Modeling the effect of weather conditions in sample day selection using an optimization method

Unmanned aerial vehicles are used today in many real-world applications. In all these applications, the vehicle endurance (flight time) is an important constraint that affects mission success. This study investigates the limitations of embedded energy for a quadrotor aerial vehicle. We consider a quadrotor simple tasked to travel from an initial hover configuration to a final hover configuration. In order to have a precise approximation of the consumed energy, we propose a power consumption model with battery dynamic, motor dynamic, and rotor efficiency function. We then introduce an optimization algorithm to minimize the energy consumption during quadrotor aerial vehicle mission. The proposed algorithm is based on an optimal control problem formulated for the quadrotor model and solved using nonlinear programming. In the optimal control problem, we seek to find control inputs (rotor velocity) and vehicle trajectory between initial and final configurations that minimize the consumed energy during a point-to-point mission. We extensively test in simulation experiments the proposed algorithm under normal and windy weather conditions. We compare the proposed optimization method with a nonlinear adaptive control approach to highlight the saved amount of energy.

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Robust Facility Location Under Disruptions

INFORMS Journal on Optimization ◽

10.1287/ijoo.2021.0054 ◽

2021 ◽

Author(s):

Chun Cheng ◽

Yossiri Adulyasak ◽

Louis-Martin Rousseau

Keyword(s):

Facility Location ◽

Approximation Scheme ◽

Weather Conditions ◽

Optimization Method ◽

Location Decisions ◽

Median Problem ◽

Constraint Generation ◽

Uncertainty Set ◽

Uncertainty Information ◽

Disruption Risks

Facility networks can be disrupted by, for example, power outages, poor weather conditions, or natural disasters, and the probabilities of these events may be difficult to estimate. This could lead to costly recourse decisions because customers cannot be served by the planned facilities. In this paper, we study a fixed-charge location problem (FLP) that considers disruption risks. We adopt a two-stage robust optimization method, by which facility location decisions are made here and now and recourse decisions to reassign customers are made after the uncertainty information on the facility availability has been revealed. We implement a column-and-constraint generation (C&CG) algorithm to solve the robust models exactly. Instead of relying on dualization or reformulation techniques to deal with the subproblem, as is common in the literature, we use a linear programming–based enumeration method that allows us to take into account a discrete uncertainty set of facility failures. This also gives the flexibility to tackle cases when the dualization technique cannot be applied to the subproblem. We further develop an approximation scheme for instances of a realistic size. Numerical experiments show that the proposed C&CG algorithm outperforms existing methods for both the robust FLP and the robust p-median problem.

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An Optimization Method for Economical Ship-Routing and Ship Operation Considering the Effect of Wind-Assisted Rotors

Volume 6A: Ocean Engineering ◽

10.1115/omae2020-18776 ◽

2020 ◽

Author(s):

Wenyu Sun ◽

Xiyang Liu ◽

Li Yang

Keyword(s):

Real Time ◽

Weather Forecasting ◽

Weather Conditions ◽

Optimization Method ◽

Optimization Strategy ◽

Fuel Cost ◽

Ship Routing ◽

The Real ◽

Critical Issues ◽

Ship Route

Abstract With the increasingly strict regulations for energy saving and emission reduction technology of ships, minimizing fuel cost is one of the most critical issues in the design and operation of merchant ships. A method to reduce the fuel cost for merchant ship is to select an economical route based on the real-time meteorological environment and weather forecasting data. So far, numerous ship routing strategies have been proposed with the development of weather routing system. More recently, many wind-assisted devices like rotors, wind sails, etc. have been investigated and designed to utilize the renewable wind energy. With the equipment of wind-assisted rotors, the optimization of ship route becomes more important because the effect of this wind-assisted device highly depends on the local wind field along the ship route. In this paper, an improved optimization strategy with the combination of the A* algorithm and a realtime optimizer has been proposed to determinate the optimal ship route and ship operations including ship heading, propeller’s rpm and rotor’s rpm. The real-time information for the weather conditions, ocean climate and sea states are obtained from European Center for Medium-range Weather Forecasts and the ship performance is evaluated by data-driven models. Finally, the proposed method was applied to test cases of ships operating in Pacific route and Indian Ocean route and the results show that the total fuel consumption could be reduced compared to the minimum distance route.

