Applying Artificial Neural Networks (ANNs) for prediction of the thermal characteristics of engine oil –based nanofluids containing tungsten oxide -MWCNTs

Soltani, Farid; Hajian, M.; Toghraie, Davood; Gheisari, Ali; Sina, Nima; Alizadeh, As’ad

doi:10.1016/j.csite.2021.101122

Cited by 19 publications

(5 citation statements)

References 45 publications

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“…For example, learning to classify known examples (i.e., supervised learning), or learning to recognize the characteristic structure of an object from input data with no additional information (i.e., unsupervised learning). 34,85,86 During learning, each synapse (i.e., the weight between layers) in the network is constantly predicted, strengthened, or weakened by the algorithm until an optimal setting has been reached. As a typical machine learning algorithm, ANN has achieved immense success in many fields, such as in image recognition, intelligent robots, automatic controls, prediction, estimation, and so on.…”

Section: In-sensor Annmentioning

confidence: 99%

“…ANN can learn from information about their surroundings by performing iterations and feedbacks and updating weights. For example, learning to classify known examples (i.e., supervised learning), or learning to recognize the characteristic structure of an object from input data with no additional information (i.e., unsupervised learning) 34,85,86 . During learning, each synapse (i.e., the weight between layers) in the network is constantly predicted, strengthened, or weakened by the algorithm until an optimal setting has been reached.…”

Section: Bionic In‐sensor Computing Based On Retinomorphic Devicesmentioning

confidence: 99%

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Retinomorphic hardware for in‐sensor computing

Feng

Zhang

Tian

et al. 2023

InfoMat

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Rapid developments in the Internet of Things and Artificial Intelligence trigger higher requirements for image perception and learning of external environments through visual systems. However, limited by von Neumann's bottleneck, the physical separation of sense, memory, and processing units in a conventional personal computer‐based vision system tend to consume a significant amount of energy, time latency, and additional hardware costs. By integrating computational tasks of multiple functionalities into the sensors themselves, the emerging bio‐inspired neuromorphic visual systems provide an opportunity to overcome these limitations. With high speed, ultralow power and strong adaptability, it is highly desirable to develop a neuromorphic vision system that is based on highly precise in‐sensor computing devices, namely retinomorphic devices. We here present a timely review of retinomorphic devices for visual in‐sensor computing. We begin with several types of physical mechanisms of photoelectric sensors that can be constructed for artificial vision. The potential applications of retinomorphic hardware are, thereafter, thoroughly summarized. We also highlight the possible strategies to existing challenges and give a brief perspective of retinomorphic architecture for in‐sensor computing.

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Section: In-sensor Annmentioning

confidence: 99%

Section: Bionic In‐sensor Computing Based On Retinomorphic Devicesmentioning

confidence: 99%

Retinomorphic hardware for in‐sensor computing

Feng

Zhang

Tian

et al. 2023

InfoMat

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“…He et al [ 11 ] employed an artificial neural network (ANN) to predict the thermal conductivity of a zinc oxide–silver/water hybrid Newtonian nanofluid. Soltani et al [ 12 ] utilized an ANN to determine the thermal conductivity of a tungsten oxide–MWCNTs/hybrid engine oil. Xia et al [ 13 ] applied a feed‐forward perceptron ANN model to predict the rolling force, power, and slip of tandem cold‐rolling.…”

Section: Introductionmentioning

confidence: 99%

Optimization of Multi‐Segment Work Roll Profile for 1340 mm HC Tandem Cold‐Rolling Mills Based on DBSCAN and NSGA‐II Algorithms

Chen

Liang-gui

Pan

et al. 2023

steel research int.

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To address issues with saturated work roll bending force (WRB), maximum contact pressure between the work roll and intermediate roll (QWI), and challenging flatness lifting in a four‐stand, six‐high tandem cold mill with a 1340 mm width, the density‐based spatial clustering of applications with noise algorithm and the non‐dominated sorting genetic algorithm II are used for optimal design and online application research of a five‐segment work roll profile. When compared to a flat roll and a parabolic roll profile with a maximum crown of 15 μm, the optimized roll profile decreases the secondary crown of the roll gap (CW2) by 5.99 and 1.055 μm, respectively, and the fourth crown of the roll gap (CW4) by 0.21 and 0.39 μm, respectively. Additionally, QWI in the field of roll gap crown adjustment decreases by 1.04 and 0.445 kN mm−1. Industrial testing shows that the average integrated value of flatness in rolling the four extreme specifications of 1340 mm tandem cold‐rolling mills lowers by 2.65 IU following the application of the new roll profile, and the average reduction of WRB in the last stand is 2.215 MPa.

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“…In this article, an analysis was performed on the convection of a Newtonian fluid flow between two asymmetric parallel surfaces in a porous medium. Mondal [17] researched irreversibility minimization in a viscoelastic liquid flow [18–49]. The viscoelastic parameters, the Peclet number, and the temperature gradient were studied in this paper.…”

Section: Introductionmentioning

confidence: 99%

Hybrid surveying of radiation and magnetic impacts on Maxwell fluid with MWCNT nanotube influence of two wire loops

et al. 2022

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In this paper, the effects of Maxwell nanoliquid transmission with MWCNT nanotube are investigated over two circular wires with opposite currents and a stretching sheet. The innovation of this paper is the examination of MWCNT nanotube temperature and nanofluid velocity and also magnetic potential and magnetic effect in the three different places of two circular wires on the sheet. The finite element method is utilized for calculating differential equations. By increasing the distance from two wires on the stretching sheet, the amount of MWCNT nanotube temperature increased, and a large vortex forms at the bottom of the sheet. Overall, the MWCNT nanotube velocity around the two circular wires at the beginning of the sheet is 46.09% higher than the middle of the sheet and 20% higher than the end of the sheet, also the MWCNT nanotube magnetic potential around the two circular wires at the beginning of the sheet is 13.40% higher than the middle of the sheet and 23% higher than the end of the sheet. The second part of the essay relates to the using response surface methodology (RSM) method in the Design‐Expert software. Based on the results obtained from the MWCNT nanotube velocity graphs and the heat transfer cantors, and magnetism effects in the Design‐Expert software, the best optimization occurred for u = 0.07, T = 1.15, and H = 4.531.

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Applying Artificial Neural Networks (ANNs) for prediction of the thermal characteristics of engine oil –based nanofluids containing tungsten oxide -MWCNTs

Cited by 19 publications

References 45 publications

Retinomorphic hardware for in‐sensor computing

Retinomorphic hardware for in‐sensor computing

Optimization of Multi‐Segment Work Roll Profile for 1340 mm HC Tandem Cold‐Rolling Mills Based on DBSCAN and NSGA‐II Algorithms

Hybrid surveying of radiation and magnetic impacts on Maxwell fluid with MWCNT nanotube influence of two wire loops

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