查看更多>>摘要：Energy consumption and food safety issues of household refrigerators have attracted considerable attention in recent years. Today, most household refrigerators use conventional controllers for adjusting the compressor speed to regulate the air temperatures inside cabinets. This research aims to design an intelligent control concept that integrates machine learning-based forecast of door opening events with fuzzy logic controllers. Firstly, bayesian neural network, logistic regression, and decision tree techniques are investigated to predict user behavior with data obtained from 18 real users of domestic refrigerators. Results show that logistic regression has the best performance in hourly predicting the door opening events after one week of training with more than 80% accuracy. Secondly, fuzzy logic controllers are designed to use the door opening predictions to regulate configuration parameters of the main refrigerator controller: maximum compressor speed, air temperature setpoint of fresh food compartment, and time offset to control the time of defrosting events. Finally, simulation studies are performed on a domestic refrigerator model developed at MATLAB Simscape. The developed model is based on an existing product in the market, and the accuracy of the model is verified by actual lab tests. Daily simulations of the household refrigerator for sample day profiles of an inactive and active user are performed for ambient temperatures of 16 °C, 25 °C, and 32 °C. Results show that the designed smart controller can achieve up to 2.5% and 4.5% of energy gain for active and passive user-profiles respectively, while maintaining the desired cabinet temperatures.
查看更多>>摘要：The authors regret the typo error of one data at T=320.11 K in Table 3. The corrected table is as follow; No other error is found. All figures and equations in this article were made based on the correct data set. The authors would like to apologise for any inconvenience caused.
查看更多>>摘要：In the present study, a new variable-volume apparatus equipped with a metal-bellows volumeter is designed to measure the critical pressure-density-temperature (p-ρ-T) properties by charging the device only once. The expanded uncertainties of the temperature, pressure and density measurements are assessed to be less than 20 mK, 4.2 kPa, and 1.6% (k = 2, 95%), respectively. Moreover, the combined expanded uncertainty of the respective critical quantities is lower than 0.084 K, 10.2 kPa, and 2.4% (k = 2, 95%). The average relative absolute deviation (ARAD) between the experimental p-ρ-T data and those calculated by the Refprop for the nitrogen is evaluated to be 0.18%. The p-ρ-T data and the critical properties of the 1,1,1,2-tetrafluoroethane and difluoromethane are measured by the apparatus for verification. All the critical p-ρ-T data agree well with the accessible experimental data from the literature. The ARAD is evaluated to be 0.67% and 0.28% for the density of 1,1,1,2-tetrafluoroethane at the vapor and liquid phases, respectively.
查看更多>>摘要：Elastocaloric cooling systems may offer a potentially more efficient as well as environmentally friendly alternative to compressor-based cooling technology. These cooling systems use stress-induced phase transformation in elastocaloric materials to pump heat. Thermodynamically consistent material models can be used to design and quantify the efficiency of these cooling systems. In this paper, we present a phenomenological material model that depicts the behavior of first-order materials during stress-induced phase transformation. This model is based on a phenomenological heat capacity equation, from which the parameters adiabatic temperature change and isothermal entropy can be derived. Hysteresis of the materials, which determines it dissipative effects, is also taken into account. Based on this model, these parameters can be calculated as a function of stress and temperature. The performance coefficients derived from the model can be used to evaluate the materials efficiency. Furthermore, the data obtained using this model coincided very closely with experimental data.
查看更多>>摘要：This article has been retracted: please see Elsevier Policy on Article Withdrawal (https://www.elsevier.com/about/our-business/policies/article-withdrawal). This article has been retracted at the request of the Editor-in-Chief. The article is a duplicate of a paper that has already been published in ENERGY & BUILDINGS, 41 (2009) 354–359. DOI: https://doi.org/10.1016/j.enbuild.2008.10.008. One of the conditions of submission of a paper for publication is that authors declare explicitly that the paper has not been previously published and is not under consideration for publication elsewhere. As such this article represents a misuse of the scientific publishing system. The scientific community takes a very strong view on this matter and apologies are offered to readers of the journal that this was not detected during the submission process.
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