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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 469 records · Page 26
All sourcesNASA NTRSDOE OSTI
NASA NTRS2018

Optimization of Second Fault Detection Thresholds to Maximize Mission Probability of Success

In order to support manned spaceflight safety requirements, the Space Launch System (SLS) has defined program-level requirements for key systems to ensure successful operation under single fault conditions. The SLS program has also levied requirements relating to the capability of the Inertial Navigation System to detect a second fault. This detection functionality is required in order to feed abort analysis and ensure crew safety. Increases in navigation state error due to sensor faults in a purely inertial system can drive the vehicle outside of its operational as-designed environmental and performance envelope. As this performance outside of first fault detections is defined and controlled at the vehicle level, it allows for the use of system level margins to increase probability of mission success on the operational edges of the design. A top-down approach is utilized to assess vehicle sensitivity to second sensor faults. A wide range of failure scenarios in terms of both fault magnitude and time is used for assessment. The approach also utilizes a schedule to change fault detection thresholds autonomously. These individual values are optimized along a nominal trajectory in order to maximize probability of mission success in terms of system-level insertion requirements while minimizing the probability of false positives. This paper will describe an approach integrating Genetic Algorithms and Monte Carlo analysis to tune the threshold parameters to maximize vehicle resilience to second fault events over an ascent mission profile. The analysis approach and performance assessment and verification will be presented to demonstrate the applicability of this approach to second fault detection optimization to maximize mission probability of success through taking advantage of existing margin.

Anzalone, Evan J.↗
NASA NTRS2018

Design of Distributed Mail-Slot Propulsion System on a Hybrid Wingbody Aircraft

This paper deals with designing a thrust distribution strategy when a Turboelectric Distributed Propulsion (TeDP) system of 16 embedded propulsors is installed on an aerodynamically optimized hybrid wing-body configuration. This HWB previously designed to satisfy conditions of trim, longitudinally static stability and specific cargo space is employed as the baseline configuration for the current study of seeking an optimal propulsion/power system. According to the nature of the entrance flow condition for each distributed propulsion passage in hybrid wing-body aircraft, the ingested boundary layer thickness differs and results in different propulsive reaction. An optimal distribution of thrust and power output is determined by how the system utilizes the propulsive characteristics of each passage. The design space and the number of design variables are selected and described accordingly. An actuator disk model is employed to model thrust generation and shaft power from the propulsor. To carry out the optimization of the propulsion/power system on a computationally expensive CFD model, a Kriging method in conjunction with a Genetic Algorithm (GA) is applied. Throughout the design process, the propulsion performances of the sampled propulsion/power system are analyzed and compared to those of a clean flow engine. The performance metrics includes mass flow rate, fan pressure ratio besides the thrust and shaft power. Minimization of total shaft power from the distributed engine is performed at multiple thrust levels. The benefit of boundary layer ingestion propulsion system is quantified via comparison of thrust equivalent, shaft power and mass flow equivalent clean flow engines with CFD based system design.

Hybrid Wingbody↗
NASA NTRS2018

Solar Sail Transfers from Invariant Objects to L5 Periodic Orbits

The rising interest in a mission to the Sun-Earth L5 point for heliophysics or Trojan asteroid search and the development of solar sails has opened the question of using solar radiation pressure for such a mission. Different solar sail trajectories to the Sun-Earth L5 point are constructed. Different invariant objects in the neighbourhood of Earth are considered for departure: equilibrium points, families of periodic orbits and their associated invariant manifold. Using a multi-objective genetic algorithm, differential correction and the optimal control solver PSOPT the transfers are obtained. The approach followed results in fast solar sail transfers which can be used for the preliminary design of a mission to the Sun-Earth L5 point.

