Method for simulating heat transfer performance of ship heat exchanger

Abstract

The invention discloses a ship heat exchanger heat transfer performance simulation method, particularly relates to the field of heat transfer, and comprises the steps of multi-source data acquisition, characteristic parameter calculation, heat transfer stability diagnosis, comprehensive heat transfer degradation evaluation, structure reliability prediction and system efficiency comprehensive evaluation. Multi-dimensional data synchronous acquisition is realized through a multi-source sensor network, a dynamic coupling analysis model is constructed to accurately calculate the heat transfer stability and the comprehensive degradation index, a three-level early warning mechanism is established to improve the state evaluation accuracy, turbulence attenuation characteristics and vibration data are fused to predict the structural reliability, and the reliability of the system is improved. And closed-loop management and control is formed by combining an efficiency index triple evaluation system, so that the spanning from single alarm to preventive maintenance is realized, the health management capability of the system is comprehensively enhanced, and full-life-cycle dynamic optimization support is provided for the ship heat exchanger.

Description

Technical Field

[0001] The present invention relates to the field of heat transfer technology, and more particularly to a method for simulating heat transfer performance of a ship heat exchanger. Background Art

[0002] As the core heat transfer unit of the power system, the performance of the ship's heat exchanger directly affects the ship's energy efficiency and equipment reliability. Traditional simulation methods mainly rely on single-dimensional parameter monitoring and static thermal testing, estimating heat transfer efficiency through temperature gradient measurement and empirical formulas, and coordinating regular manual inspections to evaluate the dirt deposition status. However, it is difficult to achieve holographic perception of the operating status and dynamic degradation warning.

[0003] The existing technical implementation process usually uses fixed temperature sensors to obtain local thermal parameters, combines periodic shutdown inspections to record dirt thickness, calculates the heat transfer coefficient based on a steady-state heat transfer model, and triggers a basic alarm when the detection value falls below a preset threshold. It lacks the ability to analyze the coupling effect of fluid and structure in real time under dynamic working conditions.

[0004] Current technology has three defects: first, the monitoring dimension is limited to a single thermal parameter, which cannot capture the synergistic effects of fluid pulsation, mechanical vibration and thermoelastic deformation; second, it adopts an isolated parameter threshold judgment mode, ignoring the comprehensive degradation law under the coupling of multiple physical fields; third, there is a lack of structural reliability prediction mechanism and system performance dynamic evaluation system, which makes it difficult to support preventive maintenance decisions. Summary of the Invention

[0005] In order to overcome the above-mentioned defects of the prior art, the present invention provides a method for simulating the heat transfer performance of a ship heat exchanger. Through the following scheme, it solves the problems proposed in the above-mentioned background technology, such as the single monitoring dimension, isolated parameter analysis, static thresholds that are not suitable for dynamic working conditions, and lack of structural reliability prediction and closed-loop management mechanism, which lead to missed abnormalities and delayed maintenance.

[0006] To achieve the above objectives, the present invention provides the following technical solution: a method for simulating heat transfer performance of a ship heat exchanger, comprising:

[0007] S1: Multi-source data acquisition: Deploy a sensor network to synchronously collect fluid dynamic characteristics data, structural vibration signal data, dirt deposition state data, thermoelastic parameter data, temperature field distribution data, and turbulence field characteristics data to obtain the original data set as the basis for analysis;

[0008] S2: Characteristic parameter calculation: The collected raw data is converted into characteristic parameters through a preset algorithm, including fluid pulsation intensity coefficient, vibration frequency response, dynamic fouling coefficient, transient thermal stress, expansion difference rate, and turbulent vortex attenuation characteristics;

[0009] S3: Heat transfer stability diagnosis: Calculates the heat transfer stability index based on fluid pulsation intensity and vibration frequency response, and uses the no-load vibration benchmark and critical vibration threshold to determine the status. The condition that triggers the high-temperature mode analysis branch is controlled by the experimental calibration threshold.

[0010] S4: Comprehensive heat transfer degradation assessment: Integrates the heat transfer stability index with the dynamic fouling coefficient, transient thermal stress, and expansion differential rate parameters to calculate the comprehensive heat transfer degradation index. This index is then compared with the threshold set by historical fault data to trigger a graded warning.

[0011] S5: Structural reliability prediction: Calculate the structural reliability coefficient based on the comprehensive heat transfer degradation index and turbulent vortex attenuation characteristics, and generate the structural safety status judgment result using the attenuation benchmark calibrated by the shaking table test and the safety threshold of the design specification;

[0012] S6: Comprehensive evaluation of system performance: The system performance index is generated by integrating the structural reliability coefficient, heat transfer stability index and thermal stress parameters. The evaluation conclusion is output according to the preset performance level classification standard and fed back to the management and control system.

