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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 NTRS2015

Prediction of Tensile Strength of Friction Stir Weld Joints with Adaptive Neuro-Fuzzy Inference System (ANFIS) and Neural Network

Friction-stir-welding (FSW) is a solid-state joining process where joint properties are dependent on welding process parameters. In the current study three critical process parameters including spindle speed (𝑁), plunge force (𝐹𝑧), and welding speed (𝑉) are considered key factors in the determination of ultimate tensile strength (UTS) of welded aluminum alloy joints. A total of 73 weld schedules were welded and tensile properties were subsequently obtained experimentally. It is observed that all three process parameters have direct influence on UTS of the welded joints. Utilizing experimental data, an optimized adaptive neuro-fuzzy inference system (ANFIS) model has been developed to predict UTS of FSW joints. A total of 1200 models were developed by varying the number of membership functions (MFs), type of MFs, and combination of four input variables (𝑁,𝑉,𝐹𝑧,𝐸𝐹𝐼) utilizing a MATLAB platform. Note EFI denotes an empirical force index derived from the three process parameters. For comparison, optimized artificial neural network (ANN) models were also developed to predict UTS from FSW process parameters. By comparing ANFIS and ANN predicted results, it was found that optimized ANFIS models provide better results than ANN. This newly developed best ANFIS model could be utilized for prediction of UTS of FSW joints.

Mohammad W Dewan↗
NASA NTRS2015

Update on HCDstruct - A Tool for Hybrid Wing Body Conceptual Design and Structural Optimization

HCDstruct is a Matlab® based software tool to rapidly build a finite element model for structural optimization of hybrid wing body (HWB) aircraft at the conceptual design level. The tool uses outputs from a Flight Optimization System (FLOPS) performance analysis together with a conceptual outer mold line of the vehicle, e.g. created by Vehicle Sketch Pad (VSP), to generate a set of MSC Nastran® bulk data files. These files can readily be used to perform a structural optimization and weight estimation using Nastran’s® Solution 200 multidisciplinary optimization solver. Initially developed at NASA Langley Research Center to perform increased fidelity conceptual level HWB centerbody structural analyses, HCDstruct has grown into a complete HWB structural sizing and weight estimation tool, including a fully flexible aeroelastic loads analysis. Recent upgrades to the tool include the expansion to a full wing tip-to-wing tip model for asymmetric analyses like engine out conditions and dynamic overswings, as well as a fully actuated trailing edge, featuring up to 15 independently actuated control surfaces and twin tails. Several example applications of the HCDstruct tool are presented.

Gern, Frank H.↗
NASA NTRS2015

An Upgrade of the Imaging for Hypersonic Experimental Aeroheating Testing (IHEAT) Software

The Imaging for Hypersonic Experimental Aeroheating Testing (IHEAT) code is used at NASA Langley Research Center to analyze global aeroheating data on wind tunnel models tested in the Langley Aerothermodynamics Laboratory. One-dimensional, semi-infinite heating data derived from IHEAT are used to design thermal protection systems to mitigate the risks due to the aeroheating loads on hypersonic vehicles, such as re-entry vehicles during descent and landing procedures. This code was originally written in the PV-WAVE programming language to analyze phosphor thermography data from the two-color, relativeintensity system developed at Langley. To increase the efficiency, functionality, and reliability of IHEAT, the code was migrated to MATLAB syntax and compiled as a stand-alone executable file labeled version 4.0. New features of IHEAT 4.0 include the options to batch process all of the data from a wind tunnel run, to map the two-dimensional heating distribution to a three-dimensional computer-aided design model of the vehicle to be viewed in Tecplot, and to extract data from a segmented line that follows an interesting feature in the data. Results from IHEAT 4.0 were compared on a pixel level to the output images from the legacy code to validate the program. The differences between the two codes were on the order of 10-5 to 10-7. IHEAT 4.0 replaces the PV-WAVE version as the production code for aeroheating experiments conducted in the hypersonic facilities at NASA Langley.

