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At least 145 records · Page 8

Session 6: Dynamic Modeling and Systems Analysis

These presentations cover some of the ongoing work in dynamic modeling and dynamic systems analysis. The first presentation discusses dynamic systems analysis and how to integrate dynamic performance information into the systems analysis. The ability to evaluate the dynamic performance of an engine design may allow tradeoffs between the dynamic performance and operability of a design resulting in a more efficient engine design. The second presentation discusses the Toolbox for Modeling and Analysis of Thermodynamic Systems (T-MATS). T-MATS is a Simulation system with a library containing the basic building blocks that can be used to create dynamic Thermodynamic Systems. Some of the key features include Turbo machinery components, such as turbines, compressors, etc., and basic control system blocks. T-MAT is written in the Matlab-Simulink environment and is open source software. The third presentation focuses on getting additional performance from the engine by allowing the limit regulators only to be active when a limit is danger of being violated. Typical aircraft engine control architecture is based on MINMAX scheme, which is designed to keep engine operating within prescribed mechanical/operational safety limits. Using a conditionally active min-max limit regulator scheme, additional performance can be gained by disabling non-relevant limit regulators

control↗

A Modular Framework for Modeling Hardware Elements in Distributed Engine Control Systems

Progress toward the implementation of distributed engine control in an aerospace application may be accelerated through the development of a hardware-in-the-loop (HIL) system for testing new control architectures and hardware outside of a physical test cell environment. One component required in an HIL simulation system is a high-fidelity model of the control platform: sensors, actuators, and the control law. The control system developed for the Commercial Modular Aero-Propulsion System Simulation 40k (C-MAPSS40k) provides a verifiable baseline for development of a model for simulating a distributed control architecture. This distributed controller model will contain enhanced hardware models, capturing the dynamics of the transducer and the effects of data processing, and a model of the controller network. A multilevel framework is presented that establishes three sets of interfaces in the control platform: communication with the engine (through sensors and actuators), communication between hardware and controller (over a network), and the physical connections within individual pieces of hardware. This introduces modularity at each level of the model, encouraging collaboration in the development and testing of various control schemes or hardware designs. At the hardware level, this modularity is leveraged through the creation of a Simulink(R) library containing blocks for constructing smart transducer models complying with the IEEE 1451 specification. These hardware models were incorporated in a distributed version of the baseline C-MAPSS40k controller and simulations were run to compare the performance of the two models. The overall tracking ability differed only due to quantization effects in the feedback measurements in the distributed controller. Additionally, it was also found that the added complexity of the smart transducer models did not prevent real-time operation of the distributed controller model, a requirement of an HIL system.

numerical simulation↗

The Vacuum-Compacted Regolith Gripping Mechanism and Unmanned Flights via Quad-Rotors

During the course of the Kennedy Space Center Summer Internship, two main experiments were performed: The Vacuum-Compacted Regolith Gripping Mechanism and Unmanned Flights via Quad-copters. The objectives of the Vacuum-Compacted Regolith Gripping Mechanism, often abbreviated as the Granular Gripper, are to exhibit Space Technology, such as a soft robotic hand, lift different apparatuses used to excavate regolith, and conserve energy while executing its intended task. The project is being conducted to test how much weight the Granular Gripper can hold. With the use of an Animatronic Robotic Hand, Arduino Uno, and other components, the system was calibrated before actually conducting the intended weight test. The maximum weight each finger could hold with the servos running, in the order of pinky, ring, middle, and index fingers, are as follows: 1.340N, 1.456 N, 0.9579 N, and 1.358 N. Using the small vacuum pump system, the maximum weight each finger could hold, in the same order, was: 4.076 N, 6.159 N, 5.454 N, and 4.052 N. The maximum torques on each of the fingers when the servos were running, in the same respective order, was: 0.0777 Nm, 0.0533 Nm, 0.0648 Nm, and 0.0532 Nm. The maximum torques on the individual fingers, when the small vacuum pump was in effect, in the same order as above, was: 0.2318 Nm, 0.3032 Nm, 0.2741 Nm, and 0.1618 Nm. In testing all the fingers with the servos running, the total weight was 5.112 N and the maximum torque on the all the fingers was 0.2515 Nm. However, when the small vacuum pump system was used, the total weight was 19.741 N and the maximum torque on the all the fingers was 0.9713 Nm. The conclusion that was drawn stated that using the small vacuum pump system proved nearly 4 times more effective when testing how much weigh the hand could hold. The resistance provided by the compacted sand in the glove allowed more weight to be held by the hand and glove. Also, when the servos turned off and the hand still retaining its position, energy is being saved because the vacuum created the same resistance the running servos did without using power. The Unmanned Flights via Quad-rotors are built because multi-rotor dynamics are an important starting point and fair analog for space craft control systems and they make good terrestrial development platforms for various aspects of control for space crafts. The project is being conducted to see what the thrust response is going to be when a pulse width modulation command is sent to the control system since the quad-rotors are PWM controlled. A simulation environment in constructed so that one can quickly iterate and test different designs such as control systems, PID control vs. LDR control, and state estimation. Using two DIY Quad Kits, APM 2.6, testing apparatus (called a data acquisition system) to test the quad-rotors, and a simulation program such as Simulink, two quad-rotors are built and controlled via a simulation program, which is designed to be as realistic as possible and not idealistic. Due to the quad-rotors not being completely built nor ready for testing, there are no results or conclusions to report.

