Optimal System Parameters Research Articles

High-efficiency high-numerical-aperture metalens designed by maximizing the efficiency limit

Theoretical bounds are commonly used to assess the limitations of photonic design. Here we introduce a more active way to use theoretical bounds, integrating them into part of the design process and identifying optimal system parameters that maximize the efficiency limit itself. As an example, we consider wide-field-of-view high-numerical-aperture metalenses, which can be used for high-resolution imaging in microscopy and endoscopy, but no existing design has achieved a high efficiency. By choosing aperture sizes to maximize an efficiency bound, setting the thickness according to a thickness bound, and then performing inverse design, we come up with high-numerical-aperture (NA=0.9) metalens designs with, to our knowledge, record-high 98% transmission efficiency and 92% Strehl ratio across all incident angles within a 60° field of view, reaching the maximized bound. This maximizing-efficiency-limit approach applies to any multi-channel system and can help a wide range of optical devices reach their highest possible performance.

FRF-based model updating of liquid-filled pipeline system considering tolerance intervals of test errors in the antiresonant frequencies

The frequency response function (FRF)-based model updating technique is an effective method to calibrate system parameters of the dynamic simulation model of pipeline systems. However, the existing FRF-based model updating methods is difficult to solve the problem of the test errors in the antiresonant frequencies, which significantly affects the accuracy of the updated results. Hence, this paper proposes a new FRF-based model updating method of the liquid-filled pipeline considering the tolerance intervals of test errors in the antiresonant frequencies. In this method, the effect of test errors on FRFs is quantified by employing the interval method, and then the tolerance intervals of test errors in the antiresonant frequencies can be identified from the interval FRFs. The obtained tolerance intervals are integrated into the objective function of the FRF-based model updating through the penalty function method, and the optimal system parameters can be obtained by using a global optimization algorithm. Compared with the existing FRF-based model updating methods, the proposed method can not only effectively reduce the effect of test errors in the measured FRFs on the updated results but also alleviate the ill-condition of model updating calculations, so that the accuracy and convergence of model updating calculations are improved simultaneously. Numerical and experimental validations of the proposed method are carried out by using an L-shaped water-filled pipeline system. The performances of the proposed method are compared with the typical FRF-based model updating method. The results demonstrate that the proposed method can obtain the updated results with higher accuracy and reliability than the typical method.

Explicit Formulas for Optimum Parameters of Viscoelastic-type Tuned Mass Dampers

Tuned mass dampers (TMDs) are passive vibration control devices that are attached to a primary system to reduce the dynamic vibrations under exciting motion. The Voigt-type TMD, which is the most widely used one, is known as a standard model of dynamic vibration absorber (DVA). The purpose of this study is to improve the vibration control performance of passive control devices by using viscoelastic-type tuned mass dampers (V-TMDs). The study adopts the Zener model to represent the viscoelastic behavior of V-TMD. In this study, the fixed-point method is used to determine the optimum parameters of a V-TMD. The displacement amplification factor (DAF) of the coupled system is obtained in the frequency domain. The optimal parameters of the V-TMD system attached to an undamped single degree-of-freedom (sdof) main system are obtained by minimizing the DAF (symbolized with 𝛽) under the effect of base excitation. The optimum parameters, such as damping ratio (ξ) and stiffness ratio (𝜅) of the coupled system are derived, and explicit expressions corresponding to the optimum parameters are presented for engineering designs. Moreover, the change in DAF values for different mass ratios (µ) is also discussed. It is proven that V-TMD is very effective in reducing the amplitudes of vibration. The study also provides valuable insights for engineering practitioners who want to design and implement V-TMDs for vibration control applications because accurate expressions, which are simple and easy to use, are derived in order to obtain optimum parameters, and step-by-step procedures are explained.

Numerical Modeling of Mid-IR Lasers Based on Tb-Doped Chalcogenide Multicore Fibers

Mid-IR fiber lasers operating at wavelengths near 5 μm are of great interest for many fundamental and industrial applications, but only a few experimental samples based on active chalcogenide fibers have been demonstrated so far. One of the limitations of the power of such lasers may be a fairly low fiber damage threshold. To solve this problem, we developed and numerically investigated in detail a mid-IR fiber laser at 5.3 µm with multi-W output power pumped into the cladding at a wavelength of 2 µm. We proposed using a Tb-doped chalcogenide multicore fiber with 25 single-mode cores arranged in a 5 × 5 square lattice as an active medium. The proposed laser design surpasses the power limit of single-core chalcogenide fibers. When simulating lasers, we specified realistic parameters of Tb-doped chalcogenide glass based on published experimental data. We performed a comprehensive theoretical analysis, studied the influence of various factors on the characteristics of generation, and found optimal system parameters and expected generation parameters.