Mason, Michelle L.↗
NASA NTRS2014

Development of Cooperative Communication Techniques for a Network of Small Satellites and Cubesats in Deep Space

This paper aims to address this problem by proposing cooperative communication approaches in which multiple CubeSats communicate cooperatively together to improve the link performance with respect to the case of a single satellite transmitting. Three approaches are proposed: a beam-forming approach, a coding approach, and a network approach. The approaches are applied to the specific case of a proposed constellation of CubeSats at the Lunar Lagrangian point L1 which aims to perform radio astronomy at very low frequencies (30 KHz -3 MHz). The paper describes the development of the approaches, the simulation and a graphical user interface developed in Matlab which allows to perform trade-offs across multiple constellation's configurations.

Babuscia, Alessandra↗
NASA NTRS2016

Features of the Upgraded Imaging for Hypersonic Experimental Aeroheating Testing (IHEAT) Software

The Imaging for Hypersonic Experimental Aeroheating Testing (IHEAT) software is used at the NASA Langley Research Center to analyze global aeroheating data on wind tunnel models tested in the Langley Aerothermodynamics Laboratory. One-dimensional, semi-infinite heating data derived from IHEAT are used in the design of thermal protection systems for hypersonic vehicles that are exposed to severe aeroheating loads, such as reentry vehicles during descent and landing procedures. This software program originally was written in the PV-WAVE(Registered Trademark) programming language to analyze phosphor thermography data from the two-color, relative-intensity system developed at Langley. To increase the efficiency, functionality, and reliability of IHEAT, the program was migrated to MATLAB(Registered Trademark) syntax and compiled as a stand-alone executable file labeled version 4.0. New features of IHEAT 4.0 include the options to perform diagnostic checks of the accuracy of the acquired data during a wind tunnel test, to extract data along a specified multi-segment line following a feature such as a leading edge or a streamline, and to batch process all of the temporal frame data from a wind tunnel run. Results from IHEAT 4.0 were compared on a pixel level to the output images from the legacy software to validate the program. The absolute differences between the heat transfer data output from the two programs were on the order of 10(exp -5) to 10(exp -7). IHEAT 4.0 replaces the PV-WAVE(Registered Trademark) version as the production software for aeroheating experiments conducted in the hypersonic facilities at NASA Langley.

Mason, Michelle L.↗
NASA NTRS2016

Simulation Development and Analysis of Crew Vehicle Ascent Abort

NASA's Commercial Crew Program is an integral step in its journey to Mars as it would expedite development of space technologies and open up partnership with U.S. commercial companies. NASA reviews and independent assessment of Commercial Crew Program is fundamental to its success, and being able to model a commercial crew vehicle in a simulation rather than conduct a live test would be a safer, faster, and less expensive way to assess and certify the capabilities of the vehicle. To this end, my project was to determine the feasibility of using a simulation tool named SOMBAT version 2.0 to model a multiple parachute system for Commercial Crew Program simulation. The main tasks assigned to me were to debug and test the main parachute system model, (capable of simulating one to four main parachute bodies), and to utilize a graphical program to animate the simulation results. To begin tackling the first task, I learned how to use SOMBAT by familiarizing myself with its mechanics and by understanding the methods used to tweak its various parameters and outputs. I then used this new knowledge to set up, run, and analyze many different situations within SOMBAT in order to explore the limitations of the parachute model. Some examples of parameters that I varied include the initial velocity and orientation of the falling capsule, the number of main parachutes, and the location where the parachutes were attached to the capsule. Each parameter changed would give a different output, and in some cases, would expose a bug or limitation in the model. A major bug that I discovered was the inability of the model to handle any number of parachutes other than three. I spent quite some time trying to debug the code logically, but was unable to figure it out until my mentor taught me that digital simulation limitations can occur when some approximations are mistakenly assumed for certain in a physical system. This led me to the realization that unlike in all of the programming classes I have taken thus far that focus on pure logic, simulation code focuses on mimicking the physical world with some approximation and can have inaccuracies or numerical instabilities. Learning from my mistake, I adopted new methods to analyze these different simulations. One method the student used was to numerically plot various physical parameters using MATLAB to confirm the mechanical behavior of the system in addition to comparing the data to the output from a separate simulation tool called FAST. By having full control over what was being outputted from the simulation, I could choose which parameters to change and to plot as well as how to plot them, allowing for an in depth analysis of the data. Another method of analysis was to convert the output data into a graphical animation. Unlike the numerical plots, where all of the physical components were displayed separately, this graphical display allows for a combined look at the simulation output that makes it much easier for one to see the physical behavior of the model. The process for converting SOMBAT output for EDGE graphical display had to be developed. With some guidance from other EDGE users, I developed a process and created a script that would easily allow one to display simulations graphically. Another limitation with the SOMBAT model was the inability for the capsule to have the main parachutes instantly deployed with a large angle between the air speed vector and the chutes drag vector. To explore this problem, I had to learn about different coordinate frames used in Guidance, Navigation & Control (J2000, ECEF, ENU, etc.) to describe the motion of a vehicle and about Euler angles (e.g. Roll, Pitch, Yaw) to describe the orientation of the vehicle. With a thorough explanation from my mentor about the description of each coordinate frame, as well as how to use a directional cosine matrix to transform one frame to another, I investigated the problem by simulating different capsule orientations. In the end, I was able to show that this limitation could be avoided if the capsule is initially oriented antiparallel to its velocity vector.

