Existing studies and methodologies do not offer designers clear guidelines for selecting optimal catamaran geometric parameters. Most focus on separate geometric characteristics or hull elements to minimize total resistance, providing only general trends that are not applicable when designing hull forms that differ from those in the mentioned studies. In this paper, a methodology is presented to evaluate the total resistance of a catamaran by determining its optimal geometric parameters. The approach involves selecting geometric parameters, defining variation limits to suit the design task, choosing appropriate evaluation methods and analyzing the results to identify the optimal catamaran geometric parameters. The application of the proposed methodology is achieved through the evaluation of different geometric parameters of an existing catamaran, such as its length, demihull separation, and hull symmetry. The CFD software Flow3D 2022 R1 is used to assess the influence of the selected geometric parameters on the total resistance of the catamaran, which is supported by experimental setup verification and validation using model tests. The results are analyzed using the system proposed in the developed methodology, enabling the selection of optimal geometric parameters for the considered catamaran design task. The research highlights the significance of optimizing catamaran hull forms by adjusting demihull spacing, symmetry, and length to achieve improved hydrodynamic performance and resistance reduction for applications during the vessel design stage.

Cycloidal reducers are widely used in precision drive systems due to their reduced backlash in meshing and compact design. However, their operational durability is limited by surface wear and lubricant degradation under elevated contact loads and boundary lubrication conditions. This study introduces a modified wear prediction model based on four-ball tribological testing, specifically adapted to simulate the complex tribological conditions in cycloidal gear contacts. The model incorporates the total acid number (TAN) and thermal conductivity coefficient of the lubricant as dimensionless factors, enabling a dynamic prediction of wear intensity as the lubrication degrades. This innovation allows an accurate estimation of service life and reliability in high-load, small-contact-area mechanical systems and offers a practical diagnostic tool for the predictive maintenance of gear transmissions.

This paper presents the results of a numerical study on the parameters that affect the efficiency of the cogeneration cycle of a ship’s power plant. The efficiency was assessed based on the excess power (N_gen.) of a free turbine, operated with the inflow of gaseous nitrogen, which was used to generate electricity. A mathematical model and simulation of the regenerative cycle were created and adjusted to operate with a dual-fuel (diesel-liquid natural gas (LNG)) six-cylinder four-stroke engine, where the energy of the exhaust gas was converted into mechanical work of the regenerative cycle turbine. The most significant factors for N_gen. were identified by parametrical analysis of the cogeneration cycle: in the presence of an ‘external’ unlimited cold potential of the LNG, N_gen. determines an exhaust gas temperature Te_g of power plant; the pressure of the turbo unit and nitrogen flow are directly proportional to N_gen. When selecting the technological units for cycle realization, it is rational to use high flow and average ${\pi }_T$ pressure (~3.0–3.5 units) turbo unit with a high adiabatic efficiency turbine. The effect of the selected heat exchangers with an efficiency of 0.9–1.0 on N_gen. did not exceed 10%. With LNG for ‘internal’ use in a ship as a fuel, the lowest possible temperature of N₂ is necessary, because each 10 K increment in N₂ entering the compressor decreases N_gen. by 5–8 kW, i.e., 5–6%.

This study presents innovative methods for solving practical challenges that occur during the operation of heavy diesel engines (DEs). The novelty of the method arises from the combined use of an artificial neural network, a single-zone DE combustion mathematical model, and data from real operation conditions. Using the proposed method, DE transient mode energy efficiency has been analysed, and the primary influencing factors have been identified: qcycl and dqcycl/dt. The adequacy of the method has been tested for CAT3512B-HD series engines installed on freight locomotives. The difference between the model results and experimental data has been 3–4%. CAT3512B-HD series DE transient operation studies have shown that at the low-load range of qcycl (up to 36% of nominal), the fuel consumption during transient operation increases by 10% compared to steady state operation. Transient operation efficiency is not influenced by the operation rate (dqcycl/dt) in the analysed -0.016 to 0.016 g/s range. Near the nominal power (qcycl increases up to 0.5 g/cycle), it is necessary to limit the dqcycl/dt range to 0.006 g/s to avoid overexploitation of the fuel by more than 100%. The proposed method has been recommended for practical use in optimizing vehicle operation load cycle structure by adapting the engine control to the concrete operation conditions, as well as for overall efficiency improvement.

In this study, we have investigated the efficiency of transport diesel engines CAT3512B-HD in transient braking and acceleration modes in 2M62M locomotives. A comparative analysis of the diesel engine performance has been performed at speeds of power increase and braking ranging from 4–5 kW/s to 17–18 kW/s. A decrease in the fuel economy occurred, and the main reason for it (compared with the steady-state operating condition at qcycl = idem) has been found to be the deterioration of the mechanical efficiency coefficient due to the loss of the additional equipment kinetic energy of the engine. The efficiency decreased by 3–3.5% under power increase operations and by 10–14% in the braking modes. The original methodology for the evaluation of the diesel engine parameters registered by the engine control units (ECU) in the engine operating conditions, mathematical modelling application AVL BOOST, and analytical summaries in artificial neural networks (ANNs) have been used. The errors in the obtained results have been 5–8% at a determination coefficient of 0.97–0.99.