Tran-Phu Nguyen

The rapid expansion of solar and wind energy systems has increased the demand for reliable monitoring solutions, particularly in regions where network infrastructure is limited or unstable. In Vietnam, most existing monitoring approaches primarily target single-source renewable installations and rely on WiFi or cellular networks, which are often unavailable in remote hybrid deployment scenarios. To address this limitation, this paper presents a low-cost IoT (Internet of Things)-based monitoring system for hybrid solar–wind energy applications, with an emphasis on reliable data acquisition under constrained connectivity conditions. The proposed system utilizes an ESP32 microcontroller integrated with a LoRa communication module to support long-range wireless transmission, while a local data buffering mechanism is employed to maintain data continuity during temporary network outages. Key electrical and environmental parameters, including voltage, current, temperature, and wind speed, are continuously monitored. Collected measurement data are stored in an InfluxDB time-series database and visualized using Grafana dashboards for real-time and historical analysis. Experimental validation demonstrates stable LoRa communication over distances of approximately 400 m in urban environments and up to 700 m in open areas, as well as consistent monitoring performance during WiFi interruptions and varying renewable input conditions. The proposed system provides a practical and reliable monitoring solution for hybrid renewable energy systems operating under limited connectivity and establishes a foundation for future extensions toward advanced data analytics and remote supervision.

The hull material of an autonomous underwater helicopter (AUH) strongly influences its descent behavior and maneuverability. Numerical computations have been conducted to study the influences of hull materials during the diving of the AUH. This study employs a coupled computational fluid dynamics (CFD)—six-degrees-of-freedom (6DOF) framework to quantify how variations in hull material density modify AUH hydrodynamic characteristics during dive maneuvers. Time-resolved simulations were conducted to produce the pressure field, velocity, and resistance force histories, which were subsequently analyzed with the vehicle's trajectory and attitude responses. The results show that the diving motion of AUH is affected by the mass of the hull of AUH generated by the density of the hull material, which causes the pressure difference between the bow and aft of the AUH. As more time passes, the pressure gradient on the surfaces of AUH is changed by the motion of AUH. The velocity, pitch angle, and resistance force accelerate rapidly in the initial stage before reaching their maximum values. An increase in the input density of hull material leads to a higher migration velocity due to stronger negative buoyancy effects. Forces and pitch angle time histories reveal clear links between material-dependent hydrodynamics and maneuvering characteristics, suggesting tangible trade-offs between mass vehicle and resistance force. The use of carbon fiber reduces the resistance force by approximately 10 times compared to that of a titanium alloy. The pitch angle is only half that observed when using the aluminum alloy. This provides an approach that helps reduce energy consumption under operating conditions. The proposed CFD–6DOF approach provides a practical, physics-based pathway to inform material selection and hull design for next-generation AUHs.

Nghiên cứu đã xây dựng mô hình mô phỏng số quá trình ép phun nhằm phân tích ảnh hưởng của các thông số công nghệ đến biến dạng và xác định bộ thông số tối ưu để giảm thiểu cong vênh. Mô hình số đã tái hiện đầy đủ các giai đoạn điền đầy, bảo áp và làm mát, đồng thời cho phép theo dõi chi tiết sự phân bố nhiệt độ, áp suất và trường biến dạng trong toàn bộ chu trình. Kết quả mô phỏng cung cấp cơ sở khoa học làm rõ rằng cong vênh của sản phẩm chủ yếu chịu chi phối bởi hai cơ chế chính là chênh lệch nhiệt độ và co rút thể tích. Đối với vật liệu PA66 Ultramid A3, co rút thể tích giữ vai trò quyết định, tác động trực tiếp đến sự biến dạng sau khi làm nguội. Kết hợp phương pháp thiết kế thí nghiệm (DOE) với mô phỏng số, bộ thông số tối ưu được xác định gồm: nhiệt độ nhựa 280 °C, nhiệt độ khuôn 80 °C, thời gian bảo áp 4 s và áp suất bảo áp 60% áp suất cực đại. Các thông số này đảm bảo sự cân bằng giữa điền đầy, bảo áp và làm mát, qua đó cải thiện đáng kể độ ổn định hình dạng và giảm cong vênh. Phương pháp đề xuất chứng minh hiệu quả trong tối ưu hóa quá trình ép phun và có tiềm năng ứng dụng thực tiễn cao.

