| 1. |
- Thomas, HS, et al.
(författare)
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- 2019
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swepub:Mat__t
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| 2. |
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| 3. |
- Tao, Hai, et al.
(författare)
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Energy and cost management of different mixing ratios and morphologies on mono and hybrid nanofluids in collector technologies
- 2023
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Ingår i: Engineering Applications of Computational Fluid Mechanics. - : Taylor & Francis. - 1994-2060 .- 1997-003X. ; 17:1
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Tidskriftsartikel (refereegranskat)abstract
- The flat-plate solar collector (FPSC) three-dimensional (3D) model was used to numerically evaluate the energy and economic estimates. A laminar flow with 500 ≤ Re ≤ 1900, an inlet temperature of 293 K, and a solar flux of 1000 W/m2 were assumed the operating conditions. Two mono nanofluids, CuO-DW and Cu-DW, were tested with different shapes (Spherical, Cylindrical, Platelets, and Blades) and different volume fractions. Additionally, hybrid nanocomposites from CuO@Cu/DW with different shapes (Spherical, Cylindrical, Platelets and Blades), different mixing ratios (60% + 40%, 50% + 50% and 40% + 60%) and different volume fractions (1 volume%, 2 volume%, 3 volume% and 4 volume%) were compared with mono nanofluids. At 1 volume% and Re = 1900, CuO-Platelets demonstrated the highest pressure drop (33.312 Pa). CuO-Platelets achieved the higher thermal enhancement with (8.761%) at 1 vol.% and Re = 1900. CuO-Platelets reduced the size of the solar collector by 25.60%. Meanwhile, CuO@Cu-Spherical (40:60) needed a larger collector size with 16.69% at 4 vol.% and Re = 1900. CuO-Platelets with 967.61, CuO – Cylindrical with 976.76, Cu Platelets with 983.84, and Cu-Cylindrical with 992.92 presented the lowest total cost. Meanwhile, the total cost of CuO – Cu – Platelets with 60:40, 50:50, and 40:60 was 994.82, 996.18, and 997.70, respectively.
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| 4. |
- Yaseen, Zaher Mundher, et al.
(författare)
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State-of-the Art-Powerhouse, Dam Structure, and Turbine Operation and Vibrations
- 2020
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Ingår i: Sustainability. - Switzerland : MDPI. - 2071-1050. ; 12:4
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Tidskriftsartikel (refereegranskat)abstract
- Dam and powerhouse operation sustainability is a major concern from the hydraulic engineering perspective. Powerhouse operation is one of the main sources of vibrations in the dam structure and hydropower plant; thus, the evaluation of turbine performance at different water pressures is important for determining the sustainability of the dam body. Draft tube turbines run under high pressure and suffer from connection problems, such as vibrations and pressure fluctuation. Reducing the pressure fluctuation and minimizing the principal stress caused by undesired components of water in the draft tube turbine are ongoing problems that must be resolved. Here, we conducted a comprehensive review of studies performed on dams, powerhouses, and turbine vibration, focusing on the vibration of two turbine units: Kaplan and Francis turbine units. The survey covered several aspects of dam types (e.g., rock and concrete dams), powerhouse analysis, turbine vibrations, and the relationship between dam and hydropower plant sustainability and operation. The current review covers the related research on the fluid mechanism in turbine units of hydropower plants, providing a perspective on better control of vibrations. Thus, the risks and failures can be better managed and reduced, which in turn will reduce hydropower plant operation costs and simultaneously increase the economical sustainability. Several research gaps were found, and the literature was assessed to provide more insightful details on the studies surveyed. Numerous future research directions are recommended.
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| 5. |
- Khatri, C, et al.
(författare)
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Outcomes after perioperative SARS-CoV-2 infection in patients with proximal femoral fractures: an international cohort study
- 2021
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Ingår i: BMJ open. - : BMJ. - 2044-6055. ; 11:11, s. e050830-
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Tidskriftsartikel (refereegranskat)abstract
- Studies have demonstrated high rates of mortality in people with proximal femoral fracture and SARS-CoV-2, but there is limited published data on the factors that influence mortality for clinicians to make informed treatment decisions. This study aims to report the 30-day mortality associated with perioperative infection of patients undergoing surgery for proximal femoral fractures and to examine the factors that influence mortality in a multivariate analysis.SettingProspective, international, multicentre, observational cohort study.ParticipantsPatients undergoing any operation for a proximal femoral fracture from 1 February to 30 April 2020 and with perioperative SARS-CoV-2 infection (either 7 days prior or 30-day postoperative).Primary outcome30-day mortality. Multivariate modelling was performed to identify factors associated with 30-day mortality.ResultsThis study reports included 1063 patients from 174 hospitals in 19 countries. Overall 30-day mortality was 29.4% (313/1063). In an adjusted model, 30-day mortality was associated with male gender (OR 2.29, 95% CI 1.68 to 3.13, p<0.001), age >80 years (OR 1.60, 95% CI 1.1 to 2.31, p=0.013), preoperative diagnosis of dementia (OR 1.57, 95% CI 1.15 to 2.16, p=0.005), kidney disease (OR 1.73, 95% CI 1.18 to 2.55, p=0.005) and congestive heart failure (OR 1.62, 95% CI 1.06 to 2.48, p=0.025). Mortality at 30 days was lower in patients with a preoperative diagnosis of SARS-CoV-2 (OR 0.6, 95% CI 0.6 (0.42 to 0.85), p=0.004). There was no difference in mortality in patients with an increase to delay in surgery (p=0.220) or type of anaesthetic given (p=0.787).ConclusionsPatients undergoing surgery for a proximal femoral fracture with a perioperative infection of SARS-CoV-2 have a high rate of mortality. This study would support the need for providing these patients with individualised medical and anaesthetic care, including medical optimisation before theatre. Careful preoperative counselling is needed for those with a proximal femoral fracture and SARS-CoV-2, especially those in the highest risk groups.Trial registration numberNCT04323644
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| 6. |
- Tao, Hai, et al.