[0013] Preferably, the fluid dynamic characteristic data is obtained by continuously collecting raw fluid velocity data at a sampling frequency of more than 1 kHz using a high-frequency ultrasonic flowmeter, and the working fluid density and dynamic viscosity data are obtained using a densitometer and a rotational viscometer, and characteristic diameter data are extracted in combination with the heat exchanger flow channel geometric design drawings.

[0014] Preferably, the structural vibration signal data is collected by a MEMS triaxial accelerometer on the pipe wall at a sampling rate of 10kHz, the pipe length and wall thickness parameters are obtained according to the design drawings, the natural frequency data is measured using a dynamic signal analyzer, and the material elastic modulus data is obtained by querying the ASME specifications.

[0015] Preferably, the dirt deposition state data is obtained by obtaining the inlet and outlet pressure drop data in real time through a differential pressure transmitter, collecting the inner and outer wall temperature difference data using a wall thermocouple array, regularly photographing the pipe wall deposition state image data with an industrial endoscope, and fitting the characteristic time constant data using historical maintenance records.

[0016] Preferably, the thermoelastic parameter data is obtained by acquiring temperature gradient data at a sampling rate of 100 Hz through a micro-thin film thermocouple array, the material thermal expansion coefficient data is measured using a thermomechanical analyzer, and the thermal conductivity and specific heat capacity data are measured using a flash thermal property analyzer.

[0017] Preferably, the temperature field distribution data is obtained by scanning the surface temperature field distribution data with an infrared thermal imager at a refresh rate of 5 Hz, the local composition data of the material is determined by an electron probe microanalyzer, and the residual stress distribution data is obtained in combination with an X-ray diffractometer.

[0018] Preferably, the turbulence field characteristic data is captured by a particle image velocimetry system to capture the flow field tracer particle motion data, a dual-pulse laser sheet light source and a high-speed camera are used to record the flow image data, a rotational viscometer is used to measure the kinematic viscosity data, and the vortex characteristic scale data is extracted through correlation function analysis.

[0019] Preferably, the fluid pulsation intensity coefficient characterizes the flow instability by statistically analyzing the ratio of the standard deviation of the fluid velocity fluctuation to the average flow velocity, and introduces a Reynolds number correction term to eliminate the scale effect; the vibration frequency response non-dimensionalizes the root mean square value of the vibration acceleration through the elastic modulus and geometric dimensions of the pipe, and reflects the resonance risk in combination with the ratio of the natural frequency to the critical frequency; the dynamic fouling coefficient uses an exponential decay weighted integral to express the time-dependent characteristics of fouling deposition, and establishes a deposition dynamics model by inverting the thermal resistance change rate through pressure difference; the transient thermal stress is based on Fourier's heat conduction law and thermoelasticity theory, and is related to the square root relationship of the temperature change rate, the thermal expansion coefficient of the material, and the thermal diffusivity; the expansion difference rate reflects the interaction between the temperature field distribution and the anisotropy of the material by normalizing the difference between the maximum and minimum local expansion amounts relative to the average expansion amount; the turbulent vortex attenuation characteristic is based on the turbulent kinetic energy transport equation, constructs the proportional relationship between the pulsation velocity cube and the dissipation rate, and combines the kinematic viscosity and the dimensionless expression of the energy attenuation mechanism.

[0020] Preferably, the fluid pulsation intensity coefficient is specifically expressed as: σ v : Standard deviation of flow rate, Average flow velocity, Re: Reynolds number, Re ref =10 4 : Reference Reynolds number; the vibration frequency response is specifically expressed as: a rms : RMS vibration acceleration, L: pipe length, E: Young's modulus, δ: wall thickness, f n : Measured natural frequency, f c =50Hz: critical frequency; the dynamic fouling coefficient is specifically expressed as: t0: the starting time of dirt monitoring, t: the current analysis time, Fouling deposition rate, τ = 3600s: characteristic time constant; the transient thermal stress is specifically expressed as: α: thermal expansion coefficient, κ: thermal conductivity, ρ: density, c p : specific heat capacity, Temperature change rate; the expansion difference rate is specifically expressed as: β: local expansion coefficient, ΔT: regional temperature difference, N: total number of partitions, β i ΔT i : The product of the local expansion coefficient and the temperature difference of the i-th region, β j ΔT j: Similar parameters of the jth region, j and i are the same region number, and the separate marks are only used to distinguish the maximum and minimum value operation objects, β k ΔT k : kth region parameter, k is used to traverse all N regions for summation operation; the turbulent eddy attenuation characteristics are specifically expressed as: u′: turbulent fluctuation velocity, ε: turbulent energy dissipation rate, L ε : characteristic eddy size, ν: kinematic viscosity.

