A tubular air conditioner heat exchanger

Abstract

This invention belongs to the field of heat exchanger technology, specifically a tubular air conditioning heat exchanger, comprising: a heat exchanger body, which internally defines a shell side for refrigerant phase change heat transfer and heat exchange tubes penetrating the shell side; an acoustic vibration excitation assembly, including a sound wave generator disposed within the heat exchanger body and a low-frequency vibrator coupled to a tube bundle support frame, for applying multi-physics field coupled excitation energy to the shell side fluid and the liquid film on the inner wall of the heat exchange tubes; a multi-mode sensing network, including a differential pressure sensor installed at the shell side inlet and outlet, an optical fiber displacement sensor embedded in the cross section of the heat exchange tubes, an electrical conductivity probe array closely attached to the gas-liquid interface, a capacitance tomography sensor surrounding the shell side periphery, and a broadband hydrophone for capturing the sound pressure at the antinode of the standing wave in the cavity; and a real-time closed-loop drive for the low-frequency vibrator, enabling the heat exchanger to maintain adaptive phase-locked operation between breaking through thermal resistance boundaries and overload protection, significantly improving the system's energy conversion extreme value and long-term disturbance rejection life.

Description

Technical Field

[0001] This invention belongs to the field of heat exchanger technology, specifically a tubular air conditioning heat exchanger. Background Technology

[0002] Tubular air conditioning heat exchangers are the core heat transfer hubs in modern HVAC systems, primarily achieving cooling or heating functions through heat exchange between the shell-side and tube-side fluids. Their heat exchange efficiency directly determines the overall energy efficiency ratio and operational stability of the unit, playing a crucial role in large-scale commercial air conditioning, industrial refrigeration, and precision environmental control.

[0003] Under long-term operation or variable operating conditions, existing tubular heat exchangers are prone to shell-side fluid thickening and flow chaos, leading to sudden increases in pressure drop. Simultaneously, the refrigerant liquid film on the heat exchange tube surface is easily unevenly distributed due to the coupling effect of surface tension and gravity. To improve heat transfer performance, some existing technologies introduce external mechanical vibration or ultrasonic auxiliary devices to disturb the flow field. However, this open-loop, single-physical-field application method often suffers from low energy conversion efficiency and cannot dynamically adjust the input energy according to the real-time phase distribution inside the tube.

[0004] When severe flow field disturbances or liquid film tears occur inside the heat exchanger, traditional vibration control logic blindly maintains or even increases the excitation power. This leads to severe nonlinear fluctuations in the shell-side fluid pressure drop, easily triggering a surge in negative resistance. Furthermore, existing equipment can only obtain macroscopic inlet and outlet temperature differences and total pressure drops, lacking microscopic quantification methods for the microscopic configuration of the gas-liquid interface in the heat exchange tubes, the thickness of liquid film fluctuations, and the acoustic field potential trapping capability. This results in the system being unable to achieve precise alignment of the excitation phase with the gas-liquid resonance beat under complex fluid conditions, leading to a severe disconnect between the microscopic phase control field and the macroscopic thermal flow field. Ultimately, this causes frequent acoustic cavitation mechanical overload and thermodynamic deterioration phenomena during high-frequency operation.

[0005] Therefore, the present invention provides a tubular air conditioning heat exchanger. Summary of the Invention

[0006] In order to overcome the shortcomings of the prior art, at least one technical problem raised in the background art is solved.

[0007] The technical solution adopted by this invention to solve its technical problem is: a tubular air conditioning heat exchanger according to this invention, comprising: The heat exchanger body has a shell side for heat transfer due to refrigerant phase change and heat exchange tubes that pass through the shell side. The acoustic vibration excitation assembly includes an acoustic wave generator disposed in the heat exchanger body and a low-frequency oscillator coupled to the tube bundle support frame, for applying multi-physics field coupled excitation energy to the shell-side fluid and the liquid film on the inner wall of the heat exchange tube; The multimodal sensing network includes a differential pressure sensor installed at the inlet and outlet of the shell side, an optical fiber displacement sensor embedded in the cross section of the heat exchange tube, an electrical conductivity probe array closely attached to the gas-liquid interface, a capacitance tomography sensor surrounding the periphery of the shell side, a broadband hydrophone for capturing the sound pressure at the antinode of the standing wave in the cavity, and a triaxial accelerometer anchored to the output end of the low-frequency oscillator. An adaptive controller is communicatively connected to the acoustic-vibration excitation component and the multimodal sensing network. It has a built-in physics field calculation module configured to receive differential pressure timing, transient liquid film thickness, reconstructed dielectric constant, underwater acoustic pressure, and vibration acceleration signals collected by various sensors. It extracts shell-side pressure drop characteristics to construct flow-state disturbance rejection robustness; extracts phase boundary configuration fit based on dynamic liquid film deviation and interface wave excitation; integrates phase cloud map information entropy, liquid film coverage, and thermal potential enhancement to evaluate the global thermal potential conversion degree; combines acoustic field potential trapping force and excitation phase-locking angle to deduce the multimodal acoustic-vibration coupling fidelity; and finally, based on the multimodal acoustic-vibration coupling fidelity, generates a target acceleration command through a low-level nonlinear feedback mechanism to drive the low-frequency oscillator to adaptively break the thermal resistance boundary in a closed loop.

[0008] The beneficial effects of this invention are as follows: 1. This invention constructs an acoustic vibration excitation component that includes a sound wave generator and a low-frequency oscillator, and combines it with a multi-modal sensor network to perceive multi-dimensional physical quantities such as shell-side pressure drop, liquid film thickness, phase distribution, sound field intensity, and vibration state in real time. This enables a comprehensive perception of the internal thermofluid state of the heat exchanger, providing a reliable data foundation for closed-loop precise control and overcoming the limitations of traditional equipment that can only obtain macroscopic inlet and outlet temperature differences and total pressure drop.

[0009] 2. This invention extracts the fluid disturbance resistance robustness, phase boundary configuration fit, and global thermal potential conversion degree through the physical field calculation module built into the adaptive controller, and jointly deduces the multimodal acoustic-vibration coupling fidelity. This enables a comprehensive quantitative evaluation of the coupling effect between excitation energy and fluid phase state, so that the excitation phase and gas-liquid resonance beat can be accurately aligned, effectively avoiding the problem of the microscopic phase control field and the macroscopic thermal flow field being disconnected.

[0010] 3. This invention uses a low-level nonlinear feedback mechanism to dynamically generate target acceleration commands based on the deviation between real-time coupling fidelity and the desired threshold. This closed-loop drive enables the low-frequency oscillator to operate adaptively, allowing the system to maintain an automatic balance between breaking through thermal resistance boundaries and preventing acoustic cavitation mechanical overload. This significantly improves the operational stability and energy conversion extreme values ​​of the equipment under long-term operation and variable operating conditions. Attached Figure Description

[0011] The invention will now be further described with reference to the accompanying drawings.

[0012] Figure 1 This is a schematic diagram of the overall structure of the present invention; Figure 2 This is the overall closed-loop control flowchart of the present invention; Figure 3 This is a diagram showing the synergistic extraction and fusion of the flow regime robustness and phase boundary configuration fit of this invention; Figure 4 This is a graph showing the global thermal potential conversion degree assessment and multimodal acoustic-vibration coupling fidelity derivation of the present invention. Figure 5 This invention generates a target acceleration command diagram based on the targeted excitation evolution control law.

[0013] In the diagram: 1. Shell side; 2. Heat exchange tubes; 3. Low-frequency oscillator; 4. Tube bundle support frame. Detailed Implementation

[0014] To make the technical means, creative features, objectives and effects of this invention easier to understand, the invention will be further described below in conjunction with specific embodiments.

[0015] like Figure 1 As shown, the present invention provides a tubular air conditioning heat exchanger, which includes a heat exchanger body as the basic structure for heat transfer. The body has a shell side 1 for heat transfer by refrigerant phase change and a heat exchange tube 2 that passes through the shell side 1. The heat exchanger body adopts a U-shaped tube bundle design, and the heat exchange tube 2 is bent into a U-shape.

[0016] The acoustic vibration excitation assembly includes an acoustic wave generator located inside the heat exchanger body and a low-frequency vibrator 3 coupled to the tube bundle support frame 4. It is used to apply multi-physics field coupled excitation energy to the fluid in the shell side 1 and the liquid film on the inner wall of the heat exchange tube 2. The acoustic wave generator adopts a piezoelectric ceramic transducer, which generates ultrasonic waves by applying a high-frequency electrical signal. These ultrasonic waves propagate in the fluid, causing microscopic cavitation and fluid disturbance. The low-frequency vibrator 3 adopts an electromagnetic vibrator, which generates electromagnetic force by controlling the current, driving the vibrator to perform mechanical vibration, thereby applying low-frequency vibration to the tube bundle support frame 4.

