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This paper summarizes the procedures of generating a polyhedral mesh derived from hanging-node elements as well as presents sample results from its application to the numerical solution of a single element lean direct injection (LDI) combustor using an open-source version of the National Combustion Code (NCC).
An analysis framework is presented that makes available multiscale analysis of composite structures using the open-source FEA solver package CalculiX CrunchiX (CCX). At the center of this framework is the coupling of the Finite Element Analysis - Micromechanics Analysis Code (FEAMAC) library from NASA's Micromechanics Analysis Code with Generalized Method of Cells (MAC/GMC) to allow micromechanics analysis. The results show that the proposed coupling can be used with appropriate care for multiscale FEA simulations of composite materials. The largest error reported in this validation was in a four-point bend test specimen with an error of less than 1% difference in the maximum deflection of the beam.
NASA’s “Volatiles Investigating Polar Exploration Rover” (VIPER) will be the first robotic mission to prospect for water ice near the south pole of the Moon in late 2023 on a 100-Earth-day mission. The information that the VIPER rover provides will help improve understanding of the composition, distribution, and accessibility of Lunar polar volatiles and will help determine how the Moon’s resources can support future human space exploration. VIPER, however, represents a radical departure from the way that NASA has traditionally developed planetary robotic missions. A key consequence of these differences is that estimating the cost of VIPER’s rover software is challenging and complex.For example, VIPER is being developed using management procedures typically applied to NASA research and technology projects, rather than space flight programs. In addition, key portions of the rover’s software are being designed as ground software to run on mission control computers (rather than on-board the rover as flight software as with prior planetary missions) taking advantage of continuous, interactive data communications between the Moon and Earth and higher performance computing available on the ground. Moreover, the rover’s software is being engineered using Agile software development practices and incorporates a significant amount of open-source, rather than following traditional (spiral, waterfall, etc.) development methods and in-house code. In this paper, we present an innovative process to estimate the life cycle cost of VIPER’s rover software. We first describe how we modeled the architecture and code counts for three software elements: Rover Flight Software (RFSW), Rover Ground Software (RGSW), and Rover Simulation Software (RSIM). We then discuss key challenges and unique aspects of our approach, such as the lack of Lunar rover analogies, the need to integrate and test large open source software, and the strategies developed to account for use of non-space flight management practices and the impact of the COVID-19 pandemic. We conclude with a summary of our results, including cumulative distribution, nearest neighbors and cluster analysis, as well as heuristics used to confirm the reasonableness of the cost estimate.
DMRT-ML is a physically based numerical model designed to compute the thermal microwave emission of a given snowpack. Its main application is the simulation of brightness temperatures at frequencies in the range 1-200 GHz similar to those acquired routinely by spacebased microwave radiometers. The model is based on the Dense Media Radiative Transfer (DMRT) theory for the computation of the snow scattering and extinction coefficients and on the Discrete Ordinate Method (DISORT) to numerically solve the radiative transfer equation. The snowpack is modeled as a stack of multiple horizontal snow layers and an optional underlying interface representing the soil or the bottom ice. The model handles both dry and wet snow conditions. Such a general design allows the model to account for a wide range of snow conditions. Hitherto, the model has been used to simulate the thermal emission of the deep firn on ice sheets, shallow snowpacks overlying soil in Arctic and Alpine regions, and overlying ice on the large icesheet margins and glaciers. DMRT-ML has thus been validated in three very different conditions: Antarctica, Barnes Ice Cap (Canada) and Canadian tundra. It has been recently used in conjunction with inverse methods to retrieve snow grain size from remote sensing data. The model is written in Fortran90 and available to the snow remote sensing community as an open-source software. A convenient user interface is provided in Python.
Formal verification and simulation are powerful tools for the verification of requirements against complex systems. Requirements are developed in early stages of the software lifecycle and are typically expressed in natural language. There is a gap between such requirements and their software implementations.We present a framework that bridges this gap by supporting a tight integration and feedback loop between high-level requirements and their analysis against software artifacts. Our framework implements an analysis portal within the fret requirements elicitation tool, thus forming an end-to-end, open-source environment where requirements are written in an intuitive, structured natural language, and are verified automatically against Simulink models.