Granular Gripper↗

Cantera Integration with the Toolbox for Modeling and Analysis of Thermodynamic Systems (T-MATS)

NASA Glenn Research Center (GRC) has recently developed a software package for modeling generic thermodynamic systems called the Toolbox for the Modeling and Analysis of Thermodynamic Systems (T-MATS). T-MATS is a library of building blocks that can be assembled to represent any thermodynamic system in the Simulink(Registered TradeMark) (The MathWorks, Inc.) environment. These elements, along with a Newton Raphson solver (also provided as part of the T-MATS package), enable users to create models of a wide variety of systems. The current version of T-MATS (v1.0.1) uses tabular data for providing information about a specific mixture of air, water (humidity), and hydrocarbon fuel in calculations of thermodynamic properties. The capabilities of T-MATS can be expanded by integrating it with the Cantera thermodynamic package. Cantera is an object-oriented analysis package that calculates thermodynamic solutions for any mixture defined by the user. Integration of Cantera with T-MATS extends the range of systems that may be modeled using the toolbox. In addition, the library of elements released with Cantera were developed using MATLAB native M-files, allowing for quicker prototyping of elements. This paper discusses how the new Cantera-based elements are created and provides examples for using T-MATS integrated with Cantera.

Systems Analysis↗

A Process for the Creation of T-MATS Propulsion System Models from NPSS Data

A modular thermodynamic simulation package called the Toolbox for the Modeling and Analysis of Thermodynamic Systems (T-MATS) has been developed for the creation of dynamic simulations. The T-MATS software is designed as a plug-in for Simulink(Trademark) and allows a developer to create system simulations of thermodynamic plants (such as gas turbines) and controllers in a single tool. Creation of such simulations can be accomplished by matching data from actual systems, or by matching data from steady state models and inserting appropriate dynamics, such as the rotor and actuator dynamics for an aircraft engine. This paper summarizes the process for creating T-MATS turbo-machinery simulations using data and input files obtained from a steady state model created in the Numerical Propulsion System Simulation (NPSS). The NPSS is a thermodynamic simulation environment that is commonly used for steady state gas turbine performance analysis. Completion of all the steps involved in the process results in a good match between T-MATS and NPSS at several steady state operating points. Additionally, the T-MATS model extended to run dynamically provides the possibility of simulating and evaluating closed loop responses.

