scholarly journals Automated Detection of Instability-Inducing Channel Geometry Transitions in Saint-Venant Simulation of Large-Scale River Networks

Water ◽  
2021 ◽  
Vol 13 (16) ◽  
pp. 2236
Author(s):  
Cheng-Wei Yu ◽  
Ben R. Hodges ◽  
Frank Liu
Keyword(s):  
Large Scale ◽  
Open Channel ◽  
Search Algorithm ◽  
Shallow Water Equation ◽  
Stable Solution ◽  
River Networks ◽  
River Systems ◽  
Channel Network ◽  
Channel Geometry ◽  
Venant Equation

A new sweep-search algorithm (SSA) is developed and tested to identify the channel geometry transitions responsible for numerical convergence failure in a Saint-Venant equation (SVE) simulation of a large-scale open-channel network. Numerical instabilities are known to occur at “sharp” transitions in discrete geometry, but the identification of problem locations has been a matter of modeler’s art and a roadblock to implementing large-scale SVE simulations. The new method implements techniques from graph theory applied to a steady-state 1D shallow-water equation solver to recursively examine the numerical stability of each flowpath through the channel network. The SSA is validated with a short river reach and tested by the simulation of ten complete river systems of the Texas–Gulf Coast region by using the extreme hydrological conditions recorded during hurricane Harvey. The SSA successfully identified the problematic channel sections in all tested river systems. Subsequent modification of the problem sections allowed stable solution by an unsteady SVE numerical solver. The new SSA approach permits automated and consistent identification of problem channel geometry in large open-channel network data sets, which is necessary to effectively apply the fully dynamic Saint-Venant equations to large-scale river networks or for city-wide stormwater networks.

scholarly journals Evolution of eastern Tibetan river systems is driven by the indentation of India

2021 ◽  
Vol 2 (1) ◽  
Author(s):  
Yi Chen ◽  
Baosheng Wu ◽  
Zhongyu Xiong ◽  
Jinbo Zan ◽  
Bangwen Zhang ◽  
...  
Keyword(s):  
Tibetan Plateau ◽  
Large Scale ◽  
Network Models ◽  
The Tibetan Plateau ◽  
River Systems ◽  
Channel Network ◽  
Erosion Rates ◽  
Digital Elevation ◽  
Tectonically Active ◽  
Elevation Model

AbstractThe main rivers that originate from the Tibetan Plateau are important as a resource and for the sedimentary and biogeochemical exchange between mountains and oceans. However, the dominant mechanism for the evolution of eastern Tibetan river systems remains ambiguous. Here we conduct geomorphological analyses of river systems and assess catchment-average erosion rates in the eastern Tibetan Plateau using a digital elevation model and cosmogenic radionuclide data. We find that major dividing ranges have northeast oriented asymmetric geometries and that erosion rates reduce in the same direction. This coincides with the northeastward indentation of India and we suggest this indicates a primarily tectonic influence on the large-scale configuration of eastern Tibetan river systems. In contrast, low-level streams appear to be controlled by fluvial self-organization processes. We propose that this distinction between high- and low-order channel evolution highlights the importance of local optimization of optimal channel network models in tectonically active areas.

scholarly journals Influence of Wind Power on Modeling of Bidding Strategy in a Promising Power Market with a Modified Gravitational Search Algorithm

2021 ◽  
Vol 11 (10) ◽  
pp. 4438
Author(s):  
Satyendra Singh ◽  
Manoj Fozdar ◽  
Hasmat Malik ◽  
Maria del Valle Fernández Moreno ◽  
Fausto Pedro García Márquez
Keyword(s):  
Wind Power ◽  
Electricity Market ◽  
Large Scale ◽  
Search Algorithm ◽  
Gravitational Search Algorithm ◽  
Optimization Methods ◽  
Bidding Strategy ◽  
Wind Speed Profile ◽  
Gravitational Search ◽  
Modified Gravitational Search Algorithm

