scholarly journals Model-based Design Optimization to Achieve the Performance Goals (16.0 SEER/9.5 HSPF)

2021 ◽  
Author(s):  
Bo Shen ◽  
Zhenning LI
Keyword(s):  
Design Optimization ◽  
Performance Goals ◽  
Model Based

Model-Based Conceptual Design Optimization Methods: Disaggregated Weather System Follow-On

2015 ◽  
Vol 52 (4) ◽  
pp. 1021-1037 ◽  
Author(s):  
Robert E. Thompson ◽  
John M. Colombi ◽  
Jonathan Black ◽  
Bradley J. Ayres
Keyword(s):  
Design Optimization ◽  
Conceptual Design ◽  
Optimization Methods ◽  
Weather System ◽  
Model Based

A Robust Design Optimization Framework for Systematic Model-Based Calibration of Engine Control Systems

2015 ◽  
Vol 137 (11) ◽  
Author(s):  
Hoseinali Borhan ◽  
Edmund Hodzen
Keyword(s):  
Control System ◽  
Design Optimization ◽  
Robust Design ◽  
Closed Loop ◽  
Optimization Technique ◽  
Loop System ◽  
Robust Design Optimization ◽  
Engine Control ◽  
Closed Loop System ◽  
Model Based

In this paper, a systematic model-based calibration framework basing on robust design optimization technique is developed for engine control system. In this framework, the control system is calibrated in an optimization fashion where both performance and robustness of the closed-loop system to uncertainties are optimized. The proposed calibration process has three steps: in the first step, the optimal performance of the system at the nominal conditions, where the effects of uncertainties are ignored, is computed by formulation of the controller calibration as an optimization problem. The capabilities of the controller are fully explored at nominal conditions. In the second step, the robustness and sensitivity of a selected control design to the system uncertainties are analyzed using Monte Carlo simulation. In the third step, robust design optimization is applied to optimize both performance and robustness of the closed-loop system to the uncertainties. The robustness capabilities of the controller are fully explored and the one that satisfies both performance and robustness requirements is selected. This process is implemented for the calibration of an advanced diesel air path control system with a variable geometry turbocharger (VGT) and dual loop exhaust gas recirculation (EGR) architecture.

Integrated Modeling and Analysis to Support Model-Based Systems Engineering

2012 ◽  
Author(s):  
Hongman Kim ◽  
David Fried ◽  
Peter Menegay ◽  
Grant Soremekun
Keyword(s):  
Design Optimization ◽  
Systems Engineering ◽  
Optimization Problems ◽  
Integrated Modeling ◽  
System Model ◽  
Engineering Analysis ◽  
Modeling Tools ◽  
Modeling And Analysis ◽  
Model Based ◽  
Model Based Systems Engineering

Model-based systems engineering (MBSE) is an approach to improve traditional document-based systems engineering approach through the use of a system model. In the current practice of system developments, there exists a large gap between systems engineering activities and engineering analyses, because systems engineers and engineering analysts are using different models, tools and terminology. The gap results in inefficiencies and quality issues that can be very expensive. This work presents an integrated modeling and analysis capability that bridges the gap. The technical approach is based on integrating SysML modeling tools with process integration and design optimization framework. This approach connects SysML models with various engineering analysis tools through a common interface. A capability was developed to automatically generate analysis models from a system model and then execute the analytical models. Requirements conformance analysis was performed using results of engineering analysis. A technique was developed to define optimization problems in SysML, where requirements were used as design constraints. The integrated system modeling and analysis capability was demonstrated using an automobile brake pad design example. The integrated toolset was used to understand impacts of requirements changes in the SysML model and to find a new design that meets the new requirements through engineering design optimization.

Dynamics Modeling to Inform Design Optimization

2014 ◽  
Author(s):  
Tim Foglesong ◽  
Rob Stone ◽  
John Parmigiani
Keyword(s):  
Design Optimization ◽  
Testing Machine ◽  
Initial Model ◽  
Dynamics Modeling ◽  
Tensile Testing Machine ◽  
Drive Mechanism ◽  
Model Based ◽  
Reinforced Plastic ◽  
Future Work ◽  
Spring Constants

This paper presents the methods employed in modeling a vibratory conveyor for use in model-based design optimization. The conveyor, essentially a large table whose top oscillates at an angle off of horizontal, uses springs between the drive mechanism and the tabletop to directly apply a sinusoidal excitation. These springs prevent the system from losing response amplitude as load is increased. The manufacturer is having difficulty optimizing performance and reliability in newer designs, and has requested a model-based approach to the design optimization. This study discusses the initial steps taken in modeling the original mechanism design, specifically the dynamic model and experimental determination of the necessary spring constants. The first full iteration of the model starts with low detail and simplified geometry, with a plan to add complexity as needed to improve accuracy. In the initial model, the parallel springs in the tabletop suspension are combined, bypassing the spring mounting geometry, and tested as one large spring. The drive mechanism springs, bars of fiber reinforced plastic (FRP), are more meticulously tested in a tensile testing machine. The resulting spring constants are used in the initial model to calculate the sinusoidal response of the tabletop at any given input frequency. The deflection response per time of the tabletop is then measured and compared to the model. Conclusions detail the initial model’s accuracy and Future Work examines how to bring it in closer agreement with the real machine’s sinusoidal response.

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