(författare)
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Groundwater level prediction using machine learning models: A comprehensive review
- 2022
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Ingår i: Neurocomputing. - : Elsevier. - 0925-2312 .- 1872-8286. ; 489, s. 271-308
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Forskningsöversikt (refereegranskat)abstract
- Developing accurate soft computing methods for groundwater level (GWL) forecasting is essential for enhancing the planning and management of water resources. Over the past two decades, significant progress has been made in GWL prediction using machine learning (ML) models. Several review articles have been published, reporting the advances in this field up to 2018. However, the existing review articles do not cover several aspects of GWL simulations using ML, which are significant for scientists and practitioners working in hydrology and water resource management. The current review article aims to provide a clear understanding of the state-of-the-art ML models implemented for GWL modeling and the milestones achieved in this domain. The review includes all of the types of ML models employed for GWL modeling from 2008 to 2020 (138 articles) and summarizes the details of the reviewed papers, including the types of models, data span, time scale, input and output parameters, performance criteria used, and the best models identified. Furthermore, recommendations for possible future research directions to improve the accuracy of GWL prediction models and enhance the related knowledge are outlined.
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| 7. |
- Afan, Haitham Abdulmohsin, et al.
(författare)
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Thermal and Hydraulic Performances of Carbon and Metallic Oxides-Based Nanomaterials
- 2022
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Ingår i: Nanomaterials. - : MDPI AG. - 2079-4991. ; 12:9
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Tidskriftsartikel (refereegranskat)abstract
- For companies, notably in the realms of energy and power supply, the essential requirement for highly efficient thermal transport solutions has become a serious concern. Current research highlighted the use of metallic oxides and carbon-based nanofluids as heat transfer fluids. This work examined two carbon forms (PEG@GNPs & PEG@TGr) and two types of metallic oxides (Al2O3 & SiO2) in a square heated pipe in the mass fraction of 0.1 wt.%. Laboratory conditions were as follows: 6401 ≤ Re ≤ 11,907 and wall heat flux = 11,205 W/m2. The effective thermal–physical and heat transfer properties were assessed for fully developed turbulent fluid flow at 20–60 °C. The thermal and hydraulic performances of nanofluids were rated in terms of pumping power, performance index (PI), and performance evaluation criteria (PEC). The heat transfer coefficients of the nanofluids improved the most: PEG@GNPs = 44.4%, PEG@TGr = 41.2%, Al2O3 = 22.5%, and SiO2 = 24%. Meanwhile, the highest augmentation in the Nu of the nanofluids was as follows: PEG@GNPs = 35%, PEG@TGr = 30.1%, Al2O3 = 20.6%, and SiO2 = 21.9%. The pressure loss and friction factor increased the highest, by 20.8–23.7% and 3.57–3.85%, respectively. In the end, the general performance of nanofluids has shown that they would be a good alternative to the traditional working fluids in heat transfer requests.
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| 8. |
- Al-Janabi, Ahmed Mohammed Sami, et al.
(författare)
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Experimental and Numerical Analysis for Earth-Fill Dam Seepage
- 2020
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Ingår i: Sustainability. - Switzerland : MDPI. - 2071-1050. ; 12:6, s. 1-14
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Tidskriftsartikel (refereegranskat)abstract
- Earth-fill dams are the most common types of dam and the most economical choice. However, they are more vulnerable to internal erosion and piping due to seepage problems that are the main causes of dam failure. In this study, the seepage through earth-fill dams was investigated using physical, mathematical, and numerical models. Results from the three methods revealed that both mathematical calculations using L. Casagrande solutions and the SEEP /Wnumerical model have a plotted seepage line compatible with the observed seepage line in the physical model. However,when the seepage flow intersected the downstream slope and when piping took place, the use of SEEP /Wto calculate the flow rate became useless as it was unable to calculate the volume of water flow in pipes. This was revealed by the big dierence in results between physical and numerical models in the first physical model, while the results were compatible in the second physical model when the seepage line stayed within the body of the dam and low compacted soil was adopted. Seepage analysis for seven dierent configurations of an earth-fill dam was conducted using the SEEP /W model at normal and maximum water levels to find the most appropriate configuration among them. The seven dam configurations consisted of four homogenous dams and three zoned dams. Seepage analysis revealed that if sucient quantity of silty sand soil is available around the proposed dam location, a homogenous earth-fill dam with a medium drain length of 0.5 m thickness is the best design configuration. Otherwise, a zoned earth-fill dam with a central core and 1:0.5 Horizontal to Vertical ratio (H:V) is preferred.
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| 9. |
- Al-Janabi, Ahmed Mohammed Sami, et al.
(författare)
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Optimizing Height and Spacing of Check Dam Systems for Better Grassed Channel Infiltration Capacity
- 2020
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Ingår i: Applied Sciences. - Switzerland : MDPI. - 2076-3417. ; 10:11
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Tidskriftsartikel (refereegranskat)abstract
- The check dams in grassed stormwater channels enhance infiltration capacity by temporarily blocking water flow. However, the design properties of check dams, such as their height and spacing, have a significant influence on the flow regime in grassed stormwater channels and thus channel infiltration capacity. In this study, a mass-balance method was applied to a grassed channel model to investigate the effects of height and spacing of check dams on channel infiltration capacity. Moreover, an empirical infiltration model was derived by improving the modified Kostiakov model for reliable estimation of infiltration capacity of a grassed stormwater channel due to check dams from four hydraulic parameters of channels, namely, the water level, channel base width, channel side slope, and flow velocity. The result revealed that channel infiltration was increased from 12% to 20% with the increase of check dam height from 10 to 20 cm. However, the infiltration was found to decrease from 20% to 19% when a 20 cm height check dam spacing was increased from 10 to 30 m. These results indicate the effectiveness of increasing height of check dams for maximizing the infiltration capacity of grassed stormwater channels and reduction of runoff volume.
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| 10. |
- Al-Sulttani, Ali Omran, et al.