It is expected that large-scale producers of wind energy will become dominant players in the future electricity market. However, wind power output is irregular in nature and it is subjected to numerous fluctuations. Due to the effect on the production of wind power, producing a detailed bidding strategy is becoming more complicated in the industry. Therefore, in view of these uncertainties, a competitive bidding approach in a pool-based day-ahead energy marketplace is formulated in this paper for traditional generation with wind power utilities. The profit of the generating utility is optimized by the modified gravitational search algorithm, and the Weibull distribution function is employed to represent the stochastic properties of wind speed profile. The method proposed is being investigated and simplified for the IEEE-30 and IEEE-57 frameworks. The results were compared with the results obtained with other optimization methods to validate the approach.

scholarly journals A Matheuristic Approach Combining Local Search and Mathematical Programming

2016 ◽  
Vol 2016 ◽  
pp. 1-7 ◽  
Author(s):  
Carolina Lagos ◽  
Guillermo Guerrero ◽  
Enrique Cabrera ◽  
Stefanie Niklander ◽  
Franklin Johnson ◽  
...  
Keyword(s):  
Mathematical Programming ◽  
Local Search ◽  
Large Scale ◽  
Search Algorithm ◽  
Facility Location Problem ◽  
Specific Information ◽  
Local Search Algorithm ◽  
Capacitated Facility Location Problem ◽  
Capacitated Facility Location ◽  
Installation Cost

A novel matheuristic approach is presented and tested on a well-known optimisation problem, namely, capacitated facility location problem (CFLP). The algorithm combines local search and mathematical programming. While the local search algorithm is used to select a subset of promising facilities, mathematical programming strategies are used to solve the subproblem to optimality. Proposed local search is influenced by instance-specific information such as installation cost and the distance between customers and facilities. The algorithm is tested on large instances of the CFLP, where neither local search nor mathematical programming is able to find good quality solutions within acceptable computational times. Our approach is shown to be a very competitive alternative to solve large-scale instances for the CFLP.

Bilateral bidding strategy in joint day-ahead energy and reserve electricity markets considering techno-economic-environmental measures

2021 ◽  
pp. 0958305X2110148
Author(s):  
Mojtaba Shivaie ◽  
Mohammad Kiani-Moghaddam ◽  
Philip D Weinsier
Keyword(s):  
Electricity Market ◽  
Large Scale ◽  
Search Algorithm ◽  
Power Grid ◽  
Mathematical Formulation ◽  
Test System ◽  
Bidding Strategy ◽  
Level Problem ◽  
Optimal Bidding ◽  
Environmental Measures

In this study, a new bilateral equilibrium model was developed for the optimal bidding strategy of both price-taker generation companies (GenCos) and distribution companies (DisCos) that participate in a joint day-ahead energy and reserve electricity market. This model, from a new perspective, simultaneously takes into account such techno-economic-environmental measures as market power, security constraints, and environmental and loss considerations. The mathematical formulation of this new model, therefore, falls into a nonlinear, two-level optimization problem. The upper-level problem maximizes the quadratic profit functions of the GenCos and DisCos under incomplete information and passes the obtained optimal bidding strategies to the lower-level problem that clears a joint day-ahead energy and reserve electricity market. A locational marginal pricing mechanism was also considered for settling the electricity market. To solve this newly developed model, a competent multi-computational-stage, multi-dimensional, multiple-homogeneous enhanced melody search algorithm (MMM-EMSA), referred to as a symphony orchestra search algorithm (SOSA), was employed. Case studies using the IEEE 118-bus test system—a part of the American electrical power grid in the Midwestern U.S.—are provided in this paper in order to illustrate the effectiveness and capability of the model on a large-scale power grid. According to the simulation results, several conclusions can be drawn when comparing the unilateral bidding strategy: the competition among GenCos and DisCos facilitates; the improved performance of the electricity market; mitigation of the polluting atmospheric emission levels; and, the increase in total profits of the GenCos and DisCos.

scholarly journals A Compound Wind Power Forecasting Strategy Based on Clustering, Two-Stage Decomposition, Parameter Optimization, and Optimal Combination of Multiple Machine Learning Approaches

