Optimization for Motorcycle Tire Using Explicit FEM

2001 ◽  
Vol 29 (4) ◽  
pp. 230-243 ◽  
Author(s):  
M. Koide ◽  
H. Heguri ◽  
T. Kamegawa ◽  
Y. Nakajima ◽  
H. Ogawa
Keyword(s):  
Neural Network ◽  
Finite Element ◽  
Design Space ◽  
Objective Functions ◽  
Explicit Finite Element ◽  
Handling Performance ◽  
The Neural Network ◽  
Practical Development ◽  
Contour Design ◽  
Good Agreement

Abstract A new procedure of the crown contour design for the motorcycle tire is proposed in this paper. The explicit finite element method (FEM) combined with the neural network has been utilized to optimize the cornering property of the motorcycle tire. For the motorcycle tire, the camber thrust is one of the most important cornering characteristics. The explicit FEM has been utilized for the camber thrust prediction to avoid poor numerical stability that will be caused by the implicit FEM. The prediction of the camber thrust that the results of finite element analyses (FEA) were in good agreement with the experimental results has been verified. For the approximation of the design space in the optimization, the neural network has been utilized to circumvent the multipeak and huge CPU time problems. The objective functions of the optimization were both the linearity of camber thrust and the uniformity of pressure distribution in the contact area. The design variable was the crown contour expressed by three variables, and the number of variables was defined in consideration of decreasing the number of FEA. The procedure has been applied to the practical development of a motorcycle tire and verified to be an effective method to improve the handling performance at the proving ground.

Linear Static Response of Suspension Arm Based on Artificial Neural Network Technique

2011 ◽  
Vol 213 ◽  
pp. 419-426
Author(s):  
M.M. Rahman ◽  
Hemin M. Mohyaldeen ◽  
M.M. Noor ◽  
K. Kadirgama ◽  
Rosli A. Bakar
Keyword(s):  
Neural Network ◽  
Finite Element Analysis ◽  
Finite Element ◽  
Network Model ◽  
Neural Network Model ◽  
Mesh Size ◽  
Maximum Displacement ◽  
Element Analysis ◽  
The Neural Network ◽  
The Finite Element Analysis

Modeling and simulation are indispensable when dealing with complex engineering systems. This study deals with intelligent techniques modeling for linear response of suspension arm. The finite element analysis and Radial Basis Function Neural Network (RBFNN) technique is used to predict the response of suspension arm. The linear static analysis was performed utilizing the finite element analysis code. The neural network model has 3 inputs representing the load, mesh size and material while 4 output representing the maximum displacement, maximum Principal stress, von Mises and Tresca. Finally, regression analysis between finite element results and values predicted by the neural network model was made. It can be seen that the RBFNN proposed approach was found to be highly effective with least error in identification of stress-displacement of suspension arm. Simulated results show that RBF can be very successively used for reduction of the effort and time required to predict the stress-displacement response of suspension arm as FE methods usually deal with only a single problem for each run.

Tail Deviation Mechanism and Neural Network Control of the Tandem Rolling Strap

2011 ◽  
Vol 103 ◽  
pp. 488-492
Author(s):  
Guang Bin Wang ◽  
Xian Qiong Zhao ◽  
Yi Lun Liu
Keyword(s):  
Neural Network ◽  
Finite Element ◽  
Finite Element Model ◽  
Control Policy ◽  
Element Model ◽  
Rolling Process ◽  
Network Control ◽  
Neural Network Control ◽  
The Neural Network ◽  
The Finite Element Model

In the rolling process, deviation is the phenomenon that the strap width direction's centerline deviates from rolling system setting centerline,serious deviation will cause product quality drop and rolling equipment fault. This paper has established the finite element model to the hot tandem rolling aluminum strap, analyzed the strap’s deviation rule under four kinds of incentives,obtained the neural network predictive model and the control policy of the tail deviation.The result to analyze a set of fact deviation data shows this method may control tail deviation in preconcerted permission range.