Wong, Chi S.↗
NASA NTRS2016

Space Launch System Vibration Analysis Support

The ultimate goal for my efforts during this internship was to help prepare for the Space Launch System (SLS) integrated modal test (IMT) with Rodney Rocha. In 2018, the Structural Engineering Loads and Dynamics Team will have 10 days to perform the IMT on the SLS Integrated Launch Vehicle. After that 10 day period, we will have about two months to analyze the test data and determine whether the integrated vehicle modes/frequencies are adequate for launching the vehicle. Because of the time constraints, NASA must have newly developed post-test analysis methods proven well and with technical confidence before testing. NASA civil servants along with help from rotational interns are working with novel techniques developed and applied external to Johnson Space Center (JSC) to uncover issues in applying this technique to much larger scales than ever before. We intend to use modal decoupling methods to separate the entangled vibrations coming from the SLS and its support structure during the IMT. This new approach is still under development. The primary goal of my internship was to learn the basics of structural dynamics and physical vibrations. I was able to accomplish this by working on two experimental test set ups, the Simple Beam and TAURUS-T, and by doing some light analytical and post-processing work. Within the Simple Beam project, my role involves changing the data acquisition system, reconfiguration of the test set up, transducer calibration, data collection, data file recovery, and post-processing analysis. Within the TAURUS-T project, my duties included cataloging and removing the 30+ triaxial accelerometers, coordinating the removal of the structure from the current rolling cart to a sturdy billet for further testing, preparing the accelerometers for remounting, accurately calibrating, mounting, and mapping of all accelerometer channels, and some testing. Hammer and shaker tests will be performed to easily visualize mode shapes at low frequencies. Short analytical projects using MATLAB were also assigned to aid in research efforts. These included integration of acceleration data for comparison to measured displacement data. Laplace and Fourier transforms were also investigated to determine viability as a method of modal decoupling. In addition to these projects, I was also able contribute work that would benefit future interns and the division as a whole. I gave a short presentation and answered questions to aid in the recruitment of subsequent interns and co-ops for the division. I also assisted in revisions and additions to Intern/Co-Op Handbook to provide incoming employees with background information on the organization they are about to work for. I further developed tutorial on Pulse software, which was used for data acquisition for both experiments and will be helpful to interns and engineers that may be unfamiliar to the software. I gained a diverse range of experience throughout my internship. I was introduced to advanced dynamics and analytical techniques. This was through new experience with both hands on experimentation and analytical post processing methods. I was exposed to the benefits of interdepartmental collaboration and developed stronger skills in time management by coordinating two different tests at once. This internship provided an excellent opportunity to see how engineering theories applied to real life scenarios, and an introduction to how NASA/JSC solves technical problems.