[0021] Preferably, the heat transfer stability index is specifically expressed as: HSI = FPI·ln(1+VFR / VFR0)+|VFR-VFR c | / VFR c , VFR0: no-load vibration reference, VFR c : Critical vibration threshold.

[0022] Preferably, HSI>H c When HSI≤H c When maintaining standard analytical procedures, H c : High temperature threshold.

[0023] Preferably, the high temperature mode analysis branch includes:

[0024] Material creep correction: Introducing time-varying Young's modulus Dynamically update the vibration response calculation, where γ is the high-temperature creep coefficient obtained through material endurance testing, T_ref is the reference temperature, E0 is the initial Young's modulus at the standard temperature, T is the real-time monitored absolute temperature of the pipe wall, and Δt is the cumulative action time of the high-temperature condition;

[0025] Thermal radiation enhancement: superimpose radiation heat flux terms in heat transfer calculations Emissivity ε q Calibrated by high temperature oxidation surface test, σ is the Stefan-Boltzmann constant, T amb is the absolute temperature of the surrounding medium;

[0026] Cooling strategy optimization: Dynamically adjust the cooling medium flow rate v based on the real-time TED value new =v design ×[1+tanh(TED / TED c )], among which TED c is the critical value of turbulence attenuation, determined by high-temperature fluid-solid coupling simulation, v design It is the rated cooling medium flow rate of the heat exchanger under standard design conditions.

[0027] Preferably, the comprehensive heat transfer degradation index is specifically expressed as: TSA0: material allowable stress threshold, taken as 10% of yield strength; CHDc: warning threshold, determined based on historical failure data statistics.

[0028] Preferably, CHD>CHD c The third level dirt warning is triggered when the CHD increase is greater than ΔCHD for three consecutive times. c When the maintenance request is triggered, ΔCHD c is the critical change rate threshold of the comprehensive heat transfer degradation index.

[0029] Preferably, the three-level fouling warning includes:

[0030] Level 1 warning: Activated when the comprehensive heat transfer degradation index exceeds the historical statistical threshold for the first time, the system automatically increases the sampling frequency of the fouling coefficient to three times the standard value and simultaneously starts the automatic comparison function of the tube wall deposition image. The fouling type is confirmed through the correlation analysis of the real-time DFC change rate and the TSA / UER ratio.

[0031] Level 2 warning: When the CHD increase ΔCHD>0.2CHD for three consecutive sampling periods c When triggered, forced intervention in operation control: the cooling medium flow rate is increased to 120% of the design value, the heat exchanger bypass valve opening is adjusted to reduce the flow rate by 5%, and the ultrasonic descaling device is activated to run intermittently at 50% power, and a deposition rate prediction curve is generated for the operator to confirm;

[0032] Level 2 warning: When the CHD value reaches 2.3 times CHD c When forced to execute, immediately start the thermal resistance inversion verification program to verify the dirt thickness distribution model. If the DFC integral value exceeds the material tolerance limit DFC max , the interlock shutdown and activation of the chemical cleaning circuit, synchronously upload the axial dirt accumulation gradient data of the tube bundle to the maintenance system to generate the cleaning path plan.

[0033] Preferably, the structural reliability coefficient is specifically expressed as: TED0 is the attenuation reference.

[0034] Preferably, a structural safety alarm is triggered when SRC < 1.0, and 1.0 ≤ SRC c Prompt preventive maintenance, S c is the safety threshold.

[0035] Preferably, the system performance index is specifically expressed as: η0 is the benchmark heat transfer efficiency of the heat exchanger under standard test conditions.

[0036] ​Preferably, CEI ≥ 0.9 is an excellent level, indicating that the system is in the optimal heat transfer state; 0.7 ≤ CEI < 0.9 is a good level, indicating that there is room for optimizing heat transfer parameters; CEI < 0.7 is a maintenance level, confirming that the system performance has fallen below the safe operation benchmark.

[0037] Technical effects and advantages of the present invention:

[0038] 1. This invention builds a multi-source heterogeneous sensor collaborative monitoring network to synchronously collect multi-dimensional data such as fluid dynamic characteristics, structural vibration signals, and thermoelastic parameters. It combines high-frequency ultrasonic flowmeters with MEMS triaxial accelerometers to achieve microsecond-level dynamic response, effectively overcoming the blind spots of traditional single-parameter monitoring systems in sensing complex working conditions and significantly improving the ability to identify abnormal working conditions early.

