Improvement of the Static and Dynamic Characteristics of Magnetic Head Sliders by Optimum Design

1999 ◽  
Vol 122 (1) ◽  
pp. 280-287 ◽  
Author(s):  
Hiromu Hashimoto ◽  
Yasuhisa Hattori
Keyword(s):  
Objective Function ◽  
Optimum Design ◽  
Amplitude Ratio ◽  
Optimization Technique ◽  
Hard Disk Drives ◽  
Maximum Amplitude ◽  
Disk Surface ◽  
Operating Conditions ◽  
Magnetic Head ◽  
Design Variables

The aim of this paper is to develop a general methodology for the optimum design of magnetic head sliders in improving the spacing characteristics between a slider and disk surface under static and dynamic operating conditions of hard disk drives and to present an application of the methodology to the IBM 3380-type slider design. To generate the optimal design variables, the objective function is defined as the weighted sum of the minimum spacing, the maximum difference in the spacing due to variation of the radial location of the head, and the maximum amplitude ratio of the slider motion. Slider rail width, taper length, taper angle, suspension position, and preload are selected as the design variables. Before the optimization of the head, the effects of these five design variables on the objective function are examined by a parametric study, and then the optimum design variables are determined by applying the hybrid optimization technique, combining the direct search method and successive quadratic programming. From the obtained results, the effectiveness of optimum design on the spacing characteristics of magnetic heads is clarified. [S0742-4787(00)03701-2]

Optimum Design of Magnetic Head Slider Under Static and Dynamic Operation Conditions of Hard Disk Drive

1999 ◽  
Author(s):  
Yasuhisa Hattori ◽  
Hiromu Hashimoto ◽  
Masayuki Ochiai
Keyword(s):  
Objective Function ◽  
Hard Disk Drive ◽  
Optimum Design ◽  
Hard Disk ◽  
Disk Drive ◽  
Magnetic Head ◽  
Head Slider ◽  
Dynamic Operation ◽  
Operation Conditions ◽  
Design Variables

Abstract The aim of this paper is to develop the general methodology for the optimum design of magnetic head slider for improving the spacing characteristics between head slider and disk surfaces under the static and dynamic operation conditions of hard disk drive and to present an application of the methodology to IBM 3380-type slider design. In the optimum design, the objective function is defined as the weighted sum of minimum spacing, maximum difference of spacing due to variation of radial location of head and maximum amplitude ratio of slider motion. Slider rail width, taper length, taper angle, suspension position and preload are selected as the design variables. Before the optimization of magnetic head slider, the effects of these five design variables on the objective function are examined by the parametric study, and then the optimum design variables are determined by applying the hybrid optimization technique combining the direct search method and the successive quadratic programming (SQP). From the results obtained, the effectiveness of optimum design on the spacing characteristics of magnetic head slider is clarified.

Shape Optimization of Hydraulic Structures: an Example of an Optimum Design of a Fish Passage

10.29007/2k64 ◽  
2018 ◽  
Author(s):  
Pat Prodanovic ◽  
Cedric Goeury ◽  
Fabrice Zaoui ◽  
Riadh Ata ◽  
Jacques Fontaine ◽  
...  
Keyword(s):  
Numerical Model ◽  
Shape Optimization ◽  
Objective Function ◽  
Optimum Design ◽  
Optimization Problem ◽  
A Priori ◽  
Coupled System ◽  
Fish Passage ◽  
Hydraulic Structures ◽  
Design Variables

This paper presents a practical methodology developed for shape optimization studies of hydraulic structures using environmental numerical modelling codes. The methodology starts by defining the optimization problem and identifying relevant problem constraints. Design variables in shape optimization studies are configuration of structures (such as length or spacing of groins, orientation and layout of breakwaters, etc.) whose optimal orientation is not known a priori. The optimization problem is solved numerically by coupling an optimization algorithm to a numerical model. The coupled system is able to define, test and evaluate a multitude of new shapes, which are internally generated and then simulated using a numerical model. The developed methodology is tested using an example of an optimum design of a fish passage, where the design variables are the length and the position of slots. In this paper an objective function is defined where a target is specified and the numerical optimizer is asked to retrieve the target solution. Such a definition of the objective function is used to validate the developed tool chain. This work uses the numerical model TELEMAC- 2Dfrom the TELEMAC-MASCARET suite of numerical solvers for the solution of shallow water equations, coupled with various numerical optimization algorithms available in the literature.