Johnson, Katie↗
NASA NTRS2016

Investigating the Simulink Auto-Coding Process

Model based program design is the most clear and direct way to develop algorithms and programs for interfacing with hardware. While coding "by hand" results in a more tailored product, the ever-growing size and complexity of modern-day applications can cause the project work load to quickly become unreasonable for one programmer. This has generally been addressed by splitting the product into separate modules to allow multiple developers to work in parallel on the same project, however this introduces new potentials for errors in the process. The fluidity, reliability and robustness of the code relies on the abilities of the programmers to communicate their methods to one another; furthermore, multiple programmers invites multiple potentially differing coding styles into the same product, which can cause a loss of readability or even module incompatibility. Fortunately, Mathworks has implemented an auto-coding feature that allows programmers to design their algorithms through the use of models and diagrams in the graphical programming environment Simulink, allowing the designer to visually determine what the hardware is to do. From here, the auto-coding feature handles converting the project into another programming language. This type of approach allows the designer to clearly see how the software will be directing the hardware without the need to try and interpret large amounts of code. In addition, it speeds up the programming process, minimizing the amount of man-hours spent on a single project, thus reducing the chance of human error as well as project turnover time. One such project that has benefited from the auto-coding procedure is Ramses, a portion of the GNC flight software on-board Orion that has been implemented primarily in Simulink. Currently, however, auto-coding Ramses into C++ requires 5 hours of code generation time. This causes issues if the tool ever needs to be debugged, as this code generation will need to occur with each edit to any part of the program; additionally, this is lost time that could be spent testing and analyzing the code. This is one of the more prominent issues with the auto-coding process, and while much information is available with regard to optimizing Simulink designs to produce efficient and reliable C++ code, not much research has been made public on how to reduce the code generation time. It is of interest to develop some insight as to what causes code generation times to be so significant, and determine if there are architecture guidelines or a desirable auto-coding configuration set to assist in streamlining this step of the design process for particular applications. To address the issue at hand, the Simulink coder was studied at a foundational level. For each different component type made available by the software, the features, auto-code generation time, and the format of the generated code were analyzed and documented. Tools were developed and documented to expedite these studies, particularly in the area of automating sequential builds to ensure accurate data was obtained. Next, the Ramses model was examined in an attempt to determine the composition and the types of technologies used in the model. This enabled the development of a model that uses similar technologies, but takes a fraction of the time to auto-code to reduce the turnaround time for experimentation. Lastly, the model was used to run a wide array of experiments and collect data to obtain knowledge about where to search for bottlenecks in the Ramses model. The resulting contributions of the overall effort consist of an experimental model for further investigation into the subject, as well as several automation tools to assist in analyzing the model, and a reference document offering insight to the auto-coding process, including documentation of the tools used in the model analysis, data illustrating some potential problem areas in the auto-coding process, and recommendations on areas or practices in the current Ramses model that should be further investigated. Several skills were required to be built up over the course of the internship project. First and foremost, my Simulink skills have improved drastically, as much of my experience had been modeling electronic circuits as opposed to software models. Furthermore, I am now comfortable working with the Simulink Auto-coder, a tool I had never used until this summer; this tool also tested my critical thinking and C++ knowledge as I had to interpret the C++ code it was generating and attempt to understand how the Simulink model affected the generated code. I had come into the internship with a solid understanding of Matlab code, but had done very little in using it to automate tasks, particularly Simulink tasks; along the same lines, I had rarely used shell script to automate and interface with programs, which I gained a fair amount of experience with this summer, including how to use regular expression. Lastly, soft-skills are an area everyone can continuously improve on; having never worked with NASA engineers, which to me seem to be a completely different breed than what I am used to (commercial electronic engineers), I learned to utilize the wealth of knowledge present at JSC. I wish I had come into the internship knowing exactly how helpful everyone in my branch would be, as I would have picked up on this sooner. I hope that having gained such a strong foundation in Simulink over this summer will open the opportunity to return to work on this project, or potentially other opportunities within the division. The idea of leaving a project I devoted ten weeks to is a hard one to cope with, so having the chance to pick up where I left off sounds appealing; alternatively, I am interested to see if there are any opening in the future that would allow me to work on a project that is more in-line with my research in estimation algorithms. Regardless, this summer has been a milestone in my professional career, and I hope this has started a long-term relationship between JSC and myself. I really enjoy the thought of building on my experience here over future summers while I work to complete my PhD at Missouri University of Science and Technology.

