Phase control method and system of thermoacoustic Stirling coupling system

Abstract

The invention provides a phase control method and system for a thermoacoustic Stirling coupling system, and the method comprises the steps: building a joint performance model, and carrying out the optimization of the joint performance model through a preset optimization algorithm, so as to obtain an optimal configuration set comprising a plurality of optimal configuration points; acquiring operation data of the system under the current working condition, and comparing the operation data with one or more optimal configuration points in the optimal configuration set to identify operation deviation between the current operation state and the optimal configuration points; according to the recognized operation deviation, phase regulation and control operation is executed, so that the phase difference between the gas speed and the pressure is adjusted to a target phase interval, self-adaptive regulation and control are conducted, and maximization of the acoustic power density and the heat regeneration efficiency is achieved; performance evaluation is carried out in a preset evaluation period, if the operation deviation exceeds a first preset threshold value, increment optimization operation is carried out, and the current optimal configuration point is updated.

Description

Technical Field

[0001] The present application relates to the field of Stirling systems, and more specifically to a phase control method and system for a thermoacoustic Stirling coupling system. Background Art

[0002] The thermoacoustic-Stirling coupled system is a high-efficiency energy conversion system that combines the thermoacoustic effect with the Stirling cycle principle. It is widely used in scenarios such as cryogenic refrigeration, industrial waste heat recovery, and aerospace energy management. In existing technologies, there is a strongly coupled thermoacoustic-dynamic interaction between the thermoacoustic cavity, the regenerator, and the Stirling working air mass, and the overall performance of the system is affected by the interaction of multiple operating parameters. Since traditional control methods are usually based on single-objective optimization or local parameter adjustment, it is difficult to achieve global optimization at the system level, which can easily lead to problems such as fluctuations in acoustic power output efficiency or low acoustic power density. In actual operation, this type of system is highly sensitive to load changes and ambient temperature difference disturbances. The phase difference between gas velocity and pressure can easily deviate from the optimal control range. If there is a lack of dynamic regulation and real-time response mechanism, it may lead to a decrease in acoustic power conversion efficiency and even cause system detuning, affecting its stable operation. Summary of the Invention

[0003] The purpose of this application is to provide a phase control method and system for a thermoacoustic Stirling coupling system to achieve control of the gas velocity and pressure phase difference in the thermoacoustic Stirling coupling system, thereby maximizing the acoustic power density and heat recovery efficiency.

[0004] In order to achieve the above-mentioned objectives, the present application provides, on the one hand, a phase control method for a thermoacoustic Stirling coupling system, comprising: establishing a joint performance model and optimizing the joint performance model using a preset optimization algorithm to obtain an optimal configuration set containing multiple optimal configuration points, wherein the joint performance model includes a thermoacoustic cavity acoustic power output model, a regenerator thermal conversion model, a pressure drop loss model and a coupling performance function; collecting operating data of the system under the current operating conditions, and comparing the operating data with one or more optimal configuration points in the optimal configuration set to identify the operating deviation between the current operating state and the optimal configuration point; performing a phase control operation based on the identified operating deviation to adjust the phase difference between the gas velocity and pressure to a target phase range and perform adaptive control; and performing performance evaluation within a preset evaluation period. If the operating deviation exceeds a first preset threshold, performing an incremental optimization operation and updating the current optimal configuration point. The joint performance model can comprehensively consider the acoustic power output, heat recovery efficiency and pressure drop loss of the thermal acoustic cavity; it can realize operating state comparison and deviation identification based on the optimal configuration set, which can achieve more accurate and timely regulation; it controls the phase difference as a variable and combines it with an adaptive control mechanism to achieve the coordinated optimization of acoustic power density and heat recovery efficiency; the incremental optimization mechanism supports the update of the optimal configuration, which can improve the stability of the system.