Formal veri fication and simulation are powerful tools for the veri fication of requirements against complex systems. Requirements are developed in early stages of the software lifecycle and are typically expressed in natural language. There is a gap between such requirements and their software implementations. We present a framework that bridges this gap by supporting a tight integration and feedback loop between high-level requirements and their analysis against software artifacts. Our framework implements an analysis portal within the fret requirements elicitation tool, thus forming an end-to-end, open-source environment where requirements are written in an intuitive, structured natural language, and are veri fied automatically against Simulink models.
In order to characterize exoplanets around nearby stars, upcoming and proposed space telescopes are being designed with high contrast coronagraph instruments. Coronagraphs are generally designed to suppress only a single, on-axis star, but there are numerous binary star systems within our observational range. Multi-star wavefront correction (MSWC) is a technique that uses deformable mirror(s) to simultaneously suppress light from more than one star to create a high contrast dark zone in the image. MSWC has already been well demonstrated in simulation and in air-based testbeds. In this paper, we describe our prepatory vacuum experiments in the Decadal Survey Testbed (DST) at the Jet Propulsion Laboratory. First we implemented super-Nyquist wavefront control (SNWC, a critical component of MSWC), and then we proceeded to simultaneous sub- and super-Nyquist control. With our existing calibration methods, SNWC reached a contrast of $8.6{\times}10^{-9}$ in a 10\% bandpass around a first-order diffraction spot; calibration improvements are needed to improve model agreement for higher-order spots. We have also implemented changes to our software (the open-source package FALCO) required for MSWC, and identified the necessary changes in hardware. Here we report our best SNWC lab results and our plans for implementing MSWC during a future set of experiments.
The state of the art for many Earth-to-orbit trajectory analysis toolsets used at NASA is somewhat dated in terms of the languages they are written in and their user interfaces. Many of these programs are written in languages like Fortran or C that are no longer considered modern in the technology industry, and are command-line based without any means of interpreting the data outputs. This presentation is meant to demonstrate a specific use case for a newly developed web app that visualizes the output data for one of these tools. Specifically, one key usage is to investigate and validate launches for notional multi-stage vehicle concepts to various non-standard high-inclination orbits. Some NASA requirements dictate that trajectories be designed such that no surviving debris lands closer than 200 nautical miles (nm) from foreign landmasses or 27 nm from the continental United States, therefore validating re-entry locations of spent stages is an integral part of the launch planning process. In addition, at programmatic levels it can prove insightful and clarifying for the decision-making process to enhance the technical results of numerical simulations with visualizations. The intent with this tool is to provide the mission analyst and program level management with an intuitive and clear grasp of key information on possible mission scenarios either departing from or arriving at Earth, and in the future, the moon or Mars as well. The software tool was developed with an agile development approach and utilizes the latest frameworks and technologies. The front end uses the React JavaScript library for making a state of the art frontend and a backend based on the Django Python framework for handling data using Python’s powerful and free scientific libraries. In addition, the CesiumJS open-source library is key for visualizing these end-to-end trajectories on a high-resolution Earth model. The presentation will demonstrate the current capability and tested use cases.
This report describes the process of acquiring and using 3-D numerical model weather data sets in NASA Langley's Research Flight Deck (RFD). A set of software tools implement the process and can be used for other purposes as well. Given time and location information of a weather phenomenon of interest, the user can download associated numerical weather model data. These data are created by the National Oceanic and Atmospheric Administration (NOAA) High Resolution Rapid Refresh (HRRR) model, and are then processed using a set of Mathworks' Matlab(TradeMark) scripts to create the usable 3-D weather data sets. Each data set includes radar re ectivity, water vapor, component winds, temperature, supercooled liquid water, turbulence, pressure, altitude, land elevation, relative humidity, and water phases. An open-source data processing program, wgrib2, is available from NOAA online, and is used along with Matlab scripts. These scripts are described with sucient detail to make future modi cations. These software tools have been used to generate 3-D weather data for various RFD experiments.