[0017] like Figure 2-5As shown, the multimodal sensing network includes a differential pressure sensor installed at the inlet and outlet of shell side 1, a fiber optic displacement sensor embedded in the cross section of heat exchange tube 2, a conductivity probe array closely attached to the gas-liquid interface, a capacitance tomography sensor surrounding the periphery of shell side 1, a broadband hydrophone capturing the sound pressure at the antinode of the standing wave in the cavity, and a triaxial accelerometer anchored at the output end of the low-frequency oscillator 3. The differential pressure sensor is manufactured using silicon-based MEMS technology and senses the pressure difference by measuring the deformation of a tiny diaphragm. The fiber optic displacement sensor uses a sensing element based on a fiber Bragg grating (FBG) and accurately measures the pressure difference by monitoring the drift of the reflected light wavelength. The minute changes in liquid film thickness are addressed by a conductivity probe array composed of multiple microelectrodes, which reflects the local state of the gas-liquid interface by measuring the conductivity between different electrode pairs. A capacitance tomography sensor, composed of multiple external electrodes arranged in a ring, reconstructs the dielectric constant distribution image inside the shell side 1 by measuring the capacitance between the electrodes. A broadband hydrophone, made of piezoelectric ceramic material, can convert sound pressure signals in the fluid into electrical signals and cover a wide frequency range. A triaxial accelerometer, using microelectromechanical systems (MEMS) technology, obtains vibration acceleration by measuring inertial forces in three orthogonal directions.

[0018] The adaptive controller communicates with the acoustic vibration excitation component and the multimodal sensor network. The controller has a built-in physics field calculation module configured to receive differential pressure timing signals, transient liquid film thickness, reconstructed dielectric constant, underwater acoustic pressure, and vibration acceleration signals collected by various sensors. The adaptive controller uses a high-performance digital signal processor (DSP) or field-programmable gate array (FPGA) as its core computing unit to achieve high-speed data processing and real-time control. The physics field calculation module contains a series of pre-programmed algorithms for filtering, calibrating, and extracting features from the received raw sensor data. For example, the module can perform Fourier transform on the differential pressure timing signal to analyze its frequency components and wave characteristics to extract the shell-side 1 pressure drop characteristics. For the transient liquid film thickness signal, the module can calculate its average value, standard deviation, and the amplitude and frequency of the interface wave. For the reconstructed dielectric constant data, the module can perform image segmentation and feature recognition to quantify the liquid film coverage and phase uniformity. For the underwater acoustic pressure and vibration acceleration signals, the module can perform cross-correlation analysis to determine their phase relationship and coupling strength.

[0019] The adaptive controller further extracts the shell-side 1 pressure drop characteristics to construct the flow regime's robustness against disturbances. The adaptive controller performs statistical analysis on the differential pressure time series data collected by the differential pressure sensor, calculates its root mean square value and fluctuation frequency, and compares them with the preset reference value to evaluate the stability of the flow regime.

[0020] The adaptive controller extracts the phase boundary configuration fit based on dynamic liquid film deviation and interface wave oscillation. The adaptive controller analyzes the transient liquid film thickness data obtained by the fiber optic displacement sensor, calculates the deviation between the average thickness of the liquid film and the theoretical optimal thickness, and combines the amplitude and frequency of the liquid film interface wave to quantify whether the configuration of the gas-liquid phase interface meets the requirements of efficient heat transfer.

[0021] The adaptive controller integrates phase cloud image information entropy, liquid film coverage, and thermal potential enhancement to evaluate the global thermal potential conversion degree. The adaptive controller calculates the information entropy of the dielectric constant image reconstructed by the capacitance tomography sensor to evaluate the uniformity of phase distribution. At the same time, it calculates the coverage area ratio of the liquid film on the surface of heat exchange tube 2 and evaluates the heat transfer gain brought by acoustic vibration excitation in combination with the thermodynamic model. Finally, it integrates these factors to quantify the heat conversion efficiency of the entire heat exchanger.

[0022] The adaptive controller, in conjunction with the acoustic field potential trapping force and the excitation phase-locking angle, derives the multimodal acoustic-vibration coupling fidelity. The adaptive controller analyzes the underwater acoustic pressure signal captured by the broadband hydrophone and calculates the trapping force of the acoustic field on the droplets or bubbles. At the same time, it analyzes the phase difference between the vibration acceleration signal measured by the triaxial accelerometer and the acoustic pressure signal to determine the degree of coupling between the acoustic excitation energy and the fluid and liquid film.

[0023] Ultimately, the adaptive controller, based on the multimodal acoustic-vibration coupling fidelity, generates a target acceleration command through a low-level nonlinear feedback mechanism. This closed-loop drive of the low-frequency oscillator 3 adaptively breaks the thermal resistance boundary. The adaptive controller employs a model predictive control (MPC) algorithm as the low-level nonlinear feedback mechanism. Based on the deviation between the current multimodal acoustic-vibration coupling fidelity and the expected value, it predicts the future system state and generates an optimized target acceleration command. This command is output through a digital-to-analog converter (DAC) to drive the power amplifier of the low-frequency oscillator 3, thereby precisely controlling the vibration frequency, amplitude, or phase of the low-frequency oscillator 3 to achieve dynamic and adaptive control of the thermal resistance boundary.

[0024] After a long period of operation, due to load fluctuations and changes in fluid properties, the refrigerant flow in the shell side 1 of the tubular air conditioning heat exchanger begins to become turbulent, and the liquid film distribution on the inner wall of the heat exchange tube 2 becomes uneven, resulting in a significant decrease in overall heat exchange efficiency. At this time, the multi-modal sensor network begins to fully perceive the internal state, and the differential pressure sensors installed at the inlet and outlet of the shell side 1 monitor the abnormal fluctuation of the pressure drop in the shell side 1 in real time, indicating a decrease in flow stability.

[0025] The fiber optic displacement sensor embedded in the cross section of heat exchange tube 2 detected that the thickness of the liquid film on the inner wall of heat exchange tube 2 fluctuated violently and the average thickness deviated from the theoretical optimal value. The conductivity probe array close to the gas-liquid interface detected that the liquid film coverage was reduced and that local areas were dry or rich in liquid. The capacitance tomography sensor surrounding the shell side 1 reconstructed the phase cloud map inside the shell side 1, showing that the gas and liquid phases were unevenly distributed and that there was local fluid stagnation or bubble aggregation. The broadband hydrophone that captured the sound pressure at the antinode of the standing wave in the cavity detected that the sound field intensity and phase deviated from the output of the exciter. The triaxial accelerometer anchored to the output end of the low-frequency oscillator 3 provided real-time feedback on the actual vibration state of the low-frequency oscillator 3.

[0026] Subsequently, the adaptive controller receives the aforementioned multi-source heterogeneous sensor signals, and its built-in physical field calculation module begins to perform in-depth processing and analysis on these signals. The module first extracts the shell-side 1 pressure drop characteristics, constructs the flow disturbance resistance robustness, and finds that its value is lower than the preset threshold, confirming that the flow is unstable. Then, based on the dynamic liquid film deviation and interface wave oscillation, it extracts the phase boundary configuration fit, and finds that its value is low, indicating that the liquid film distribution is poor.

[0027] Furthermore, by integrating the phase cloud map information entropy, liquid film coverage, and thermal potential enhancement evaluation results, the global thermal potential conversion degree was assessed. It was found that the value was far lower than expected, confirming that the overall heat transfer efficiency was low. Finally, by combining the acoustic field potential trapping force and the excitation phase-locking angle, the multimodal acoustic-vibration coupling fidelity was deduced. It was found that the value was not ideal, indicating that the acoustic excitation energy failed to be effectively coupled to the fluid and liquid film.

[0028] Based on the quantitative evaluation results, the adaptive controller generates an optimized target acceleration command through its underlying nonlinear feedback mechanism. This command is sent to the acoustic excitation system. After receiving the command, the low-frequency oscillator 3 precisely adjusts its vibration frequency, amplitude, and phase to achieve optimal resonance with the real-time state of the fluid and liquid film in shell side 1. Simultaneously, the acoustic generator may also adjust its acoustic parameters according to the command, working in conjunction with the low-frequency oscillator 3 to jointly apply multi-physics field coupled excitation energy to the fluid in shell side 1 and the liquid film on the inner wall of the heat exchange tube 2.

[0029] Thus, under closed-loop drive, the low-frequency oscillator 3 adaptively breaks the thermal resistance boundary, the fluid boundary layer is effectively disturbed, the liquid film distribution tends to be uniform, and the interface wave excitation is optimized, thereby reducing the heat transfer resistance and improving the heat exchange efficiency. Since the excitation energy and the fluid phase state are precisely coupled, the energy waste and potential acoustic cavitation overload caused by ineffective excitation are avoided, ensuring that the equipment maintains good operational stability while operating efficiently.

[0030] 2. Preferably, the process of the adaptive controller extracting the shell-side 1 pressure drop characteristics to construct the flow regime disturbance robustness is based on a continuous bounded nonlinear mapping mechanism. The adaptive controller is configured to: calculate the ratio of the physical reference pressure drop scale to the real-time shell-side 1 pressure drop fluctuation dispersion, and combine the ratio with the shell-side 1 pressure drop state ratio and the preset sensitivity weight as a dimensionless input term. Through a monotonically increasing nonlinear mathematical function with limit boundary values, the turbulent disturbance state of the physical flow regime is mapped to a normalized coefficient characterizing the smoothness of the flow field, and the flow regime disturbance robustness is output.

[0031] The process of extracting the shell-side 1 pressure drop characteristics and constructing the flow regime's disturbance rejection robustness using an adaptive controller is based on a full-positive-domain inverse triangular mapping model, whose analytical equation is:

[0032] In the formula, This represents the flow regime's robustness against disturbances, and is dimensionless. The preset sensitivity weighting coefficient is dimensionless. The pressure drop ratio for shell-side 1 is dimensionless. This is a reference pressure drop scale determined based on Darcy-Weisbach's principle, with units of . ; The standard deviation of the shell-side 1 pressure drop fluctuation is obtained in real time, in units of ; To prevent the denominator from having a singularly small constant, the unit is 1. .