(författare)
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Thermal effectiveness of solar collector using Graphene nanostructures suspended in ethylene glycol–water mixtures
- 2022
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Ingår i: Energy Reports. - : Elsevier BV. - 2352-4847. ; 8, s. 1867-1882
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Tidskriftsartikel (refereegranskat)abstract
- Flat plate solar collectors (FPSCs) are the most often used as solar collectors due to their easiness of installation and usage. The current research investigates the energy efficiency of FPSC using different mass concentration with varied base fluids containing Graphene nanofluids (T-Gr). Mass concentration of 0.1%-wt., 0.075%-wt., 0.050%-wt. and 0.025%-wt. were mixed with ethylene glycol (EG) and distilled water (DW) in different rations. The operating conditions were volumetric flowrate (1.5, 1 and 0.5) LPM 50 °C-input fluid temperature and 800 W/m2-global solar irradiation. Scanning electron microscope (SEM) and energy dispersive X-ray (EDX) were used to synthesize the thermally treated nanomaterial. The theoretical investigation indicated that using T-Gr nanosuspensions increased the FPSC efficiency in comparison with the host fluid for all examined mass concentrations and volumetric flowrates. In quantitative terms, the maximum thermal effectiveness improvement for the EG, (DW:70 + EG:30) and DW:EG (DW:50 + EG:50) and using flowrates of (1.5, 1 and 0.5) LPM were 12.54%, 12.46% and 12.48%. In addition, the research results pointed that the essential parameters (i.e., loss energy (FRUL)) and gain energy (FR (τα)) of the T-Gr nanofluids were increased significantly.
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| 11. |
- Alawi, Omer A., et al.
(författare)
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Heat transfer and hydrodynamic properties using different metal-oxide nanostructures in horizontal concentric annular tube : An optimization study
- 2021
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Ingår i: Nanomaterials. - : MDPI AG. - 2079-4991. ; 11:8
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Tidskriftsartikel (refereegranskat)abstract
- Numerical studies were performed to estimate the heat transfer and hydrodynamic properties of a forced convection turbulent flow using three-dimensional horizontal concentric annuli. This paper applied the standard k–ε turbulence model for the flow range 1 × 104 ≤ Re ≥ 24 × 103. A wide range of parameters like different nanomaterials (Al2O3, CuO, SiO2 and ZnO), different particle nanoshapes (spherical, cylindrical, blades, platelets and bricks), different heat flux ratio (HFR) (0, 0.5, 1 and 2) and different aspect ratios (AR) (1.5, 2, 2.5 and 3) were examined. Also, the effect of inner cylinder rotation was discussed. An experiment was conducted out using a field-emission scanning electron microscope (FE-SEM) to characterize metallic oxides in spherical morphologies. Nano-platelet particles showed the best enhancements in heat transfer properties, followed by nano-cylinders, nano-bricks, nano-blades, and nano-spheres. The maximum heat transfer enhancement was found in SiO2, followed by ZnO, CuO, and Al2O3, in that order. Meanwhile, the effect of the HFR parameter was insignificant. At Re = 24,000, the inner wall rotation enhanced the heat transfer about 47.94%, 43.03%, 42.06% and 39.79% for SiO2, ZnO, CuO and Al2O3, respectively. Moreover, the AR of 2.5 presented the higher heat transfer improvement followed by 3, 2, and 1.5.
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| 12. |
- Ameen, Ameen Mohammed Salih, et al.
(författare)
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Minimizing the Principle Stresses of Powerhoused Rock-Fill Dams Using Control Turbine Running Units: Application of Finite Element Method
- 2018
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Ingår i: Water. - : MDPI. - 2073-4441. ; 10:9
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Tidskriftsartikel (refereegranskat)abstract
- This study focuses on improving the safety of embankment dams by considering theeffects of vibration due to powerhouse operation on the dam body. The study contains two ainparts. In the first part, ANSYS-CFX is used to create the three-dimensional (3D) Finite Volume (FV)model of one vertical Francis turbine unit. The 3D model is run by considering various reservoirconditions and the dimensions of units. The Re-Normalization Group (RNG) k-?? turbulence modelis employed, and the physical properties of water and the flow haracteristics are defined in theturbine model. In the second phases, a 3D finite element (FE) numerical model for a rock-fill dam iscreated by using ANSYS®, considering the dam connection with its powerhouse represented by fourvertical Francis turbines, foundation, and the upstream reservoir. Changing the upstream watertable minimum and maximum water levels, standers earth gravity, fluid-solid interface, hydrostaticpressure, and the soil properties are onsidered. The dam model runs to cover all possibilities forturbines operating in accordance with the reservoir discharge ranges. In order to minimize stressesin the dam body and increase dam safety, this study optimizes the turbine operating system byintegrating turbine and dam models.
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| 13. |
- Ehteram, Mohammad, et al.
(författare)
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Hybridization of artificial intelligence models with nature inspired optimization algorithms for lake water level prediction and uncertainty analysis
- 2021
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Ingår i: Alexandria Engineering Journal. - Netherlands : Elsevier. - 1110-0168 .- 2090-2670. ; 60:2, s. 2193-2208
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Tidskriftsartikel (refereegranskat)abstract
- In the present study, an improved adaptive neuro fuzzy inference system (ANFIS) and multilayer perceptron (MLP) models are hybridized with a sunflower optimization (SO) algorithm and are introduced for lake water level simulation. The Urmia Lake water level is predicted and assessed using the potential of the proposed advanced artificial intelligence (AI) models. The sunflower optimization algorithm is implemented to find the optimal tuning parameters. The results indicated that the ANFIS-SO model with the combination of three lags of rainfall and temperature as input attributes attained the best predictability performance. The minimal values of the root mean square error were RMSE = 1.89 m and 1.92 m for the training and testing modeling phases, respectively. The worst prediction capacity was attained for the long lead (i.e., six months rainfall lag times). The uncertainty analysis showed that the ANFIS-SO model had less uncertainty based on the percentage of more responses in the confidence band and lower bandwidth. Also, different scenarios of water harvesting were investigated with the consideration of environmental restrictions and fair water allocation to stakeholders. Further, studying Urmia Lake water harvesting scenarios displayed that the 30% water harvesting scenario of the lake water improves the lake’s water level.
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| 14. |
- El Baba, Hamizah, et al.
