The group-contribution (GC) methods suffer from a limitation concerning to the prediction of process-related indexes, e.g., thermal efficiency. Recently developed analytical models for thermal efficiency of organic Ra...The group-contribution (GC) methods suffer from a limitation concerning to the prediction of process-related indexes, e.g., thermal efficiency. Recently developed analytical models for thermal efficiency of organic Rankine cycles (ORCs) provide a possibility of overcoming the limitation of the GC methods because these models formulate thermal efficiency as functions of key thermal properties. Using these analytical relations together with GC methods, more than 60 organic fluids are screened for medium-low temperature ORCs. The results indicate that the GC methods can estimate thermal properties with acceptable accuracy (mean relative errors are 4.45%-11.50%);the precision, however, is low because the relative errors can vary from less than 0.1% to 45.0%. By contrast, the GC-based estimation of thermal efficiency has better accuracy and precision. The relative errors in thermal efficiency have an arithmetic mean of about 2.9% and fall within the range of 0-24.0%. These findings suggest that the analytical equations provide not only a direct way of estimating thermal efficiency but an accurate and precise approach to evaluating working fluids and guiding computer-aided molecular design of new fluids for ORCs using GC methods.展开更多
A statistical damage detection and condition assessment scheme for existing structures is developed. First a virtual work error estimator is defined to express the discrepancy between a real structure and its analytic...A statistical damage detection and condition assessment scheme for existing structures is developed. First a virtual work error estimator is defined to express the discrepancy between a real structure and its analytical model, with which a system identification algorithm is derived by using the improved Newton method. In order to investigate its properties in the face of measurement errors, the Monte Carlo method is introduced to simulate the measured data. Based on the identified results, their statistical distributions can be assumed, the status of an existing structure can be statistically evaluated by hypothesis tests. A 5-story, two-bay steel frame is used to carry out numerical simulation studies in detail, and the proposed scheme is proved to be effective.展开更多
基金Project(51778626) supported by the National Natural Science Foundation of China
文摘The group-contribution (GC) methods suffer from a limitation concerning to the prediction of process-related indexes, e.g., thermal efficiency. Recently developed analytical models for thermal efficiency of organic Rankine cycles (ORCs) provide a possibility of overcoming the limitation of the GC methods because these models formulate thermal efficiency as functions of key thermal properties. Using these analytical relations together with GC methods, more than 60 organic fluids are screened for medium-low temperature ORCs. The results indicate that the GC methods can estimate thermal properties with acceptable accuracy (mean relative errors are 4.45%-11.50%);the precision, however, is low because the relative errors can vary from less than 0.1% to 45.0%. By contrast, the GC-based estimation of thermal efficiency has better accuracy and precision. The relative errors in thermal efficiency have an arithmetic mean of about 2.9% and fall within the range of 0-24.0%. These findings suggest that the analytical equations provide not only a direct way of estimating thermal efficiency but an accurate and precise approach to evaluating working fluids and guiding computer-aided molecular design of new fluids for ORCs using GC methods.
基金The National Natural Science Foundation of China(No50538020)
文摘A statistical damage detection and condition assessment scheme for existing structures is developed. First a virtual work error estimator is defined to express the discrepancy between a real structure and its analytical model, with which a system identification algorithm is derived by using the improved Newton method. In order to investigate its properties in the face of measurement errors, the Monte Carlo method is introduced to simulate the measured data. Based on the identified results, their statistical distributions can be assumed, the status of an existing structure can be statistically evaluated by hypothesis tests. A 5-story, two-bay steel frame is used to carry out numerical simulation studies in detail, and the proposed scheme is proved to be effective.