[0033] The full positive domain inverse triangular mapping model is a mathematical transformation mechanism designed to map input parameters to a normalized output range with nonlinear characteristics. Its role is to transform complex physical quantities (such as pressure drop fluctuations) into a unified, quantifiable index. At the same time, through its nonlinear characteristics, it can effectively suppress the excessive influence of abnormal fluctuations on the evaluation results and enhance the robustness of the evaluation. In terms of implementation, this model can be used as a software module inside the adaptive controller, and the above analytical equations can be calculated through a programming language; or, its core algorithm can be embedded in a dedicated digital signal processor (DSP).

[0034] Fluid dynamic disturbance robustness It is a dimensionless index used to quantify the ability of a shell-side fluid to maintain a stable flow state when subjected to external excitation or internal disturbance. The value of this index ranges from 0 to 1. The higher the value, the more stable the flow state and the stronger the resistance to disturbance. Its function is to provide an intuitive and quantitative basis for the adaptive controller so that the controller can accurately judge the stability of the current fluid state and thus make subsequent excitation energy adjustments.

[0035] Preset sensitivity weighting coefficient These are dimensionless preset parameters used to adjust the model's sensitivity to pressure drop fluctuations in the shell side. Their function is to allow the system to adjust its focus on flow stability based on different operating conditions, fluid medium characteristics, or desired control strategies. For example, in precision heat transfer scenarios with extremely high stability requirements, a higher setting can be used. This value makes the model highly sensitive to even small fluctuations in pressure drop, while a lower value can be set in scenarios where a certain range of fluctuations is acceptable. The value, this coefficient, is calibrated using experimental data from the system debugging phase.

[0036] Shell-side 1 pressure drop ratio This is a dimensionless parameter used to reflect the changing trend of the pressure drop in the shell side 1 before and after the acoustic vibration excitation component is turned on. Its function is to evaluate the dynamic impact of the excitation component on fluid resistance, thereby indirectly reflecting the excitation effect. When the pressure drop decreases significantly after the excitation component is turned on, The value will change accordingly, indicating that the excitation has a positive impact on the fluid resistance. This parameter is obtained by measuring the pressure drop in the shell side 1 under different excitation conditions and calculating the ratio.

[0037] Reference pressure drop scale It is a reference voltage drop value determined based on Darcy-Weisbach's principle, with units of . Its function is to provide a theoretical or empirical reference benchmark for the pressure drop fluctuation in the shell side 1, so that the pressure drop fluctuation measured in real time can be compared with a stable, ideal or expected state. This benchmark can be theoretically calculated based on parameters such as the geometric dimensions of the heat exchanger body, fluid properties and design flow rate, using the Darcy-Weisbach formula.

[0038] Real-time acquisition of standard deviation of shell-side pressure drop fluctuation It is the standard deviation of the real-time acquired shell-side 1 pressure drop data sequence, in units of Its function is to quantify the instantaneous fluctuation of the pressure drop of the fluid in the shell side 1, reflecting the turbulence or stability of the flow. The larger the standard deviation, the more violent the pressure drop fluctuation and the more unstable the flow. This parameter is obtained by collecting pressure drop data in real time through a micro differential pressure sensor in a multimodal sensor network and by performing statistical calculations within the adaptive controller through a sliding time window or a fixed sampling period.

[0039] Minimal constant to prevent singular denominators The unit is The minimum positive value of is used to prevent the standard deviation of the pressure drop fluctuation in the shell side 1 from being affected during the calculation process. When the denominator approaches zero, a singularity (i.e., zero) occurs, thus ensuring the numerical stability of the mathematical model. In practical applications, even if the flow regime is stable, It may also approach zero due to factors such as measurement noise. This constant can be preset to a very small fixed value, for example... Alternatively, its size can be dynamically adjusted based on the sensor's measurement accuracy and the system's data range.

[0040] By introducing a full-positive-domain inverse triangular mapping model into the adaptive controller, the precise quantification of the flow disturbance robustness of the shell-side 1 of a tubular air conditioning heat exchanger is achieved. This model receives real-time shell-side 1 pressure drop data collected from a multimodal sensor network and, combined with preset parameters, calculates a dimensionless flow disturbance robustness. .

[0041] Specifically, the adaptive controller first obtains the standard deviation of the real-time shell-side 1 pressure drop fluctuation. This value reflects the degree of turbulence in the current flow regime, and is combined with a reference pressure drop scale determined based on Darcy-Weisbach's principle. And the shell-side pressure drop ratio, which reflects the pressure drop change before and after excitation. These parameters together constitute the core input of the model.

[0042] By substituting these physical quantities into the full positive domain inverse triangular mapping model, and utilizing preset sensitivity weighting coefficients... Adjusting the model's sensitivity to fluctuations ultimately outputs the flow regime's robustness against disturbances. This model, through its nonlinear mapping characteristics, can unify physical quantities of different dimensions into a normalized range and effectively suppress the excessive influence of abnormal pressure drop fluctuations on the evaluation results. This ensures a more accurate and robust assessment of flow stability under complex fluid conditions, enabling the adaptive controller to accurately determine whether the current shell-side 1 pressure drop fluctuation is within a controllable range. This provides a reliable decision-making basis for the subsequent energy adjustment of the acoustic vibration excitation components, avoiding blind control caused by the lack of effective evaluation methods in traditional methods, and thus improving the operating efficiency and stability of the entire heat exchanger system.

[0043] 3 Preferably, the physical reference pressure drop scale is based on the principle of fluid friction resistance along the flow path, combined with the effective geometric dimensions of the heat exchange tube 2, the gas phase fluid density, and the characteristic velocity of the inlet flow field; the pressure drop fluctuation dispersion of the shell side 1 is determined by the statistical standard deviation of the transient differential pressure time series discretely sampled by the micro differential pressure sensor relative to its mean; the pressure drop state ratio of the shell side 1 is defined as the ratio of the reference pressure drop when the acoustic vibration excitation component is not turned on to the dynamic excitation pressure drop.

[0044] Reference pressure drop scale Standard deviation of pressure drop fluctuation in shell side 1 Compared with the pressure drop state of shell side 1 Obtained through the following system of equations:

[0045]

[0046]

[0047] In the formula, The coefficient of frictional resistance of the pipe wall is dimensionless. For effective management, the unit is... ; Equivalent inner diameter, unit: ; This is the gas phase density, in units of... ; Inlet flow rate, unit: ; This represents the total number of discrete sampling points. and The first The instantaneous differential pressure value and the time series average differential pressure, in units of... ; and These are the reference shell-side 1 pressure drops under conditions of no acoustic excitation and with acoustic excitation applied, respectively, in units of... .

[0048] Reference pressure drop scale This is a theoretical benchmark value used to evaluate the fluid flow state in the shell side 1. It characterizes the ideal pressure drop that the fluid should experience when passing through the shell side 1 under specific geometric and fluid property conditions. Its function is to provide a stable and quantifiable reference point for comparison with real-time measured pressure drops, thereby determining the degree of deviation from the flow regime. This benchmark can be based on classical fluid dynamics models, such as the Darcy-Weisbach equation, combined with the heat exchanger's geometric parameters (such as effective tube length). Equivalent inner diameter ) and fluid properties (such as gas phase density) Inlet flow rate Pipe wall friction coefficient ) to perform calculations.

[0049] Standard deviation of pressure drop fluctuation in shell side 1 It is a statistical indicator that measures the degree of transient instability or turbulence in the pressure drop of the shell-side fluid. It reflects the dispersion of the pressure drop in the shell-side 1 relative to its average value within a certain time window. This feature can capture transient disturbances inside the flow field, such as local eddies, bubble rupture, or liquid film tearing. These phenomena are often the cause of decreased heat transfer performance and increased pressure drop. It is calculated by using the instantaneous differential pressure value collected by the micro differential pressure sensor. Perform continuous sampling, and within a preset total number of discrete sampling points. Within this process, calculate the difference between these instantaneous values ​​and the average differential pressure over the time series. The standard deviation between them.

[0050] Shell-side 1 pressure drop ratio It is a dimensionless parameter used to directly quantify the actual effect of the acoustic vibration excitation component on the pressure drop of the shell-side 1 fluid. This is achieved by comparing the pressure drop of the reference shell-side 1 when the acoustic vibration excitation component is not applied. The reference shell-side pressure drop during operation of the acoustic vibration excitation assembly This ratio can intuitively reflect whether the acoustic vibration excitation component leads to an increase in pressure drop (which may have a negative impact) or a decrease (which may optimize the flow state). It is obtained by periodically turning the acoustic vibration excitation component on and off during system operation and recording the shell side 1 pressure drop value under the corresponding steady state.

[0051] Equation system acquisition refers to the process of transforming raw data collected by sensors into physically meaningful parameters using pre-defined mathematical models and algorithms. This method ensures precise quantification of fluid dynamic characteristics, providing accurate and reliable input for the adaptive controller. This equation system can be integrated into the physics field solution module within the adaptive controller, implemented through firmware or software programs. Specifically, high-performance microprocessors (such as digital signal processors (DSPs) or embedded microcontrollers (MCUs) are used to perform these complex floating-point operations to meet real-time requirements.

