Results for “Interdependency” — Page 34

NASA NTRS2016

Streamlining the Design Tradespace for Earth Imaging Constellations

Distributed Spacecraft Missions (DSMs) are gaining momentum in their application to Earth Observation (EO) missions owing to their unique ability to increase observation sampling in spatial, spectral, angular and temporal dimensions simultaneously. DSM design includes a much larger number of variables than its monolithic counterpart, therefore, Model-Based Systems Engineering (MBSE) has been often used for preliminary mission concept designs, to understand the trade-offs and interdependencies among the variables. MBSE models are complex because the various objectives a DSM is expected to achieve are almost always conflicting, non-linear and rarely analytical. NASA Goddard Space Flight Center is developing a pre-Phase A tool called "Trade-space Analysis Tool for Constellations" (TAT-C) to initiate constellation mission design. The tool will allow users to explore the tradespace between various performance, cost and risk metrics (as a function of their science mission) and select Pareto optimal architectures that meet their requirements. This paper focuses on the tradespace search and how it can be streamlined by combining physical rules, as well as well-designed orbit and coverage computations, thus yielding significant speed-ups. Two use cases are shown as representative examples of the utility of TAT-C generated trades, and results are preliminarily validated against AGI's Systems Tool Kit.

Science Data Processing↗

NASA NTRS2018

Evaluation of Multiple Flow Constrained Area Capacity Setting Methods for Collaborative Trajectory Options Program

The purpose of this study was to compare flow constrained area (FCA) capacity setting methods for Collaborative Trajectory Options Program (CTOP) as they pertain to the Integrated Demand Management (IDM) concept. IDM uses flow balancing to manage air traffic across multiple FCAs with a common downstream constraint, as well as constraints at the respective FCA locations. FCA capacity rates can be set manually, but generating capacities for multiple, interdependent FCAs could potentially over-burden a user. A new enhancement to CTOP called the FCA Balance Algorithm (FBA) was developed at NASA Ames Research Center to improve the process of allocating capacity across multiple flow constrained segments in the airspace. The FBA evaluates the predicted demand and capacity across multiple FCAs and dynamically generates capacity settings for the FCAs that best meet capacity limits for all identified constraints. In a human-in-the-loop simulation study, both manual and automated capacity setting methods were evaluated in terms of their overall feasibility using measures of system performance, human performance, and qualitative feedback. Subject matter experts were asked to use three different methods to allocate capacity to three FCAs, either (1) by manually setting capacity for every 60-minute time window, (2) by manually setting capacity for every 15-minute time window, or (3) by using the FBA capability to automatically generate capacity settings. Results showed no significant differences in terms of overall system performance, indicated by similar ground delay and airport throughput numbers between methods. However, differences in individual strategies afforded by the manual methods allowed some participants to achieve system-wide delay that was much lower than the average. The FBA was the fastest method of capacity setting, and it received the lowest subjective rating scores on physical task load, mental task load, task difficulty and task complexity out of the three methods. Finally, participants explained through qualitative feedback that there were many benefits to using the FBA, such as ease of use, accuracy, and low risk of human input error. Participants did not experience the same limitations with the FBA that they did with the manual methods, such as reduced accuracy in the 60-minute manual condition, or high complexity in the 15-minute/manual condition. These results suggest that the FBA automation enhancement to CTOP maintains system performance while improving human performance. Therefore, the FBA could be introduced as a way to mitigate operator workload while planning a CTOP.

