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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 NTRS2010

Simulation Framework for Rapid Entry, Descent, and Landing (EDL) Analysis: Appendices - Volume 2

The NASA Engineering and Safety Center (NESC) was requested to establish the Simulation Framework for Rapid Entry, Descent, and Landing (EDL) Analysis assessment, which involved development of an enhanced simulation architecture using the Program to Optimize Simulated Trajectories II (POST2) simulation tool. The assessment was requested to enhance the capability of the Agency to provide rapid evaluation of EDL characteristics in systems analysis studies, preliminary design, mission development and execution, and time-critical assessments. Many of the new simulation framework capabilities were developed to support the Agency EDL Systems Analysis (EDL-SA) team, that is conducting studies of the technologies and architectures that are required to enable higher mass robotic and human mission to Mars. The appendices to the original report are contained in this document.

Murri, Daniel G.↗
NASA NTRSDate not supplied

Assessing Huygens Probe Entry, Descent, and Landing at Titan Simulation using Dragonfly Atmosphere Model

Dragonfly is a New Frontiers Program mission that will deliver a rotorcraft to Saturn's moon, Titan. This mission follows Huygens as the previous mission that successfully landed a vehicle on Titan. A flight mechanics simulation of Dragonfly's Entry, Descent, and Landing sequence has been developed using the Program to Optimize Simulated Trajectories II. The simulation incorporates several subsystem models, including aerodynamics, gravity, and mass properties, to fully capture the multi-body six degree of freedom dynamics. Among all the subsystem models that inform the Entry, Descent, and Landing dynamics, the atmosphere model of Titan is a critical component. The atmosphere model characterizes the density, temperature, pressure, and winds that the entry vehicle experiences during the descent. This impacts several aspects of the descent such as the peak heating, aerodynamics, parachute release conditions, the dynamics of the vehicle and parachutes, and the landing ellipse. In the course of developing Dragonfly, an updated model of the Titan atmosphere has been created corresponding to Dragonfly's arrival in the mid-2030s, approximately one Titan year after the Huygens mission successfully landed a probe on Titan. This work leverages previous work done to investigate Huygens EDL sequence to assess the atmosphere model developed for Dragonfly. This is done by utilizing the updated Titan atmosphere model, the Dragonfly atmosphere model, within the Huygens POST2-based flight simulation with the goal of characterizing the differences between the atmospheric models and assessing how the Dragonfly atmosphere model impacts Huygens entry dynamics.

Huygens↗
NASA NTRSDate not supplied

Assessing Huygens Probe Entry, Descent, and Landing at Titan Simulation using Dragonfly Atmosphere Model

Dragonfly is a New Frontiers Program mission that will deliver a rotorcraft to Saturn's moon, Titan. This mission follows Huygens as the previous mission that successfully landed a vehicle on Titan. A flight mechanics simulation of Dragonfly's Entry, Descent, and Landing sequence has been developed using the Program to Optimize Simulated Trajectories II. The simulation incorporates several subsystem models, including aerodynamics, gravity, and mass properties, to fully capture the multi-body six degree of freedom dynamics. Among all the subsystem models that inform the Entry, Descent, and Landing dynamics, the atmosphere model of Titan is a critical component. The atmosphere model characterizes the density, temperature, pressure, and winds that the entry vehicle experiences during the descent. This impacts several aspects of the descent such as the peak heating, aerodynamics, parachute release conditions, the dynamics of the vehicle and parachutes, and the landing ellipse. In the course of developing Dragonfly, an updated model of the Titan atmosphere has been created corresponding to Dragonfly's arrival in the mid-2030s, approximately one Titan year after the Huygens mission successfully landed a probe on Titan. This work leverages previous work done to investigate Huygens EDL sequence to assess the atmosphere model developed for Dragonfly. This is done by utilizing the updated Titan atmosphere model, the Dragonfly atmosphere model, within the Huygens POST2-based flight simulation with the goal of characterizing the differences between the atmospheric models and assessing how the Dragonfly atmosphere model impacts Huygens entry dynamics.

