2019
DOI: 10.1017/s0890060419000039
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Artificial intelligence-based Monte-Carlo numerical simulation of aerodynamics of tire grooves using computational fluid dynamics

Abstract: In the current work, the effects of design (groove depth and groove width) and operational (temperature and velocity) parameters on aerodynamic performance parameters (coefficient of drag and coefficient of lift) of an isolated passenger car tire have been investigated. The study is conducted by using neural network-based Monte-Carlo analysis on computational fluid dynamics (CFD). The computer experiments are designed to obtain the causal relationship between tire design, operational, and aerodynamic performan… Show more

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Cited by 10 publications
(8 citation statements)
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“…Gradient descent with momentum is employed as a training function, tangent hyperbolic and purelin is employed as transfer functions at the hidden and output layer of MLP, respectively [2,44]. ANN training is carried out until one of the two stopping criteria is met, i.e., either a 0.0000001 change in convergence error or a maximum number of epochs is reached [2,49]. In this work, multiple ANNs are trained, and the number of neurons in the hidden layer is varied from 10 to 36 to find the optimal number of hidden layer neurons based on the validation test, as mentioned in the Section 5.2.…”
Section: Development Of Process Modelsmentioning
confidence: 99%
“…Gradient descent with momentum is employed as a training function, tangent hyperbolic and purelin is employed as transfer functions at the hidden and output layer of MLP, respectively [2,44]. ANN training is carried out until one of the two stopping criteria is met, i.e., either a 0.0000001 change in convergence error or a maximum number of epochs is reached [2,49]. In this work, multiple ANNs are trained, and the number of neurons in the hidden layer is varied from 10 to 36 to find the optimal number of hidden layer neurons based on the validation test, as mentioned in the Section 5.2.…”
Section: Development Of Process Modelsmentioning
confidence: 99%
“…In modeling such a nonlinear and quantitative nature of the objective function built on the hyperdimensional input space, the backpropagation algorithm works well to approximate the system . System- and component-level problems of industrial-scale production facilities are continuous data functional approximation problems. ,, On top of that, large industrial complexes generate control data for which established function approximators like backpropagation-based fully connected multilayer perceptron models would perform better . Such machine learning algorithms, which are fundamentally and architecturally classifiers (like SVM), and their modified variants for regression-based learning algorithms cannot perform on par with ANN for component/system-level complexity and nonlinearity. …”
Section: Resultsmentioning
confidence: 99%
“…ANN and SVM models as bottom-up approaches for modeling the isentropic efficiency of HP steam turbine are selected. The two modeling techniques have presented useful results in scientific and industrial research studies. ANN possesses nonlinear learning characteristics desirable for approximating an ill-defined and complex objective function, which is modeled on the hyperdimensional and interacting input parameters . On the other hand, SVM has demonstrated an excellent generalization ability in numerous applications ranging from atomic domain to enterprise-level optimization. , The performance comparison of the two models is investigated so that a better-performing model could be selected.…”
Section: Objectives and Methodsmentioning
confidence: 99%
“…Many studies have been published in the journal focusing on using AI in design, particularly for varying design activities. This includes supporting idea and concept generation (Luo et al, 2018; Sarica et al, 2021; Hanifi et al, 2022), concept and product evaluation (Chen et al, 2021), design simulation (Uddin et al, 2019), data analysis (López et al, 2019), and design automation (Kang et al, 2023). Recently, large language AI models, such as OpenAI’s GPT-3 and GPT-4, Google’s Gemini, and Meta’s LLaMA, which possess extensive common knowledge and powerful semantic reasoning abilities, have been used to support design.…”
Section: Artificial Intelligence and Cognitive Science In Designmentioning
confidence: 99%