Design Optimization of Angular Contact Ball Bearing Using Genetic Algorithm and Grid Search Method

Despite the high level of interpretability of the Classification and Regression Tree (CART) as one of the widely-used data mining models, its results usually are less accurate than other models. To address the low accuracy of the CART model, this paper developed two new and enhanced CART-based algorithms that can provide both high interpretability and high accuracy for flood susceptibility mapping. In the developed models, various hyperparameters of the CART were explicitly tuned using the genetic algorithm (GA) and grid search (GS) methods. Eleven variables and 222 flood locations were used in Sardabroud watershed in Iran for modelling. The results showed area under the receiver operating characteristic curve (AUROC) values of 0.884, 0.927 and 0.908, respectively, for the CART, CART-GS and CART-GA models in the validation run. The results showed that the developed models can produce the output that satisfies both accuracy and interpretability criteria.

To increase the rigidity of bearings-shaft systems, the shaft is often supported by a series of double-row rolling bearings. Little work on multiple-row rolling bearings has been published. A comprehensive model is proposed which permits the determination of the internal interactions. The total elastic deflection between bearing rings is described using a vector-and-matrix method. A variety of double-row rolling bearing types are analysed, such as tapered roller bearing, cylindrical roller bearing, spherical roller bearing, self-aligning ball bearings and angular contact ball bearing. The basic internal geometry (including the internal clearance) and the effect of the initial preload (in term of the initial axial compression) are considered.

The method and results of 4E (energy, exergy, economic and environmental) analysis and multi-objective optimization for a gas turbine, heat recovery steam generator and multi-effect thermal vapour compression desalination unit are investigated in this article. To optimize the system and to determine the optimal values of design parameters, the genetic algorithm was used. The first objective function was considered as the sum of investment and operational costs as well as penalty for producing [Formula: see text] emissions. The second objective function was the cycle total amount of exergy destruction. The optimal values of design parameters such as drum pressure of heat recovery steam generator, pinch point temperature in heat recovery steam generator, top brine temperature, last stage temperature, minimum temperature difference of condenser and number of effects were then estimated. Also, the effects of gas turbine part load as well as ambient temperature and fuel cost changes on the optimal values of design parameters were analysed.

An analytical investigation of turbocharger rotor-bearing dynamics with rolling element bearings and squeeze film dampers

This paper proposed a new hybrid method combining genetic algorithm(GA) and coordinate search method (CSM). Genetic algorithm is good at global exploration but bad at accuracy and local search. Whereas, coordinate search method is good at local exploitation, and its accuracy is reliable when searching in a local area. Thus we combine those two methods in this paper to design a hybrid method called genetic algorithm with coordinate search (GACS). Experimental tests shows that this method are good at both global search and local accuracy. © 2012 IEEE.

Nonlinear time series model is a time series model applied to data that has the nonlinear pattern. One of the nonlinear time series models is Self-Exciting Threshold Autoregressive (SETAR). The SETAR model is a time series model that data modeling is done by dividing data into multiple regimes, whereas each regime following an autoregressive (AR) model. The division of the regime based on the score of the delay and threshold of the data itself. The number of SETAR model parameters not only resulted from the best model search process but also resulted in a SETAR model that is not yet optimum. Based on these findings, this study used Genetic Algorithm (GA) to produce the best and optimum SETAR model. In this research, using SETAR simulation data modeling and return data of Bank Rakyat Indonesia (BRI) were performed. The method used to model the data is Grid Search (GS) and Genetic Algorithm (GA). The result of analysis of SETAR simulation data shows that GA method gives better modeling result than GS method. The GA motive AIC value for the amount of 200 data is -3.976178 which is smaller than the AIC GS method of 1.361723. For the amount of data of 500 AIC values, GA method is also smaller than AIC GS method. In BRI stock return data, GA method also gives better modeling result compared to GS. It is marked by the GA AIC method value of -11147.66 less than -11146.26 which is the AIC method of GS. Thus, the result of analysis of SETAR model simulation data and BRI stock return shows that GA method gives better modeling result compared to GS method based on generated AIC value.

Optimization of building thermal design and control by multi-criterion genetic algorithm

This paper presents a novel design scheme for traveling wave thermo-acoustic Stirling engines (TASEs) based on a target frequency using genetic algorithm (GA). First, the effects of engine design parameters on the system performance are studied via root locus method. Accordingly, it is found that the resonator length, the inertance diameter, and the hot gas temperature are the most effective parameters in the engine design procedure. Next, the relation between the closed-loop poles positions of the thermo-acoustic system and the effective design variables are described parametrically. Consequently, in order to estimate the optimum values of the effective design parameters as well as the unknown positions of the nondominant poles, an appropriate fitness function based on the desired engine frequency is proposed. Subsequently, a GA is used to achieve the optimal values of design parameters so as to minimize the considered cost function. Finally, the validity of the proposed design technique is verified by comparing the obtained results with the available data of an experimental case study.