[0039] 2. Based on a dynamic coupling analysis method, a calculation model for the heat transfer stability index and comprehensive degradation index is established. The correlation analysis between fluid pulsation intensity and vibration response reveals the heat transfer performance degradation mechanism. Combined with the dynamic weighted integration of the fouling coefficient and thermal stress parameters, this system achieves a leapfrog upgrade from a single threshold alarm to a multi-level early warning mechanism, significantly enhancing the comprehensiveness and accuracy of system status assessment.

[0040] 3. The innovative structural reliability prediction module integrates turbulence attenuation characteristics and vibration table test data to build a safety evaluation system. Combined with the system efficiency index triple evaluation model, it forms a "monitoring-diagnosis-early warning-decision-making" closed-loop control chain, completely changing the traditional passive maintenance mode and providing full life cycle health management support for the heat exchange system. BRIEF DESCRIPTION OF THE DRAWINGS

[0041] Figure 1 It is a schematic diagram of the overall structure of the present invention.

[0042] Figure 2 Schematic diagram of the control logic structure of the present invention. DETAILED DESCRIPTION

[0043] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative efforts are within the scope of protection of the present invention.

[0044] refer to Figure 1-Figure 2 A method for simulating heat transfer performance of a ship heat exchanger is shown, comprising:

[0045] S1: Multi-source data acquisition: Deploy a sensor network to synchronously collect fluid dynamic characteristics data, structural vibration signal data, dirt deposition status data, thermoelastic parameter data, temperature field distribution data, and turbulence field characteristics data to obtain the original data set as the basis for analysis.

[0046] The fluid dynamic characteristic data is obtained by continuously collecting fluid velocity raw data at a sampling frequency of more than 1 kHz using a high-frequency ultrasonic flowmeter, and the working fluid density and dynamic viscosity data are obtained using a densitometer and a rotational viscometer, and characteristic diameter data are extracted in combination with the heat exchanger flow channel geometric design drawings.

[0047] The structural vibration signal data is collected by a MEMS triaxial accelerometer at a sampling rate of 10 kHz on the pipe wall, the pipe length and wall thickness parameters are obtained according to the design drawings, the natural frequency data is measured using a dynamic signal analyzer, and the material elastic modulus data is obtained by querying the ASME specifications.

[0048] The dirt deposition status data is obtained by obtaining the inlet and outlet pressure drop data in real time through a differential pressure transmitter, and the inner and outer wall temperature difference data is collected by using a wall thermocouple array. The pipe wall deposition status image data is regularly taken with an industrial endoscope, and the characteristic time constant data is fitted using historical maintenance records.

[0049] Thermoelastic parameter data were obtained by acquiring temperature gradient data at a sampling rate of 100 Hz through a micro-thin film thermocouple array. The thermal expansion coefficient data of the material was determined by a thermomechanical analyzer, and the thermal conductivity and specific heat capacity data were measured using a flash thermal physical property analyzer.

[0050] The surface temperature field distribution data was obtained by scanning with an infrared thermal imager at a refresh rate of 5 Hz, the local composition data of the material was determined by an electron probe microanalyzer, and the residual stress distribution data was obtained by combining an X-ray diffractometer.

[0051] The turbulent field characteristic data are captured by a particle image velocimetry system to capture the flow field tracer particle motion data, a dual-pulse laser sheet light source and a high-speed camera are used to record the flow image data, a rotational viscometer is used to measure the kinematic viscosity data, and the vortex characteristic scale data are extracted through correlation function analysis.

[0052] S2: Characteristic parameter calculation: The collected raw data is converted into characteristic parameters through a preset algorithm, including fluid pulsation intensity coefficient, vibration frequency response, dynamic fouling coefficient, transient thermal stress, expansion difference rate and turbulent vortex attenuation characteristics.

[0053] The fluid pulsation intensity coefficient characterizes flow instability by statistically analyzing the ratio of the standard deviation of fluid velocity fluctuation to the average flow velocity, and introduces a Reynolds number correction term to eliminate scale effects. The vibration frequency response non-dimensionalizes the root mean square value of the vibration acceleration through the elastic modulus and geometric dimensions of the pipe, and reflects the resonance risk by combining the ratio of the natural frequency to the critical frequency. The dynamic fouling coefficient uses an exponential decay weighted integral to express the time-dependent characteristics of fouling deposition, and establishes a deposition dynamics model by inverting the thermal resistance change rate through pressure difference. The transient thermal stress is based on Fourier's heat conduction law and thermoelasticity theory, and is related to the square root relationship of the temperature change rate, the thermal expansion coefficient of the material, and the thermal diffusivity. The expansion difference rate reflects the interaction between the temperature field distribution and the anisotropy of the material by normalizing the difference between the maximum and minimum local expansion amounts relative to the average expansion amount. The turbulent vortex attenuation characteristic is based on the turbulent kinetic energy transport equation, constructs the proportional relationship between the pulsating velocity cube and the dissipation rate, and combines the kinematic viscosity and the dimensionless vortex scale to express the energy attenuation mechanism.