NextGen↗

NASA NTRS2017

Non-Linear Analysis of the NASA Super Pressure Balloons: Whole Flight Simulations

Tensys have a long-established background in the shape generation and load analysis of architectural stressed membrane structures. Founded upon their inTENS finite element analysis suite, these activities have broadened to encompass 'lighter than air' structures such as aerostats, airships, hybrid air vehicles and stratospheric balloons. Tensys have acted as consultants to the NASA Super Pressure Balloon (SPB) Program since 2004. Previous papers have focussed upon the application of inTENS to the overall structural and stability analysis of pumpkin type balloons as used in the SPB Program. Particular emphasis has been placed upon the ability to study both stress and stability at all stages of a flight. As the program has developed, increasing modelling fidelity has been introduced as design refinement has moved emphasis from the overall shape to the performance of individual details. Examples include the introduction of a contact capability into inTENS to better represent the separate cap layer of film. Localised investigations have considered the consequence of local geometric anomalies introduced during fabrication and the effects of debonding of the PBO tendon within its sleeve. Analysis to date has used a material model for the polyethylene shell film developed by Dr Rand of Winzen Engineering, supported by a program of fine resolution material tests at the Balloon Research and Development Laboratory (BRDL) at the GSFC Wallops Island Facility. Based upon work first reported by Schapery, this model has been incorporated into so-called 'snapshot' analyses within inTENS. For a given elapsed time point and temperature distribution, non-linear material properties are iteratively updated for the current stress state in each individual element until changes in those material properties are insignificant. This process has to be iterative as the film stresses and material properties are interdependent. Attempts to derive a time-stepping incremental viscoelastic capability using the Schapery Rand model encountered problems when dealing with the varying temperatures associated with balloon deployment, pressurisation and diurnal behaviour. An alternative approach has been developed by Pellegrino et al at Caltech, again with support from the NASA Balloon Program Office. This is a large strain non-linear viscoelastic model that includes film out-of-plane mechanical and thermal effects. Working from a unified base, this model can be utilised for time-stepping analyses in both a modulus or compliance mode, or a combination of both. This new capability is intended to enhance the current NASA balloon design process. This paper presents the implementation of the Caltech model into a specialist finite element suite, inTENS and its application to whole flight simulations.

Wakefield, David↗

NASA NTRS2016

Real-Time Early Warning Techniques

Contemporary approaches to aerospace vehicle system monitoring rely heavily on thresholds that represent a compromise between providing warning early enough to avert a mishap while simultaneously minimizing false alarms. While this reliance on thresholds has been in place for decades and has permeated both cockpits and control rooms, we often find it insufficient when retrospectively analyzing data from an accident. To modernize and enhance flight test safety, we introduce new methods of monitoring for anomalous patterns of interaction rather than for thresholds exceedance. For systems with a well characterized baseline we show how the Inductive Monitoring System (IMS), utilized by NASA in the aftermath of the Columbia accident, might be implemented in real-time to provide earlier warning than currently employed techniques. For systems without such a baseline, we introduce new developments in statistical methods relating to critical slowing down, first applied in medicine and physics, which show promise for adaptation to flight test. Finally, the familiar resource constrained environment leads to a reliance on increased instrumentation that is challenging the limits of the current one-sensor, one-indicator threshold paradigm. Existing methods thus fail to accurately reflect the true complexity of a vehicle rich with interdependent interacting systems. We highlight these concepts in a brief summary of the 2001 Air Transat Flight 236 deadstick landing in the Azores. We then suggest control room and cockpit modifications to better display the information gleaned using these novel monitoring methods.

Javorsek, Dan↗

NASA NTRS2016

Enhancing Flight Test Safety with Real-Time Early Warning Techniques

Contemporary approaches to aerospace vehicle system monitoring rely heavily on thresholds that represent a compromise between providing warning early enough to avert a mishap while simultaneously minimizing false alarms. While this reliance on thresholds has been in place for decades and has permeated both cockpits and control rooms, we often find it insufficient when retrospectively analyzing data from an accident. To modernize and enhance flight test safety, we introduce new methods of monitoring for anomalous patterns of interaction rather than for thresholds exceedance. For systems with a well characterized baseline we show how the Inductive Monitoring System (IMS), utilized by NASA in the aftermath of the Columbia accident, might be implemented in real-time to provide earlier warning than currently employed techniques. For systems without such a baseline, we introduce new developments in statistical methods relating to critical slowing down, first applied in medicine and physics, which show promise for adaptation to flight test. Finally, the familiar resource constrained environment leads to a reliance on increased instrumentation that is challenging the limits of the current "one-sensor, one-indicator" threshold paradigm. Existing methods thus fail to accurately reflect the true complexity of a vehicle rich with interdependent interacting systems. We highlight these concepts in a brief summary of the 2001 Air Transat Flight 236 deadstick landing in the Azores. We then suggest control room and cockpit modifications to better display the information gleaned using these novel monitoring methods.

