Blade thickness effect on the aerodynamic performance of an asymmetric NACA six series blade vertical axis wind turbine in low wind speed

ABSTRACT

Vertical axis wind turbine (VAWT) is an economic and widely used energy converter for converting wind energy into useful form of energy, like mechanical and electrical energy. For efficient energy conversion in low wind speed and to have improved power coefficient of asymmetric blade VAWT, selection of optimum blade thickness is needed thus entailing its detailed investigation with respect to different operating wind speed conditions. Present study methodically explores the impact of thickness to chord (t/c) ratio on aerodynamic performance of a three bladed asymmetrical blade H-Darrieus VAWT at different low wind speed conditions by using 2D unsteady CFD simulations. The optimal t/c is obtained on the basis of maximum power coefficient and average moment coefficient of the turbine. The aerodynamic performance curves are obtained at different operating and t/c conditions and the performance insights are corroborated with the findings from the flow physics study to come to some concrete conclusions on the effects of the thickness to chord ratio. The present study identifies large blade curvature to create a large diverging passage on the blade suction surface as the prominent reason for aerodynamic performance drop at a high t/c ratio.     

A novel blade design for Savonius wind turbine based on polynomial bezier curves for aerodynamic performance enhancement

ABSTRACT

Advanced wind turbine designs and technologies have been evolved to take advantage of wind energy. Despite the significant progress already attained, the need for a dependable wind energy converter particularly devoted to small-scale applications remains a challenging issue. Due to its design simplicity, Savonius wind turbine is the most suitable candidate for such applications. It operates at low wind speed, with the necessary starting capacity and insensitivity to wind directions. Moreover, in the literature related to wind energy, the Savonius rotor is known for its low performance compared to other types of wind turbines. In this paper, we present a study into the utilization of Bézier curves and transient computational fluid dynamics (CFD) to optimize the conventional Savonius blade design. The k-ω SST turbulence model is employed to perform a series of CFD simulations in order to assess the power coefficient of each generated design. A validation of optimization results using the Taguchi method was carried out. The comparative analysis of the torque and power coefficients shows a significant increase in the power coefficient (C_p). The optimal C_p is 0.35 and is 29% higher than the conventional Savoniu wind turbine (SWT). Subsequently, the effectiveness of the innovative geometry is proved by improved pressure and velocity distributions around blades of novel design.     

Optimum Heat Conductance Distribution for Power Optimization of a Regenerated Closed Brayton Cycle

In this paper, the power output of the cycle is taken as objective for performance optimization of an irreversible regenerated closed Brayton cycle coupled to constant-temperature thermal energy reservoirs in the viewpoint of finite time thermodynamics (FTT) or entropy generation minimization (EGM). The analytical formulae about the relations between power output and pressure ratio are derived with the heat resistance losses in the hot- and cold-side heat exchangers and the regenerator, the irreversible compression and expansion losses in the compressor and turbine, and the pressure drop loss in the piping. The maximum power output optimization is performed by searching the optimum heat conductance distribution corresponding to the optimum power output among the hot- and cold-side heat exchangers and the regenerator for the fixed total heat exchanger inventory. The influence of some design parameters, including the temperature ratio of the heat reservoirs, the total heat exchanger inventory, the efficiencies of the compressor and the turbine, and the pressure recovery coefficient, on the optimum heat conductance distribution and the maximum power output are provided. The power plant design with optimization leads to smaller size including the compressor, turbine, and the hot- and cold-side heat exchangers and the regenerator.     

