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NASA NTRSDate not supplied

MLtool++ package for machine learning and its applications to materials data

We are developing Mltool++ package of software programs for machine learning (ML). Given the MLtool Python code, we create a faster C++ code with the potential for parallelization. We have extracted materials data from the literature. One dataset contains melting temperatures of stoichiometric 1:1 metallic compounds XZ, composed by elements X={Al, Ti, V, Cr, Zr, Nb, Mo, Hf, Ta, W} and Z={Co, Ni, Cu, Rh, Pd, Ag, Ir, Pt, Au}, and another contains solid-solid symmetry-breaking phase transition temperatures. We studied dependences of temperatures on composition, found several correlations, and parametrized them by analytical functions. Mltool++ package is generic and applicable to any tabulated numeric data.

Pierce M. Pettit↗
NASA NTRS2016

Station Program Note Pull Automation

Upon commencement of my internship, I was in charge of maintaining the CoFR (Certificate of Flight Readiness) Tool. The tool acquires data from existing Excel workbooks on NASA's and Boeing's databases to create a new spreadsheet listing out all the potential safety concerns for upcoming flights and software transitions. Since the application was written in Visual Basic, I had to learn a new programming language and prepare to handle any malfunctions within the program. Shortly afterwards, I was given the assignment to automate the Station Program Note (SPN) Pull process. I developed an application, in Python, that generated a GUI (Graphical User Interface) that will be used by the International Space Station Safety & Mission Assurance team here at Johnson Space Center. The application will allow its users to download online files with the click of a button, import SPN's based on three different pulls, instantly manipulate and filter spreadsheets, and compare the three sources to determine which active SPN's (Station Program Notes) must be reviewed for any upcoming flights, missions, and/or software transitions. Initially, to perform the NASA SPN pull (one of three), I had created the program to allow the user to login to a secure webpage that stores data, input specific parameters, and retrieve the desired SPN's based on their inputs. However, to avoid any conflicts with sustainment, I altered it so that the user may login and download the NASA file independently. After the user has downloaded the file with the click of a button, I defined the program to check for any outdated or pre-existing files, for successful downloads, to acquire the spreadsheet, convert it from a text file to a comma separated file and finally into an Excel spreadsheet to be filtered and later scrutinized for specific SPN numbers. Once this file has been automatically manipulated to provide only the SPN numbers that are desired, they are stored in a global variable, shown on the GUI, and transferred over to a new Excel worksheet for comparison. I managed to get my application to acquire the CSWG (Computer Safety Working Group) and the SPNWG (Space Station Working Group) SPN's with just two mouse clicks for each pull, as opposed to several from the original process. When all three pulls are performed, an Excel sheet containing all three different results will be generated for the user to compare and determine which SPN's will be presented or reviewed the following month. The experience from this internship has been spectacular. As a high school senior who will begin attending college in the fall, this internship has been both educationally and occupationally beneficial. The internship has allowed me the opportunities to learn new programming languages, effectively network with NASA personnel from a variety of departments at JSC, and allowed me to learn new professional skills and etiquette. My internship at NASA's Johnson Space Center has further motivated me to pursue a Master's degree in Software Engineering and strive for a prosperous career with NASA as a civil servant.