Entry Descent Landing↗
NASA NTRS2016

Augmenting Parametric Optimal Ascent Trajectory Modeling with Graph Theory

It has been well documented that decisions made in the early stages of Conceptual and Pre-Conceptual design commit up to 80% of total Life-Cycle Cost (LCC) while engineers know the least about the product they are designing [1]. Once within Preliminary and Detailed design however, making changes to the design becomes far more difficult to enact in both cost and schedule. Primarily this has been due to a lack of detailed data usually uncovered later during the Preliminary and Detailed design phases. In our current budget-constrained environment, making decisions within Conceptual and Pre-Conceptual design which minimize LCC while meeting requirements is paramount to a program's success. Within the arena of launch vehicle design, optimizing the ascent trajectory is critical for minimizing the costs present within such concerns as propellant, aerodynamic, aeroheating, and acceleration loads while meeting requirements such as payload delivered to a desired orbit. In order to optimize the vehicle design its constraints and requirements must be known, however as the design cycle proceeds it is all but inevitable that the conditions will change. Upon that change, the previously optimized trajectory may no longer be optimal, or meet design requirements. The current paradigm for adjusting to these updates is generating point solutions for every change in the design's requirements [2]. This can be a tedious, time-consuming task as changes in virtually any piece of a launch vehicle's design can have a disproportionately large effect on the ascent trajectory, as the solution space of the trajectory optimization problem is both non-linear and multimodal [3]. In addition, an industry standard tool, Program to Optimize Simulated Trajectories (POST), requires an expert analyst to produce simulated trajectories that are feasible and optimal [4]. In a previous publication the authors presented a method for combatting these challenges [5]. In order to bring more detailed information into Conceptual and Pre-Conceptual design, knowledge of the effects originating from changes to the vehicle must be calculated. In order to do this, a model capable of quantitatively describing any vehicle within the entire design space under consideration must be constructed. This model must be based upon analysis of acceptable fidelity, which in this work comes from POST. Design space interrogation can be achieved with surrogate modeling, a parametric, polynomial equation representing a tool. A surrogate model must be informed by data from the tool with enough points to represent the solution space for the chosen number of variables with an acceptable level of error. Therefore, Design Of Experiments (DOE) is used to select points within the design space to maximize information gained on the design space while minimizing number of data points required. To represent a design space with a non-trivial number of variable parameters the number of points required still represent an amount of work which would take an inordinate amount of time via the current paradigm of manual analysis, and so an automated method was developed. The best practices of expert trajectory analysts working within NASA Marshall's Advanced Concepts Office (ACO) were implemented within a tool called multiPOST. These practices include how to use the output data from a previous run of POST to inform the next, determining whether a trajectory solution is feasible from a real-world perspective, and how to handle program execution errors. The tool was then augmented with multiprocessing capability to enable analysis on multiple trajectories simultaneously, allowing throughput to scale with available computational resources. In this update to the previous work the authors discuss issues with the method and solutions.

Patrick D Dees↗
NASA NTRS2010

Guidance and Control Algorithms for the Mars Entry, Descent and Landing Systems Analysis

The purpose of the Mars Entry, Descent and Landing Systems Analysis (EDL-SA) study was to identify feasible technologies that will enable human exploration of Mars, specifically to deliver large payloads to the Martian surface. This paper focuses on the methods used to guide and control two of the contending technologies, a mid- lift-to-drag (L/D) rigid aeroshell and a hypersonic inflatable aerodynamic decelerator (HIAD), through the entry portion of the trajectory. The Program to Optimize Simulated Trajectories II (POST2) is used to simulate and analyze the trajectories of the contending technologies and guidance and control algorithms. Three guidance algorithms are discussed in this paper: EDL theoretical guidance, Numerical Predictor-Corrector (NPC) guidance and Analytical Predictor-Corrector (APC) guidance. EDL-SA also considered two forms of control: bank angle control, similar to that used by Apollo and the Space Shuttle, and a center-of-gravity (CG) offset control. This paper presents the performance comparison of these guidance algorithms and summarizes the results as they impact the technology recommendations for future study.

Davis, Jody L.↗
NASA NTRS2012

MSL DSENDS EDL Analysis and Operations

This paper presents the operations experience of the JPL EDL (Entry, Descent, and Landing) trajectory simulation team, focusing on events in the week before landing and EDL itself. A brief description of the EDL trajectory simulation tool used for targeting and simulation verification analysis will be presented, including a high level discussion of the required modeling. Initialization and output interfaces used for landing hazard analysis will also be covered, along with discussion of real-time EDL event detection using radiometric Doppler data.