Gualdoni, Matthew J.↗
NASA NTRS2016

Computational Approaches to Simulation and Optimization of Global Aircraft Trajectories

This study examines three possible approaches to improving the speed in generating wind-optimal routes for air traffic at the national or global level. They are: (a) using the resources of a supercomputer, (b) running the computations on multiple commercially available computers and (c) implementing those same algorithms into NASAs Future ATM Concepts Evaluation Tool (FACET) and compares those to a standard implementation run on a single CPU. Wind-optimal aircraft trajectories are computed using global air traffic schedules. The run time and wait time on the supercomputer for trajectory optimization using various numbers of CPUs ranging from 80 to 10,240 units are compared with the total computational time for running the same computation on a single desktop computer and on multiple commercially available computers for potential computational enhancement through parallel processing on the computer clusters. This study also re-implements the trajectory optimization algorithm for further reduction of computational time through algorithm modifications and integrates that with FACET to facilitate the use of the new features which calculate time-optimal routes between worldwide airport pairs in a wind field for use with existing FACET applications. The implementations of trajectory optimization algorithms use MATLAB, Python, and Java programming languages. The performance evaluations are done by comparing their computational efficiencies and based on the potential application of optimized trajectories. The paper shows that in the absence of special privileges on a supercomputer, a cluster of commercially available computers provides a feasible approach for national and global air traffic system studies.

global air traffic optimization↗
NASA NTRS2016

Modeling in the State Flow Environment to Support Launch Vehicle Verification Testing for Mission and Fault Management Algorithms in the NASA Space Launch System

Analysis methods and testing processes are essential activities in the engineering development and verification of the National Aeronautics and Space Administration's (NASA) new Space Launch System (SLS). Central to mission success is reliable verification of the Mission and Fault Management (M&FM) algorithms for the SLS launch vehicle (LV) flight software. This is particularly difficult because M&FM algorithms integrate and operate LV subsystems, which consist of diverse forms of hardware and software themselves, with equally diverse integration from the engineering disciplines of LV subsystems. M&FM operation of SLS requires a changing mix of LV automation. During pre-launch the LV is primarily operated by the Kennedy Space Center (KSC) Ground Systems Development and Operations (GSDO) organization with some LV automation of time-critical functions, and much more autonomous LV operations during ascent that have crucial interactions with the Orion crew capsule, its astronauts, and with mission controllers at the Johnson Space Center. M&FM algorithms must perform all nominal mission commanding via the flight computer to control LV states from pre-launch through disposal and also address failure conditions by initiating autonomous or commanded aborts (crew capsule escape from the failing LV), redundancy management of failing subsystems and components, and safing actions to reduce or prevent threats to ground systems and crew. To address the criticality of the verification testing of these algorithms, the NASA M&FM team has utilized the State Flow environment6 (SFE) with its existing Vehicle Management End-to-End Testbed (VMET) platform which also hosts vendor-supplied physics-based LV subsystem models. The human-derived M&FM algorithms are designed and vetted in Integrated Development Teams composed of design and development disciplines such as Systems Engineering, Flight Software (FSW), Safety and Mission Assurance (S&MA) and major subsystems and vehicle elements such as Main Propulsion Systems (MPS), boosters, avionics, Guidance, Navigation, and Control (GN&C), Thrust Vector Control (TVC), liquid engines, and the astronaut crew office. Since the algorithms are realized using model-based engineering (MBE) methods from a hybrid of the Unified Modeling Language (UML) and Systems Modeling Language (SysML), SFE methods are a natural fit to provide an in depth analysis of the interactive behavior of these algorithms with the SLS LV subsystem models. For this, the M&FM algorithms and the SLS LV subsystem models are modeled using constructs provided by Matlab which also enables modeling of the accompanying interfaces providing greater flexibility for integrated testing and analysis, which helps forecast expected behavior in forward VMET integrated testing activities. In VMET, the M&FM algorithms are prototyped and implemented using the same C++ programming language and similar state machine architectural concepts used by the FSW group. Due to the interactive complexity of the algorithms, VMET testing thus far has verified all the individual M&FM subsystem algorithms with select subsystem vendor models but is steadily progressing to assessing the interactive behavior of these algorithms with LV subsystems, as represented by subsystem models. The novel SFE applications has proven to be useful for quick look analysis into early integrated system behavior and assessment of the M&FM algorithms with the modeled LV subsystems. This early MBE analysis generates vital insight into the integrated system behaviors, algorithm sensitivities, design issues, and has aided in the debugging of the M&FM algorithms well before full testing can begin in more expensive, higher fidelity but more arduous environments such as VMET, FSW testing, and the Systems Integration Lab7 (SIL). SFE has exhibited both expected and unexpected behaviors in nominal and off nominal test cases prior to full VMET testing. In many findings, these behavioral characteristics were used to correct the M&FM algorithms, enable better test coverage, and develop more effective test cases for each of the LV subsystems. This has improved the fidelity of testing and planning for the next generation of M&FM algorithms as the SLS program evolves from non-crewed to crewed flight, impacting subsystem configurations and the M&FM algorithms that control them. SFE analysis has improved robustness and reliability of the M&FM algorithms by revealing implementation errors and documentation inconsistencies. It is also improving planning efficiency for future VMET testing of the M&FM algorithms hosted in the LV flight computers, further reducing risk for the SLS launch infrastructure, the SLS LV, and most importantly the crew.

