Optimal Performance Analysis of a Three-Terminal Thermoelectric Refrigerator with Ideal Tunneling Quantum Dots

The model of a three-terminal thermoelectric refrigerator with ideal tunneling quantum dots is established. It consists of a cavity connected to two quantum dots embedded between two electron reservoirs at different temperatures and chemical potentials. According to the Landauer formula the expressions for the heat current, the cooling rate and the coefficient of performance （COP） are derived analytically. The performance characteristic curves of the cooling rate versus the coefficient of performance are plotted with numerical calculation. The optimal regions of the cooling rate and the COP are determined. Moreover, we optimize the cooling rate and the COP with respect to the position of energy level of the right quantum dot, respectively. The influence of the width of energy level and the temperature ratio on performance of the three-terminal thermoelectric refrigerator is analyzed. Lastly, when the width of energy level is small enough, the optimal performance of the refrigerator is discussed in detail.

Full-face excavation of large tunnels in difficult conditions

Following a few preliminary remarks on the tunneling methods at the beginning of the 20th century, thesuccessful applications of the full-face method also in difficult conditions are underlined. The attention isposed on the use of a systematic reinforcement of the face and of the ground, by means of fiber-glasselements. A selection of tunnels where this method was used successfully is reported with the purposeof illustrating the wide spectrum of ground conditions where it has been applied. Then, following adescription of the main concepts behind the method, the attention moves from the so-called “heavymethod”, where deformations are restrained, to the “light method”, where deformations are allowedwith the intention to decrease the stresses acting on the primary and final linings. The progress in theapplication of the “light method” is underlined, up to the development of a novel technique, which relieson the use of a yielding support composed of top head steel sets with sliding joints and specialdeformable elements inserted in the primary lining. The well-known case study of the Saint Martin LaPorte access adit, along the Lyon-Turin Base Tunnel, is described. In this tunnel, a yield-control supportsystem combined with full-face excavation has been adopted successfully in order to cope with the largedeformations experienced during face advance through the Carboniferous formation. The monitoringresults obtained during excavation are illustrated, together with the modeling studies performed whenpaying attention to the rock mass time-dependent behavior.

Utilizing partial least square and support vector machine for TBM penetration rate prediction in hard rock conditions

Rate of penetration(ROP) of a tunnel boring machine(TBM) in a rock environment is generally a key parameter for the successful accomplishment of a tunneling project. The objectives of this work are to compare the accuracy of prediction models employing partial least squares(PLS) regression and support vector machine(SVM) regression technique for modeling the penetration rate of TBM. To develop the proposed models, the database that is composed of intact rock properties including uniaxial compressive strength(UCS), Brazilian tensile strength(BTS), and peak slope index(PSI), and also rock mass properties including distance between planes of weakness(DPW) and the alpha angle(α) are input as dependent variables and the measured ROP is chosen as an independent variable. Two hundred sets of data are collected from Queens Water Tunnel and Karaj-Tehran water transfer tunnel TBM project. The accuracy of the prediction models is measured by the coefficient of determination(R2) and root mean squares error(RMSE) between predicted and observed yield employing 10-fold cross-validation schemes. The R2 and RMSE of prediction are 0.8183 and 0.1807 for SVMR method, and 0.9999 and 0.0011 for PLS method, respectively. Comparison between the values of statistical parameters reveals the superiority of the PLSR model over SVMR one.