Characteristics of pulsating heat pipe with variation of tube diameter, filling ratio, and SiO2 nanoparticles: Biomedical and engineering implications

Case Studies in Thermal Engineering, Год журнала: 2024, Номер 55, С. 104065 - 104065

Опубликована: Фев. 10, 2024

Pulsating heat pipe (PHP) is an implicit technique through a passive two-stage transfer system. This paper presents the experimentations on PHP contrived using copper with different inner tube diameters of 1, 1.5, 2, and 2.5 mm, respectively. The accused acetone as functional liquid filling proportions varying from 50 to 90% its volume increment 10%. effects proportion diameter thermal performance were investigated. evaporator zone electrically heated mica heater in range 20–80 W, condenser area kept cool by water circulation method. results show that 2 mm performs best compared other diameters, lower rate resistance 0.49 K/W. Also, enhanced at 60% for all diameters. Further, CFD analysis was carried out ratios constant input 80 it revealed test outcomes line results. deviation between experimental numerical studies Considering optimized parameters, i.e., ratio, work extended adding SiO2 nanoparticles base fluid 1–5 % mass concentration. showed value 0.3 W/K higher coefficient 828.64 W/m2 °K obtained 2% concentration SiO2. proportional rise 60 W 11.46, 17, 14, 4.15, 1.94% 3, 4, 5% nanoparticles, Hence, operates better ratio nanoparticles.

Язык: Английский

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Machinability investigation of natural fibers reinforced polymer matrix composite under drilling: Leveraging machine learning in bioengineering applications

AIP Advances, Год журнала: 2024, Номер 14(4)

Опубликована: Апрель 1, 2024

The growing demand for fiber-reinforced polymer (FRP) in industrial applications has prompted the exploration of natural fiber-based composites as a viable alternative to synthetic fibers. Using jute–rattan composite offers potential environmentally sustainable waste material decomposition and cost reduction compared conventional fiber materials. This article focuses on impact different machining constraints surface roughness delamination during drilling process FRP composite. Inspired by this unexplored research area, emphasizes influence various Response methodology designs experiment using drill bit material, spindle speed, feed rate input variables measure factors. technique order preference similarity ideal solution method is used optimize parameters, predicting delamination, two machine learning-based models named random forest (RF) support vector (SVM) are utilized. To evaluate accuracy predicted values, correlation coefficient (R2), mean absolute percentage error, squared error were used. RF performed better comparison with SVM, higher value R2 both testing training datasets, which 0.997, 0.981, 0.985 roughness, entry exit respectively. Hence, study presents an innovative through learning techniques.

Язык: Английский

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Plain-Woven Areca Sheath Fiber-Reinforced Epoxy Composites: The Influence of the Fiber Fraction on Physical and Mechanical Features and Responses of the Tribo System and Machine Learning Modeling

ACS Omega, Год журнала: 2024, Номер unknown

Опубликована: Фев. 5, 2024

Recent studies focus on enhancing the mechanical features of natural fiber composites to replace synthetic fibers that are highly useful in building, automotive, and packing industries. The novelty work is woven areca sheath (ASF) with different fraction epoxy has been fabricated tested for its tribological responses three-body abrasion wear testing machines along features. impact various examined. study also revolves around development validation a machine learning predictive model using random forest (RF) algorithm, aimed at forecasting two critical performance parameters: specific rate (SWR) coefficient friction (COF). void observed vary between 0.261 3.8% as incremented. hardness mat rises progressively from 40.23 84.26 HRB. A fair ascent tensile strength modulus observed. Even though short descent flexural seen 0 12 wt % composite specimens, they incrementally raised finest values 52.84 2860 MPa, respectively, pertinent 48 fiber-loaded specimen. progressive rise ILSS perceptible. behavior specimens reported. worn surface morphology studied understand interface ASF matrix. RF exhibited outstanding prowess, evidenced by high R-squared coupled low mean-square error mean absolute metrics. Rigorous statistical employing paired t tests confirmed model's suitability, revealing no significant disparities predicted actual both SWR COF.