Delgado, Ivan↗
NASA NTRS2016

Simulation of Mission Phases

This position with the Simulation and Graphics Branch (ER7) at Johnson Space Center (JSC) provided an introduction to vehicle hardware, mission planning, and simulation design. ER7 supports engineering analysis and flight crew training by providing high-fidelity, real-time graphical simulations in the Systems Engineering Simulator (SES) lab. The primary project assigned by NASA mentor and SES lab manager, Meghan Daley, was to develop a graphical simulation of the rendezvous, proximity operations, and docking (RPOD) phases of flight. The simulation is to include a generic crew/cargo transportation vehicle and a target object in low-Earth orbit (LEO). Various capsule, winged, and lifting body vehicles as well as historical RPOD methods were evaluated during the project analysis phase. JSC core mission to support the International Space Station (ISS), Commercial Crew Program (CCP), and Human Space Flight (HSF) influenced the project specifications. The simulation is characterized as a 30 meter +V Bar and/or -R Bar approach to the target object's docking station. The ISS was selected as the target object and the international Low Impact Docking System (iLIDS) was selected as the docking mechanism. The location of the target object's docking station corresponds with the RPOD methods identified. The simulation design focuses on Guidance, Navigation, and Control (GNC) system architecture models with station keeping and telemetry data processing capabilities. The optical and inertial sensors, reaction control system thrusters, and the docking mechanism selected were based on CCP vehicle manufacturer's current and proposed technologies. A significant amount of independent study and tutorial completion was required for this project. Multiple primary source materials were accessed using the NASA Technical Report Server (NTRS) and reference textbooks were borrowed from the JSC Main Library and International Space Station Library. The Trick Simulation Environment and User Training Materials version 2013.0 release was used to complete the Trick tutorial. Multiple network privilege and repository permission requests were required in order to access previous simulation models. The project was also an introduction to computer programming and the Linux operating system. Basic C++ and Python syntax was used during the completion of the Trick tutorial. Trick's engineering analysis and Monte Carlo simulation capabilities were observed and basic space mission planning procedures were applied in the conceptual design phase. Multiple professional development opportunities were completed in addition to project duties during this internship through the System for Administration, Training, and Education Resources for NASA (SATERN). Topics include: JSC Risk Management Workshop, CCP Risk Management, Basic Radiation Safety Training, X-Ray Radiation Safety, Basic Laser Safety, JSC Export Control, ISS RISE Ambassador, Basic SharePoint 2013, Space Nutrition and Biochemistry, and JSC Personal Protective Equipment. Additionally, this internship afforded the opportunity for formal project presentation and public speaking practice. This was my first experience at a NASA center. After completing this internship I have a much clearer understanding of certain aspects of the agency's processes and procedures, as well as a deeper appreciation from spaceflight simulation design and testing. I will continue to improve my technical skills so that I may have another opportunity to return to NASA and Johnson Space Center.

Carlstrom, Nicholas Mercury↗
NASA NTRS2020

Julia Programming Language Benchmark Using a Flight Simulation

Julia’s goal to provide scripting language ease-of-coding with compiled language speed is explored. The runtime speed of the relatively new Julia programming language is assessed against other commonly used languages including Python, Java, and C++. An industry-standard missile and rocket simulation, coded in multiple languages, was used as a test bench for runtime speed. All language versions of the simulation, including Julia, were coded to a highly-developed object-oriented simulation architecture tailored specifically for time-domain flight simulation. A “speed-of-coding” second-dimension is plotted against runtime for each language to portray a space that characterizes Julia’s scripting language efficiencies in the context of the other languages. With caveats, Julia runtime speed was found to be in the class of compiled or semi-compiled languages. However, some factors that affect runtime speed at the cost of ease-of-coding are shown. Julia’s built-in functionality for multi-core processing is briefly examined as a means for obtaining even faster runtime speed. The major contribution of this research to the extensive language benchmarking body-of-work is comparing Julia to other mainstream languages using a complex flight simulation as opposed to benchmarking with single algorithms.

Sells, Ray↗
NASA NTRSDate not supplied

Parallel Hybrid Turboprop Performance Modeling and Optimization

NASA’s Electrified Powertrain Flight Demonstration (EPFD) project conducts ground and flight tests of integrated Megawatt (MW) class hybrid-electric powertrain systems on regional turboprop aircraft demonstrators. To meet the increased demand for assessment of potential capabilities and benefits from these novel vehicle configurations, NASA is developing tooling and models to estimate the performance of hybridized regional turboprops. This paper covers the development of a parametrically driven performance model for a De Havilland Canada Dash 8-400 (Q400) regional turboprop integrated with a novel parallel hybrid architecture using the Gascon framework. Gascon is a modern reimplementation of the General Aviation Synthesis Program (GASP) built using the Condor mathematical modeling framework in Python. Within Gascon, a parametric representation of the parallel hybrid architecture was synthesized, which features the electric motor coupled to the power turbine. This capability allows for in-the-loop optimization of the parametric parallel hybrid architecture to characterize the mission capabilities and fuel savings of the design and determine optimal power scheduling strategies for efficient electric power management for a given mission. The study shows that a fuel savings of up to 20% can be achieved, but that increased fuel savings comes at the expense of payload capacity.

Gascon↗
NASA NTRSDate not supplied

Parallel Hybrid Turboprop Performance Modeling and Optimization

NASA’s Electrified Powertrain Flight Demonstration (EPFD) project conducts ground and flight tests of integrated Megawatt (MW) class hybrid-electric powertrain systems on regional turboprop aircraft demonstrators. To meet the increased demand for assessment of potential capabilities and benefits from these novel vehicle configurations, NASA is developing tooling and models to estimate the performance of hybridized regional turboprops. This paper covers the development of a parametrically driven performance model for a De Havilland Canada Dash 8-400 (Q400) regional turboprop integrated with a novel parallel hybrid architecture using the Gascon framework. Gascon is a modern reimplementation of the General Aviation Synthesis Program (GASP) built using the Condor mathematical modeling framework in Python. Within Gascon, a parametric representation of the parallel hybrid architecture was synthesized, which features the electric motor coupled to the power turbine. This capability allows for in-the-loop optimization of the parametric parallel hybrid architecture to characterize the mission capabilities and fuel savings of the design and determine optimal power scheduling strategies for efficient electric power management for a given mission. The study shows that a fuel savings of up to 20% can be achieved, but that increased fuel savings comes at the expense of payload capacity.

