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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 55 records · Page 3
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NASA NTRSDate not supplied

In-Situ Resource Utilization Modeling of a Lunar Water Processing System

A key element of achieving a sustained surface presence, such as defined in NASA’s Artemis plan, is In-Situ Resource Utilization (ISRU). ISRU is the practice of using local resources to provide mission consumables that reduce system launch mass requirements, and regenerate resources (chiefly, water and oxygen) for propulsion and life support supporting both Lunar and Martian missions. ISRU systems require multiple complex processes, such as excavation, chemical reactors, and electrolysis subsystems that must operate in harmony to optimize the overall system process from beginning to end. The Mission Analysis and Integration Tool (MAIT) was previously developed with MATLAB in FY22 to connect individual subsystem models into a customized, flexible framework for the purpose of technology downselect, optimization, and end-to-end process planning. Beginning in FY24, MAIT was leveraged and evolved using MATLAB/Simulink due to its ability to communicate with a vast number of other programming languages and makes up the backbone of data flow between inputs and outputs to the subsystem models. MAIT initially evaluated a suite of ISRU-related technologies, including the water processing Lunar Auger Dryer for ISRU (LADI) system with integrated upstream excavation and downstream electrolysis subsystems. With individual models consolidated, the MAIT tool generated over 60,000 cases during its parametric sweeps; these system iterations produced valuable insight into the optimal LADI geometry for minimizing energy demands, estimating carbothermal reactor and radiator mass relationships, and calculated the power dynamics of the electrolysis unit. Advanced efforts with advanced models will include examining multiple production targets to demonstrate the ability to scale ISRU technologies supporting the Space Technology Mission Directorate’s (STMD) commercialization strategy, and increase the MAIT software capability to handle a wide array of ISRU system models beyond the Lunar environment, e.g. production of propellant for a Martian lander.

Avery Carlson↗
NASA NTRS2002

Modeling Composite Laminate Crushing for Crash Analysis

Crash modeling of composite structures remains limited in application and has not been effectively demonstrated as a predictive tool. While the global response of composite structures may be well modeled, when composite structures act as energy-absorbing members through direct laminate crushing the modeling accuracy is greatly reduced. The most efficient composite energy absorbing structures, in terms of energy absorbed per unit mass, are those that absorb energy through a complex progressive crushing response in which fiber and matrix fractures on a small scale dominate the behavior. Such failure modes simultaneously include delamination of plies, failure of the matrix to produce fiber bundles, and subsequent failure of fiber bundles either in bending or in shear. In addition, the response may include the significant action of friction, both internally (between delaminated plies or fiber bundles) or externally (between the laminate and the crushing surface). A figure shows the crushing damage observed in a fiberglass composite tube specimen, illustrating the complexity of the response. To achieve a finite element model of such complex behavior is an extremely challenging problem. A practical crushing model based on detailed modeling of the physical mechanisms of crushing behavior is not expected in the foreseeable future. The present research describes attempts to model composite crushing behavior using a novel hybrid modeling procedure. Experimental testing is done is support of the modeling efforts, and a test specimen is developed to provide data for validating laminate crushing models.

Fleming, David C.↗
NASA NTRS2017

Technical Challenges and Barriers Affecting Turbo-Electric and Hybrid Electric Aircraft Propulsion

In order to meet aggressive aircraft performance goals set by NASA's Aeronautics Research Mission Directorate, the Glenn Research Center (GRC) is leading research and development of electrified aircraft propulsion systems with electricity being generated from a gas turbine engine or combination of gas turbine engine and an alternate energy source. The presentation will provide an overview of technical challenges and barriers affecting the development and implementation of turboelectric and hybrid electric systems. Advances will be required in multiple areas, which include energy storage, electrical machines, power transmission, power electronics, control systems, materials, thermal management, and multi-scale modeling tools. The presentation will summarize current GRC activities in these areas. Challenges associated with integration and demonstration of multiple technologies at the system level will be presented.

Propulsion↗
NASA NTRS2017

Evolution of Fundamental Technologies for Future Electrified Aircraft

Gradual progression of electric and hybrid electric aircraft from small planes to large planes will require technology advances in multiple areas, which include energy storage, electrical machines, power transmission, power electronics, control systems, materials, thermal management, and multi-scale modeling tools. Advances in both fundamental research and applied interdisciplinary research will be required to realize the goals for future electric and hybrid electric aircraft. The presentation will provide an overview of long-range research and technology needs for the next thirty years and how evolution of several early stage technologies will influence the development of electrified aircraft in the future.