Entry, descent, and landing (EDL)↗
DOE OSTI2024

Data Science Shows that Entropy Correlates with Accelerated Zeolite Crystallization in Monte Carlo Simulations

We have performed a data science study of Monte Carlo simulation trajectories to understand factors that can accelerate formation of zeolite nanoporous crystals, a process that can take days or even weeks. In previous work, Monte Carlo simulations predicted and experiments confirmed that using a secondary organic structure-directing agent (OSDA) accelerates crystallization of all-silica LTA zeolite, with experiments finding a three-fold speedup [PCCP 24, 142-148 (2022)]. However, it remains unclear what physical factors cause the speed-up. Here, we apply data science to analyze the simulation trajectories to discover what drives accelerated zeolite crystallization in Monte Carlo going from a one-OSDA synthesis (1OSDA) to a two-OSDA version (2OSDA). We encoded simulation snapshots using the Smooth Overlap of Atomic Positions approach, which represents all 2- and 3-body correlations within a given cutoff distance. Principal component analyses failed to discriminate datasets of structures from 1OSDA and 2OSDA simulations, while the Support Vector Machine (SVM) approach succeeded at classifying such structures with an area-under-curve (AUC) score of 0.99 (where AUC = 1 is a perfect classification) with all 3-body correlations, and as high as 0.94 with only 2-body correlations. SVM decision functions reveal relatively broad / narrow histograms for 1OSDA / 2OSDA datasets, suggesting that the two simulations differ strongly in information heterogeneity. Informed by these results, we performed pair (2-body) entropy calculations during crystallization, resulting in entropy differences that semi-quantitatively account for the speedup observed in the previous Monte Carlo simulations. We conclude that altering synthesis conditions in ways that substantially changes the entropy of labile silica networks may accelerate zeolite crystallization, and we discuss possible approaches for achieving such acceleration.

77 NANOSCIENCE AND NANOTECHNOLOGY↗
NASA NTRSDate not supplied

Comparison of Entry Descent and Landing Aerodynamic Databases with Uncertainty Quantification Developed Using Machine Learning Techniques

When developing the aerodynamic databases for use in trajectory simulations, it is important to develop a system of metrics to qualify which aerodynamic models are best to use. Since aerodynamics are just one input into trajectory simulations, the results of these simulations do not reflect on the quality of the aerodynamic database used. This means that aerodynamic database comparisons must be done offline. While traditional metrics that focus on mean/nominal predictions are a good first step, more robust estimates of the prediction interval become important as more focused uncertainty models are developed. We explore the limitations of evaluating aerodynamic models based purely on nominal-centered response surfaces. Before elaborating and evaluating metrics based on distributed models, the value of evaluating prediction interval and confidence interval are discussed to conclude that prediction intervals are more relevant to the use of trajectory analysis. Several metrics to evaluate the prediction interval are introduced with a focus on the standard calibration metric. Finally, we compare candidate models using both mean and distributed metrics. A finalized candidate model developed using state of the art machine learning methods is compared to a baseline model developed using traditional aerodynamic database modeling techniques.

Aerodynamic Database↗
NASA NTRS1989

A further study of cumulus interactions and mergers - Three-dimensional simulations with trajectory analysis

Cumulus processes involved in the interaction and merging of clouds under the influence of different imposed conditions (including large-scale lifting forcing, environmental wind shear, and cloud microphysical processes) were studied using simulations with a three-dimensional model. The design of the study was to generate several convective clouds randomly inside the model domain, and then to observe and analyze the interactions and merging between the simulated clouds. Ten merged clouds were identified. Seven of these, each involving two previously separated clouds, generally lie along a line parallel to the initial environmental wind shear vector, while one (also a two-cloud system) lies along a line perpendicular to the wind shear vector prior to merging. The remaining two merging systems involve three parent clouds each; they are a combination of parallel and perpendicular cells. The merging mechanisms associated with three-cloud merging cases are studied by examining the temperature, pressure, and wind fields prior to, during, and following the merging.

Tao, Wei-Kuo↗
NASA NTRS2002

Trajectories for High Specific Impulse High Specific Power Deep Space Exploration

Flight times and deliverable masses for electric and fusion propulsion systems are difficult to approximate. Numerical integration is required for these continuous thrust systems. Many scientists are not equipped with the tools and expertise to conduct interplanetary and interstellar trajectory analysis for their concepts. Several charts plotting the results of well-known trajectory simulation codes were developed and are contained in this paper. These charts illustrate the dependence of time of flight and payload ratio on jet power, initial mass, specific impulse and specific power. These charts are intended to be a tool by which people in the propulsion community can explore the possibilities of their propulsion system concepts. Trajectories were simulated using the tools VARITOP and IPOST. VARITOP is a well known trajectory optimization code that involves numerical integration based on calculus of variations. IPOST has several methods of trajectory simulation; the one used in this paper is Cowell's method for full integration of the equations of motion. An analytical method derived in the companion paper was also evaluated. The accuracy of this method is discussed in the paper.

