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An Improved flow direction algorithm That Considers Mass Conservation for Sediment Transport Simulations

WATER 2023年第23期15卷 4111页

作者： Wei, Hong Dai, Wen Wang, Bo Zhu, Hui Zhao, Fei Jiao, Haoyang Li, Penghui Nanjing Univ Informat Sci & Technol Sch Remote Sensing & Geomatics Engn Nanjing 210044 Peoples R China Nanjing Normal Univ Sch Geog Nanjing 210023 Peoples R China Nanjing Normal Univ Key Lab Virtual Geog Environm Minist Educ Nanjing 210023 Peoples R China Jiangsu Ctr Collaborat Innovat Geog Informat Resou Nanjing 210023 Peoples R China Nanjing Univ Informat Sci & Technol Sch Geog Sci Nanjing 210044 Peoples R China Beijing Normal Univ Fac Geog Sci Beijing 100875 Peoples R China

The sediment transport process in watersheds is an important research component of geomorphology and surface dynamics. Previous work has inferred the spatial distribution of the sediment transport rate (STR) by the flow direction algorithm and measured topographic variation;however, the simple application of the flow direction algorithm contributes to mass non-conservation during a simulation. This study designs an improved flow direction algorithm for a sediment transport process simulation by judging the mass conservation situation in the simulation process. The specific implementation is to evaluate the existence of negative values for the STR;if they exist, the negative values of the STR are reset to stop the propagation of the negative values downstream. Experiments are conducted to improve the classical D8, MFD-se, and MFD-md flow algorithms in this paper, and the experimental results show that the method in this paper can effectively improve the simulation effect of STR. The STR simulations of the three models, D8, MFD-se, and MFD-md, improved by 1.26%, 4.17%, and 4.54%, respectively. Moreover, the MFD-se model is more suitable for the simulation of the STR when comparing the three models. The improved flow algorithm can be used to simulate the STR, sediment content, and pollutant migration in watersheds, providing a new method for the fine-grained characterization of surface processes in watersheds.

关键词： flow direction algorithm sediment transport rate multiple flow direction DEM

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A novel hybrid flow direction optimizer-dynamic oppositional based learning algorithm for solving complex constrained mechanical design problems

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MATERIALS TESTING 2023年第1期65卷 134-143页

作者： Yildiz, Betul S. Pholdee, Nantiwat Mehta, Pranav Sait, Sadiq M. Kumar, Sumit Bureerat, Sujin Yildiz, Ali Riza Uludag Univ Bursa Uludag Univ Dept Mech Engn TR-16059 Bursa Turkiye Khon Kaen Univ Dept Mech Engn Khon Kaen 40002 Thailand Dharmsinh Desai Univ Dept Mech Engn Nadiad 387001 Gujarat India King Fand Univ Petr & Minerals Dhahran Saudi Arabia Univ Tasmania Launceston Tas 7250 Australia

In this present work, mechanical engineering optimization problems are solved by employing a novel optimizer (HFDO-DOBL) based on a physics-based flow direction optimizer (FDO) and dynamic oppositional-based learning. Five real-world engineering problems, viz. planetary gear train, hydrostatic thrust bearing, robot gripper, rolling bearing, and multiple disc clutch brake, are considered. The computational results obtained by HFDO-DOBL are compared with several newly proposed algorithms. The statistical analysis demonstrates the HFDO-DOBL dominance in finding optimal solutions relatively and competitiveness in solving constraint design optimization problems.

关键词： dynamic oppositional based learning flow direction algorithm hydrostatic thrust bearing mechanical design planetary gear train robot gripper

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Enhanced flow direction Arithmetic Optimization algorithm for mathematical optimization problems with applications of data clustering

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ENGINEERING ANALYSIS WITH BOUNDARY ELEMENTS 2022年第0期138卷 13-29页

作者： Abualigah, Laith Almotairi, Khaled H. Abd Elaziz, Mohamed Shehab, Mohammad Altalhi, Maryam Amman Arab Univ Fac Comp Sci & Informat Amman 11953 Jordan Univ Sains Malaysia Sch Comp Sci George Town 11800 Malaysia Umm Al Qura Univ Comp Engn Dept Mecca Saudi Arabia Galala Univ Fac Comp Sci & Engn Suze 435611 Egypt Zagazig Univ Fac Sci Dept Math Zagazig Egypt Ajman Univ Artificial Intelligence Res Ctr AIRC Ajman 346 U Arab Emirates World Islamic Sci & Educ Univ Informat Technol Amman Jordan Taif Univ Coll Business Adm Dept Management Informat Syst POB 11099 Taif 21944 Saudi Arabia