Germany↗
NASA NTRSDate not supplied

Machine-Learned Committor Functions for Reactive Molecular Dynamics

Reactive molecular dynamics (MD) is a powerful tool for atomistic-scale modeling of a diverse range of chemical processes. However, scaling these simulations to large systems and long times scales remains a challenge because of the complexity of the potential energy function required. The authors previously developed a heuristic approach, called REACTER, that incorporates reactivity in MD simulations in a less general but much more computationally efficient manner. REACTER uses standard, fixed valence force fields as the underlying potentialenergy surface for describing all interatomic interactions but adds a procedure for enforcing user-defined reactions that occur when certain geometric constraints on relative atomic positions are satisfied. Further, these bonding changes can be accepted or rejected with a probability related tothe local thermal energy. This work seeks to generalize this approach by replacing the set of user defined geometric constraints and energetic criteria with a committor function that specifies the probability of a reaction occurring on the basis of the local atomic configuration. The committor function is a useful mathematical tool for modeling rare events but, unfortunately, is very difficult to compute for realistic systems in a general way. This work describes a method for approximating the committor function using a machine learning approach, specifically a deep neural network trained with data from reactive MD and DFT-based dynamics simulations. This network is coupled to the existing REACTER protocol, as implemented in the LAMMPS MD package, and used to make on-the-fly predictions of reaction probabilities without the more extensive user input previously required. The new method is demonstrated using the polymerization of polystyrene as a case study. Although very dependent on the quality and quantity of training data, machine-learned committor functions show promise as a method for incorporating reaction probability from higher level calculations into highly scalable MD simulations.

polymer simulations↗
NASA NTRS1991

Low energy trajectories to Mars via gravity assist from Venus to earth

The analytical determination of launch dates and proposed trajectories is reviewed with respect to the search for a low-energy trajectory to Mars with gravitational assist from Venus for the years 1995-2024. Both Ballistic and Venus-Earth gravity assist (VEGA) trajectories are calculated with an automated design tool by the authors (1990). The trajectories are modeled as conic sections from one gravitating body to the next, and gravity assist is considered to act impulsively. VEGA trajectories to Mars require similar launch energies for 6 years listed and have moderate arrival C3s, with the lowest C3 requirement in 2015. The flight time and arrival energies of the trajectories are found to be larger than those of ballistic trajectories, but the low-energy launch window makes them desirable for unmanned Mars missions, in particular.

Williams, S. N.↗
NASA NTRS2003

Production of Loop-Top Hard X-Ray Emission

The main goal of this work has been to understand the particle acceleration mechanism (or mechanisms) in impulsive solar flares, using hard X-ray observations of high spatial and spectral resolution. Several new observations, including the observations by YOHKOH that reveal emission from both footpoints and loop tops, have suggested to us that a model employing stochastic acceleration is the most likely candidate for explaining energetic particles in a majority (if not all) of impulsive flares. In this model, most of the flare energy is initially converted into plasma waves and turbulence, which in turn can accelerate particles from thermal to relativistic energies on sub-second timescales. This research has provided important tools for the interpretation of the high spectral and spatial resolution observations obtained from the Ramaty High Energy Solar Spectroscopic Imager (RHESSI) . In the course of our research, we have focused on two different (but possibly related) stochastic acceleration models: (1) a model that employs high-frequency whistler turbulence and concentrates on the energetic electrons, and (2) a model that employs low frequency MHD waves, and is able to accelerate both the ambient electrons and ions.