A New Multi-Objective Optimization Strategy for Improved C3MR Liquefaction Process

In the traditional C3MR process (T-C3MR), the boiling gas (BOG) output from the last stage of the gas–liquid separator is directly discharged, in which the excellent low-temperature capability is not utilized, and the system efficiency is decreased. In liquefied natural gas (LNG), single-objective optimization methods are commonly used to optimize system parameters, which may result in incomplete system analysis. To solve the above problems, this paper proposes a multi-objective optimization strategy for the improved C3MR process(I-C3MR) based on a new multi-objective optimization algorithm called EHR-GWO-GA. Firstly, the main work proposes an I-C3MR structure. Secondly, an optimization strategy of the I-C3MR with the maximization of liquefaction amount, minimization of unit energy consumption and minimization of exergy loss as objective functions are proposed. Based on the optimization results, the influence of decision variables on liquefaction amount, unit energy consumption and exergy loss are analyzed, and the results show that the decision variables have good adaptability. Finally, a detailed exergy analysis of the equipment used is made, and the results show that the main exergy losses come from the water coolers and compressors, accounting for 32% and 34%, respectively. Compared to the T-C3MR, the improved C3MR based on EHR-GWO-GA(E-C3MR) has an approximate 8% increase in liquefaction amount—a roughly 23% decrease in unit energy consumption and a decrease of nearly 24% in exergy loss.

Research on a Vibrationally Tuned Directional Seed Supply Method Based on ADAMS-EDEM Coupling and the Optimization of System Parameters

We have combined the theory of bulk dynamics and the agronomic requirements of precision sowing with the aim of resolving the technical problems of poor seed mobility and the difficulty in controlling suction posture, which leads to an increase in the leakage rate and a reduction in seed qualification index scores. In this study, a vibrationally tuned directional seed supply method and system are proposed. We carried out a force analysis of seeds, constructed kinematic equations for seeds and seed boxes to specify the state of the seed motion, and determined the structural parameters and the range of structural parameters that affect the seed suction posture. In addition, we coupled the ADAMS-EDEM simulation of the motion process of the seed and seed boxes and analyzed the vibrational tuning process of the seeds and the angle of inclination of the bottom surface of the seed box. The speed of the eccentric wheel and the eccentric distance were used as test factors. Three-factor and three-level Box–Behnken central combination testing with a single-grain rate, multiple-grain rate, and cavity rate were used as response indicators. Mathematical models were obtained between the experimental factors and the response indicators. Multi-objective optimization of mathematical regression models was carried out with Design-Expert 10.0.4 software. The optimal parameter combination obtained was a tilt angle of 14.27°, an eccentric wheel speed of 4.48 rad/s, and an eccentricity of 1.94 mm. The rate of single grains was 90.75%, the rate of multiple grains was 3.63%, and the rate of cavities was 5.62%. In bench performance tests, using an angle of inclination of 14°, the speed of the eccentric wheel was 4.50 rad/s and the eccentricity was 2 mm. The mean value of the single-grain rate was 89.28%, the mean value of the multiple-grain rate was 3.89%, and the mean value of the cavity rate was 6.83%. The test error was within permissible limits, and reliable results were achieved for parameter optimization. The results met the technical requirements for precision sowing. The results of the study can provide academic references for theoretical research on the methodology of posturing and directional seed supply. They can also provide ideas for the design and development of seed supply systems for precision sowing machinery.

Multi-Performance Economical Optimization of Base Isolation System with Tuned Inerter Negative Stiffness Damper Subjected to Non-Stationary Seismic Excitation

Base isolation proves to be an effective vibration control strategy for buildings. However, the base-isolation floor (BIF) may undergo substantial displacements. To improve the seismic performance of base-isolation system, this paper proposes a multi-performance economical optimization procedure (MPEOP) for exploring the optimal parameters of base isolation system with tuned inerter negative stiffness damper (BIS-TINSD) subjected to non-stationary seismic excitation. The simplified analysis model for BIS-TINSD is developed, followed by the formulation of the state-space representation. The non-stationary seismic excitation is represented as a stationary Gaussian process with a time-modulating function based on the Clough–Penzien spectrum, and combining the equations of motion with seismic excitation results in the augmented state-space representation of structure-damper-excitation, followed by the formulation of differential Lyapunov equation. The multi-objective performance economical index is proposed, in which the performance optimization objective is defined as a weighted combination of base isolation floor displacement and superstructure acceleration, and the economic optimization objective is set as the support stiffness. The Pareto optimal fronts (POF) are adopted to deal with optimal objectives. The examination of optimal design parameters is extended under real earthquake records. The results demonstrate the effectiveness of the MPEOP, and indicate the advantages of the BIS-TINSD compared with the inerter amplify damper (IAD) and negative stiffness inerter damper (NSID).