A heuristic optimization approach for Air Quality Monitoring Network design with the simultaneous consideration of multiple pollutants

An interactive optimization methodology for allocating the number and configuration of an Air Quality Monitoring Network (AQMN) in a vast area to identify the impact of multiple pollutants is described. A mathematical model based on the multiple cell approach (MCA) was used to create monthly spatial distributions for the concentrations of the pollutants emitted from different emission sources. These spatial temporal patterns were subject to a heuristic optimization algorithm to identify the optimal configuration of a monitoring network. The objective of the optimization is to provide maximum information about multi-pollutants (i.e., CO, NO(x) and SO(2)) emitted from each source within a given area. The model was applied to a network of existing refinery stacks and the results indicate that three stations can provide a total coverage of more than 70%. In addition, the effect of the spatial correlation coefficient (R(C)) on total area coverage was analyzed. The modeling results show that as the cutoff correlation coefficient R(C) is increased from 0.75 to 0.95, the number of monitoring stations required for total coverage is increased. A high R(C) based network may not necessarily cover the entire region, but the covered region will be well represented. A low R(C) based network, on the other hand, would offer more coverage of the region, but the covered region may not be satisfactorily represented.     

A novel objective function for optimal DG allocation in distribution systems using meta-heuristic algorithms

Meta heuristic algorithms have been introduced as a powerful method to solve the nonlinear optimization problems. These algorithms have been employed in many complex engineering problems due to their high capability in finding the solutions and reaching the optimal results within a short period of time. Optimization of distributed generation units in distribution systems, which have profoundly impacted on the system losses and voltage profile, is one of these nonlinear problems. In this study, a novel objective function was proposed for optimization procedure by meta-heuristic algorithms. The related objective function consists of the total cost of distributed generation units, cost of the purchased natural gas, cost of distribution system power losses, and penalty for greenhouse gas emissions. The electrical, cooling, and heating loads were considered in this study. In the distribution system, the waste and fuel cell were used to supply the required heating and cooling loads. The meta-heuristic algorithms including Particle Swarm Optimization (PSO), Genetic Algorithm (GA), and Imperialist Competitive Algorithm (ICA) were employed to find the optimal location and size of distributed generation units in a distribution system. A detailed performance analysis was done on 13 bus radial distribution system. The performances of three algorithms were compared with each other and results showed that the PSO was the fastest; and had the best solution and optimum results. Furthermore, the PSO reached the optimum solution in a fewer number of iterations than the GA and ICA algorithms.     

A NONLINEAR PROGRAMMING ALGORITHM FOR REAL-TIME HOURLY RESERVOIR OPERATIONS1

ABSTRACT: The optimization of real-time operations for a single reservoir system is studied. The objective is to maximize the sum of hourly power generation over a period of one day subject to constraints of hourly power schedules, daily flow requirement for water supply and other purposes, and the limitations of the facilities. The problem has a nonlinear concave objective function with nonlinear concave and linear constraints. Nonlinear Duality Theorems and Lagrangian Procedures are applied to solve the problem where the minimization of the Lagrangian is carried out by a modified gradient projection technique along with an optimal stepsize determination routine. The dimension of the problem in terms of the number of variables and constraints is reduced by eliminating the 24 continuity equations with a special implicit routine. A numerical example is presented using data provided by the Bureau of Reclamation, Sacramento, California.     

A COMPARISON OF TRANSFORMATION METHODS FOR FLOOD FREQUENCY ANALYSIS1

ABSTRACT: The SMEMAX transformation, its modified versions and power transformation were applied to 55 long-term records of annual maximum flood flows tested previously for independence, homogeneity and completeness. Even though SMEMAX transformation reduced the coefficient of skewness to near zero for flood data, their distribution was not a true normal distribution. In almost all cases, the coefficient of kurtosis was quite different from 3.0 of the normal distribution. Empirical criteria showed that SMEMAX transformation performed well only for 40 (70 percent) of the 55 stations tested. Its performance level dropped, especially for stations which had both the coefficient of skewness and kurtosis greater than 3.0 and 10.0, respectively. Power transformation was generally better in transforming the flood data to a normal distribution. It performed well for 50 (90 percent) of the 55 stations tested. The coefficient of skewness in case of the data transformed by power transformation was much closer to the zero value than in the case of SMEMAX transformed series. The SMEMAX transformation and its two modified versions yielded identical results when flood frequency analysis was performed. Computationally, all three methods were equally simple and easy to apply for flood frequency analysis. In some cases, the coefficient of kurtosis for the transformed distributions obtained both by SMEMAX and power transformations deviated farther from that for the normal distribution than for the parent distribution.     