scholarly journals Design Space Exploration for YOLO Neural Network Accelerator

Electronics ◽  
2020 ◽  
Vol 9 (11) ◽  
pp. 1921
Author(s):  
Hongmin Huang ◽  
Zihao Liu ◽  
Taosheng Chen ◽  
Xianghong Hu ◽  
Qiming Zhang ◽  
...  
Keyword(s):  
Neural Network ◽  
Design Space Exploration ◽  
Design Space ◽  
Space Exploration ◽  
Computation Time ◽  
Memory Bandwidth ◽  
Block Transmission ◽  
Transmission Strategy ◽  
The Neural Network ◽  
The Given

The You Only Look Once (YOLO) neural network has great advantages and extensive applications in computer vision. The convolutional layers are the most important part of the neural network and take up most of the computation time. Improving the efficiency of the convolution operations can greatly increase the speed of the neural network. Field programmable gate arrays (FPGAs) have been widely used in accelerators for convolutional neural networks (CNNs) thanks to their configurability and parallel computing. This paper proposes a design space exploration for the YOLO neural network based on FPGA. A data block transmission strategy is proposed and a multiply and accumulate (MAC) design, which consists of two 14 × 14 processing element (PE) matrices, is designed. The PE matrices are configurable for different CNNs according to the given required functions. In order to take full advantage of the limited logical resources and the memory bandwidth on the given FPGA device and to simultaneously achieve the best performance, an improved roofline model is used to evaluate the hardware design to balance the computing throughput and the memory bandwidth requirement. The accelerator achieves 41.99 giga operations per second (GOPS) and consumes 7.50 W running at the frequency of 100 MHz on the Xilinx ZC706 board.

Optimization of Pressure Path in Sheet Hydroforming Process Using Artificial Intelligence and Simulated Annealing

2014 ◽  
Vol 622-623 ◽  
pp. 772-779 ◽  
Author(s):  
Amirreza Yaghoobi ◽  
Mohammad Bakhshi-Jooybari ◽  
Abdolhamid Gorji ◽  
Hamid Baseri
Keyword(s):  
Neural Network ◽  
Finite Element ◽  
Simulated Annealing ◽  
Network Model ◽  
Neural Network Model ◽  
Critical Region ◽  
Element Model ◽  
Sheet Hydroforming ◽  
The Neural Network ◽  
Hydroforming Process

The success of sheet hydroforming process largely depends on the loading pressure path. Pressure path is one of the most important parameters in sheet hydroforming process. In this study, a combination of finite element simulation, artificial intelligence and simulated annealing optimization have been utilized to optimize the pressure path in producing cylindrical-spherical parts. In the beginning, the finite element model was verified based on laboratory experimental results. The experiments were designed and a radial basis neural network model was developed using data generated from verified finite element model to predict the thickness in the critical region of the product. Results indicated that the neural network model could be applied successfully to predict the sheet thickness in the critical region. In addition, the neural network model was used as a fitness function in simulated annealing algorithm to minimize the thickening in the above mentioned critical region. The final results showed that utilization of the optimized pressure path yields good thickness distribution of the part.

Neural Networks for Tubular Joint Optimization in Offshore Jacket Structures

2016 ◽  
Author(s):  
Niels Hørbye Christiansen ◽  
Benny Korsholm Tang
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Design Space ◽  
Joint Optimization ◽  
Joint Models ◽  
Tubular Joints ◽  
Design Variables ◽  
The Neural Network ◽  
Jacket Structures ◽  
Tubular Joint

