Propulsion motors are vital components in marine propulsion systems and industrial machinery, where high torque and operational reliability are paramount. During operation, high-power propulsion motors generate considerable heat, which can adversely affect efficiency, durability, and stability. Therefore, an effective thermal management system is necessary to maintain optimal performance and ensure long-term reliability. Cooling technologies, such as water jackets, are commonly employed to regulate temperature distribution, prevent localized overheating, and preserve insulation integrity under high-power conditions. This paper examines the cooling performance of water jackets for high-power propulsion motors through numerical analysis. We evaluated the effects of three different cooling pipe locations and varying coolant flow rates on thermal balance and cooling efficiency. Additionally, we analyzed temperature variations in the windings and key heat-generating components to determine if a specific cooling flow rate and pipe configuration can effectively keep the winding insulation (Class H) within its 180^oC limit. The findings of this study highlight the significance of optimized cooling system design and contribute to the development of efficient thermal management technologies, ultimately enhancing motor reliability, operational stability, and energy efficiency.

The rapid growth of semiconductor and display manufacturing highlights the demand for fast, precise motion stages. Advanced systems such as lithography and bio-stages require accuracy at the μm and nm levels, but linear motor stages face challenges from disturbances, model uncertainties, and measurement noise. Disturbances and uncertainties cause deviations from models, while noise limits control gains and performance. Disturbance Observers (DOBs) enhance performance by compensating for these effects using input–output data and a nominal inverse model. However, widening the disturbance estimation bandwidth increases noise sensitivity. Conversely, the Kalman Filter (KF) estimates system states from noisy measurements, reducing noise in position feedback, but it does not treat disturbances as states, limiting compensation. To address this, we propose an Augmented Kalman Filter (AKF)–based position control for linear motor stages. The system was modeled and identified through frequency response analysis, and DOB and AKF were implemented with a PIV servo filter. Experimental validation showed reduced following error, jitter, and control effort, demonstrating the improved control performance of the AKF approach over conventional methods.

Amyotrophic lateral sclerosis (ALS) is a fatal neurodegenerative disorder marked by the progressive degeneration of motor neurons and muscle atrophy. Despite extensive clinical research, effective treatments remain scarce due to the complexity of the disease's mechanisms and the inadequacy of current preclinical models. Recent advancements in microphysiological systems (MPS) present promising alternatives to traditional animal models for studying ALS pathogenesis and evaluating potential therapies. This review outlines the latest developments in ALS MPS, including co-culture membrane-based systems, microfluidic compartmentalization, microarray platforms, and modular assembly approaches. We also discuss key studies that replicate ALS-specific pathologies, such as TDP-43 aggregation, neuromuscular dysfunction, and alterations in astroglial mitochondria. Additionally, we identify significant challenges that need to be addressed for more physiologically relevant ALS modeling: replicating neural fluid flow, incorporating immune responses, reconstructing the extracellular matrix, and mimicking the pathological microenvironment. Finally, we emphasize the potential of ALS MPS as valuable tools for preclinical screening, mechanistic studies, and personalized medicine applications.