[0054] The fluid pulsation intensity coefficient is specifically expressed as: σ v : Standard deviation of flow rate, Average flow velocity, Re: Reynolds number, Re ref =10 4 : Reference Reynolds number; the vibration frequency response is specifically expressed as: a rms : RMS vibration acceleration, L: pipe length, E: Young's modulus, δ: wall thickness, f n : Measured natural frequency, f c =50Hz: critical frequency; the dynamic fouling coefficient is specifically expressed as: t0: the starting time of dirt monitoring, t: the current analysis time, Fouling deposition rate, τ = 3600s: characteristic time constant; the transient thermal stress is specifically expressed as: Coefficient of thermal expansion, κ: thermal conductivity, ρ: density, c p : specific heat capacity, Temperature change rate; the expansion difference rate is specifically expressed as: β: local expansion coefficient, ΔT: regional temperature difference, N: total number of partitions, β i ΔT i : The product of the local expansion coefficient and the temperature difference of the i-th region, β j ΔT j : Similar parameters of the jth region, j and i are the same region number, and the separate marks are only used to distinguish the maximum and minimum value operation objects, β k ΔT k : kth region parameter, k is used to traverse all N regions for summation operation; the turbulent eddy attenuation characteristics are specifically expressed as: u′: turbulent fluctuation velocity, ε: turbulent energy dissipation rate, L ε : characteristic eddy size, ν: kinematic viscosity.

[0055] S3: Heat transfer stability diagnosis: The heat transfer stability index is calculated based on the fluid pulsation intensity and vibration frequency response. The state is judged by combining the no-load vibration benchmark and critical vibration threshold. The condition for triggering the high-temperature mode analysis branch is controlled by the experimental calibration threshold.

[0056] The heat transfer stability index is constructed by integrating the fluid pulsation intensity and the pipe wall vibration frequency response. A logarithmic function is used to quantify the vibration energy accumulation effect, and the absolute value term is combined to characterize the degree to which the vibration state deviates from the critical threshold. The fluid pulsation intensity reflects the contribution of flow instability, and the vibration frequency response includes the influence of structural dynamic characteristics. The key threshold no-load vibration reference value is determined by analyzing the vibration spectrum of the shutdown state. The critical vibration threshold is calibrated based on material fatigue test data. When the index calculation result exceeds the high temperature threshold, the operating mode switch is triggered. The high temperature threshold is obtained through regression analysis of bench test data simulating extreme working conditions.

[0057] The heat transfer stability index is specifically expressed as: HSI = FPI·ln(1+VFR / VFR0)+|VFR-VFR c | / VFR c , VFR0: no-load vibration reference, VFR c : critical vibration threshold;

[0058] HSI>H c When HSI≤H c When maintaining standard analytical procedures, H c : High temperature threshold.

[0059] The high temperature mode analysis branch includes:

[0060] Material creep correction: Introducing time-varying Young's modulus Dynamically update the vibration response calculation, where γ is the high-temperature creep coefficient obtained through material endurance testing, T_ref is the reference temperature, E0 is the initial Young's modulus at the standard temperature, T is the real-time monitored absolute temperature of the pipe wall, and Δt is the cumulative action time of the high-temperature condition;

[0061] Thermal radiation enhancement: superimpose radiation heat flux terms in heat transfer calculations Emissivity ε q Calibrated by high temperature oxidation surface test, σ is the Stefan-Boltzmann constant, T amb is the absolute temperature of the surrounding medium;

[0062] Cooling strategy optimization: Dynamically adjust the cooling medium flow rate v based on the real-time TED value new=v design ×[1+tanh(TED / TED c )], among which TED c is the critical value of turbulence attenuation, determined by high-temperature fluid-solid coupling simulation, v design It is the rated cooling medium flow rate of the heat exchanger under standard design conditions.

[0063] S4: Comprehensive heat transfer degradation assessment: Integrates the heat transfer stability index with the dynamic fouling coefficient, transient thermal stress, and expansion differential rate parameters to calculate the comprehensive heat transfer degradation index. A graded warning is triggered by comparing the thresholds set against historical fault data.

[0064] The construction of the comprehensive heat transfer degradation index adopts the product of the main controlling factors of heat transfer stability and fouling deposition as the core term, dynamically suppresses and adjusts through the transient thermal stress square term, and superimposes the cube root effect of non-uniform expansion differences to form a dual-path coupling mechanism. The numerator term reflects the synergistic degradation effect of fluid vibration and fouling deposition, the denominator term establishes negative feedback compensation for thermal stress excess, and the cube root term reflects the progressive impact of local expansion differences. Multi-dimensional normalization is achieved through the benchmark stress threshold.