Trevino, Luis↗
NASA NTRS2015

PISCES High Contrast Integral Field Spectrograph Simulations and Data Reduction Pipeline

The PISCES (Prototype Imaging Spectrograph for Coronagraphic Exoplanet Studies) is a lenslet array based integral field spectrograph (IFS) designed to advance the technology readiness of the WFIRST (Wide Field Infrared Survey Telescope)-AFTA (Astrophysics Focused Telescope Assets) high contrast Coronagraph Instrument. We present the end to end optical simulator and plans for the data reduction pipeline (DRP). The optical simulator was created with a combination of the IDL (Interactive Data Language)-based PROPER (optical propagation) library and Zemax (a MatLab script), while the data reduction pipeline is a modified version of the Gemini Planet Imager's (GPI) IDL pipeline. The simulations of the propagation of light through the instrument are based on Fourier transform algorithms. The DRP enables transformation of the PISCES IFS data to calibrated spectral data cubes.

The PISCES (Prototype Imaging Spectrograph for Cor↗
NASA NTRS2016

Full-Field Reconstruction of Structural Deformations and Loads from Measured Strain Data on a Wing Using the Inverse Finite Element Method

A study was undertaken to investigate the measurement of wing deformation and internal loads using measured strain data. Future aerospace vehicle research depends on the ability to accurately measure the deformation and internal loads during ground testing and in flight. The approach uses the inverse Finite Element Method (iFEM). The iFEM is a robust, computationally efficient method that is well suited for real-time measurement of real-time structural deformation and loads. The method has been validated in previous work, but has yet to be applied to a large-scale test article. This work is in preparation for an upcoming loads test of a half-span test wing in the Flight Loads Laboratory at the National Aeronautics and Space Administration Armstrong Flight Research Center (Edwards, California). The method has been implemented into an efficient MATLAB® (The MathWorks, Inc., Natick, Massachusetts) code for testing different sensor configurations. This report discusses formulation and implementation along with the preliminary results from a representative aerospace structure. The end goal is to investigate the modeling and sensor placement approach so that the best practices can be applied to future aerospace projects.