Polsgrove, Tara↗
NASA NTRS1973

A preliminary design and implementation of the low-thrust simulation and trajectory search program (LOWTRAJ)

The results are presented of one phase of research conducted for the JPL Solar Electric Propulsion (SEP) Navigation Software System development program. It deals only with the problem of designing the flight quality trajectory program, which is a major subset of the entire navigation software system. In this phase of research (breadboard development phase), attempts were made to assess the SEP trajectory software functional requirements, to investigate the program design method satisfying these requirements, to identify the primary anticipated problem areas, and to provide solutions to these problem areas. These efforts culminated in the development of a compact breadboard program. A functional description and the mathematical formulation of the program are presented.

Yen, C. L.↗
NASA NTRS2000

Parametric Model of an Aerospike Rocket Engine

A suite of computer codes was assembled to simulate the performance of an aerospike engine and to generate the engine input for the Program to Optimize Simulated Trajectories. First an engine simulator module was developed that predicts the aerospike engine performance for a given mixture ratio, power level, thrust vectoring level, and altitude. This module was then used to rapidly generate the aerospike engine performance tables for axial thrust, normal thrust, pitching moment, and specific thrust. Parametric engine geometry was defined for use with the engine simulator module. The parametric model was also integrated into the iSIGHT multidisciplinary framework so that alternate designs could be determined. The computer codes were used to support in-house conceptual studies of reusable launch vehicle designs.

Korte, J. J.↗
NASA NTRS2000

Parametric Model of an Aerospike Rocket Engine

A suite of computer codes was assembled to simulate the performance of an aerospike engine and to generate the engine input for the Program to Optimize Simulated Trajectories. First an engine simulator module was developed that predicts the aerospike engine performance for a given mixture ratio, power level, thrust vectoring level, and altitude. This module was then used to rapidly generate the aerospike engine performance tables for axial thrust, normal thrust, pitching moment, and specific thrust. Parametric engine geometry was defined for use with the engine simulator module. The parametric model was also integrated into the iSIGHTI multidisciplinary framework so that alternate designs could be determined. The computer codes were used to support in-house conceptual studies of reusable launch vehicle designs.

Korte, J. J.↗
NASA NTRS2013

Parachute Models Used in the Mars Science Laboratory Entry, Descent, and Landing Simulation

An end-to-end simulation of the Mars Science Laboratory (MSL) entry, descent, and landing (EDL) sequence was created at the NASA Langley Research Center using the Program to Optimize Simulated Trajectories II (POST2). This simulation is capable of providing numerous MSL system and flight software responses, including Monte Carlo-derived statistics of these responses. The MSL POST2 simulation includes models of EDL system elements, including those related to the parachute system. Among these there are models for the parachute geometry, mass properties, deployment, inflation, opening force, area oscillations, aerodynamic coefficients, apparent mass, interaction with the main landing engines, and off-loading. These models were kept as simple as possible, considering the overall objectives of the simulation. The main purpose of this paper is to describe these parachute system models to the extent necessary to understand how they work and some of their limitations. A list of lessons learned during the development of the models and simulation is provided. Future improvements to the parachute system models are proposed.

Cruz, Juan R.↗
NASA NTRS2017

Flight Deck Surface Trajectory-Based Operations (STBO): A Four-Dimensional Trajectory (4DT) Simulation