Optimization of single and double pass solar air heater-phase change material (SAH-PCM) system based on thickness to length ratio

This paper presents a two-dimensional transient model for a solar air heater with phase change material (SAH-PCM), focusing on the thickness-to-length ratio (t/L) of the PCM container. Verified through experiments, the model considers single (SP) and double pass (DP) flow configurations, assessing liquid fraction, dead length, outlet temperature, energy, exergy, and system efficiency during PCM charging and discharging. Increasing the t/L ratio improves peak liquid fraction, reduces dead length, and delays PCM discharge in both configurations, enhancing system energy and exergy efficiencies. In a SP flow system, optimizing t/L at 0.042 yields a peak liquid fraction of 0.928, a 92.28% dead length reduction, and a 2 h and 22 min delay for PCM discharge (base-case t/L ratio: 0.0182), achieving a highest discharging efficiency of 39.1%. For a DP flow system, the optimized t/L ratio of 0.058 results in a peak liquid fraction of 0.976 and a 1 hour and 34 min delay for PCM discharge, attaining the highest discharging efficiency of 63.34%. Moreover, an improvement in the energy payback time (EPBT) is observed for both SP and DP flow as the t/L is increased. For the DP flow, a minimum EPBT of 0.68 years (energy basis) and 6.55 years (exergy basis) is observed at a t/L ratio of 0.058. In terms of energy cost, SP flow reaches a minimum of Rs. 20.824 Rs/kWh at a t/L ratio of 0.042. For DP flow, the minimum energy cost is 13.033 Rs/kWh at a t/L ratio of 0.058, representing a noTable 37.42% improvement compared to SP flow. These findings underscore the critical role of optimizing system parameters, specifically the t/L ratio, for enhanced energy and exergy performance, and economic viability of SAH-PCM.

Preventing malware propagation in wireless sensor networks: Hybrid optimization algorithm for controlling

Malware transmission is a significant security issue in WSN, however, the influence of the attack and defensive processes on malware propagation is rarely taken into account in traditional malware propagation prevention methods. Advanced methods are in need to stop the propagation of malware of sensor nodes. With the formulation of representing dynamics among states, a new decision-making problem as the optimal control problem via hybrid optimization algorithm. The proposing model is termed as Butterfly Updated Bald Eagle Optimization based Prevention of Malware Propagation in Wireless Sensor Network (BUBEO-PMPWSN). In the proposed controlling system, optimal system parameters are analyzed via the BUBEO for preventing malware propagation in WSN. Particularly, the sensor node states considered are Susceptible, Infectious, Infectious and sleeping, recovered, Recovered and sleeping, and finally Dead. The system parameter tuning will be under the evaluation of fitness calculation under probability of infectious sensor node becoming recovered and the probability of infectious sensor node entering sleeping state. This optimal tuning strategy ensures the preventing of malware propagation. Finally, the performance of proposed BUBEO-PMPWSN model is evaluated and validated successfully by comparing other state-of-the-art models. The BUBEO-PMPWSN achieved 250 recovered nodes for time 500, while the HGS, BOA, HBA, COOT, and HHO scored 123, 115, 236, 172, and 180, respectively, for recovered nodes.

Inverse Design of Photonic Systems

AbstractInverse design methods use optimization and learning algorithms to pair desired functionalities with the corresponding high‐performing systems. Such methods have significant potential for discovering novel photonics solutions, with inverse design techniques already mediating significant milestones in nanophotonics, quantum optics, and lens systems. However, while computational tools for identifying optimal system parameters (i.e., component settings) have reached significant maturity, the identification of suitable system topologies (i.e., component choice and arrangement) has remained challenging, especially for the design of complex photonic schemes. Here, a framework for the inverse design of practical photonic systems is presented, capable of efficiently and automatically searching for high‐performance topologies and their associated operational parameters. It is demonstrated that the approach can aid in the discovery of practical photonic systems, that are both physically feasible and non‐trivial, by leveraging system‐level automatic differentiation and discrete topological changes. The versatility of the platform is supported with example designs for waveform generation, noise suppression, and sensing, among others.

