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Coning motion has been detected in flight experiments of many spinning projectiles for decades and the stability analysis of this motion has been extensively studied recently. In this paper, a novel optimal control algorithm based on stability analyses is developed to suppress the coning motion. A Lagrangian functional is built to compute the optimal control that minimizes the coning motion for a given control cost. Then a linearized approach, which produces accurate enough control within the range of parameters considered, is proposed to reduce the computational cost. It is analytically presented that the optimal solution of this linearized optimization is unique at any given values of control cost. The analytical analysis also indicates that there exists a direct solution of the control that approaches the linearized optimal control when the control cost is small enough and that this direct solution can be obtained much more efficiently than the linearized optimal control. The controllability problem associated with this optimal control algorithm is also discussed and the results shed lights on the choice of numerical parameters in the optimization. These optimal control algorithms are implemented to calculate the optimal control that minimizes the coning motion induced by initial disturbances to a wrap-around-fin missile. [ABSTRACT FROM AUTHOR]

In this paper the authors propose a global-local methodology for optimizing the short-term operation of hydroelectric plants. The authors determine the tradeoffs between minimizing the daily release from the plant and minimizing the number of startups and shutdowns of the generating units. The model is formulated as a mixed integer, nonlinear programming optimization problem with multiple objectives. The authors consider the nonlinearities of the generating units without simplifications or approximations. The authors develop a solution method that combines an evolutionary algorithm for the global search of the integer variables and a gradient-based local optimizer for the continuous variables. The local optimizer is embedded in the global search algorithm. Convergence is achieved by iterating between the global search and the local optimizer. The proposed methodology is applied to a moderately sized Brazilian hydroelectric plant that belongs to the national interconnected system. Additionally, a comparative study was conducted using historical operational records. The results demonstrate that the proposed methodology is feasible for online daily operations and delivers two specific benefits. The first is the efficiency gained, as the model seeks to operate the generating units as close as possible to their most efficient operating points. The second benefit is reduction of the units' maintenance costs, as the model minimizes switching on/off of the generating units. [ABSTRACT FROM AUTHOR]

This paper shows how pipe replacements and control valve installations can be optimized in water distribution networks to reduce leakage, under minimum nodal pressure constraints. To this end, a hybrid multiobjective algorithm, which has pipe diameters and valve positions and settings as decisional variables, was set up. The algorithm also enables identification of the isolation valves that have to be closed in order to improve effectiveness of the control valves installed. The algorithm is initially applied to the optimal valve location problem, where it explores the trade-off between the number of installed control valves and the daily leakage volume. In this context, the analysis of the results proves the new algorithm more effective than a multiobjective genetic algorithm widely adopted in the scientific literature. Furthermore, it shows that if some isolation valves identified ad hoc are closed in the network, the installation of control valves determines larger leakage volume reductions. In a second application of the algorithm, pipe replacements and control valve installations are simultaneously performed. In this case, a Pareto front of trade-off solutions between installation costs and daily leakage volume is obtained. For the choice of the final solution within the front, an economic criterion based on the long-term convenience analysis is also illustrated. [ABSTRACT FROM AUTHOR]

Various multiobjective evolutionary algorithms (MOEAs) have been applied to solve the optimal design problems of a water distribution system (WDS). Such methods are able to find the near-optimal trade-off between cost and performance benefit in a single run. Previously published work used a number of small benchmark networks and/or a few large, real-world networks to test MOEAs on design problems of WDS. A few studies also focused on the comparison of different MOEAs given a limited computational budget. However, no consistent attempt has been made before to investigate and report the best-known approximation of the true Pareto front (PF) for a set of benchmark problems, and thus there is not a single point of reference. This paper applied 5 state-of-the-art MOEAs, with minimum time invested in parameterization (i.e., using the recommended settings), to 12 design problems collected from the literature. Three different population sizes were implemented for each MOEA with respect to the scale of each problem. The true PFs for small problems and the best-known PFs for the other problems were obtained. Five MOEAs were complementary to each other on various problems, which implies that no one method was completely superior to the others. The nondominated sorting genetic algorithm-II (NSGA-II), with minimum parameters tuning, remains a good choice as it showed generally the best achievements across all the problems. In addition, a small population size can be used for small and medium problems (in terms of the number of decision variables). However, for intermediate and large problems, different sizes and random seeds are recommended to ensure a wider PF. The publicly available best-known PFs obtained from this work are a good starting point for researchers to test new algorithms and methodologies for WDS analysis. [ABSTRACT FROM AUTHOR]

