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While ventilation is crucial for ensuring indoor air quality and occupant health, introducing fresh air can lead to increased latent loads due to dehumidification, particularly, in humid climates. The Dedicated Outdoor Air System (DOAS) has gained attention for its ability to supply all fresh air while decoupling latent and sensible loads in buildings. Integrating a desiccant wheel (DW) into the DOAS further enhances its dehumidification performance compared to conventional cooling-based methods. This study proposes a modeling approach leveraging the Julia language and its equation-based acausal modeling framework to analyze the energy performance of DOAS systems, highlighting Julia’s potential in heating, ventilation, and air-conditioning (HVAC) applications. Specifically, we modeled two configurations of DW-based DOAS: one using an active DW and the other using a passive DW. The main modeling components include a desiccant wheel, a thermal network-based office room model considering sensible and latent loads, coils and local controllers. A simulation case study was then conducted to compare the energy performance of the two DOAS configurations over one week during the cooling season in Houston, TX. This study presents a preliminary workflow for HVAC modeling and optimization using a Julia-based symbolic modeling framework, highlighting its potential over conventional methods. The framework is extendable for a further high-fidelity modeling and advanced control analysis of DOAS, and other HVAC equipment and systems. 

This letter aims to develop a new learning-based flocking control framework that ensures inter-agent free collision. To achieve this goal, a leader-following flocking control based on a deep Q-network (DQN) is designed to comply with the three Reynolds’ flocking rules. However, due to the inherent conflict between the navigation attraction and inter-agent repulsion in the leader-following flocking scenario, there exists a potential risk of inter-agent collisions, particularly with limited training episodes. Failure to prevent such collision not only caused penalties in training but could lead to damage when the proposed control framework is executed on hardware. To address this issue, a control barrier function (CBF) is incorporated into the learning strategy to ensure collision-free flocking behavior. Moreover, the proposed learning framework with CBF enhances training efficiency and reduces the complexity of reward function design and tuning. Simulation results demonstrate the effectiveness and benefits of the proposed learning methodology and control framework. 

This white paper is on the HMCS Firefly mission concept study. Firefly focuses on the global structure and dynamics of the Sun's interior, the generation of solar magnetic fields, the deciphering of the solar cycle, the conditions leading to the explosive activity, and the structure and dynamics of the corona as it drives the heliosphere.