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An Evolutionary Optimization System for Spacecraft Design
Published in Don Potter, Manton Matthews, Moonis Ali, Industrial and Engineering Applications of Artificial Intelligence and Expert Systems, 2020
Alex S. Fukunaga, Andre D. Stechert
The Multidisciplinary Integrated Design Assistant for Spacecraft (MIDAS) [GPS95] is a computer-aided design environment developed at JPL that allows a user to integrate a system of (possibly distributed) design model components using a methogram, a graphical diagram depicting the data flow of the system. Each node in the methogram corresponds to a design model component, which may be one of 1) a model in a commercial design tool such as IDEAS, NASTRAN, or SPICE, 2) a program written in C, C++, or FORTRAN, 3) a MIDAS built-in tool, or 4) an embedded methogram (i.e., methograms can be hierarchical). Inputs to nodes in the methogram correspond to input parameters for the component represented by the node, and outputs from a methogram node correspond to output values computed by the component. Since it was implemented as a distributed object system and since an output node can be used to compute an arbitrary function of the parameters in the model, MIDAS provides a uniform interface to a wide variety of design models without requiring optimization algorithms to have strong dependencies on the target simulation. Figure 2 shows a screen shot of the MIDAS methogram for the Neptune Orbiter model.
Performance Modeling and Analysis Using VHDL and SystemC
Published in Wai-Kai Chen, Computer Aided Design and Design Automation, 2018
Robert H. Klenke, Jonathan A. Andrews, James H. Aylor
Methodology for integrated design and simulation (MIDAS) supports the design of distributed systems via iterative refinement of partially implemented performance specification (PIPS) models [26]. A PIPS model is a partially implemented design where some components exist as simulation models and others as operational subsystems (i.e., implemented components). Although they use the term “hybrid model” in this context it refers to a different type of modeling. MIDAS is an “integrated approach to software design” [26]. It supports the performance evaluation of software being executed on a given machine. It does not allow the integration of components expressed in an HDL into the model.
Mock-Ups, Models, Simulations, and Embedded Testing
Published in Samuel G. Charlton, Thomas G. O’Brien, Handbook of Human Factors Testing and Evaluation, 2019
Valerie J. Gawron, Thomas W. Dennison, Michael A. Biferno
MIDAS is an integrated set of tools for crew station designers to apply human factors principles and human performance models to the conceptual design of helicopters. In the developer’s own words: MIDAS is intended to be used at the early stages of conceptual design as an environment wherein designers can use computational representations of the crew station and operator, instead of hardware simulators and man-in-the-loop studies, to discover first-order problems and ask “what if” questions regarding the projected operator tasks, equipment and environment. (MATRIS, 2001, p. 1)
Automated leak localization performance without detailed demand distribution data
Published in Urban Water Journal, 2018
J. Moors, L. Scholten, J. P. van der Hoek, J. den Besten
The performance of the leak localization method and the location of the sensors are interdependent (Bonada and Meseguer 2014a; Bonada, Meseguer, and Mirats-Tur 2014b; Meseguer et al. 2014). Initially it was planned to install all 15 smart meters that Oasen, the water company servicing DMA Leimuiden, had acquired. The smart meters comprised a pressure sensor, temperature sensor and a pulse counter that can be attached to and installed next to an original water meter to derive the inflow. The pressure device included a pressure transmitter (JUMO MIDAS C08, measurement error 0.35% of full scale: 0–4 bar, 20°C), one measurement per five seconds, reported resolution 0.01 bar). The locations were selected based on the highest flow velocities and preferred flow direction as identified by modeling the current hydraulic situation. At these points the change of pressure was expected to be the most sensitive. In our case, the maximum velocity ranged from 0.01 to 0.15 m/s. This amounted to simulated nighttime pressure drops between 0.20 kPa in location I and 10.98 kPa in location III. Furthermore, the sensors were planned at the outer areas and on long pipe segments. They were installed in front of the existing water meters at the customer connections, from which inflow measurements were obtained. When the customer does not use water, the pressure at the house is the same as the pressure in the network. Due to unacceptance by customers, holidays, practical limitations of the location and firmware problems, only six of the 15 planned smart meters were installed and in operation before the start of the artificial leak campaign. Therefore, seven pressure loggers (PrimeLog+, measurement error 0.1% of full scale: 0–10 bar, one measurement per second) were additionally installed on fire hydrants. Their location was defined with the optimal model-based sensor location deployment procedure and used a Genetic Algorithm (Bonada, Meseguer, and Mirats-Tur 2014b) implemented in the Python package DEAP (Fortin et al. 2012). To perform this procedure a uniform demand model is calibrated with pressure measurement at nine locations across the DMA, in the same way as explained in the section ‘Hydraulic model set up, calibration and leak simulation’. The optimization objective was to minimize the largest group with the same leak response (leak response group) after truncating the pressure response to one decimal. The parameters were; population size: 4000, crossover fraction: 0.5, individual mutation probability: 0.2, attribute mutation probability: 0.1, number of generations: 20.