@article{AlsaadVoelker,
  author    = {Alsaad, Hayder and V{\"o}lker, Conrad},
  title     = {Performance evaluation of ductless personalized ventilation in comparison with desk fans using numerical simulations},
  series = {Indoor Air},
  volume    = {2020},
  journal   = {Indoor Air},
  publisher = {John Wiley \& Sons Ltd},
  doi       = {10.1111/ina.12672},
  url       = {http://nbn-resolving.de/urn:nbn:de:gbv:wim2-20200422-41407},
  pages     = {14},
  abstract  = {The performance of ductless personalized ventilation (DPV) was compared to the performance of a typical desk fan since they are both stand-alone systems that allow the users to personalize their indoor environment. The two systems were evaluated using a validated computational fluid dynamics (CFD) model of an office room occupied by two users. To investigate the impact of DPV and the fan on the inhaled air quality, two types of contamination sources were modelled in the domain: an active source and a passive source. Additionally, the influence of the compared systems on thermal comfort was assessed using the coupling of CFD with the comfort model developed by the University of California, Berkeley (UCB model). Results indicated that DPV performed generally better than the desk fan. It provided better thermal comfort and showed a superior performance in removing the exhaled contaminants. However, the desk fan performed better in removing the contaminants emitted from a passive source near the floor level. This indicates that the performance of DPV and desk fans depends highly on the location of the contamination source. Moreover, the simulations showed that both systems increased the spread of exhaled contamination when used by the source occupant.},
  subject      = {Behaglichkeit},
  language  = {en}
}
@article{SadeghzadehMaddahAhmadietal.,
  author    = {Sadeghzadeh, Milad and Maddah, Heydar and Ahmadi, Mohammad Hossein and Khadang, Amirhosein and Ghazvini, Mahyar and Mosavi, Amir Hosein and Nabipour, Narjes},
  title     = {Prediction of Thermo-Physical Properties of TiO2-Al2O3/Water Nanoparticles by Using Artificial Neural Network},
  series = {Nanomaterials},
  volume    = {2020},
  journal   = {Nanomaterials},
  number    = {Volume 10, Issue 4, 697},
  publisher = {MDPI},
  address   = {Basel},
  doi       = {10.3390/nano10040697},
  url       = {http://nbn-resolving.de/urn:nbn:de:gbv:wim2-20200421-41308},
  abstract  = {In this paper, an artificial neural network is implemented for the sake of predicting the thermal conductivity ratio of TiO2-Al2O3/water nanofluid. TiO2-Al2O3/water in the role of an innovative type of nanofluid was synthesized by the sol-gel method. The results indicated that 1.5 vol.\% of nanofluids enhanced the thermal conductivity by up to 25\%. It was shown that the heat transfer coefficient was linearly augmented with increasing nanoparticle concentration, but its variation with temperature was nonlinear. It should be noted that the increase in concentration may cause the particles to agglomerate, and then the thermal conductivity is reduced. The increase in temperature also increases the thermal conductivity, due to an increase in the Brownian motion and collision of particles. In this research, for the sake of predicting the thermal conductivity of TiO2-Al2O3/water nanofluid based on volumetric concentration and temperature functions, an artificial neural network is implemented. In this way, for predicting thermal conductivity, SOM (self-organizing map) and BP-LM (Back Propagation-Levenberq-Marquardt) algorithms were used. Based on the results obtained, these algorithms can be considered as an exceptional tool for predicting thermal conductivity. Additionally, the correlation coefficient values were equal to 0.938 and 0.98 when implementing the SOM and BP-LM algorithms, respectively, which is highly acceptable. View Full-Text},
  subject      = {W{\"a}rmeleitf{\"a}higkeit},
  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}
}