Javorsek, Daniel, II↗

NASA NTRS2017

Navigating the Deployment and Downlink Tradespace for Earth Imaging Constellations

Distributed Spacecraft Missions (DSMs) are gaining momentum in their application to Earth Observation (EO) missions owing to their unique ability to increase observation sampling in spatial, spectral, angular and temporal dimensions simultaneously. DSM design includes a much larger number of variables than its monolithic counterpart, therefore, Model-Based Systems Engineering (MBSE) has been often used for preliminary mission concept designs, to understand the trade-offs and interdependencies among the variables. MBSE models are complex because the various objectives a DSM is expected to achieve are almost always conflicting, non-linear and rarely analytical. NASA Goddard Space Flight Center (GSFC) is developing a pre-Phase A tool called Tradespace Analysis Tool for Constellations (TAT-C) to initiate constellation mission design. The tool will allow users to explore the tradespace between various performance, cost and risk metrics (as a function of their science mission) and select Pareto optimal architectures that meet their requirements. This paper will describe the different types of constellations that TAT-C’s Tradespace Search Iterator is capable of enumerating (homogeneous Walker, heterogeneous Walker, precessing type, ad-hoc) and their impact on key performance metrics such as revisit statistics, time to global access and coverage. We will also discuss the ability to simulate phased deployment of the given constellations, as a function of launch availabilities and/or vehicle capability, and show the impact on performance. All performance metrics are calculated by the Data Reduction and Metric Computation module within TAT-C, which issues specific requests and processes results from the Orbit and Coverage module. Our TSI is also capable of generating tradespaces for downlinking imaging data from the constellation, based on permutations of available ground station networks - known (default) or customized (by the user). We will show the impact of changing ground station options for any given constellation, on data latency and required communication bandwidth, which in turn determines the responsiveness of the space system.

Nag, Sreeja↗

NASA NTRS2018

Evaluation of Multiple Flow Constrained Area Capacity Setting Methods for Collaborative Trajectory Options Program

The purpose of this study was to compare flow constrained area (FCA) capacity setting methods for Collaborative Trajectory Options Program (CTOP) as they pertain to the Integrated Demand Management (IDM) concept. IDM uses flow balancing to manage air traffic across multiple FCAs with a common downstream constraint, as well as constraints at the respective FCA locations. FCA capacity rates can be set manually, but generating capacities for multiple, interdependent FCAs could potentially over-burden a user. A new enhancement to CTOP called the FCA Balance Algorithm (FBA) was developed at NASA Ames Research Center to improve the process of allocating capacity across multiple flow constrained segments in the airspace. The FBA evaluates the predicted demand and capacity across multiple FCAs and dynamically generates capacity settings for the FCAs that best meet capacity limits for all identified constraints. In a human-in-the-loop simulation study, both manual and automated capacity setting methods were evaluated in terms of their overall feasibility using measures of system performance, human performance, and qualitative feedback. Subject matter experts were asked to use three different methods to allocate capacity to three FCAs, either (1) by manually setting capacity for every 60-minute time window, (2) by manually setting capacity for every 15-minute time window, or (3) by using the FBA capability to automatically generate capacity settings. Results showed no significant differences in terms of overall system performance, indicated by similar ground delay and airport throughput numbers between methods. However, differences in individual strategies afforded by the manual methods allowed some participants to achieve system-wide delay that was much lower than the average. The FBA was the fastest method of capacity setting, and it received the lowest subjective rating scores on physical task load, mental task load, task difficulty and task complexity out of the three methods. Finally, participants explained through qualitative feedback that there were many benefits to using the FBA, such as ease of use, accuracy, and low risk of human input error. Participants did not experience the same limitations with the FBA that they did with the manual methods, such as reduced accuracy in the 60-minute manual condition, or high complexity in the 15-minute/manual condition. These results suggest that the FBA automation enhancement to CTOP maintains system performance while improving human performance. Therefore, the FBA could be introduced as a way to mitigate operator workload while planning a CTOP.