[0005] Preferably, the thermal acoustic cavity acoustic power output model takes the excitation frequency, hot end temperature and cavity geometric parameters as input, and the acoustic power density generated per unit time as output; the regenerator thermal conversion model takes the air mass displacement, regenerator thermal conductivity and structural parameters as input, and the heat recovery efficiency as output; the pressure drop loss model takes the air flow channel size, flow velocity and temperature gradient as input, and the total pressure loss value as output; the output results of the thermal acoustic cavity acoustic power output model, the regenerator thermal conversion model and the pressure drop loss model are used to construct a coupling performance function.

[0006] Preferably, the coupling performance function is as follows:

[0007]

[0008] Where, P s is the acoustic power density per unit volume, η r is the regenerator thermal efficiency, ΔP is the system pressure drop loss, and ω1, ω2, and ω3 are the weighting coefficients for acoustic power output, regenerator efficiency, and pressure drop loss, respectively. Flexible adjustment of different performance weights allows for adapting operational strategies to different application scenarios.

[0009] Preferably, the preset optimization algorithm is a coupling performance optimization method based on a genetic algorithm, which includes: initializing a population, where each individual in the population corresponds to a set of device configuration parameters; using the coupling performance function as the fitness function to calculate the fitness value of each individual; performing selection, crossover, and mutation operations to generate the next generation population; and outputting the configurations with the highest fitness under preset termination conditions as the optimal configuration set. This population search avoids falling into local optimality; using the coupling performance function as the fitness function improves optimization efficiency and adapts to parameter search problems in complex nonlinear systems.

[0010] Preferably, the preset optimization algorithm adopts a phased optimization approach, including: generating multiple candidate solutions from an initial population; performing multiple rounds of iterations through selection, crossover, and mutation operations in a genetic algorithm to globally search the entire solution space to obtain multiple local optimal regions; performing local searches for each of the multiple local optimal regions obtained from the global search, using the current optimal configuration point as the initial solution to improve the accuracy of the optimal configuration point; selecting the configuration point with the highest overall performance score from each local optimal region and using it as the final optimal configuration point in the optimal configuration set. This splits the search process into two phases, exploration and refinement, balancing breadth and precision; reducing search redundancy and enhancing the applicability of the evolutionary algorithm in practical deployments.

[0011] Preferably, the incremental optimization operation includes re-optimizing within the neighborhood of the current optimal configuration point based on the range of perturbation variables, including: constructing a perturbation space guided by the current operating deviation; adjusting the search endpoint to a high-sensitivity parameter that affects phase difference stability; obtaining a local optimal configuration point that better suits the current operating conditions, and adding this configuration point as the updated optimal configuration point to the optimal configuration set. The search direction is dynamically adjusted based on the operating deviation to optimize the response effect.

[0012] Preferably, the phase control operation includes adjusting structural parameters of the thermoacoustic cavity, and the structural parameters include the length, cross-sectional area and resonant frequency of the thermoacoustic cavity.

[0013] Preferably, the phase control operation includes: detecting the phase difference between the current gas velocity and pressure in real time;

[0014] The phase difference is compared with a target phase range, and control parameters are adjusted based on the phase difference to keep the phase difference within the target phase range. The control parameters include one or more of the following: drive frequency, drive amplitude, and heat source input power. Multiple control parameter adjustments are available to easily adapt to various interferences and requirements.

[0015] Preferably, adaptively maintaining the phase difference within the target phase range includes: continuously monitoring the real-time phase difference between gas velocity and pressure; determining the current control effect based on the phase change trend within a sliding time window; and adjusting the control parameters if the phase change trend detected based on the current control effect exceeds a second preset threshold. The control parameters are adjusted to maximize the weighted terms corresponding to acoustic power density and heat recovery efficiency in the coupling performance function, thereby achieving adaptive control of the phase difference. The adaptability of control is enhanced through the time series trend judgment mechanism.