Fernandez, Alvaro↗
NASA NTRS2015

A Stochastic Scheduler for Integrated Arrival, Departure and Surface Operations in Los Angeles

In terminal airspace, integrating arrivals, departures, and surface operations with competing resources provides the potential of improving operational efficiency by removing barriers between different operations. This work develops a centralized stochastic scheduler for operations in a terminal area including airborne and surface operations using Non-dominated sorting genetic algorithm and Monte Carlo simulations. The scheduler handles completing resources between different flows, such as runway allocations, runway crossing, departure fixes, and other interaction way points between arrivals and departures. Meanwhile, the scheduler also takes time-varied uncertainties into account when optimizing schedules. The scheduler is run sequentially to identify the best and robust schedule for the next planning window. Resulting schedules decide the routes, speed or delays, and runway assignments with separation constraints at mergingdiverging waypoints in the air and crossing and separations on runways. The Los Angels terminal area was used as an example. The implementation of this stochastic scheduler for integrated arrival, departure and surface operations is completed. And several preliminary runs are finished for over 1,200 flights in LAX in a typical day. Sensitivity studies on various planning window sizes are presented, which shows that trade-off exits between planning window size and achievable minimum delay. Preliminary results on runway usage are also presented in this abstract. Because arrivals on the outer runways have to be followed by crossings on the inner runways, algorithmic runway allocation prefers inner runways for arrivals and outer runways for departures. More results will be presented in the final paper. And current terminal arrival and departure procedures based on first-come-first-serve procedure will also be set up and used as a baseline for comparison.

integrated scheduler↗
NASA NTRS2019

Conceptual Design of a Counter-Rotating Fan System for Distributed Boundary Layer Ingesting Propulsion

The present paper details the design of the counter rotating fans for a Turboelectric Distributed Propulsion (TeDP) system. Sixteen propulsors installed in mail-slot-shape nacelles are embedded on an aerodynamically optimized hybrid wing-body configuration. The hybrid-wing/body (HWB) configuration which was previously designed to satisfy the conditions of trim, longitudinally static stability and specific cargo space is employed as the baseline configuration in pursuing an optimal distributed propulsion system. A set of distributed propulsors is conceptually designed and the collective performance is evaluated against the target thrust mandated by the mission requirements. The concept of the distributed propulsion allows the fan pressure ratio to be around 1.27~1.32 for the target thrust. In addition, further splitting of the fan pressure ratio by using the counter-rotating fans for each slot realizes the target pressure ratio with low tip speed. In the distributed propulsion system, the nature of the flow conditions and/or the thickness of the ingested boundary layer may differ and result in different propulsive reaction of each individual propulsor. The optimization is, thus, approached from both the propulsion system and individual propulsor perspectives. An optimal distribution of the thrust and power output is determined by how the system utilizes each passage's propulsive characteristics and its interaction with the airframe. These system level analysis and optimization are conducted using an actuator disk model to account for the propulsion-airframe integration numerically. With respect to the propulsor level, aerodynamic shape optimizations of the fan blades are performed in a sequential multi-objective optimization process for various design objectives, such as mass flow rate condition, fan pressure ratio, efficiency and the exit flow angle of the fan stage by using a genetic algorithm, NSGA-II. The radial chord distribution, and meanline distribution of the rotors are designed on the circumferentially averaged axi-symmetric inlet profiles and tested on the six inlet profiles from six divided sectors to reckon flow distortion. The performances of the counter rotating fans are, thus, evaluated accordingly for obtaining distortion tolerant fan. The performance of the distributed propulsion system is evaluated by two CFD tools, i.e., a multi-stage turbo-machinery CFD code and one propulsion-airframe integration flow solver coupled with a body-force model. The optimized boundary layer ingestion propulsion system of 16 distributed slots not only reaches the system target thrust, but also delivers a close to 20% fuel saving benefit against its counterpart 12 distributed clean inlet propulsion system.

Boundary-Layer-Ingestion Propulsion↗
NASA NTRS2019

Using Deep Learning to Automate Inference of Meteoroid Pre-Entry Properties

Properly assessing the asteroid threat depends on the knowledge of asteroid pre-entry parameters, such as size, velocity, mass, density, and strength. Although a vast number of possible bodies to study exist, such characterization of asteroid populations is currently limited by substantial costs associated with space rendezvous missions and rare meteorite findings. As asteroids fragment, ablate, and decelerate in the atmosphere, they emit light detectable by ground-based and space-borne instruments. Earth’s atmosphere, thus, becomes an accessible laboratory that enables impactor risk assessments by facilitating inference of the pre-entry parameters. These asteroid pre-entry conditions are typically deduced by modeling the entry and breakup physics that best reproduce the observed light or energy deposition curve. However, this process requires extensive manual trial-and-error of uncertain modeling parameters. Automating meteor modeling and inference would improve property distributions used in risk assessments and enable population characterization as more light curves become more readily available through the presence of space assets and ground-based camera networks. We previously developed a genetic algorithm to automate meteor modeling by using the fragment-cloud model (FCM) to search for the values of the FCM input parameters (e.g., diameter) that generate energy deposition profiles that match the observed one. Now, we apply deep learning to infer asteroid diameter, velocity, and density from observed energy deposition curves. We trained and tested our neural network models with synthetic energy deposition curves modeled using the FCM rubble pile implementation. We present an application of a 1D convolutional neural network and compare its performance to other attempted regressors and machine learning techniques, such as a fully connected neural network and Random Forest regression, to demonstrate its capabilities. We validate our model weights and approach using the Chelyabinsk, Tagish Lake, Benešov, Košice, and Lost City meteors.