Energies ◽  
2019 ◽  
Vol 12 (18) ◽  
pp. 3586 ◽  
Author(s):  
Sizhou Sun ◽  
Jingqi Fu ◽  
Ang Li
Keyword(s):  
Wind Power ◽  
Large Scale ◽  
Wind Farm ◽  
Search Algorithm ◽  
Interpolation Method ◽  
Least Square ◽  
Support Vector ◽  
Two Stage ◽  
Wind Power Forecasting ◽  
Power Forecasting

Given the large-scale exploitation and utilization of wind power, the problems caused by the high stochastic and random characteristics of wind speed make researchers develop more reliable and precise wind power forecasting (WPF) models. To obtain better predicting accuracy, this study proposes a novel compound WPF strategy by optimal integration of four base forecasting engines. In the forecasting process, density-based spatial clustering of applications with noise (DBSCAN) is firstly employed to identify meaningful information and discard the abnormal wind power data. To eliminate the adverse influence of the missing data on the forecasting accuracy, Lagrange interpolation method is developed to get the corrected values of the missing points. Then, the two-stage decomposition (TSD) method including ensemble empirical mode decomposition (EEMD) and wavelet transform (WT) is utilized to preprocess the wind power data. In the decomposition process, the empirical wind power data are disassembled into different intrinsic mode functions (IMFs) and one residual (Res) by EEMD, and the highest frequent time series IMF1 is further broken into different components by WT. After determination of the input matrix by a partial autocorrelation function (PACF) and normalization into [0, 1], these decomposed components are used as the input variables of all the base forecasting engines, including least square support vector machine (LSSVM), wavelet neural networks (WNN), extreme learning machine (ELM) and autoregressive integrated moving average (ARIMA), to make the multistep WPF. To avoid local optima and improve the forecasting performance, the parameters in LSSVM, ELM, and WNN are tuned by backtracking search algorithm (BSA). On this basis, BSA algorithm is also employed to optimize the weighted coefficients of the individual forecasting results that produced by the four base forecasting engines to generate an ensemble of the forecasts. In the end, case studies for a certain wind farm in China are carried out to assess the proposed forecasting strategy.

Context-aware Adaptive Surgery

2021 ◽  
Vol 5 (3) ◽  
pp. 1-22
Author(s):  
Hongli Wang ◽  
Bin Guo ◽  
Jiaqi Liu ◽  
Sicong Liu ◽  
Yungang Wu ◽  
...  
Keyword(s):  
Real Time ◽  
Large Scale ◽  
Resource Constraints ◽  
Search Algorithm ◽  
Search Time ◽  
State Graph ◽  
Context Aware ◽  
Optimal Partition ◽  
Research Attention ◽  
Neighbor Effect

Deep Neural Networks (DNNs) have made massive progress in many fields and deploying DNNs on end devices has become an emerging trend to make intelligence closer to users. However, it is challenging to deploy large-scale and computation-intensive DNNs on resource-constrained end devices due to their small size and lightweight. To this end, model partition, which aims to partition DNNs into multiple parts to realize the collaborative computing of multiple devices, has received extensive research attention. To find the optimal partition, most existing approaches need to run from scratch under given resource constraints. However, they ignore that resources of devices (e.g., storage, battery power), and performance requirements (e.g., inference latency), are often continuously changing, making the optimal partition solution change constantly during processing. Therefore, it is very important to reduce the tuning latency of model partition to realize the real-time adaption under the changing processing context. To address these problems, we propose the Context-aware Adaptive Surgery (CAS) framework to actively perceive the changing processing context, and adaptively find the appropriate partition solution in real-time. Specifically, we construct the partition state graph to comprehensively model different partition solutions of DNNs by import context resources. Then "the neighbor effect" is proposed, which provides the heuristic rule for the search process. When the processing context changes, CAS adopts the runtime search algorithm, Graph-based Adaptive DNN Surgery (GADS), to quickly find the appropriate partition that satisfies resource constraints under the guidance of the neighbor effect. The experimental results show that CAS realizes adaptively rapid tuning of the model partition solutions in 10ms scale even for large DNNs (2.25x to 221.7x search time improvement than the state-of-the-art researches), and the total inference latency still keeps the same level with baselines.

scholarly journals Exact and Heuristic Methods for Observing Task-Oriented Satellite Cluster Agent Team Formation