[0016] In another aspect, the present application provides a phase control system for a thermoacoustic Stirling coupling system, comprising: a performance modeling module configured to establish a joint performance model, the joint performance model comprising a thermoacoustic cavity acoustic power output model, a regenerator thermal conversion model, a pressure drop loss model, and a coupling performance function constructed based on the thermoacoustic cavity acoustic power output model, the regenerator thermal conversion model, and the pressure drop loss model;

[0017] an optimization module configured to optimize the coupling performance function using a preset optimization algorithm to obtain an optimal configuration set including a plurality of optimal configuration points;

[0018] a data collection and deviation identification module, configured to collect operating data under current operating conditions and compare the operating data with one or more optimal configuration points in the optimal configuration set to identify deviations between the current operating state and the optimal configuration points;

[0019] a phase control module configured to perform a phase control operation according to the identified operating deviation to adjust the phase difference between the gas velocity and pressure to a target phase range;

[0020] an adaptive control module configured to monitor the phase difference in real time and adjust control parameters according to the monitoring results, adaptively maintaining the phase difference within the target phase range to maximize the acoustic power density and heat recovery efficiency;

[0021] The evaluation and incremental optimization module is configured to perform a performance evaluation on the current operating state within a preset evaluation period. If the difference between the current operating state and the current optimal configuration point exceeds a first preset threshold, an incremental optimization operation is performed and the current optimal configuration point is updated.

[0022] It should be understood that the foregoing general description and the following detailed description are merely illustrative and are not restrictive of the present application. BRIEF DESCRIPTION OF THE DRAWINGS

[0023] The accompanying drawings, which are incorporated herein and form a part of the specification, illustrate one or more embodiments of the present application and, together with the description, serve to explain the principles of the present application and to enable one of ordinary skill in the relevant art to make and use the present application.

[0024] Figure 1 This is a flow chart of a phase control method for a thermoacoustic Stirling coupling system provided in an embodiment of the present application;

[0025] Figure 2 This is a schematic diagram of the structure of the joint performance model provided in the embodiment of the present application;

[0026] Figure 3 This is a flow chart of a method for generating an optimal configuration set based on an improved genetic algorithm provided in an embodiment of the present application;

[0027] Figure 4 This is a standard genetic evolution flow chart provided in the examples of this application;

[0028] Figure 5 This is a flow chart of a method for extracting configuration points using a clustering method provided in an embodiment of the present application;

[0029] Figure 6 This is a flow chart of the operating status comparison and deviation identification method provided in an embodiment of the present application;

[0030] Figure 7 is a flow chart of a matching method for identifying multiple optimal configuration points provided by an embodiment of the present application;

[0031] Figure 8 This is a flow chart of a phase difference calculation method provided in an embodiment of the present application;

[0032] Figure 9 This is a flow chart of the phase difference adaptive maintenance method provided in an embodiment of the present application;

[0033] Figure 10 This is a schematic diagram of the phase control system structure of the thermoacoustic Stirling coupling system provided in the embodiment of the present application.

[0034] Figure 11 Schematic diagram of the operation of the phase control system of the thermoacoustic Stirling coupling system provided in an embodiment of the present application. DETAILED DESCRIPTION

[0035] Example embodiments will now be described more fully with reference to the accompanying drawings. However, example embodiments may be implemented in a variety of forms and should not be construed as limited to the examples set forth herein; rather, the description of these embodiments is intended to make this application more comprehensive and complete and to fully convey the concepts of the example embodiments to those skilled in the art. The described features, structures, or characteristics may be combined in any suitable manner in one or more embodiments. In the following description, numerous specific details are provided to provide a deeper understanding of the embodiments of this application.

[0036] In one embodiment of the present application, a phase control method for a thermoacoustic Stirling coupling system is provided, such as Figure 1 As shown, the following steps are included:

[0037] S1: Establish a joint performance model and optimize it using a preset optimization algorithm to obtain an optimal configuration set containing multiple optimal configuration points. The joint performance model includes a thermoacoustic cavity acoustic power output model, a regenerator thermal conversion model, a pressure drop loss model, and a coupling performance function. The schematic diagram of the joint performance model is shown in the figure. Figure 2 shown.

