, and is intended to pair with the next generation of Stirling convertor technology, the SunPower Robust Stirling Converter (SRSC). The SRSC is able to operate more efficiently at lower thermal power levels than the previous Advanced Stirling Convertor (ASC), making it more suitable for the 241Am system.

Lunar↗
NASA NTRS2009

General Purpose Data-Driven Monitoring for Space Operations

As modern space propulsion and exploration systems improve in capability and efficiency, their designs are becoming increasingly sophisticated and complex. Determining the health state of these systems, using traditional parameter limit checking, model-based, or rule-based methods, is becoming more difficult as the number of sensors and component interactions grow. Data-driven monitoring techniques have been developed to address these issues by analyzing system operations data to automatically characterize normal system behavior. System health can be monitored by comparing real-time operating data with these nominal characterizations, providing detection of anomalous data signatures indicative of system faults or failures. Data-driven techniques have a number of advantages over other methods for monitoring complex space vehicles. Unlike model-based systems, the developer does not need to understand or encode the internal operation of the system. The knowledge required to monitor the system is automatically derived from archived data from system operation. Unlike rule-based systems, data-driven systems do not require system analysts to define nominal relationships among sensors. Analysts can and often do determine these relationships for a system with few sensors; it is more difficult to analytically determine the nominal relationship among a large number of sensors. Data-driven techniques are not limited to low-dimensional spaces and work as effectively with dozens of parameters as they do with a few. Knowledge bases formed by data-driven techniques are also easy to update. As the operating envelope of the monitored system is expanded, data-driven techniques can be quickly retrained to incorporate the new behavior into the knowledge base. The expertise and time-consuming process of updating a model or rule base to maintain consistency with the new operation is not required. The Inductive Monitoring System (IMS) is a data-driven system health monitoring software tool that has been successfully applied to several aerospace applications. IMS uses a data mining technique called clustering to analyze archived system data and characterize normal interactions between parameters. This characterization, or model, of nominal operation is stored in a knowledge base that can be used for real-time system monitoring or analysis of archived events. System data is compared with the nominal IMS model to produce a measure of how well current system behavior matches the normal behavior defined by the training data. Significant deviations from the nominal system model can provide alerts to system malfunctions or precursors of significant failures. The scope of IMS based data-driven monitoring applications continues to expand with current development activities. Successful IMS deployment in the International Space Station (ISS) flight control room to monitor ISS attitude control systems has led to applications in other ISS flight control disciplines, such as thermal control. It has also generated interest in data-driven monitoring capability for Constellation, NASA's program to replace the Space Shuttle with new launch vehicles and spacecraft capable of returning astronauts to the moon, and then on to Mars. Several projects are currently underway to evaluate and mature the IMS technology and complementary tools for use in the Constellation program. These include an experiment on board the Air Force TacSat-3 satellite, and ground systems monitoring for NASA's Ares I-X and Ares I launch vehicles. The TacSat-3 Vehicle System Management (TVSM) project is a software experiment to integrate fault and anomaly detection algorithms and diagnosis tools with executive and adaptive planning functions contained in the flight software on-board the Air Force Research Laboratory TacSat-3 satellite. The TVSM software package will be uploaded after launch to monitor spacecraft subsystems such as power and guidance, navigation, and control (GN&C). It will analyze data in real-time to demonstrate detection of faults and unusual conditions, diagnose problems, and react to threats to spacecraft health and mission goals. The experiment will demonstrate the feasibility and effectiveness of integrated system health management (ISHM) technologies with both ground and on-board experiments. Initially, the TVSM software will run open loop, providing system health information and recommendations to ground operators, without automatically performing fault-mitigating corrective actions. After the end of the satellite's mission, closed loop tests combining TVSM monitoring and diagnosis with reactive capabilities by the flight software will be performed. In addition to monitoring for long periods of actual operation, the experiment will include fault injection into TacSat-3 data as well as commanded operations to test and evaluate automatic ISHM monitoring and recovery under controlled conditions.

Satellites↗
NASA NTRS1994

Control of an Axisymmetric Jet Using Vortex Generators

The results of an experimental investigation on the effect of vortex generators, in the form of small tabs at the nozzle exit on the evolution of a jet, are reported in this paper. Primarily tabs of triangular shape are considered, and the effect is studied up to a jet Mach number of 1.8. Each tab is found to produce a dominant pair of counter-rotating streamwise vortices having a sense of rotation opposite to that expected from the wrapping of the boundary layer. This results in an inward indentation of the mixing layer into the core of the jet. A triangular-shaped tab with its apex leaning downstream, referred to as a delta tab, is found to be the most effective in producing such vortices, with a consequential large influence on the overall jet evolution. Two delta tabs, spaced 180 deg apart, completely bifurcate the jet. Four delta tabs stretch the mixing layer into four 'fingers,' resulting in a significant increase in the jet mixing downstream. For six delta tabs the mixing layer distortion settles back to a three finger configuration through an interaction of the streamwise vortices. The tabs are found to be equally effective in jets with turbulent or laminar initial boundary layers. Two sources of streamwise vorticity are postulated for the flow under consideration. One is the upstream 'pressure hill,' generated by the tab, which constitutes the main contributor of vorticity to the dominant pair. Another is due to vortex filaments shed from the sides of the tab and reoriented downstream by the mean shear of the mixing layer. Depending on the orientation of the tab, the latter source can produce a vortex pair having a sense of rotation opposite to that of the dominant pair. In the case of the delta tab, vorticity from the two sources add, explaining the strong effect in that configuration.

Streamwise Vorticity↗
NASA NTRS2012

General Purpose Data-Driven System Monitoring for Space Operations

Modern space propulsion and exploration system designs are becoming increasingly sophisticated and complex. Determining the health state of these systems using traditional methods is becoming more difficult as the number of sensors and component interactions grows. Data-driven monitoring techniques have been developed to address these issues by analyzing system operations data to automatically characterize normal system behavior. The Inductive Monitoring System (IMS) is a data-driven system health monitoring software tool that has been successfully applied to several aerospace applications. IMS uses a data mining technique called clustering to analyze archived system data and characterize normal interactions between parameters. This characterization, or model, of nominal operation is stored in a knowledge base that can be used for real-time system monitoring or for analysis of archived events. Ongoing and developing IMS space operations applications include International Space Station flight control, spacecraft vehicle system health management, launch vehicle ground operations, and fleet supportability. As a common thread of discussion this paper will employ the evolution of the IMS data-driven technique as related to several Integrated Systems Health Management (ISHM) elements. Thematically, the projects listed will be used as case studies. The maturation of IMS via projects where it has been deployed or is currently being integrated to aid in fault detection will be described. The paper will also explain how IMS can be used to complement a suite of other ISHM tools, providing initial fault detection support for diagnosis and recovery

Space Propulsion↗