gas path dynamics↗

A Process for the Creation of T-MATS Propulsion System Models from NPSS Data

A modular thermodynamic simulation package called the Toolbox for the Modeling and Analysis of Thermodynamic Systems (T-MATS) has been developed for the creation of dynamic simulations. The T-MATS software is designed as a plug-in for Simulink(Registered TradeMark) and allows a developer to create system simulations of thermodynamic plants (such as gas turbines) and controllers in a single tool. Creation of such simulations can be accomplished by matching data from actual systems, or by matching data from steady state models and inserting appropriate dynamics, such as the rotor and actuator dynamics for an aircraft engine. This paper summarizes the process for creating T-MATS turbo-machinery simulations using data and input files obtained from a steady state model created in the Numerical Propulsion System Simulation (NPSS). The NPSS is a thermodynamic simulation environment that is commonly used for steady state gas turbine performance analysis. Completion of all the steps involved in the process results in a good match between T-MATS and NPSS at several steady state operating points. Additionally, the T-MATS model extended to run dynamically provides the possibility of simulating and evaluating closed loop responses.

gas path dynamics↗

A Modular Framework for Modeling Hardware Elements in Distributed Engine Control Systems

Progress toward the implementation of distributed engine control in an aerospace application may be accelerated through the development of a hardware-in-the-loop (HIL) system for testing new control architectures and hardware outside of a physical test cell environment. One component required in an HIL simulation system is a high-fidelity model of the control platform: sensors, actuators, and the control law. The control system developed for the Commercial Modular Aero-Propulsion System Simulation 40k (40,000 pound force thrust) (C-MAPSS40k) provides a verifiable baseline for development of a model for simulating a distributed control architecture. This distributed controller model will contain enhanced hardware models, capturing the dynamics of the transducer and the effects of data processing, and a model of the controller network. A multilevel framework is presented that establishes three sets of interfaces in the control platform: communication with the engine (through sensors and actuators), communication between hardware and controller (over a network), and the physical connections within individual pieces of hardware. This introduces modularity at each level of the model, encouraging collaboration in the development and testing of various control schemes or hardware designs. At the hardware level, this modularity is leveraged through the creation of a Simulink (R) library containing blocks for constructing smart transducer models complying with the IEEE 1451 specification. These hardware models were incorporated in a distributed version of the baseline C-MAPSS40k controller and simulations were run to compare the performance of the two models. The overall tracking ability differed only due to quantization effects in the feedback measurements in the distributed controller. Additionally, it was also found that the added complexity of the smart transducer models did not prevent real-time operation of the distributed controller model, a requirement of an HIL system.

numerical simulation↗

A Process for the Creation of T-MATS Propulsion System Models from NPSS data

A modular thermodynamic simulation package called the Toolbox for the Modeling and Analysis of Thermodynamic Systems (T-MATS) has been developed for the creation of dynamic simulations. The T-MATS software is designed as a plug-in for Simulink (Math Works, Inc.) and allows a developer to create system simulations of thermodynamic plants (such as gas turbines) and controllers in a single tool. Creation of such simulations can be accomplished by matching data from actual systems, or by matching data from steady state models and inserting appropriate dynamics, such as the rotor and actuator dynamics for an aircraft engine. This paper summarizes the process for creating T-MATS turbo-machinery simulations using data and input files obtained from a steady state model created in the Numerical Propulsion System Simulation (NPSS). The NPSS is a thermodynamic simulation environment that is commonly used for steady state gas turbine performance analysis. Completion of all the steps involved in the process results in a good match between T-MATS and NPSS at several steady state operating points. Additionally, the T-MATS model extended to run dynamically provides the possibility of simulating and evaluating closed loop responses.

gas path dynamics↗

Cantera Integration with the Toolbox for Modeling and Analysis of Thermodynamic Systems (T-MATS)

NASA Glenn Research Center (GRC) has recently developed a software package for modeling generic thermodynamic systems called the Toolbox for the Modeling and Analysis of Thermodynamic Systems (T-MATS). T-MATS is a library of building blocks that can be assembled to represent any thermodynamic system in the Simulink (The MathWorks, Inc.) environment. These elements, along with a Newton Raphson solver (also provided as part of the T-MATS package), enable users to create models of a wide variety of systems. The current version of T-MATS (v1.0.1) uses tabular data for providing information about a specific mixture of air, water (humidity), and hydrocarbon fuel in calculations of thermodynamic properties. The capabilities of T-MATS can be expanded by integrating it with the Cantera thermodynamic package. Cantera is an object-oriented analysis package that calculates thermodynamic solutions for any mixture defined by the user. Integration of Cantera with T-MATS extends the range of systems that may be modeled using the toolbox. In addition, the library of elements released with Cantera were developed using MATLAB native M-files, allowing for quicker prototyping of elements. This paper discusses how the new Cantera-based elements are created and provides examples for using T-MATS integrated with Cantera.