[0052] By establishing a system of mathematical equations, the fluid dynamics characteristics of the shell side 1 were refined and quantified. The adaptive controller first utilizes Darcy-Weisbach's principle, combined with the geometric parameters of the heat exchanger (such as effective tube length). Equivalent inner diameter ) and fluid physical properties (such as gas phase density) Inlet flow rate Pipe wall friction coefficient ), calculate the reference pressure drop scale This scale provides a theoretical reference for pressure drop, offering a solid physical basis for subsequent assessments of flow stability.

[0053] Meanwhile, the adaptive controller uses the instantaneous differential pressure values ​​acquired by the differential pressure sensors in the multimodal sensor network. Discrete sampling is performed, and the time-series average differential pressure is combined. Calculate the standard deviation of the pressure drop fluctuation in shell side 1. This standard deviation can capture the degree of transient turbulence within the flow field, such as local turbulence and bubble motion, thereby reflecting the smoothness of fluid flow.

[0054] Furthermore, the reference shell-side 1 pressure drop was compared when the component was running without acoustic excitation. The reference shell-side pressure drop during operation of the acoustic vibration excitation assembly The adaptive controller can calculate the shell-side 1 pressure drop state ratio. This ratio directly quantifies the actual impact of the acoustic vibration excitation component on the pressure drop in the flow field, i.e., whether the excitation promotes flow stability or exacerbates pressure drop fluctuations.

[0055] Reference pressure drop scale Standard deviation of pressure drop fluctuation in shell side 1 Compared with the pressure drop state of shell side 1 Together, they constitute the fluid dynamics robustness. Based on computational foundations, by transforming macroscopic pressure drop data into these calculable parameters with clear physical meaning, the adaptive controller can perceive the dynamic state of the shell-side 1 flow field in real time and comprehensively. This enables the controller to overcome the limitations of traditional single-indicator methods, accurately assess the flow regime's disturbance rejection capability, and lay the foundation for subsequent construction of flow regime disturbance rejection robustness based on a full-domain inverse triangular mapping model. Providing input data significantly improves the accuracy and reliability of adaptive controllers in making decisions under complex fluid conditions, effectively solving the control blind zone problem caused by the lack of micro-quantification methods, thus providing solid data support for the closed-loop drive of the low-frequency oscillator to adaptively break the thermal resistance boundary.

[0056] 4. Preferably, the adaptive controller extracts the phase boundary configuration fit based on dynamic liquid film deviation and interface wave oscillation. This process is based on a rational polynomial composite mapping mechanism with bell-shaped attenuation characteristics. The adaptive controller is configured to: quantify the relative error of the transient average liquid film thickness deviating from the theoretical optimal heat transfer thickness, and perform dimensionless processing using the fluid capillary characteristic length; simultaneously, extract the disturbance penalty ratio of the liquid film surface oscillation amplitude relative to the average liquid film thickness; and use the above deviation and disturbance as attenuation factors to construct a composite dynamic fidelity evaluation index that tends to the theoretical extreme boundary, so as to output the phase boundary configuration fit.

[0057] The adaptive controller's process for extracting phase boundary configuration fit based on dynamic liquid film deviation and interface wave oscillation is based on the Cauchy-Lorentz rational composite mapping model, whose analytical equation is:

[0058] In the formula, Represents the phase boundary configuration fit, dimensionless and with an output range of . ; The average thickness of the dynamic liquid film within the integration time window of the fiber optic displacement sensor, in units of ; The theoretically optimal liquid film thickness, in units of ; The capillary characteristic length of the gas-liquid interface, in units of ; The amplitude of the liquid film interface wave is expressed in units of... ; This is a preset amplitude attenuation penalty factor, which is dimensionless.

[0059] The process of extracting the phase boundary configuration fit between dynamic liquid film deviation and interface wave oscillation aims to quantify the degree of matching between the microstructure of the gas-liquid interface in heat exchange tube 2 and the ideal state. By comprehensively considering the degree of deviation of liquid film thickness from the optimal value and the stability of interface fluctuation, feedback indicators are provided to the adaptive controller to guide the application of excitation energy. This process is calculated by running a preset algorithm model on the digital signal processor inside the adaptive controller.

[0060] The Cauchy-Lorentz rational composite mapping model combines the characteristics of the Cauchy and Lorentz distributions, mapping multiple input variables to a single output variable through rational functions. It can describe phenomena with sharp peaks or broad distributions and imposes nonlinear penalties on inputs that deviate from the central value. In this application, this model is used to nonlinearly map the parameters of liquid film thickness deviation and interface wave oscillation into a comprehensive phase boundary configuration fit index, enabling this index to reflect the health of the micro-interface. The analytical equations of this model can be directly calculated within the firmware of the adaptive controller using a floating-point arithmetic unit.

[0061] The analytical equation is the specific mathematical expression of the Cauchy-Lorentz rational composite mapping model, which defines how to calculate the phase boundary configuration fit from the input parameters. The first part of the equation reflects the degree of matching between the liquid film thickness and the optimal value, while the second part reflects the stability of the interface fluctuations. The calculation logic of this equation can be implemented in the adaptive controller using a programming language.

[0062] The phase boundary configuration fit is a dimensionless index with a value between 0 and 1 (excluding 0 and including 1). It is used to quantify the degree of matching between the microstructure of the gas-liquid interface in heat exchange tube 2 and the theoretical optimal state. The closer the value is to 1, the higher the fit and the better the heat exchange performance. It serves as the input for the adaptive controller to make decisions and guide the adjustment of the excitation strategy.

[0063] The average thickness of the dynamic liquid film within the integration time window of the fiber optic displacement sensor is the average value of the liquid film thickness within a certain time window, obtained by real-time measurement of the liquid film thickness by the fiber optic displacement sensor. The fiber optic displacement sensor can employ sensing technology based on fiber Bragg gratings (FBG) and infer the liquid film thickness by measuring the wavelength shift of the reflection spectrum.

[0064] The theoretically optimal liquid film thickness is the ideal liquid film thickness that achieves the best heat transfer efficiency and minimum flow resistance under specific operating conditions. This value is obtained offline through pre-established heat transfer and fluid dynamics models, combined with the geometric parameters of the heat exchanger and the fluid properties.

[0065] The capillary characteristic length of the gas-liquid interface is a physical quantity that characterizes the relative importance of the surface tension effect and the gravity effect of the fluid. It determines the spreading and stability of the liquid film on the surface and is calculated using known physical formulas based on the basic physical parameters of the fluid, such as surface tension, density, and gravitational acceleration.

[0066] The amplitude of the liquid film interface wave is the magnitude of the fluctuations on the liquid film surface, reflecting the dynamic instability of the gas-liquid interface. It is measured in real time by an electrical conductivity probe array to detect the transient changes in the liquid film thickness, and then the amplitude information is extracted by performing Fourier transform or peak and trough detection on the signal. The preset amplitude attenuation penalty factor is a dimensionless constant used to adjust the degree of influence of the liquid film interface wave amplitude on the phase boundary configuration fit. The larger the value, the heavier the penalty for interface fluctuations, that is, the less desirable large interface fluctuations are. This parameter can be manually set during the system debugging phase through experiments or simulations, according to the desired control effect and the requirements for interface stability.

[0067] By introducing the Cauchy-Lorentz rational composite mapping model, a quantitative assessment of the configuration fit of the gas-liquid interface within heat exchanger tube 2 was achieved, providing microscopic state feedback for closed-loop control. This model consists of a product of two parts; the first part utilizes the average dynamic liquid film thickness obtained from a fiber optic displacement sensor. Compared with the theoretical optimal liquid film thickness The difference, combined with the capillary feature length Normalization was performed to assess the extent to which the liquid film thickness deviated from the optimal value.

[0068] By utilizing the characteristics of the Cauchy distribution, this term approaches its maximum value when the liquid film thickness is close to the optimal value, thus reflecting the rationality of the liquid film thickness distribution. The second part introduces the amplitude of the liquid film interface wave. With average thickness The ratio, and set the amplitude attenuation penalty factor. The purpose is to evaluate the stability of interface fluctuations.

[0069] By penalizing the amplitude of interface waves, heat transfer deterioration caused by severe fluctuations can be effectively suppressed, ensuring that the phase boundary configuration remains relatively stable during dynamic changes. By performing a composite mapping of these two parts, the system can comprehensively consider the liquid film thickness and the interface fluctuation state, thereby calculating the phase boundary configuration fit. The phase boundary configuration fit As input for the adaptive controller to make decisions, it is combined with other macroscopic fluid parameters (such as shell-side 1 pressure drop characteristics) provided by the multimodal sensing network, enabling the controller to dynamically adjust the excitation strategy of the acoustic vibration excitation component according to the microscopic interface state and macroscopic flow state information, so as to ensure that the heat exchanger always operates in a highly efficient heat transfer state.

[0070] 5 Preferably, the characteristic length of the fluid capillary is determined by the balance relationship between the surface tension of the gas-liquid interface and the density difference between the gas and liquid under gravity; the amplitude of the oscillation wave on the liquid film surface is obtained by extracting the peak-valley fluctuation difference of the transient voltage signal output by the conductivity probe array and converting it by spatial calibration coefficient.

[0071] Capillary feature length in phase boundary configuration fit model Amplitude of wave at the liquid film interface Obtained by performing the following equations:

[0072]

[0073] In the formula, Surface tension of liquid and gas, unit: ; This is the acceleration due to gravity, with units of 1. ; and These are the densities of the liquid and gas phases, respectively, in units of... ; These are the calibration coefficients for the conductivity probe array, in units of... ; The transient voltage signal output by the probe, in units of .