Development of the DeCoM comes from the requirement of analyzing the performance of a condenser. A component of a loop heat pipe (LHP), the condenser, is interfaced with the radiator in order to reject heat. DeCoM simulates the condenser, with certain input parameters. Systems Improved Numerical Differencing Analyzer (SINDA), a thermal analysis software, calculates the adjoining component temperatures, based on the DeCoM parameters and interface temperatures to the radiator. Application of DeCoM is (at the time of this reporting) restricted to small-scale analysis, without the need for in-depth LHP component integrations. To efficiently develop a model to simulate the LHP condenser, DeCoM was developed to meet this purpose with least complexity. DeCoM is a single-condenser, single-pass simulator for analyzing its behavior. The analysis is done based on the interactions between condenser fluid, the wall, and the interface between the wall and the radiator. DeCoM is based on conservation of energy, two-phase equations, and flow equations. For two-phase, the Lockhart- Martinelli correlation has been used in order to calculate the convection value between fluid and wall. Software such as SINDA (for thermal analysis analysis) and Thermal Desktop (for modeling) are required. DeCoM also includes the ability to implement a condenser into a thermal model with the capability of understanding the code process and being edited to user-specific needs. DeCoM requires no license, and is an open-source code. Advantages to DeCoM include time dependency, reliability, and the ability for the user to view the code process and edit to their needs.
OpenMDAO is an open-source MDAO framework. It is used to develop an integrated analysis and design environment for engineering challenges. This Phase II project integrated additional modules and design tools into OpenMDAO to perform discipline-specific analysis across multiple flight regimes at varying levels of fidelity. It also showcased a refined system architecture that allows the system to be less customized to a specific configuration (i.e., system and configuration separation). By delivering a capable and validated MDAO system along with a set of example applications to be used as a template for future users, this work greatly expands NASA's high-fidelity, physics-based MDAO capabilities and enables the design of revolutionary vehicles in a cost-effective manner. This proposed work complements M4 Engineering's expertise in developing modeling and simulation toolsets that solve relevant subsonic, supersonic, and hypersonic demonstration applications.
The OpenSE Cookbook is an open-sourced collection of patterns, procedures, and best practices targeted for systems engineers who seek guidance on applying model-based and executable systems engineering (MBSE) using SysML. Its content has emerged from the system level modeling effort on the European Framework Program 6 (FP6) and the Thirty Meter Telescope (TMT). The TMT MBSE approach applied the Executable Systems Engineering Method (ESEM) and the open-source Engineering Environment (OpenMBEE) to specify, analyze, and verify requirements of TMT’s Alignment and Phasing System (APS) and the Narrow Field Infrared Adaptive Optics System (NFIRAOS). In these applications, implicit dependencies are made explicit in a formal model through the use of ESEM, OpenMBEE, and SysML modeling constructs. The value proposition for applying this MBSE approach was to establish precise requirements and fine-grained traceability to system designs, and to verify key requirements beginning early in development. The integration of ESEM and the OpenMBEE tooling infrastructure (providing linked-data and web-operability) is a significant added value for the MBSE approach. The APS is responsible for the overall pre-adaptive optics wavefront quality, using starlight to measure wavefront errors and align the TMT optics. In the formally integrated and executable SysML model, simulations are performed to analyze the impact of changed requirements and verify specified constraints for various operational scenarios. The APS team used several modeling patterns to capture information such as the requirements, the operational scenarios, involved subsystems and their interaction points, the estimated or required time durations, and the mass and power consumption. Adaptive optics systems are designed to sense real-time atmospheric turbulence and correct the telescope’s optical beam to remove its effect. The system model for the adaptive optics operational modes was developed to capture sequence behaviors and operational scenarios to run Monte-Carlo simulations for verifying acquisition time, observing efficiency, and operational behavior requirements. The model is particularly useful for investigating the effect of parallelization, identifying interface issues, and re-ordering sequence acquisition tasks. A former version of the Cookbook (which is now updated to MBSE challenges, goals, and lessons learned) included modeling guidelines and conventions for all system aspects, hierarchy levels, and views, which were developed during for the Active Phasing Experiment (APE), an opto-mechatronical system technology demonstrator for the Extremely Large Telescope (ELT). The Cookbook utilizes the above mentioned system models as real-world case-studies to demonstrate and document the applications of the recipes, providing also instructional examples and addressing the available tooling support. The Cookbook is accompanied by a number of SysML models and aodel libraries which facilitate model authoring and maintenance. The Cookbook covers the different aspects of Systems Engineering such as management of Requirements, Design (behavior and structure), Interfaces, Interdisciplinary Integration, Analysis, Trade Studies, and Technical Resources. This paper presents the background, motivation, architecture, and highlights some key content of the Cookbook. For example, interface management, error budget management, requirements verification, Monte Carlo driven analysis, and timing analysis of operational scenarios. The paper discusses how the capabilities of OpenMBEE contributed significantly to the adoption of executable systems engineering.