Finite element↗
NASA NTRS2017

Metallic Junction Thermoelectric Device Simulations

Thermoelectric junctions made of semiconductors have existed in radioisotope thermoelectric generators (RTG) for deep space missions, but are currently being adapted for terrestrial energy harvesting. Unfortunately, these devices are inefficient, operating at only 7% efficiency. This low efficiency has driven efforts to make high-figure-of-merit thermoelectric devices, which require a high electrical conductivity but a low thermal conductivity, a combination that is difficult to achieve. Lowered thermal conductivity has increased efficiency, but at the cost of power output. An alternative setup is to use metallic junctions rather than semiconductors as thermoelectric devices. Metals have orders of magnitude more electrons and electronic conductivities higher than semiconductors, but thermal conductivity is higher as well. To evaluate the viability of metallic junction thermoelectrics, a two dimensional heat transfer MATLAB simulation was constructed to calculate efficiency and power output. High Seebeck coefficient alloys, Chromel (90%Ni-10%Cr) and Constantan (55%Cu-45%Ni), produced efficiencies of around 20-30%. Parameters such as the number of layers of junctions, lateral junction density, and junction sizes for both series- and parallel-connected junctions were explored.

Duzik, Adam J.↗
NASA NTRS2017

A Comparison of Three Random Number Generators for Aircraft Dynamic Modeling Applications

Three random number generators, which produce Gaussian white noise sequences, were compared to assess their suitability in aircraft dynamic modeling applications. The first generator considered was the MATLAB (registered) implementation of the Mersenne-Twister algorithm. The second generator was a website called Random.org, which processes atmospheric noise measured using radios to create the random numbers. The third generator was based on synthesis of the Fourier series, where the random number sequences are constructed from prescribed amplitude and phase spectra. A total of 200 sequences, each having 601 random numbers, for each generator were collected and analyzed in terms of the mean, variance, normality, autocorrelation, and power spectral density. These sequences were then applied to two problems in aircraft dynamic modeling, namely estimating stability and control derivatives from simulated onboard sensor data, and simulating flight in atmospheric turbulence. In general, each random number generator had good performance and is well-suited for aircraft dynamic modeling applications. Specific strengths and weaknesses of each generator are discussed. For Monte Carlo simulation, the Fourier synthesis method is recommended because it most accurately and consistently approximated Gaussian white noise and can be implemented with reasonable computational effort.

Grauer, Jared A.↗
NASA NTRS2017

MPCV Exercise Operational Volume Analysis

In order to minimize the loss of bone and muscle mass during spaceflight, the Multi-purpose Crew Vehicle (MPCV) will include an exercise device and enough free space within the cabin for astronauts to use the device effectively. The NASA Digital Astronaut Project (DAP) has been tasked with using computational modeling to aid in determining whether or not the available operational volume is sufficient for in-flight exercise.Motion capture data was acquired using a 12-camera Smart DX system (BTS Bioengineering, Brooklyn, NY), while exercisers performed 9 resistive exercises without volume restrictions in a 1g environment. Data were collected from two male subjects, one being in the 99th percentile of height and the other in the 50th percentile of height, using between 25 and 60 motion capture markers. Motion capture data was also recorded as a third subject, also near the 50th percentile in height, performed aerobic rowing during a parabolic flight. A motion capture system and algorithms developed previously and presented at last years HRP-IWS were utilized to collect and process the data from the parabolic flight [1]. These motions were applied to a scaled version of a biomechanical model within the biomechanical modeling software OpenSim [2], and the volume sweeps of the motions were visually assessed against an imported CAD model of the operational volume. Further numerical analysis was performed using Matlab (Mathworks, Natick, MA) and the OpenSim API. This analysis determined the location of every marker in space over the duration of the exercise motion, and the distance of each marker to the nearest surface of the volume. Containment of the exercise motions within the operational volume was determined on a per-exercise and per-subject basis. The orientation of the exerciser and the angle of the footplate were two important factors upon which containment was dependent. Regions where the exercise motion exceeds the bounds of the operational volume have been identified by determining which markers from the motion capture exceed the operational volume and by how much. A credibility assessment of this analysis was performed in accordance with NASA-STD-7009 prior to delivery to the MPCV program.

