Empirically derived optimization model: Theoretical formulation and validation

Zengeya, Miles; Gadala, Mohamed S.

doi:10.1016/j.triboint.2011.10.009

Cited by 4 publications

(5 citation statements)

References 15 publications

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“…Furthermore, 10 ∘ C limit is adopted by the two main references used for results comparison [12,15]. The last reason why 10 ∘ C is chosen instead of 20 ∘ C is that a large amount of lubricants, without additives, suffer from rapid degradation due to temperature rise as explained in [2] specially if higher than 10 ∘ C.…”

Section: Resultsmentioning

confidence: 99%

“…However, some authors optimize only two objectives composed by the single performance or by a nondimensional form of it. Others in [2] chose to optimize two performances and constrain the others (temperature rise, maximum pressure, and minimum film thickness). In this work, all the performances are aggregated into two objectives ( m 1 = 2) in order to minimize or maximize their value without constraining it.…”

Section: Optimization Methodologymentioning

confidence: 99%

“…Numerous techniques have been developed to predict the performances of HJB and can be divided into four categories: approximated methods [2][3][4], rigorous solution of the Reynolds equation [5][6][7], curve fitting, and CFD solution with commercial software. The first group is based on the fact that the pressure profile and solution of the Reynolds equation of lubrication can be imitated using the proper combination of functions whose variables are eccentricity and length to diameter ratio.…”

Section: Introductionmentioning

confidence: 99%

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Journal Bearing Optimization Using Nonsorted Genetic Algorithm and Artificial Bee Colony Algorithm

Gorasso

Wang

2014

Advances in Mechanical Engineering

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In this work, a journal bearing optimization process has been developed and is divided into two stages. Each one has a set of decision variables and custom objectives aggregating performances with a weighting strategy. The performance functions used are an artificial neural network, trained with Reynolds equation solutions, and a CFD simulation of the bearings carried out with commercial software. The results show the capabilities of the algorithm to design and optimize journal bearings by reducing both power loss and mass flow with respect to ones designed with traditional methods, as well as by minimizing the maximum and average temperature.

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Section: Resultsmentioning

confidence: 99%

Section: Optimization Methodologymentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Journal Bearing Optimization Using Nonsorted Genetic Algorithm and Artificial Bee Colony Algorithm

Gorasso

Wang

2014

Advances in Mechanical Engineering

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“…Alternatively, a mathematical model with reduced computational effort that captures the variation of the bearing parameters for different operating conditions is essential for design optimization. In this work, an experimentally validated empirical mathematical model for hydrodynamic journal bearings operating in laminar flow is adopted (Zengeya et al, 2007;Zengeya and Gadala, 2012). This model can estimate, with reasonable accuracy, different bearing state variables while considering the effect of design variable variations and loading conditions on the bearing performance.…”

Section: Introductionmentioning

confidence: 99%

Multi-objective design optimization of hydrodynamic journal bearings using a hybrid approach

Shaltout

Hegazi

2021

ILT

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Purpose In this work, the design problem of hydrodynamic plain journal bearings is formulated as a multi-objective optimization problem to improve bearing performance under different operating conditions. Design/methodology/approach The problem is solved using a hybrid approach combining genetic algorithm and sequential quadratic programming. The selected state variables are oil leakage flow rate, power loss and minimum oil film thickness. The selected design variables are the radial clearance, length-to-diameter ratio, oil viscosity, oil supply pressure and oil supply groove angular position. A validated empirical model is adopted to provide relatively accurate estimation of the bearing state variables with reduced computations. Pareto optimal solution sets are obtained for different operating conditions, and secondary selection criteria are proposed to choose a final optimum design. Findings The adopted hybrid optimization approach is a random search algorithm that generates a different solution set for each run, thus a different bearing design. For a number of runs, it is found that the key design variables that significantly affect the optimum state variables are the bearing radial clearance, oil viscosity and oil supply pressure. Additionally, oil viscosity is found to represent the significant factor that distinguishes the optimum designs obtained using the implemented secondary selection criteria. Finally, the results of the proposed optimum design framework at different operating conditions are presented and compared. Originality/value The proposed multi-objective formulation of the bearing design problem can provide engineers with a systematic approach and an important degree of flexibility to choose the optimum design that best fits the application requirements.

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“…These are geometry parameters such as the rotary inertia, the gyroscopic inertia, the shear deformation of shaft, and the geometric asymmetry of shaft (Dakel et al, 2014). Moreover, the effect of groove geometry, groove location, supply pressure and bearing geometry were considered in model development of the two journal bearings model (Zengeya & Gadala, 2012).…”

Section: Introductionmentioning

confidence: 99%

Optimum modeling of a flexible multi-bearing rotor system

Falah¹,

Khorshid²

2014

J Engin Res

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Modeling and system identification on flexible multi-bearing rotors with two-concentrated disks are presented in this study. Both rotor unbalance vibration responses through critical speed were experimentally obtained through accurate control of journal bearing static load. Vibration simulations of this laboratory rotor-bearing system were performed as a linear system. The simulation predicted that critical speeds were higher than the experimental ones. These differences strongly occurred because of errors in estimating journal bearing damping and stiffness coefficients. Estimated journal bearing coefficients were carried out by using optimization methods to enhance the model prediction capabilities to the actual test results. The pattern search method was used to solve an inverse problem for the global parameters. A good agreement, in terms of instability threshold speeds and system responses, was found between the experimental results and the optimized model. Both the Response Surface method and the Neural Network method were used to build a Metamodel for predicting the system coefficients. For all tested static loads, the predicted error of the original model was in the range of 9% -18%.

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Empirically derived optimization model: Theoretical formulation and validation

Cited by 4 publications

References 15 publications

Journal Bearing Optimization Using Nonsorted Genetic Algorithm and Artificial Bee Colony Algorithm

Journal Bearing Optimization Using Nonsorted Genetic Algorithm and Artificial Bee Colony Algorithm

Multi-objective design optimization of hydrodynamic journal bearings using a hybrid approach

Optimum modeling of a flexible multi-bearing rotor system

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