Gascon↗
NASA NTRS2007

State-Chart Autocoder

A computer program translates Unified Modeling Language (UML) representations of state charts into source code in the C, C++, and Python computing languages. ( State charts signifies graphical descriptions of states and state transitions of a spacecraft or other complex system.) The UML representations constituting the input to this program are generated by using a UML-compliant graphical design program to draw the state charts. The generated source code is consistent with the "quantum programming" approach, which is so named because it involves discrete states and state transitions that have features in common with states and state transitions in quantum mechanics. Quantum programming enables efficient implementation of state charts, suitable for real-time embedded flight software. In addition to source code, the autocoder program generates a graphical-user-interface (GUI) program that, in turn, generates a display of state transitions in response to events triggered by the user. The GUI program is wrapped around, and can be used to exercise the state-chart behavior of, the generated source code. Once the expected state-chart behavior is confirmed, the generated source code can be augmented with a software interface to the rest of the software with which the source code is required to interact.

Clark, Kenneth↗
NASA NTRSDate not supplied

Analysis and Optimization of Baseline Single Aisle Aircraft for Future Electrified Powertrain Flight Demonstrator Comparisons

The purpose of this study is to provide baseline single-aisle vehicles for future comparisons with NASA’s Electrified Powertrain Flight Demonstration (EPFD) turbofan-powered Vision Systems. State-of-the-art single-aisle transports with varying design capacities of 100 to 150 passengers are modeled using NASA Ames Research Center’s General Aviation Synthesis Program (GASP) as well as GASPy. GASPy is a modernized Python-based version of GASP built on the OpenMDAO framework to allow for future, efficient gradient-based optimization and coupled airframe-propulsion design. In order to meet projected NASA Aeronautics goals for 2035, advanced aircraft technologies must be incorporated into these vehicle systems. Methodology to parametrically infuse baseline aircraft models with advanced technologies simulating improvements in aerodynamics, structures, and propulsion systems is detailed, along with the results of technology sensitivity studies. Comparison of the baseline and advanced configurations will allow for future analysis of the benefits of future hybrid and fully electric aircraft concepts in the EPFD project, where fuel consumption and emissions will be modeled and assessed. This study has been conducted under the EPFD project to establish benchmark turbofan models and demonstrate System Analysis capabilities in multi-disciplinary aircraft design, analysis, and optimization for advanced turbofan concepts.

Carl J. Recine↗
NASA NTRS2021

Aircraft Flaps Modeling in OpenMDAO

The goal of this project was to develop a model for a single subsystem in the aerodynamics discipline, in this case the flaps of an aircraft. Flaps are high-lift devices used by planes to allow for quicker takeoffs, and slower landings. A computer-based aircraft model of the flaps of an aircraft was developed using the Python based open-source framework OpenMDAO. OpenMDAO is used to develop multi-disciplinary aircraft models using gradient-based optimization; design optimization (MDO) is concerned with solving design problems involving numerical models of complex engineering systems. There were 4 components in the model; each has input values, output variables, and equations to calculate said outputs. The variables and equations are sourced from NASA Fortran code from the 1970s, in a project called the General Aviation Synthesis Program (GASP). These variables and equations which create the model are being converted to Python for ease of use. The flaps model developed will be integrated into a larger model of a conventional aircraft’s flight phases. All subsystems of the model will first be built using the parameters of a Boeing 737 MAX-8, to validate its functionality and accuracy. Then, the aircraft model will be used for hybrid-electric research; running optimizations to improve efficiency, minimize fuel burn, and advance hybrid-electric technology in the aerospace field.