scholarly journals Prof. Comparing H2 and H∞ Algorithms for Optimum Design of Tuned Mass Dampers under Near-Fault and Far-Fault Earthquake Motions

2020 ◽  
Author(s):  
Ali Kaveh ◽  
Mazyar Fahimi Fazam ◽  
Rasool Maroofiazar
Keyword(s):  
Objective Function ◽  
Optimum Design ◽  
Ground Motions ◽  
Optimization Technique ◽  
Large Set ◽  
Near Fault ◽  
Colliding Bodies Optimization ◽  
Controlled Structure ◽  
Robust Optimum Design ◽  
Far Fault

In this study, the robust optimum design of Tuned Mass Damper (TMD) is established. The H2 and H∞ norm of roof displacement transfer function are implemented and compared as the objective functions under Near-Fault (NF) and Far-Fault (FF) earthquake motions. Additionally, the consequences of different characteristics of NF ground motions such as forward-directivity and fling-step are investigated on the behavior of a benchmark 10-story controlled structure. The Colliding Bodies Optimization (CBO) is employed as an optimization technique to calculate the optimum parameters of the TMDs. The resulting statistical assessment shows that the H∞ objective function is rather superior to H2 objective function for optimum design of TMDs under NF and FF earthquake excitations. Finally, the robustness of the designed TMDs is evaluated under a large set of natural ground motions.

scholarly journals The Impact of Surrogate Models on the Multi-Objective Optimization of Pump-As-Turbine (PAT)

Energies ◽  
2020 ◽  
Vol 13 (9) ◽  
pp. 2271 ◽  
Author(s):  
Stephen Ntiri Asomani ◽  
Jianping Yuan ◽  
Longyan Wang ◽  
Desmond Appiah ◽  
Kofi Asamoah Adu-Poku
Keyword(s):  
Neural Network ◽  
Objective Function ◽  
Surrogate Model ◽  
Surrogate Models ◽  
Operating Conditions ◽  
Pareto Optimal ◽  
Objective Functions ◽  
Operating Range ◽  
Design Variables ◽  
Pareto Optimal Points

Pump-as-turbine (PAT) technology permits two operating states—as a pump or turbine, depending on the demand. Nevertheless, designing the geometrical components to suit these operating states has been an unending design issue, because of the multi-conditions for the PAT technology that must be attained to enhance the hydraulic performance. Also, PAT has been known to have a narrow operating range and operates poorly at off-design conditions, due to the lack of flow control device and poor geometrical designs. Therefore, for the PAT to have a wider operating range and operate effectively at off-design conditions, the geometric parameters need to be optimized. Since it is practically impossible to optimize more than one objective function at the same time, a suitable surrogate model is needed to mimic the objective functions for it to be solvable. In this study, the Latin hypercube sampling method was used to obtain the objective function values, the Adaptive Neuro-Fuzzy Inference System (ANFIS), Artificial Neural Network (ANN) and Generalized Regression Neural Network (GRNN) were used as surrogate models to approximate the objective functions in the design space. Then, a suitable surrogate model was chosen for the optimization. The Pareto-optimal solutions were obtained by using the Pareto-based genetic algorithm (PBGA). To evaluate the results of the optimization, three representative Pareto-optimal points were selected and analyzed. Compared to the baseline model, the Pareto-optimal points showed a great improvement in the objective functions. After optimization, the geometry of the impeller was redesigned to suit the operating conditions of PAT. The findings show that the efficiencies of the optimized design variables of PAT were enhanced by 23.7%, 11.5%, and 10.4% at part load, design point, and under overload flow conditions, respectively. Moreover, the results also indicated that the chosen design variables (b2, β2, β1, and z) had a substantial impact on the objective functions, justifying the feasibility of the optimization method employed in this study.

Three-dimensional optimum design of the cooling lines of injection moulds based on boundary element design sensitivity analysis

2002 ◽  
Vol 216 (7) ◽  
pp. 1067-1071 ◽  
Author(s):  
H Zhou ◽  
D Li ◽  
S Cui
Keyword(s):  
Sensitivity Analysis ◽  
Objective Function ◽  
Boundary Element ◽  
Optimum Design ◽  
Three Dimensional ◽  
Design Sensitivity ◽  
Boundary Integral ◽  
Optimization Techniques ◽  
Design Sensitivity Analysis ◽  
Design Variables

A three-dimensional numerical simulation using the boundary element method is proposed, which can predict the cavity temperature distributions in the cooling stage of injection moulding. Then, choosing the radii and positions of cooling lines as design variables, the boundary integral sensitivity formulations are deduced. For the optimum design of cooling lines, the squared difference between the objective temperature and the temperature of the cavity is taken as the objective function. Based on the optimization techniques with design sensitivity analysis, an iterative algorithm to reach the minimum value of the objective function is introduced, which leads to the optimum design of cooling lines at the same time.