[0074] Among them, capillary feature length It is a key physical quantity characterizing the geometric scale features of the gas-liquid interface under the combined effects of gravity and surface tension. It defines a length scale where the surface tension effect is comparable to the gravitational effect, and is crucial for understanding and predicting the stability, wettability, and propagation behavior of liquid films and interface waves. This length can be determined based on the liquid-gas surface tension of the fluid. Liquid phase density gas phase density and gravitational acceleration It is calculated using physical formulas.

[0075] Liquid film interface wave amplitude It is a direct indicator for quantifying the degree of dynamic disturbance at the gas-liquid interface, reflecting the intensity of fluctuations on the liquid film surface. This amplitude is expressed as a transient voltage signal output by the conductivity probe array. The range and the calibration coefficients of the pre-calibrated conductivity probe array Multiply them to get the result.

[0076] Liquid-gas surface tension It is a physical quantity that measures the intermolecular forces on the surface of a liquid. It causes the liquid surface to tend to contract and has a decisive influence on the formation, stability, and propagation characteristics of the liquid film and interface waves. This parameter can usually be obtained from the refrigerant's physical property database based on the current operating temperature and pressure.

[0077] gravitational acceleration It is the acceleration of an object falling freely in Earth's gravitational field, which affects the overall force balance and flow behavior of the liquid film. Standard gravitational acceleration is used. However, for applications requiring extremely high precision, fine adjustments can be made based on the actual geographical location and altitude.

[0078] Liquid phase density and gas phase density These are the masses of the liquid and gaseous fluids per unit volume, respectively. They are key parameters for fluid inertia, gravity, and buoyancy, and directly affect the calculation results of capillary characteristic length. These density values ​​can be obtained from the refrigerant's physical property database based on the current operating temperature and pressure.

[0079] Calibration coefficients of conductivity probe array This is a conversion factor used to convert the electrical signal (voltage) output by the conductivity probe array into the actual physical dimension (liquid film interface wave amplitude). The calibration coefficient is typically obtained through experimental calibration. For example, under controlled experimental conditions, by introducing a liquid film interface wave with a known amplitude and simultaneously measuring the voltage output of the conductivity probe array, the correspondence between the voltage signal and the actual wave amplitude is established, thereby determining... .

[0080] transient voltage signal output by the probe It is an electrical signal generated when the conductivity probe array monitors the gas-liquid interface fluctuations in real time. When the liquid film thickness or interface wave changes, the conductivity between the probe electrodes will change accordingly, resulting in fluctuations in the output voltage signal. The acquisition frequency of this signal needs to be high enough to ensure that the rapid transient changes of the interface wave can be captured. This signal is usually input to the adaptive controller for further analysis and calculation after preprocessing such as signal amplification and filtering.

[0081] The adaptive controller receives the dynamic average thickness of the liquid film from the fiber optic displacement sensor in the multimodal sensor network. and transient voltage signals output by the conductivity probe array At the same time, combined with preset fluid properties, such as liquid-gas surface tension Liquid phase density gas phase density and gravitational acceleration The controller first calculates the capillary feature length. Next, by analyzing the transient voltage signal The extreme values, combined with the calibration coefficients of the conductivity probe array. The amplitude of the liquid film interface wave was calculated. These physical quantities, namely capillary characteristic length and liquid film interface wave amplitude This data is then input into the built-in physics field solution module of the adaptive controller, serving as the Cauchy-Lorentz rational composite mapping model (used to extract phase boundary configuration fit). The parameters of the phase boundary configuration are transformed into microscopic interface characteristic parameters with clear physical meaning, thereby providing a physical basis for calculating the phase boundary configuration fit.

[0082] 6. Preferably, the adaptive controller evaluates the global thermal potential conversion degree by adopting a global translation-limited algebraic mapping mechanism. The adaptive controller is configured to: extract the acoustic-vibration synergistic efficiency coefficient characterizing the macroscopic heat transfer state of the flow field, and translate and compress it to the preset positive domain boundary through an algebraic limit function with a scaling factor to adaptively accommodate positive and negative heat transfer benefits; and perform power-law synergistic calculations on the energy efficiency parameter after the limit, the two-phase cloud uniformity index, and the effective liquid film coverage rate to output the global thermal potential conversion degree.

[0083] The adaptive controller evaluates the global thermal potential conversion rate based on a global translational algebraic S-mapping model, adaptively encompassing both positive and negative heat transfer gains. Its analytical equation is:

[0084] In the formula, Represents the global thermal potential conversion degree, dimensionless and with an output range of [value missing]. ; is the two-phase cloud evenness index, dimensionless; Effective liquid film coverage, dimensionless; The acoustic-vibration synergistic enhancement coefficient based on thermodynamic sensible heat extraction is dimensionless. , The spatial distribution sensitivity power exponent is dimensionless. The energy efficiency conversion gain coefficient is dimensionless.

[0085] The adaptive controller's process of evaluating the global thermal potential conversion degree aims to quantify the overall thermodynamic performance conversion efficiency of the tubular air conditioning heat exchanger during operation. Its role is to comprehensively consider multiple key physical quantities to form a unified performance index to guide subsequent control strategies. This process is executed by a dedicated processing unit inside the adaptive controller, for example, through calculation by a pre-programmed algorithm module or through dynamic evaluation by a real-time data stream-driven computing engine.

[0086] The global translational algebraic S-mapping model is a nonlinear mapping function. Its characteristic is that it can integrate multiple input variables through specific algebraic operations and S-shaped functions, and output a normalized index within a specific range. It is used to process input data with complex nonlinear relationships and map it to an evaluation scale with clear physical meaning. It can be implemented by embedding pre-compiled mathematical model code in the firmware of the adaptive controller. The adaptive inclusive positive and negative heat transfer gain means that the evaluation model can flexibly handle the positive enhancement effect (positive gain) and the possible negative deterioration effect (negative gain) of acoustic vibration excitation components on heat transfer performance. The adaptiveness is reflected in the model's ability to dynamically adjust the evaluation weights and influences of these gains according to actual operating data, ensuring the accuracy and robustness of the evaluation results. This can be achieved through the model's internal parameter self-adjustment mechanism, such as parameter calibration based on historical data or preset operating conditions.

[0087] This represents the overall efficiency of a tubular air conditioning heat exchanger in converting input energy into effective thermal potential under current operating conditions, and its dimensionless characteristics and Its output range makes it an easy-to-understand and comparable performance quantification standard, which serves as the basis for adaptive controllers to make decisions, such as adjusting excitation energy and optimizing fluid conditions.

[0088] It is an index that quantifies the uniformity of the refrigerant two-phase flow distribution within the shell side 1. A uniform two-phase flow distribution usually means more efficient heat transfer. This index is obtained by analyzing the dielectric constant distribution within the shell side 1, for example, by using reconstructed dielectric constant information obtained by a capacitance tomography sensor around the periphery of the shell side 1.

[0089] This indicates the proportion of the surface area of ​​heat exchange tube 2 that is effectively covered by the refrigerant liquid film. A higher liquid film coverage usually means a larger effective heat transfer area. This indicator is obtained by measuring and analyzing the thickness of the liquid film on the inner wall of heat exchange tube 2, for example, by using transient liquid film thickness information obtained by a fiber optic displacement sensor embedded in the cross section of heat exchange tube 2 or an electrical conductivity probe array closely attached to the gas-liquid interface.

[0090] It is a coefficient that reflects the actual gain effect of the acoustic vibration excitation component on the heat transfer process. This gain is quantified by comparing the sensible heat change of the fluid before and after applying the acoustic vibration excitation component. The calculation of this coefficient requires obtaining thermodynamic parameters such as the mass flow rate, specific heat capacity, and inlet and outlet temperatures of the fluid, for example, by obtaining data through temperature sensors and flow meters installed at the inlet and outlet of shell side 1.

[0091] , These two power exponents are used to adjust the two-phase cloud uniformity index. and effective liquid film coverage global thermal potential conversion degree The influence weights of these indices can be adjusted to make the model more or less sensitive to changes in specific spatial distribution characteristics (such as evenness or coverage), thereby highlighting or weakening their importance in the evaluation. These indices can be determined through experimental data fitting, numerical simulation optimization, or expert experience setting. This coefficient is used to adjust the acoustic-vibration synergistic effect coefficient. global thermal potential conversion degree The contribution of the coefficient reflects the efficiency of converting the energy input to the acoustic vibration excitation component into actual heat transfer gain. This coefficient can be calibrated through system energy efficiency testing, energy balance analysis, or performance optimization based on specific operating conditions.

[0092] By introducing a global translational algebraic S-mapping model, an evaluation index that can comprehensively reflect the overall thermodynamic performance of the heat exchanger is constructed, thus solving the problem of the disconnect between local parameter optimization and global heat transfer objectives. After receiving various data collected by the multimodal sensor network, the adaptive controller first preprocesses and extracts features from these raw data to obtain the two-phase cloud uniformity index. Effective liquid film coverage And the acoustic-vibration synergistic enhancement coefficient based on thermodynamic sensible heat extraction. .