[0038] Specifically, a joint performance model of the system was constructed based on the acoustic power output model of the thermoacoustic cavity, the thermal conversion model of the regenerator, and the system's pressure drop loss model. This joint performance model integrates the output results of these models through a coupled performance function to comprehensively reflect the overall performance of the system under different configuration parameters.

[0039] The joint performance model is optimized using a pre-set optimization algorithm to generate an optimal configuration set containing multiple optimal configuration points. Each optimal configuration point represents the best operating parameter combination for the system under specific operating conditions.

[0040] Furthermore, the thermoacoustic cavity is the core energy conversion component in the thermoacoustic-Stirling coupled system. The thermoacoustic cavity acoustic power output model is used to describe the acoustic power output per unit volume of the thermoacoustic cavity under certain thermal excitation conditions. The input parameters of the thermoacoustic cavity acoustic power output model include the hot-end temperature, cold-end temperature, excitation frequency, cavity length, cross-sectional shape, sound pressure amplitude, and velocity amplitude. The output parameter of the thermoacoustic cavity acoustic power output model is the acoustic power density per unit volume. The thermoacoustic cavity acoustic power output model reflects the mechanical acoustic power intensity that can be extracted from the cavity under given structural and thermal excitation conditions.

[0041] In a thermoacoustic-Stirling system, a regenerator is responsible for heat recovery and temperature gradient maintenance during the thermal-to-mechanical energy conversion process. Its performance significantly impacts system efficiency. A regenerator thermal conversion model is used to evaluate the thermal efficiency conversion of gas passing through the regenerator. Input parameters include the air mass displacement amplitude, gas specific heat, regenerator structural parameters, hot-end and cold-end temperature difference, flow velocity distribution, and heat exchange surface area. Regenerator structural parameters include packing material, porosity, length, and thermal conductivity. The output parameter of the regenerator thermal conversion model is the regeneration efficiency, which is expressed as the ratio of heat energy recovered per unit volume of gas during a single reciprocating displacement to the total heat exchanged. Regenerator thermal conversion models are developed based on numerical simulations (such as 1D distributed parameter models) or empirical models and are used to evaluate the thermal energy cycle efficiency of thermoacoustic systems.

[0042] Gas flow generates non-negligible pressure drop in the regenerator and thermoacoustic channel. A pressure drop model is used to estimate the total pressure drop in the gas flow channel due to viscous friction and geometric disturbances. The input parameters of the pressure drop model include gas flow rate, channel geometry (channel diameter and length), gas density and viscosity, temperature gradient, and the resistance coefficient within the regenerator. The output parameter of the pressure drop model is the pressure drop. The pressure drop model is used to identify fluid resistance bottlenecks that limit system efficiency and provide a basis for optimizing the acoustic-thermal-flow channel structure.

[0043] Combining the above three models, the thermoacoustic cavity acoustic power output model uses temperature, frequency, structure, and sound field as input parameter dimensions to characterize the acoustic energy output capacity of the thermoacoustic cavity under different configurations; the regenerator thermal conversion model uses gas displacement, thermal conductivity, heat exchange area, etc. as input parameter dimensions to characterize thermal efficiency and thermodynamic stability; the pressure drop loss model uses flow velocity, structural size, temperature, and viscosity as input parameter dimensions to characterize flow resistance and energy loss; the above three models are used as sub-models and are uniformly mapped to a coupled performance function in the joint performance model for overall performance optimization.

[0044] S2: Collecting operating data of the system under the current working condition, and comparing the operating data with one or more optimal configuration points in the optimal configuration set to identify the operating deviation between the current operating state and the optimal configuration.

[0045] Specifically, the system continuously collects operating data under current operating conditions, including parameters such as gas velocity and pressure. The collected real-time operating data is compared with one or more optimal configuration points in the optimal configuration set to identify operational deviations between the current operating state and the optimal configuration point.