The use of jacket structures to support offshore installations has for a long time been a popular choice in places with appropriate water depths. In recent years the use of jacket structures as offshore wind turbine foundations has also attracted increasing attention and is becoming a feasible alternative to traditional monopile foundations. One of the key challenges in jacket design is optimizing tubular joints in terms of fatigue resistance. As it is not practically possible to include detailed FEM joint models in global jacket models designers are forced to look for alternative methods to obtain realistic joint representations. This is done by calculating influence factors (INF) and stress concentration factors (SCF) to be applied to simplified models of relevant tubular joints in global models in order to achieve a realistic force flow in the structure. One simple and widely used method is to apply parametric formulas, e.g. those suggested by Efthymiou. However, these approximating formulas have a fairly limited validity range. Therefore, on complex joint the most reliable way to determine INF’s is by setting up refined FEM models of relevant joints and then subsequently using the calculated factors in the global model. This strategy is computationally demanding and hence, very time consuming, as a new detailed FEM analysis of the tubular joint must be conducted for each step in the optimization process. The present paper demonstrates how this time consuming procedure can be avoided by use of artificial neural networks (ANN) trained to estimate INF’s on tubular joints. The neural network is trained on a pre-generated library of detailed FEM joint models and is then able to predict INF’s on joints that are not part of the library — and thereby providing a significant reduction in calculation time during the jacket/joint optimization process. The analysis is conducted on a typical joint on a three legged jacket structure. The joint is located on a jacket leg and has two incoming braces. Such a joint has a finite number of free design variables, e.g. chord diameter/thickness, brace diameter/thickness, brace angle, gap etc. Each of these free variables can be considered as a dimension in the joint design space. Having a sufficient number of FEM joint models in the library the neural network can be trained to recognize and predict underlying patterns in this design space. The method is demonstrated on a limited number of design variables but should easily be extended to cover all variables as the joint library is expanded to include all dimensions.

Prediction of Detonation Pressure of Aluminized Explosive by Artificial Neural Network

2013 ◽  
Vol 641-642 ◽  
pp. 460-463
Author(s):  
Yong Gang Liu ◽  
Xin Tian ◽  
Yue Qiang Jiang ◽  
Gong Bing Li ◽  
Yi Zhou Li
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Optimal Condition ◽  
Detonation Pressure ◽  
Ann Model ◽  
Training Set ◽  
The Neural Network ◽  
Artificial Neural ◽  
Artificial Neural Network Ann ◽  
Good Agreement

In this study, a three-layer artificial neural network(ANN) model was constructed to predict the detonation pressure of aluminized explosive. Elemental composition and loading density were employed as input descriptors and detonation pressure was used as output. The dataset of 41 aluminized explosives was randomly divided into a training set (30) and a prediction set (11). After optimized by adjusting various parameters, the optimal condition of the neural network was obtained. Simulated with the final optimum neural network [6–9–1], calculated detonation pressures show good agreement with experimental results. It is shown here that ANN is able to produce accurate predictions of the detonation pressure of aluminized explosive.

scholarly journals The new Passive microwave Neural network Precipitation Retrieval (PNPR) algorithm for the cross-track scanning ATMS radiometer: description and verification study over Europe and Africa using GPM and TRMM spaceborne radars

2016 ◽  
Author(s):  
Paolo Sanò ◽  
Giulia Panegrossi ◽  
Daniele Casella ◽  
Anna Cinzia Marra ◽  
Francesco Di Paola ◽  
...  
Keyword(s):  
Neural Network ◽  
Mean Squared Error ◽  
Passive Microwave ◽  
Precipitation Radar ◽  
Water Vapor Absorption ◽  
The Neural Network ◽  
Trmm Pr ◽  
The Cross ◽  
Cross Track ◽  
Good Agreement

Abstract. The objective of this paper is to describe the development and evaluate the performance of a totally new version of the Passive microwave Neural network Precipitation Retrieval (PNPR v2), an algorithm based on a neural network approach, designed to retrieve the instantaneous surface precipitation rate using the cross-track ATMS radiometer measurements. This algorithm, developed within the EUMETSAT H-SAF program, represents an evolution of the previous version (PNPR v1), developed for AMSU/MHS radiometers (and used and distributed operationally within H-SAF), with improvements aimed at exploiting the new precipitation sensing capabilities of ATMS with respect to AMSU/MHS. In the design of the neural network the new ATMS channels compared to AMSU/MHS, and their combinations, including the brightness temperature differences in the water vapor absorption band, around 183 GHz, are considered . The algorithm is based on a single neural network, for all types of surface background, trained using a large database based on 94 cloud-resolving model simulations over the European and the African areas. The performance of PNPR v2 has been evaluated through an intercomparison of the instantaneous precipitation estimates with co-located estimates from the TRMM Precipitation Radar (TRMM-PR) and from the GPM Core Observatory Ku-band Precipitation Radar (GPM-KuPR). In the comparison with TRMM-PR, over the African area, the statistical analysis was carried out for a two-year (2013-2014) dataset of coincident observations, over a regular grid at 0.5° × 0.5° resolution. The results have shown a good agreement between PNPR v2 and TRMM-PR for the different surface types. The correlation coefficient (CC) was equal to 0.69 over ocean and 0.71 over vegetated land (lower values were obtained over arid land and coast), and the root mean squared error (RMSE) was equal to 1.30 mm h−1 over ocean and 1.11 mm h−1 over vegetated land. The results showed a slight tendency to underestimate moderate to high precipitation, mostly over land, and overestimate moderate to light precipitation over ocean. Similar results were obtained for the comparison with GPM-KuPR over the European area (15 months, from March 2014 to May 2015 of coincident overpasses) with slightly lower CC (0.59 over vegetated land and 0.57 over ocean) and RMSE (0.82 mm h−1 over vegetated land and 0.71 mm h−1 over ocean), confirming a good agreement also between PNPR v2 and GPM-KuPR. The performance of PNPR v2 over the African area was also compared to that of PNPR v1. PNPR v2 has higher R over the different surfaces, with general better estimate of low precipitation, mostly over ocean, thanks to improvements in the design of the neural network and also to the improved capabilities of ATMS compared to AMSU/MHS. Both versions of PNPR algorithm have shown a general consistency with the TRMM-PR.

