Criticality and Sensitivity Analysis for Incremental Performance Optimization of Asynchronous Pipelines

2017 ◽  
Author(s):  
Chun-Hong Shih ◽  
Jie-Hong R. Jiang
Keyword(s):  
Sensitivity Analysis ◽  
Performance Optimization

Structure Sensitivity Analysis and Dynamic Performance Optimization of Marine Gearbox

2017 ◽  
Author(s):  
Tengjiao Lin ◽  
Daokun Xie ◽  
Ziran Tan ◽  
Bo Liu
Keyword(s):  
Sensitivity Analysis ◽  
Performance Optimization ◽  
Optimization Design ◽  
Dynamic Performance ◽  
Structure Sensitivity ◽  
Superposition Method ◽  
Structure Parameters ◽  
Analysis Model ◽  
Vibration Acceleration ◽  
Response Optimization

The aim of this paper is to investigate the influence of structure parameters on the vibration characteristics and improve the dynamic performance of marine gearbox. A finite element model was established to solve the dynamic response by using modal superposition method. Based on the theory of multi-objective optimization design, the structure sensitivity analysis model of marine gearbox was established, which takes the structure parameters of the housing as design variables. The modal and response sensitivity was obtained by using the optimal gradient method. According to the results of sensitivity analysis, a modal and response optimization model of marine gearbox was established. The objective was to avoid natural frequencies from the excitation frequencies and minimize the root mean square of vibration acceleration of the evaluating points on the surface of housing. Then the modal optimization and response optimization of gearbox were carried out by using zero-order and first-order optimization method. The results indicate that the dynamic optimization of the gearbox can be achieved. After optimization, the amplitude of vibration acceleration of the evaluating points on the housing surface has been reduced and the resonance of marine gearbox can be avoided.

scholarly journals An Innovative Multicriteria Decision-Making Tool for Building Performance Optimization

2021 ◽  
Vol 11 (1) ◽  
pp. 6603-6608
Author(s):  
A. Serbouti ◽  
M. Rattal ◽  
E. M. Oualim ◽  
A. Mouhsen
Keyword(s):  
Sensitivity Analysis ◽  
Global Warming ◽  
Performance Optimization ◽  
Multicriteria Decision Making ◽  
Building Performance ◽  
Global Dynamic ◽  
Energy Efficient Buildings ◽  
Overall Performance ◽  
New Generation ◽  
The Impact

Buildings are accountable for nearly 40% of global greenhouse gas emissions. Their overall efficiency is thus a major pillar to optimize energy consumption and to mitigate engendered global warming. The current work takes part in this global dynamic. Indeed, we developed a standalone decision-aid tool based on sensitivity analysis, multiobjective optimization, and artificial neural networks to design a new generation of energy-efficient buildings. The tool aims to allow benefiting from Sobol’ sensitivity analysis samplings to instantaneously generate sensitivity indexes and perform multicriteria optimizations. This efficient process allows both understanding buildings’ complex behavior (by ranking the impact of the inputs parameters on the outputs and highlighting their interactions) and optimizing their overall performance. The main advantages of this method are the time gaining and the provision of relevant outputs to analyze the buildings’ design. The tool was successfully used to solve constrained 13-input parameters with 5-criteria on TRNSYS simulation program, considering the impact of global warming

Performance optimization model n 802.11n networks using multi-objective programming

2017 ◽  
pp. 2907-2918
Author(s):  
Carlos Arturo Castillo Medina ◽  
Octavio Jose Salcedo Parra ◽  
Miguel J. Espitia R.
Keyword(s):  
Sensitivity Analysis ◽  
Wireless Networks ◽  
Performance Optimization ◽  
Optimization Model ◽  
Performance Data ◽  
Past Performance ◽  
Multi Objective ◽  
Multi Objective Programming ◽  
Objective Programming

This project aims to develop a method to find an alternative to analyze the performance of wireless networks (for this case 802.11n) using the Multiobjetive Programming. They perform three functions with their respective objectives and constraints finds its solution. The model was validated against and past performance data against data taken from two wireless networks possess a different complement infrastructure, this in order to perform a sensitivity analysis.

scholarly journals Assessment of Sensitivity to Evaluate the Impact of Operating Parameters on Stability and Performance in Proton Exchange Membrane Fuel Cells

Energies ◽  
2021 ◽  
Vol 14 (14) ◽  
pp. 4069
Author(s):  
Mingzhang Pan ◽  
Chengjie Pan ◽  
Jinyang Liao ◽  
Chao Li ◽  
Rong Huang ◽  
...  
Keyword(s):  
Sensitivity Analysis ◽  
Fuel Cell ◽  
Performance Optimization ◽  
Proton Exchange Membrane ◽  
Proton Exchange ◽  
Operating Conditions ◽  
Output Performance ◽  
Highly Nonlinear ◽  
Exchange Membrane ◽  
The Impact

As a highly nonlinear system, the performance of proton exchange membrane fuel cell (PEMFC) is controlled by various parameters. If the effects of all parameters are considered during the performance optimization, low working efficiency and waste of resources will be caused. The development of sensitivity analysis for parameters can not only exclude the parameters which have slight effects on the system, but also provide the reasonable setting ranges of boundary values for simulation of performance optimization. Therefore, sensitivity analysis of parameters is considered as one of the methods to optimize the fuel cell performance. According to the actual operating conditions of PEMFC, the fluctuation ranges of seven sets of parameters affecting the output performance of PEMFC are determined, namely cell operating temperature, anode/cathode temperature, anode/cathode pressure, and anode/cathode mass flow rate. Then, the control variable method is used to qualitatively analyze the sensitivity of main parameters and combines with the Monte Carlo method to obtain the sensitivity indexes of the insensitive parameters under the specified current density. The results indicate that among these parameters, the working temperature of the fuel cell is the most sensitive to the output performance under all working conditions, whereas the inlet temperature is the least sensitive within the range of deviation. Moreover, the cloud maps of water content distribution under the fluctuation of three more sensitive parameters are compared; the results verify the simulated data and further reveal the reasons for performance changes. The workload of PEMFC performance optimization will be reduced based on the obtained results.

Performance improvement of a crystallization system through optimization and sensitivity analysis

2020 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Anil Kr. Aggarwal
Keyword(s):  
Sensitivity Analysis ◽  
Performance Optimization ◽  
Initial Boundary ◽  
Time To Failure ◽  
Transition Diagram ◽  
Content Type ◽  
Crystallization System ◽  
Sugar Plant ◽  
Maintenance Personnel ◽  
Mean Time

PurposeThis paper deals with the performance optimization and sensitivity analysis for crystallization system of a sugar plant.Design/methodology/approachCrystallization system comprises of five subsystems, namely crystallizer, centrifugal pump and sugar grader. The Chapman–Kolmogorov differential equations are derived from the transition diagram of the crystallization system using mnemonic rule. These equations are solved to compute reliability and steady state availability by putting the appropriate combinations of failure and repair rates using normalizing and initial boundary conditions. The performance optimization is carried out by varying number of generations, population size, crossover and mutation probabilities. Finally, sensitivity analysis is performed to analyze the effect of change in failure rates of each subsystem on availability, mean time to failure (MTBF) and mean time to repair (MTTR).FindingsThe highest performance observed is 96.95% at crossover probability of 0.3 and sugar grader subsystem comes out to be the most critical and sensitive subsystem.Originality/valueThe findings of the paper highlights the optimum value of performance level at failure and repair rates for subsystems and also helps identify the most sensitive subsystem. These findings are highly beneficial for the maintenance personnel of the plant to plan the maintenance strategies accordingly.

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