Nowadays, engine room simulators have become an important tool for maritime training, but programming engine room simulators often involves handling large amounts of data, making the process inefficient. This paper proposes an innovative visual modeling method for the ship pipeline network program in engine room simulators, aimed at addressing the heavy programming tasks associated with traditional text-based design and calculation methods when dealing with complex and large-scale pipeline systems. By creating Scalable Vector Graphics (SVG) images and using Windows Presentation Foundation (WPF) to place controls, an intuitive graphical user interface is built, allowing programmers to easily operate through the graphical interface. Subsequently, You Only Look Once version 5 (YOLOv5) object detection technology is used to identify the completed SVG images and WPF controls, generating corresponding Comma-Separated Values (CSV) files, which are then used as data input via C# (C Sharp). Through automated data processing and equipment recognition, compared to traditional manual design processes (such as using Matlab or C++ for pipeline design), this method reduces human errors and improves programming accuracy. Customization of key pipeline characteristics (such as maximum flow and flow direction) enhances the accuracy and applicability of the pipeline network model. The intuitive user interface design also allows nonprofessional users to easily design and optimize pipeline systems. The results show that this tool not only improves the efficiency of data processing and calculation but also demonstrates excellent performance and broad application prospects in the design and optimization of ship pipeline systems. In the future, this tool is expected to be more widely promoted in ship pipeline network education and practical applications, driving the field towards more efficient and intelligent development.

This paper researches the heat transfer equation and thermal balance equation of a shell-and-tube evaporator; constructs an accurate mathematical model for the evaporator; and derives equations including detailed and accurate calculation methods for all heat transfer coefficients, such as the refrigerant side heat transfer coefficient, water side heat transfer coefficient, refrigerant kinematic viscosity, density, and specific enthalpy. Adopting this approach involves fitting the relationships between the density, thermal conductivity, kinematic viscosity, and enthalpy of R134a refrigerants in saturated vapor and liquid states. The relationships between superheated gas enthalpy, density, and temperature were also assessed, and heat transfer coefficients were obtained through calculation methods and microelement heat transfer relationships in both the single-phase and two-phase zones, matching empirical formulas concerning the relationship between superheated enthalpy and temperature. Notably, the research utilizes the Simulink approach without relying on M files and S functions to establish the evaporator’s two-phase and superheated zones, as well as an overall simulation model which provides intuitive internal coupling relationships and the coefficient calculation process in the formulas and uses the function “Algebraic Constraint” instead of “memory” or “1/z” to solve algebraic loops, thereby avoiding computation deviations introduced by delays and iterations. Finally, simulation calculations were conducted, and an experimental platform was designed and built for experimental verification which can validate the derived mathematical models. The simulation results, including the evaporator pressure, and chilled water outlet temperature with variation in chilled water mass flow rate, closely matched the experimental outcomes. The simulation model is concise and intuitive. Modifying parameters such as the thermal conductivity of the model material is straightforward, thereby alleviating the workload for researchers. It also facilitates an understanding of model principles for beginners. Moreover, the database generated from the model allows for fault analysis, diagnosis, and decision evaluation.

Magnetoresistance, the change in resistance with applied magnetic fields, is crucial to the magnetic sensor technology. Linear magnetoresistance has been intensively studied in semimetals and semiconductors. However, the air-stable oxides with a large linear magnetoresistance are highly desirable but remain to be fully explored. In this paper, we report the direct observation of linear magnetoresistance in polycrystalline MoO₂ without any sign of saturation up to 7 T under 50 K. Interestingly, the linear magnetoresistance reaches as large as 1500% under 7 T at 2 K. The linear field dependence is in great contrast to the parabolic behavior observed in single-crystal MoO₂, probably due to phonon scattering near the grain boundaries. Our results pave the way to comprehending magneto-transport behavior in oxides and their potential applications in magnetic sensors.

To address noise interference and feature overlap in multiclass diesel-engine fault-state classification, this study proposes a dual-decoder denoising autoencoder with dual-channel feature fusion. Multi-SNR denoising training pairs are encoded by a shared encoder and reconstructed by a general denoising decoder under reconstruction and cross-SNR latent consistency constraints. With the encoder frozen, a normal-manifold decoder is trained on normal samples to generate a normal-state reference. The difference between the decoder outputs forms a normal-manifold residual that characterizes deviations from normal operation. Latent features are processed by a residual multilayer perceptron, whereas residuals are processed by multi-scale 1D convolution with efficient channel attention; gated fusion combines the two channels for classification. On the 3500-DEFault dataset, reconstruction MAE decreased by 93.75% at 0 dB and 90.86% at 15 dB. Anomaly detection based on normal-manifold residuals achieved an ROC-AUC of 0.9749. The proposed method attained an accuracy of 94.21% and a Macro-F1 of 94.28% on the independent test set, improving Macro-F1 by 3.07 percentage points over the DAE classifier. These results demonstrate the effectiveness of the proposed framework for noise-robust multiclass fault-state classification under the evaluated SNR conditions.

This study is dedicated to the development of an advanced ship piping network programming tool to address the challenges faced by traditional text-based design and computation methods when dealing with complex and large-data-volume piping systems, such as burdensome programming tasks, high error rates, and difficulty in troubleshooting faults. Leveraging Microsoft’s WPF technology and the C# language, combined with Excel as a data input platform, this tool provides an intuitive graphical user interface, allowing users to intuitively build and analyze ship piping network models by dragging and dropping controls. The tool not only simplifies the design process of complex piping systems but also significantly improves efficiency and accuracy through automated data processing and calculations. It supports user customization of key pipeline characteristics, such as maximum flow and direction, further enhancing the applicability and accuracy of the piping network model. In addition, with optimized interaction design and data management methods, the tool significantly reduces the learning difficulty for users, while improving the reliability of design and efficiency of troubleshooting. The results of this study show the tool not only technically outperforms traditional methods but also provides a new efficient, intuitive, and user-friendly tool for the teaching and engineering applications of ship piping networks, paving a new path for the design and optimization of ship piping network systems, with significant practical application value and theoretical significance. Looking forward, this tool is expected to play a broader role in the instruction and industrial practices associated with ship piping networks, moving the field toward more efficient and intelligent development.