Data clustering is one of the most common and challenging problems in the machine learning domain. It requires an efficient method to be addressed. This paper proposed a new version of the flow direction algorithm (FDA) to solve various optimization problems. The proposed method is called FDAOA, which enhanced the performance of the original flow direction algorithm by the arithmetic operators that have been used in the Arithmetic Optimization algorithm (AOA). The main aim of the proposed FDAOA is to avoid the recognized weaknesses in the original methods;stuck in the local area, premature convergence, and weak equilibrium between the exploration and exploitation search mechanisms. The proposed method is tested on two sets of various problems to validate its performance. In the first set, twenty-three benchmark functions are used, which belong to three categories;seven unimodal functions, six multimodal functions, and ten fixed dimension functions. In the second set, eight common data clustering problems are used to prove the ability of the proposed FDAOA to deal with real-world optimization problems. The results of the proposed method are compared with other well-established methods, and the proposed FDAOA achieved promising output compared to the other methods on various tested problems. The proposed method got the optimal clustering solutions almost in all the tested data clustering problems with clear significant improvements against the other comparative methods.

关键词： Arithmetic Optimization algorithm flow direction algorithm Meta-heuristics Mathematical benchmark problems Data clustering problems

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A complex synthetic surface for assessing flow direction algorithms based on total contributing area

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GEOMORPHOLOGY 2024年 450卷

作者： Song, Ying Yang, Tao Li, Zhenya Xu, Chong-Yu Hohai Univ Ctr Global Change & Water Cycle State Key Lab Hydrol Water Resources & Hydraul Eng Nanjing 210098 Peoples R China Hohai Univ Natl Cooperat Innovat Ctr Water Safety & Hydrosci Nanjing 210098 Peoples R China Chinese Acad Sci Key Lab Watershed Geog Sci Nanjing Inst Geog & Limnol Nanjing 210008 Peoples R China Univ Oslo Dept Geosci & Hydrol POB 1047 Blindern N-0316 Oslo Norway

flow direction algorithms have important application in attracting geomorphic features and topographic attributes, which serve as inputs for some hydrological and topographical models. Evaluating flow direction algorithms is of great significance and often conducted on synthetic surfaces instead of real digital elevation models for free of approximation errors. However, most widely-used synthetic surfaces are too simplified to represent complex topographical relief of real-world terrains. For this, this work applies a complex synthetic surface of modified Himmelblau's function (HF) to simulate sophisticated terrains encountered in real landscapes. HF surface is spatially smooth and continuous with four hilltops and one valley, where plan curvatures are clustered mainly from -0.1 to 0.1. In addition, a slope line-based discretization numerical (SLDN) approach is designed for obtaining numerical solution to theoretical total contributing area (TCA) on synthetic surfaces of non-integrable slope lines (e.g. HF surface). TCAs estimated by several flow direction algorithms are compared with SLDNderived TCA quantitatively. Results indicate that the largest and smallest mean size errors are obtained by Random eight-node (Rho8) (i.e. 82.6 %) and Freeman multiple flow direction (FMFD) (i.e. 17.9 %), while the largest and smallest mean extent errors by Eight drainage directions (D8) (i.e. 128.2 %) and Eight drainage directions, least transversal deviation (D8-LTD) (i.e.55.4 %). Most mean errors are larger than 20.0 %, which may not be satisfactory in practice. This work can provide a reference for flow direction algorithms application in digital terrain analysis, and therefore improve accuracy of hydrological, geological and geomorphological models.

关键词： Synthetic surface flow direction algorithm Total contributing area flow path Numerical size and spatial extent error assessment Digital elevation model

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Evaluating the area and position accuracy of surface water paths obtained by flow direction algorithms

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JOURNAL OF HYDROLOGY 2020年第0期583卷 124619-000页

作者： Li, Zhenya Yang, Tao Xu, Chong-Yu Shi, Pengfei Yong, Bin Huang, Ching-sheng Wang, Chao Hohai Univ Ctr Global Change & Water Cycle State Key Lab Hydrol Water Resources & Hydraul En Nanjing 210098 Peoples R China Hohai Univ Sch Earth Sci & Engn Nanjing 210098 Peoples R China Univ Oslo Dept Geosci POB 1047 Blindern N-0316 Oslo Norway Hohai Univ Natl Cooperat Innovat Ctr Water Safety & Hydrosci Nanjing 210098 Peoples R China New Mexico Inst Min & Technol Dept Earth & Environm Sci Hydrol Program Socorro NM 87801 USA