On-site construction of industrial projects relies heavily on large mobile cranes for the lifting of prefabricated modules, which over the years have become heavier and more voluminous. Industrial construction sites are frequently congested, a condition that reduces the area where cranes can freely maneuver. As a result, in some cases mobile cranes may not have sufficient clearance to perform the lifts and thus need to pick and walk with loads to a point from which the modules can ultimately be set in their resting positions. Since this type of crane operation presents higher risks of failure or accident in comparison to the traditional scenario, in which the location of the crane does not change for the duration of the lifting, a detailed analysis of these operations is paramount to ensure safety and efficiency. Paradoxically, many practitioners still rely on their best judgment for crane walking planning, which in the case of highly congested and complex construction sites is likely to lead to inefficiencies and errors since numerous obstacles may interfere with the crane body and the load. Furthermore, manual analysis is tedious, time-consuming and cannot respond rapidly to changes in work orders or project scope, especially considering the large number of lifts involved in modern industrial projects. In this paper, a generic algorithm is proposed for mobile crane walking path planning, which considers the typical site constraints, the geometry of lifted modules, and crane configuration. The proposed algorithm calculates the pick area and crane collision-free operation area, based upon which the crane's walking paths are determined. A case study, based on an actual industrial project, is presented to validate the developed algorithm. [ABSTRACT FROM AUTHOR]

This paper describes the influence of steel fiber-reinforcement on the design of cost-optimized, prestressed concrete, precast road bridges, with a double U-shaped crosssection and isostatic spans. A memetic algorithm with variable-depth neighborhood search is applied to the economic cost of these structures at different stages of manufacturing, transportation, and construction. The problem involved 41 discrete design variables for the geometry of the beam and the slab, materials in the two elements, active and passive reinforcement, as well as residual flexural tensile strength corresponding to the fibers. The use of fibers decreases the mean weight of the beam by 1.72% and reduces the number of strands an average of 3.59%, but it increases the passive reinforcement by 8.71% on average, respectively. Finally, despite the higher cost of the fibers, their use is economically feasible since the average relative difference in cost is less than 0.19%. [ABSTRACT FROM AUTHOR]

Sustainable construction is critical to the architecture-engineering-construction (AEC) industry. The consideration and selection of optimal building components is necessary in order to fulfill multiple objectives of sustainable construction, which are often conflicting. Today, information and communication technologies (ICT) such as building information modeling (BIM) are widely used in the construction industry for decision making during design and construction. However, due to the complexity of building and construction, supporting multiple-objective decision making is not trivial. Currently, BIM has limited capability to support such a process. This paper introduces Simulation of Environmental Impact of Construction (SimulEICon), which is an extension of BIM designed specifically to aid in the decision-making process during the design stage of a construction project. SimulEICon is an add-on to the Autodesk Revit Architecture software, and it uses Microsoft Access for database-related operations. In addition, the genetic algorithm (GA), NSGA-II, is used for optimization in order to find solutions that best conform to project objectives. Moreover, SimulEICon has the capability of finding optimal solutions for all components at a building level or only for specific components and visualizing the solutions in a three- or four-dimensional model to support the decision-making process. Furthermore, the results can be constrained to conform to certain limitations. A case study of a real building, the Future House USA, is used with SimulEICon to demonstrate its use and results. A demonstration of SimulEICon is shown by comparing a set of possible solutions obtained by SimulEICon to those implemented in the building; it is expected that some of the solutions from SimulEICon would match those in the building at a component level, or even at a building level. Moreover, additional solutions to those present in the building show that SimulEICon is capable of presenting a wider range of possible solutions from which the user might choose the most appropriate one for the project goals. [ABSTRACT FROM AUTHOR]

The paper presents the results of the calibration, by means of a microgenetic algorithm, of the mechanical parameters of high-density polyethylene pipes, to reproduce experimental unsteady flow runs in an installation consisting primarily of a steel pipe and of an additional polyethylene pipe. The mechanical parameters of viscoelastic models are estimated using both one-dimensional and quasi-two-dimensional models. Two sets of runs carried out at a distance of about 12 years are considered. The calibration of a three-parameter Kelvin-Voigt model provides evidence of the impossibility of obtaining general results. The use of a model with a single Kelvin-Voigt element (two-parameter model) gives good results in terms of comparison between numerical and experimental results for both pressure head and velocity. The calibration for the second series of tests shows increased deformability of the material (the reduction of the elastic modulus is more than 30%). The calibrated values of the retardation time result linearly correlated to the oscillations period, increasing with time due to the reduction of the elastic modulus. The obtained results allow evaluation of the viscoelastic parameters starting only with the knowledge of the equivalent elastic modulus. [ABSTRACT FROM AUTHOR]