Computer-based aircraft modeling↗
NASA NTRS2016

MONTE: the Next Generation of Mission Design and Navigation Software

The Mission Analysis, Operations and Navigation Toolkit Environment (MONTE) is an astrodynamic toolkit produced by the Mission Design and Navigation Software Group at the Jet Propulsion Laboratory. It provides a single integrated environment for all phases of deep space and Earth orbiting missions. Capabilities include: trajectory optimization and analysis, operational orbit determination, flight path control, and 2D/3D visualization. MONTE is presented to the user as an importable Python language module. This allows a simple but powerful user interface via CLUI or script. In addition, the Python interface allows MONTE to be used seamlessly with other canonical scientific programming tools such as SciPy, NumPy, and Matplotlib. MONTE is the prime operational orbit determination software for all JPL navigated missions.

Optimization↗
NASA NTRSDate not supplied

Design of an Application Programming Interface for the Program to Optimize Simulated Trajectories II

A significant effort to upgrade the Program to Optimize Simulated Trajectories II (POST2), a heritage flight mechanics tool developed at NASA Langley Research Center, is ongoing to support current and future NASA missions. To meet mission requirements, it may be necessary for multiple specialized computational tools to interact to properly assess a system. An application programming interface for POST2 was developed to allow easier access for users and to enable communication between external applications. A demonstration of the POST2application programming interface is presented by utilizing common engineering platforms such as MATLAB and Python.

R Anthony Williams↗
NASA NTRS2012

The Virtual Solar Observatory: What Are We Up To Now?

In the nearly ten years of a functional Virtual Solar Observatory (VSO), http://virtualsolar.org/ we have made it possible to query and access sixty-seven distinct solar data products and several event lists from nine spacecraft and fifteen observatories or observing networks. We have used existing VSO technology, and developed new software, for a distributed network of sites caching and serving SDO HMI and/ or AlA data. We have also developed an application programming interface (API) that has enabled VSO search and data access capabilities in IDL, Python, and Java. We also have quite a bit of work yet to do, including completion of the implementation of access to SDO EVE data, and access to some nineteen other data sets from space- and ground-based observatories. In addition, we have been developing a new graphic user interface that will enable the saving of user interface and search preferences. We solicit advice from the community input prioritizing our task list, and adding to it

Gurman, J. B.↗
NASA NTRS2022

MLtool Python Code

Machine Learning (ML) is a subfield of Artificial Intelligence that gives computers the ability to learn from past data without being explicitly programmed. The predictive capabilities of ML models have already been used to facilitate several scientific breakthroughs. However, the practical application of ML is often limited due to the gaps in technical knowledge of its users. The common issue faced by many scientific researchers is the inability to choose the appropriate ML pipelines that are needed to treat real-world data, which is often sparse and noisy. To solve this problem, we have developed an automated Machine Learning tool (MLtool) that includes a set of ML algorithms and approaches to aid scientific researchers. The current version of MLtool is implemented as an object-oriented Python code that is easily extensible. It includes 44 different regression algorithms used to model data. MLtool helps users select the best model for their data, based on the scoring metrics used. Besides regression algorithms, MLtool also includes a suite of pre- and post-processing techniques such as missing value imputation, categorical variable encoding, input feature normalization, uncertainty quantification, exploratory data analysis (EDA), etc. MLtool was tested on several publicly available multi-dimensional data sets and was found capable of making accurate predictions.

Machine Learning↗
NASA NTRS2016

MONTE Python for Deep Space Navigation

The Mission Analysis, Operations, and Navigation Toolkit Environment (MONTE) is the Jet Propulsion Laboratory’s (JPL) signature astrodynamic computing platform. It was built to support JPL’s deep space exploration program, and has been used to fly robotic spacecraft to Mars, Jupiter, Saturn, Ceres, and many solar system small bodies. At its core, MONTE consists of low-level astrodynamic libraries that are written in C++ and presented to the end user as an importable Python language module. These libraries form the basis on which Python-language applications are built for specific astrodynamic applications, such as trajectory design and optimization, orbit determination, flight path control, and more. The first half of this paper gives context to the MONTE project by outlining its history, the field of deep space navigation and where MONTE fits into the current Python landscape. The second half gives an overview of the main MONTE libraries and provides a narrative example of how it can be used for astrodynamic analysis.

aerospace↗
NASA NTRS2019

An Automated Behavioral Analysis of Drosophila Melanogaster

Behavioral characteristics of D.melanogaster are strongly influenced by intrinsic and extrinsic factors, allowing scientists to assess how changes in physiology or environment manifest into behavior. Conversely, assessing changes in behavior of specimens provides valuable information about how the physiology of that organism responds to external changes. In this project, we developed a computer program to automate behavioral analyses of larvae and adult D. melanogaster aboard the International Space Station using on-board video recordings. Utilizing freely available libraries for Python, we set parameters to compute the number of animals, amount of locomotion as distance or movement, and the change in the perimeter of the larvae's outer shape to quantify behaviors such as curling or peristaltic full body wall contractions. Results show that our program is an efficient tool for analysis of larvae and adult locomotive behavior, thus providing scientists with a low-cost, efficient, and reliable method of quantifying behavioral data.

