@unpublished{MosaviTorabiHashemietal.,
  author    = {Mosavi, Amir and Torabi, Mehrnoosh and Hashemi, Sattar and Saybani, Mahmoud Reza and Shamshirband, Shahaboddin},
  title     = {A Hybrid Clustering and Classification Technique for Forecasting Short-Term Energy Consumption},
  doi       = {10.25643/bauhaus-universitaet.3755},
  url       = {http://nbn-resolving.de/urn:nbn:de:gbv:wim2-20180907-37550},
  abstract  = {Electrical energy distributor companies in Iran have to announce their energy demand at least three 3-day ahead of the market opening. Therefore, an accurate load estimation is highly crucial. This research invoked methodology based on CRISP data mining and used SVM, ANN, and CBA-ANN-SVM (a novel hybrid model of clustering with both widely used ANN and SVM) to predict short-term electrical energy demand of Bandarabbas. In previous studies, researchers introduced few effective parameters with no reasonable error about Bandarabbas power consumption. In this research we tried to recognize all efficient parameters and with the use of CBA-ANN-SVM model, the rate of error has been minimized. After consulting with experts in the field of power consumption and plotting daily power consumption for each week, this research showed that official holidays and weekends have impact on the power consumption. When the weather gets warmer, the consumption of electrical energy increases due to turning on electrical air conditioner. Also, con-sumption patterns in warm and cold months are different. Analyzing power consumption of the same month for different years had shown high similarity in power consumption patterns. Factors with high impact on power consumption were identified and statistical methods were utilized to prove their impacts. Using SVM, ANN and CBA-ANN-SVM, the model was built. Sine the proposed method (CBA-ANN-SVM) has low MAPE 5 1.474 (4 clusters) and MAPE 5 1.297 (3 clusters) in comparison with SVM (MAPE 5 2.015) and ANN (MAPE 5 1.790), this model was selected as the final model. The final model has the benefits from both models and the benefits of clustering. Clustering algorithm with discovering data structure, divides data into several clusters based on similarities and differences between them. Because data inside each cluster are more similar than entire data, modeling in each cluster will present better results. For future research, we suggest using fuzzy methods and genetic algorithm or a hybrid of both to forecast each cluster. It is also possible to use fuzzy methods or genetic algorithms or a hybrid of both without using clustering. It is issued that such models will produce better and more accurate results. This paper presents a hybrid approach to predict the electric energy usage of weather-sensitive loads. The presented methodutilizes the clustering paradigm along with ANN and SVMapproaches for accurate short-term prediction of electric energyusage, using weather data. Since the methodology beinginvoked in this research is based on CRISP data mining, datapreparation has received a gr eat deal of attention in thisresear ch. Once data pre-processing was done, the underlyingpattern of electric energy consumption was extracted by themeans of machine learning methods to precisely forecast short-term energy consumption. The proposed approach (CBA-ANN-SVM) was applied to real load data and resulting higher accu-racy comparing to the existing models. 2018 American Institute of Chemical Engineers Environ Prog, 2018 https://doi.org/10.1002/ep.12934},
  subject      = {Data Mining},
  language  = {en}
}
@unpublished{KavrakovMorgenthal,
  author    = {Kavrakov, Igor and Morgenthal, Guido},
  title     = {A synergistic study of a CFD and semi-analytical models for aeroelastic analysis of bridges in turbulent wind conditions},
  doi       = {10.25643/bauhaus-universitaet.4087},
  url       = {http://nbn-resolving.de/urn:nbn:de:gbv:wim2-20200206-40873},
