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This document explains all the necessary steps in order to generate optimized C code from Simulink Models. This document also covers some general information on good programming practices, selection of variable types, how to organize models and subsystems, and finally how to test the generated C code and compare it with data from MATLAB.
The success of JPL's Next Generation Imaging Spectrometer (NGIS) in Earth remote sensing has inspired a follow-on instrument project, the MaRSPlus Sensor System (MSS). One of JPL's responsibilities in the MSS project involves updating the documentation from the previous JPL airborne imagers to provide all the information necessary for an outside customer to operate the instrument independently. As part of this documentation update, I created detailed electrical cabling diagrams to provide JPL technicians with clear and concise build instructions and a database to track the status of cables from order to build to delivery. Simultaneously, a distributed motor control system is being developed for potential use on the proposed 2018 Mars rover mission. This system would significantly reduce the mass necessary for rover motor control, making more mass space available to other important spacecraft systems. The current stage of the project consists of a desktop computer talking to a single "cold box" unit containing the electronics to drive a motor. In order to test the electronics, I developed a graphical user interface (GUI) using MATLAB to allow a user to send simple commands to the cold box and display the responses received in a user-friendly format.
This work presents a measurement system to characterize the pulsed response of high-power GaN amplifiers for use in space-based SAR platforms that require very strict amplitude and phase stability. The measurement system is able to record and analyze data on three different time scales: fast, slow, and long, which allows for greater detail of the mechanisms that impact amplitude and phase stability. The system is fully automated through MATLAB, which offers both instrument control capability and in-situ data processing. To validate this system, a high-power GaN HEMT amplifier operated in saturation was characterized. The fast time results show that variations to the amplitude and phase are correlated to DC supply transients, while long time characteristics are correlated to temperature changes.
Calculating an accurate nutation time constant for a spinning spacecraft is an important step for ensuring mission success. Spacecraft nutation is caused by energy dissipation about the spin axis. Propellant slosh in the spacecraft fuel tanks is the primary source for this dissipation and can be simulated using a forced motion spin table. Mechanical analogs, such as pendulums and rotors, are typically used to simulate propellant slosh. A strong desire exists for an automated method to determine these analog parameters. The method presented accomplishes this task by using a MATLAB Simulink/SimMechanics based simulation that utilizes the Parameter Estimation Tool.
The Capsule Parachute Assembly System (CPAS) Analysis Team is responsible for determining parachute inflation parameters and dispersions that are ultimately used in verifying system requirements. A model memo is internally released semi-annually documenting parachute inflation and other key parameters reconstructed from flight test data. Dispersion probability distributions published in previous versions of the model memo were uniform because insufficient data were available for determination of statistical based distributions. Uniform distributions do not accurately represent the expected distributions since extreme parameter values are just as likely to occur as the nominal value. CPAS has taken incremental steps to move away from uniform distributions. Model Memo version 9 (MMv9) made the first use of non-uniform dispersions, but only for the reefing cutter timing, for which a large number of sample was available. In order to maximize the utility of the available flight test data, clusters of parachutes were reconstructed individually starting with Model Memo version 10. This allowed for statistical assessment for steady-state drag area (CDS) and parachute inflation parameters such as the canopy fill distance (n), profile shape exponent (expopen), over-inflation factor (C(sub k)), and ramp-down time (t(sub k)) distributions. Built-in MATLAB distributions were applied to the histograms, and parameters such as scale (sigma) and location (mu) were output. Engineering judgment was used to determine the "best fit" distribution based on the test data. Results include normal, log normal, and uniform (where available data remains insufficient) fits of nominal and failure (loss of parachute and skipped stage) cases for all CPAS parachutes. This paper discusses the uniform methodology that was previously used, the process and result of the statistical assessment, how the dispersions were incorporated into Monte Carlo analyses, and the application of the distributions in trajectory benchmark testing assessments with parachute inflation parameters, drag area, and reefing cutter timing used by CPAS.