NextGen↗

NASA NTRS2016

Creating Systems Engineering Products with Executable Models in a Model-Based Engineering Environment

Applying systems engineering across the life-cycle results in a number of products built from interdependent sources of information using different kinds of system level analysis. This paper focuses on leveraging the Executable System Engineering Method (ESEM) which automates requirements verification (e.g. power and mass budget margins and duration analysis of operational modes) using executable SysML models. The particular value proposition is to integrate requirements, and executable behavior and performance models for certain types of system level analysis. The models are created with modeling patterns that involve structural, behavioral and parametric diagrams, and are managed by an open source Model Based Engineering Environment (named OpenMBEE). This paper demonstrates how the ESEM is applied in conjunction with OpenMBEE to create key engineering products (e.g. operational concept document) for the Alignment and Phasing System (APS) within the Thirty Meter Telescope (TMT) project, which is under development by the TMT International Observatory (TIO).

MBSE↗

NASA NTRS2018

Understanding Mercury's Exosphere: Models Derived from MESSENGER Observations: Chapter - 15

The observations of Mercury's exosphere described in Chapter 14 have led to many modeling efforts. Early models were based upon a few simple assumptions and primarily explored the dynamics of sodium atoms pushed anti-sunward by radiation pressure [Ip, 1986; Smyth and Marconi, 1995]. More recently, these early models have been superseded by simulations with an increasing number of interdependent source processes [Leblanc and Johnson, 2003; Mura et al., 2009; Leblanc and Johnson, 2010; Burger et al., 2010, 2012, 2014]. We briefly summarize the source and loss processes before describing the published exosphere models, first for the three species observed almost continuously during the MESSENGER mission by the Ultraviolet and Visible Spectrometer (UVVS) channel of the Mercury Atmospheric and Surface Composition2Spectrometer (MASCS), (Na, Mg and Ca), and then more briefly for other species that have been observed or for which new upper limits have been derived.15.1 Overview of Source and Loss Processes15.1.1 Source Processes15.1.1.1 Thermal DesorptionThermal desorption (or thermal evaporation) is the release of adsorbed atoms from a surface via heating. Thermal desorption is related to the binding energy of the atom on the surface and the vibrational frequency of the bound atom, such that the rate of thermal desorption is given by, (15.1)where TD,

Thermal↗

NASA NTRS2017

Measuring the Effectiveness of Human Autonomy Teaming

Human-Automation Teaming (HAT), is now recognized as a promising solution to the problems of humans managing increasingly complex work systems. A human-automation team can be defined as the interdependent coupling between one or more human operators and one or more autonomous systems requiring collaboration and coordination to accomplish system and task goals (e.g., Langan-Fox et al., 2009). In this conception, automated agents are considered team members that can operate at various levels of automation, be focused on one or more human-information-processing stages, and the interactions with human operators may be adaptable, adjustable or mixed initiative. We investigated some metrics for assessing HAT effectiveness in a demonstration of a HAT tool used by ground station operators in a Reduced Crew Operations project that was conducted at NASA Ames Human Automation Teaming Laboratory. In this paper, we focus on operator metrics of HAT effectiveness, specifically workload and operator behaviors.