Tarano, Ana Maria↗
NASA NTRS2019

Numerical Investigation and Optimization of a Flushwall Injector for Scramjet Applications at Hypervelocity Flow Conditions

An investigation utilizing Reynolds-averaged simulations (RAS) was performed in order to find optimal designs for an interdigitated flushwall injector suitable for scramjet applications at hypervelocity conditions. The flight Mach number, duct height, spanwise width, and injection angle were the design variables selected to maximize two objective functions: the thrust potential and combustion efficiency. A Latin hypercube sampling design-of-experiments method was used to select design points for RAS. A methodology was developed that automated building geometries and generating grids for each design. The ensuing RAS analysis generated the performance database from which the two objective functions of interest were computed using a one-dimensional performance utility. The data were fitted using four surrogate models: an artificial neural network (ANN) model, a cubic polynomial, a quadratic polynomial, and a Kriging model. Variance-based decomposition showed that both objective functions were primarily driven by changes in the duct height. Multiobjective design optimization was performed for all four surrogate models via a genetic algorithm method. Optimal solutions were obtained at the upper and lower bounds of the flight Mach number range. The Kriging model obtained an optimal solution set that predicted high values for both objective functions. Additionally, three challenge points were selected to assess the designs on the Pareto fronts. Further sampling among the designs of the Pareto fronts are required in order to lower the errors and perform more accurate surrogate-based optimization. sed optimization.

Shenoy, Rajiv R.↗
NASA NTRS2019

Numerical Investigation and Optimization of a Flushwall Injector for Scramjet Applications at Hypervelocity Flow Conditions

An investigation utilizing Reynolds-averaged simulations (RAS) was performed in order to demonstrate the use of design and analysis of computer experiments (DACE) methods in Sandia’s DAKOTA software package for surrogate modeling and optimization. These methods were applied to a flow- path fueled with an interdigitated flushwall injector suitable for scramjet applications at hyper- velocity conditions and ascending along a constant dynamic pressure flight trajectory. The flight Mach number, duct height, spanwise width, and injection angle were the design variables selected to maximize two objective functions: the thrust potential and combustion efficiency. Because the RAS of this case are computationally expensive, surrogate models are used for optimization. To build a surrogate model a RAS database is created. The sequence of the design variables comprising the database were generated using a Latin hypercube sampling (LHS) method. A methodology was also developed to automatically build geometries and generate structured grids for each design point. The ensuing RAS analysis generated the simulation database from which the two objective functions were computed using a one-dimensionalization (1D) of the three-dimensional simulation data. The data were fitted using four surrogate models: an artificial neural network (ANN), a cubic polynomial, a quadratic polynomial, and a Kriging model. Variance-based decomposition showed that both objective functions were primarily driven by changes in the duct height. Multiobjective design optimization was performed for all four surrogate models via a genetic algorithm method. Optimal solutions were obtained at the upper and lower bounds of the flight Mach number range. The Kriging model predicted an optimal solution set that exhibited high values for both objective functions. Additionally, three challenge points were selected to assess the designs on the Pareto fronts. Further sampling among the designs of the Pareto fronts may be required to lower the surrogate model errors and perform more accurate surrogate-model-based optimization.