Parallel computers built out of mass-market parts are cost-effectively performing data processing and simulation tasks. The Supercomputing (now known as "SC") series of conferences celebrated its 10th anniversary last November. While vendors have come and gone, the dominant paradigm for tackling big problems still is a shared-resource, commercial supercomputer. Growing numbers of users needing a cheaper or dedicated-access alternative are building their own supercomputers out of mass-market parts. Such machines are generally called Beowulf-class systems after the 11th century epic. This modern-day Beowulf story began in 1994 at NASA's Goddard Space Flight Center. A laboratory for the Earth and space sciences, computing managers there threw down a gauntlet to develop a $50,000 gigaFLOPS workstation for processing satellite data sets. Soon, Thomas Sterling and Don Becker were working on the Beowulf concept at the University Space Research Association (USRA)-run Center of Excellence in Space Data and Information Sciences (CESDIS). Beowulf clusters mix three primary ingredients: commodity personal computers or workstations, low-cost Ethernet networks, and the open-source Linux operating system. One of the larger Beowulfs is Goddard's Highly-parallel Integrated Virtual Environment, or HIVE for short.
Defining requirements, missions, technologies, and concepts for space exploration involves multiple levels of organizations, teams of people with complementary skills, and analytical models and simulations. Analytical activities range from filling a To-Be-Determined (TBD) in a requirement to creating animations and simulations of exploration missions. In a program as large as returning to the Moon, there are hundreds of simultaneous analysis activities. A way to manage and integrate efforts of this magnitude is to deploy a centralized database that provides the capability to define tasks, identify resources, describe products, schedule deliveries, and generate a variety of reports. This paper describes a web-accessible task management system and explains the lessons learned during the development and deployment of the database. Through the database, managers and team leaders can define tasks, establish review schedules, assign teams, link tasks to specific requirements, identify products, and link the task data records to external repositories that contain the products. Data filters and spreadsheet export utilities provide a powerful capability to create custom reports. Import utilities provide a means to populate the database from previously filled form files. Within a four month period, a small team analyzed requirements, developed a prototype, conducted multiple system demonstrations, and deployed a working system supporting hundreds of users across the aeros pace community. Open-source technologies and agile software development techniques, applied by a skilled team enabled this impressive achievement. Topics in the paper cover the web application technologies, agile software development, an overview of the system's functions and features, dealing with increasing scope, and deploying new versions of the system.
BACKGROUND: Marginal stability of astronaut movement while performing lunar surface tasks has been well documented, and is clearly demonstrated in videos of falls, and near falls, during Apollo Lunar Extravehicular Activities. Referencing mission reports from Apollo 15 and 16 [1, 2], suspected causes for falls include: surface conditions, visibility, and gravitational effects (hypogravity). In this preliminary test, we employ the open-source biomechanical tool OpenSim [3, 4] to analyze the impact of lunar gravity (Lg) on two object-pickup motions performed by a single shirt-sleeved subject. Specifically, we attempt to answer the following questions based on an estimation of the Center of Mass Projection (CoMP) and ground reaction force Center of Pressure (COP) as it relates to the astronaut Base of Support (BOS) for 1g and Lg conditions: 1. Is the task motion, as performed in 1g, dynamically feasible in 1g and Lg? 2. Can we make the motion dynamically feasible in Lg by slowing it down? 3. Is the Lg COP equal to the 1g COP at a theoretically predicted reduction in motion speed? METHODS AND RESULTS: To answer the first question, the gravitational acceleration in the OpenSim model