[0065] The comprehensive heat transfer degradation index is specifically expressed as: TSA0: material allowable stress threshold, taken as 10% of yield strength; CHDc: warning threshold, determined based on historical failure data statistics;

[0066] In CHD>CHD c The third level dirt warning is triggered when the CHD increase is greater than ΔCHD for three consecutive times. c When the maintenance request is triggered, ΔCHD c is the critical change rate threshold of the comprehensive heat transfer degradation index.

[0067] The three-level fouling warning includes:

[0068] Level 1 warning: Activated when the comprehensive heat transfer degradation index exceeds the historical statistical threshold for the first time, the system automatically increases the sampling frequency of the fouling coefficient to three times the standard value and simultaneously starts the automatic comparison function of the tube wall deposition image. The fouling type is confirmed through the correlation analysis of the real-time DFC change rate and the TSA / UER ratio.

[0069] Level 2 warning: When the CHD increase ΔCHD>0.2CHD for three consecutive sampling periods c When triggered, forced intervention in operation control: the cooling medium flow rate is increased to 120% of the design value, the heat exchanger bypass valve opening is adjusted to reduce the flow rate by 5%, and the ultrasonic descaling device is activated to run intermittently at 50% power, and a deposition rate prediction curve is generated for the operator to confirm;

[0070] Level 2 warning: When the CHD value reaches 2.3 times CHD c When forced to execute, immediately start the thermal resistance inversion verification program to verify the dirt thickness distribution model. If the DFC integral value exceeds the material tolerance limit DFC max , the interlock shutdown and activation of the chemical cleaning circuit, synchronously upload the axial dirt accumulation gradient data of the tube bundle to the maintenance system to generate the cleaning path plan.

[0071] S5: Structural reliability prediction: The structural reliability coefficient is calculated based on the comprehensive heat transfer degradation index and turbulent vortex attenuation characteristics, and the structural safety status judgment result is generated using the attenuation benchmark calibrated by the shaking table test and the design specification safety threshold.

[0072] The structural reliability coefficient integrates the material elastic modulus, tube wall geometric parameters, comprehensive heat transfer degradation index and turbulent vortex attenuation characteristics to construct an evaluation model, and associates the inherent mechanical properties of the tube bundle with real-time operating parameters: the elastic modulus and the cube of the wall thickness are used to characterize the structural deformation resistance, the square of the tube length reflects the span effect, the comprehensive heat transfer degradation index quantifies the degree of performance attenuation, and the turbulent attenuation characteristics are standardized and then characterized by an exponential function to characterize the vortex-induced vibration energy dissipation efficiency. Finally, the multi-dimensional parameters are integrated into a dimensionless reliability coefficient, and a dynamic evaluation of the structural safety margin is achieved by comparing the attenuation benchmark calibrated by the vibration table test with the safety threshold determined by the design specification.

[0073] The structural reliability coefficient is specifically expressed as: TED0 is the attenuation reference;

[0074] When SRC < 1.0, the structural safety alarm is triggered. 1.0 ≤ SRC c Prompt preventive maintenance, S c is the safety threshold.

[0075] S6: Comprehensive evaluation of system performance: The system performance index is generated by integrating the structural reliability coefficient, heat transfer stability index and thermal stress parameters. The evaluation conclusion is output according to the preset performance level classification standard and fed back to the management and control system.

[0076] The system efficiency index is constructed through the coupling of multi-dimensional parameters, with the structural reliability coefficient representing the mechanical safety margin as the core base number. The attenuation correction term of the heat transfer stability index is introduced to reflect the impact of dynamic working conditions. The inverse square relationship between transient thermal stress and expansion difference rate is used to quantify the thermal imbalance risk. Finally, the initial efficiency parameters of the equipment are integrated through the efficiency weight coefficient to form a triple evaluation system from structural integrity, heat transfer stability to thermal balance.

[0077] The system performance index is specifically expressed as: η0 is the benchmark heat transfer efficiency of the heat exchanger under standard test conditions; ​

[0078] When CEI ≥ 0.9, it is an excellent level, indicating that the system is in the optimal heat transfer state; when 0.7 ≤ CEI < 0.9, it is a good level, indicating that there is room for optimizing heat transfer parameters; when CEI < 0.7, it is a maintenance level, confirming that the system performance has fallen below the safe operation benchmark.