measurements↗

NASA NTRS2019

2040 Vision Study: an Enlargement of Model Based Engineering

Over the last few decades, advances in high-performance computing, new materials characterization methods, and, more recently, an emphasis on integrated computational materials engineering (ICME) and additive manufacturing have been a catalyst for multiscale modeling and simulation-based design of materials and structures in the aerospace industry. As a result, NASA's Transformational Tools and Technology (TTT) Project sponsored a study (performed by a team led by Pratt & Whitney) to define the potential 25-year future state required for integrated multiscale modeling of materials and systems (e.g., load-bearing structures) to accelerate the pace and reduce the expense of innovation in future aerospace and aeronautical systems. This talk will briefly review the findings of this 2040 Vision study (e.g., the 2040 vision state; the required interdependent core technical work areas, Key Element (KE); associated critical gaps and actions to close those gaps; and major recommendations). The study, NASA CR 2018- 219771, envisions the development of a cyber-physical-social ecosystem comprised of experimentally verified and validated computational models, tools, and techniques, along with the associated digital tapestry, that marries two non-mutually exclusive paradigms _ "design of the materials" (material scientist viewpoint) and "design with the materials" (structural analyst viewpoint) _ into a concurrent transformational paradigm that impacts the entire supply chain to enable cost-effective, rapid, and revolutionary design of fit-for-purpose materials, components, and systems. Although the vision focused on aeronautics and space applications, it is believed that other engineering communities (e.g., automotive, biomedical, etc.) can benefit as well from the proposed framework with only minor modifications. Finally, it is TTT's hope and desire that this vision provides the strategic guidance to both public and private research and development decision makers to make the proposed 2040 vision state a reality and thereby provide a significant advancement in the United States global competitiveness.

Arnold, Steven M.↗

NASA NTRS2016

Heat Exchanger Performance Impacts on Optimum Cost Conditions in Thermoelectric Energy Recovery Designs

Cost is just as important as power density or efficiency for the adoption of waste heat recovery thermoelectric generators (TEG). Prior work [1] has shown that the system design that minimizes cost (e.g., the $/W value) can be close to the designs that maximize the system’s efficiency or power density, however, it is important to understand the relationship between those designs to optimize TEG performance-cost compromises. Expanding on recent work [1, 2, 3] the impact of heat exchanger conditions on the optimum TEG fill factors and cost scaling of a waste heat recovery thermoelectric generator with a detailed treatment of the hot side exhaust heat exchanger has been investigated further. The effect of the heat lost to the environment and updated relationships between the hot-side and cold-side conductances [4] that maximize power output are considered. The optimum fill factor to minimize TEG energy recovery system costs is strongly dependent on the heat leakage fraction, σ, the mass flow rate of the exhaust, the hot-side heat exchanger effectiveness, heat exchanger UAh, and heat flux. These relationships are explored and characterized for typical exhaust gas-flow conditions to show the inherent design complexities. The heat exchanger costs often dominate the TEG cost equation and it is critical to fully understand the tradeoff between heat exchanger performance, optimum TEG fill factors, and cost to establish potentially optimum design points within the cost-performance design space. This work will explore the design tradeoffs and relationships within the cost-efficiency-power density design space for a typical thermoelectric energy recovery system application. The interplay between optimum TEG fill factors and heat exchanger design can impact system footprint, volume, and mass in weight-sensitive applications. Less-effective, low-cost heat exchangers may outperform higher cost alternatives from a market adoption perspective. This shift of emphasis acknowledging the interdependence of optimum TEG fill factors and heat exchanger performance has significant implications on thermoelectric waste heat recovery systems designs and their operation. In addition, preferred TEG design regimes exist that accommodate reasonable compromises in TE performance and cost. This effort highlights how the optimum fill factor–heat exchanger performance relations couple to these optimum TEG performance-cost domains based on TEG-system-level analyses and provides a focus for future system research and development efforts.