  abstract  = {Long-span bridges are prone to wind-induced vibrations. Therefore, a reliable representation of the aerodynamic forces acting on a bridge deck is of a major significance for the design of such structures. This paper presents a systematic study of the two-dimensional (2D) fluid-structure interaction of a bridge deck under smooth and turbulent wind conditions. Aerodynamic forces are modeled by two approaches: a computational fluid dynamics (CFD) model and six semi-analytical models. The vortex particle method is utilized for the CFD model and the free-stream turbulence is introduced by seeding vortex particles upstream of the deck with prescribed spectral characteristics. The employed semi-analytical models are based on the quasi-steady and linear unsteady assumptions and aerodynamic coefficients obtained from CFD analyses. The underlying assumptions of the semi-analytical aerodynamic models are used to interpret the results of buffeting forces and aeroelastic response due to a free-stream turbulence in comparison with the CFD model. Extensive discussions are provided to analyze the effect of linear fluid memory and quasi-steady nonlinearity from a CFD perspective. The outcome of the analyses indicates that the fluid memory is a governing effect in the buffeting forces and aeroelastic response, while the effect of the nonlinearity is overestimated by the quasi-steady models. Finally, flutter analyses are performed and the obtained critical velocities are further compared with wind tunnel results, followed by a brief examination of the post-flutter behavior. The results of this study provide a deeper understanding of the extent of which the applied models are able to replicate the physical processes for fluid-structure interaction phenomena in bridge aerodynamics and aeroelasticity.},
  subject      = {Ingenieurwissenschaften},
  language  = {en}
}
@unpublished{KavrakovMorgenthal,
  author    = {Kavrakov, Igor and Morgenthal, Guido},
  title     = {Aeroelastic analyses of bridges using a Pseudo-3D vortex method and velocity-based synthetic turbulence generation},
  doi       = {10.25643/bauhaus-universitaet.4086},
  url       = {http://nbn-resolving.de/urn:nbn:de:gbv:wim2-20200206-40864},
  abstract  = {The accurate representation of aerodynamic forces is essential for a safe, yet reasonable design of long-span bridges subjected to wind effects. In this paper, a novel extension of the Pseudo-three-dimensional Vortex Particle Method (Pseudo-3D VPM) is presented for Computational Fluid Dynamics (CFD) buffeting analysis of line-like structures. This extension entails an introduction of free-stream turbulent fluctuations, based on the velocity-based turbulence generation. The aerodynamic response of a long-span bridge is obtained by subjecting the 3D dynamic representation of the structure to correlated free-stream turbulence in two-dimensional (2D) fluid planes, which are positioned along the bridge deck. The span-wise correlation of the free-stream turbulence between the 2D fluid planes is established based on Taylor's hypothesis of frozen turbulence. Moreover, the application of the laminar Pseudo-3D VPM is extended to a multimode flutter analysis. Finally, the structural response from the Pseudo-3D flutter and buffeting analyses is verified with the response, computed using the semi-analytical linear unsteady model in the time-domain. Meaningful merits of the turbulent Pseudo-3D VPM with respect to the linear unsteady model are the consideration of the 2D aerodynamic nonlinearity, nonlinear fluid memory, vortex shedding and local non-stationary turbulence effects in the aerodynamic forces. The good agreement of the responses for the two models in the 3D analyses demonstrates the applicability of the Pseudo-3D VPM for aeroelastic analyses of line-like structures under turbulent and laminar free-stream conditions.},
  subject      = {Bridge},
  language  = {en}
}
@unpublished{SteinerBourinetLahmer,
  author    = {Steiner, Maria and Bourinet, Jean-Marc and Lahmer, Tom},
  title     = {An adaptive sampling method for global sensitivity analysis based on least-squares support vector regression},
  doi       = {10.25643/BAUHAUS-UNIVERSITAET.3832},
  url       = {http://nbn-resolving.de/urn:nbn:de:gbv:wim2-20181218-38320},
  pages     = {1 -- 33},
