The air conditioning (AC) condenser plays a vital role in heating, ventilating, and air conditioning systems. In vehicles, it is typically located within a cooling pack, alongside other heat exchangers. Hence, the dissipation of heat from the AC condenser directly affects surrounding components. Therefore, accurate modeling of heat transfer between the refrigerant and air is crucial for vehicle development, especially nowadays for modern battery electric vehicles. This article focuses on the development and verification of a simplified AC condenser model that reduces input data requirements by utilizing directly measured heat performance characteristics. This enables a more general and broadly applicable approach. Despite the simplified input, the model captures the spatial distribution of heat transfer, leading to improved predictions of air outlet temperatures. The model is based on the well-established ϵ-NTU method and iteratively applies phase-dependent relations during condensation. Dedicated test equipment was developed as part of this study to support the input data acquisition and model verification. The proposed model was evaluated under two distinct operating conditions and verified against the experimental data. The model exhibited good agreement with the measurements in predicting refrigerant inlet and outlet temperatures. The proposed model was verified to capture the refrigerant phase change under the evaluated operating conditions.
This study presents a machine learning–based approach for predicting tool wear and preventing tool breakage using vibration diagnostics in machining processes. By analysing vibration signals (and, where applicable, acoustic emission), the proposed method enables early fault detection and supports predictive maintenance strategies. The approach contributes to sustainable manufacturing by reducing material waste, improving resource efficiency, and extending tool lifetime. Experimental results demonstrate that vibration features effectively distinguish between normal and abnormal tool conditions, highlighting the potential of AI-assisted diagnostics in green innovation and Industry 4.0 applications.
The article presents an improved approach to thermodynamic modelling and early, preventive prediction of spinodal decomposition processes with phase delamination of irregular three-component α - solid solutions into separate equilibrium, immiscible phases. Using the obtained model, it is possible to analytically predict the critical concentration-temperature conditions under which the noted phase segregation can be induced, contributing to the premature aging of metallic materials and reducing the operational reliability of machine parts made from them. Consequently, the proposed approach will allow in advance, even at the stage of development of the alloy, to eliminate the risk associated with its structural-phase decay during the operation of the product made from it. The results of calculations obtained on a widely used model alloy of the Fe-Cr-C system are presented. It has been established that the spinodal decomposition of the noted three-component stainless heat-resistant solid solution can lead to concentration stratification into the following three equilibrium phases, with the content of elements in molar parts: 1) Fe=0.14, Cr=0.29, C=0.57; 2) Fe=0.53, Cr=0.29, C=0.18; 3) Fe=0.14, Cr=0.68, C=0.18. Such phase segregation can be initiated in a solid solution with an initial content of these elements of 0.72, 0.25, and 0.03 mol, in the case of its rapid forced cooling to a critical temperature of 342 K for this system, since this leads to the maximization of free energy and transfers it in a thermodynamically non-equilibrium state. As a preventive measure to avoid the process of spinodal decomposition, seeking to zeroing the free energy of the system by its concentrative enriched or depleted delamination and, consequently, microstructural embrittlement, it is recommended to technologically exclude the probability of producing and operating an alloy with a predetermined non-equilibrium chemical composition and critical extent of forced cooling.
This paper shows the possibilities of applying similarity and dimensionality methods for mathematical modelling of tribosystem — a friction pair in which, in addition to solid surfaces, there is a multi-component lubricant.Multifactorial tribological phenomena often cannot be described using formal laws, the models we propose allow us to carry out studies of such processes. The essence of the method is that the dependent variable — in this case, stationary wear of friction surfaces — is represented as a set of independent (or weakly dependent) variables.Two basic theorems of the method of dimension analysis were applied to the general model: the theorem on the dimensionality of quantities in the system of basic dimensions of mechanics and Buckingham’s theorem on finding the number of dimensionless complexes.The possibility of reducing the number of model variables and forming a system of new, simpler models, which allow to describe the process explicitly, was shown.
The safe transportation of steel coil sheets within a factory is of vital importance for the machinery manufacturing sector. This study presents the design and finite element analysis of a hydraulic-driven coil tilting machine intended for positioning steel coils weighing up to 10 tons. The machine is designed to position coils both horizontally and vertically to facilitate various production processes. Considering the industry's demand for positioning a wide range of coil sizes, design criteria were established, and a 3D model was developed. Structural analysis was performed using the finite element method to evaluate the machine's performance under various loading and operational conditions. The results showed a maximum stress of 125.5 MPa and a maximum displacement of 7.9 mm, demonstrating compliance with the Turkish Machinery Safety Directive. This research contributes to the development of efficient and safe coil handling solutions for the manufacturing industry, potentially enhancing productivity and reducing labor costs.