(författare)
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First case of chronic cell leukemia discovered incidentally in extra-saccular inguinal lymph node during laparoscopic bilateral inguinal hernia repair : Case report and literature review
- 2021
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Ingår i: International Journal of Surgery Case Reports. - : Elsevier. - 2210-2612. ; 88
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Tidskriftsartikel (refereegranskat)abstract
- Introduction: Chronic cell leukemia discovered incidentally in extra-saccular inguinal lymph node during laparoscopic bilateral inguinal hernia repair is extremely rare. Presentation of case: 62-year-old Romanian male presented at the outpatient general surgery clinic in April 2019 complaining of bilateral inguinal swelling that gradually increased in size mainly on right side and was diagnosed with bilateral inguinal hernia. During the laparoscopic repair of the hernia, a large lymph node in the left femoral canal was incidentally observed. Histopathologic, immunohistochemical, and flowcytometric evaluation of the excised specimen confirmed chronic lymphocytic leukemia/small lymphocytic lymphoma. Discussion: Whole body CT showed supra and infra-diaphragmatic lymphadenopathy, and few small subsolid pulmonary nodules, possibly metastatic. Splenomegaly and pancreatomegaly were also noted, suggesting lymphomatoid infiltration. Conclusion: There is need for cautious inspection and meticulous palpation of the inguinal area for any lymphadenopathy during routine inguinal hernia repair.
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| 15. |
- El-Seedi, Hesham, et al.
(författare)
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Gelatin nanofibers : Recent insights in synthesis, bio-medical applications and limitations
- 2023
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Ingår i: Heliyon. - : Elsevier. - 2405-8440. ; 9:5
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Forskningsöversikt (refereegranskat)abstract
- The use of gelatin and gelatin-blend polymers as environmentally safe polymers to synthesis electrospun nanofibers, has caused a revolution in the biomedical field. The development of efficient nanofibers has played a significant role in drug delivery, and for use in advanced scaffolds in regenerative medicine. Gelatin is an exceptional biopolymer, which is highly versatile, despite variations in the processing technology. The electrospinning process is an efficient technique for the manufacture of gelatin electrospun nanofibers (GNFs), as it is simple, efficient, and cost-effective. GNFs have higher porosity with large surface area and biocompatibility, despite that there are some drawbacks. These drawbacks include rapid degradation, poor mechanical strength, and complete dissolution, which limits the use of gelatin electrospun nanofibers in this form for biomedicine. Thus, these fibers need to be cross-linked, in order to control its solubility. This modification caused an improvement in the biological properties of GNFs, which made them suitable candidates for various biomedical applications, such as wound healing, drug delivery, bone regeneration, tubular scaffolding, skin, nerve, kidney, and cardiac tissue engineering. In this review an outline of electrospinning is shown with critical summary of literature evaluated with respect to the various applications of nanofibers-derived gelatin.
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| 16. |
- Falih, Ali Hasan, et al.
(författare)
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Comparative study on salinity removal methods: an evaluation-based stable isotopes signatures in ground and sea water
- 2023
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Ingår i: Applied water science. - : Springer. - 2190-5487 .- 2190-5495. ; 13
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Tidskriftsartikel (refereegranskat)abstract
- This research aims to attain the optimal method of removing the high salinity concentrations without its effect on the balance or accuracy of stable isotopes measurement of deuterium and oxygen-18 (δ18O, δ2H). Four treatment methods (i.e., distillation, vacuum distillation, electro dialysis and ion exchange) were applied for nine samples, which were obtained from different water sources (sea, groundwater, river).l Worth to notice that the samples have Electrical Conductivity (EC) ranged (1000–60,000 µs/cm). Liquid–Water Isotope Analyzer used to measure the isotope concentration of δ18O, δ2H. The research findings of the four applied methods revealed their effectiveness with various percentages (normal distillation: 92.37%; vacuum distillation: 88.31%; electro dialysis: 94.85%; ion exchange: 99.62%). In addition, the investigation was conducted a clear correspondence measurement of (δ18O, δ2H) isotopes before and after treatment. The four methods results indicated that samples with EC ranged (1000–5000 µs/cm) have no effect on stable isotope readings. Whereas, samples with EC higher than 10,000, have substantial influence on the stable isotope readings. Finally, vacuum distillation method attained the best results among the treatment methods for EC ranged (10,000–60,000 µs/cm) without affecting the isotopic content of (δ18O, δ2H). There is a clear correspondence of the stable isotopic measurements before and after treatment, for all the selected samples.
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| 17. |
- Hai, Tao, et al.
(författare)
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Global Solar Radiation Estimation and Climatic Variability Analysis Using Extreme Learning Machine Based Predictive Model
- 2020
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Ingår i: IEEE Access. - USA : IEEE. - 2169-3536. ; 8, s. 12026-12042
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Tidskriftsartikel (refereegranskat)abstract
- Sustainable utilization of the freely available solar radiation as renewable energy source requires accurate predictive models to quantitatively evaluate future energy potentials. In this research, an evaluation of the preciseness of extreme learning machine (ELM) model as a fast and efficient framework for estimating global incident solar radiation (G) is undertaken. Daily meteorological datasets suitable for G estimation belongs to the northern parts of the Cheliff Basin in Northwest Algeria, is used to construct the estimation model. Cross-correlation functions are applied between the inputs and the target variable (i.e., G) where several climatological information’s are used as the predictors for surface level G estimation. The most significant model inputs are determined in accordance with highest cross-correlations considering the covariance of the predictors with the G dataset. Subsequently, seven ELM models with unique neuronal architectures in terms of their input-hidden-output neurons are developed with appropriate input combinations. The prescribed ELM model’s estimation performance over the testing phase is evaluated against multiple linear regressions (MLR), autoregressive integrated moving average (ARIMA) models and several well-established literature studies. This is done in accordance with several statistical score metrics. In quantitative terms, the root mean square error (RMSE) and mean absolute error (MAE) are dramatically lower for the optimal ELM model with RMSE and MAE = 3.28 and 2.32 Wm −2 compared to 4.24 and 3.24 Wm −2 (MLR) and 8.33 and 5.37 Wm −2 (ARIMA).
