Influence of Gear Loss Factor on the Power Loss Prediction

Fernandes, Carlos; Marques, Pedro M.T.; Martins, Ramiro C.; Seabra, Jorge

doi:10.1007/978-3-319-09411-3_84

Cited by 7 publications

(6 citation statements)

References 3 publications

(6 reference statements)

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“…Moreover, auxiliary losses should also be considered. Gear and bearing losses are separated into two different categories: load and no-load dependent losses [12,13], the transmission losses (∆ ℎ ) are classified in Fig. 3.…”

Section: The Efficiency Of the Transmissionmentioning

confidence: 99%

“…Gear loss factor ( ) is a constant calculated based on the base helix angle, load distribution, the path of contact, and other gear parameters. In their study, C. Fernandes et al [12] give three different equations for the calculation and propose the calculation method that does not consider the elastic effects of the gears. They show the influence of the calculation of the gear loss factor that is dependent on the geometry of the gear in the prediction of the power loss.…”

Section: Shift Position Switchmentioning

confidence: 99%

“…No-load bearing losses highly depend on the bearing type and size (e.g., lowest no-load losses of radial bearings are expected for cylindrical roller bearings [12]), bearing arrangement, lubricant viscosity, and supply. Load dependent bearing losses rely on the size, type, rolling (load) and sliding conditions, and lubricant type.…”

Section: Shift Position Switchmentioning

confidence: 99%

See 2 more Smart Citations

Parametric Digital Twin of Autonomous Electric Vehicle Transmission

Rassõlkin¹,

Rjabtšikov²,

Kuts³

et al. 2021

Journal of Machine Engineering

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Variable applications and methodologies are used in the Digital Twin technology. Digital Twin as a trending technology is also a general topic of many industry-oriented research projects. To develop and implement a novel technology, a detailed study of any single part of a system is required. This paper presents a development case study of the parametric Digital Twin of autonomous electric vehicle transmission. Digital Twin combines the advantages of software models and real equipment to reduce total test runs and safe maintenance. The primary duty of the Digital Twin is to allow complete synchronization and connectivity between virtual and real entities. The paper presents a detailed structural description of the virtual entity that considers the parametrization of the transmission.

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Section: The Efficiency Of the Transmissionmentioning

confidence: 99%

Section: Shift Position Switchmentioning

confidence: 99%

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Parametric Digital Twin of Autonomous Electric Vehicle Transmission

Rassõlkin¹,

Rjabtšikov²,

Kuts³

et al. 2021

Journal of Machine Engineering

View full text Add to dashboard Cite

show abstract

“…Besides Mao's FD model, the finite element (FE) model is also commonly implemented. Fernandes et al 10,11 predicted the maximum meshing temperature of POM gears via a semi-analytical method. They analytically calculated the frictional heat with a power loss model and numerically simulated the heat transfer process of generated frictional heat.…”

Section: Introductionmentioning

confidence: 99%

Numerical investigation on meshing temperature of multicomponent nylon gears by incorporating viscoelasticity

Gong

et al. 2023

Proceedings of the Institution of Mechanical Engineers, Part L:

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This study concerns a numerical analysis of the meshing temperature for a multicomponent nylon gear by incorporating typical viscoelastic responses of polymers. Firstly, the generalized Maxwell model parameters were calibrated by the dynamic mechanical analysis to capture the viscoelasticity of polyamide 66. Next, the finite element model predicting the meshing temperature was established, including the heat generation analysis model, which calculates the generation of the frictional and hysteretic heat, and the heat propagation analysis model, which simulates the heat transmission. Different material models were used for polyamide 66 to investigate the influences of temperature dependence, strain rate dependence, and hysteretic effect. The effect of the iteration sequence on the meshing temperature calculation was also analyzed, i.e., how the heat generation should be updated as the loading cycles increase. A reasonable iteration scheme was determined by balancing the computational precision and consumption. It is noteworthy that the evolution of the ambient temperature around the gear and the worm within the housing was also considered. Finally, the accuracy of such meshing temperature analysis was validated on a self-developed gear test rig, in terms of the peak temperature evolution on the gear side surface. The experimental results and the simulation with the ambient temperature correction match well and the maximum error is 5 °C.

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“…[23][24][25][26] Investigation of the load-dependent power loss for the sliding friction was estimated by the prediction of the gear loss factor. 27 Fernandes 28 prediction of sliding frictional power loss in the drive by including profile modifications 29 stated the enhancement of performance. He also suggested that alteration in the profile initiates variations in the contact load, sliding velocity along the line of action, load-dependent power loss and its resulting efficiency values.…”

Section: Introductionmentioning

confidence: 99%

Load share model based gear loss factor prediction in high contact ratio spur gear drive

Ravivarman

Sekar

2022

Proceedings of the Institution of Mechanical Engineers, Part J:

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Gear loss factor is considered to be a crucial parameter, and is governed by factors like contact load, sliding velocity, contact ratio, etc. Sliding power loss is a function of the gear loss factor and friction coefficient. It depends on the contact tooth load and its contact surfaces. A small decline in the gear loss factor and variation in the friction coefficient reduces the energy consumption which improves gear efficiency significantly. Hence, accurate prediction of the gear loss factor is important. This paper deliberates on measuring the contact load of High Contact Ratio (HCR) non-standard gear drive, using the finite element contact model. The input for the gear loss factor is obtained from a 2D elastic contact model using ANSYS. In the previous works the load is considered as a rigid hypothetical element, but in the real case it is varying at each instantaneous contact point. To address this, a more accurate method of predicting the power loss using the contact load share model is proposed. The effectiveness of this technique, built on the computational data and its accuracy are examined. Based on the accuracy, the load share model was suggested to be the most effective one compared to other techniques. The results show that the proposed model provides an effective tool for making reliability assessments on power loss and efficiency of high contact ratio gear drives under diverse operating conditions.

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Influence of Gear Loss Factor on the Power Loss Prediction

Cited by 7 publications

References 3 publications

Parametric Digital Twin of Autonomous Electric Vehicle Transmission

Parametric Digital Twin of Autonomous Electric Vehicle Transmission

Numerical investigation on meshing temperature of multicomponent nylon gears by incorporating viscoelasticity

Load share model based gear loss factor prediction in high contact ratio spur gear drive

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