Suitable error evaluation criteria selection in the wind energy assessment via the K-means clustering algorithm

In this paper, wind energy potential of four locations in Xinjiang region is assessed. The Weibull distribution as well as the Logistic and the Lognormal distributions are applied to describe the distributions of the wind speed at different heights. In determining the parameters in the Weibull distribution, four intelligent parameter optimization approaches including the differential evolutionary, the particle swarm optimization, and two other approaches derived from these two algorithms and combined advantages of these two approaches are employed. Then the optimal distribution is chosen through the Chi-square error (CSE), the Kolmogorov–Smirnov test error (KSE), and the root mean square error (RMSE) criteria. However, it is found that the variation range of some criteria is quite large, thus these criteria are analyzed and evaluated both from the anomalous values and by the K-means clustering method. Anomaly observation results have shown that the CSE is the first one should be considered to be eliminated from the consequent optimal distribution function selection. This idea is further confirmed by the K-means clustering algorithm, by which the CSE is clustered into a different group with KSE and RMSE. Therefore, only the reserved two error evaluation criteria are utilized to evaluate the wind power potential.     

Economic dispatch of power system containing wind power and photovoltaic considering carbon trading and spare capacity variation

The drying up of the fossil energy sources and the damage from unchecked carbon emissions demand the development of low carbon economy, which promotes the development of new energy sources, such as wind power and photovoltaic. However, the direct connections of wind/photovoltaic power into power grid bring great impacts on power systems, thus affecting the security and stability of power system operations, which challenges the power system dispatching. In despite of many methods for power system dispatch, lack of the models, for power system containing wind power and photovoltaic considering carbon trading and spare capacity variation (PSCWPCCTSCV), restricts the further optimal operations of power systems. This paper studies the economic dispatch modeling problem of power system containing wind power and photovoltaic, establishes the model of economic dispatch of PSCWPCCTSCV. On this basis, adaptive immune genetic algorithm is applied to conduct the economic operation optimization, which can provide the optimal carbon trading price and the optimal power distribution coefficient. Finally, simulations based on the newly proposed models are made to illustrate the economic dispatch of PSCWPCCTSCV. The results show that optimization with the proposed model can not only weaken the volatility of the new energy effectively, but also reduce carbon emissions and reduce power generation costs.     

A numerical investigation of fluid-structure coupling of 3 MW wind turbine blades

In this paper, computational modeling is created for 3 MW wind turbine blade using the software Solidworks, and the computations are carried out using Workbench for the fluid–structure interaction of blades due to both the rated and the extreme wind load. The methods developed are applied to the simulation of the NREL Phase VI wind turbine blades, and validation against published data is presented. Moreover, the static structural analysis for wind turbine rotor is performed using Mechanical APDL. The results show that the maximum values of stress reach 458 MPa and 76.9 MPa due to the rated and extreme wind load, respectively, which are lower than 78.3 MPa, the flexural strength of Swancor 2511-A epoxy resin for blade material.     

Research on Coupling Mechanism of China’s Wind Power Industry Chain

ABSTRACT

Firstly, on the basis of literature research, sort out and summarize the critical coupling relationship among the upstream, middle, and downstream enterprises in the wind power industry chain. Secondly, the evaluation index system of coupling coordination degree of China’s wind power industry chain was established. Based on entropy weight method and subsystem efficiency function, the capacity coupling (CC) coefficient model of wind power industry chain subsystem was established. The coupling coordination degree between the upstream subsystem and the midstream subsystem of the wind power industry chain, and between the midstream subsystem and the downstream subsystem is dynamically evaluated, and the coupling coordination degree evaluation model of the wind power industry chain in China is proposed. Thirdly, according to the relevant statistical data of China from 2010 to 2017, this paper conducts an empirical study on the coupling of the upstream, middle and downstream subsystems of the wind power industry chain. Finally, based on the collaborative coupling study of China’s wind power industry chain, this paper analyzes the key factors influencing the collaborative development of wind power industry chain, and puts forward Suggestions on the optimization of the collaborative development of China’s wind power industry chain.     

