The Counter-Rotating Vortex Pair in Film-Cooling Flow and its Effect on Cooling Effectiveness

A fundamental investigation on a key vortical structure in film cooling flow, which is called counter-rotating vortex pair (CRVP), has been performed. Traditionally, the coolant’s momentum flux ratio is thought as the most critical parameter on film cooling effectiveness, which is the index of film cooling performance, and this performance is also influenced notably by CRVP. About the sources of CRVP, the in-tube vortex, the in-tube boundary layer vorticity, the jet/mainstream interaction effect, alone or combined, are proposed as the main source in the literature. A numerical approach was applied in present study. By simulating a general inclined cylindrical cooling hole on a flat plate (the baseline case), the CRVP was visualized as well as the in-tube vortex. Another case, which is identical with the baseline except the boundary condition of the in-tube wall was set as free-slip to isolate its boundary layer effect, was simulated for comparing. Their comparisons have clarified that the jet/mainstream interaction is the only essential source of CRVP. Through further analyzing its mechanism, CRVP was found to be a pair of x direction (mainstream wise direction) vortices. Hence, the velocity gradients -v/z and w/y were the promoters of CRVP. Applying this mechanism, a new scheme named nozzle scheme was designed to control the CRVP intensity and isolate the overall momentum flux ratio Iov, a parameter used in literature. Analysis of the effects of CRVP intensity and momentum flux ratio on film cooling effectiveness has demonstrated that the CRVP intensity, instead of the momentum flux ratio, was the most critical factor governing the film cooling performance

Selfishness in device-to-device communication underlaying cellular networks

In a device-to-device (D2D) communication underlaying cellular network, user equipments are required to operate cooperatively and unselfishly to transmit data as relays. However, most users behave in a more or less selfish way, which makes user selfishness a key factor that affects the performance of the whole communication system. We focus on the impact of node selfishness on D2D communications. By separating the user selfishness into two types in accordance with two D2D transmission modes – connected D2D transmission and opportunistic D2D transmission, we propose a time-varying graph model that characterizes the impacts of both individual and social selfishness on D2D communications. Simulation results obtained under the realistic networking settings indicate that the interaction between connected and opportunistic selfishness worsens the impairment caused by individual selfishness, while the harmful interaction caused by social selfishness can be alleviated

A 7.3-μ W 13-ENOB 98-dB SFDR Noise-Shaping SAR ADC With Duty-Cycled Amplifier and Mismatch Error Shaping

This article presents a second-order noise-shaping successive-approximation-register (SAR) analog-to-digital converter (ADC) that employs a duty-cycled amplifier and digital-predicted mismatch error shaping (MES). The loop filter is composed of an active amplifier and two cascaded passive integrators to provide a theoretical 30-dB in-band noise attenuation. The amplifier achieves 18\times gain in a power-efficient way thanks to its inverter-based topology and duty-cycled operation. The capacitor mismatch in the digital-to-analog converter (DAC) array is mitigated by first-order MES. A two-level digital prediction scheme is adopted with MES to avoid input range loss. Fabricated in 65-nm CMOS technology, the prototype achieves 80-dB peak signal-to-noise-and-distortion-ratio (SNDR) and 98-dB peak spurious-free-dynamic-range (SFDR) in a 31.25-kHz bandwidth with 16\times oversampling ratio (OSR), leading to a Schreier figure-of-merit (FoM) of 176.3 dB and a Walden FoM of 14.3 fJ/conversion-step.</p

Moccasin: Efficient Tensor Rematerialization for Neural Networks

The deployment and training of neural networks on edge computing devices pose many challenges. The low memory nature of edge devices is often one of the biggest limiting factors encountered in the deployment of large neural network models. Tensor rematerialization or recompute is a way to address high memory requirements for neural network training and inference. In this paper we consider the problem of execution time minimization of compute graphs subject to a memory budget. In particular, we develop a new constraint programming formulation called \textsc{Moccasin} with only $O(n)$ integer variables, where $n$ is the number of nodes in the compute graph. This is a significant improvement over the works in the recent literature that propose formulations with $O(n^2)$ Boolean variables. We present numerical studies that show that our approach is up to an order of magnitude faster than recent work especially for large-scale graphs