mathematical models↗
NASA NTRS2016

The Framework for 0-D Atmospheric Modeling (F0AM) v3.1

The Framework for 0-D Atmospheric Modeling(F0AM) is a flexible and user-friendly MATLAB-based platform for simulation of atmospheric chemistry systems. The F0AM interface incorporates front-end configuration of observational constraints and model setups, making it readily adaptable to simulation of photochemical chambers, Lagrangian plumes, and steady-state or time-evolving solar cycles. Six different chemical mechanisms and three options for calculation of photolysis frequencies are currently available. Example simulations are presented to illustrate model capabilities and, more generally, highlight some of the advantages and challenges of 0-D box modeling.

chemistry↗
NASA NTRS2017

Data are from Mars, Tools are from Venus

Although during the data production phase, the data producers will usually ensure the products to be easily used by the specific power users the products serve. However, most data products are also posted for general public to use. It is not straightforward for data producers to anticipate what tools that these general end-data users are likely to use. In this talk, we will try to help fill in the gap by going over various tools related to Earth Science and how they work with the existing NASA HDF (Hierarchical Data Format) data products and the reasons why some products cannot be visualized or analyzed by existing tools. One goal is for to give insights for data producers on how to make their data product more interoperable. On the other hand, we also provide some hints for end users on how to make tools work with existing HDF data products. (tool category list: check the comments) HDF-EOS tools: HDFView HDF-EOS Plugin, HEG, h4tonccf, hdf-eos2 dumper, NCL, MATLAB, IDL, etc.net; CDF-Java tools: Panoply, IDV, toosUI, NcML, etc.net; CDF-C tools: ArcGIS Desktop, GrADS, NCL, NCO, etc.; GDAL tools: ArcGIS Desktop, QGIS, Google Earth, etc.; CSV tools: ArcGIS Online, MS Excel, Tableau, etc.

hdf↗
NASA NTRS2017

Investigation of Vapor Cooling Enhancements for Applications on Large Cryogenic Systems

The need to demonstrate and evaluate the effectiveness of heat interception methods for use on a relevant cryogenic propulsion stage at a system level has been identified. Evolvable Cryogenics (eCryo) Structural Heat Intercept, Insulation and Vibration Evaluation Rig (SHIIVER) will be designed with vehicle specific geometries (SLS Exploration Upper Stage (EUS) as guidance) and will be subjected to simulated space environments. One method of reducing structure-born heat leak being investigated utilizes vapor-based heat interception. Vapor-based heat interception could potentially reduce heat leak into liquid hydrogen propulsion tanks, increasing potential mission length or payload capability. Due to the high number of unknowns associated with the heat transfer mechanism and integration of vapor-based heat interception on a realistic large-scale skirt design, a sub-scale investigation was developed. The sub-project effort is known as the Small-scale Laboratory Investigation of Cooling Enhancements (SLICE). The SLICE aims to study, design, and test sub-scale multiple attachments and flow configuration concepts for vapor-based heat interception of structural skirts. SLICE will focus on understanding the efficiency of the heat transfer mechanism to the boil-off hydrogen vapor by varying the fluid network designs and configurations. Various analyses were completed in MATLAB, Excel VBA, and COMSOL Multiphysics to understand the optimum flow pattern for heat transfer and fluid dynamics. Results from these analyses were used to design and fabricate test article subsections of a large forward skirt with vapor cooling applied. The SLICE testing is currently being performed to collect thermal mechanical performance data on multiple skirt heat removal designs while varying inlet vapor conditions necessary to intercept a specified amount of heat for a given system. Initial results suggest that applying vapor-cooling provides a 50 heat reduction in conductive heat transmission along the skirt to the tank. The information obtained by SLICE will be used by the SHIIVER engineering team to design and implement vapor-based heat removal technology into the SHIIVER forward skirt hardware design.

small scale testing↗