Shenoy, Rajiv R.↗
NASA NTRS2021

Generation-based Evolutionary Tool for the Optimization of Constellations (GenETOC)

With the rapid growth in the capabilities of smaller satellites, satellite architectures that replace a single, extremely capable spacecraft with multiple, cheaper ones are gaining in popularity. Unfortunately, the orbit design process for constellations can be significantly more involved, especiallywhen the relative placement of the individual spacecraft within the constellation is not constrained by mission and/or science objectives. Optimizing a satellite constellation in the presence of multiple, competing objectives is a highly complex problem to which many traditional mathematical optimization methods cannot be applied and few tools exist to help mission designers search for promising candidate mission designs. The Generation-based Evolutionary Tool for the Optimization of Constellations (GenETOC) has been created to search for near-optimal constellation design options. GenETOC combines a modified version of the Non-dominated Sorting Genetic Algorithm II (NSGA II) with STK Components libraries (a 3rdparty .NET package created by Analytical Graphics Inc.) to create a framework that enables a mission designer to generate a simulation that models the design problem and obtain a family of potential, near-optimal solutions that can be investigated more in detail.

mission design↗
NASA NTRS1999

Integrated Design and Optimization of Microelectronic Devices

A genetic algorithm is used for design of infrared filters and in the understanding of the material structure of a resonant tunneling diode. These two components are examples of microdevices and nanodevices that can be numerically simulated using fundamental mathematical and physical models.

Microelectronic↗
NASA NTRS2017

GTOC9: Methods and Results from the Jet Propulsion Laboratory Team

The removal of 123 pieces of debris from the Sunsynchronous LEO environment is accomplished by a 10-spacecraft campaign wherein the spacecraft, flying in succession over an 8-yr period, rendezvous with a series of the debris objects, delivering a de-orbit package at each one before moving on to the next object by means of impulsive manoeuvres. This was the GTOC9 problem, as posed by the European Space Agency. The methods used by the Jet Propulsion Laboratory team are described, along with the winning solution found by the team. Methods include branch-and-bound searches that exploit the natural nodal drift to compute long chains of rendezvous with debris objects, beam searches for synthesising campaigns, ant colony optimisation, and a genetic algorithm. Databases of transfers between all bodies on a fine time grid are made, containing an easyto- compute yet accurate estimate of the transfer V . Lastly, a final non-linear programming optimisation is performed to ensure the trajectories meet all the constraints and are locally optimal in initial mass.

Sims, Jon↗
NASA NTRSDate not supplied

Using Machine Learning to Infer Pre-Entry Properties for Asteroid Threat Analysis

Accurately assessing asteroid threats relies on knowledge of the asteroid’s pre-entry properties such as size, velocity, and mass. Directly measuring these properties can be infeasible due to the sparsity of events and the accuracy and fidelity of various sensors. Current analysis of an asteroid’s pre-entry properties involves modeling the asteroid’s entry into the Earth’s atmosphere. This process can be time consuming and can require manual adjustment of uncertain modeling specific parameters. NASA Ames has developed a genetic algorithm that can help automate asteroid modeling using the Fragment-Cloud Model (FCM). The algorithm generates realistic energy deposition curves based on actual energy deposition curves from real, observed asteroids. By using these synthetic, labeled energy deposition curves, we developed a one-dimensional convolutional neural network that can predict an asteroid’s pre-entry parameters.

ATAP↗
NASA NTRSDate not supplied

Copernicus-LinCov (COPCOV) Software Integration in Support of Robust Trajectory Optimization

Robust trajectory optimization is the process of optimizing a trajectory while accounting for system uncertainty due to a variety of potential error sources. This work highlights the development and features of a novel tool known as CopCov to support robust trajectory optimization efforts. CopCov acts as an interface between Copernicus, a generalized trajectory design and optimization tool, and LinCov, a linear covariance analysis tool. By having a direct interface between these two software packages, Copernicus can receive covariance information from LinCov through a direct feedback loop, thus enabling optimization of a trajectory that is robust to trajectory dispersions and navigation errors. This paper details the architecture of CopCov and its flexibility to operate under varying configurations, including with both tools running locally or alternatively with the tools communicating via a remote connection. Additionally, the CopCov tool is demonstrated on a simple Hohmann transfer reference trajectory with varying numbers of Trajectory Correction Maneuvers (TCMs) and varying problem formulations. This example scenario is used to highlight how the inclusion of the CopCov interface affects burn placement of both major burns and minor burns (i.e., TCMs) in the optimized solution. Results are compared against analytical solutions and against a Genetic Algorithm (GA) optimizer for independent verification and validation.

