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52. Thermohydraulic performance of thermal system integrated with twisted turbulator inserts using ternary hybrid nanofluids

Author

Alawi Omer A., Kamar Haslinda Mohamed, Falah Mayadah W., Hussein Omar A., Abdelrazek Ali H., Ahmed Waqar, Eltaweel Mahmoud, Homod Raad Z., Al-Ansari Nadhir, and Yaseen Zaher Mundher

Subjects
heat transfer properties, hydrodynamic performance, ternary hybrid nanofluid, twisted-tape pipes, energy turbulators, Technology, Chemical technology, TP1-1185, Physical and theoretical chemistry, QD450-801
Abstract

Mono, hybrid, and ternary nanofluids were tested inside the plain and twisted-tape pipes using k-omega shear stress transport turbulence models. The Reynolds number was 5,000 ≤ Re ≤ 15,000, and thermophysical properties were calculated under the condition of 303 K. Single nanofluids (Al2O3/distilled water [DW], SiO2/DW, and ZnO/DW), hybrid nanofluids (SiO2 + Al2O3/DW, SiO2 + ZnO/DW, and ZnO + Al2O3/DW) in the mixture ratio of 80:20, and ternary nanofluids (SiO2 + Al2O3 + ZnO/DW) in the mixture ratio of 60:20:20 were estimated in different volumetric concentrations (1, 2, 3, and 4%). The twisted pipe had a higher outlet temperature than the plain pipe, while SiO2/DW had a lower T out value with 310.933 K (plain pipe) and 313.842 K (twisted pipe) at Re = 9,000. The thermal system gained better energy using ZnO/DW with 6178.060 W (plain pipe) and 8426.474 W (twisted pipe). Furthermore, using SiO2/DW at Re = 9,000, heat transfer improved by 18.017% (plain pipe) and 21.007% (twisted pipe). At Re = 900, the pressure in plain and twisted pipes employing SiO2/DW reduced by 167.114 and 166.994%, respectively. In general, the thermohydraulic performance of DW and nanofluids was superior to one. Meanwhile, with Re = 15,000, DW had a higher value of η Thermohydraulic = 1.678.

Published
2023
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70. Application of statistical and machine learning techniques for landslide susceptibility mapping in the Himalayan road corridors

Author

Sarfraz Yasir, Basharat Muhammad, Riaz Muhammad Tayyib, Akram Mian Sohail, Xu Chong, Ahmed Khawaja Shoaib, Shahzad Amir, Al-Ansari Nadhir, and Thuy Linh Nguyen Thi

Subjects
landslide susceptibility, machine learning, the weight of evidence, analytical hierarchy process, random forest, road corridors, Geology, QE1-996.5
Abstract

Landslides are frequent geological hazards, mainly in the rainy season along road corridors worldwide. In the present study, we have comparatively analyzed landslide susceptibility by employing integrated geospatial approaches, i.e., data-driven, knowledge-driven, and machine learning (ML), along the main road corridors of the Muzaffarabad district. The landslide inventory of three road corridors is developed to evaluate landslide susceptibility, and eleven landslide causative factors (LCFs) were analyzed. After statistical significance analysis, these eleven LCFs generated susceptibility models using WoE, AHP, LR, and RF. Distance from roads, landcover, lithological units, and slopes are considered more influential LCFs. The performance matrix of different LSMs is evaluated through the area under the curve (AUC-ROC), overall accuracy, Kappa index, F1 score, Mean Absolute Error, and Root Mean Square Error. The AUC-ROC for WoE, AHP, LR, and RF techniques along Neelum road is 0.86, 0.82, 0.91, and 0.97, respectively, along Jhelum Valley road is 0.83, 0.81, 0.93, and 0.95, respectively, while along Kohala road is 0.89, 0.88, 0.89, and 0.92, respectively. The produced LSMs through ML (i.e., RF and LR) showed better prediction accuracies than WoE and AHP along these three road corridors. The LSMs are categorized into very high, high, moderate, and low susceptible zones along these roads. The LSM generated through hybrid models can facilitate the concerned local agencies to implement landslide mitigation policies for the landslide-prone zones along road corridors.

Published
2022
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78. Groundwater level prediction using machine learning models: A comprehensive review

Author

Tao, Hai, Hameed, Mohammed Majeed, Marhoon, Haydar Abdulameer, Zounemat-Kermani, Mohammad, Heddam, Salim, Kim, Sungwon, Sulaiman, Sadeq Oleiwi, Tan, Mou Leong, Sa’adi, Zulfaqar, Mehr, Ali Danandeh, Allawi, Mohammed Falah, Abba, S.I., Zain, Jasni Mohamad, Falah, Mayadah W., Jamei, Mehdi, Bokde, Neeraj Dhanraj, Bayatvarkeshi, Maryam, Al-Mukhtar, Mustafa, Bhagat, Suraj Kumar, Tiyasha, Tiyasha, Khedher, Khaled Mohamed, Al-Ansari, Nadhir, Shahid, Shamsuddin, and Yaseen, Zaher Mundher

Published
2022
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88. Long-term application of FYM and fertilizer N improve soil fertility and enzyme activity in 51st wheat cycle under pearl millet-wheat.

Author

Sheoran, Sunita, Prakash, Dhram, Yadav, Parmod Kumar, Gupta, Rajeev Kumar, Al-Ansari, Nadhir, El-Hendawy, Salah, and Mattar, Mohamed A.

Abstract

Our study from an ongoing research experiment initiated in Rabi 1967 at the Research Farm of CCS Haryana Agricultural University, Haryana, India, reports that during the 51st wheat cycle in pearl millet-wheat sequence, adding FYM in both seasons significantly impacted various soil parameters at different wheat growth stages compared to the rabi season. The application of 15 t of FYM ha−1 resulted in a considerable increase in dissolved organic carbon content (9.1–11.2%), available P (9.7–12.1%), and available S (12.6–17.1%), DHA levels by 7.3–22.0%, urease activity (10.1 and 17.0%), β-Glucosidase activity (6.2–8.4%), and APA activity (5.2–10.6%), compared to 10 t FYM ha−1. Application of N120 exhibited a considerable improvement in DHA (11.0–23.2%), β-Glucosidase (9.4–19.2%), urease (13.3–28.3%), and APA (3.3–6.2%) activity compared to control (N0). At stage 3, the box plot revealed that 50% of the available N, P, and S values varied from 223.1 to 287.9 kg ha−1, 53.0 to 98.2 kg ha−1, and 50.0 to 97.6 kg ha−1, respectively. Principal component analysis, with PC1 explaining 94.7% and PC2 explaining 3.15% of the overall variability, and SOC had a polynomial relationship with soil characteristics (R2 = 0.89 to 0.99). Applying FYM15 × N120 treatment during both seasons proved beneficial in sustaining the health of sandy loam soil in North-West India. [ABSTRACT FROM AUTHOR]

Published
2024
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