[0079] The present invention first deploys a multi-source sensor network to synchronously collect fluid dynamic characteristics, structural vibration signals, fouling deposition status, thermoelastic parameters, temperature field distribution and turbulence field characteristic data, and obtains the original data set through high-frequency ultrasonic flowmeters, MEMS three-axis accelerometers, differential pressure transmitters, infrared thermal imagers and other equipment; then the collected data are converted into characteristic parameters such as fluid pulsation intensity coefficient, vibration frequency response, dynamic fouling coefficient, transient thermal stress, expansion difference rate and turbulent vortex attenuation characteristics through a preset algorithm, involving key operations such as standard deviation ratio calculation, dimensionless processing, exponential decay weighted integration and turbulent kinetic energy equation construction; then the heat transfer stability index is calculated based on the fluid pulsation intensity and vibration response, and the system state is judged by combining the no-load vibration benchmark and the critical threshold. When the high temperature threshold is exceeded, the heat transfer stability index is calculated. When the value is set, the high-temperature analysis branch including material creep correction, thermal radiation enhancement and cooling strategy optimization is activated; then the heat transfer stability index is integrated with the fouling coefficient, thermal stress, and expansion difference rate parameters to generate a comprehensive heat transfer degradation index. By comparing the historical fault thresholds, a three-level early warning mechanism is triggered, including sampling frequency increase, flow rate adjustment, ultrasonic descaling activation and shutdown cleaning decision; then the structural reliability coefficient is calculated according to the comprehensive degradation index and turbulence attenuation characteristics, and the structural safety status is determined by combining the vibration table test data and safety regulations; finally, the system efficiency index is constructed by integrating the structural reliability, heat transfer stability and thermal stress parameters, and the benchmark heat transfer efficiency is integrated through the weight coefficient to form a triple evaluation system, which outputs excellent, good or maintenance-pending conclusions according to the preset grade standards and feeds back to the control system to complete closed-loop management.

[0080] Secondly: The drawings of the embodiments disclosed in the present invention only involve structures related to the embodiments disclosed in the present invention. Other structures may refer to conventional designs. The same embodiment and different embodiments of the present invention may be combined with each other without conflict.

[0081] Finally: The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of the present invention should be included in the scope of protection of the present invention.

Claims

1. A method for simulating heat transfer performance of a ship heat exchanger, characterized in that: include: S1: Multi-source data acquisition: Deploy a sensor network to synchronously collect fluid dynamic characteristics data, structural vibration signal data, dirt deposition state data, thermoelastic parameter data, temperature field distribution data, and turbulence field characteristics data to obtain the original data set as the basis for analysis; S2: Characteristic parameter calculation: The collected raw data is converted into characteristic parameters through a preset algorithm, including fluid pulsation intensity coefficient, vibration frequency response, dynamic fouling coefficient, transient thermal stress, expansion difference rate, and turbulent vortex attenuation characteristics; S3: Heat transfer stability diagnosis: Calculates the heat transfer stability index based on fluid pulsation intensity and vibration frequency response, and uses the no-load vibration benchmark and critical vibration threshold to determine the status. The condition that triggers the high-temperature mode analysis branch is controlled by the experimental calibration threshold. S4: Comprehensive heat transfer degradation assessment: Integrates the heat transfer stability index with the dynamic fouling coefficient, transient thermal stress, and expansion differential rate parameters to calculate the comprehensive heat transfer degradation index. This index is then compared with the threshold set by historical fault data to trigger a graded warning. S5: Structural reliability prediction: Calculate the structural reliability coefficient based on the comprehensive heat transfer degradation index and turbulent vortex attenuation characteristics, and generate the structural safety status judgment result using the attenuation benchmark calibrated by the shaking table test and the safety threshold of the design specification; S6: Comprehensive evaluation of system performance: The system performance index is generated by integrating the structural reliability coefficient, heat transfer stability index and thermal stress parameters. The evaluation conclusion is output according to the preset performance level classification standard and fed back to the management and control system.

2. The method for simulating heat transfer performance of a ship heat exchanger according to claim 1, characterized in that: The fluid pulsation intensity coefficient is specifically expressed as: σ v : Standard deviation of flow rate, Average flow velocity, Re: Reynolds number, Re ref =10 4 : Reference Reynolds number; the vibration frequency response is specifically expressed as: a rms : RMS vibration acceleration, L: pipe length, E: Young's modulus, δ: wall thickness, f n : Measured natural frequency, f c =50Hz: critical frequency; the dynamic fouling coefficient is specifically expressed as: t0: the starting time of dirt monitoring, t: the current analysis time, Dirt deposition rate, τ = 3600 s: characteristic time constant; The transient thermal stress is specifically expressed as: α: thermal expansion coefficient, κ: thermal conductivity, ρ: density, c p : specific heat capacity, Temperature change rate; the expansion difference rate is specifically expressed as: β: local expansion coefficient, ΔT: regional temperature difference, N: total number of partitions, β i ΔT i : The product of the local expansion coefficient and the temperature difference of the i-th region, β j ΔT j : Similar parameters of the jth region, j and i are the same region number, and the separate marks are only used to distinguish the maximum and minimum value operation objects, β k ΔT k : kth region parameter, k is used to traverse all N regions for summation operation; the turbulent eddy attenuation characteristics are specifically expressed as: u′: turbulent fluctuation velocity, ε: turbulent energy dissipation rate, L ε : characteristic eddy size, ν: kinematic viscosity.