Anisotropic plates in different applications may have geometric defects such as openings and cracks. The presence of the opening disturbs the heat flow, which creates significant thermal stress around the opening. When the heat flux is high enough, these extreme stresses can lead to structural failure. This article aims to obtain the optimal parameters for achieving the minimum value of the normalized stress near the cutout's boundary in perforated anisotropic plates utilizing the genetic algorithm. Optimization parameters include the curvature of opening's corners, orientation angle of opening, fibers angle, heat flux angle, and opening's elongation. The plate is under heat flux, and the opening's border is thermally insulated. The stress distribution around the opening is calculated using Lekhnitskii's complex variable method and complex potential functions. The genetic algorithm is then implemented to find the optimal values for design parameters. The results show that by selecting the optimal parameters related to the anisotropic material and the opening's geometry, the stress intensity factor of the perforated anisotropic plates is remarkably reduced. Furthermore, this optimization algorithm can be extended to find the optimized parameters and achieve the optimal designs in anisotropic and isotropic perforated plates under thermal loadings.

The paper describes the optimization of the design of a sensor which performs parts inspection by measuring the inertial properties of a part. The sensor comprises a platform supported on a column which is fixed to a rigid base. The part under inspection is attached to the platform to form a single body. By measuring the static deflections of the column and the natural frequencies of vibration of the system, it is possible to determine the mass and the spatial location of the centre of mass of the part. The information obtained is used to distinguish defective parts from good ones. The application of genetic algorithms to determine the optimal values of design parameters is presented.

Deposition and characteristics of chromium nitride thin film coatings on precision balls for tribological applications

In order to solve the problem of low fault detection rate of combinatorial navigation due to the mismatch of support vector machine parameters, this paper uses genetic algorithm and lattice search method to find the optimal support vector machine penalty parameter C and kernel function parameter g. The result of the search is brought into the support vector machine to obtain the classification model and finally classify the combinatorial navigation data. The results show that the genetic algorithm has a faster search speed and a higher classification accuracy.

This paper presents the results of an exploratory study aimed at investigating the impacts of optimisation of PV plant locations in South Africa in the context of the seasonal and diurnal cycles associated with the system load profiles and solar generation profiles. The distribution of a normalised per-unit generation capacity is optimised across a set of locations chosen to represent the diurnal cycle along a west-east axis and the seasonal cycle along a north-south axis, using local solar irradiance profiles for the candidate sites. Optimisations are conducted for a range of objective functions representing different scenarios defined in terms of seasonal considerations and time-of-use (TOU) periods using both genetic algorithm (GA) and pattern search methods in the MATLAB simulation environment. Comparative analysis of the optimisation results indicate that the latitude of PV plant locations plays a significant role in seasonal performance, while optimisation along a longitude dimension offers higher power generation during daily peak demand periods.

Shape Memory Alloys (SMA) have been researched extensively in the past forty years. The different available compositions of these alloys can make them suitable for certain applications with specific requirements. The increasing demand for high precision machinery and equipment brought forward the need to search for alloys that that can perform better under stress than conventional materials. This is the case of Shape Memory Alloys. As their name suggests, these alloys can regain their initial shape after being subjected to external stresses, up to a specific degree. Due to the research carried out over the past two decades on these alloys, they became better known and more accessible, being tested in order to be integrated in various applications. One such application consists of using a shape memory alloy formed into disc springs in order to optimize the preloading of angular contact ball bearings. The tests were conducted using a device especially developed for this research, with the purpose of recording the friction torque evolution and the preload induced by disc springs during the operation of the rolling bearings in order to assess the possibility of using elastic elements for active ball bearings preloading systems.

In this paper, a quasi-static angular contact ball bearing model, considering assembly accuracy is constructed, while a numerical solution method for bearing stiffness under bad assembly state is established. A 7014C angular contact ball bearing is used as the research object and five groups of different spacer inclinations are designed to imitate the installation error of the spindle bearing. The bearing stiffness performance was comparatively analyzed, according to the five spacers. The effect of preload and rotation speed on bearing stiffness are systematically investigated, considering different parallelism errors, as induced by the spacers. The influence mechanism of the badly assembled bearing on the respective stiffness anisotropy is studied based on the proposed model. The results show that the variations of the inclination between the inner and outer rings of the bearing exhibit a very weak effect on the axial stiffness, while the influence on the radial and angular stiffness is more significant.