Thermodynamics↗

Applied Time Domain Stability Margin Assessment for Nonlinear Time-Varying Systems

The baseline stability margins for NASA's Space Launch System (SLS) launch vehicle were generated via the classical approach of linearizing the system equations of motion and determining the gain and phase margins from the resulting frequency domain model. To improve the fidelity of the classical methods, the linear frequency domain approach can be extended by replacing static, memoryless nonlinearities with describing functions. This technique, however, does not address the time varying nature of the dynamics of a launch vehicle in flight. An alternative technique for the evaluation of the stability of the nonlinear launch vehicle dynamics along its trajectory is to incrementally adjust the gain and/or time delay in the time domain simulation until the system exhibits unstable behavior. This technique has the added benefit of providing a direct comparison between the time domain and frequency domain tools in support of simulation validation. This technique was implemented by using the Stability Aerospace Vehicle Analysis Tool (SAVANT) computer simulation to evaluate the stability of the SLS system with the Adaptive Augmenting Control (AAC) active and inactive along its ascent trajectory. The gains for which the vehicle maintains apparent time-domain stability defines the gain margins, and the time delay similarly defines the phase margin. This method of extracting the control stability margins from the time-domain simulation is relatively straightforward and the resultant margins can be compared to the linearized system results. The sections herein describe the techniques employed to extract the time-domain margins, compare the results between these nonlinear and the linear methods, and provide explanations for observed discrepancies. The SLS ascent trajectory was simulated with SAVANT and the classical linear stability margins were evaluated at one second intervals. The linear analysis was performed with the AAC algorithm disabled to attain baseline stability margins. At each time point, the system was linearized about the current operating point using Simulink's built-in solver. Each linearized system in time was evaluated for its rigid-body gain margin (high frequency gain margin), rigid-body phase margin, and aero gain margin (low frequency gain margin) for each control axis. Using the stability margins derived from the baseline linearization approach, the time domain derived stability margins were determined by executing time domain simulations in which axis-specific incremental gain and phase adjustments were made to the nominal system about the expected neutral stability point at specific flight times. The baseline stability margin time histories were used to shift the system gain to various values around the zero margin point such that a precise amount of expected gain margin was maintained throughout flight. When assessing the gain margins, the gain was applied starting at the time point under consideration, thereafter following the variation in the margin found in the linear analysis. When assessing the rigid-body phase margin, a constant time delay was applied to the system starting at the time point under consideration. If the baseline stability margins were correctly determined via the linear analysis, the time domain simulation results should contain unstable behavior at certain gain and phase values. Examples will be shown from repeated simulations with variable added gain and phase lag. Faithfulness of margins calculated from the linear analysis to the nonlinear system will be demonstrated.

Kiefer, J. M.↗

Non-Maximally Decimated Filter Banks Enable Adaptive Frequency Hopping for Unmanned Aircraft Vehicles

In the last few years, radio technologies for unmanned aircraft vehicle (UAV) have advanced very rapidly. The increasing need to fly unmanned aircraft systems (UAS) in the national airspace system (NAS) to perform missions of vital importance to national security, defense, and science has pushed ahead the design and implementation of new radio platforms. However, a lot still has to be done to improve those radios in terms of performance and capabilities. In addition, an important aspect to account for is hardware cost and the feasibility to implement these radios using commercial off-the-shelf (COTS) components. UAV radios come with numerous technical challenges and their development involves contributions at different levels of the design. Cognitive algorithms need to be developed in order to perform agile communications using appropriate frequency allocation while maintaining safe and efficient operations in the NAS and, digital reconfigurable architectures have to be designed in order to ensure a prompt response to environmental changes. Command and control (C2) communications have to be preserved during "standard" operations while crew operations have to be minimized. It is clear that UAV radios have to be software-defined systems, where size, weight and power consumption (SWaP) are critical parameters. This paper provides preliminary results of the efforts performed to design a fully digital radio architecture as part of a NASA Phase I STTR. In this paper, we will explain the basic idea and technical principles behind our dynamic/adaptive frequency hopping radio for UAVs. We will present our Simulink model of the dynamic FH radio transmitter design for UAV communications and show simulation results and FPGA system analysis.