Energy recovery↗

NASA NTRS2020

Effects of Hydrogen Bonding and Molecular Chain Flexibility of Substituted n-Alkyldimethylsilanes On Impact Ice Adhesion Shear Strength

The effects of hydrogen bonding and molecular flexibility upon ice adhesion shear strength were investigated using aluminum substrates coated with substituted n-alkyldimethylalkoxysilanes. The location of the chemical group substitution was on the opposing end of the linear n-alkyl chain with respect to silicon. Three hydrogen-bonding characteristics were evaluated: 1) non-hydrogen bonding, 2) donor/acceptor, and 3) acceptor. Varying the length of the n-alkyl chain provided an assessment of molecular chain flexibility. Coated and uncoated aluminum surfaces were characterized by receding water contact angle and surface roughness. Ice adhesion shear strength was determined in the Adverse Environment Rotor Test Stand facility from -16 to -8°C that simulated aircraft in-flight icing conditions within the FAR Part 25/29 Appendix C icing envelope. Surface roughness of the coatings was similar allowing for comparison of the test results. An adhesion reduction factor, based on the ice adhesion shear strength data with respect to uncoated aluminum obtained at the same temperature, was calculated to compare the data. The results revealed complex interactions with impacting supercooled water droplets that were interdependent upon ice accretion temperature, surface energy characteristics of water and ice, hydrogen bonding characteristic of the substituent, and length of the n-alkyl chain. To aid in explaining the results, 1) changes in the surface energy component (i.e., non-polar and polar) values that water undergoes during its’ phase change from liquid to solid that arise from the freezing of impacting supercooled water droplets on the surface depended upon the temperature during accretion were taken into account and 2) the physical properties (i.e., water solubility and melting point) of small compounds analogous to the substituted n-alkyldimethylalkoxysilanes used in this study were compared.

Smith, Joseph G.↗

NASA NTRS2019

Design Considerations and Development Status for Atmospheric Powered Descent of High-Mass Payloads at Mars

Retropropulsion, initiated at supersonic conditions, is the only presently viable approach to the descent and landing of human-scale payloads on Mars, providing both deceleration and control authority for precision landing. Atmospheric powered descent challenges traditional processes and dependencies in system design. Changes to a design by one discipline have a greater potential to affect overall vehicle performance than in the case of powered descent with no atmosphere. The interdependence is due to the sensitivity of propulsive-aerodynamic interference effects to small changes in configuration or operational environment. The fidelity of Mars exploration architectures has increased in recent years, and the conditions and designs with the lowest aerosciences uncertainties are no longer as flight-relevant. At this time, there is no human-rated vehicle relying on retropropulsion for descent and landing in an atmospheric environment. Continued development of powered descent for atmospheric environments is enabled by progressive balanced investments in ground testing, predictive computational capabilities, and flight testing and demonstration.

EDL↗

NASA NTRSDate not supplied

A Multiphysics Study to Improve Specific Energy of Primary Batteries for Low Temperature Operation for Deep Space Missions

Several lander missions on the outer planets such as Europa, Enceladus, and Titan require electrical power to operate scientific and communication equipment. The traditional power generation methods, such as a photovoltaic array, are not feasible as their efficiency drops significantly at these vast distances. The novel radioisotope power systems are not practical today based on current lander designs and the effectiveness of these systems. To perform in situ science on distant planets, a high specific energy battery (>700 Wh/kg) needs to operate for about 480 hours under cold temperatures (-40C or 0C) [1]. While a primary battery such as Li-CFx can provide high specific energy at room temperature, its specific capacity decreases significantly at low temperatures. One of the causes for this drop is low ion and electrical conductivity, and slower reaction kinetics. Slower transport and facile kinetics lead to an increase in the battery’s resistance and higher voltage drops during the cell operation, thus reducing specific capacity. Both the transport and kinetics show a strong dependence on temperature. Thus, a small temperature rise can lead to an increase in the reaction rate and ion conductivity; since the temperature, cell resistance, and specific capacity are interdependent. A conventional battery model accounts for ohmic, thermodynamic, and, electrochemical, and chemical decomposition heating. The ohmic heating can be controlled by designing a resistive microstructure and varying the ratios of the active materials [2]. The kinetics can be improved by increasing the surface area, reducing the particle size, or adding a catalyst. These parameters are often optimized to achieve high specific energy at room temperatures. A similar optimization study is not available at low temperatures and for a primary (high specific energy) battery. For this presentation, we will explore the effect of geometrical, microstructural, and material properties on optimal specific capacity at low temperatures through multiphysics simulations. The ion transport resistance depends on the porosity and the tortuosity of an electrode and the separator.