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Meteorological Conditions Influences on the Variability of Algae Bloom in Taihu Lake and its Risk Prediction

Applied Mechanics and Materials ◽

10.4028/www.scientific.net/amm.253-255.935 ◽

2012 ◽

Vol 253-255 ◽

pp. 935-938

Author(s):

Yuan Shu Jing ◽

Zhi Hao Jing ◽

Jing Yuan Hu ◽

Fei Chen

Keyword(s):

Taihu Lake ◽

Meteorological Factors ◽

Chlorophyll Concentration ◽

Weather Conditions ◽

Optimization Method ◽

Middle Level ◽

Lake Eutrophication ◽

Algae Bloom ◽

Average Wind Speed ◽

Average Wind

Lake eutrophication and algal bloom is one of the most important environmental problems facing China's lakes, and it is also the focus of lake eutrophication control of the world's attention. The monitoring data on chlorophyll concentration was analyzed every one month, combined with corresponding weather conditions from 2004 to 2006. According to the degree of eutrophication in Taihu Lake, it is divided into five Lakes: heavy eutrophication region V, eutrophication region IV, middle-level eutrophication region III, light eutrophication region II and nutrition region I. Based on fuzzy factor optimization method, the average wind speed, average pressure, average temperature and sunshine hours was selected to discuss the influence mechanism of meteorological factors on the algae bloom in Taihu Lake. Considered the four meteorological factors as the input layer nodes, BP neural network model was applied to build the zoning monitoring and early warning model of blue algae in Taihu Lake.

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Fuzzy Graph Learning Regularized Sparse Filtering for Visual Domain Adaptation

Applied Sciences ◽

10.3390/app11104503 ◽

2021 ◽

Vol 11 (10) ◽

pp. 4503

Author(s):

Lingtong Min ◽

Deyun Zhou ◽

Xiaoyang Li ◽

Qinyi Lv ◽

Yuanjie Zhi

Keyword(s):

Domain Adaptation ◽

Weather Conditions ◽

Optimization Method ◽

Superior Performance ◽

Target Domain ◽

Graph Learning ◽

Learning Framework ◽

Cross Domain ◽

Sparse Filtering ◽

Visual Domain

Distribution mismatch can be easily found in multi-sensor systems, which may be caused by different shoot angles, weather conditions and so on. Domain adaptation aims to build robust classifiers using the knowledge from a well-labeled source domain, while applied on a related but different target domain. Pseudo labeling is a prevalent technique for class-wise distribution alignment. Therefore, numerous efforts have been spent on alleviating the issue of mislabeling. In this paper, unlike existing selective hard labeling works, we propose a fuzzy labeling based graph learning framework for matching conditional distribution. Specifically, we construct the cross-domain affinity graph by considering the fuzzy label matrix of target samples. In order to solve the problem of representation shrinkage, the paradigm of sparse filtering is introduced. Finally, a unified optimization method based on gradient descent is proposed. Extensive experiments show that our method achieves comparable or superior performance when compared to state-of-the-art works.

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Optimization of Flight Rescheduling Problem under Carbon Tax

Sustainability ◽

10.3390/su12145576 ◽

2020 ◽

Vol 12 (14) ◽

pp. 5576

Author(s):

Mohamed Ali Kammoun ◽

Sadok Turki ◽

Nidhal Rezg

Keyword(s):

Carbon Emissions ◽

Traffic Congestion ◽

Carbon Tax ◽

Weather Conditions ◽

Optimization Method ◽

Air Traffic ◽

Economic Losses ◽

Aircraft Emissions ◽

Bad Weather ◽

Air Capacity

The flight rescheduling problem is one of the major challenges of air traffic issue. Unforeseen bad weather conditions stimulate air traffic congestion and make the initial scheduling infeasible, resulting in significant economic losses for passengers and airlines. Furthermore, due to rigorous environmental legislations, flight rescheduling becomes a more complicated problem, as it has to deal with flight delays on the one hand, and carbon emissions on the other hand. In this paper, we address the flight rescheduling problem with an environmental requirement subject to the air capacity limitation due to bad weather conditions. A new strategy is proposed to minimize the disruption effects on planned flights, which adopted ground delay, longer route change, flight cancellation, as well speed adjustment to arrive at a scheduled time. Firstly, the objective of this study is to determine the economical flights plan in line with the new available air capacity. Secondly, by considering the environmental impact of the kerosene consumption, we illustrate the contribution of an economical decision to aircraft emissions. Experiment results are provided to show the efficiency of the proposed strategies and genetic algorithm as the used optimization method. Furthermore, the impacts of carbon tax and cost of arrival delay on the flights carbon emissions are studied.

