| 1. |
- 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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| 2. |
- Jianfeng, Lin, 1994, et al.
(författare)
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Hydrodynamic performance of a rim-driven thruster improved with gap geometry adjustment
- 2023
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Ingår i: Engineering Applications of Computational Fluid Mechanics. - : Informa UK Limited. - 1994-2060 .- 1997-003X. ; 17:1
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Tidskriftsartikel (refereegranskat)abstract
- The hubless rim-driven thruster (RDT) has become increasingly interesting for ship propulsion. Gap flow has been proven as the main feature of RDT that cannot be simply neglected. In this study, based on a classical hubless RDT, the effects of the gap geometry are studied by adjusting its axial passage length, and inlet and outlet oblique angles. The hydrodynamic characteristics of the RDT were simulated with OpenFOAM based on the k – ω shear stress transport turbulence model. Due to the pressure increase after the main flow passes through the rotating blades, the flow inside gap is driven upstream, which is opposite to the main flow direction. It is found that the hydrodynamic efficiency is increased as the gap axial passage length is shortened, which is realized by increasing the oblique angle with the fixed inlet and outlet positions. Moving the inlet and outlet to further downstream and upstream positions has negligible effects on the hydrodynamic efficiency and leads to recirculating flow within the gap near its inlet. These findings shed light on the design of the gap geometry to improve the RDT hydrodynamic performance.
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| 3. |
- Khosravi, Khabat, et al.
(författare)
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Fluvial bedload transport modelling: advanced ensemble tree-based models or optimized deep learning algorithms?
- 2024
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Ingår i: Engineering Applications of Computational Fluid Mechanics. - : Informa UK Limited. - 1994-2060 .- 1997-003X. ; 18:1
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Tidskriftsartikel (refereegranskat)abstract
- The potential of advanced tree-based models and optimized deep learning algorithms to predict fluvial bedload transport was explored, identifying the most flexible and accurate algorithm, and the optimum set of readily available and reliable inputs. Using 926 datasets for 20 rivers, the performance of three groups of models was tested: (1) standalone tree-based models Alternating Model Tree (AMT) and Dual Perturb and Combine Tree (DPCT); (2) ensemble tree-based models Iterative Absolute Error Regression (IAER), ensembled with AMT and DPCT; and (3) optimized deep learning models Long Short-Term Memory (LSTM) and Recurrent Neural Network (RNN) ensembled with Grey Wolf Optimizer. Comparison of the predictive performance of the models with that of commonly used empirical equations and sensitivity analysis of the driving variables revealed that: (i) the coarse grain-size percentile D90 was the most effective variable in bedload transport prediction (where Dx is the xth percentile of the bed surface grain size distribution), followed by D84, D50, flow discharge, D16, and channel slope and width; (ii) all tree-based models and optimized deep learning algorithms displayed ‘very good’ or ‘good’ performance, outperforming empirical equations; and (iii) all algorithms performed best when all input parameters were used. Thus, a range of different input variable combinations must be considered in the optimization of these models. Overall, ensemble algorithms provided more accurate predictions of bedload transport than their standalone counterpart. In particular, the ensemble tree-based model IAER-AMT performed most strongly, displaying great potential to produce robust predictions of bedload transport in coarse-grained rivers based on a few readily available flow and channel variables.
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| 4. |
- Li, Shicheng, et al.
(författare)
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Flow features in a pooled fishway with V-shaped weir formation
- 2020
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Ingår i: Engineering Applications of Computational Fluid Mechanics. - : Informa UK Limited. - 1994-2060 .- 1997-003X. ; 14:1, s. 1337-1350
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Tidskriftsartikel (refereegranskat)abstract
- A pooled fishway is an artificial structure to improve the river flow connectivity and facilitate fish migration. Its conventional layout is characterized by straight weirs and suffers from limited passage rate. To improve its flow behaviors, an alternative design, featuring a V-shaped weir arrangement, is devised. CFD simulations are performed to examine its effects on the flow conditions. The governing geometrical parameters include weir angling, height, and spacing. Depending on weir arm angle, the flow is directed towards either the channel's central part or the sidewalls, leading to both across- and along-channel differences in flow velocity and turbulent kinetic energy. This offers conducive zones for multiple fish species with distinct swimming preferences. For a given location in a pool, the cross-sectional velocity exhibits a cosine-type variation; a relationship is established to predict the maximum longitudinal velocity. The pressure distribution exhibits a similar pattern, with high-pressure areas residing upstream of the weir. A power law describes the relationship between the energy dissipation rate and unit discharge with an exponent at approximately 2/3. With an increase in either weir spacing or height, the energy dissipation rate declines. A change in weir angle shows however a negligible influence on it.
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| 5. |
- Li, Shicheng, et al.