is modified from a nominal 9.81 m/s2 to 1.64 m/s2, and the 1g joint trajectory is input to an OpenSim based method [5] for estimating ground reaction forces and moments. From this method, the position of the CoMP and COP can be estimated and checked to see if they remain within a simulated BOS formed from the footprint of the OpenSim model to determine whether the motion is dynamically feasible. As expected, both of the motions were estimated to be feasible in a 1g environment, however, both motions had periods of infeasibility in Lg. It is well known that crew members make adjustments to motion trajectories in altered gravity fields to maintain balance. As a first step, we considered the simple adjustment of slowing the motion in Lg by a constant factor. This was accomplished by scaling the time stamps in the motion trajectory file by that factor. For the two motions considered, it was found that scale factors of 1.3 and 1.4 kept the COP just within the subject BOS. The CoMP is unchanged by the gravity field. Simple analysis of an inverted pendulum in the Lg environment, which generalizes to a general multibody system, leads to a theoretical prediction that a reduction in speed factor of √1g/Lg, or 2.445, will make the COP trajectory in Lg equivalent to that in 1g. When the above procedure was performed with a factor of 2.445, the estimated COP in Lg, was observed to be very close to that in 1g. In summary, we have developed a method for estimating the CoMP and COP in Lg, for subject motion collected in 1g. We believe this method can prove to be a valuable check and balance for simulated Lg training and testing by exposing potential simulator-induced artifacts that make the simulated task motion seem possible, when in fact, it would violate the above criteria. We also note that a reduction in task speed should tend the task motion towards stability, with a theoretical slowdown factor of √1g/Lg making the motion stability equal to that in 1g according to the CoMP and COP criteria.
The number of spaceflight bioscience mission opportunities is too small to allow all relevant biological and environmental parameters to be experimentally identified. Simulated spaceflight experiments in ground-based facilities (GBFs), such as clinostats, are each suitable only for particular investigations -- a rotating-wall vessel may be 'simulated microgravity' for cell differentiation (hours), but not DNA repair (seconds) -- and introduce confounding stimuli, such as motor vibration and fluid shear effects. This uncertainty over which biological mechanisms respond to a given form of simulated space radiation or gravity, as well as its side effects, limits our ability to baseline spaceflight data and validate mission science. Machine learning techniques autonomously identify relevant and interdependent factors in a data set given the set of desired metrics to be evaluated: to automatically identify related studies, compare data from related studies, or determine linkages between types of data in the same study. System-of-systems (SoS) machine learning models have the ability to deal with both sparse and heterogeneous data, such as that provided by the small and diverse number of space biosciences flight missions; however, they require appropriate user-defined metrics for any given data set. Although machine learning in bioinformatics is rapidly expanding, the need to combine spaceflight/GBF mission parameters with omics data is unique. This work characterizes the basic requirements for implementing the SoS approach through the System Map (SM) technique, a composite of a dynamic Bayesian network and Gaussian mixture model, in real-world repositories such as the GeneLab Data System and Life Sciences Data Archive. The three primary steps are metadata management for experimental description using open-source ontologies, defining similarity and consistency metrics, and generating testing and validation data sets. Such approaches to spaceflight and GBF omics data may soon enable unique insight into which measured phenomena correlate to biological mechanisms that are truly affected by spaceflight conditions; which are most likely to be confounded by other variables; and which are insufficiently characterized, significantly increasing existing and future science return from ISS and spaceflight missions.
