Optimization of Magnetorheological Brake

2011 ◽  
Author(s):  
Snegdha Gupta ◽  
Harish Hirani
Keyword(s):  
Minimum Weight ◽  
Hybrid Genetic Algorithm ◽  
Brake System ◽  
Outer Diameter ◽  
Current Accounting ◽  
Hydraulic Brake ◽  
Rate Dependent ◽  
Design Variables ◽  
Braking Torque ◽  
Gradient Based

Quick response and rheological properties as a function of magnetic field are well known features of MR fluids which inspire their usage as brake materials. Controllable torque and minimum weight of brake system are the deciding functions based on which the viability of the MR brake against the conventional hydraulic brake system can be judged. The aim of this study is to optimize a multi-disk magneto-rheological brake system considering torque and weight as objective functions and geometric dimensions of conventional hydraulic brake as constraints. The electric current accounting magnetic saturation, MR gap, number of disk, thickness of disk, and outer diameter of disk have been considered as design variables. To model the behavior of MR Fluid, Bingham and Herschel Bulkley models have been compared. To implement these models in estimating the braking torque a modification in shear rate dependent component has been proposed. The overall design of MR brake has been optimized using a hybrid (Genetic algorithm plus gradient based) optimization scheme of MATLAB software.

The Car Brake System Design and Calculation

2013 ◽  
Vol 457-458 ◽  
pp. 340-343
Author(s):  
Yong Wang
Keyword(s):  
Distribution Coefficient ◽  
System Design ◽  
Brake System ◽  
Force Distribution ◽  
Drive Mechanism ◽  
Hydraulic Brake ◽  
Braking Torque ◽  
Braking Force

Calculation of car brake system design, also according to the known automotive related parameters is obtained by calculating the main parameters. The brake and braking torque, braking moment and braking force distribution coefficient and hydraulic brake drive mechanism related parameters. Finally, the braking performances are analyzed in detail.

CAD and Optimization of Helical Torsion Springs

1994 ◽  
Author(s):  
Sayed M. Metwalli ◽  
M. Alaa Radwan ◽  
Abdel Aziz M. Elmeligy
Keyword(s):  
Iterative Process ◽  
Minimum Weight ◽  
Design Parameters ◽  
Optimal Parameters ◽  
Direct Evaluation ◽  
Torsion Spring ◽  
Performance Requirements ◽  
Design Variables ◽  
Method Of Lagrange Multipliers ◽  
Torsion Springs

Abstract The convensional procedure of helical torsion spring design is an iterative process because of large number of requirements and relations that are to be attained once at a time. The design parameters are varied at random until the spring design satisfies performance requirements. A CAD of the spring for minimum weight is formulated with and without the variation of the maximum normal stress with the wire diameter. The CAD program solves by employing the method of Lagrange-Multipliers. The optimal parameters, in a closed form are obtained, normalized and plotted. These explicit relations of design variables allow direct evaluation of optimal design objective and hence, an absolute optimum could be achieved. The comparison of optimum results with those previously published, shows a pronounced achievement in the reduction of torsion spring weight.

Autonomous Emergency Braking Control Based on Hierarchical Strategy Using Integrated-Electro-Hydraulic Brake System

2017 ◽  
Author(s):  
Xiangkun He ◽  
Xuewu Ji ◽  
Kaiming Yang ◽  
Yulong Liu ◽  
Jian WU ◽  
...  
Keyword(s):  
Brake System ◽  
Emergency Braking ◽  
Hydraulic Brake ◽  
Braking Control

scholarly journals Application of FURIA for Finding the Faults in a Hydraulic Brake System Using a Vibration Analysis through a Machine Learning Approach

2019 ◽  
Vol 10 (1) ◽  
Author(s):  
Alamelu Manghai T. M ◽  
Jegadeeshwaran R
Keyword(s):  
Machine Learning ◽  
Fault Diagnosis ◽  
Decision Tree ◽  
Continuous Monitoring ◽  
Selection Process ◽  
Brake System ◽  
Learning Approach ◽  
Vibration Signals ◽  
Hydraulic Brake ◽  
Machine Learning Approach