Introduction: The Integrated Medical Model (IMM) utilizes historical data, cohort data, and external simulations as input factors to provide estimates of crew health, resource utilization and mission outcomes. The Cervical Spine Injury Module (CSIM) is an external simulation designed to provide the IMM with parameter estimates for 1) a probability distribution function (PDF) of the incidence rate, 2) the mean incidence rate, and 3) the standard deviation associated with the mean resulting from injury/trauma of the neck. Methods: An injury mechanism based on an idealized low-velocity blunt impact to the superior posterior thorax of an ISS crewmember was used as the simulated mission environment. As a result of this impact, the cervical spine is inertially loaded from the mass of the head producing an extension-flexion motion deforming the soft tissues of the neck. A multibody biomechanical model was developed to estimate the kinematic and dynamic response of the head-neck system from a prescribed acceleration profile. Logistic regression was performed on a dataset containing AIS1 soft tissue neck injuries from rear-end automobile collisions with published Neck Injury Criterion values producing an injury transfer function (ITF). An injury event scenario (IES) was constructed such that crew 1 is moving through a primary or standard translation path transferring large volume equipment impacting stationary crew 2. The incidence rate for this IES was estimated from in-flight data and used to calculate the probability of occurrence. The uncertainty in the model input factors were estimated from representative datasets and expressed in terms of probability distributions. A Monte Carlo Method utilizing simple random sampling was employed to propagate both aleatory and epistemic uncertain factors. Scatterplots and partial correlation coefficients (PCC) were generated to determine input factor sensitivity. CSIM was developed in the SimMechanics/Simulink environment with a Monte Carlo wrapper (MATLAB) used to integrate the components of the module. Results: The probability of generating an AIS1 soft tissue neck injury from the extension/flexion motion induced by a low-velocity blunt impact to the superior posterior thorax was fitted with a lognormal PDF with mean 0.26409, standard deviation 0.11353, standard error of mean 0.00114, and 95% confidence interval [0.26186, 0.26631]. Combining the probability of an AIS1 injury with the probability of IES occurrence was fitted with a Johnson SI PDF with mean 0.02772, standard deviation 0.02012, standard error of mean 0.00020, and 95% confidence interval [0.02733, 0.02812]. The input factor sensitivity analysis in descending order was IES incidence rate, ITF regression coefficient 1, impactor initial velocity, ITF regression coefficient 2, and all others (equipment mass, crew 1 body mass, crew 2 body mass) insignificant. Verification and Validation (V&V): The IMM V&V, based upon NASA STD 7009, was implemented which included an assessment of the data sets used to build CSIM. The documentation maintained includes source code comments and a technical report. The software code and documentation is under Subversion configuration management. Kinematic validation was performed by comparing the biomechanical model output to established corridors.
We discuss here the relative merits of these numbers as possible random sequence sources. The quality of these sequences is not judged directly based on the outcome of all known tests for the randomness of a sequence. Instead, it is determined implicitly by the accuracy of the Monte Carlo integration in a statistical sense. Since our main motive of using a random sequence is to solve real world problems, it is more desirable if we compare the quality of the sequences based on their performances for these problems in terms of quality/accuracy of the output. We also compare these sources against those generated by a popular pseudo-random generator, viz., the Matlab rand and the quasi-random generator ha/ton both in terms of error and time complexity. Our study demonstrates that consecutive blocks of digits of each of these numbers produce a good random sequence source. It is observed that randomly chosen blocks of digits do not have any remarkable advantage over consecutive blocks for the accuracy of the Monte Carlo integration. Also, it reveals that pi is a better source of a random sequence than theta when the accuracy of the integration is concerned.
The Crew Exploration Vehicle Parachute Assembly System (CPAS) is the parachute system for NASA s Orion spacecraft. The test program consists of numerous drop tests, wherein a test article rigged with parachutes is extracted or released from an aircraft. During such tests, range safety is paramount, as is the recoverability of the parachutes and test article. It is crucial to establish an aircraft release point that will ensure that the article and all items released from it will land in safe locations. A new footprint predictor tool, called Sasquatch, was created in MATLAB. This tool takes in a simulated trajectory for the test article, information about all released objects, and atmospheric wind data (simulated or actual) to calculate the trajectories of the released objects. Dispersions are applied to the landing locations of those objects, taking into account the variability of winds, aircraft release point, and object descent rate. Sasquatch establishes a payload release point (e.g., where the payload will be extracted from the carrier aircraft) that will ensure that the payload and all objects released from it will land in a specified cleared area. The landing locations (the final points in the trajectories) are plotted on a map of the test range. Sasquatch was originally designed for CPAS drop tests and includes extensive information about both the CPAS hardware and the primary test range used for CPAS testing. However, it can easily be adapted for more complex CPAS drop tests, other NASA projects, and commercial partners. CPAS has developed the Sasquatch footprint tool to ensure range safety during parachute drop tests. Sasquatch is well correlated to test data and continues to ensure the safety of test personnel as well as the safe recovery of all equipment. The tool will continue to be modified based on new test data, improving predictions and providing added capability to meet the requirements of more complex testing.
