Prognostic modeling of polydisperse SiO2/Aqueous glycerol nanofluids' thermophysical profile using an explainable artificial intelligence (XAI) approach

Sharma, K.V.; Talpa Sai, P.H.V. Sesha; Sharma, Prabhakar; Kanti, Praveen Kumar; Bhramara, P.; Akilu, Suleiman

doi:10.1016/j.engappai.2023.106967

Cited by 10 publications

(2 citation statements)

References 60 publications

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“…These nanofluids, which are made up of nanoparticles that are dispersed throughout a base fluid, have better thermal characteristics, which makes them potential candidates for enhanced heat transfer applications. Experimentation or simulation is used to gather data on critical factors such as nanoparticle concentration, temperature, fluid flow rate, and thermal conductivity 18 , 19 . However, when data is large and complex-nonlinear, it becomes difficult to model the data and find the correlation.…”

Section: Introductionmentioning

confidence: 99%

Machine learning analysis of thermophysical and thermohydraulic properties in ethylene glycol- and glycerol-based SiO2 nanofluids

Akilu,

Sharma,

Baheta

et al. 2024

Sci Rep

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The study investigates the heat transfer and friction factor properties of ethylene glycol and glycerol-based silicon dioxide nanofluids flowing in a circular tube under continuous heat flux circumstances. This study tackles the important requirement for effective thermal management in areas such as electronics cooling, the automobile industry, and renewable energy systems. Previous research has encountered difficulties in enhancing thermal performance while handling the increased friction factor associated with nanofluids. This study conducted experiments in the Reynolds number range of 1300 to 21,000 with particle volume concentrations of up to 1.0%. Nanofluids exhibited superior heat transfer coefficients and friction factor values than the base liquid values. The highest enhancement in heat transfer was 5.4% and 8.3% for glycerol and ethylene glycol -based silicon dioxide Nanofluid with a relative friction factor penalty of ∼30% and 75%, respectively. To model and predict the complicated, nonlinear experimental data, five machine learning approaches were used: linear regression, random forest, extreme gradient boosting, adaptive boosting, and decision tree. Among them, the decision tree-based model performed well with few errors, while the random forest and extreme gradient boosting models were also highly accurate. The findings indicate that these advanced machine learning models can accurately anticipate the thermal performance of nanofluids, providing a dependable tool for improving their use in a variety of thermal systems. This study's findings help to design more effective cooling solutions and improve the sustainability of energy systems.

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Section: Introductionmentioning

confidence: 99%

Machine learning analysis of thermophysical and thermohydraulic properties in ethylene glycol- and glycerol-based SiO2 nanofluids

Akilu,

Sharma,

Baheta

et al. 2024

Sci Rep

Self Cite

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“…By promptly addressing these cracks, potential safety hazards can be mitigated, ensuring the longevity and structural integrity of the building. Recent advancements in science and technology have led to the development of automatic crack detection models, employing image processing and machine learning (ML) techniques [3][4][5][6] .…”

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confidence: 99%

Transfer learned deep feature based crack detection using support vector machine: a comparative study

Bhalaji Kharthik,

Onyema,

Mallik

et al. 2024

Sci Rep

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Technology offers a lot of potential that is being used to improve the integrity and efficiency of infrastructures. Crack is one of the major concerns that can affect the integrity or usability of any structure. Oftentimes, the use of manual inspection methods leads to delays which can worsen the situation. Automated crack detection has become very necessary for efficient management and inspection of critical infrastructures. Previous research in crack detection employed classification and localization-based models using Deep Convolutional Neural Networks (DCNNs). This study suggests and compares the effectiveness of transfer learned DCNNs for crack detection as a classification model and as a feature extractor to overcome this restriction. The main objective of this paper is to present various methods of crack detection on surfaces and compare their performance over 3 different datasets. Experiments conducted in this work are threefold: initially, the effectiveness of 12 transfer learned DCNN models for crack detection is analyzed on three publicly available datasets: SDNET, CCIC and BSD. With an accuracy of 53.40%, ResNet101 outperformed other models on the SDNET dataset. EfficientNetB0 was the most accurate (98.8%) model on the BSD dataset, and ResNet50 performed better with an accuracy of 99.8% on the CCIC dataset. Secondly, two image enhancement methods are employed to enhance the images and are transferred learned on the 12 DCNNs in pursuance of improving the performance of the SDNET dataset. The results from the experiments show that the enhanced images improved the accuracy of transfer-learned crack detection models significantly. Furthermore, deep features extracted from the last fully connected layer of the DCNNs are used to train the Support Vector Machine (SVM). The integration of deep features with SVM enhanced the detection accuracy across all the DCNN-dataset combinations, according to analysis in terms of accuracy, precision, recall, and F1-score.

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Optimization of thermophysical properties of nanofluids using a hybrid procedure based on machine learning, multi-objective optimization, and multi-criteria decision-making

Zhang,

Manafi Khajeh Pasha,

Mohammad Sajadi

et al. 2024

Chemical Engineering Journal

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Prognostic modeling of polydisperse SiO2/Aqueous glycerol nanofluids' thermophysical profile using an explainable artificial intelligence (XAI) approach

Cited by 10 publications

References 60 publications

Machine learning analysis of thermophysical and thermohydraulic properties in ethylene glycol- and glycerol-based SiO2 nanofluids

Machine learning analysis of thermophysical and thermohydraulic properties in ethylene glycol- and glycerol-based SiO2 nanofluids

Transfer learned deep feature based crack detection using support vector machine: a comparative study

Optimization of thermophysical properties of nanofluids using a hybrid procedure based on machine learning, multi-objective optimization, and multi-criteria decision-making

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