The surface water path (SWP) extracted from digital elevation model (DEM) by flow direction algorithms is widely employed to obtain a variety of topographic variables used in hydrological modeling. Accurate SWPs can facilitate understanding the underlying mechanisms of water movement on Earth's surface. However, the accuracy of extracted SWPs by different flow direction algorithms has not been systematically studied. In this work, two indicators are developed to measure the area and position errors of extracted SWPs relative to theoretical SWPs on four synthetic surfaces representing typical terrains of natural watersheds. Based on the formulas of the synthetic surfaces, theoretical true SWP can be derived for any grid cell on the DEM discretized from the synthetic surfaces. Several widely used flow direction algorithms including three single flow direction (SFD) algorithms (i.e. D8, Rho8 and D8-LTD approaches) and three multiple flow direction (MFD) algorithms (i.e. FDFM, MFD-md and D8 approaches) are implemented to extract SWPs. Results suggest that significant distinctions can be detected in SWPs extracted by different flow direction algorithms. The SWPs extracted by SFD algorithms are always one-dimensional non-dispersive lines because SFD algorithms allow only one flow direction at each grid cell. In contrast, the SWPs extracted by MFD algorithms show excessive artificial dispersion. The average area error of extracted SWPs ranges from 16.3% to 75.2% on different synthetic surfaces and the minimum is obtained by FDFM approach for all synthetic surfaces. The average position error falls in the range of 46.0% to 161.4%. The maximum is gained by D8 or FDFM approach, and the minimum by D8-LTD or D8 approach. The cross compensation of SWP area induced by artificial dispersion leads to relatively high area accuracy but relatively low position accuracy of MFD algorithms. In addition, increasing DEM resolution without capturing more topographic variability can decrease

关键词： Surface water path (SWP) flow direction algorithm Synthetic surfaces Artificial dispersion Area and position precision

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Assessing the Precision of Total Contributing Area (TCA) Estimated by flow direction algorithms Based on the Analytical Solution of Theoretical TCA on Synthetic Surfaces

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WATER RESOURCES RESEARCH 2021年第4期57卷 e2020WR028546-e2020WR028546页

作者： Li, Zhenya Yang, Tao Wang, Chao Shi, Pengfei Yong, Bin Song, Ying Hohai Univ Ctr Global Change & Water Cycle State Key Lab Hydrol Water Resources & Hydraul En Nanjing Peoples R China Hohai Univ Sch Earth Sci & Engn Nanjing Peoples R China Hohai Univ Natl Cooperat Innovat Ctr Water Safety & Hydrosci Nanjing Peoples R China New Mexico Inst Min & Technol Dept Earth & Environm Sci Hydrol Program Socorro NM 87801 USA

Total contributing area (TCA) has important hydrological, geological, and geomorphological implications in digital terrain analysis. Currently, the validity of estimated TCA is challenged by the approximation error of digital elevation models (DEMs) to real-world terrains, the choice of flow direction algorithms and the difficulty in a quantitative evaluation. To solve these problems, this work employs a range of synthetic surfaces for free from approximation errors. Theoretical TCA for any cell on synthetic surfaces is analytically solved by using subsection integral and L'Hospital's rule based on the topographical definition of TCA. The impacts of grid discretization on the spatial patterns of theoretical TCA are explained mathematically and geometrically. In case studies, the analytically solved theoretical TCA is treated as a benchmark to quantitatively evaluate the TCAs estimated by three SFD algorithms (i.e., D8, Rho8, and D8-LTD) and three MFD algorithms (i.e., FDFM, MFD-md, and D infinity). Results show that (1) SFD algorithms obtain unsmooth and discontinuous spatial patterns of estimated TCA, due to the existence of source cells caused by the basic hypothesis of allowing only one receiving cell;(2) Cross compensation induced by artificial dispersion in MFD algorithms leads to a less error in the quantity of estimated TCA but a larger error in the physical position of estimated TCA;(3) The addition of contour length errors does not promise larger specific contributing area errors than TCA errors. To summarize, this work offers a theoretical and quantitative evaluation on the precision of the TCAs estimated by flow direction algorithms.