UAV↗

150 kW Class Solar Electric Propulsion Spacecraft Power Architecture Model

The National Aeronautics and Space Administration (NASA) Solar Electric Propulsion Technology Demonstration Mission (SEP TDM), in conjunction with PC Krause and Associates, has created a Simulink-based power architecture model for a 50 kilo-Watt (kW) solar electric propulsion system. NASA has extended this model to investigate 150 kW solar electric propulsion systems. Increasing the power capability to 150 kW is an intermediate step to the anticipated power requirements for Mars and other deep space applications. The high-power solar electric propulsion capability has been identified as a critical part of NASA’s future beyond-low-Earth-orbit for human-crewed exploration missions. This paper presents four versions of a 150 kW architecture, simulation results, and a discussion of their merits.

Power Control↗

150 kW Class Solar Electric Propulsion Spacecraft Power Architecture Model

The National Aeronautics and Space Administration (NASA) Solar Electric Propulsion Technology Demonstration Mission in conjunction with PC Krause and Associates has created a Simulink-based power architecture model for a 50 kilo-Watt (kW) solar electric propulsion system. NASA has extended this model to investigate 150 kW solar electric propulsion systems. Increasing the power system capability from 50 kW to 150 kW better aligns with the anticipated power requirements for Mars and other deep space explorations. The high-power solar electric propulsion capability has been identified as a critical part of NASAs future beyond-low-Earth-orbit for human-crewed exploration missions. This paper presents multiple 150 kW architectures, simulation results, and a discussion of their merits.

Power Control↗

Automated Analysis of Stateflow Models

Stateflow is a widely used modeling framework for embedded and cyber physical systems where control software interacts with physical processes. In this work, we present a framework a fully automated safety verification technique for Stateflow models. Our approach is two-folded: (i) we faithfully compile Stateflow models into hierarchical state machines, and (ii) we use automated logic-based verification engine to decide the validity of safety properties. The starting point of our approach is a denotational semantics of State flow. We propose a compilation process using continuation-passing style (CPS) denotational semantics. Our compilation technique preserves the structural and modal behavior of the system. The overall approach is implemented as an open source toolbox that can be integrated into the existing Mathworks Simulink Stateflow modeling framework. We present preliminary experimental evaluations that illustrate the effectiveness of our approach in code generation and safety verification of industrial scale Stateflow models.

Stateflow↗

Application of the Core Flight System to a Lunar Rover

Resource Prospector (RP) is a lunar mission sponsored by NASAs Advanced Exploration Systems (AES) division, that aims to study in-situ resource utilization (ISRU) feasibility and technologies on the surface of the moon. The RP missions lunar surface segment includes a rover equipped with with a suite of instruments specifically designed to measure and map volatiles both at the surface and in the subsurface. Of particular interest is the quantity and state of volatiles in permanently shadowed regions. To conduct the mission, ground system operators will remotely drive the rover, directing it to waypoints along the surface in order to achieve measurement objectives. At selected locations, an onboard drill will be deployed to collect material and obtain direct measurements of the subsurface constituents. RP is currently planned for launch in 2022. RP is managed at NASA Ames Research Center. The RP Rover is being designed and developed by NASA Johnson Space Center (JSC) in partnership with NASA Ames. NASA Kennedy Space Center (KSC) is responsible for the Honeybee drilling system and science payload.In order to better understand the technical challenges and demonstrate capability, in 2015 the RP project developed a rover testbed (known as RP15). In this mission in a year, a rover was designed, developed, and outfitted with science instruments and a drill. The rover was operated from a remote operations center, and operated in an outdoor lunar rock yard at Johnson space center. The study was a resounding success meeting all objectives. The RP Rover software architecture and development processes were based on the successful Lunar Atmosphere and Dust Environment Explorer spacecraft. This architecture is built on the Core Flight System software and an interface to MatlabSimulink auto-generated software components known as the Simulink Interface Layer (SIL). The application of this lunar satellite inspired framework worked well for the rover application, and is currently being planned for the mission. This presentation provides an overview of the architecture and processes, and describes some of the changes and challenges for the rover application.