Reliability-based optimum design for mechanical problems using genetic algorithms

2008 ◽  
Vol 222 (9) ◽  
pp. 1791-1799 ◽  
Author(s):  
Y-T Tsai ◽  
H-C Chang
Keyword(s):  
Genetic Algorithms ◽  
Optimum Design ◽  
Optimization Technique ◽  
Structural Complexity ◽  
Optimal Solution ◽  
Unconstrained Minimization ◽  
Reliability Design ◽  
Performance Indexes ◽  
Design Variables ◽  
Relationship Of

A reliability oriented design approach for mechanical or structural components is implemented primarily based on strength—stress interference (SSI) theory. This paper demonstrates a principle for combining SSI theory and an optimization technique for developing a reliability-based optimum design for mechanical problems. The independently paired information (strength and stress distributions) are basic while progressing reliability design. For a complex system, the independently paired information sometimes is not easily clarified due to the structural complexity or the coupled relationship of the loads. To treat these problems, the paper proposes to express the independently paired information from the viewpoint of supply-requirement of a design in performance. The supply (provided by a design) is analogized to the strength as well as the requirement (requested by the customer) to the stress. Based on the viewpoint of supply-requirement, the paper presents four types of performance-related reliability measurement to fulfil reliability design for mechanical problems. The reliability measurements are derived according to the related design variables that formulate the performance indexes. Next, the designed problem expressed with probabilistic formulation is transformed into an unconstrained minimization problem subjected to the constraints of the performance needs and its reliability target. Genetic algorithms (GAs) are used to find the optimal solution for the reliability design problem. The related theories and an example of design are reported in this paper to depict the proposed method.

Improving Emission and Blow-Out Characteristics of Novel Natural Gas Low NOx Burners for Heavy Duty Gas Turbine

2019 ◽  
Author(s):  
Matteo Cerutti ◽  
Michele Roma ◽  
Alessio Picchi ◽  
Riccardo Becchi ◽  
Bruno Facchini
Keyword(s):  
Objective Function ◽  
Gas Turbine ◽  
Mathematical Formulation ◽  
Flame Stabilization ◽  
Operating Conditions ◽  
Design Parameters ◽  
Design Variables ◽  
Geometrical Features ◽  
Heavy Duty Gas Turbine ◽  
Operating Window

Abstract The development and the optimization of a novel dry low NOx burner may require several steps of improvement. The first step of the overall development process has been documented by authors in a previous paper and included an exhaustive experimental characterization of a set of novel geometries. The in-depth results analysis allowed to correlate the investigated design parameters to burner performances, discovering possible two-fold optimization paths. Recurrent verifications of the assumptions made to define prototypes design are considered a mandatory step to avoid significant deviation from the correct optimization path, which strongly depends on both objective function definition and selection of design variables. Concerning the objective function, a proper mathematical formulation was proposed in the previous work, which represented a balance between two apparently conflicting aspect like flame stability and low emissions. Concerning design variables, outcomes of the first test campaign have been used in the present work to define new burner geometries. Starting from a new baseline who has showed the widest low NOx operating window, additional geometrical features have been considered in this survey as potentially affecting flame stabilization. Thanks to the degree of freedom offered by DMLM technology, rapid prototyping of alternative geometries allowed to easily setup a new experimental plan for the second optimization step. Exploiting the same approach used in the first test campaign, new geometries have been tested in a single-cup test rig at gas turbine relevant operating conditions, showing Stable low-NOx operating windows have been evaluated throughout dedicated objective functions for all geometries and results showed lower NOx and CO emissions as a consequence of the newly introduced geometrical modifications. Moreover, the comparison with the estimates of the previous campaign proved the existence of the identified optimization path. Indeed, it furnished valid elements for further using of the proposed methodology for the improvement of emission and blow-out characteristics of novel burners and, more in general, for the development of a novel dry low NOx technology.