Brief Presenter Biography (35 word limit): Bohdan Wesely is an Aerospace Engineer in the Entry Systems and Technology Division at Ames. He has worked on a variety of projects for NASA including integrated TPS (thermal protection system) flight hardware deliveries and testing services for commercial partners. Introduction: TEMPEST is a new trajectory analysis framework that is designed to fill the gap between dedicated flight mechanics tools and aerothermal and TPS sizing tools. The project started as an SJSU master’s thesis and has since evolved into a general conceptual design tool capable of studying a wide variety of entry problems. Development is ongoing in the Entry Systems and Technology Division at NASA ARC. Why TEMPEST: Space missions involving entry into a planetary atmosphere involve a series of unique requirements across multiple disciplines. Whether it is traditional entry descent and landing (EDL), or aerocapture, the vehicle must navigate to its target landing location or orbit state, and the TPS must protect the payload during entry. The design process typically involves iterative handoffs between various flight mechanics, flow solver, and material response level tools. During the early conceptual phase, a wide variety of feasible trajectories are simulated in a Monte Carlo scenario which broadly satisfy the mission or landing requirements. Next, computational fluid dynamics (CFD), direct simulation Monte Carlo (DSMC), and other flow solver analyses are performed at various key trajectory points to generate an aero-database, heating and TPS design requirements also emerge at this stage. At this point, with updated aerodynamics from the various flow solvers, trajectories can be re-run, this in turn can change the required freestream conditions for the CFD tools, and as a project progresses, these analyses converge, and uncertainty is reduced. However, there is always a “hand-off” occurring between two inherently coupled phenomena. Analysis Description: One of the goals with TEMPEST is to use a variety of first principles estimation methods coupled with an atmosphere model to predict vehicle aerothermodynamics across the entire flight regime while propagating a 3 or 6 degree of freedom (DoF) trajectory. Aerodynamics methods include modified Newtonian, Maxwell and Cercignani- Lampis-Lord (CLL) for continuum, transitional, and free molecular flow regimes. Aerothermodynamics include boundary layer and reference enthalpy methods, and Mutation++ for non-equilibrium chemistry modeling. TEMPEST is also capable of stitching multiple trajectory segments together to study mission scenarios like multi-pass aerocapture and aero-gravity assists. Most of the program is implemented in MATLAB using modern system objects, it relies on several C++ shared libraries for supporting tools like Gmsh, the Global Reference Atmospheric Model (GRAM), and Mutation++. The various first principles aerothermal estimation methods are discretized across either a structured axisymmetric panel mesh or an unstructured tri-mesh generated from an open-source tool such as Gmsh, this allows solutions on the same mesh to be compared across tools such as CB-Aero. CFD Coupling. A physics-aware, gaussian process CFD anchoring scheme is proposed to adjust the various first principles methods as a CFD database is populated. One goal for this anchoring module is to inform the project where CFD should be run. Full knowledge of the entire trajectory, atmosphere, and aerothermodynamics allows for easier identification of high sensitivity areas and uncertainty quantification. While the first principles effects are well known and proven accurate in existing tools such as CB- Aero and Cart3D, a physics aware CFD anchoring scheme increases tool credibility across a project lifecycle. Material Response Modeling. Correct TPS sizing is critical for optimizing mass for science payloads and ensuring mission success. The process typically involves a thermal analysis along the trajectory with surface heating environments as a boundary condition. Several design constraints are maximum bondline temperature and maximum recession with various margining techniques. The material response tool FIAT, developed out of NASA Ames, is currently being integrated into the TEMPEST environment. TPS recession, shape change, mass loss, and mass property alteration are all factors that can perturb an entry trajectory. For missions like Mars 2020, recession was minimal and was safely handled separately as a post process. For missions such as Jupiter Galileo with a high TPS mass fraction or asteroid entries, recession plays a major role. The proposed fully coupled scheme is to use an epoch-based approach where the trajectory integration is halted after a recession threshold, the energy balance and FIAT are solved at each panel, the mesh, aerodynamics, and mass properties are updated, and the trajectory continues. Several computational tradeoffs have been made during the development of TEMPEST to limit the cost of a single trajectory and preserve its utility as a conceptual, rapid iteration tool. Conclusion: Development of TEMPEST is ongoing and the project is still in its infancy. This talk aims to showcase its unique capabilities to support future NASA entry systems missions.
Software that enables authorized users to monitor and control scientific payloads aboard the International Space Station (ISS) from diverse terrestrial locations equipped with Internet connections is undergoing development. This software reflects a data-driven approach to distributed operations. A Web-based software framework leverages prior developments in Java and Extensible Markup Language (XML) to create portable code and portable data, to which one can gain access via Web-browser software on almost any common computer. Open-source software is used extensively to minimize cost; the framework also accommodates enterprise-class server software to satisfy needs for high performance and security. To accommodate the diversity of ISS experiments and users, the framework emphasizes openness and extensibility. Users can take advantage of available viewer software to create their own client programs according to their particular preferences, and can upload these programs for custom processing of data, generation of views, and planning of experiments. The same software system, possibly augmented with a subset of data and additional software tools, could be used for public outreach by enabling public users to replay telescience experiments, conduct their experiments with simulated payloads, and create their own client programs and other custom software.