关键词： analytical solution flow direction algorithm synthetic surface total contributing area

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The Implementation of a New Optimization Method for Hydropower Generation and Multi-Reservoir Systems

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WATER RESOURCES MANAGEMENT 2024年第5期38卷 1711-1735页

作者： Moghani, Abbas Karami, Hojat Semnan Univ Fac Civil Engn Dept Water Engn & Hydraul Struct Semnan Iran

This research tries to find the best operation strategies for a reservoir system with the flow direction algorithm (FDA), which was recently introduced. This study evaluates the implementation of the FDA, for the first time, for optimizing the hydropower operation of the Karun-4 reservoir in Iran for 106 months (from October 2010 to July 2019) and for the multi-reservoir systems for 12 months. Multi-Reservoir systems which are hypothetical 4 and 10-reservoir systems are studied to demonstrate the effectiveness and robustness of the algorithms. The results are compared to those of the three most commonly used evolutionary algorithms, namely the Particle Swarm Optimization algorithm (PSO), the Weed algorithm (WA), and the Genetic algorithm (GA). The multi-reservoir results indicated that the absolute optimal solution was 308.292 in the four-reservoir benchmark system (FRBS) and 1194.441 in the ten-reservoir benchmark system (TRBS), and according to these results, FDA outperformed three other algorithms. In the Karun-4 reservoir, the best approach was chosen with the analytical hierarchy process (AHP) method, and according to the results, the FDA outperformed PSO, WA, and GA. The reliability percentage for FDA, PSO, WA, and GA was 95%, 86%, 78%, and 64%, respectively. The average optimal objective function value generated by FDA was 0.138, compared with PSO, WA, and GA, with the values of 0.322, 0.631, and 1.112, respectively, being better. The hydropower produced by FDA was more than three other algorithms in less time, with the lowest coefficient of variation value, which demonstrates the power of the FDA.

关键词： Reservoir optimization Hydropower flow direction algorithm Evolutionary algorithm Water resource management

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Optimal operation and control of hybrid power systems with stochastic renewables and FACTS devices: An intelligent multi-objective optimization approach

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ALEXANDRIA ENGINEERING JOURNAL 2024年 93卷 90-113页

作者： Premkumar, Manoharan Hashim, Tengku Juhana Tengku Ravichandran, Sowmya Sin, Tan Ching Chandran, Ramakrishnan Alsoud, Anas R. Jangir, Pradeep Univ Tenaga Nas Inst Power Engn IPE Coll Engn Dept Elect & Elect Engn Kajang 43000 Selangor Malaysia Dayananda Sagar Coll Engn Dept Elect & Elect Engn Bengaluru 560078 Karnataka India Manipal Acad Higher Educ Manipal Inst Technol Dept Elect & Elect Engn Manipal 576104 Karnataka India SNS Coll Technol Dept Elect & Elect Engn Coimbatore 641035 Tamil Nadu India Al Ahliyya Amman Univ Hourani Ctr Appl Sci Res Amman Jordan Saveetha Inst Med & Tech Sci Saveetha Sch Engn Dept Biosci Chennai 602105 India

This paper delves into the increasingly complex domain of Optimal Power flow (OPF) within modern power systems, enhanced by the integration of unpredictable renewable energy sources. The research originally integrates stochastic photovoltaic and wind energy sources, along with a suite of Flexible AC Transmission System (FACTS) components - including thyristor-controlled series compensators, static VAR compensators, and thyristor-controlled phase shifters. The primary objective is to solve the OPF problem by reducing generation costs while accommodating the variable nature of renewable energy sources and load demands. This study prioritizes the examination of both constant and fluctuating load requirements. The inherent variability of PV and wind energy, along with load demand, is captured through the modelling of probability density functions. This approach enables a more detailed optimization process, incorporating not just the cost of thermal energy generation but also the scheduling costs of renewable sources and associated penalty costs. Moreover, the study examines the strategic placement and sizing of FACTS components, an aspect essential in minimizing the overall cost of power production. Employing both single- and multi-objective optimization algorithms, the research addresses the OPF problem in a modified IEEE-30 bus system through various case studies. The application of the recently developed flow direction algorithm, including its multi-objective variant with an epsilon-based constrainthandling mechanism to OPF problem is the primary contributions of this work. The results, benchmarked against several advanced metaheuristic algorithms, reveal the proposed algorithm's superior performance. This comprehensive study not only underscores the potential of integrating renewable energy sources into the grid but also highlights the efficacy of intelligent optimization strategies in managing the complexities of modern power systems.