Response of UHPC-Concrete Composite Structural Members Using Implicit and Explicit Finite Element Method

2019 ◽  
Vol 793 ◽  
pp. 93-97 ◽  
Author(s):  
Hor Yin ◽  
Kazutaka Shirai ◽  
Wee Teo
Keyword(s):  
Finite Element ◽  
Fe Simulation ◽  
Fe Analysis ◽  
Experimental Results ◽  
Structural Members ◽  
Explicit Finite Element ◽  
Explicit Analysis ◽  
Cracking Pattern ◽  
Implicit And Explicit ◽  
Good Agreement

This paper investigates the response of UHPC-concrete composite structural members using implicit and explicit finite element (FE) methods. Both methods were prepared and conducted individually for the FE analysis under static loading condition. Results of the implicit and explicit analysis were compared to experimental results conducted in previous study. Both the implicit and explicit methods showed similar overall response with fair accuracy compared with the experimental results. In addition, the effective plastic strain obtained from the FE simulation was in good agreement with the damage cracking pattern in the experiment.

scholarly journals Artificial Neural Network Modelling for Optimizing the Optical Parameters of Plasmonic Paired Nanostructures

Nanomaterials ◽  
2022 ◽  
Vol 12 (1) ◽  
pp. 170
Author(s):  
Sneha Verma ◽  
Sunny Chugh ◽  
Souvik Ghosh ◽  
B. M. Azizur Rahman
Keyword(s):  
Neural Network ◽  
Finite Element Method ◽  
Artificial Neural Network ◽  
Finite Element ◽  
Plasmonic Nanostructures ◽  
Optical Parameters ◽  
Complex Data ◽  
The Neural Network ◽  
Artificial Neural ◽  
Element Method

The Artificial Neural Network (ANN) has become an attractive approach in Machine Learning (ML) to analyze a complex data-driven problem. Due to its time efficient findings, it has became popular in many scientific fields such as physics, optics, and material science. This paper presents a new approach to design and optimize the electromagnetic plasmonic nanostructures using a computationally efficient method based on the ANN. In this work, the nanostructures have been simulated by using a Finite Element Method (FEM), then Artificial Intelligence (AI) is used for making predictions of associated sensitivity (S), Full Width Half Maximum (FWHM), Figure of Merit (FOM), and Plasmonic Wavelength (PW) for different paired nanostructures. At first, the computational model is developed by using a Finite Element Method (FEM) to prepare the dataset. The input parameters were considered as the Major axis, a, the Minor axis, b, and the separation gap, g, which have been used to calculate the corresponding sensitivity (nm/RIU), FWHM (nm), FOM, and plasmonic wavelength (nm) to prepare the dataset. Secondly, the neural network has been designed where the number of hidden layers and neurons were optimized as part of a comprehensive analysis to improve the efficiency of ML model. After successfully optimizing the neural network, this model is used to make predictions for specific inputs and its corresponding outputs. This article also compares the error between the predicted and simulated results. This approach outperforms the direct numerical simulation methods for predicting output for various input device parameters.

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