Design and optimization of a combined heat and power system with a fluidized-bed combustor and stirling engine

In this study, a fluidized-bed biofuel system was integrated with a Stirling engine (SE) to create a combined heat and power (CHP) system with high energy efficiency. Methods for ensuring the stability of the SE and fluidized-bed combustion were investigated and implemented. The proposed design was tested experimentally, and the system successfully produced electricity from the heat of the flue gas generated during biomass combustion. The Taguchi method was employed to maximize the temperature of the fluidized bed by modifying three parameters: the sand height, air flow rate, and biomass feed rate. The biomass used in this study was discarded mushroom sawdust waste. The biomass feed rate was varied from 9.5 to 17 g/min, and the air flow rate was varied from 50 to 60 L/min. The integrated CHP system was found to yield 90−100 W of electric power and 1077.3 W of heat energy for producing hot water. The oxygen, carbon dioxide, carbon monoxide, and nitric oxide concentrations of the flue gas produced under the optimized system parameters were analyzed, and minimal emissions of carbon monoxide and nitric oxides, which are harmful gases, were discovered. In most experiments on SEs in the literature, liquid or gaseous fuels have been used to power the engine. However, this study successfully used solid biomass fuel to power a practical SE, thus widening the knowledge on the utilization of this renewable energy source for electricity generation. The results of this study indicate that the proposed CHP system is stable, clean, and efficient. This system potentially provides a solution for two problems, namely the disposal of mushroom sawdust waste and the eco-friendly generation of heat and electricity. Therefore, the proposed system is promising for green and sustainable power generation in the future.

Theoretical exploration of a free-piston Stirling generator with a gas-compressing self-circulating heat exchanger

The free-piston Stirling generator (FPSG) holds great promise as a thermal-to-electrical conversion device for space applications. However, heat transfer remains a critical factor obstructing its scalability to high power levels. In this study, we propose a high-power FPSG with a gas-compressing self-circulating heat exchanger (GSHX) that utilizes high-pressure helium as the heat transfer fluid, enhancing the reliability of the heat exchanger. By incorporating a built-in gas-compressing device and check valves, the performance degradation caused by significant pressure fluctuations in the resonant self-circulating heat exchanger is effectively addressed. Through detailed simulations, we demonstrate the principle feasibility of the GSHX with a 4 m heat transfer loop, achieving an output electric power of 11 kW and a thermal-to-electric efficiency of 20.4%. Furthermore, the GSHX exhibits similar operating characteristics to traditional FPSGs, as we analyze the systems' stability under various operating conditions. Importantly, our findings reveal that the acoustic characteristics of the loop play a pivotal role in system performance, with operating parameters exhibiting periodic variations related to helium wavelength. By carefully optimizing system parameters, GSHX with a long loop length of 29 m can achieve comparable performance to systems with shorter loops. This study provides valuable insights into the operational characteristics and performance optimization of the GSHX, offering a comprehensive heat transfer solution for long-distance, high-efficiency, and highly reliable applications of high-power FPSGs in the space domain.

Parametric Study and Optimization of Hydrogen Production Systems Based on Solar/Wind Hybrid Renewable Energies: A Case Study in Kuqa, China

Based on the concept of sustainable development, to promote the development and application of renewable energy and enhance the capacity of renewable energy consumption, this paper studies the design and optimization of renewable energy hydrogen production systems. For this paper, six different scenarios for grid-connected and off-grid renewable energy hydrogen production systems were designed and analyzed economically and technically, and the optimal grid-connected and off-grid systems were selected. Subsequently, the optimal system solution was optimized by analyzing the impact of the load data and component capacity on the grid dependency of the grid-connected hydrogen production system and the excess power rate of the off-grid hydrogen production system. Based on the simulation results, the most matched load data and component capacity of different systems after optimization were determined. The grid-supplied power of the optimized grid-connected hydrogen production system decreased by 3347 kWh, and the excess power rate of the off-grid hydrogen production system decreased from 38.6% to 10.3%, resulting in a significant improvement in the technical and economic performance of the system.