(författare)
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Modelling of suspended sediment load by Bayesian optimized machine learning methods with seasonal adjustment
- 2022
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Ingår i: Engineering Applications of Computational Fluid Mechanics. - : Informa UK Limited. - 1994-2060 .- 1997-003X. ; 16:1, s. 1883-1901
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Tidskriftsartikel (refereegranskat)abstract
- Suspended sediment load (SSL) is essential to river and dam engineering. Due to the complexity and stochastic nature of sedimentation, SSL prediction is a challenging task and conventional methods often fail to generate accurate results. Aiming to provide an improved estimation, this paper contributes to a new forecasting framework by integrating the seasonal adjustment (SA) and Bayesian optimization (BOP) into a machine learning (ML) model (denoted as BMS). The SA is used for de-seasonalisation and trend extraction; the BOP is to optimize the ML architecture. The BMS is evaluated using the daily SSL records from the Yangtze River. Its performance is appraised by statistical criteria of Nash-Sutcliffe efficiency (NSE), correlation coefficient (CC), root mean squared error (RMSE) and mean absolute error (MAE). With the de-noising and hyper-parameter tuning modules, the BMS effectively heightens the accuracy of the standard ML models. The most significant improvement occurs in the Boosting model, with its augments in NSE and CC by 4.3% and 1.6%, and reductions in RMSE and MAE by 24.9% and 24.2%. The BMS gains by comparison even under flood conditions, where it remarkably reduces the errors of the constituent models by up to 47.9% for RMSE and 48.3% for MAE.
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| 6. |
- Li, Shicheng, et al.
(författare)
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Two-phase flow modelling by an error-corrected population balance model
- 2023
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Ingår i: Engineering Applications of Computational Fluid Mechanics. - : Informa UK Limited. - 1994-2060 .- 1997-003X. ; 17:1
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Tidskriftsartikel (refereegranskat)abstract
- High-velocity aerated flow is a common phenomenon in spillways. Its accurate modelling is challenging, mainly due to the lack of realistic physics in the conventional two-phase models. To this end, this study establishes a population balance model (PBM) approach to account for the evolutionary process of air bubbles. The air-water flow in a stepped chute is examined. The model performance is evaluated by statistical metrics: correlation coefficient (CC), root mean squared error (RMSE), and mean absolute error (MAE). Compared with conventional models, the PBM generates improved air-water predictions. However, the flow parameters are still underestimated, particularly in areas with intense air-water interactions. For further development, an error-corrected PBM (EPBM) is proposed by incorporating machine learning (ML) techniques into the PBM. Compared with the PBM, the EPBM leads to a mean augmentation in velocity prediction by 19.8% for the CC, 73.0% for the RMSE, and 77.1% for the MAE. The gains in air concentration estimation are 2.0%, 67.6% and 73.5%, respectively. The EPBM generates the most accurate results, with 99.6% and 89.6% of the velocity and air concentration predictions within a 20% relative error range. The main contributions are establishing a PBM for air-water flows and developing an error-corrected PBM using ML.
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| 7. |
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Machine learning model development for predicting aeration efficiency through Parshall flume
- 2021
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Ingår i: Engineering Applications of Computational Fluid Mechanics. - : Taylor & Francis. - 1994-2060 .- 1997-003X. ; 15:1, s. 889-901
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Tidskriftsartikel (refereegranskat)abstract
- This study compares several advanced machine learning models to obtain the most accurate method for predicting the aeration efficiency (E20) through the Parshall flume. The required dataset is obtained from the laboratory tests using different flumes fabricated in National Institute Technology Kurukshetra, India. Besides, the potential of K Nearest Neighbor (KNN), Random Forest Regression (RFR), and Decision Tree Regression (DTR) models are evaluated to predict the aeration efficiency. In this way, several input combinations (e.g. M1-M15) are provided using the laboratory parameters (e.g. W/L, S/L, Fr, and Re). Different predictive models are obtained based on those input combinations and machine learning models proposed in the present study. The predictive models are assessed based on several performance metrics and visual indicators. Results show that the KNN-M11 model (RMSEtesting=0.002,R2testing=0.929), which includes W/L, S/L, and Fr as predictive variables outperforms the other predictive models. Furthermore, an enhancement is observed in KNN model estimation accuracy compared to the previously developed empirical models. In general, the predictive model dominated in the present study provides adequate performance in predicting the aeration efficiency in the Parshall flume.
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| 8. |
- Malik, Anurag, et al.
