Analysis of Supercritical CO2 Cycle Using Zigzag Channel Pre-Cooler: A Design Optimization Study Based on Deep Neural Network

The role of a pre-cooler is critical to the sCO2-BC as it not only acts as a sink but also controls the conditions at the main compressor’s inlet that are vital to the cycle’s overall performance. Despite their prime importance, studies on the pre-cooler’s design are hard to find in the literature. This is partly due to the unavailability of data around the complex thermohydraulic characteristics linked with their operation close to the critical point. Henceforth, the current work deals with designing and optimizing pre-cooler by utilizing machine learning (ML), an in-house recuperator and pre-cooler design, an analysis code (RPDAC), and a cycle design point code (CDPC). Initially, data computed using 3D Reynolds averaged Navier-Stokes (RANS) equation is used to train the machine learning (ML) model based on the deep neural network (DNN) to predict Nusselt number (Nu) and friction factor (f). The trained ML model is then used in the pre-cooler design and optimization code (RPDAC) to generate various designs of the pre-cooler. Later, RPDAC was linked with the cycle design point code (CDPC) to understand the impact of various designs of the pre-cooler on the cycle’s performance. Finally, a multi-objective genetic algorithm was used to optimize the pre-cooler geometry in the environment of the power cycle. Results suggest that the trained ML model can approximate 99% of the data with 90% certainty in the pre-cooler’s operating regime. Cycle simulation results suggest that the cycle’s performance calculation can be misleading without considering the pre-cooler’s pumping power. Moreover, the optimization study indicates that the compressor’s inlet temperature ranging from 307.5 to 308.5 and pre-cooler channel’s Reynolds number ranging from 28,000 to 30,000 would be a good compromise between the cycle’s efficiency and the pre-cooler’s size.

Effects of Combined Versus Singular Verbal or Haptic Feedback on Acquisition, Retention, Difficulty, and Competence Perceptions in Motor Learning

While augmented feedback (AF) is widely acknowledged to affect motor learning, the effects of mode of feedback on motor learning acquisition, retention, and perceived competence has rarely been studied. The present investigation analyzes the effects of verbal, haptic, and combined (verbal and haptic) feedback when learning a novel gymnastic parallel bars task. Forty-eight physical education students and four expert gymnastics teachers participated in the study. We divided the students into three AF groups (verbal, haptic, and combined) and a no-feedback control group (CG). One gymnastics teacher led the learning sessions, while the others evaluated student performances following familiarization, acquisition, and retention learning phases. All sessions were video recoded, and the experts gave blind assessments according to an adapted gymnastic point code. We recorded task perceived difficulty (PD) and students' perceived self-competency throughout the sessions. A repeated measures analysis of variance revealed a significant effect of AF mode on acquisition and retention such that combined AF was best for learning stability and retention (19.1% improvement for combined vs. 9.9% for haptic and 6.9% for verbal). Similarly, participants in the combined AF group, relative to the verbal and haptic AF groups, also reported lower perceived difficulty and higher perceived self-competency after the retention phase. PE teachers are encouraged to combine verbal and haptic AF when teaching new motor skills.