This paper describes an experimental study of the cooling capabilities of microchannel and micro-pin-fin based on-chip cooling systems. The on-chip cooling systems integrated with a micro heat sink, simulated power IC (integrated circuit) and temperature sensors are fabricated by micromachining and silicon-to-silicon direct bonding. Three micro heat sink structures: a microchannel heat sink (MCHS), an inline micro-pin-fin heat sink (I-MPFHS) and a staggered micro-pin-fin heat sink (S-MPFHS) are tested in the Reynolds number range of 79.2 to 882.3. The results show that S-MPFHS is preferred if the water pump can provide enough pressure drop. However, S-MPFHS has the worst performance when the rated pressure drop of the pump is lower than 1.5 kPa because the endwall effect under a low Reynolds number suppresses the disturbance generated by the staggered micro pin fins but S-MPFHS is still preferred when the rated pressure drop of the pump is in the range of 1.5 to 20 kPa. When the rated pressure drop of the pump is higher than 20 kPa, I-MPFHS will be the best choice because of high heat transfer enhancement and low pressure drop price brought by the unsteady vortex street.

Conducting large-scale numerical computations to obtain flow field during the hypersonic vehicle engineering design phase can be excessively costly. Although deep learning algorithms enable rapid flow field prediction with high-precision, they require a significant investment in training samples, contradicting the motivation of reducing the cost of acquiring flow field. The combination of feature extraction algorithms and regression algorithms can also achieve high-precision prediction of flow fields, which is more suitable to tackle three-dimensional flow prediction with a small dataset. In this study, we propose a reduced-order model (ROM) for the three-dimensional hypersonic vehicle flow prediction utilizing proper orthogonal decomposition to extract representative features and Gaussian process regression with improved automatic kernel construction (AKC-GPR) to perform a nonlinear mapping of physical features for prediction. The selection of variables is based on sensitivity analysis and modal assurance criterion. The underlying relationship is unveiled between flow field variables and inflow conditions. The ROM exhibits high predictive accuracy, with mean absolute percentage error (MAPE) of total field less than 3.5%, when varying altitudes and Mach numbers. During angle of attack variations, the ROM only effectively reconstructs flow distribution by interpolation with a MAPE of 7.02%. The excellent small-sample fitting capability of our improved AKC-GPR algorithm is demonstrated by comparing with original AKC-GPRs with a maximum reduction in a MAPE of 35.28%. These promising findings suggest that the proposed ROM can serve as an effective approach for rapid and accurate vehicle flow predicting, enabling its application in engineering design analysis.

In the field of rarefied gas dynamics, the presence of non-equilibrium flow characteristics poses significant challenges for achieving efficient and accurate numerical simulation methods. These challenges arise from the complex coexistence of these phenomena at multiple scales. The recent advent of intelligent fluid mechanics has introduced the data-driven nonlinear constitutive relation (DNCR) method as a promising approach for expeditious physical modeling of non-equilibrium rarefied flows. To enhance the generalization capabilities of the DNCR method, this study proposes a deep convolutional neural network model (DNCR-CNN) based on data-driven nonlinear constitutive relations, integrated with free-form deformation (FFD). Employing FFD technology, a series of hypersonic geometric shapes are generated for model training, and a multi-task learning-based deep convolutional neural network model is subsequently trained. The prediction of the hypersonic geometric shapes test set is carried out, and the results of the model prediction are substituted in the conservation equation for the iterative solution, thereby enhancing the DNCR method's generalization performance for varying geometric shapes. Upon conducting a comparative analysis of the outcomes obtained from DNCR, Navier–Stokes (NS), and unified gas kinetic scheme (UGKS), it is revealed that the DNCR method can maintain computational resource levels equivalent to those of the NS equation while achieving a level of accuracy comparable to UGKS under diverse geometric shapes and grid resolutions. The enhancements in usability render the DNCR method a potent tool for addressing the challenges posed by rarefied gas, thereby expanding its applicability within the field.

A high quality factor ( Q ) piezoelectric double-ended tuning fork (DETF) resonator with dual-driving capacity based on aluminum nitride (AlN) is reported in this paper. This novel dual-driving electrode design in a piezoelectric resonator can not only enhance the symmetry in the in-plane mode but also weaken the crosstalk from the out-of-plane mode. By trapping the vibration energy in the wanted mode, the dual-driving DETF improves Q to 8173 compared to the conventional single-driving DETF with a Q of 3632. Through this dual-driving strategy, the DETF is excited to vibrate more closely with the in-plane resonant mode, which has less energy loss and balanced stress distribution. In addition, the dual-driving method also increases the signal intensity at the symmetric mode while reducing the signal intensity at the out-of-plane mode. This suppressed multimode effect contributes to a high performance of the DETF resonators and shows good potential in piezoelectric resonant sensors with high resolution.