Two-stage vertex analysis method for planning electric power systems with greenhouse gas abatement consideration

This study presents a two-stage vertex analysis (TSVA) method for the planning of electric power systems (EPS) under uncertainty. TSVA has advantages in comparison to other optimization techniques. Firstly, TSVA can incorporate greenhouse gas (GHG) abatement policies directly into its optimization process, and, secondly, it can readily integrate inherent system uncertainties expressed as fuzzy sets and probability distributions directly into its modeling formulation and solution procedure. The TSVA method is applied to a case study of planning EPS and it is demonstrated how the TSVA efficiently identify optimal electricity-generation schemes that could help to minimize system cost under different GHG-abatement considerations. Different combinative considerations on the uncertain inputs lead to varied system costs and GHG emissions. Results reveal that the total electricity supply will rise up along with the time period due to the increasing demand and, at the same time, more non-fossil fuels should be used to satisfy the increasing requirement for GHG mitigation. Moreover, uncertainties in connection with complexities in terms of information quality (e.g., capacity, efficiency, and demand) result in changed electricity-generation patterns, GHG-abatement amounts, as well as system costs. Minimax regret (MMR) analysis technique is employed to identify desired alternative that reflects compromises between system cost and system-failure risk.     

Inexact credibility constrained programming for environmental system management

This study proposed an inexact credibility constrained programming (ICCP) to deal with multi-formats of uncertainties in parameters and variables for an agricultural water planning system. The study system includes three subareas with different crop distributions. The redundant water in the wet season can be stored in the reservoir and utilized in the dry season. The ICCP method can reflect not only inexact uncertainties in the objective function, variables and parameters, but also fuzzy uncertainties in the right-hand side. Interval credibility levels which represent satisfaction degrees of the constraints can be analyzed. Scenario analysis is conducted to analyze possible events in wet and dry years. The resulting solutions can provide stable intervals for the objective function and decision variables with different levels of risk when violating the constraints.     

Stochastical analysis of surfactant-enhanced remediation of denser-than-water nonaqueous phase liquid (DNAPL)-contaminated soils

Stochastical analysis was performed to assess the effect of soil spatial variability and heterogeneity on the recovery of denser-than-water nonaqueous phase liquids (DNAPL) during the process of surfactant-enhanced remediation. UTCHEM, a three-dimensional, multicomponent, multiphase, compositional model, was used to simulate water flow and chemical transport processes in heterogeneous soils. Soil spatial variability and heterogeneity were accounted for by considering the soil permeability as a spatial random variable and a geostatistical method was used to generate random distributions of the permeability. The randomly generated permeability fields were incorporated into UTCHEM to simulate DNAPL transport in heterogeneous media and stochastical analysis was conducted based on the simulated results. From the analysis, an exponential relationship between average DNAPL recovery and soil heterogeneity (defined as the standard deviation of log of permeability) was established with a coefficient of determination (r2) of 0.991, which indicated that DNAPL recovery decreased exponentially with increasing soil heterogeneity. Temporal and spatial distributions of relative saturations in the water phase, DNAPL, and microemulsion in heterogeneous soils were compared with those in homogeneous soils and related to soil heterogeneity. Cleanup time and uncertainty to determine DNAPL distributions in heterogeneous soils were also quantified. The study would provide useful information to design strategies for the characterization and remediation of nonaqueous phase liquid-contaminated soils with spatial variability and heterogeneity.     

Optimal design of rainwater collecting systems for domestic use into a residential development

This paper proposes an optimization-based approach for designing rainwater harvesting systems for domestic use in a residential development. The optimization model accounts for the implementation of rainwater harvesting devices, pipes and reservoirs for the optimal sitting, collecting, storing and distribution of harvested rainwater. The optimization model consists in satisfying the water domestic demands and considers as objective function the minimization of the total annual cost associated to the fresh water, the capital costs for the catchment areas, storages and pumps, and the cost associated to the pumping, maintenance and treatment. A case study for a residential development in Morelia, Mexico is presented. The city of Morelia is characterized for having complications to satisfy the water demands, especially during dry seasons. The application of the proposed optimization approach shows that it is possible to satisfy a significant percentage of the domestic water demands using a harvesting rainwater system decreasing the associated cost in the time horizon. Several scenarios have been presented to show the potential solutions identified in the case study.     

