ORCID Profile
0000-0002-3740-4389
Current Organisations
Taizhou University
,
Sultan Qaboos University
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Publisher: Elsevier BV
Date: 09-2023
Publisher: Springer Science and Business Media LLC
Date: 07-03-2019
Publisher: Elsevier BV
Date: 11-2020
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 2021
Publisher: Springer Science and Business Media LLC
Date: 23-03-2022
DOI: 10.1007/S11356-022-19620-1
Abstract: Groundwater management is essential in water and environmental engineering from both quantity and quality aspects due to the growing urban population. Groundwater vulnerability evaluation models play a prominent role in groundwater resource management, such as the DRASTIC model that has been used successfully in numerous areas. Several studies have focused on improving this model by changing the initial parameters or the rates and weights. The presented study investigated results produced by the DRASTIC model by simultaneously exerting both modifications. For this purpose, two land use-based DRASTIC-derived models, DRASTICA and susceptibility index (SI), were implemented in the Shiraz plain, Iran, a semi-arid region and the primary resource of groundwater currently struggling with groundwater pollution. To develop the novel proposed framework for the progressive improvement of the mentioned rating-based techniques, three main calculation steps for rates and weights are presented: (1) original rates and weights (2) modified rates by Wilcoxon tests and original weights and (3) adjusted rates and optimized weights using the genetic algorithm (GA) and particle swarm optimization (PSO) algorithms. To validate the results of this framework applied to the case study, the concentrations of three contamination pollutants, NO
Publisher: MDPI AG
Date: 19-10-2022
DOI: 10.3390/EN15207734
Abstract: Ocean energy is one potential renewable energy alternative to fossil fuels that has a more significant power generation due to its better predictability and availability. In order to harness this source, wave energy converters (WECs) have been devised and used over the past several years to generate as much energy and power as is feasible. While it is possible to install these devices in both nearshore and offshore areas, nearshore sites are more appropriate places since more severe weather occurs offshore. Determining the optimal location might be challenging when dealing with sites along the coast since they often have varying capacities for energy production. Constructing wave farms requires determining the appropriate location for WECs, which may lead us to its correct and optimum design. The WEC size, shape, and layout are factors that must be considered for installing these devices. Therefore, this review aims to explain the methodologies, advancements, and effective hydrodynamic parameters that may be used to discover the optimal configuration of WECs in nearshore locations using evolutionary algorithms (EAs).
Publisher: Elsevier BV
Date: 07-2023
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 03-2023
Publisher: Elsevier BV
Date: 04-2020
Publisher: Elsevier BV
Date: 05-2023
Publisher: Hindawi Limited
Date: 15-02-2021
DOI: 10.1155/2021/6675720
Abstract: In an attempt to meet the global demand, renewable energy systems (RES) have gained an interest in it due to the availability of the resources, especially solar photovoltaic system that has been an importance since many years because of per watt cost reduction, improvement in efficiency, and abundant availability. Photovoltaic system in remote and rural areas is very useful where a grid supply is unavailable. In this scenario, power electronic converters are an integral part of the renewable energy systems particularly for electronic devices which are operated from renewable energy sources and energy storage system (fuel cell and batteries). In this article, a new topology of charge pump capacitor (CPC) which is based on high voltage gain technique DC-DC boost converter (DCBC) with dynamic modeling is proposed. To testify the efficacy of the introduced topology, a prototype has been developed in a laboratory, where input was given 10VDC and 80VDC output voltage achieved at the load side. Furthermore, the experimental result shows that the voltage stress of MOSFET switches is very less in comparison with the conventional boost converter with the same parameters as the proposed converter.
Publisher: Elsevier BV
Date: 05-2023
Publisher: Informa UK Limited
Date: 12-05-2023
Publisher: Springer Nature Singapore
Date: 2022
Publisher: Springer Science and Business Media LLC
Date: 13-08-2022
Publisher: AIP Publishing
Date: 2022
DOI: 10.1063/5.0079791
Abstract: Dam-break wave propagation in a debris flood event is strongly influenced by accumulated reservoir-bound sediment and downstream obstacles. For instance, the Brumadinho dam disaster in Brazil in 2019 released 12 × 106 m3 of mud and iron tailings and inflicted 270 casualties. The present work was motivated by the apparent lack of experimental or numerical studies on silted-up reservoir dam-breaks with downstream semi-circular obstacles. Accordingly, 24 dam-break scenarios with different reservoir sediment depths and with or without obstacles were observed experimentally and verified numerically. Multiphase flood waves were filmed, and sediment depths, water levels, and values of front wave celerity were measured to improve our scientific understanding of shock wave propagation over an abruptly changing topography. Original data generated in this study is available online in the public repository and may be used for practical purposes. The strength of OpenFOAM software in estimating such a complex phenomenon was assessed using two approaches: volume of fluid (VOF) and Eulerian. An acceptable agreement was attained between numerical and experimental records (errors ranged from 1 to 13.6%), with the Eulerian outperforming the VOF method in estimating both sediment depth and water level profiles. This difference was most notable when more than half of the reservoir depth was initially filled by sediment (≥0.15 m), particularly in bumpy bed scenarios.