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| 18. |
- Homod, R. Z., et al.
(författare)
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Crude oil production prediction based on an intelligent hybrid modelling structure generated by using the clustering algorithm in big data
- 2023
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Ingår i: Geoenergy Science and Engineering. - : Elsevier. - 2949-8910. ; 225
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Tidskriftsartikel (refereegranskat)abstract
- Since the behavior of a complex dynamic system for a large oil field in Iraq is significantly influenced by many nonlinearities, its dependent parameters exhibit non-stationary with a very high delay time. Developing white-box modelling approaches for such dynamic oil well production cannot handle these large data sets with all dependent dimensions and their non-linear effects. Therefore, this study adopts the hybrid model that combines white-box and black-box to address such problems because the model outputs require various variable types to achieve optimal fitness to measured values. The hybrid model structure needs to evolve with changes in the physical parameters (white-box part) and Neural Networks' Weights (black-box part). The model structure of the proposed hybrid network relied on converting fuzzy rules in a Takagi–Sugeno–Kang Fuzzy System (TSK-FS) into a multilayer perceptron network (MLP). The hybrid parameters are formulated concerning six-dimensional dependent variables to describe them in matrix form or layer and by which can quantify total model outputs. After mapping categorical variables to tuples of MLP, the Gauss-Newton regression (GNR) provides an optimal update of the hybrid parameters to get the best fitting of the model outputs with the target of the dataset. The clustering technique and GNR promote predictive performance due to reducing uncertainties in the hybrid parameters. Due to time being the most effective of the independent variables for predicting oil production, datasets are classified into different clusters based on time. The actual field dataset for training and validation is collected from Zubair Oil Field (9 oil wells), which is implemented to build the proposed model. The results of the hybrid model indicate that the development of the proposed structure has achieved the high capability to represent such big data which is the most imperative feature of the proposed model. Furthermore, obtained results show its accuracy far outpacing competitors and achieving a significant improvement in predictive performance.
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| 19. |
- Homsi, Rajab, et al.
(författare)
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Precipitation projection using a CMIP5 GCM ensemble model : a regional investigation of Syria
- 2020
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Ingår i: Engineering Applications of Computational Fluid Mechanics. - UK : Taylor & Francis. - 1994-2060 .- 1997-003X. ; 14:1, s. 90-106
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Tidskriftsartikel (refereegranskat)abstract
- The possible changes in precipitation of Syrian due to climate change are projected in this study. The symmetrical uncertainty (SU) and multi-criteria decision-analysis (MCDA) methods are used to identify the best general circulation models (GCMs) for precipitation projections. The effectiveness of four bias correction methods, linear scaling (LS), power transformation (PT), general quantile mapping (GEQM), and gamma quantile mapping (GAQM) is assessed in downscaling GCM simulated precipitation. A random forest (RF) model is performed to generate the multi model ensemble (MME) of precipitation projections for four representative concentration pathways (RCPs) 2.6, 4.5, 6.0, and 8.5. The results showed that the best suited GCMs for climate projection of Syria are HadGEM2-AO, CSIRO-Mk3-6-0, NorESM1-M, and CESM1-CAM5. The LS demonstrated the highest capability for precipitation downscaling. Annual changes in precipitation is projected to decrease by −30 to −85.2% for RCPs 4.5, 6.0, and 8.5, while by < 0.0 to −30% for RCP 2.6. The precipitation is projected to decrease in the entire country for RCP 6.0, while increase in some parts for other RCPs during wet season. The dry season of precipitation is simulated to decrease by −12 to −93%, which indicated a drier climate for the country in the future.
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| 20. |
- Li, Jing, et al.
(författare)
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A Systematic Operation Program of a Hydropower Plant Based on Minimizing the Principal Stress : Haditha Dam Case Study
- 2018
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Ingår i: Water. - Switerland : MDPI. - 2073-4441. ; 10:1270, s. 2-20
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Tidskriftsartikel (refereegranskat)abstract
- Dam operation and management have become more complex recently because of the need for considering hydraulic structure sustainability and environmental protect on. An Earthfill dam that includes a powerhouse system is considered as a significant multipurpose hydraulic structure. Understanding the effects of running hydropower plant turbines on the dam body is one of the major safety concerns for earthfill dams. In this research, dynamic analysis of earthfill dam, integrated with a hydropower plant system containing six vertical Kaplan turbines (i.e., Haditha dam), is investigated.In the first stage of the study, ANSYS-CFX was used to represent one vertical Kaplan turbine unit by designing a three-dimensional (3-D) finite element (FE) model. This model was used to differentiate between the effect of turbine units’ operation on dam stability in accordance to maximum and minimum reservoir upstream water levels, and the varying flowrates in a fully open gate condition. In the second stage of the analysis, an ANSYS-static modeling approach was used to develop a 3-D FE earthfill dam model. The water pressure pattern determined on the boundary of the running turbine model is transformed into the pressure at the common area of the dam body with turbines. The model is inspected for maximum and minimum upstream water levels. Findings indicate that the water stress fluctuations on the dam body are proportional to the inverse distance from the turbine region. Also, it was found that the cone and outlet of the hydropower turbine system are the most affected regions when turbine is running. Based on the attained results, a systematic operation program was proposed in order to control the running hydropower plant with minimized principal stress atselected nodes on the dam model and the six turbines.
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| 21. |
- Liu, Suhong, et al.
(författare)
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Energy analysis using carbon and metallic oxides-based nanomaterials inside a solar collector
- 2020
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Ingår i: Energy Reports. - Germany : Elsevier. - 2352-4847. ; 6, s. 1373-1381
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Tidskriftsartikel (refereegranskat)abstract
- The effectiveness of a flat-plate solar collector was studied by using SiO2, Al2O3, Graphene, and graphene nanoplatelets nanofluids with distilled water as the working fluids. The energy efficiency was theoretically compared using MATLAB programming. The prepared carbon and metallic oxides nanomaterials were structurally and morphologically characterized via field emission scanning electron microscope. The study was conducted under different operating conditions such as different volume fractions (0.25%, 0.5%, 0.75% and 1%), fluid mass flow rate (0.0085, 0.017, and 0.0255 kg/s), input temperatures (30, 40, and 50 °C), and solar irradiance (500, 750, and 1000 W/m2). Nanofluids showed better thermophysical properties compared to standard working fluids. With the addition of the nanofluids SiO2, Al2O3, Gr and GNPs to the FPSC the highest efficiency of 64.45%, 67.03%, 72.45%, and 76.56% respectively was reached. The results suggested that nanofluids made from carbon nanostructures and metallic oxides can be used in solar collectors to increase the parameters of heat absorbed/loss compared to water only usage.