WATER RESOURCES MODELING AND OPTIMIZATION BASED ON CONSERVATION AND FLOODING POOLS1

ABSTRACT: A new and practical concept in water resources modeling and optimization is introduced. Instead of unrealistically assuming a multipurpose reservoir to be composed of a single lumped pool of water, it is treated as two different pools namely conservation and flood pools. Based on this treatment, the optimization problem is stated using the concepts of Lagrange multipliers and parameter optimization. The optimization problem consists of the material balance equation, the constraints on control and state variables and the objective function.     

MULTI-OBJECTIVE RESERVOIR OPERATION INCLUDING TURBIDITY CONTROL1

ABSTRACT: The operational problems of a reservoir are expressed by three coordinates: space, time stage, and objective. The operational procedure is formulated using dynamic programming as a multi-objective problem. After comparing the scalar and the vector optimization, the scalar optimization technique is applied to turbidity analysis in a reservoir.     

Optimization of carbon dioxide absorption in a continuous bubble column reactor using response surface methodology

Carbon dioxide absorption using amine based solvents is a well-known approach for carbon dioxide removal. Especially with the increasing concerns about greenhouse gas emissions, there is a need for an optimization approach capable of multifactor calibration and prediction of interactions. Since conventional methods based on empirical relations are not efficiently applicable, this study investigates use of Response Surface Methodology as a strong optimization tool. A bubble column reactor was used and the effect of solvent concentration (10.0, 20.0 and 30.0 vol%), flow rate (4.0, 5.0 and 6.0 L min⁻¹), diffuser pore size (0.5, 1.0 and 1.5 mm) and temperature (20.0, 25.0 and 30.0°C) on the absorption capacity and also overall mass transfer coefficient was evaluated. The optimization results for maintaining maximum capacity and overall mass transfer coefficient revealed that different optimization targets led to different tuned operational factors. Overall mass transfer coefficient decreased to 34.7 min⁻¹ when the maximum capacity was the desired target. High reaction rate along with the highest absorption capacity was set as desirable two factor target in this application. As a result, a third scenario was designed to maximize both mass transfer coefficient and absorption capacity simultaneously. The optimized condition was achieved when a gas flow rate of 5.9 L min⁻¹, MEA solution of 29.6 vol%, diffuser pore size of 0.5 mm and temperature of 20.6°C was adjusted. At this condition, mass transfer coefficient reached a maximum of 38.4 min⁻¹, with a forecasted achievable absorption capacity of 120.5 g CO₂ per kg MEA.     

ESTIMATING STREAMFLOW AND ASSOCIATED HYDRAULIC GEOMETRY,THE MID‐ATLANTIC REGION,USA1

ABSTRACT: Methods to estimate streamflow and channel hydraulic geometry were developed for unpaged streams in the Mid‐Atlantic Region. Observed mean annual streamflow and associated hydraulic geometry data from 75 gaging stations in the Appalachian Plateau, the Ridge and Valley, and the Piedmont Physiographic Provinces of the Mid‐Atlantic Region were used to develop a set of power functions that relate streamflow to drainage area and hydraulic geometry to streamflow. For all three physiographic provinces, drainage area explained 95 to 98 percent of the variance in mean annual streamflow. Relationships between mean annual streamflow and water surface width and mean flow depth had coefficients of determination that ranged from R²= 0.55 to R²= 0.91, but the coefficient of determination between mean flow velocity and mean annual streamflow was lower (R²= 0.44 to R²= 0.54). The advantages of using the regional regression models to estimate streamflow over a conceptual model or a water balance model are its ease of application and reduced input data needs. The prediction of the regression equations were tested with data collected as part of the U.S. Environmental Protection Agency (USEPA) Environmental Monitoring and Assessment Program (EMAP). In addition, equations to transfer streamflow from gaged to ungaged streams are presented.