2018 ◽  
Vol 2018 ◽  
pp. 1-23 ◽  
Author(s):  
Hao Chen ◽  
Shu Yang ◽  
Jun Li ◽  
Ning Jing
Keyword(s):  
Large Scale ◽  
Search Algorithm ◽  
Low Cost ◽  
Team Formation ◽  
Satellite Systems ◽  
Heuristic Search Algorithm ◽  
Earth Observation Satellite ◽  
Novel Algorithms ◽  
Swarm Intelligence Optimization ◽  
Task Oriented

With the development of aerospace science and technology, Earth Observation Satellite cluster which consists of heterogeneous satellites with many kinds of payloads appears gradually. Compared with the traditional satellite systems, satellite cluster has some particular characteristics, such as large-scale, heterogeneous satellite platforms, various payloads, and the capacity of performing all the observation tasks. How to select a subset from satellite cluster to perform all observation tasks effectively with low cost is a new challenge arousing in the field of aerospace resource scheduling. This is the agent team formation problem for observation task-oriented satellite cluster. A mathematical scheduling model is built. Three novel algorithms, i.e., complete search algorithm, heuristic search algorithm, and swarm intelligence optimization algorithm, are proposed to solve the problem in different scales. Finally, some experiments are conducted to validate the effectiveness and practicability of our algorithms.

A New Solution Seed for Job Shop Scheduling Problem

2011 ◽  
Vol 110-116 ◽  
pp. 3899-3905
Author(s):  
Parviz Fattahi ◽  
Mojdeh Shirazi Manesh ◽  
Abdolreza Roshani
Keyword(s):  
Tabu Search ◽  
Large Scale ◽  
Production Management ◽  
Job Shop ◽  
Job Shop Scheduling ◽  
Search Algorithm ◽  
Tabu Search Algorithm ◽  
Np Hard ◽  
Shop Scheduling ◽  
Large Scale Problems

Scheduling for job shop is very important in both fields of production management and combinatorial optimization. Since the problem is well known as NP-Hard class, many metaheuristic approaches are developed to solve the medium and large scale problems. One of the main elements of these metaheuristics is the solution seed structure. Solution seed represent the coding structure of real solution. In this paper, a new solution seed for job shop scheduling is presented. This solution seed is compared with a famous solution seed presented for the job shop scheduling. Since the problem is well known as NP-Hard class, a Tabu search algorithm is developed to solve large scale problems. The proposed solution seed are examined using an example and tabu search algorithm.

scholarly journals Finding A Small Vertex Cover in Massive Sparse Graphs: Construct, Local Search, and Preprocess

2017 ◽  
Vol 59 ◽  
pp. 463-494 ◽  
Author(s):  
Shaowei Cai ◽  
Jinkun Lin ◽  
Chuan Luo
Keyword(s):  
Local Search ◽  
Real World ◽  
Large Scale ◽  
Heuristic Algorithms ◽  
Search Algorithm ◽  
Vertex Cover ◽  
Search Algorithms ◽  
Theory And Practice ◽  
Sparse Graphs ◽  
Massive Graphs

The problem of finding a minimum vertex cover (MinVC) in a graph is a well known NP-hard combinatorial optimization problem of great importance in theory and practice. Due to its NP-hardness, there has been much interest in developing heuristic algorithms for finding a small vertex cover in reasonable time. Previously, heuristic algorithms for MinVC have focused on solving graphs of relatively small size, and they are not suitable for solving massive graphs as they usually have high-complexity heuristics. This paper explores techniques for solving MinVC in very large scale real-world graphs, including a construction algorithm, a local search algorithm and a preprocessing algorithm. Both the construction and search algorithms are based on low-complexity heuristics, and we combine them to develop a heuristic algorithm for MinVC called FastVC. Experimental results on a broad range of real-world massive graphs show that, our algorithms are very fast and have better performance than previous heuristic algorithms for MinVC. We also develop a preprocessing algorithm to simplify graphs for MinVC algorithms. By applying the preprocessing algorithm to local search algorithms, we obtain two efficient MinVC solvers called NuMVC2+p and FastVC2+p, which show further improvement on the massive graphs.

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