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| 22. |
- Mohammad, Reem Sabah, et al.
(författare)
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Frictional pressure drop and cost savings for graphene nanoplatelets nanofluids in turbulent flow environments
- 2021
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Ingår i: Nanomaterials. - : MDPI AG. - 2079-4991. ; 11:11
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Tidskriftsartikel (refereegranskat)abstract
- Covalent-functionalized graphene nanoplatelets (CF-GNPs) inside a circular heated-pipe and the subsequent pressure decrease loss within a fully developed turbulent flow were discussed in this research. Four samples of nanofluids were prepared and investigated in the ranges of 0.025 wt.%, 0.05 wt.%, 0.075 wt.%, and 0.1 wt.%. Different tools such as field emission scanning electron microscopy (FE-SEM), ultraviolet-visible-spectrophotometer (UV-visible), energy-dispersive X-ray spectroscopy (EDX), zeta potential, and nanoparticle sizing were used for the data preparation. The thermophysical properties of the working fluids were experimentally determined using the testing conditions established via computational fluid dynamic (CFD) simulations that had been designed to solve governing equations involving distilled water (DW) and nanofluidic flows. The average error between the numerical solution and the Blasius formula was ~4.85%. Relative to the DW, the pressure dropped by 27.80% for 0.025 wt.%, 35.69% for 0.05 wt.%, 41.61% for 0.075 wt.%, and 47.04% for 0.1 wt.%. Meanwhile, the pumping power increased by 3.8% for 0.025 wt.%, 5.3% for 0.05 wt.%, 6.6% for 0.075%, and 7.8% for 0.1 wt.%. The research findings on the cost analysis demonstrated that the daily electric costs were USD 214, 350, 416, 482, and 558 for DW of 0.025 wt.%, 0.05 wt.%, 0.075 wt.%, and 0.1 wt.%, respectively.
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| 23. |
- Mohammed, Mariamme, et al.
(författare)
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Shallow Foundation Settlement Quantification : Application of Hybridized Adaptive Neuro-Fuzzy Inference System Model
- 2020
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Ingår i: Advances in Civil Engineering / Hindawi. - UK : Hindawi Publishing Corporation. - 1687-8086 .- 1687-8094. ; 2020
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Tidskriftsartikel (refereegranskat)abstract
- Settlement simulating in cohesion materials is a crucial issue due to complexity of cohesion soil texture. This research emphasis on the implementation of newly developed machine learning models called hybridized Adaptive Neuro-Fuzzy Inference System (ANFIS) with Particle Swarm Optimization (PSO) algorithm, Ant Colony optimizer (ACO), Differential Evolution (DE), and Genetic Algorithm (GA) as efficient approaches to predict settlement of shallow foundation over cohesion soil properties. The width of footing (B), pressure of footing (qa), geometry of footing (L/B), count of SPT blow (N), and ratio of footing embedment (Df/B) are considered as predictive variables. Nonhomogeneity and inconsistency of employed dataset is a major concern during prediction modeling. Hence, two different modeling scenarios (i) preprocessed dataset (PP) and (ii) nonprocessed (initial) dataset (NP) were inspected. To assess the accuracy of the applied hybrid models and standalone one, multiple statistical metrics were computed and analyzed over the training and testing phases. Results indicated ANFIS-PSO model exhibited an accurate and reliable prediction data intelligent and had the highest predictability performance against all employed models. In addition, results demonstrated that data preprocessing is highly essential to be performed prior to building the predictive models. Overall, ANFIS-PSO model showed a robust machine learning for settlement prediction.
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| 24. |
- Naji, Laith A., et al.
(författare)
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Modification of Langmuir model for simulating initial pH and temperature effects on sorption process
- 2020
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Ingår i: Separation science and technology (Print). - UK : Taylor & Francis. - 0149-6395 .- 1520-5754. ; 55:15, s. 2729-2736
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Tidskriftsartikel (refereegranskat)abstract
- The present study modifies the sorption isothermfor simulating the influences of initial pH and temperature variations on thecadmium sorption from contaminated water using waste foundry sand based on Langmuir,Freundlich and Temkin models. Results proved that the Langmuir expression is ableto adopt these effects by relating sorption capacity and affinity constantswith pH and temperature of aqueous solution through exponential relationships (determinationcoefficient = 0.9375). The present model is assumed that the sorption process occursthrough acidic functional groups and this is consistent with FTIR outputs. Interactionof cadmium/WFS is found to be exothermic by thermodynamic analysis.
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| 25. |
- Qutbudin, Ishanch, et al.
(författare)
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Seasonal Drought Pattern Changes Due to Climate Variability : Case Study in Afghanistan
- 2019
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Ingår i: Water. - : MDPI. - 2073-4441. ; 11:5, s. 1-20
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Tidskriftsartikel (refereegranskat)abstract
- We assessed the changes in meteorological drought severity and drought return periods during cropping seasons in Afghanistan for the period of 1901 to 2010. The droughts in the country were analyzed using the standardized precipitation evapotranspiration index (SPEI). Global Precipitation Climatology Center rainfall and Climate Research Unit temperature data both at 0.5 resolutions were used for this purpose. Seasonal drought return periods were estimated using the values of the SPEI fitted with the best distribution function. Trends in climatic variables and SPEI were assessed using modified Mann–Kendal trend test, which has the ability to remove the influence of long-term persistence on trend significance. The study revealed increases in drought severity and frequency in Afghanistan over the study period. Temperature, which increased up to 0.14 C/decade, was the major factor influencing the decreasing trend in the SPEI values in the northwest and southwest of the country during rice- and corn-growing seasons, whereas increasing temperature and decreasing rainfall were the cause of a decrease in SPEI during wheat-growing season. We concluded that temperature plays a more significant role in decreasing the SPEI values and, therefore, more severe droughts in the future are expected due to global warming.