The social quality of life (SQOL) for single-family housing residents can be improved by enhancing their thermal comfort, indoor lighting quality, indoor air quality, and locating the housing unit in high-quality and safe neighborhoods. Accomplishing these SQOL improvements often leads to an increase in the life-cycle cost (LCC) of the housing unit. This paper presents a multiobjective optimization model that is capable of generating optimal trade-offs between the two conflicting objectives of maximizing the SQOL for single-family housing residents and minimizing the LCC of single-family housing units. The model is designed to maximize the SQOL for housing residents by improving their thermal comfort, indoor lighting quality, indoor air quality, and neighborhood quality. The model is also designed to minimize the LCC of housing units by minimizing the initial housing unit cost as well as its annual energy, utility, and maintenance costs. The model is developed in three main stages that focus on (1) identifying relevant criteria and metrics to measure the performance of these two optimization objectives; (2) modeling all decision variables, objective functions, and constraints; and (3) implementing a multiobjective genetic algorithm model to evaluate its performance. An application example of a housing unit is analyzed to illustrate the use of the developed model and demonstrate its capabilities in identifying optimal configurations of single-family housing design and construction decisions. [ABSTRACT FROM AUTHOR]

Control valves are commonly used for the operation of water distribution systems. Modeling these devices typically requires that their operating states are known, or that a computationally expensive search is undertaken over all possible operating states. This paper presents a novel method of modeling control valves (including flow control, pressure sustaining, pressure reducing and check valves) in extended-period simulations of water distribution systems. Instead of the normal discrete control problem formulation, it is approached with the Karush-Kuhn-Tucker equations for an optimization problem with constraints. The proposed method does not prerequire the operating state (open, closed, active) of each valve to be determined, as this is done implicitly. Pipe and valve flow rates and nodal heads are determined by: (1) minimizing deviations from targets at control valves, and (2) satisfy the state equations (conservation of mass and energy) by solving a constrained least-squares problem. Sensitivity equations with respect to the control variables (valve settings) are derived from the state equations, and the control variables are updated using Levenberg-Marquardt iterations. The results of simple problems and case studies are presented to demonstrate the effectiveness of the approach. [ABSTRACT FROM AUTHOR]

Different from the current studies on the impulsive rendezvous which are mainly normal propellant-optimal or time-optimal designs, the safety-optimal impulsive rendezvous with consideration of practical trajectory uncertainties is investigated in this paper. An optimization model for safety-optimal rendezvous is established based on the Clohessy-Wiltshire (C-W) equations, which employs one new, recently proposed quantitative performance index of the rendezvous trajectory safety as the objective function. A real-coded genetic algorithm is adopted to locate the optimal solution. The safety index and the optimization approach are tested by several rendezvous problems with different numbers of impulses and different approach directions. The practical application value of the safety-optimal rendezvous trajectory is demonstrated by comparing with the traditional propellant-optimal and time-optimal rendezvous trajectories. [ABSTRACT FROM AUTHOR]

The radio frequency identification (RFID) technology has proven its potential in locating and tracking construction resources, a critical task in construction project control. However, the main challenge is how to achieve desired levels of locating accuracy. This paper presents an enhanced boundary condition method that incorporates the tag-reader angle and the reader geometric configuration factors to control the accuracy of a locating system that integrates RFID and real time kinematic (RTK) global positioning system (GPS). Controlled laboratory experiments were conducted to assess their effects and create quality control filters. This study demonstrated the relationship between the detecting range and the tag-reader angle and used it to separate valid/invalid boundary points. Spatial dilution of precision (SDOP) was formulated to measure the geometric configuration of readers forming the boundary constraint. Correlating SDOP to the locating error through a polynomial regression model, a mechanism was created to use SDOP to predict and control the locating accuracy. Open field experiments validated that appropriate SDOP, and tag-reader angle filters could be created to achieve varying levels of accuracy. [ABSTRACT FROM AUTHOR]