CFS↗

Application of CFS to a Lunar Rover: Resource Prospector (RP)

Resource Prospector (RP) is a lunar mission sponsored by NASA's Advanced Exploration Systems (AES) division, that aims to study in-situ resource utilization (ISRU) feasibility and technologies on the surface of the moon. The RP mission's lunar surface segment includes a rover equipped with with a suite of instruments specifically designed to measure and map volatiles both at the surface and in the subsurface. Of particular interest is the quantity and state of volatiles in permanently shadowed regions. To conduct the mission, ground system operators will remotely drive the rover, directing it to waypoints along the surface in order to achieve measurement objectives. At selected locations, an onboard drill will be deployed to collect material and obtain direct measurements of the subsurface constituents. RP is currently planned for launch in 2022. RP is managed at NASA Ames Research Center. The RP Rover is being designed and developed by NASA Johnson Space Center (JSC) in partnership with NASA Ames. NASA Kennedy Space Center (KSC) is responsible for the Honeybee drilling system and science payload. In order to better understand the technical challenges and demonstrate capability, in 2015 the RP project developed a rover testbed (known as RP15). In this mission in a year, a rover was designed, developed, and outfitted with science instruments and a drill. The rover was operated from a remote operations center, and operated in an outdoor lunar rock yard at Johnson space center. The study was a resounding success meeting all objectives. The RP Rover software architecture and development processes were based on the successful Lunar Atmosphere and Dust Environment Explorer spacecraft. This architecture is built on the Core Flight System software and an interface to Matlab/Simulink auto-generated software components known as the Simulink Interface Layer (SIL). The application of this lunar satellite inspired framework worked well for the rover application, and is currently being planned for the mission. This presentation provides an overview of the architecture and processes, and describes some of the changes and challenges for the rover application.

RP↗

Investigation of a Verification and Validation Tool with a Turbofan Aircraft Engine Application

The development of more advanced control architectures for turbofan aircraft engines can yield gains in performance and efficiency over the lifetime of an engine. However, the implementation of these increasingly complex controllers is contingent on their ability to provide safe, reliable engine operation. Therefore, having the means to verify the safety of new control algorithms is crucial. As a step towards this goal, CoCoSim, a publicly available verification tool for Simulink, is used to analyze C-MAPSS40k, a 40,000 lbf class turbo-fan engine model developed at NASA for testing new control algorithms. Due to current limitations of the verification software, several modifications are made to C-MAPSS40k to achieve compatibility with CoCoSim. Some of these modifications sacrifice fidelity to the original model. Several safety and performance requirements typical for turbofan engines are identified and constructed into a verification framework. Preliminary results using an industry standard baseline controller for these requirements are presented. While verification capabilities are demonstrated, a truly comprehensive analysis will require further development of the verification tool.

V&↗

Are We There Yet? Determining the Adequacy of Formalized Requirements and Test Suites

Structural coverage metrics have traditionally categorized code as either covered or uncovered. Recent work presents a stronger notion of coverage, checked coverage, which counts only statements whose execution contributes to an outcome checked by an oracle. While this notion of coverage addresses the adequacy of the oracle, for Model-Based Development of safety critical systems, it is still not enough; we are also interested in how much of the oracle is covered, and whether the values of program variables are masked when the oracle is evaluated. Such information can help system engineers identify missing requirements as well as missing test cases. In this work, we add to the idea of checked coverage an analysis of requirements coverage to help provide insight to engineers as to whether the requirements or the test suite need to be improved. We implement our approach based on a dynamic backward slicing technique and evaluate it on several systems developed in Simulink. The results of our preliminary study show that even for systems with comprehensive test suites and good sets of requirements, our approach can identify cases where more tests or more requirements are needed to improve coverage numbers.

Dynamic Slicing↗