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| 26. |
- Salih, Abdalla E. M., et al.
(författare)
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Marine Sulfated Polysaccharides as Promising Antiviral Agents : A Comprehensive Report and Modeling Study Focusing on SARS CoV-2
- 2021
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Ingår i: Marine Drugs. - : MDPI. - 1660-3397. ; 19:8
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Tidskriftsartikel (refereegranskat)abstract
- SARS-CoV-2 (severe acute respiratory syndrome coronavirus-2) is a novel coronavirus strain that emerged at the end of 2019, causing millions of deaths so far. Despite enormous efforts being made through various drug discovery campaigns, there is still a desperate need for treatments with high efficacy and selectivity. Recently, marine sulfated polysaccharides (MSPs) have earned significant attention and are widely examined against many viral infections. This article attempted to produce a comprehensive report about MSPs from different marine sources alongside their antiviral effects against various viral species covering the last 25 years of research articles. Additionally, these reported MSPs were subjected to molecular docking and dynamic simulation experiments to ascertain potential interactions with both the receptor-binding domain (RBD) of SARS CoV-2's spike protein (S-protein) and human angiotensin-converting enzyme-2 (ACE2). The possible binding sites on both S-protein's RBD and ACE2 were determined based on how they bind to heparin, which has been reported to exhibit significant antiviral activity against SARS CoV-2 through binding to RBD, preventing the virus from affecting ACE2. Moreover, our modeling results illustrate that heparin can also bind to and block ACE2, acting as a competitor and protective agent against SARS CoV-2 infection. Nine of the investigated MSPs candidates exhibited promising results, taking into consideration the newly emerged SARS CoV-2 variants, of which five were not previously reported to exert antiviral activity against SARS CoV-2, including sulfated galactofucan (1), sulfated polymannuroguluronate (SPMG) (2), sulfated mannan (3), sulfated heterorhamnan (8), and chondroitin sulfate E (CS-E) (9). These results shed light on the importance of sulfated polysaccharides as potential SARS-CoV-2 inhibitors.
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| 27. |
- Salih, Sinan Q., et al.
(författare)
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Thin and sharp edges bodies-fluid interaction simulation using cut-cell immersed boundary method
- 2019
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Ingår i: Engineering Applications of Computational Fluid Mechanics. - UK : Taylor & Francis. - 1994-2060 .- 1997-003X. ; 13:1, s. 860-877
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Tidskriftsartikel (refereegranskat)abstract
- This study aims to develop an adaptive mesh refinement (AMR) algorithm combined with Cut-Cell IBM using two-stage pressure–velocity corrections for thin-object FSI problems. To achieve the objective of this study, the AMR-immersed boundary method (AMR-IBM) algorithm discretizes and solves the equations of motion for the flow that involves rigid thin structures boundary layer at the interface between the structure and the fluid. The body forces are computed in proportion to the fraction of the solid volume in the IBM fluid cells to incorporate fluid and solid motions into the boundary. The corrections of the velocity and pressure is determined by using a novel simplified marker and cell scheme. The new developed AMR-IBM algorithm is validated using a benchmark data of fluid past a cylinder and the results show that there is good agreement under laminar flow. Simulations are conducted for three test cases with the purpose of demonstration the accuracy of the AMR-IBM algorithm. The validation confirms the robustness of the new algorithms in simulating flow characteristics in the boundary layers of thin structures. The algorithm is performed on a staggered grid to simulate the fluid flow around thin object and determine the computational cost.
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| 28. |
- Salih, Sinan Q., et al.
(författare)
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Viability of the advanced adaptive neuro-fuzzy inference system model on reservoir evaporation process simulation : case study of Nasser Lake in Egypt
- 2019
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Ingår i: Engineering Applications of Computational Fluid Mechanics. - UK : Taylor & Francis. - 1994-2060 .- 1997-003X. ; 13:1, s. 878-891
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Tidskriftsartikel (refereegranskat)abstract
- Reliable prediction of evaporative losses from reservoirs is an essential component of reservoir management and operation. Conventional models generally used for evaporation prediction have a number of drawbacks as they are based on several assumptions. A novel approach called the co-active neuro-fuzzy inference system (CANFIS) is proposed in this study for the modeling of evaporation from meteorological variables. CANFIS provides a center-weighted set rather than global weight sets for predictor–predictand relationship mapping and thus it can provide a higher prediction accuracy. In the present study, adjustments are made in the back-propagation algorithm of CANFIS for automatic updating of membership rules and further enhancement of its prediction accuracy. The predictive ability of the CANFIS model is validated with three well-established artificial intelligence (AI) models. Different statistical metrics are computed to investigate the prediction efficacy. The results reveal higher accuracy of the CANFIS model in predicting evaporation compared to the other AI models. CANFIS is found to be capable of modeling evaporation from mean temperature and relative humidity only, with a Nash–Sutcliffe efficiency of 0.93, which is much higher than that of the other models. Furthermore, CANFIS improves the prediction accuracy by 9.2–55.4% compared to the other AI models.
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| 29. |
- Salman, Saleem A., et al.
