Water medium retarders for heavy-duty vehicles: Computational fluid dynamics and experimental analysis of filling ratio control method

Zheng, Hongpeng; Lei, Yulong; Song, Panpan

doi:10.1016/s1001-6058(16)60820-x

Cited by 7 publications

(3 citation statements)

References 8 publications

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“…During normal operation, the road excitation frequency varies with the driving speed, typically remaining below 3 Hz for highway and smoother urban road surfaces. 26 Setting the lower frequency extraction limit to 1 Hz, the first three free modes of the subframe, post-filtering of the rigid body mode, are detailed in Table 5, with the corresponding vibration modes depicted in Figure 5. The first order natural frequency of the frame is different from the idle speed and working excitation frequency, which meets the dynamic design requirements.…”

Section: Multi-condition Analysis Of Frame Structurementioning

confidence: 99%

Multi-condition classification optimization of subframe of dust suppression vehicle

Cui,

Zhao,

Dong

et al. 2024

Proceedings of the Institution of Mechanical Engineers, Part C:

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In this paper, a forward classification optimization design idea for the subframe structure with discrete material distribution and take a typical subframe of a dust suppression vehicle is taken as an example to optimize and verify. Using the finite element method, we established a simulation model for the dust suppression vehicle, and the feasibility of lightweight design was determined through the multi-condition stress analysis and modal analysis of the subframe structure of the model. Firstly, the water tank bracket structure is taken as the topology optimization space, the compromise topology optimization based on grey correlation is carried out, and the conceptual design is carried out according to the material distribution of the water tank bracket shown in the topological cloud image. Then the response surface optimization model is established, and the thickness of each structure of the subframe is parameterized. Before optimization, a Pareto graph was drawn through sensitivity analysis to exclude variables with weak correlation with optimization objectives, and a high-precision response surface was fitted by least square method to meet the requirements of response surface model adaptation. Finally, the best solution was found by GRSM algorithm. After adjusting the original model according to the parameters of the best solution, the optimized subframe not only meets the operational requirements of the dust suppression vehicle but also achieves a 23.8% reduction in weight, demonstrating significant practical engineering value.

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Section: Multi-condition Analysis Of Frame Structurementioning

confidence: 99%

Multi-condition classification optimization of subframe of dust suppression vehicle

Cui,

Zhao,

Dong

et al. 2024

Proceedings of the Institution of Mechanical Engineers, Part C:

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“…The outlet oil pressure of the hydrodynamic retarder at maximal and minimal charging rate at different rotor rotational speeds was simulated by CFD [21]. Here are the assumptions and conditions of the simulation.…”

Section: A Controllable Regionmentioning

confidence: 99%

A Prediction Model Based on Artificial Neural Network for the Temperature Performance of a Hydrodynamic Retarder in Constant-Torque Braking Process

et al. 2021

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Excessively high brake temperature of hydrodynamic retarders may lead to brake fading and failure, resulting in a decrease in brake effectiveness. However, the temperature performance modeling of hydrodynamic retarders is a challenge because of the non-linear characteristics of the system. In this study, a temperature model based on an artificial neural network is constructed to predict the temperature performance of a hydrodynamic retarder in constant-torque braking process. The model is developed from a back-propagation neural network trained with the Levenberg-Marquardt algorithm. Before the application of the neural network, computational fluid dynamics is used to obtain the controllable region where experimental tests were performed to collect data for neural network training and validation. The linear regression method is adopted to check the quality of the training. It is shown that the constructed backpropagation neural network model is within 98% accuracy. Furthermore, the temperature model of the hydrodynamic retarder, which consists of the back-propagation neural network and thermal balance models, is simulated for 1500N•m, 2000N•m, and 2500N•m constant-torque braking processes. The simulation results are in agreement with experimental data within 2.87% error. The proposed temperature model can predict the temperature of the hydrodynamic retarder accurately and provide theoretical guidance for brake control strategy and thermal management. INDEX TERMS Hydrodynamic retarder, back-propagation neural network, constant-torque braking, temperature performance.

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“…С. 3928-3931. торых модели используются для различных целей: исследования гидро-и термодинамических процессов в проточной области замедлителя и их влияние на процесс торможения [3,4,5,6,7,8,9,10,11] разработки систем управления [12,13] сравнения различных вариантов конструкции тормоза-замедлителя [14], моделирования процесса торможения транспортного средства 1 .…”

Section: Introductionunclassified

Mathematical and Computer Simulation of the Commertial Vehicle’s Hydraulic Retarder

Kovalev¹

2018

Vestn. SibADI

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Introduction.The article represents the mathematical model of commercial vehicle’s hydraulic retarder. The model is based on the mathematical model of the variable filling hydraulic dynamometer.Materials and methods.The retarder model was designed with the possibility of its integration with the mathematical model of the engine cooling system. For this purpose, the state function of fluid temperature in a working chamber of the retarder was added to the retarder model. Consequently, fluid compression in the working chamber was included into the model to avoid unlimited filling because of possible high pressure at the retarder inlet.Results.The simulation model of the retarder was established as LMS Amesim submodel using C-programming language. For testing, the retarder simulation model was integrated with the Amesim models of the engine cooling system and with the powertrain and vehicle movement dynamics. In addition, brake torque regulation wassynthesized on PI controllers.Discussion and conclusions. During simulation of the vehicle movement on the mountain route expectable results were obtain, such as continuous braking via the retarder, which led to increasing of the cooling system’s fluid temperature. Therefore, overfilling of the retarder working chamber didn’t occur through the fluid compression. The retarder model could be applied to determine retarder design influence at vehicle functioning. For instance, it could be used for development of the algorithms’ control.

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Water medium retarders for heavy-duty vehicles: Computational fluid dynamics and experimental analysis of filling ratio control method

Cited by 7 publications

References 8 publications

Multi-condition classification optimization of subframe of dust suppression vehicle

Multi-condition classification optimization of subframe of dust suppression vehicle

A Prediction Model Based on Artificial Neural Network for the Temperature Performance of a Hydrodynamic Retarder in Constant-Torque Braking Process

Mathematical and Computer Simulation of the Commertial Vehicle’s Hydraulic Retarder

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