Vibration-based continuous monitoring system for fault diagnosis of automobile hydraulic brake system is presented in this study. This study uses a machine learning approach for the fault diagnosis study. A hydraulic brake system test rig was fabricated. The vibration signals were acquired from the brake system under different simulated fault conditions using a piezoelectric transducer. The histogram features were extracted from the acquired vibration signals. The feature selection process was carried out using a decision tree. The selected features were classified using fuzzy unordered rule induction algorithm ( FURIA ) and Repeated Incremental Pruning to Produce Error Reduction ( RIPPER ) algorithm. The classification results of both algorithms for fault diagnosis of a hydraulic brake system were presented. Compared to RIPPER and J48 decision tree, the FURIA performs better and produced 98.73 % as the classification accuracy.

scholarly journals Surrogate Assisted Design Optimization of an Air Turbine

2014 ◽  
Vol 2014 ◽  
pp. 1-8 ◽  
Author(s):  
Rameez Badhurshah ◽  
Abdus Samad
Keyword(s):  
Design Space ◽  
Hybrid Genetic Algorithm ◽  
Navier Stokes ◽  
Cfd Analysis ◽  
Impulse Turbine ◽  
Optimal Point ◽  
Ocean Wave Energy ◽  
Design Variables ◽  
Air Turbine ◽  
Full Factorial

Surrogates are cheaper to evaluate and assist in designing systems with lesser time. On the other hand, the surrogates are problem dependent and they need evaluation for each problem to find a suitable surrogate. The Kriging variants such as ordinary, universal, and blind along with commonly used response surface approximation (RSA) model were used in the present problem, to optimize the performance of an air impulse turbine used for ocean wave energy harvesting by CFD analysis. A three-level full factorial design was employed to find sample points in the design space for two design variables. A Reynolds-averaged Navier Stokes solver was used to evaluate the objective function responses, and these responses along with the design variables were used to construct the Kriging variants and RSA functions. A hybrid genetic algorithm was used to find the optimal point in the design space. It was found that the best optimal design was produced by the universal Kriging while the blind Kriging produced the worst. The present approach is suggested for renewable energy application.

Efficient Gradient-Based Algorithms for the Construction of Pareto Fronts

2011 ◽  
Author(s):  
Sriram Shankaran ◽  
Brian Barr
Keyword(s):  
Pareto Front ◽  
Computational Cost ◽  
High Fidelity ◽  
Simulation Tools ◽  
Pareto Points ◽  
Uniform Spacing ◽  
Design Variables ◽  
Gradient Based ◽  
Pareto Fronts ◽  
Sampling Approach

The objective of this study is to develop and assess a gradient-based algorithm that efficiently traverses the Pareto front for multi-objective problems. We use high-fidelity, computationally intensive simulation tools (for eg: Computational Fluid Dynamics (CFD) and Finite Element (FE) structural analysis) for function and gradient evaluations. The use of evolutionary algorithms with these high-fidelity simulation tools results in prohibitive computational costs. Hence, in this study we use an alternate gradient-based approach. We first outline an algorithm that can be proven to recover Pareto fronts. The performance of this algorithm is then tested on three academic problems: a convex front with uniform spacing of Pareto points, a convex front with non-uniform spacing and a concave front. The algorithm is shown to be able to retrieve the Pareto front in all three cases hence overcoming a common deficiency in gradient-based methods that use the idea of scalarization. Then the algorithm is applied to a practical problem in concurrent design for aerodynamic and structural performance of an axial turbine blade. For this problem, with 5 design variables, and for 10 points to approximate the front, the computational cost of the gradient-based method was roughly the same as that of a method that builds the front from a sampling approach. However, as the sampling approach involves building a surrogate model to identify the Pareto front, there is the possibility that validation of this predicted front with CFD and FE analysis results in a different location of the “Pareto” points. This can be avoided with the gradient-based method. Additionally, as the number of design variables increases and/or the number of required points on the Pareto front is reduced, the computational cost favors the gradient-based approach.