Copernicus↗
NASA NTRSDate not supplied

Transient Optimization of an Electrified Gas Turbine Engine Using Machine Learning

Gas turbine engines are designed with sufficient margin to prevent stall under normal operating conditions throughout their life. This compromise ensures that during rapid accelerations, compressor operation remains stable, but at the cost of efficiency and thrust responsiveness. The design margin encompasses multiple sources of uncertainty and systematic deviances from the operating line, the largest of which is the transient allowance. This set-aside accounts for the temporary incoordination of the engine spools during an acceleration while still enabling it to meet the certification requirement to accelerate from low to high power within a specified time, and without experiencing overtemperature, surge, stall, or other detrimental factors. Electrification of the powertrain provides the opportunity to address this reserve and truly optimize the design. The addition of electric machines inherent in hybrid propulsion concepts offers a means to interact with the engine shafts such that the necessary margin can be reduced, which can positively impact the engine design. By adjusting the amount of power extracted from or injected to the engine spools by the electric machines during transient operation, excursions from the operating line can be minimized. Past work using a dynamic engine model has shown that optimization of the fuel flow schedule during acceleration can reduce the required margin while still meeting the time requirement, and results are further improved when combined with power injection and extraction. The current work uses machine learning through a genetic algorithm to address the problem holistically by concurrently optimizing the electric machine power command and fuel flow acceleration schedule using an updated, higher fidelity version of the original engine model.

Stall Margin↗
NASA NTRSDate not supplied

Transient Optimization of an Electrified Gas Turbine Engine Using Machine Learning

Gas turbine engines are designed with sufficient margin to prevent stall under normal operating conditions throughout their life. This compromise ensures that during rapid accelerations, compressor operation remains stable, but at the cost of efficiency and thrust responsiveness. The design margin encompasses multiple sources of uncertainty and systematic deviances from the operating line, the largest of which is the transient allowance. This set-aside accounts for the temporary incoordination of the engine spools during an acceleration while still enabling it to meet the certification requirement to accelerate from low to high power within a specified time, and without experiencing overtemperature, surge, stall, or other detrimental factors. Electrification of the powertrain provides the opportunity to address this reserve and truly optimize the design. The addition of electric machines inherent in hybrid propulsion concepts offers a means to interact with the engine shafts such that the necessary margin can be reduced, which can positively impact the engine design. By adjusting the amount of power extracted from or injected to the engine spools by the electric machines during transient operation, excursions from the operating line can be minimized. Past work using a dynamic engine model has shown that optimization of the fuel flow schedule during acceleration can reduce the required margin while still meeting the time requirement, and results are further improved when combined with power injection and extraction. The current work uses machine learning through a genetic algorithm to address the problem holistically by concurrently optimizing the electric machine power command and fuel flow acceleration schedule using an updated, higher fidelity version of the original engine model.

Stall Margin↗
NASA NTRS2024

Transient Optimization of an Electrified Gas Turbine Engine Using Machine Learning

Gas turbine engines are designed with sufficient margin to prevent stall under normal operating conditions throughout their life. This compromise ensures that during rapid accelerations, compressor operation remains stable, but at the cost of efficiency and thrust responsiveness. The design margin encompasses multiple sources of uncertainty and systematic deviances from the operating line, the largest of which is the transient allowance. This set-aside accounts for the temporary incoordination of the engine spools during an acceleration while still enabling it to meet the certification requirement to accelerate from low to high power within a specified time, and without experiencing overtemperature, surge, stall, or other detrimental factors. Electrification of the powertrain provides the opportunity to address this reserve and truly optimize the design. The addition of electric machines inherent in hybrid propulsion concepts offers a means to interact with the engine shafts such that the necessary margin can be reduced, which can positively impact the engine design. By adjusting the amount of power extracted from or injected to the engine spools by the electric machines during transient operation, excursions from the operating line can be minimized. Past work using a dynamic engine model has shown that optimization of the fuel flow schedule during acceleration can reduce the required margin while still meeting the time requirement, and results are further improved when combined with power injection and extraction. The current work uses machine learning through a genetic algorithm to address the problem holistically by concurrently optimizing the electric machine power command and fuel flow acceleration schedule using an updated, higher fidelity version of the original engine model.

Stall Margin↗