It has always been the goal of structural engineers to construct safe and stable buildings using the least amount of materials. Utilizing a quick and effective way to optimize the cross-section size is crucial because...
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It has always been the goal of structural engineers to construct safe and stable buildings using the least amount of materials. Utilizing a quick and effective way to optimize the cross-section size is crucial because conventional building design methods are impacted by the designers' knowledge, and it is challenging to prevent material waste. Due to its implicit optimization function, discrete design variables, and expensive individual evaluation, sizing optimization of high-rise buildings is very challenging to achieve. In order to effectively handle this optimization problem, a two-stage discrete sizing optimization method for high-rise buildings based on the DIviding RECTangles (direct) algorithm and local response surface is proposed in this paper. The optimization method suggested in this research consists of two key stages: the global search stage and the local search stage. In the global search stage, the entire design domain is divided using a modified direct algorithm to swiftly identify potentially optimal subregions that may contain the best points. In the local search stage, the local response surface model is constructed to approximate the objective and constraint functions using the sampling points from the previous stage, and the discrete optimal solution is rapidly found through mathematical iterative solving. A sizing optimization calculation program for high-rise buildings was developed in Microsoft Visual Studio 2015 on the basis of C++ to achieve automatic optimization. The new method was applied to optimize three high-rise steel frame buildings with different heights and plan shapes. The results showed that the material cost could be successfully saved compared with the conventional design, and the over-limit constraints could be adjusted automatically, which demonstrated the viability and efficacy of the two-stage optimization method.
In the evolving landscape of sustainable energy, optimizing geothermal power systems presents a critical challenge. This study explores the energy and exergy efficiencies of a power production system utilizing a singl...
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In the evolving landscape of sustainable energy, optimizing geothermal power systems presents a critical challenge. This study explores the energy and exergy efficiencies of a power production system utilizing a single-flash geothermal cycle integrated with a trans-critical CO2 cycle. The study's methodology involves a detailed examination of key performance parameters-separator pressure, CO(2 )turbine intake pressure, and steam turbine output pressure. Utilizing the EES software environment, the study innovatively employs a combination of Genetic algorithm (GA), Nelder-Mead Simplex (NMS) method, and direct algorithm (DA). When using GA, NMS and DA, the system's exergy efficiency increases from 32.46% in the default operating mode to 39.21%, 36.16%, and 38.82%, respectively. One of the notable outcomes is the identification of optimal separator pressure for maximum energy efficiency. Furthermore, the study reveals that increasing the CO2 turbine's inlet pressure adversely impacts the system's efficiency. The study's results contribute significantly to the field of renewable energy, offering practical guidelines for enhancing the performance of geothermal power systems.
Entropy generation is always a matter of concern in a heat transfer system. It denotes the amount of energy lost as a result of irreversibility. As a result, it must be reduced. The present work considers an investiga...
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Entropy generation is always a matter of concern in a heat transfer system. It denotes the amount of energy lost as a result of irreversibility. As a result, it must be reduced. The present work considers an investigation on the turbulent forced convective heat transfer and entropy generation of Al2O3-Ethylene glycol (EG) nanofluid inside a circular tube subjected to constant wall temperature. The study is focused on the development of an analytical framework by using mathematical models to simulate the characteristics of nanofluids in the as-mentioned thermal system. The simulated result is validated using published data. Further, Genetic algorithm (GA) and direct algorithm are implemented to determine the optimal condition which yields minimum entropy generation. According to the findings, heat transfer increases at a direct proportion to the mass flow, Reynolds number (Re), and volume concentration of nanoparticles. Furthermore, as Re increases, particle concentration should be decreased in order to reduce total entropy generation (TEG) and to improve heat transfer rate of any given particle size. A minimal concentration of nanoparticles is required to reduce TEG when Re is maintained constant. The highest increase in TEG with nanofluids was 2.93 times that of basefluid. The optimum condition for minimum entropy generation is Re = 4000, nanoparticle size = 65 nm, volume concentration = 0.2% and mass flow rate = 0.54 kg/s.
The factorization method is introduced to electrical capacitance tomography for sensors of non-circular cross sections in the paper. Unlike the traditional methods based on the sensitivity theorem, it is a direct algo...
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The factorization method is introduced to electrical capacitance tomography for sensors of non-circular cross sections in the paper. Unlike the traditional methods based on the sensitivity theorem, it is a direct algorithm for image reconstruction, as the gray value at each pixel can be obtained directly and independently. Since the test function was calculated according to sensors of circular cross sections, the reconstruction process was simple. Reconstructed images were obtained for sensors of non-circular cross sections using the conformal transformation. Both simulated and experimental results validated the feasibility and effectiveness of the factorization method in electrical capacitance tomography.
The present study deals with the application of artificial intelligence techniques coupled with Box–Behnken (BB) design to model the process parameters for biosorption of cadmium using live Spirulina (Arthrospira) sp...
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The present study deals with the application of artificial intelligence techniques coupled with Box–Behnken (BB) design to model the process parameters for biosorption of cadmium using live Spirulina (Arthrospira) spp. as adsorbent in open race way pond with Zarrouk medium. The biomass concentration of Spirulina spp. decreased to half at 4 ppm Cd (II) after 8 days. Based on the LCt50 values, 3.69 ppm (8th day), Spirulina (Arthospira) maxima showed maximum tolerance. Considerable growth and bioaccumulation of Spirulina spp . is observed below 1 ppm and tolerant up to 3 ppm. The cadmium adsorption on Spirulina spp. showed good correlation (R 2 = 0.99) when applied to Freundlich equation and data fit into pseudo second order kinetics. A four factorial, three blocks and three level Box–Behnken design with initial concentration (1 ppb to 5 ppb), biosorbant dosage (0.1 gdw to 0.2 gdw), agitation speed (12 rpm to 16 rpm) and pH (6 to 8) as independent variables and percentage adsorption as dependent variable were selected for study. The data were further processed using artificial neural network model and direct algorithm for better optimization. The final Cd (II) concentration of <0.5 ppb was achieved with 1 ppb initial concentration under optimal conditions. A continuous desorption process was also developed for removal of cadmium from Spirulina (Arthrospira) sp.
The formation velocity is an important factor affecting the precise location of microseismic source. The establishment of elastic wave velocity model in the monitoring area to satisfy the requirements for precise loca...
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The formation velocity is an important factor affecting the precise location of microseismic source. The establishment of elastic wave velocity model in the monitoring area to satisfy the requirements for precise location of seismic source has been a technical problem for the mine microseismic monitoring. Based on the assumption of horizontal layered medium condition, a new velocity model inversion method has been proposed. According to the concept of equal difference time surface, the first arrival travel time difference between the measured points and datum points is investigated on the basis of the observation point of first arrival travel time duration placed in the middle in the observational network. The minimal difference (double time difference) between the measured first arrival time difference and the calculated first arrival time difference is taken as the constraint condition, and the objective function is constructed to solve the velocity model. The direct fast search algorithm with global optimization characteristics is applied to solve the objective function. This method is used to carry out the trial treatment for the mine microseismic model data and the measured data. The results show that the stratified velocity model under the horizontal layered medium can be obtained by his method using the microseismic data of known seismic source, with a better adaptability to different monitoring systems. Through the test for the actual data of well-ground joint microseismic monitoring, the velocity model obtained by the method in this paper can get more accurate location of the seismic source.
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