3. The method for simulating heat transfer performance of a ship heat exchanger according to claim 1, characterized in that: The heat transfer stability index is specifically expressed as: HSI = FPI·ln(1+VFR / VFR0)+|VFR-VFR c | / VFR c , VFR0: no-load vibration reference, VFR c : critical vibration threshold; HSI>H c When HSI≤H c When maintaining standard analytical procedures, H c : High temperature threshold.

4. The method for simulating heat transfer performance of a ship heat exchanger according to claim 3, characterized in that: The high temperature mode analysis branch includes: Material creep correction: Introducing time-varying Young's modulus Dynamically update the vibration response calculation, where γ is the high-temperature creep coefficient obtained through material endurance testing, T_ref is the reference temperature, E0 is the initial Young's modulus at the standard temperature, T is the real-time monitored absolute temperature of the pipe wall, and Δt is the cumulative action time of the high-temperature condition; Thermal radiation enhancement: superimpose radiation heat flux terms in heat transfer calculations Emissivity ε q Calibrated by high temperature oxidation surface test, σ is the Stefan-Boltzmann constant, T amb is the absolute temperature of the surrounding medium; Cooling strategy optimization: Dynamically adjust the cooling medium flow rate v based on the real-time TED value new =v design ×[1+tanh(TED / TED c )], among which TED c is the critical value of turbulence attenuation, determined by high-temperature fluid-solid coupling simulation, v design It is the rated cooling medium flow rate of the heat exchanger under standard design conditions.

5. The method for simulating heat transfer performance of a ship heat exchanger according to claim 1, characterized in that: The comprehensive heat transfer degradation index is specifically expressed as: TSA0: material allowable stress threshold, taken as 10% of yield strength; CHDc: warning threshold, determined based on historical failure data statistics; In CHD>CHD c The third level dirt warning is triggered when the CHD increase is greater than ΔCHD for three consecutive times. c When the maintenance request is triggered, ΔCHD c is the critical change rate threshold of the comprehensive heat transfer degradation index.

6. The method for simulating heat transfer performance of a ship heat exchanger according to claim 5, characterized in that: The three-level fouling warning includes: Level 1 warning: Activated when the comprehensive heat transfer degradation index exceeds the historical statistical threshold for the first time, the system automatically increases the sampling frequency of the fouling coefficient to three times the standard value and simultaneously starts the automatic comparison function of the tube wall deposition image. The fouling type is confirmed through the correlation analysis of the real-time DFC change rate and the TSA / UER ratio. Level 2 warning: When the CHD increase ΔCHD>0.2CHD for three consecutive sampling periods c When triggered, forced intervention in operation control: the cooling medium flow rate is increased to 120% of the design value, the heat exchanger bypass valve opening is adjusted to reduce the flow rate by 5%, and the ultrasonic descaling device is activated to run intermittently at 50% power, and a deposition rate prediction curve is generated for the operator to confirm; Level 2 warning: When the CHD value reaches 2.3 times CHD c When forced to execute, immediately start the thermal resistance inversion verification program to verify the dirt thickness distribution model. If the DFC integral value exceeds the material tolerance limit DFC max , the interlock shutdown and activation of the chemical cleaning circuit, synchronously upload the axial dirt accumulation gradient data of the tube bundle to the maintenance system to generate the cleaning path plan.

7. The method for simulating heat transfer performance of a ship heat exchanger according to claim 1, characterized in that: The structural reliability coefficient is specifically expressed as: TED0 is the attenuation reference; When SRC < 1.0, the structural safety alarm is triggered. 1.0 ≤ SRC c Prompt preventive maintenance, S c is the safety threshold.​ 8. The method for simulating heat transfer performance of a ship heat exchanger according to claim 1, characterized in that: The system performance index is specifically expressed as: η0 is the benchmark heat transfer efficiency of the heat exchanger under standard test conditions; CEI ≥ 0.9 is an excellent grade, indicating that the system is in the optimal heat transfer state; When 0.7≤CEI<0.9, it is a good level, indicating that there is room for optimizing heat transfer parameters; when CEI<0.7, it is a maintenance level, confirming that the system performance has fallen below the safe operation benchmark.

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