Miller, James A.↗
NASA NTRS1995

Surface temperature of a magnetized neutron star and interpretation of the ROSAT data. 1: Dipole fields

We model the temperature distribution at the surface of a magnetized neutron star and study the effects on the observed X-ray spectra and light curves. Generalrelativistic effects, i.e., redshift and lensing, are fully taken into account. Atmospheric effects on the emitted spectral flux are not included: we consider only blackbody emission at the local effective temperature. In this first paper we restrict ourselves to dipole fields. General features are studied and compared with the ROSAT data from the pulsars 0833 - 45 (Vela), 0656 + 14, 0630 + 178 (Geminga), and 1055 - 52, the four cases for which there is strong evidence that thermal radiation from the stellar surface is detected. The composite spectra we obtain are not very different from a blackbody spectrum at the star's effective temperature. We conclude that, as far as blackbody spectra are considered, temperature estimates using single-temperature models give results practically identical to our composite models. The change of the (composite blackbody) spectrum with the star's rotational phase is also not very large and may be unobservable inmost cases. Gravitational lensing strongly suppresses the light curve pulsations. If a dipole field is assumed, pulsed fractions comparable to the observed ones can be obtained only with stellar radii larger than those which are predicted by current models of neutron star struture, or with low stellar masses. Moreover, the shapes of the theoretical light curves with dipole fields do not correspond to the observations. The use of magnetic spectra may raise the pulsed fraction sufficiently but will certainly make the discrepancy with the light curve shapes worse: dipole fields are not sufficient to interpret the data. Many neutron star models with a meson condensate or hypersons predict very small radii, and hence very strong lensing, which will require highly nondipolar fields to be able to reproduce the observed pulsed fractions, if possible at all: this may be a new tool to constrain the size of neutron stars. The pulsed fractions obtained in all our models increase with photon energy: the strong decrease observed in Geminga at energies 0.3-0.5 keV is definitely a genuine effect of the magnetic field on the spectrum in contradistinction to the magnetic effects on the surface temperature considered her. Thus, a detailed analysis of thermal emission from the four pulsars we consider will require both complex surface field configurations and the inclusion of magnetic effects in the atmosphere (i.e., on the emitted spectrum).

Page, Dany↗
NASA NTRSDate not supplied

Integrated In-Situ Resource Utilization Modeling of a Lunar Water Processing System

A key element of achieving a sustained surface presence, such as defined in NASA’s Artemis plan, is In-Situ Resource Utilization (ISRU). ISRU is the practice of using local resources to provide mission consumables that reduce system launch mass requirements, and regenerate resources (chiefly, water and oxygen) for propulsion and life support supporting both Lunar and Martian missions. ISRU systems require multiple complex processes, such as excavation, chemical reactors, and electrolysis subsystems that must operate in harmony to optimize the overall system process from beginning to end. The Mission Analysis and Integration Tool (MAIT) connects individual subsystem models into a customized, flexible framework for the purpose of technology downselect, optimization, and end-to-end process planning. MATLAB was favorable to use as the main software integration tool due to its ability to communicate with a vast number of other programming languages and makes up the backbone of data flow between inputs and outputs to the subsystem models. MAIT initially evaluated a suite of technologies, including but not limited to, the water processing Lunar Auger Dryer for ISRU (LADI) system and an oxygen extraction carbothermal reduction process. With individual models consolidated, the MAIT tool generated over 60,000 cases during its parametric sweeps; these system iterations produced valuable insight into the optimal LADI geometry for minimizing heater energy demands, estimating carbothermal reactor and radiator mass relationships, and calculated the power dynamics of the electrolysis unit. Efforts to update the MAIT tool are ongoing to accommodate and scale ISRU technologies supporting the Space Technology Mission Directorate’s (STMD) commercialization strategy, and increase the MAIT software capability to handle a wide array of ISRU system models beyond the Lunar environment, e.g. production of propellant for a Martian lander.

Avery Carlson↗
NASA NTRS2008

Modeling Bird Migration in Changing Habitats: Space-based Ornithology using Satellites and GIS

Understanding bird migration and avian biodiversity is one of the most compelling and challenging problems of modern biology with major implications for human health and conservation biology. Migration and conservation efforts cross national boundaries and are subject to numerous international agreements and treaties presenting challenges in both geographic space and time. Space based technology, coupled with geographic information systems, yields new opportunities to shed light on the distribution and movement of organisms on the planet and their sensitivity to human disturbances and environmental changes. At NASA, we are creating ecological forecasting tools for science and application users to address the consequences of loss of wetlands, flooding, drought or other natural disasters such as hurricanes on avian biodiversity and bird migration. In our work, we use individual organism biophysical models and drive these models with satellite observations and numerical weather predictions of the spatio-temporal gradients in climate and habitat. Geographic information system technology comprises one component of our overall simulation framework, especially for characterizing the changing habitats and conditions encountered by en-route migratory birds. Simulation provides a tool for studying bird migration across multiple scales and can be linked to mechanistic processes describing the time and energy budget states of migrating birds. Such models yield an understanding of how a migratory flyway and its component habitats function as a whole and link stop-over ecology with biological conservation and management. We present examples of our simulation of shorebirds, principally, pectoral sandpipers, along the central flyways of the United States and Canada from the Gulf of Mexico to Alaska.