In the injection molding of thin-walled plastic parts, premature solidification of the polymer melt upon contact with the relatively cold mold surface reduces cavity filling capability and adversely affects product quality. Therefore, proper control and distribution of mold cavity temperature during the heating stage play a crucial role in improving melt flow behavior without significantly extending the injection molding cycle. However, studies focusing on layered heating channels for mold cavities and the application of artificial intelligence methods for predicting temperature distribution remain limited. This study investigates the feasibility of using artificial neural networks (ANN) to predict the temperature distribution of an injection mold cavity equipped with a layered heating channel. Temperature data were collected during the mold heating process and used to develop and train an ANN model, which was further compared with a random forest model. When the number of neurons in the hidden layer was increased from 7 to 150, MSE decreased from 15.2575 to 0.6670, while the Rall increased from 0.9579 to 0.9981. Meanwhile, the Random Forest (RF) model also achieved a low prediction error, with MSE values ranging from 0.24 to 0.64 and Rall of 0.9991. The results indicate that the artificial neural network achieves high prediction accuracy and effectively captures the nonlinear relationships governing heat transfer, demonstrating strong potential for application in mold temperature optimization in injection molding processes.

Weld lines in injection-molded plastics often act as structural weak points that reduce mechanical performance. Enhancing weld line strength is therefore essential to improve product reliability and service life. This study aims to develop and validate an injection mold system capable of localized cavity temperature control to strengthen weld line regions. A specialized mold with an integrated cooling layer was designed to enable rapid thermal response during molding. The Taguchi method was applied to optimize three key parameters—part thickness, melt temperature, and injection pressure—to maximize weld line flexural strength. Experiments based on an L25 orthogonal array revealed that weld line stress varied significantly across parameter combinations, with a maximum of 109.23 MPa. A subsequent validation test conducted under the optimal conditions (250 °C melt temperature, 1.5 mm part thickness, and 16 MPa injection pressure) achieved an enhanced weld line stress of 121.88 MPa, confirming the reliability of the Taguchi-based optimization. Among the factors studied, part thickness had the greatest influence, followed by injection pressure, while melt temperature had the smallest effect. These results demonstrate that combining cavity temperature control with systematic parameter optimization provides an effective strategy to enhance weld line strength in high-performance plastic components.

A new autonomous underwater vehicle (AUV) has high maneuverability near the bottom and a direction turnaround ability, called the autonomous underwater helicopter (AUH). This paper numerically investigates the hydrodynamic performance of the AUH. A Reynolds-Averaged Navier–Stokes (RANS) equation, a computational fluid dynamics (CFD) technique, is applied to analyze the AUH’s behavior. Investigations of the AUH’s hydrodynamic characteristics become more obvious with a service speed in the range of 0.4–1.2 m/s. For the same velocity condition, the resistance of the AUH increases, and the irregular eddy at the rear of the AUH expands with changes in the angles of attack and the length/height ratio. Essential design characteristics including pressure, velocity distribution, and velocity streamlines are shown and analyzed. These insights can be used as a guideline to reduce drag force and optimize the AUH profile for future designs. It has great potential for improving the AUH’s control algorithms.

In this study, the flow, temperature and solute concentration distributions in the melt during the CZ growth process are numerically investigated. The results show that the magnitude and distribution of the solute concentration in the melt is strongly affected by the convective flow and the thermal distribution, since both Prandtl and Schmidt numbers are very high. The buoyancy convection forms a vortex within the melt, flowing up at the sidewall and down at the centerline. When the chemical reaction between the alumina oxide and the tungsten crucible wall is taken into account, the maximum tungsten trioxide concentration always occurs at the crucible sidewall where the maximum temperature in the melt is found and its concentration at the crystal-melt interface increases from the triple point to the centerline. When the graphite effect is considered, the maximum carbon concentration always occurs at the free surface of the melt closed to the crucible sidewall and the carbon concentration at the crystal-melt interface decreases from the triple point to the centerline of crystal.