(författare)
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Changes in Climatic Water Availability and Crop Water Demand for Iraq Region
- 2020
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Ingår i: Sustainability. - Switzerland : MDPI. - 2071-1050. ; 12:8
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Tidskriftsartikel (refereegranskat)abstract
- Decreases in climatic water availability (CWA) and increases in crop water demand (CWD) in the background of climate change are a major concern in arid regions because of less water availability and higher irrigation requirements for crop production. Assessment of the spatiotemporal changes in CWA and CWD is important for the adaptation of irrigated agriculture to climate change for such regions. The recent changes in CWA and CWD during growing seasons of major crops have been assessed for Iraq where rapid changes in climate have been noticed in recent decades. Gridded precipitation of the global precipitation climatology center (GPCC) and gridded temperature of the climate research unit (CRU) having a spatial resolution of 0.5°, were used for the estimation of CWA and CWD using simple water balance equations. The Mann–Kendall (MK) test and one of its modified versions which can consider long-term persistence in time series, were used to estimate trends in CWA for the period 1961–2013. In addition, the changes in CWD between early (1961–1990) and late (1984–2013) periods were evaluated using the Wilcoxon rank test. The results revealed a deficit in water in all the seasons in most of the country while a surplus in the northern highlands in all the seasons except summer was observed. A significant reduction in the annual amount of CWA at a rate of −1 to −13 mm/year was observed at 0.5 level of significance in most of Iraq except in the north. Decreasing trends in CWA in spring (−0.4 to −1.8 mm/year), summer (−5.0 to −11 mm/year) and autumn (0.3 to −0.6 mm/year), and almost no change in winter was observed. The CWA during the growing season of summer crop (millet and sorghum) was found to decrease significantly in most of Iraq except in the north. The comparison of CWD revealed an increase in agricultural water needs in the late period (1984–2013) compared to the early period (1961–1990) by 1.0–8.0, 1.0–14, 15–30, 14–27 and 0.0–10 mm for wheat, barley, millet, sorghum and potato, respectively. The highest increase in CWD was found in April, October, June, June and April for wheat, barley, millet, sorghum and potato, respectively.
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| 30. |
- Sharafati, Ahmad, et al.
(författare)
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Development of Advanced Computer Aid Model for Shear Strength of Concrete Slender Beam Prediction
- 2020
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Ingår i: Applied Sciences. - Switzerland : MDPI. - 2076-3417. ; 10:11
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Tidskriftsartikel (refereegranskat)abstract
- High-strength concrete (HSC) is highly applicable to the construction of heavy structures. However, shear strength (Ss) determination of HSC is a crucial concern for structure designers and decision makers. The current research proposes the novel models based on the combination of adaptive neuro-fuzzy inference system (ANFIS) with several meta-heuristic optimization algorithms, including ant colony optimizer (ACO), differential evolution (DE), genetic algorithm (GA), and particle swarm optimization (PSO), to predict the Ss of HSC slender beam. The proposed models were constructed using several input combinations incorporating several related dimensional parameters such as effective depth of beam (d), shear span (a), maximum size of aggregate (ag), compressive strength of concrete (fc), and percentage of tension reinforcement (ρ). To assess the impact of the non-homogeneity of the dataset on the prediction result accuracy, two possible modeling scenarios, (i) non-processed (initial) dataset (NP) and (ii) pre-processed dataset (PP), are inspected by several performance indices. The modeling results demonstrated that ANFIS-PSO hybrid model attained the best prediction accuracy over the other models and for the pre-processed input parameters. Several uncertainty analyses were examined (i.e., model, variables, and data), and results indicated predicting the HSC shear strength was more sensitive to the model structure uncertainty than the input parameters.
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| 31. |
- Tao, Hai, et al.
(författare)
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Global solar radiation prediction over North Dakota using air temperature : Development of novel hybrid intelligence model
- 2021
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Ingår i: Energy Reports. - Netherland : Elsevier. - 2352-4847. ; 7, s. 136-157
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Tidskriftsartikel (refereegranskat)abstract
- Accurate solar radiation (SR) prediction is one of the essential prerequisites of harvesting solar energy. The current study proposed a novel intelligence model through hybridization of Adaptive Neuro-Fuzzy Inference System (ANFIS) with two metaheuristic optimization algorithms, Salp Swarm Algorithm (SSA) and Grasshopper Optimization Algorithm (GOA) (ANFIS-muSG) for global SR prediction at different locations of North Dakota, USA. The performance of the proposed ANFIS-muSG model was compared with classical ANFIS, ANFIS-GOA, ANFIS-SSA, ANFIS-Grey Wolf Optimizer (ANFIS-GWO), ANFIS-Particle Swarm Optimization (ANFIS-PSO), ANFIS-Genetic Algorithm (ANFIS-GA) and ANFISDragonfly Algorithm (ANFIS-DA). Consistent maximum, mean and minimum air temperature data for nine years (2010–2018) were used to build the models. ANFIS-muSG showed 25.7%–54.8% higher performance accuracy in terms of root mean square error compared to other models at different locations of the study areas. The model developed in this study can be employed for SR prediction from temperature only. The results indicate the potential of hybridization of ANFIS with the metaheuristic optimization algorithms for improvement of prediction ccuracy.
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| 32. |
- Yaseen, Zaher Mundher, et al.
(författare)
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Implementation of Univariate Paradigm for Streamflow Simulation Using Hybrid Data-Driven Model : Case Study in Tropical Region
- 2019
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Ingår i: IEEE Access. - USA : IEEE. - 2169-3536. ; 7, s. 74471-74481
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Tidskriftsartikel (refereegranskat)abstract
- The performance of the bio-inspired adaptive neuro-fuzzy inference system (ANFIS) models are proposed for forecasting highly non-linear streamflow of Pahang River, located in a tropical climatic region of Peninsular Malaysia. Three different bio-inspired optimization algorithms namely particle swarm optimization (PSO), genetic algorithm (GA), and differential evolution (DE) were individually used to tune the membership function of ANFIS model in order to improve the capability of streamflow forecasting. Different combination of antecedent streamflow was used to develop the forecasting models. The performance of the models was evaluated using a number of metrics including mean absolute error (MAE), root mean square error (RMSE), coefficient of determination ( R2 ), and Willmott’s Index (WI) statistics. The results revealed that increasing number of inputs has a positive impact on the forecasting ability of both ANFIS and hybrid ANFIS models. The comparison of the performance of three optimization methods indicated PSO improved the capability of ANFIS model (RMSE = 7.96; MAE = 2.34; R2=0.998 and WI = 0.994) more compared to GA and DE in forecasting streamflow. The uncertainty band of ANFIS-PSO forecast was also found the lowest (±0.217), which indicates that ANFIS-PSO model can be used for reliable forecasting of highly stochastic river flow in tropical environment.
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