Publisher: Elsevier BV
Date: 08-2021
DOI: 10.1016/J.JENVMAN.2021.112807
Abstract: Groundwater level drawdown changes the hydrological cycle and poses challenges such as land subsidence and reduction of the groundwater quality. In this study, a new approach using a simulation-optimization framework was developed for shared groundwater management under water bankruptcy conditions (where water demand is greater than the allowable discharge capacity of water resources). The novelty of this study lies in using bankruptcy rules and a game model to manage a bankrupted shared groundwater resource considering aquifer sustainability. Accordingly, groundwater flow in the aquifer was numerically simulated by a finite-differences model (MODFLOW). Then, the repeated performance code of the finite-differences model was run for different discharge scenarios, and the results were applied to develop an MLP-ANN meta-model. By coupling the meta-model with a non-dominated sorting genetic algorithm II (NSGA-II)-based multi-objective optimization model, an optimized cultivation pattern under water bankruptcy conditions was achieved. Then, six different bankruptcy methods were utilized to specify groundwater allocation between three stakeholders. To manage the water bankruptcy conditions, different scenarios considering various groundwater extraction rates and cultivation areas were defined, and the optimization model was recoded for each scenario to find the corresponding optimized cultivation pattern. To consider the competition between stakeholders for groundwater extraction, a non-cooperative 3-player game was applied to achieve a compromise for different combinations of management strategies, which maximizes the profit and yields the best cultivation scenario. Applicability of the proposed methodology was investigated in an aquifer system located in Golestan Province, Iran, including three regions (Minudasht, Azadshahr, and Gonbade-kavus). Results show that the proposed method is capable of managing the bankruptcy conditions by generating greater agricultural profit and reducing groundwater drawdowns.
Publisher: Elsevier BV
Date: 07-2023
Publisher: Springer Nature Switzerland
Date: 2023
Publisher: Elsevier
Date: 2023
Publisher: Springer Science and Business Media LLC
Date: 24-06-2023
Publisher: Springer Science and Business Media LLC
Date: 16-01-2023
Publisher: Informa UK Limited
Date: 19-05-2023
Publisher: Elsevier BV
Date: 06-2021
Publisher: Springer Science and Business Media LLC
Date: 06-07-2022
Publisher: Elsevier BV
Date: 09-2023
Publisher: Elsevier BV
Date: 08-2023
Publisher: Springer Science and Business Media LLC
Date: 26-10-2022
Publisher: Elsevier BV
Date: 10-2018
DOI: 10.1016/J.WATRES.2018.06.050
Abstract: Optimization-based deployment of contamination warning system in water distribution systems has been widely used in the literature, due to their superior performance compared to rule- and opinion-based approaches. However, optimization techniques impose an excessive computational burden, which in turn is compensated for by shrinking the problem's decision space and/or using faster optimization algorithms with less accuracy. This imposes subjectivity in interpretation of the system and associated risks, and undermines model's accuracy by not exploring the entire feasible space. We propose a framework that uses information theoretic techniques, including value of information and transinformation entropy, for optimal sensor placement. This can be used either as pre-selection, i.e. pinpointing best potential locations of sensors to be in turn used in optimization framework, or ultimate selection, i.e. single-handedly selecting sensor locations from the feasible space. The proposed framework is then applied to Lamerd water distribution system, in Fars province, Iran, and the results are compared to the suggested potential locations of sensors in previous studies and results of TEVA-SPOT model. The proposed information theoretic scheme enhances the decision space, provides more accurate results, significantly reduces the computational burden, and warrants objective selection of sensor placement.
Publisher: Elsevier BV
Date: 03-2019
Publisher: Elsevier BV
Date: 05-2021
Publisher: IOP Publishing
Date: 17-09-2020
Abstract: Wildfire danger is often ascribed to increased temperature, decreased humidity, drier fuels, or higher wind speed. However, the concurrence of drivers—defined as climate, meteorological and biophysical factors that enable fire growth—is rarely tested for commonly used fire danger indices or climate change studies. Treating causal factors as independent additive influences can lead to inaccurate inferences about shifting hazards if the factors interact as a series of switches that collectively modulate fire growth. As evidence, we show that in Southern California very large fires and ‘megafires’ are more strongly associated with multiple drivers exceeding moderate thresholds concurrently, rather than direct relationships with extreme magnitudes of in idual drivers or additive combinations of those drivers. Days with concurrent fire drivers exceeding thresholds have increased more rapidly over the past four decades than in idual drivers, leading to a tripling of annual ‘megafire critical danger days’. Assessments of changing wildfire risks should explicitly address concurrence of fire drivers to provide a more precise assessment of this hazard in the face of a changing climate.
Publisher: MDPI AG
Date: 14-01-2022
DOI: 10.3390/EN15020578
Abstract: Nowadays, learning-based modeling methods are utilized to build a precise forecast model for renewable power sources. Computational Intelligence (CI) techniques have been recognized as effective methods in generating and optimizing renewable tools. The complexity of this variety of energy depends on its coverage of large sizes of data and parameters, which have to be investigated thoroughly. This paper covered the most resent and important researchers in the domain of renewable problems using the learning-based methods. Various types of Deep Learning (DL) and Machine Learning (ML) algorithms employed in Solar and Wind energy supplies are given. The performance of the given methods in the literature is assessed by a new taxonomy. This paper focus on conducting comprehensive state-of-the-art methods heading to performance evaluation of the given techniques and discusses vital difficulties and possibilities for extensive research. Based on the results, variations in efficiency, robustness, accuracy values, and generalization capability are the most obvious difficulties for using the learning techniques. In the case of the big dataset, the effectiveness of the learning techniques is significantly better than the other computational methods. However, applying and producing hybrid learning techniques with other optimization methods to develop and optimize the construction of the techniques is optionally indicated. In all cases, hybrid learning methods have better achievement than a single method due to the fact that hybrid methods gain the benefit of two or more techniques for providing an accurate forecast. Therefore, it is suggested to utilize hybrid learning techniques in the future to deal with energy generation problems.
Publisher: Elsevier BV
Date: 09-2023
Publisher: Elsevier
Date: 2023
Location: Pakistan
No related grants have been discovered for Mohammad Reza Nikoo.