(författare)
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Daily pan-evaporation estimation in different agro-climatic zones using novel hybrid support vector regression optimized by Salp swarm algorithm in conjunction with gamma test
- 2021
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Ingår i: Engineering Applications of Computational Fluid Mechanics. - : Taylor & Francis. - 1994-2060 .- 1997-003X. ; 15:1, s. 1075-1094
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Tidskriftsartikel (refereegranskat)abstract
- Ensuring accurate estimation of evaporation is weighty for effective planning and judicious management of available water resources for agricultural practices. Thus, this work enhances the potential of support vector regression (SVR) optimized with a novel nature-inspired algorithm, namely, Slap Swarm Algorithm (SVR-SSA) against Whale Optimization Algorithm (SVR-WOA), Multi-Verse Optimizer (SVR-MVO), Spotted Hyena Optimizer (SVR-SHO), Particle Swarm Optimization (SVR-PSO), and Penman model (PM). Daily EP (pan-evaporation) was estimated in two different agro-climatic zones (ACZ) in northern India. The optimal combination of input parameters was extracted by applying the Gamma test (GT). The outcomes of the hybrid of SVR and PM models were equated with recorded daily EP observations based on goodness-of-fit measures along with graphical scrutiny. The results of the appraisal showed that the novel hybrid SVR-SSA-5 model performed superior (MAE = 0.697, 1.556, 0.858 mm/day; RMSE = 1.116, 2.114, 1.202 mm/day; IOS = 0.250, 0.350, 0.303; NSE = 0.0.861, 0.750, 0.834; PCC = 0.929, 0.868, 0.918; IOA = 0.960, 0.925, 0.956) than other models in testing phase at Hisar, Bathinda, and Ludhiana stations, respectively. In conclusion, the hybrid SVR-SSA model was identified as more suitable, robust, and reliable than the other models for daily EP estimation in two different ACZ.
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| 9. |
- Malik, Anurag, et al.
(författare)
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Modeling monthly pan evaporation process over the Indian central Himalayas : application of multiple learning artificial intelligence model
- 2020
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Ingår i: Engineering Applications of Computational Fluid Mechanics. - UK : Taylor & Francis. - 1994-2060 .- 1997-003X. ; 14:1, s. 323-338
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Tidskriftsartikel (refereegranskat)abstract
- The potential of several predictive models including multiple model-artificial neural network (MM-ANN), multivariate adaptive regression spline (MARS), support vector machine (SVM), multi-gene genetic programming (MGGP), and ‘M5Tree’ were assessed to simulate the pan evaporation in monthly scale (EPm) at two stations (e.g. Ranichauri and Pantnagar) in India. Monthly climatological information were used for simulating the pan evaporation. The utmost effective input-variables for the MM-ANN, MGGP, MARS, SVM, and M5Tree were determined using the Gamma test (GT). The predictive models were compared to each other using several statistical criteria (e.g. mean absolute percentage error (MAPE), Willmott's Index of agreement (WI), root mean squared error (RMSE), Nash-Sutcliffe efficiency (NSE), and Legate and McCabe’s Index (LM)) and visual inspection. The results showed that the MM-ANN-1 and MGGP-1 models (NSE, WI, LM, RMSE, MAPE are 0.954, 0.988, 0.801, 0.536 mm/month, 9.988% at Pantnagar station, and 0.911, 0.975, 0.724, and 0.364 mm/month, 12.297% at Ranichauri station, respectively) with input variables equal to six were more successful than the other techniques during testing period to simulate the monthly pan evaporation at both Ranichauri and Pantnagar stations. Thus, the results of proposed MM-ANN-1 and MGGP-1 models will help to the local stakeholders in terms of water resources management.
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| 10. |
- Moazenzadeh, Roozbeh, et al.
(författare)
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Soil moisture estimation using novel bio-inspired soft computing approaches
- 2022
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Ingår i: Engineering Applications of Computational Fluid Mechanics. - : Informa UK Limited. - 1994-2060 .- 1997-003X. ; 16:1, s. 826-840
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Tidskriftsartikel (refereegranskat)abstract
- Soil moisture (SM) is of paramount importance in irrigation scheduling, infiltration, runoff, and agricultural drought monitoring. This work aimed at evaluating the performance of the classical ANFIS (Adaptive Neuro-Fuzzy Inference System) model as well as ANFIS coupled with three bio-inspired metaheuristic optimization methods including whale optimization algorithm (ANFIS-WOA), krill herd algorithm (ANFIS-KHA) and firefly algorithm (ANFIS-FA) in estimating SM. Daily air temperature, relative humidity, wind speed and sunshine hours data at Istanbul Bolge station in Turkey and soil temperature values measured over 2008–2009 were fed into the models under six different scenarios. ANFIS-WOA (RMSE = 1.68, MAPE = 0.04) and ANFIS (RMSE = 2.55, MAPE = 0.07) exhibited the best and worst performance in SM estimation, respectively. All three hybrid models (ANFIS-WOA, ANFIS-KHA and ANFIS-FA) improved SM estimates, reducing RMSE by 34, 28 and 27% relative to the base ANFIS model, respectively. A more detailed analysis of model performances in estimating moisture content over three intervals including [15–25), [25–35) and ≥35% revealed that ANFIS-WOA has had the lowest errors with RMSEs of 1.69, 1.89 and 1.55 in the three SM intervals, respectively. From the perspective of under- or over-estimation of moisture values, ANFIS-WOA (RMSE = 1.44, MAPE = 0.03) in under-estimation set and ANFIS-KHA (RMSE = 1.94, MAPE = 0.05) in over-estimation set showed the highest accuracies. Overall, all three hybrid models performed better in the underestimation set compared to overestimation set.
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