关键词： epsilon-based constraint-handling Energy FACTS controllers flow direction algorithm Multiobjective optimization Optimal power flow analysis

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Reduced-Complexity and Robust Surface Water Path Delineation for Large Real-World Terrain Using Boundary Element Method

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IEEE ACCESS 2024年 12卷 132565-132591页

作者： Chantaveerod, Ajalawit Woradit, Kampol Seagar, Andrew Limpiti, Thunyawat Walailak Univ Ctr Excellence Sustainable Disaster Management Sch Engn & Technol Dept Elect Engn Nakhon Si Thammarat 80161 Thailand Chiang Mai Univ Fac Engn Dept Comp Engn Optimized AI Syst Energy & Environm Sustainabil OA Chiang Mai 50200 Thailand Griffith Univ Sch Engn & Built Environm Gold Coast QLD 4222 Australia

Hydrologists delineate surface water path (SWP) from digital elevation model (DEM) images to gain insights into catchment characteristics. Currently, high-resolution DEM can be obtained to improve the SWP delineation accuracy. It was found that accuracy is still the same for the popular existing grid-based algorithms, but is much higher for the physically-based and contour-based algorithms. Unfortunately, the contour-based algorithms suffer complexity at high resolution. Accordingly, we develop the algorithm to decrease the error with high-resolution DEMs by enhancing an existing contour-based algorithm, constructed with a physically-based concept, derived from the semi-analytical solution of Laplace's partial differential equation through the boundary element method (BEM). To reduce the complexity, we propose the framework integrating the enhanced algorithm with pre-processing that reduces the input data size from very long contours by transforming those contours into the closed paths, exploited by the enhanced algorithm to delineate SWPs with the numerical BEM-based solution. The results show the framework's ability to successfully delineate the forward SWPs from hilltops to reservoirs over a real-world large-terrain DEM image and to depict the drainage networks flowing into real rivers. Analyzing the catchment boundary at the outlet along the real river, the framework successfully delineates the reverse SWPs from the outlet to hilltops and accurately demarcates the catchment boundary, consistent with the results from a popular GIS software. These findings indicate that the framework, which leverages the BEM-based numerical solution, reduces the complexity of the physically-based and contour-based algorithm in delineating the SWPs over a real-world large-terrain DEM image while maintaining robustness in problematic regions.

关键词： Accuracy Software algorithms Complexity theory Software Noise measurement Computational modeling Land surface Boundary element method (BEM) digital elevation model (DEM) flow direction algorithm physically-based algorithms real-world terrain

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flow direction optimizer tuned robust FOPID-(1

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JOURNAL OF ENERGY STORAGE 2024年 83卷

作者： Ansari, Zafar Ayub Raja, G. Lloyds Natl Inst Technol Patna Dept Elect Engn Patna Bihar India Loknayak Jai Prakash Inst Technol LNJPIT Dept Elect & Elect Engn Chapra Bihar India

The incorporation of renewable energy sources (REs) in modern interconnected power systems (PSs) raises concerns about stability, flexibility, and appropriateness. Furthermore, the stochastic nature of REs, load disruptions, physical constraints and system parameter variations escalate the issue of frequency oscillations in modern PSs. Owing to this, load frequency control (LFC) is vital to providing high quality, reliable and stable power supply. Hence, this study offers a novel fractional-order proportional-integral-derivative (FOPID) cascaded with 1 + tilted-derivative (1 + TD) controller (FOPID-(1 + TD)) for LFC of the multi-area renewableintegrated hybrid power system (RIHPS). The parameters of the proposed controller are optimized using the flow direction algorithm (FDA) with integral time square error (ITSE) as the fitness function. To begin with, thermal systems with/without governor dead band (GDB) nonlinearity are used for the validation of the FDA-tuned FOPID-(1 + TD) control strategy. Then suggested control strategy is further expanded to a challenging RIHPS where the hybrid micro-grid (H mu G) is integrated with the utility grid. The H mu G contains wind turbine generator (WTG), organic Rankine cycle (ORC) solar thermal power (STP), biodiesel generator (BDG) and hybrid electrical energy storage (HEES). The superiority in dynamic behavior and robustness of the FDA-tuned FOPID-(1 + TD) strategy is vindicated by simulating various practical scenarios such as step and random load variations, fluctuations in REs generation and aberration of +/- 25 % in PS parameters. The HEES unit which consists of a redox flow battery (RFB) and capacitive energy storage (CES) is included to advance system performance even further by offsetting the load power requirement. With the recommended FOPID-(1 + TD) controller, significant improvement in dynamic performance is obtained at attaining the depletion of 2 % to 90 %, 26 % to 83 % and 50 % to 95 % in settling time, undersho

关键词： flow direction algorithm FOPID-(1 + TD) cascade controller Hybrid electrical energy storage Microgrid Renewables Nonlinearities

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