[0093] Two-phase cloud evenness index The uniformity of the refrigerant phase distribution within shell-side 1 was quantified using the power-law exponent of spatial distribution sensitivity. Weighting is applied so that the controller can dynamically adjust the weight of each phase distribution's influence on the overall thermal potential conversion based on the quality of the phase distribution, resulting in effective liquid film coverage. This reflects the actual heat transfer area of ​​the liquid film on the surface of heat exchange tube 2, as shown by the power-law exponent of spatial distribution sensitivity. By mapping, the contribution of liquid film distribution to heat transfer efficiency can be accurately assessed, thereby strengthening the focus on liquid film integrity in the control logic.

[0094] Based on this, the acoustic-vibration synergistic enhancement coefficient extracted from thermodynamic sensible heat is... Combined with energy efficiency conversion gain coefficient By utilizing the nonlinear function term in the global translation algebraic S-mapping, the actual thermal gain brought by the acoustic vibration excitation component is transformed into a normalized global thermal potential conversion degree. This nonlinear mapping mechanism enables the model to adaptively accommodate the positive heat transfer gain that the acoustic vibration excitation components may bring, as well as the negative thermodynamic deterioration that may occur under certain operating conditions. In this way, the adaptive controller can dynamically adjust the evaluation benchmark according to the actual effect of acoustic vibration synergy, providing a quantitative basis for subsequent closed-loop feedback control that can characterize the global thermal potential conversion level. This evaluation process is closely integrated with a multimodal sensor network, which provides rich, real-time microscopic and macroscopic data required for the evaluation. For example, a capacitance tomography sensor is used to obtain the reconstructed dielectric constant to calculate the two-phase cloud uniformity index. Fiber optic displacement sensors and conductivity probe arrays are used to obtain transient liquid film thickness to assess effective liquid film coverage. Temperature and power sensors are used to calculate the acoustic-vibration synergy coefficient. This data-driven evaluation approach enables the adaptive controller to comprehensively perceive the operating status of the heat exchanger from multiple dimensions and to conduct a global assessment of the thermal potential conversion rate, thereby overcoming the limitations of traditional methods that only focus on local optimization and ignore overall performance.

[0095] 7. Preferably, the uniformity index of the two-phase cloud is quantified by the information entropy of the dielectric constant ratio of discrete pixels reconstructed by the capacitance tomography sensor; the acoustic-vibration synergistic efficiency coefficient is defined as the ratio of the net apparent heat exchange increment under the start-stop condition of the excitation system to the real-time total energy consumption of the input acoustic wave generator and the low-frequency oscillator 3.

[0096] Two-phase cloud evenness index Synergistic effect coefficient with acoustic vibration Obtained through the following system of equations:

[0097]

[0098] In the formula, The total number of pixels reconstructed by the capacitive tomography sensor; For the first The proportion of information about the dielectric constant of each pixel; Specific heat capacity at constant pressure of a fluid, in units of q_m represents mass flow rate, in units of... ; and These are the shell-side outlet thermodynamic temperatures after starting and stopping the acoustic vibration excitation assembly, in units of... ; and These are the instantaneous electrical power inputs to the sound wave generator and the low-frequency oscillator 3, respectively, in units of... .

[0099] Among them, the two-phase cloud uniformity index It is a quantitative index that measures the uniformity of the distribution of two-phase flow (such as gas and liquid phases of refrigerant) within shell side 1. Based on the information entropy theory, it evaluates the spatial uniformity of the fluid by analyzing the dielectric constant distribution reconstructed by the capacitance tomography sensor. There is a significant difference between the dielectric constant in the gas phase and the liquid phase, so its distribution can reflect the relative content and mixing state of the two phases. This index can reflect the uniformity of the refrigerant distribution within shell side 1 from the spatial distribution dimension, providing a spatial distribution basis for assessing the stability of thermal potential conversion.

[0100] when A higher value indicates a more uniform two-phase flow distribution, which is beneficial for efficient heat transfer; conversely, a lower value may indicate localized liquid phase enrichment or gas phase cavitation, affecting heat transfer efficiency. Capacitance tomography sensors can employ a multi-electrode array configuration. By measuring the capacitance values ​​between different electrode pairs and combining this with image reconstruction algorithms (such as linear back projection and iterative reconstruction), the shell-side section 1 can be divided into... Each pixel is analyzed, and the dielectric constant of each pixel is calculated. Then, the information about the proportion of each pixel's dielectric constant relative to the total dielectric constant is considered. Substitute the values ​​into the Shannon entropy formula for calculation.

[0101] Acoustic-Vibration Synergistic Effect Coefficient It is an indicator that quantifies the actual contribution of the acoustic vibration excitation component to the heat transfer performance of the heat exchanger. It is calculated by comparing the sensible heat gain generated by the thermodynamic temperature change of the outlet fluid in the shell side 1 before and after the acoustic vibration excitation component is turned on, and the ratio of the total input electrical power of the acoustic generator and the low-frequency oscillator 3. This coefficient directly links the thermodynamic output gain to the cost of electrical energy input, and can effectively quantify the actual contribution of acoustic vibration excitation to the heat transfer performance, providing a feedback indicator for the adaptive controller to judge the effectiveness of the excitation energy.

[0102] A higher The values ​​indicate that acoustic vibration excitation brings about a significant heat transfer enhancement effect with a relatively small energy input. In actual operation, this can be monitored in real time by installing a high-precision thermocouple or platinum resistance temperature sensor at the shell-side 1 outlet. and mass flow Measurements were taken using a mass flow meter and an acoustic generator. and low-frequency oscillator 3 The instantaneous electrical power can be calculated using a power sensor or by measuring its voltage and current. To improve the accuracy of the measurement, further steps can be taken. and During measurement, ensure that other operating parameters (such as inlet temperature, inlet flow rate, refrigerant pressure, etc.) remain stable to isolate the influence of the acoustic vibration excitation components.

[0103] By introducing the two-phase cloud uniformity index Synergistic effect coefficient of sound and vibration This technology enables a refined and quantitative assessment of the internal flow field state and energy conversion efficiency of a tubular air conditioner heat exchanger. Specifically, a capacitance tomography sensor surrounds the outer periphery of shell side 1, capturing in real time the dielectric constant distribution information of the refrigerant two-phase flow within shell side 1. The adaptive controller utilizes this information to reconstruct the total number of pixels. The proportion of information about the dielectric constant of each pixel The two-phase cloud evenness index was calculated. This index intuitively reflects the uniformity of the gas-liquid two-phase distribution within the shell side 1, providing a spatial distribution basis for assessing the stability of the heat transfer process.

[0104] When the two-phase flow is unevenly distributed, such as when liquid phase accumulation or gas phase cavitation occurs, it can lead to localized deterioration of heat transfer. The value will decrease, indicating that the system needs to intervene. At the same time, in order to quantify the actual synergistic effect of the acoustic-vibration excitation component, the adaptive controller also calculates the acoustic-vibration synergistic efficiency coefficient. This coefficient is activated by comparing the acoustic vibration excitation components. ) and close ( The difference in the outlet thermodynamic temperature of the shell side 1 under the two conditions, combined with the fluid's isobaric specific heat capacity and mass flow The actual thermodynamic gain resulting from acoustic excitation is calculated, and this gain is then compared with that of the acoustic wave generator. and low-frequency oscillator 3 The ratio of the total instantaneous electrical power input directly quantifies the heat transfer enhancement effect produced by a unit energy input, and directly links the thermodynamic output with the electrical energy input, enabling the adaptive controller to clearly determine the energy efficiency ratio of the current excitation strategy.

[0105] By using the two-phase cloud uniformity index Synergistic effect coefficient of sound and vibration Introduced into global thermal potential conversion degree In the evaluation model, the adaptive controller can more comprehensively and accurately evaluate the thermal potential conversion efficiency of the entire heat exchange system. As a two-phase cloud uniformity index, it directly affects In The term reflects the contribution of fluid distribution to the heat potential conversion, while As the acoustic-vibration synergistic enhancement coefficient, it influences... In This directly reflects the contribution of acoustic vibration excitation to heat transfer gain, enabling the adaptive controller to not only sense macroscopic temperature and pressure drop changes, but also delve into the microscopic two-phase flow distribution and energy conversion efficiency, thereby generating the target acceleration command. At the same time, it can more accurately adjust the operating parameters of the acoustic vibration excitation component to adaptively break the thermal resistance boundary, avoiding the problems of energy waste and negative resistance surge in traditional open-loop control.

[0106] 8. Preferably, the adaptive controller's process for deriving the fidelity of multimodal acoustic-vibration coupling combines frequency domain phase-locked state and potential well depth theory, and its analytical equations and built-in acoustic trapping potential energy index The equation is:

[0107]

[0108] In the formula, It represents the multimodal acoustic-vibration coupling fidelity and is dimensionless. The acoustic-vibration cooperative phase-locked angle is calculated by converting the peak time difference of the cross-correlation between the external excitation fundamental frequency and the signal, with units of . ; , To satisfy and for complementary weighting coefficients; The reference saturation constant; The droplet's mean Soder diameter is expressed in units of 1000 oz. ; The effective value of the antinode sound pressure obtained by the broadband hydrophone, in units of ; The velocity of sound in fluids, in units of ; The acoustic contrast factor is dimensionless. The characteristic velocity of the liquid phase, in units of .

[0109] Among them, multimodal acoustic-vibration coupling fidelity It is a comprehensive index used to quantify the degree of matching between acoustic vibration excitation components and fluid thermodynamic state, reflecting the effectiveness of excitation energy acting on thermal resistance boundary. Its concept is to integrate macroscopic heat exchange efficiency with microscopic interface stability, and further consider the synchronization of excitation and the ability to control microscopic fluid.