Zavaleta, Jhony↗
NASA NTRSDate not supplied

Trajectory Simulation Using Multi Model Monte Carlo with Python (MXMCPy)

EDL (Entry, Descent and Landing) is the process from a vehicle approaching a surface to landing on it, such as a Mars rover approaching the planet before landing. POST2 (Program to Optimize Simulated Trajectories 2) is Langley’s primary EDL simulation tool and is used NASA-wide for simulations. POST2 can generate highly accurate results by running a precise, but time consuming, Monte Carlo (MC) simulation hundreds or thousands of times. Though POST2 can produce highly accurate results, it can take unrealistic time spans to generate these results, which has created a need to speed up the simulations. The new NASA software MXMCPy offers various ways to speed up the simulations while getting just as precise results. Instead of running high-precision POST2 simulations many times for traditional MC, MXMCPy can run fewer high-precision POST2 simulations and many less precise POST2 simulations and merge the results. MXMCPy contains 30+ different methods which will each suggest different allocations between model precision levels, which result in results of varying precision based on the POST2 simulation. I created Python and Bash code to automate the 5 steps of MXMCPy’s application to POST2. I also tested the precision of traditional Monte Carlo simulations to MXMCPy aided simulations and found that MXMCPy can achieve substantially more precise solutions at the same computer runtime. I learned Test Driven Development (TDD), a software programming workflow which involves writing computer-automated tests before writing the code which is being tested. These tests are ran every time the code is changed and they can find glitches in the code much quicker than a human can. This programming workflow saved me a lot of time because the automated tests could tell me exactly where the code had stopped working. I plan on using this software development method for future academic and professional software projects. I have greatly enjoyed my work at NASA, so I have been applying to NASA internships and Pathways positions. In addition, I plan on applying what I have learned about Test Driven Development to my computer science courses next semester

James Warner↗
NASA NTRSDate not supplied

Introducing Tropical Geometric Approaches to Delay Tolerant Networking Optimization

Delay Tolerant Networking (DTN) is the standard approach to the networking of space systems with the goal of supporting the Solar System Internet (SSI). Current space networks have a small scale and often depend on rigorously scheduled (pre-determined) contact opportunities; this manual approach inhibits scalability. The goal of this paper is to recast these scheduling problems in order to apply the optimization machinery of tropical geometry. Contact opportunities in space are dependent on such factors as orbital mechanics and asset availability, which induce time-varying connectivity; indeed, end-to-end connectivity might never occur. Routing optimization within this structure is classically difficult and typically utilizes Dijkstra's algorithm as applied to contact graphs. Alternatively, we follow the successes of tropical geometry in train schedule optimization, job assignments, and even traditional networking, by extending this approach to this more general (i.e. disconnected) problem space. These successes imply tropical geometry provides a useful framework in the context of DTNs, starting with applications to queuing theory and long-haul links. Recently, tropical geometry has been applied to parametric path optimization on graphs with variable edge weights. In this work, we extend these advances to account for the problem of routing in a space network, and find that tropical geometry is well-suited to the challenges offered by this new setting, including contact schedules featuring probabilities. Our approach leverages the combinatorial nature of the problem to give feasible shortest path trees in the presence of variable channel conditions and latency, evolving topologies, and uncertainty inherent in space routing. We discuss our tropical approach to DTN for two Python implementations, a Verilog Tropical ALU implementation, tropical frameworks for other parametric graph problems, and solution stability. Lastly, a program for future work is included to illuminate the path ahead.

Delay Tolerant Networking↗
NASA NTRSDate not supplied

POST Explorer: A Design Space Exploration Tool for POST2

Recent improvements for the Program to Optimize Simulated Trajectories II (POST2) have included the development of an application programming interface (API). This API allows POST2 simulation inputs to be directly manipulated from other applications (such as MATLAB or Python), and the outputs from POST2 are streamed directly to the external application that enables visualization, data manipulation, etc. Through this framework, a new tool called POST Explorer is being developed that provides a user the capability to modify the simulation inputs and interrogate the outputs within the same application, with raw data inspection and visualization embedded. This tool can be leveraged for multiple types of analyses, such as parametric sweeps and sensitivity studies, and will be available with a future release of the POST2 software.

Robert Anthony Williams↗