Язык: Английский

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Material performance, manufacturing methods, and engineering applications in aviation of Carbon fiber reinforced polymers: A comprehensive review

Thin-Walled Structures, Год журнала: 2024, Номер unknown, С. 112899 - 112899

Опубликована: Дек. 1, 2024

Язык: Английский

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Impedance Value Prediction of Carbon Nanotube/Polystyrene Nanocomposites Using Tree-Based Machine Learning Models and the Taguchi Technique

Results in Engineering, Год журнала: 2024, Номер unknown, С. 103599 - 103599

Опубликована: Дек. 1, 2024

Язык: Английский

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A systematic review of nanotechnology for electric vehicles battery

International Journal of Low-Carbon Technologies, Год журнала: 2024, Номер 19, С. 747 - 765

Опубликована: Янв. 1, 2024

Abstract Nanotechnology has increased electric vehicle (EV) battery production, efficiency and use. is explored in this car illustration. Nanoscale materials topologies research energy density, charge time cycle life. Nanotubes, graphene metal oxides improve storage, flow charging/discharge. Solid-state lithium-air high-energy batteries are safer, more dense stable using nanoscale catalysts. improves parts. Nanostructured fluids reduce lithium dendrite, improving batteries. Nanocoating electrodes may damage extend benefits the planet. Nanomaterials allow parts to employ ordinary, safe instead of rare, harmful ones. promotes recycling, reducing waste. Change does not influence stable, cost-effective or scalable items. Business opportunities for nanotechnology-based EV need research. High-performance, robust environmentally friendly might make cars popular transportation sustainable with development. An outline nanotechnology researchexamines publication patterns, notable articles, collaborators contributions. This issue was researched extensively, indicating interest. Research focuses on anode materials, storage performance. A landscape assessment demonstrates nanotechnology’s growth future. comprehensive literature review examined nanosensors EVs. Our study provides a solid foundation understanding current state research, identifying major trends discovering breakthroughs sensors by carefully reviewing, characterizing rating important papers.

Язык: Английский

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Prediction of Wear Rate of Glass-Filled PTFE Composites Based on Machine Learning Approaches

Polymers, Год журнала: 2024, Номер 16(18), С. 2666 - 2666

Опубликована: Сен. 22, 2024

Wear is induced when two surfaces are in relative motion. The wear phenomenon mostly data-driven and affected by various parameters such as load, sliding velocity, distance, interface temperature, surface roughness, etc. Hence, it difficult to predict the rate of interacting from fundamental physics principles. machine learning (ML) approach has not only made possible establish relation between operating but also helps predicting behavior material polymer tribological applications. In this study, an attempt apply different algorithms experimental data for prediction specific glass-filled PTFE (Polytetrafluoroethylene) composite. Orthogonal array L25 used experimentation evaluating with variations applied distance. analysed using ML linear regression (LR), gradient boosting (GB), random forest (RF). R2 value obtained 0.91, 0.97, 0.94 LR, GB, RF, respectively. GB model highest among models, close 1.0, indicating almost perfect fit on data. Pearson’s correlation analysis reveals that load distance have a considerable impact compared velocity.

Язык: Английский

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Machine learning-enabled powder-spreading process

Elsevier eBooks, Год журнала: 2025, Номер unknown, С. 107 - 130

Опубликована: Янв. 1, 2025

Язык: Английский

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Comparative Study of Wear Behaviour of ZA37 Alloy, ZA37/SiC Composite, and Grey Cast Iron under Lubricated Conditions: Predictive Modeling by Machine Learning

Tribology International, Год журнала: 2025, Номер unknown, С. 110623 - 110623

Опубликована: Март 1, 2025

Язык: Английский

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Advancements in CNT research: Integrating machine learning with microscopic simulations, macroscopic techniques, and application of performance prediction and functional optimization

Materials Today Chemistry, Год журнала: 2025, Номер 45, С. 102616 - 102616

Опубликована: Март 5, 2025

Язык: Английский

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