G-CAT is a covariance analysis tool that enables fast and accurate computation of error ellipses for descent, landing, ascent, and rendezvous scenarios, and quantifies knowledge error contributions needed for error budgeting purposes. Because GCAT supports hardware/system trade studies in spacecraft and mission design, it is useful in both early and late mission/ proposal phases where Monte Carlo simulation capability is not mature, Monte Carlo simulation takes too long to run, and/or there is a need to perform multiple parametric system design trades that would require an unwieldy number of Monte Carlo runs. G-CAT is formulated as a variable-order square-root linearized Kalman filter (LKF), typically using over 120 filter states. An important property of G-CAT is that it is based on a 6-DOF (degrees of freedom) formulation that completely captures the combined effects of both attitude and translation errors on the propagated trajectories. This ensures its accuracy for guidance, navigation, and control (GN&C) analysis. G-CAT provides the desired fast turnaround analysis needed for error budgeting in support of mission concept formulations, design trade studies, and proposal development efforts. The main usefulness of a covariance analysis tool such as G-CAT is its ability to calculate the performance envelope directly from a single run. This is in sharp contrast to running thousands of simulations to obtain similar information using Monte Carlo methods. It does this by propagating the "statistics" of the overall design, rather than simulating individual trajectories. G-CAT supports applications to lunar, planetary, and small body missions. It characterizes onboard knowledge propagation errors associated with inertial measurement unit (IMU) errors (gyro and accelerometer), gravity errors/dispersions (spherical harmonics, masscons), and radar errors (multiple altimeter beams, multiple Doppler velocimeter beams). G-CAT is a standalone MATLAB- based tool intended to run on any engineer's desktop computer.
In order to have the capability to use satellite data from its own missions to inform future sea-level rise projections, JPL needed a full-fledged ice-sheet/iceshelf flow model, capable of modeling the mass balance of Antarctica and Greenland into the near future. ISSM was developed with such a goal in mind, as a massively parallelized, multi-purpose finite-element framework dedicated to ice-sheet modeling. ISSM features unstructured meshes (Tria in 2D, and Penta in 3D) along with corresponding finite elements for both types of meshes. Each finite element can carry out diagnostic, prognostic, transient, thermal 3D, surface, and bed slope simulations. Anisotropic meshing enables adaptation of meshes to a certain metric, and the 2D Shelfy-Stream, 3D Blatter/Pattyn, and 3D Full-Stokes formulations capture the bulk of the ice-flow physics. These elements can be coupled together, based on the Arlequin method, so that on a large scale model such as Antarctica, each type of finite element is used in the most efficient manner. For each finite element referenced above, ISSM implements an adjoint. This adjoint can be used to carry out model inversions of unknown model parameters, typically ice rheology and basal drag at the ice/bedrock interface, using a metric such as the observed InSAR surface velocity. This data assimilation capability is crucial to allow spinning up of ice flow models using available satellite data. ISSM relies on the PETSc library for its vectors, matrices, and solvers. This allows ISSM to run efficiently on any parallel platform, whether shared or distrib- ISSM: Ice Sheet System Model NASA's Jet Propulsion Laboratory, Pasadena, California uted. It can run on the largest clusters, and is fully scalable. This allows ISSM to tackle models the size of continents. ISSM is embedded into MATLAB and Python, both open scientific platforms. This improves its outreach within the science community. It is entirely written in C/C++, which gives it flexibility in its design, and the power/speed that C/C++ allows. ISSM is svn (subversion) hosted, on a JPL repository, to facilitate its development and maintenance. ISSM can also model propagation of rifts using contact mechanics and mesh splitting, and can interface to the Dakota software. To carry out sensitivity analysis, mesh partitioning algorithms are available, based on the Scotch, Chaco, and Metis partitioners that ensure equal area mesh partitions can be done, which are then usable for sampling and local reliability methods.