M. Mehta↗

NASA NTRSDate not supplied

Enabling Advanced Air Mobility Operations through Appropriate Trust in Human-Autonomy Teaming: Foundational Research Approaches and Applications

Emerging Advanced Air Mobility(AAM)operations will be enabled by increasingly autonomous systems, requiring technologies to take on more responsibilities and fundamentally altering traditional human-automation interaction paradigms. The growing reliance on higher levels of automation will necessitate research to identify capabilities and principles that facilitate humans and machines working and thinking better together, i.e., human-autonomy teaming (HAT). Trust is an inherent requirement in effective teams because when members work interdependently, those agents (human, automation) must be willing to accept a level of risk to rely upon each other to reach goals and contribute to team tasks. This work provides an initial approach to enabling AAM operations through appropriate trust within HAT. The main contributions of this approach resides in connecting the construct of trust to mental models. Using the outlined mental model approach, we propose novel HAT strategies, such as Adaptive Trust Calibration, and preview planned research activities derived from this approach. Additionally, we propose several practical applications that can currently be employed by AAM development communities.

Eric T Chancey↗

NASA NTRS2021

Machine Learning to Predict Joint Performance in Epoxy Composites Based on Process Parameters

Polymer matrix composites are gaining popularity in the aerospace industry due to their high specific strength, fatigue properties, and processability. However, based on current FAA certification guidelines, manufacturers utilizing current state-of-the-art composites made with adhesive bonds commonly install redundant fasteners to guarantee the strength of these adhesively bonded composite parts. The number of fasteners in a single-aisle commercial transport aircraft is typically on the order of 105, which reduces manufacturing rate, increases cost tremendously, and reduces the advantage of the specific strength composites provide. Due to this, the Adhesive Free Bonding of Composites (AERoBOND) project at NASA Langley Research Center has developed a novel assembly process to manufacture complex composite parts without the use of adhesives and fasteners. However, optimization of the process is currently challenging due to the complex and interdependent process parameters. To assist with the optimization, four machine learning algorithms utilizing gradient boosting decision trees were created to provide predictions for the mechanical and characterization properties of the composite parts. Approximately 200 random states from each algorithm were tested, and the models from each state were isolated and analyzed based on their accuracy, a validation process, and their feature importance. This analysis concluded that the models created from the machine learning algorithms could accelerate a parametric study for the AERoBOND process by rapidly optimizing process parameters to achieve desired performance characteristics.

Brennen Michael Middleton↗

NASA NTRSDate not supplied

Keplerian Analysis for Versatile Evaluation of Arbitrary Trajectories

Designing interplanetary missions is an iterative process, with a tight coupling between mission analysis and vehicle design. The ability to simultaneously process both analyses early on can provide significant benefits in both the concept formulation and in overall mission feasibility. To implement such a simultaneous analysis process, there is a need to model interplanetary trajectories rapidly while maintaining an acceptable level of fidelity. Many simple patched conics tools make too many assumptions to satisfactorily address all relevant constraints and objectives, while full-fidelity trajectory design tools often require more time and expertise than is available in early conceptual studies. Hence, some form of middle-ground modeling tool would provide value. This paper introduces the development of such a tool using Python. This tool, Keplerian Analysis for Versatile Evaluation of Arbitrary Trajectories (KAVEAT), implements a patched conics method that enforces continuity of state (time, mass, position, velocity) through all transitions between various spheres of influence on the trajectory. The trajectory is constructed in an object-oriented manner, dynamically assembling unique maneuvers such as departure burns, heliocentric transfers (ballistic or with electric propulsion), and planetary flybys (powered or unpowered). Each maneuver has its own specific set of basic inputs needed to define it. The tool then takes these defined maneuvers, strings them together creating all the interdependencies required, and solves for the ideal trajectory using a gradient-based optimizer.

Katherine T McBrayer↗