A Decomposition Method for Concurrent Design of Mixed Discrete/Continuous Systems

1993 ◽  
Author(s):  
Jeffrey M. Ford ◽  
Christina L. Bloebaum
Keyword(s):  
Design Process ◽  
Decomposition Method ◽  
Organizational Design ◽  
Method Development ◽  
Minimum Weight ◽  
Optimization Methods ◽  
Continuous Systems ◽  
Computationally Efficient ◽  
Subspace Optimization ◽  
Design Variables

Abstract Interest in Concurrent Engineering (CE) has increased as industry looks for more efficient means of product design. Design optimization methods that facilitate the CE approach are an important aspect of current research. Among the methods that have been proposed is the Concurrent Subspace Optimization (CSSO) method, which allows the optimization problem to be decomposed into coupled subproblems. These subproblems may correspond to the different disciplines involved in the design process or to participating organizational design or manufacturing groups. The decomposition allows each discipline to apply their own optimization criteria to the problem. While this method may not be as computationally efficient as other methods, it allows the design process to conform to the departmental divisions that already exist in industry. The method development to date has focused on continuous systems only. However, problems that can not be modeled as continuous systems, such as those involving the placement of active controllers in CSI applications, would benefit from a method that allows the use of discrete parameters. The paper presents a decomposition method (based on CSSO) for the optimal design of mixed discrete/continuous systems. The method is applied to the design of a composite plate for minimum weight, with design variables contributed from sizing variables (continuous) and material combinations (discrete).

Adjoint-based aerodynamic drag minimisation with trim penalty

2021 ◽  
pp. 1-25
Author(s):  
S. Shitrit
Keyword(s):  
Drag Reduction ◽  
Aerodynamic Drag ◽  
Lift Coefficient ◽  
Free Form ◽  
Shape Optimisation ◽  
Control Points ◽  
Aerodynamic Shape ◽  
Design Variables ◽  
Gradient Based ◽  
Mesh Warping

Abstract The aerodynamic performance of conventional aircraft configurations are mainly affected by the wing and horizontal tail. Drag reduction by shape optimisation of the wing, while taking into account the aircraft trimmed constraint, has more benefit than focusing solely on the wing. So in order to evaluate this approach, the following study presents results of a single and multipoint aerodynamic shape optimisation of the wing-body-tail configuration, defined by the Aerodynamic Design Discussion Group (ADODG). Most of the aerodynamic shape optimisation problems published in the last years are focused mainly on the wing as the main driver for performance improvement, with no trim constraint and/or excess drag obtained from the fuselage, fins or other parts. This work partially fills this gap by an investigation of RANS-based aerodynamic optimisation for transonic trimmed flight. Mesh warping and geometry parametrisation is accomplished by fitting the multi-block structured grid to a B-spline volumes and performing the mesh movement by using surface control points embedded within the free-form deformation (FFD) volumes. A gradient-based optimisation algorithm is used with an adjoint method in order to compute the derivatives of the objective and constraint functions with respect to the design variables. In this work the aerodynamic shape optimisation of the CRM wing-body-tail configuration is investigated, including a trim constraint that is satisfied by rotating the horizontal tail. The shape optimisation is driven by 432 design variables that envelope the wing surface, and 120 shape variables for the tail, as well as the angle of attack and tail rotation angles. The constraints are the lift coefficient, wing’s thickness controlled by 1,000 control points, and the wing’s volume. For the untrimmed configuration the drag coefficient is reduced by 5.76%. Optimising the wing with a trim condition by tail rotation results in shock-free design with a considerably improved drag, even better than the untrimmed-optimised case. The second optimisation problem studied is a single and multi-point lift constraint drag minimisation of a gliding configuration wing in transonic viscous flow. The shock is eliminated, reducing the drag of the untrimmed configuration by more than 60%, using 192 design variables. Further robustness is achieved through a multi-point optimisation with more than 45% drag reduction.

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