Smith, James A.↗
NASA NTRS1991

Integrated energy balance analysis for Space Station Freedom

An integrated simulation model is described which characterizes the dynamic interaction of the energy transport subsystems of Space Station Freedom for given orbital conditions and for a given set of power and thermal loads. Subsystems included in the model are the Electric Power System (EPS), the Internal Thermal Control System (ITCS), the External Thermal Control System (ETCS), and the cabin Temperature and Humidity Control System (THC) (which includes the avionics air cooling, cabin air cooling, and intermodule ventilation systems). Models of the subsystems were developed in a number of system-specific modeling tools and validated. The subsystem models are then combined into integrated models to address a number of integrated performance issues involving the ability of the integrated energy transport system of Space Station Freedom to provide power, controlled cabin temperature and humidity, and equipment thermal control to support operations.

Tandler, John↗
NASA NTRS2017

In Pursuit of Improving Airburst and Ground Damage Predictions: Recent Advances in Multi-Body Aerodynamic Testing and Computational Tools Validation

An airburst from a large asteroid during entry can cause significant ground damage. The damage depends on the energy and the altitude of airburst. Breakup of asteroids into fragments and their lateral spread have been observed. Modeling the underlying physics of fragmented bodies interacting at hypersonic speeds and the spread of fragments is needed for a true predictive capability. Current models use heuristic arguments and assumptions such as pancaking or point source explosive energy release at pre-determined altitude or an assumed fragmentation spread rate to predict airburst damage. A multi-year collaboration between German Aerospace Center (DLR) and NASA has been established to develop validated computational tools to address the above challenge.

Asteroid↗
NASA NTRS1994

An investigation of current and future satellite and in-situ data for the remote sensing of the land surface energy balance

This final report from the University of Wisconsin-Madison Cooperative Institute for Meteorological Satellite Studies (CIMSS) summarizes a research program designed to improve our knowledge of the water and energy balance of the land surface through the application of remote sensing and in-situ data sources. The remote sensing data source investigations to be detailed involve surface radiometric ('skin') temperatures and also high-spectral-resolution infrared radiance data from atmospheric sounding instruments projected to be available at the end of the decade, which have shown promising results for evaluating the land-surface water and energy budget. The in-situ data types to be discussed are measurements of the temporal changes of the height of the planetary boundary layer and measurements of air temperature within the planetary boundary layer. Physical models of the land surface, planetary boundary layer and free atmosphere have been used as important tools to interpret the in-situ and remote sensing signals of the surface energy balance. A prototype 'optimal' system for combining multiple data sources into a three-dimensional estimate of the surface energy balance was developed and first results from this system will be detailed. Potential new sources of data for this system and suggested continuation research will also be discussed.

Diak, George R.↗
NASA NTRSDate not supplied

Modeling Space-Radiation Induced Chromosome Aberrations

Exposure to ionizing radiation is identified as one of the main hazards of space flight. Galactic cosmic rays (GCRs) are composed of energetic protons (87%), helium nuclei (12%) and high-charge and energy (HZE) ions. At the cellular scale, HZE ions have a pattern of energy deposition that depends on the ion linear energy transfer (LET), with high LET ions favoring the formation of complex DNA damages that induce chromosome aberrations (CAs). Understanding how physical (e.g., ion type and energy) and biological (e.g., cell type) parameters impact CA formation is required to assess space radiation risks such as carcinogenesis. The Monte Carlo tool RITCARD/RITRACKS [1,2] developed by NASA allows simulation of the transport of HZE ions and subsequent energy deposition at the cellular scale and models the creation of DNA double strand breaks (DSBs), DNA repair and formation of CAs. We used this radiation transport code to further understand how radiation quality and cell size affects CA formation.