Atmospheric errors due to the troposphere are a limiting error source for spaceborne interferometric synthetic aperture radar (InSAR) imaging. This software generates tropospheric delay maps that can be used to correct atmospheric artifacts in InSAR data. The software automatically acquires all needed GPS (Global Positioning System), weather, and Digital Elevation Map data, and generates a tropospheric correction map using a novel algorithm for combining GPS and weather information while accounting for terrain. Existing JPL software was prototypical in nature, required a MATLAB license, required additional steps to acquire and ingest needed GPS and weather data, and did not account for topography in interpolation. Previous software did not achieve a level of automation suitable for integration in a Web portal. This software overcomes these issues. GPS estimates of tropospheric delay are a source of corrections that can be used to form correction maps to be applied to InSAR data, but the spacing of GPS stations is insufficient to remove short-wavelength tropospheric artifacts. This software combines interpolated GPS delay with weather model precipitable water vapor (PWV) and a digital elevation model to account for terrain, increasing the spatial resolution of the tropospheric correction maps and thus removing short wavelength tropospheric artifacts to a greater extent. It will be integrated into a Web portal request system, allowing use in a future L-band SAR Earth radar mission data system. This will be a significant contribution to its technology readiness, building on existing investments in in situ space geodetic networks, and improving timeliness, quality, and science value of the collected data
During my internship at NASA, I was a model developer for Ground Support Equipment (GSE). The purpose of a model developer is to develop and unit test model component libraries (fluid, electrical, gas, etc.). The models are designed to simulate software for GSE (Ground Special Power, Crew Access Arm, Cryo, Fire and Leak Detection System, Environmental Control System (ECS), etc. ~.) before they are implemented into hardware. These models support verifying local control and remote software for End-Item Software Under Test (SUT). The model simulates the physical behavior (function, state, limits and 110) of each end-item and it's dependencies as defined in the Subsystem Interface Table, Software Requirements & Design Specification (SRDS), Ground Integrated Schematic (GIS), and System Mechanical Schematic.(SMS). The software of each specific model component is simulated through MATLAB's Simulink program. The intensiv~ model development life cycle is a.s follows: Identify source documents; identify model scope; update schedule; preliminary design review; develop model requirements; update model.. scope; update schedule; detailed design review; create/modify library component; implement library components reference; implement subsystem components; develop a test script; run the test script; develop users guide; send model out for peer review; the model is sent out for verific~tionlvalidation; if there is empirical data, a validation data package is generated; if there is not empirical data, a verification package is generated; the test results are then reviewed; and finally, the user. requests accreditation, and a statement of accreditation is prepared. Once each component model is reviewed and approved, they are intertwined together into one integrated model. This integrated model is then tested itself, through a test script and autotest, so that it can be concluded that all models work conjointly, for a single purpose. The component I was assigned, specifically, was a fluid component, a discrete pressure switch. The switch takes a fluid pressure input, and if the pressure is greater than a designated cutoff pressure, the switch would stop fluid flow.
The Spaceport Command and Control System (SCCS) Simulation Computer Software Configuration Item (CSCI) is,. responsible for providing simulations to support test and verification of SCCS hardware and software. The Universal Coolant Transporter System (UCTS) is a Space Shuttle Orbiter support piece of the Ground Servicing Equipment (GSE). The purpose of the UCTS is to provide two support services to the Space Shuttle Orbiter immediately after landing at the Shuttle Landing Facility. The Simulation uses GSE Models to stand in for the actual systems to support testing of SCCS systems s:luring their development. As an intern at KSC, my assignment was to develop a model component for the UCTS. I was given a fluid component (drier) to model in Matlab. The drier was a Catch All replaceable core type filter-drier. The filter-drier provides maximum protection for the thermostatic expansion valve and solenoid valve from dirt that may be in the system. The filter-drier also protects the valves from freezing up. I researched fluid dynamics to understand the function of my component. I completed training for UNIX and Simulink to help aid in my assignment. The filter-drier was modeled by determining affects it has on the pressure, velocity and temperature of the system. I used Bernoulli's Equation to calculate the pressure and velocity differential through the dryer. I created my model filter-drier in Simulink and wrote the test script to test the component. I completed component testing and captured test data. The finalized model was sent for peer review for any improvements.