Design of PMSM Dual-Loop Control Systems Integrating LADRC and PI Controllers via an Improved PSO Algorithm

This paper is concerned with the design of a dual-loop control system for permanent magnet synchronous motor (PMSM). An improved linear extended state observer (LESO) with excellent estimation capability is employed to develop an improved linear active disturbance rejection control (LADRC) suitable for PMSM speed regulation, achieving outstanding disturbance suppression in PMSM speed control. The use of an internal model control scheme to initialize the parameters of the proportional-integral- (PI-) based current controller simplifies the search space of the control system parameter optimization. An improved particle swarm optimization (PSO) algorithm is applied to optimize the controller parameters, thereby enhancing the overall system performance. Finally, through a series of simulations and experiments, we validate that our proposed controller exhibits superior performance compared to some other control methods.

Optimization of system parameters during conjugate mixed convective flow in a square domain with an oscillating spinning cylinder

A computational analysis has been executed to analyze the combined conduction-mixed convection heat transfer of a rotationally oscillating solid cylinder in a differentially heated square box filled with air. The conjugate mixed convective flow initiates the heat transfer process, where the left-side boundary is isothermally kept to a higher temperature, and the right-side boundary is maintained at a lower temperature. Conduction heat transfer takes place inside the solid cylinder. Navier-Stokes and heat energy conservation equations model the system in the dimensionless pressure-velocity formulation. All these equations are solved via the Galerkin finite element approach. Three different combinations of Grashof (103-105), Reynolds (32–316), and Richardson (0.1–10) numbers are examined to systematically investigate the variations of governing parameters on instantaneous Nusselt numbers and the respective time-averaged values along the hot wall. In each combination, the impacts of the oscillating amplitude and frequency and the variation of cylinder diameter are examined to perform the optimization study. Power spectrum analysis is also done using the Fast Fourier Transform in the frequency domain to visualize the principal frequency of the system. The instantaneous values of the Nusselt number exhibit a wavering pattern over time owing to the recurrent waning and waxing of the thermal boundary layer. For all the cases, the maximum diameter and oscillating amplitude of the cylinder are found to maximize the heat transfer. However, the optimized frequency of the oscillation strongly depends on the selection of the governing parameters. In addition, the principal thermal frequency of the system is determined to be independent of the oscillation frequency.

5G network model development in AnyLogic environment

Problem statement. The development of mobile communication networks does not stop for a minute, new standards and various new technologies are emerging. The opportunities provided by mobile technologies have long gone beyond voice services. The sharp growth of Internet traffic in networks around the world is explained by the widespread use of devices connected to mobile networks. With the development of mobile networks, new and diverse requirements are being imposed on them. Technology development is aimed at increasing productivity and expanding opportunities. Therefore, it becomes necessary to study the influence of the characteristics of additional parameters (delays, station loads) on the quality of data packet processing in order to optimize the operation of 5G networks and ensure more efficient use of resources. Many scientists who conducted research on 5G networks using modeling did not take into account various variants of queue processing algorithms, did not consider how the values of data transmission delays and the number of lost packets would change with an increase or decrease in the number of base stations, models consisting of several base stations were not considered. Based on this, we will conclude that it is advisable to develop a 5G network model, with the help of which various studies can be carried out, which will take into account the above parameters. The goal is to investigate the resource model of data transmission in 5G networks, to analyze the operation of the 5G network model in the AnyLogic environment. Results. During the study of traffic maintenance in the 5G network, the optimal system parameters were determined, allowing the most efficient use of network resources. It turned out that the use of methods of dynamic resource allocation and automatic change of base station parameters reduces the load on the network and increases its performance. The developed model allows us to assess the reliability of the system and its ability to process user requests, because the probability of losing an application or non-fulfillment of an application is high, this can lead to user dissatisfaction and a decrease in the quality of service. Practical significance. The developed model can be used in practice to study and evaluate the parameters of the 5G network, as well as to analyze various characteristics that affect the quality of the network in order to improve it. This can help telecom operators optimize their networks and ensure more efficient use of resources.

Digital twin of heat exchange station

The article describes the identification of a heat exchanger system using PLC for measurement, followed by system model identification and reconstruction in Matlab Simulink and SIMIT. The root-mean-square error of the identified system was 2.49 % on training data and 5.33 % in the validation scenario. The system model is suitable for predicting and simulating errors and anomalies; it can be used for the optimisation of control processes or system parameters. Therefore, the model can serve as an educational tool, allowing training of students, operators and other experts. They can experiment without fear of damaging components and without having to wait for the long-time transients of a real system.