Urban land-use planning is becoming increasingly complex in China because it requires estimating the future urban extent and balancing its various aspects, e.g., provision of enough housing and employment opportunities and preservation of the environment. This paper presents an integrative approach to the problem of sustainable land-use planning. Specifically, an urban growth simulation tool, a cellular automata model, was used to determine the extent of the urban area, and a neural network approach was used to quantitatively predict the land-use structure in the projected year. A multiobjective optimization genetic algorithm was then developed to search for Pareto-optimal urban land-use plans within the urban extent determined by the urban growth model. To search for a sustainable urban land-use plan, social, economic, and environmental objectives were defined that reflect the multiple objectives of the urban system. In addition to these nonspatial goals, objectives concerning the spatial distribution of land usages were proposed. These are classified as local and global spatial objectives. A case study of the Donghu Lake watershed was conducted. Donghu Lake is one of the largest downtown lakes in central China, and the watershed area is undergoing rapid urbanization and suffering from nonpoint source water pollution. The study was carried out to validate the proposed method, and it demonstrated that the models are appropriate for areas undergoing urban expansion. [ABSTRACT FROM AUTHOR]

This paper presents a combined simulation-optimization model for simulating reservoir operations without any detailed operation rules; it features the integration of a nonlinear, multiple-objective function and a heuristic search method, the Shuffled Complex Evolution method, developed at the University of Arizona. The model is applied to optimal water allocations from both Balan Reservoir and Seomjingang Dam in South Korea, an irrigation reservoir and a multipurpose, multioutlet reservoir, respectively, that primarily supply irrigation water. In the case of the optimal operation of Balan Reservoir, the simulation results show that the optimal release patterns are similar to those of historical operations because the demands for irrigation water are accurately predicted and guide optimal reservoir operations. In addition, the model is successfully applied to establishing a long-term reservoir operation plan. As for the optimal operation of Seomjingang Dam, the reservoir operations are affected by the target water stages, and the model provides reasonable results, irrespective of inflow conditions. Moreover, the model is employed to create a new operation rule that is necessary to adapt to changes in the circumstances related to water management. Consequently, it is concluded that the model is useful for assessing reservoirs' irrigation water supply capacities when establishing operation plans and providing feasible alternatives for new operation rules, and it could be applied to real-time reservoir operations. [ABSTRACT FROM AUTHOR]

This paper studies and compares the results of a simple and fast heuristic method, genetic algorithm (GA), and the exhaustive search method (ES) concerning the minimization of fuel consumption of a natural gas compressor station (CS). The results, obtained for an input data set [natural gas flow rate of 150 million standard cubic meters per day (MMSCMD), suction pressure of 5.45 MPa, and discharge pressure of 6.9 MPa], showed that for a CS with similar turbo-compressor (TC) units, all of the applied methods achieved the same solution (fuel consumption rate of ). By contrast, for a CS with dissimilar TC units, the GA and ES methods attained a lower fuel consumption rate () than that obtained by the heuristic method (). The effect of changing the CS flow rate, suction, and discharge pressures on optimal fuel consumption rate was also investigated. In the first case study, a 100 MMSCMD (or 100%) increase in the flow rate, 8.3 bar (or 13%) increase in discharge pressure, and 8.3 bar (or 14%) reduction in the suction pressure of the CS caused the optimal fuel consumption rate to increase by (or 99%), (or 33%), and (or 60%), respectively. In the second case study, for the same changes of flow rate, discharge, and suction pressures mentioned previously, the optimal fuel consumption rate increased by (or 112%), (or 32%), and (or 58%), respectively. [ABSTRACT FROM AUTHOR]

Longitudinal dispersion in pipelines leads to changes in the characteristics of contaminants. It is critical to quantify these changes because the contaminants travel through water networks or through chemical reactors. The essential characteristics of longitudinal dispersion in pipes can be described by the longitudinal dispersion coefficient. This paper presents the application of evolutionary gene expression programming (GEP) to develop new empirical formulas for the prediction of longitudinal dispersion coefficients in pipe flow using 220 experimental case studies of the dispersion coefficient with a range of 2,000-500,000 spanning transitional and turbulent pipe flow. Gene expression programming is used to develop empirical relations between the longitudinal dispersion coefficient and various control variables, including the Reynolds number, the average velocity, the pipe friction coefficient, and the pipe diameter. Four GEP models are developed, and the weight and importance of each control variable is presented. The prediction uncertainties of all of the developed GEP models are quantified and compared with those of existing models. Finally, a parametric analysis is performed for further verification of the developed GEP models. The results indicate that the proposed relations are simple and can effectively evaluate the longitudinal dispersion coefficients in pipe flow. [ABSTRACT FROM AUTHOR]

The author reflects on the discussion of the article "Optimization of Water Distribution Networks Using Integer Linear Programming (ILP)." He notes that the paper aims to introduce the integer linear programming approach as a new tool useful for solving water distribution system optimization problems. He suggests that ILP's approach should be more appropriate for solving water distribution problems, due to its simple and computationally fast widely used algorithms.