  abstract  = {In the field of engineering, surrogate models are commonly used for approximating the behavior of a physical phenomenon in order to reduce the computational costs. Generally, a surrogate model is created based on a set of training data, where a typical method for the statistical design is the Latin hypercube sampling (LHS). Even though a space filling distribution of the training data is reached, the sampling process takes no information on the underlying behavior of the physical phenomenon into account and new data cannot be sampled in the same distribution if the approximation quality is not sufficient. Therefore, in this study we present a novel adaptive sampling method based on a specific surrogate model, the least-squares support vector regresson. The adaptive sampling method generates training data based on the uncertainty in local prognosis capabilities of the surrogate model - areas of higher uncertainty require more sample data. The approach offers a cost efficient calculation due to the properties of the least-squares support vector regression. The opportunities of the adaptive sampling method are proven in comparison with the LHS on different analytical examples. Furthermore, the adaptive sampling method is applied to the calculation of global sensitivity values according to Sobol, where it shows faster convergence than the LHS method. With the applications in this paper it is shown that the presented adaptive sampling method improves the estimation of global sensitivity values, hence reducing the overall computational costs visibly.},
  subject      = {Approximation},
  language  = {en}
}
@unpublished{RezakazemiMosaviShirazian,
  author    = {Rezakazemi, Mashallah and Mosavi, Amir and Shirazian, Saeed},
  title     = {ANFIS pattern for molecular membranes separation optimization},
  volume    = {2018},
  doi       = {10.25643/BAUHAUS-UNIVERSITAET.3821},
  url       = {http://nbn-resolving.de/urn:nbn:de:gbv:wim2-20181122-38212},
  pages     = {1 -- 20},
  abstract  = {In this work, molecular separation of aqueous-organic was simulated by using combined soft computing-mechanistic approaches. The considered separation system was a microporous membrane contactor for separation of benzoic acid from water by contacting with an organic phase containing extractor molecules. Indeed, extractive separation is carried out using membrane technology where complex of solute-organic is formed at the interface. The main focus was to develop a simulation methodology for prediction of concentration distribution of solute (benzoic acid) in the feed side of the membrane system, as the removal efficiency of the system is determined by concentration distribution of the solute in the feed channel. The pattern of Adaptive Neuro-Fuzzy Inference System (ANFIS) was optimized by finding the optimum membership function, learning percentage, and a number of rules. The ANFIS was trained using the extracted data from the CFD simulation of the membrane system. The comparisons between the predicted concentration distribution by ANFIS and CFD data revealed that the optimized ANFIS pattern can be used as a predictive tool for simulation of the process. The R2 of higher than 0.99 was obtained for the optimized ANFIS model. The main privilege of the developed methodology is its very low computational time for simulation of the system and can be used as a rigorous simulation tool for understanding and design of membrane-based systems. Highlights are, Molecular separation using microporous membranes. Developing hybrid model based on ANFIS-CFD for the separation process, Optimization of ANFIS structure for prediction of separation process},
  subject      = {Fluid},
  language  = {en}
}
@unpublished{MosaviMoeiniAhmadpouretal.,
  author    = {Mosavi, Amir and Moeini, Iman and Ahmadpour, Mohammad and Alharbi, Naif and E. Gorji, Nima},
  title     = {Modeling the time-dependent characteristics of perovskite solar cells},
  doi       = {10.25643/bauhaus-universitaet.3757},
  url       = {http://nbn-resolving.de/urn:nbn:de:gbv:wim2-20180907-37573},
  abstract  = {We proposed two different time-dependent modeling approaches for variation of device characteristics of perovskite solar cells under stress conditions. The first approach follows Sah-Noyce-Shockley (SNS) model based on Shockley-Read-Hall recombination/generation across the depletion width of pn junction and the second approach is based on thermionic emission model for Schottky diodes. The connecting point of these approaches to time variation is the time-dependent defect generation in depletion width (W) of the junction. We have fitted the two models with experimental data reported in the literature to perovskite solar cell and found out that each model has a superior explanation for degradation of device metrics e.g. current density and efficiency by time under stress conditions. Nevertheless, the Sah-Noyce-Shockley model is more reliable than thermionic emission at least for solar cells.},
  subject      = {Solarzelle},
  language  = {en}
}