Within human factors there is burgeoning interest in the Human-Autonomy Teaming (HAT) concept as away to address the challenges of interacting with complex, increasingly autonomous systems. The HAT concept comes out of an aspiration to interact with increasingly autonomous automation as a team member, rather than simply use automation as a tool. The authors, and others, have proposed core tenets for HAT that include bi-directional communication, automation and system transparency, and advanced coordination between human and automated teammates via predefined, dynamic task sequences known as plays (Shively et al., 2017). It is believed that, with proper implementation, HAT should foster appropriate teamwork, thus increasing trust and reliance on the system, which in turn will reduce workload, increase situation awareness, and improve performance. To this end, HAT has been demonstrated and/or studied in multiple applications including search and rescue operations (Nourbakhsh et al., 2005), healthcare and medicine (Tsui Yanco, 2007), autonomous vehicles (Parasuraman, Barnes, Cosenzo, Mulgund, 2007), photography (Lachter, Brandt, Sadler, Shively, in press), and aviation (Shively et al., in press). The current paper presents one such effort to apply HAT. It details the design of a R-HAT Agent developed as part of a NASA Research Agreement awarded to Human-Autonomy Teaming Solutions Inc. (HATS Inc), and developed in collaboration with the Human-Autonomy Teaming Laboratory at NASA Ames Research Center. The role of this Agent is to mediate interaction between the automation and the human operator of an advanced ground dispatch station, with this mediation based upon previously mentioned core tenets for HAT and the many lessons learned from the HAT research literature. This dispatch station was developed to support a NASA project investigating a concept called Reduced Crew Operations (RCO; Lachter, Brandt, Battiste, Matessa, Johnson, in press). Part of the RCO concept involves a ground operator providing enhanced support to a large number of aircraft with a single pilot on the flight deck. When assisted by the Agent, operators can monitor and support or manage a large number of aircraft and use plays to respond in real-time to complicated, workload-intensive events (e.g., an airport closure). A play is a plan that encapsulates goals, tasks, and a task allocation strategy appropriate for a particular situation. In the current implementation, when a play is initiated by a user, the Agent determines what tasks need to be done and has the ability to autonomously execute them (e.g., determining diversion options and uplinking new routes to aircraft) when it is safe and appropriate. The R-HAT Agent has been designed to both support end users and research in RCO and HAT. Additionally, the Agent and its underlying architecture were developed with generalizability in mind as a modular piece of software applicable outside of RCO aviation in domains such as those mentioned above. This paper will also discuss future further development and testing of the R-HAT Agent.

Bakowski, Deborah L.↗
NASA NTRSDate not supplied

SpaceFOM: An Interoperability Standard for Space Systems Simulations

There is a long history of simulation supporting space systems development. This includes relatively simple parametric simulations to more complex trajectory simulations to large scale integrated vehicle simulation. One area of relatively recent development is in the area of distributed or interoperable simulation. Distributed simulation has been in wide use by the US military for years but is being used more widely in the aerospace community. To support large scale distributed simulation, the military community has developed a number of standards to support a priori interoperability between large collections of disparate simulation. For example, the IEEE 1516 High Level Architecture (HLA) and the Real-time Platform Reference Federation Object Model (RPR FOM). While HLA is suitable for space systems, there are a number of design decisions made in the development of the RPR FOM that prevent it from working well for space applications. In order to address these deficiencies, the Simulation Interoperability Standards Organization (SISO) developed a new HLS-compatible interoperability standard to support the needs of complex space systems. This standard is the Space Reference Federation Object Model (SpaceFOM). This paper presents on overview of the SpaceFOM including the fundamentals of the SpaceFOM, the key features of the SpaceFOM, and how the SpaceFOM supports large scale distributed simulation of complex space systems.

SISO↗
NASA NTRSDate not supplied

SpaceFOM: An Interoperability Standard for Space Systems Simulations

There is a long history of simulation supporting space systems development. This includes relatively simple parametric simulations to more complex trajectory simulations to large scale integrated vehicle simulation. One area of relatively recent development is in the area of distributed or interoperable simulation. Distributed simulation has been in wide use by the US military for years but is being used more broadly in the aerospace community. To support large scale distributed simulation, the military community has developed a number of standards to support a-priori interoperability between large collections of disparate simulations. For example, the IEEE 1516 High Level Architecture (HLA) and the Real-time Platform Reference Federation Object Model (RPR FOM). While HLA is suitable for space systems, there are a number of design decisions made in the development of the RPR FOM that prevent it from working well for space applications. In order to address these deficiencies, the Simulation Interoperability Standards Organization (SISO) developed a new HLA-based interoperability standard to support the needs of complex space systems. This standard is the Space Reference Federation Object Model (SpaceFOM). This paper presents an overview of the SpaceFOM including the fundamentals of the SpaceFOM, the key features of the SpaceFOM, and how the SpaceFOM supports large scale distributed simulation of complex space systems.

SpaceFOM↗