[0110] Frequency domain phase-locked state refers to the degree of synchronization between the frequency and phase of the external excitation signal and the response frequency and phase of the shell-side fluid or liquid film. Its function is to ensure that the excitation energy can be efficiently and accurately transferred to the target medium, avoiding energy loss due to phase mismatch.

[0111] Potential well depth theory describes the ability of an acoustic field to capture and manipulate microscopic particles (such as droplets and bubbles) in a fluid. The acoustic field forms a sound pressure gradient in the fluid, generating acoustic radiation force, thereby forming a potential well at a specific location to capture or drive particles. Its role is to quantify the potential of the acoustic field to effectively control the microstructure of liquid films or gas-liquid interfaces.

[0112] Acoustic capture potential index It is a dimensionless index used to quantify the relative strength between the sound field's ability to capture droplets and the inertial force of the liquid phase's characteristic motion, reflecting the sound field's manipulation efficiency of microscopic droplets. It is the acoustic-vibration cooperative phase-locked angle calculated from the peak time difference of the cross-correlation between the external excitation fundamental frequency and the signal. It is a key parameter for measuring the synchronization between the excitation system and the fluid response. By analyzing the cross-correlation function between the excitation signal and the fluid response signal, the time difference corresponding to the peak value is found, and then converted into a phase angle, which directly reflects the phase alignment between the excitation and the response.

[0113] Complementary weighting coefficients and The contributions of frequency-domain phase-locked states and potential well depth theory to the fidelity calculation of multimodal acoustic-vibration coupling are used to balance the contributions. They satisfy the condition that their sum equals 1, allowing the system to dynamically adjust the emphasis on excitation synchronization or microscopic manipulation capability according to actual operating conditions or optimization objectives. For example, in scenarios requiring precise control of liquid film fluctuations, the emphasis can be appropriately increased. The weighting, and in scenarios requiring enhanced droplet breakup or mixing, can be increased. The weights, referencing the saturation constant In the calculation of the acoustic capture potential energy index, it is used to prevent the denominator from being zero or logarithmically singular, and to provide a reference benchmark, so that... Item in It has a linear response when it is small, When the value is large, it tends to saturate.

[0114] droplet mean diameter This is a statistical parameter characterizing the droplet size distribution, defined as the average ratio of the volume to the surface area of ​​all droplets. This parameter can be measured or estimated using a laser diffractometer, image analysis techniques, or conductivity probe arrays combined with statistical models. The effective value of the ventral sound pressure obtained by a broadband hydrophone is also relevant. It refers to the root mean square value of the sound pressure measured at the antinode of a standing wave in a sound field. It directly reflects the intensity of the sound wave and is the input for calculating the sound capture potential energy.

[0115] Fluid sound speed The acoustic contrast factor is the speed at which sound waves propagate in a fluid. It is affected by factors such as the fluid medium's density, compressibility, temperature, and pressure, and is calculated using an ultrasonic velocimeter or empirical formulas based on fluid properties. It is a dimensionless parameter used to describe the difference in response between different phases in a fluid under the action of a sound field. It is related to the density and compressibility of the two phases, and determines the direction and magnitude of the sound radiation force and the characteristic velocity of the liquid phase. This represents the typical or average velocity of the liquid phase in shell side 1, used to characterize the inertial effect of the liquid phase, and can be obtained by a flow rate sensor or calculated based on mass flow rate and cross-sectional area.

[0116] By combining the theory of frequency domain phase-locked state and potential well depth with an adaptive controller, the fidelity of multimodal acoustic-vibration coupling is derived. It can comprehensively and dynamically evaluate the actual coupling state between the acoustic vibration excitation component and the fluid in shell side 1 and the liquid film on the inner wall of heat exchange tube 2.

[0117] Specifically, the adaptive controller first calculates the global thermal potential conversion degree based on the signals collected by the multimodal sensor network. Phase boundary configuration fit Global thermal potential conversion degree This reflects the macroscopic heat exchange efficiency, while the phase boundary configuration fit is... This characterizes the stability of the microscopic liquid film interface. Taking a geometric average of these two values ​​ensures a comprehensive consideration of both macroscopic heat transfer performance and microscopic interface stability.

[0118] Based on this, the system further introduces acoustic-vibration coordinated phase-locked angle. This angle accurately reflects the synchronization of the external excitation fundamental frequency and the fluid response signal in the time dimension. By mapping the phase-locked deviation through the cosine square function, the alignment of the excitation energy in the time dimension can be accurately captured, thereby avoiding energy waste caused by phase mismatch.

[0119] Simultaneously, it captures potential energy through a built-in acoustic capture index. The equation quantifies the ability of the sound field to trap microscopic droplets, revealing the potential of the sound field to control the microstructure of fluids, and the complementary weighting coefficients. and The introduction of this technology allows the system to dynamically adjust its emphasis on phase-locked state and potential energy capture capability according to actual operating conditions. Ultimately, this multi-physics, multi-scale information is organically fused to generate a high-fidelity multimodal acoustic-vibration coupling. This high-fidelity feedback signal enables the adaptive controller to more accurately determine whether the excitation energy is effectively applied to the thermal resistance boundary, thereby providing precise instructions for the subsequent closed-loop drive of the low-frequency oscillator 3 and realizing the adaptive breaking of the thermal resistance boundary. This comprehensive evaluation mechanism enables the excitation phase and the gas-liquid resonance beat to be precisely aligned, and the problem of the disconnect between the microscopic phase control field and the macroscopic thermal flow field is effectively solved.

[0120] 9. Preferably, the underlying nonlinear feedback mechanism is a targeted excitation evolution control law. The control law generates a target acceleration command based on a nonlinear exponential ratio feedback mechanism. The adaptive controller is configured to: calculate the ratio between the preset desired coupling fidelity threshold and the real-time multimodal acoustic-vibration coupling fidelity performance of the system; use the ratio as the control basis for nonlinear gain amplification or convergence, and combine it with the root mean square of the real vibration acceleration sampled by the triaxial accelerometer to output a dynamically evolved and corrected target acceleration command; so that the acoustic-vibration excitation component maintains adaptive phase-locked operation between breaking through the thermal resistance boundary and preventing acoustic cavitation mechanical overload.

[0121] The underlying nonlinear feedback mechanism is a targeted excitation evolution control law. The adaptive controller generates target acceleration commands based on the current vibration state and the fidelity deviation of the coupled target. The closed-loop drive of the low-frequency oscillator 3 has the following control equation:

[0122] In the formula, Units are ; The root mean square of the vibration acceleration calculated from samples taken by a triaxial accelerometer is expressed in units of 1000 m / s. ; The preset desired coupling fidelity threshold is dimensionless. The flow disturbance robustness is dimensionless; For multimodal acoustic-vibration coupling fidelity, dimensionless; The vibration evolution gain index is a dimensionless value.

[0123] The targeted excitation evolution control law is a strategy for dynamically adjusting excitation energy. Its core lies in generating precise driving commands in a nonlinear manner based on the vibration state and coupling fidelity deviation fed back by the system in real time. This control law can be implemented in various ways. For example, it can be based on an adaptive fuzzy logic control algorithm, using a fuzzy rule base and inference mechanism to handle the uncertainty and nonlinearity of the system.

[0124] The adaptive controller is responsible for receiving and processing data from the multimodal sensor network, and calculating and outputting the target acceleration command in real time according to the preset control logic and algorithm. The controller can be a high-performance digital signal processor (DSP) or an embedded microcontroller, which integrates the necessary computing modules and communication interfaces.

[0125] Closed-loop driven low-frequency oscillator 3 refers to the target acceleration command generated by the adaptive controller. This is transformed into actual physical excitation, and the response of the low-frequency oscillator 3 is continuously monitored. The response data is fed back to the controller to form a closed loop, which is achieved through a power amplifier. This amplifier converts the low-power signal from the controller into a power signal sufficient to drive the low-frequency oscillator 3 (e.g., a piezoelectric actuator or an electromagnetic exciter). The control equation is the core mathematical expression of this targeted excitation evolution control law, which defines the target acceleration command. How to determine the current vibration state Expected Coupling Fidelity Threshold Actual multimodal acoustic-vibration coupling fidelity Fluid dynamic disturbance robustness and vibration evolution gain index The calculation shows that the equation dynamically adjusts the excitation intensity through a nonlinear function to adapt to different working conditions.

[0126] The root mean square of the vibration acceleration is calculated by sampling from the triaxial accelerometer. It represents the current vibration intensity of the low-frequency oscillator 3 and is a quantitative indicator of the real-time vibration state of the system. This value is obtained by performing root mean square calculation on the raw acceleration signal collected by the triaxial accelerometer and can reflect the overall energy level of the vibration.

[0127] The preset desired coupling fidelity threshold is a target value determined during system design, representing the ideal level of acoustic-vibration coupling effect that is desired to be achieved. This threshold can be set according to the specific application scenario and performance requirements of the heat exchanger. For example, a higher value can be set when pursuing the ultimate heat exchange efficiency.

[0128] To assess the robustness of the fluid flow under disturbances, the stability and disturbance resistance of the shell-side 1 fluid under excitation were quantified. The calculation method is as described above, based on the full positive domain inverse triangular mapping model. To ensure the fidelity of multimodal acoustic-vibration coupling, it evaluates the synergistic effect between the sound field and the vibration field. Its calculation method is as described above, combining the frequency domain phase-locked state and potential well depth theory.