Cost-Optimal Design of Dry Cooling Towers Through Mathematical Programming Techniques

1989 ◽  
Vol 111 (2) ◽  
pp. 322-327 ◽  
Author(s):  
J. D. Buys ◽  
D. G. Kro¨ger
Keyword(s):  
Objective Function ◽  
Cooling System ◽  
Cost Effective ◽  
Operating Conditions ◽  
Variable Metric ◽  
Design Variables ◽  
Specific Performance ◽  
Programming Techniques ◽  
Effective Design ◽  
Dry Cooling

The Constrained Variable Metric Algorithm is chosen to minimize the objective function (cost) in the design of a natural draft dry cooling tower. An existing cooling system design that has specific performance characteristics under prescribed operating conditions is selected as a reference unit. By changing design variables, but not exceeding prescribed constraints, a more cost-effective design is achieved. The influence of various parameters, and the sensitivity of the objective function to these parameters, are evaluated.

Optimum design of rolling element bearings using a genetic algorithm—differential evolution (GA—DE) hybrid algorithm

2010 ◽  
Vol 225 (3) ◽  
pp. 714-721 ◽  
Author(s):  
W Y Lin
Keyword(s):  
Genetic Algorithm ◽  
Differential Evolution ◽  
Objective Function ◽  
Optimum Design ◽  
Dynamic Loads ◽  
Representative Case ◽  
De Algorithm ◽  
Design Variables ◽  
Deep Groove ◽  
Rolling Element

Binary-code genetic algorithms (BGA) have been used to obtain the optimum design for deep groove ball bearings, based on maximum fatigue life as an objective function. The problem has ten design variables and 20 constraint conditions. This method can find better basic dynamic loads rating than those listed in standard catalogues. However, the BGA algorithm requires a tremendous number of evaluations of the objective function per case to achieve convergence (e.g. about 5 200 000 for a representative case). To overcome this difficulty, a hybrid evolutionary algorithm by combining real-valued genetic algorithm (GA) with differential evolution (DE) is used together with the proper handling of constraints for this optimum design task. Findings show that the GA—DE algorithm can successfully find the better dynamic loads rating, about 1.3—11.1 per cent higher than those obtained using the traditional BGA. Moreover, the mean number of evaluations of the objective function required to achieve convergence is about 3011, using the GA—DE algorithm, as opposed to about 5 200 000 for a representative case using the BGA. Comparison shows the GA—DE algorithm to be much more effective and efficient than the BGA.

scholarly journals Optimal design for improving torque performance of permanent magnet motor

2018 ◽  
Vol 7 (2.12) ◽  
pp. 292
Author(s):  
Tae Kyoung Bang ◽  
Kyung Hun Shin ◽  
Jeong In Lee ◽  
Cheol Han ◽  
Sung Kook Cho ◽  
...  
Keyword(s):  
Optimal Design ◽  
Taguchi Method ◽  
Objective Function ◽  
Permanent Magnet ◽  
Optimum Design ◽  
Optimization Process ◽  
Bldc Motor ◽  
Original Design ◽  
Design Point ◽  
Design Variables

Background/Objectives: This paper deals with the optimal design of the BLDC motor considering a rotor structure that is used to electrically drive tools. Generally, electrically driven tools employ the BLDC motor, which should be able to operate in high-speed and high-vibration environments. However, it has the disadvantages of a high torque ripple and significant waveform fluctuation. Therefore, it is necessary to optimize it according to the usage condition.Methods/Statistical analysis: In improving the torque performance, this study performed the optimization process by employing the Taguchi method, which can achieve a robust design based on the design variables. In the optimization process, the objective functions are set using a weighting ratio depending on the importance of the objective function as back EMF, torque performance, and loss. Through the optimization process, the optimal design point that improved the performance of the objective function is derived. The improved design that applied the optimal design point is compared with the original design by using the finite element method (FEM) analysis results.Findings: In this study, the optimum design of the motor according to the design variables and the objective function is derived through the optimum design method using the Taguchi method by adopting the motor for the electrically driven tool as the interior permanent magnet type BLDC motor and the FEM results. Moreover, by comparing the analysis results with the optimized model and the initial model, the optimum design point that satisfies the restriction specification and the rated specification was found.Improvements/Applications: The optimum design point was found by using the Taguchi method and the loss and torque characteristics were improved. 

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