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

Presentation_1_Calcitonin gene-related peptide is a potential autoantigen for CD4 T cells in type 1 diabetes.pdf

The calcitonin gene-related peptide (CGRP) is a 37-amino acid neuropeptide with critical roles in the development of peripheral sensitization and pain. One of the CGRP family peptides, islet amyloid polypeptide (IAPP), is an important autoantigen in type 1 diabetes. Due to the high structural and chemical similarity between CGRP and IAPP, we expected that the CGRP peptide could be recognized by IAPP-specific CD4 T cells. However, there was no cross-reactivity between the CGRP peptide and the diabetogenic IAPP-reactive T cells. A set of CGRP-specific CD4 T cells was isolated from non-obese diabetic (NOD) mice. The T-cell receptor (TCR) variable regions of both α and β chains were highly skewed towards TRAV13 and TRBV13, respectively. The clonal expansion of T cells suggested that the presence of activated T cells responded to CGRP stimulation. None of the CGRP-specific CD4 T cells were able to be activated by the IAPP peptide. This established that CGRP-reactive CD4 T cells are a unique type of autoantigen-specific T cells in NOD mice. Using IAg7-CGRP tetramers, we found that CGRP-specific T cells were present in the pancreas of both prediabetic and diabetic NOD mice. The percentages of CGRP-reactive T cells in the pancreas of NOD mice were correlated to the diabetic progression. We showed that the human CGRP peptide presented by IAg7 elicited strong CGRP-specific T-cell responses. These findings suggested that CGRP is a potential autoantigen for CD4 T cells in NOD mice and probably in humans. The CGRP-specific CD4 T cells could be a unique marker for type 1 diabetes. Given the ubiquity of CGRP in nervous systems, it could potentially play an important role in diabetic neuropathy.</p