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Parameter Extraction of Solar Cell Models Using the Lightning Search Algorithm in Different Weather Conditions

Journal of Solar Energy Engineering ◽

10.1115/1.4033333 ◽

2016 ◽

Vol 138 (4) ◽

Cited By ~ 13

Author(s):

Reza Sirjani ◽

Hussain Shareef

Keyword(s):

Experimental Data ◽

Solar Cells ◽

Entire Range ◽

Search Algorithm ◽

Weather Conditions ◽

Optimization Method ◽

Parameter Extraction ◽

Solar Panels ◽

Bee Colony ◽

Pv Module

Recently, accurate modeling of the differences between the current and voltage (I–V) characteristics of solar cells has been the main focus of many research studies. Mostly the results were obtained only for single diode or double diode solar cells, not for both or even for photovoltaic (PV) modules. Moreover, the effect of different shading conditions and different temperatures should be considered; otherwise, the obtained results would be reliable for specific weather conditions and unreliable for all real conditions. In this study, a novel nature-inspired optimization method known as the lightning search algorithm (LSA) was developed to extract the parameters of single diode and double diode solar cells as well as for a PV module. LSA is formulated based on lightning, which originates from thunderstorms. Experimental data from multicrystalline KC200GT solar panels were used to test the single diode and double diode solar panel models, and experimental data from the monocrystalline SQ150-PC solar panels were used to test the PV module model. The experimental data are first collected at the same temperature at five different irradiance levels. In the second stage, variations in temperature are considered at the same irradiance level. The extraction results in the LSA I–V curves accurately fit the entire range of the experimental data, while many fluctuations were seen in the particle swarm optimization (PSO) and bee colony optimization (BCO) I–V curves. The convergence characteristics of LSA were also evaluated in terms of accuracy and speed. For all cases, when LSA was used, the accuracies matched well with the entire range of experimental data. In addition, the value of the objective function using LSA was lower, and that method converged much faster than PSO and BCO.

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Development of a GIS-Based System for Three-Dimensional Spatial Modeling of Offshore Site Investigation Information

Volume 10: Offshore Geotechnics ◽

10.1115/omae2020-19252 ◽

2020 ◽

Author(s):

Mingi Kim ◽

Choong-Ki Chung

Keyword(s):

Spatial Modeling ◽

Three Dimensional ◽

Weather Conditions ◽

Site Investigation ◽

Optimization Method ◽

Three Dimensions ◽

Borehole Data ◽

Geostatistical Methods ◽

Three Dimensional Modeling ◽

Investigation Information

Abstract Offshore site investigation is time-consuming and relatively expensive compared to onshore site investigation, because it is affected by severe weather conditions and has low accessibility from the land. Due to these economic and spatial-temporal constraints, it is essential to integrate the available site investigation information for the planning and design of offshore infrastructures. In this study, a GIS-based system was developed to manage and utilize the offshore site investigation data using opensource software. The system mainly performs the function of modeling the geo-information in three dimensions based on the offshore site investigation database. Through the input module, borehole data, geophysical survey data and numerical terrain information are standardized and stored in the database. The three-dimensional spatial modeling module performs outlier analysis of borehole data, subsurface geo-layer stratification, and interpolation of geotechnical properties. For the geo-layer stratification, the geostatistical integration method of borehole and geophysical datasets is adopted as well as conventional geostatistical methods. The optimization method of the results from geostatistical conditional simulation can be used for three-dimensional modeling of geotechnical property. The geoinformation predicted by the analysis module is inserted back into the database. Although this system is designed for offshore site investigation information, it can be used to manage inland and onshore site investigation information. Three-dimensional spatial modeling of site investigation information of an expressway construction site in South Korea was carried out using the developed system and its applicability was verified.

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Optimal Airflow Control of Laboratory Air Handling Unit (LAHU) Systems

Journal of Solar Energy Engineering ◽

10.1115/1.1667984 ◽

2004 ◽

Vol 126 (2) ◽

pp. 750-758 ◽

Cited By ~ 3

Author(s):

Yujie Cui ◽

Mingsheng Liu ◽

Kirk Conger

Keyword(s):

Linear Optimization ◽

Weather Conditions ◽

Optimization Method ◽

Energy Characteristics ◽

Laboratory Section ◽

Air Handling Unit ◽

Air Temperatures ◽

Laboratory Supply ◽

Air Intake ◽

Air Control

The Laboratory Air Handling Unit (LAHU) system conditions both the office section and the laboratory section. It improves indoor air quality by maximizing outside air intake to the office section and minimizes thermal energy consumption by re-circulating the office section air to the laboratory section. This paper presents a theoretical linear optimization method and results of optimal outside air control in LAHUs. The optimal outside airflows are expressed as functions of the weather conditions (outside air temperature and enthalpy), office and laboratory airflow rates, office and laboratory supply air temperatures, and four dimensionless parameters that describe the building system and energy characteristics. The optimization method used in this paper can be used to identify the optimal control schedules in other HVAC systems.

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Evaluating Microbial Partitioning Differences Under Storm and Dry-Weather Conditions

Impacts of Global Climate Change ◽

10.1061/40792(173)118 ◽

2005 ◽

Author(s):

Gregory W. Characklis ◽

Mackenzie J. Dilts ◽

Otto D. Simmons ◽

Leigh-Anne H. Krometis ◽

Christina Likirdopulos ◽

...

Keyword(s):

Weather Conditions ◽

Microbial Partitioning

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