Several analytical mechanical slosh models for a cylindrical tank with flat bottom are reviewed. Even though spacecrafts use cylinder shaped tanks, most of those tanks usually have elliptical domes. To extend the application of the analytical models for a cylindrical tank with elliptical domes, the modified slosh parameter models are proposed in this report by mapping an elliptical dome cylindrical tank to a flat top/bottom cylindrical tank while maintaining the equivalent liquid volume. For the low Bond number case, the low-g slosh models were also studied. Those low-g models can be used for Bond number > 10. The current low-g slosh models were also modified to extend their applications for the case that liquid height is smaller than the tank radius. All modified slosh models are implemented in MATLAB m-functions and are collected in the developed MST (Mechanical Slosh Toolbox).
Computational fluid dynamic (CFD) analyses of the University of Michigan (UM) Shock/Boundary-Layer Interaction (SBLI) experiments were performed as an extension of the CFD SBLI Workshop held at the 48th AIAA Aerospace Sciences Meeting in 2010. In particular, the UM Mach 2.75 Glass Tunnel with a semi-spanning 7.75deg wedge was analyzed in attempts to explore key physics pertinent to SBLI's, including thermodynamic and viscous boundary conditions as well as turbulence modeling. Most of the analyses were 3D CFD simulations using the OVERFLOW flow solver, with additional quasi-1D simulations performed with an in house MATLAB code interfacing with the NIST REFPROP code to explore perfect verses non-ideal air. A fundamental exploration pertaining to the effects of particle image velocimetry (PIV) on post-processing data is also shown. Results from the CFD simulations showed an improvement in agreement with experimental data with key contributions including adding a laminar zone upstream of the wedge and the necessity of mimicking PIV particle lag for comparisons. Results from the quasi-1D simulation showed that there was little difference between perfect and non-ideal air for the configuration presented.
For Orion missions beyond low Earth orbit (LEO), the Guidance, Navigation, and Control (GN&C) system is being developed using a model-based approach for simulation and flight software. Lessons learned from the development of GN&C algorithms and flight software for the Orion Exploration Flight Test One (EFT-1) vehicle have been applied to the development of further capabilities for Orion GN&C beyond EFT-1. Continuing the use of a Model-Based Development (MBD) approach with the Matlab®/Simulink® tool suite, the process for GN&C development and analysis has been largely improved. Furthermore, a model-based simulation environment in Simulink, rather than an external C-based simulation, greatly eases the process for development of flight algorithms. The benefits seen by employing lessons learned from EFT-1 are described, as well as the approach for implementing additional MBD techniques. Also detailed are the key enablers for improvements to the MBD process, including enhanced configuration management techniques for model-based software systems, automated code and artifact generation, and automated testing and integration.
A reduced dynamical model describing temperature stratification effects driven by natural convection in a liquid hydrogen cryogenic fuel tank has been developed. It accounts for cryogenic propellant loading, storage, and unloading in the conditions of normal, increased, and micro- gravity. The model involves multiple horizontal control volumes in both liquid and ullage spaces. Temperature and velocity boundary layers at the tank walls are taken into account by using correlation relations. Heat exchange involving the tank wall is considered by means of the lumped-parameter method. By employing basic conservation laws, the model takes into consideration the major multi-phase mass and energy exchange processes involved, such as condensation-evaporation of the hydrogen, as well as flows of hydrogen liquid and vapor in the presence of pressurizing helium gas. The model involves a liquid hydrogen feed line and a tank ullage vent valve for pressure control. The temperature stratification effects are investigated, including in the presence of vent valve oscillations. A simulation of temperature stratification effects in a generic cryogenic tank has been implemented in Matlab and results are presented for various tank conditions.
Future engineering of structures and equipment on the lunar surface requires significant understanding of particle characteristics of the lunar regolith. Nearly all sediment characteristics are influenced by particle shape; therefore a method of quantifying particle shape is useful both in lunar and terrestrial applications. We have created a method to quantify particle shape, specifically for lunar regolith, using image processing. Photomicrographs of thin sections of lunar core material were obtained under reflected light. Three photomicrographs were analyzed using ImageJ and MATLAB. From the image analysis measurements for area, perimeter, Feret diameter, orthogonal Feret diameter, Heywood factor, aspect ratio, sieve diameter, and sieve number were recorded. Probability distribution functions were created from the measurements of Heywood factor and aspect ratio.