[0129] The vibration evolution gain exponent is a dimensionless exponential parameter used to adjust the nonlinear response characteristics of the control equations. By adjusting this exponent, the system's response speed and intensity to deviations can be controlled; for example, a larger exponent... The value will make the system's response to deviations more aggressive.

[0130] By comparing the desired coupling fidelity with the weighted product of the actual coupling fidelity and the flow regime robustness, the deviation between the current excitation state and the ideal state can be evaluated in real time. When the system detects that the flow regime robustness is low or the coupling fidelity is insufficient, the mechanism will automatically adjust the target acceleration command to compensate for the performance loss of the system under complex working conditions. By introducing the vibration evolution gain index, the scheme gives the controller the ability to adjust the nonlinear response of the excitation energy, so that the drive command can evolve smoothly and in a targeted manner according to the real-time data fed back by the system, thereby ensuring that the low-frequency oscillator 3 can always work at the optimal excitation frequency and amplitude, effectively breaking the thermal resistance boundary and improving the overall heat exchange efficiency.

[0131] This control law is closely integrated with the aforementioned multimodal sensing network and physics field calculation module, enabling the adaptive controller to obtain accurate flow disturbance robustness. and multimodal acoustic-vibration coupling fidelity The real-time acquisition of parameters provides an accurate decision-making basis for the targeted excitation evolution control law, thereby achieving precise matching between excitation energy and thermal resistance boundary breaking requirements, avoiding the problems of energy waste or poor excitation effect that may occur in traditional open-loop or single feedback control.

[0132] The foregoing has shown and described the basic principles, main features, and advantages of the present invention. Those skilled in the art should understand that the present invention is not limited to the above embodiments. The embodiments and descriptions in the specification are merely illustrative of the principles of the invention. Various changes and modifications can be made to the invention without departing from its spirit and scope, and all such changes and modifications fall within the scope of the present invention as claimed. The scope of protection of the present invention is defined by the appended claims and their equivalents.

Claims

1. A tubular air conditioning heat exchanger, characterized in that: include: The heat exchanger body has a shell side for heat transfer due to refrigerant phase change and heat exchange tubes that pass through the shell side. The acoustic vibration excitation assembly includes an acoustic wave generator disposed in the heat exchanger body and a low-frequency oscillator coupled to the tube bundle support frame, for applying multi-physics field coupled excitation energy to the shell-side fluid and the liquid film on the inner wall of the heat exchange tube; The multimodal sensing network includes a differential pressure sensor installed at the inlet and outlet of the shell side, an optical fiber displacement sensor embedded in the cross section of the heat exchange tube, an electrical conductivity probe array closely attached to the gas-liquid interface, a capacitance tomography sensor surrounding the periphery of the shell side, a broadband hydrophone for capturing the sound pressure at the antinode of the standing wave in the cavity, and a triaxial accelerometer anchored to the output end of the low-frequency oscillator. An adaptive controller is communicatively connected to the acoustic-vibration excitation component and the multimodal sensing network. It has a built-in physics field calculation module configured to receive differential pressure timing, transient liquid film thickness, reconstructed dielectric constant, underwater acoustic pressure, and vibration acceleration signals collected by various sensors. It extracts shell-side pressure drop characteristics to construct flow-state disturbance rejection robustness; extracts phase boundary configuration fit based on dynamic liquid film deviation and interface wave excitation; integrates phase cloud map information entropy, liquid film coverage, and thermal potential enhancement to evaluate the global thermal potential conversion degree; combines acoustic field potential trapping force and excitation phase-locking angle to deduce the multimodal acoustic-vibration coupling fidelity; and finally, based on the multimodal acoustic-vibration coupling fidelity, generates a target acceleration command through a low-level nonlinear feedback mechanism to drive the low-frequency oscillator to adaptively break the thermal resistance boundary in a closed loop.

2. A tubular air conditioning heat exchanger according to claim 1, characterized in that: The process of the adaptive controller extracting shell-side pressure drop characteristics to construct the flow regime's robustness against disturbances is based on a continuous bounded nonlinear mapping mechanism. The adaptive controller is configured to: calculate the ratio of the physical reference pressure drop scale to the real-time shell-side pressure drop fluctuation dispersion, and combine the ratio with the shell-side pressure drop state ratio and a preset sensitivity weight as a dimensionless input term. Through a monotonically increasing nonlinear mathematical function with limit boundary values, the turbulent disturbance state of the physical flow regime is mapped to a normalized coefficient characterizing the smoothness of the flow field, and the flow regime's robustness against disturbances is output.

3. A tubular air conditioning heat exchanger according to claim 2, characterized in that: The physical reference pressure drop scale is based on the principle of fluid friction resistance, combined with the effective geometric dimensions of the heat exchange tube, the density of the gas phase fluid, and the characteristic velocity of the inlet flow field; the shell-side pressure drop fluctuation dispersion is determined by the statistical standard deviation of the transient differential pressure time series discretely sampled by the micro differential pressure sensor relative to its mean; the shell-side pressure drop state ratio is defined as the ratio of the reference pressure drop when the acoustic vibration excitation component is not turned on to the dynamic excitation pressure drop.

4. A tubular air conditioning heat exchanger according to claim 3, characterized in that: The adaptive controller extracts the phase boundary configuration fit based on dynamic liquid film deviation and interface wave oscillation. This process is based on a rational polynomial composite mapping mechanism with bell-shaped attenuation characteristics. The adaptive controller is configured to: quantify the relative error of the transient average liquid film thickness deviating from the theoretical optimal heat transfer thickness, and perform dimensionless processing using the fluid capillary characteristic length; simultaneously, extract the perturbation penalty ratio of the liquid film surface oscillation amplitude relative to the average liquid film thickness; and use the above deviation and perturbation as attenuation factors to construct a composite dynamic fidelity evaluation index that tends to the theoretical extreme boundary, so as to output the phase boundary configuration fit.

5. A tubular air conditioning heat exchanger according to claim 4, characterized in that: The fluid capillary characteristic length is derived from the balance between the surface tension of the gas-liquid interface and the density difference between the gas and liquid under gravity; the agitation amplitude of the liquid film surface is obtained by extracting the peak-valley fluctuation difference of the transient voltage signal output by the conductivity probe array and converting it with spatial calibration coefficients.

6. A tubular air conditioning heat exchanger according to claim 5, characterized in that: The adaptive controller evaluates the global thermal potential conversion degree using a global translation-limited algebraic mapping mechanism. The adaptive controller is configured to: extract the acoustic-vibration synergistic efficiency coefficient characterizing the macroscopic heat transfer state of the flow field, and translate and compress it to a preset positive domain boundary using an algebraic limit function with a scaling factor to adaptively encompass both positive and negative heat transfer benefits; and perform power-law synergistic calculations on the limited energy efficiency parameter, the two-phase cloud uniformity index, and the effective liquid film coverage rate to output the global thermal potential conversion degree.

7. A tubular air conditioning heat exchanger according to claim 6, characterized in that: The two-phase cloud uniformity index is quantified by the information entropy of the dielectric constant ratio information of discrete pixels reconstructed by the capacitance tomography sensor; the acoustic-vibration synergistic efficiency coefficient is defined as the ratio of the net apparent heat exchange increment under the start-up and shutdown conditions of the excitation system to the real-time total energy consumption power input to the acoustic wave generator and the low-frequency oscillator.

8. A tubular air conditioning heat exchanger according to claim 7, characterized in that: The adaptive controller's process for deriving the multimodal acoustic-vibration coupling fidelity combines the frequency domain phase-locked state and acoustic radiation potential well depth theory. Its multimodal acoustic-vibration coupling fidelity calculation equation and the built-in acoustic trapping potential energy index calculation equation are as follows: In the formula, It represents the multimodal acoustic-vibration coupling fidelity and is dimensionless. The global thermal potential conversion degree obtained from the aforementioned solution is dimensionless; The phase boundary configuration fit obtained from the aforementioned solution is dimensionless. The acoustic-vibration cooperative phase-locked angle is calculated by converting the peak time difference of the cross-correlation between the external excitation fundamental frequency and the signal, with units of . ; , The complementary weight coefficients, which sum to 1, are dimensionless. The potential energy index for sound capture is dimensionless. The reference potential energy saturation constant is dimensionless. The droplet's Sørgren average diameter is expressed in units of 1000 ppm. ; The effective value of the ventral sound pressure level obtained by the broadband hydrophone, in units of ; This is the density of the liquid phase, in units of... ; The velocity of sound in fluids, in units of ; The acoustic contrast factor is dimensionless. The characteristic velocity of the liquid phase, in units of .

9. A tubular air conditioning heat exchanger according to claim 8, characterized in that: The underlying nonlinear feedback mechanism is a targeted excitation evolution control law. The control law generates a target acceleration command based on a nonlinear exponential ratio feedback mechanism. The adaptive controller is configured to calculate the ratio between a preset desired coupling fidelity threshold and the real-time multimodal acoustic-vibration coupling fidelity performance of the system. Using the ratio relationship as the control basis for nonlinear gain amplification or convergence, and combining it with the root mean square of the real vibration acceleration sampled by the triaxial accelerometer, a target acceleration command for dynamic evolution correction is output; so that the acoustic vibration excitation component can maintain adaptive phase-locked operation between breaking through the thermal resistance boundary and preventing acoustic cavitation mechanical overload.

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