Journal of Energy Engineering

Solar radiation data are essential for the work of energy planners, engineers, and agricultural scientists. However, most solar radiation recording stations measure only global radiation in China. It is, therefore, necessary to elaborate correlations between the rarely available diffuse radiation and other climatic data. In the present study, 10-year data (1995–2004) of daily global diffuse solar radiation and sunshine duration obtained at Beijing meteorological station of China was analyzed to guide future projects. Nine models correlating the diffuse fraction $\left(K_d\right)$ with both the clearness index $\left(K_t\right)$ and percentage possible sunshine $\left(S/S_0\right)$ and each variable separately were tested. The accuracy of the correlations is performed in terms of the two widely used statistical indicators, mean bias error, and root mean square error. The results indicate that the correlations relating $K_d$ with both $K_t$ and $S/S_0$ are more reliable than using each variable separately and Model 9 is preferred for its accuracy. The recorded data of Zhengzhou meteorological station are compared with the corresponding values predicted by Model 9 of Beijing. Model 9 provides predictions very close to the measured values of Zhengzhou. Therefore, it may be concluded that Model 9 can be used for estimating diffuse solar radiation for locations of semiwet region of the north of China where only global solar radiation and sunshine duration are available.

Adaptive fin motion has long been the evolutionary means by which birds, fish, and various water-dwelling mammals create a propulsive thrust to propel them either through air or water. During the past decade, this particular phenomenon of kinematic motion has been studied by various scholars to gain an understanding of how and why the large relative efficiencies and body accelerations are attained. Invariably, the studies are interested in gaining an understanding of the phenomena so that these motions and kinematics can be applied to engineering design efforts. Various groups have built propulsive devices that are intended to mimic motions of these highly efficient adaptive motion propulsors with various degrees of successes. This paper summarizes some of these findings, bridging constructal theory and adaptive motions, with emphasis on their application. It also presents results of a research effort made by the writers to incorporate these concepts into low Reynolds number flexible turbine design.

This paper presents new results for numerical predictions of air flow and pressure distribution in two commonly used elbows: (1) 90° mitered duct elbows with turning vanes having 0.05 m radius, 0.038 m vane spacing and (2) 90° mitered duct elbows without turning vanes, in $0.2\times 0.2\text{m}$ $\left(8\text{in}\text{.}\times 8\text{in}\text{.}\right)$ duct cross section using the STAR-CD computational fluid dynamics (CFD) code. A $k\text{-}\epsilon$ turbulence model for high Reynolds number and $k\text{-}\epsilon$ Chen model were used for that purpose for comparative purposes. The simulation used 13 different Reynolds numbers chosen between the range of $1\times {10}^5$ and $2\times {10}^6$ . To validate the CFD results, the results of two experimental papers using guided vanes were compared with simulated vane runs under the same condition. The first experimental study used a $0.6\times 0.6\text{m}$ $\left(24\text{in}\text{.}\times 24\text{in}\text{.}\right)$ square elbow with 0.05 m radius, 0.038 m vane spacing and air velocities at 2.54 m/s (500 fpm) and 25.4 m/s (5,000 fpm), the second experiment used a $0.81\times 0.2\text{m}$ $\left(32\text{in}\text{.}\times 8\text{in}\text{.}\right)$ rectangular elbow geometry with 0.05 m radius, 0.038 m vane spacing with air velocities from 10.16 m/s (2,000 fpm) to 13.97 m/s (2,750 fpm). For Reynolds numbers $\left(1.00–2.00\right)\times {10}^5$ the pressure drop difference between vaned and unvaned elbows was found to be 35 Pa as compared to 145 Pa. The simulations also agreed reasonably well with published experimental results. For the $0.6\times 0.6\text{m}$ $\left(24\text{in}\text{.}\times 24\text{in}\text{.}\right)$ square elbow and $0.81\times 0.2\text{m}$ $\left(32\text{in}\text{.}\times 8\text{in}\text{.}\right)$ rectangular elbow with vanes, the difference in pressure drop was 3.9 and 4.1% respectively and indicates that CFD models can be used for predictive purposes in this important HVAC applications area.

This paper presents a sensor fault detection and diagnosis approach for industrial combustion processes. Clustering algorithms are applied to the measurements of controllable process variables involved in single-input-single-output feedback control loops. Current data points from the process are compared with the clusters to identify sensor faults. Once the measurements of controllable process variables are obtained, a decision-tree algorithm monitors response process variables based on the controllable and noncontrollable process variables as predictors (inputs). Test data and training data residuals generated by the decision-tree algorithm are analyzed with statistical process control limits to identify sensor faults. The proposed approach handles data from temporal processes by periodic updates of the knowledge base. An industrial boiler combustion process is used to test the ideas presented in this paper.