Beyond helium: hydrogen as a carrier gas in multiresidue pesticide analysis in fruits and vegetables by GC-MS/MS.

In this comprehensive study, we evaluated the feasibility of using hydrogen instead of helium as a carrier gas in a GC-MS/MS system for pesticide residue analysis, spanning three matrices: pepper, tomato, and zucchini. Initial assessments focused on the ion source's chemical inertness, employing nitrobenzene as a benchmark to monitor the hydrogenation process. A method with a duration of less than 12 minutes was developed, achieving good chromatographic peak resolution attributable to the enhanced chromatographic performance of hydrogen as a carrier gas. The study emphasized the optimization of system parameters, testing various ion source temperatures, detector voltages, and injection volumes. Sensitivity assessments, based on the DG-SANTE criteria, indicated that the majority of compounds were identifiable at a concentration of 5 μg kg-1 (81% in tomato, 84% in pepper and 73% in zucchini). Detailed validation for reproducibility, matrix effects, and linearity across 150 pesticides unveiled generally favorable outcomes, with a notable majority of compounds displaying low matrix effects, satisfactory linearity ranges and good reproducibility with most compounds returning a relative standard deviation (RSD) below 10%. When applied to 15 real samples, the hydrogen-based system's performance was juxtaposed against a helium-based counterpart, revealing that results are very comparable between both systems. This comparative approach highlights hydrogen's potential as a reliable and efficient carrier gas in pesticide residue analysis for routine food control laboratories, overcoming difficulties resulting from the lack of helium supplies.

The design and operation optimization for conductive rubber heated snow melting bridge deck pavement

In order to solve the problem of ice and snow disasters on roads, this study proposed a new technology for active deicing of bridge decks using conductive rubber heating system (CRHS) as the heat source. The existing measures to improve the operation efficiency of snowmelt pavement often only optimize 1–2 influencing factors, and lack of optimization of multiple factors such as paving design, operation and environment. Therefore, in order to improve the operation efficiency, this study carried out multi-objective optimization on the design and operation of conductive rubber heated snow melting bridge deck pavement. The bridge deck pavement scheme of conductive rubber heating system was proposed, and its two-dimensional numerical model of heat transfer was established. The laying spacing of conductive rubber (s), heating power (p), ambient temperature (t) and wind speed (v) were used as input parameters for system operation optimization. Predictive models for total heating time (THT) and the loss of energy rate (LER) were developed using response surface methodology (RSM). The specific effects of input parameters on THT and LER and their interactions were analyzed by thermal simulation. Aiming at less THT and lost energy (LE), the optimal input parameters of conductive rubber heating system were obtained by the multi-objective particle swarm algorithm. The results indicated that shorter THT and lower LE could be achieved when the laying spacing (s) was 0.25 m, the ambient temperature (t) was −3 °C, the wind speed (v) was 2 m/s, and the input power was 550 W/m2. And adjusting s was more effective than adjusting p to obtain low THT and LER. The results and recommendations can effectively guide the operation of conductive rubber heating system. The multi-objective optimization research idea of improving the operation efficiency of snowmelt pavement in this paper can provide reference for similar deicing pavement applications.

Formation of optimal parameters of the cargo ship maintenance system through digital strategies

Contemporary merchant ships play a key role in the international transportation system, ensuring the transportation of large volumes of cargo around the world. This places great demands on the efficiency and reliability of shipboard equipment. Cargo ship maintenance systems play an important role in ensuring the proper functioning of ships and their safety. However, there are certain problems associated with the current state of ship maintenance systems. In particular, the issues of reliability and efficiency of maintenance are critical for optimizing costs and increasing the efficiency of maritime transport. Optimal parameters of the maintenance system can solve these problems and improve the operation of cargo ships. The purpose of the article is to consider and justify the optimal parameters of the ship maintenance system based on the application of digital strategies. The problem statement includes the need to increase the reliability of ship equipment, reduce costs and improve operational performance. An analysis of previous research and publications is used to identify potential challenges and demonstrate how the use of digital strategies can address these issues. The article includes a detailed literature review and discusses state-of-the-art methods for optimizing maintenance parameters, including stochastic aspects and reliability methods. The conclusions of the article point to the importance of reorganizing and optimizing ship maintenance systems and emphasize the role of digital strategies in achieving this goal. The study helps to solve key problems in the field of cargo ship maintenance and improve the efficiency and reliability of ship equipment. The results of the study have practical application for shipping companies and organizations operating in the field of maritime transport