Floriane Poignant↗
NASA NTRSDate not supplied

Machine Learning based Aircraft Performance Model Estimation for Trajectory Prediction

The accurate prediction of aircraft trajectory by ground-based decision support tools is a critical component of air traffic management in the US National Airspace System (NAS). Accurate predictions of where the aircraft will be in the future or when they will arrive at specific locations (e.g., fixes) is a key enabler for sequencing and efficient arrival management of flights. Traditional physics based aircraft trajectory prediction relies on a simplified point-mass total energy model whose parameters are referred to as Aircraft Performance Model (APM) parameters. Even though the performance coefficients and weight of an aircraft are a vital part of the aircraft performance model’s predictions and accuracy, these coefficients are proprietary in nature and therefore, unavailable to decision-support tools. Current approaches freeze some coefficients to default base of aircraft data (BADA) values and optimize others. However, the APM parameters are highly coupled by the flight dynamics and prioritizing one parameter over others leads to bias and skewed predictions. To alleviate this problem, we provide a combined optimization framework to predict all the critical (thrust, drag and weight) APM parameters. This paper is focused on training Machine Learning (ML) models that map historical flights to optimized APM parameters that provide the best fit (in terms of prediction error). Our dataset obtained from NASA’s Sherlock data warehouse is comprised of thousands of historical flights and includes weather and track data collected from 2019. Using different subsets of relevant features (e.g., aircraft type), we trained several ML models to estimate the aircraft’s take off weight, drag polar coefficients (both parasitic and lift induced), and thrust settings (multiplier applied to the maximum engine thrust). The chosen flights are from three of the most common aircraft types (B738, B737, and A320) arriving at four airports (LAX, DEN, MSP, and DFW). Our ML approach is comprised of two different solutions: 1- using a subset of features that are known prior to the flight departure and do not change during flight (such as engine type, current temperature at departure & destination airports, aircraft type) and 2 - using a subset of temporal features of the flight trajectory (such as cruise altitude, Mach, airspeed, and rate of climb) in addition to the pre-departure features from the first solution. The labels or target variables are the APM parameters that were obtained by an optimized ordinary differential equations (ODE) fitting process (applied to individual flights). The ODE-fitting is very time intensive and is therefore performed offline. Thus, training an ML model to learn the relationship between the flight features and ODE-generated labels enables faster estimation of the APM parameters and is therefore amenable to real-time prediction. Various ML models including linear regression, random forest, XGBoost, and neural network were trained, and the results are compared. After model validation and hyperparameter-tuning, we observed that the Random Forest model outperformed the other three models by the overall mean square error (MSE) of 2% for the first solution and 1.5% for the second solution. Finally, the ML-derived parameters are compared against default BADA APM parameters using NASA’s Autonomy Development toolkit (ADK) simulation software. The simulation results for one of each aircraft type is shown and discussed.

Aida Sharif Rohani↗
NASA NTRS1971

Monopropellant hydrazine resisto jet

Mathematical models for the electrothermal hydrazine thruster are provided which can be utilized to aid in preliminary design definition, interpretation of test data, and the evaluation of design and operational changes. Three computer programs were formulated to provide the necessary steady state and transient performance design tools. These included: preliminary evaluation of the energy balance characteristics of the thruster; a means of evaluating steady state performance relative to the primary design, configuration, and operational parameters; and a model to provide a means of evaluating transient (pulse mode) operation of the thruster relative to primary design and operational parameters.

Muenzer, F. B.↗
NASA NTRS1985

Mixing line analysis of clouds and cloudy boundary layers

The diagnostic study of the thermodynamic structure of nonprecipitating clouds and cloudy boundary layers is formulated using a mixing line and saturation point approach. A parametric model for the mean structure is developed as a tool for diagnostic and prognostic modeling. Cloud-scale mixing processes are analyzed in the same framework, together with the energies of the evaporative instability in cumulus clouds. A velocity scale emerges for this evaporative instability. The statistical study of saturation level distribution in partially cloudy boundary layers is proposed to related cloud fraction to the mean thermodynamic mixing processes.

Betts, A. K.↗
NASA NTRS1987

The geosynchronous plasma environment

The geosynchronous plasma environment in the 0 to 100 keV energy range as input in the modeling of spacecraft charging phenomena is summarized. Models for spacecraft design are reviewed, emphasizing practical tools for predicting the effects of spacecraft interactions on satellite systems.

Garrett, Henry Berry↗