[0046] S3: Based on the identified operating deviation, perform phase control operations and adjust system parameters to adjust the phase difference between gas velocity and pressure to the target phase range and perform adaptive phase adjustment to continuously maintain the phase difference between gas velocity and pressure within the target phase range to maximize acoustic power density and heat recovery efficiency.

[0047] S4: Perform performance evaluation within a preset evaluation period. If the operating deviation exceeds a first preset threshold, perform incremental optimization and update the current optimal configuration point.

[0048] Specifically, within a preset performance evaluation cycle, the system evaluates the current operating state. If the difference between the current operating state and the optimal configuration point exceeds a first preset threshold, an incremental optimization operation is automatically triggered to optimize the configuration parameters based on the current operating conditions and update the current optimal configuration point to ensure that the system continues to operate in the optimal state.

[0049] In one example, linear thermoacoustic theory is used to derive the acoustic work density per unit volume, which is given by the following formula:

[0050]

[0051] Where p′ is the complex amplitude of the sound pressure, and u′ is the complex amplitude of the particle velocity.

[0052] The heat recovery efficiency is calculated based on the non-steady-state energy balance formula, and the formula is as follows:

[0053]

[0054] Where Q1 is the recovered heat and Q2 is the total input heat.

[0055] The simplified Darcy-Weisbach model is used to calculate the pressure drop loss, and the formula is as follows:

[0056]

[0057] Where ζ is the local drag coefficient, ρ is the density, and u is the average air velocity.

[0058] The outputs of the above three models are uniformly incorporated into the coupling performance function to construct a unified evaluation index.

[0059] In one example, the performance of a thermoacoustic-Stirling coupled system is affected by multiple coupling parameters. To achieve unified evaluation and optimal control, this example provides a method for constructing a coupling performance function centered around three types of performance indicators. The coupling performance function is used to quantify the overall system performance under different device configurations. It comprehensively considers acoustic power density per unit volume, heat recovery efficiency, and pressure drop loss. The coupling performance function is constructed as follows:

[0060]

[0061] Where, P s is the acoustic power density per unit volume, η r is the thermal efficiency of the regenerator, ΔP is the system pressure drop loss, ω1, ω2, ω3∈[0,1], satisfying v1+v2+ω3=1, ω1, ω2, ω3 represent the weight coefficients of acoustic power output, regenerator efficiency, and pressure drop loss, respectively. s,max is the maximum reference value of the acoustic power density per unit volume in the training data, η r,max is the maximum reference value of the regenerative efficiency in the training data, ΔP max is the maximum reference value of the voltage drop loss in the training data, P s,max ,η r,max , ΔP max Used for normalization.

[0062] In one example, ω1=0.5, ω2=0.3, and ω3=0.2 can be set. They can also be flexibly set according to the application scenario. For example, when the cooler prioritizes thermal efficiency, the value of ω2 can be increased.

[0063] The goal of the coupling performance function is to minimize F, that is, to maximize the beneficial index and minimize the harmful index.

[0064] In order to solve the optimal solution of the above coupling performance function, this example provides a method for generating an optimal configuration set based on an improved genetic algorithm. In order to solve the optimal solution of the coupling performance function F constructed in the thermoacoustic-Stirling coupling system, this example adopts an optimization method based on a genetic algorithm. By constructing an optimal configuration set that adapts to different operating conditions, the system can be operated in a complex environment, such as Figure 2 As shown, the specific steps include:

[0065] S11: Encoding and population initialization; Encode the design variables such as the thermoacoustic cavity geometry parameters, regenerator filling parameters, and driving frequency; Among them, according to the physical meaning and controllability of the adjustable parameters in the thermoacoustic-Stirling system, select several optimized design variables for encoding. In this example, a total of six design variables are selected, including the thermoacoustic cavity length L tc, cross-sectional area of ​​the thermoacoustic cavity A tc , thermal conductivity of regenerator filler λ r , driving frequency f, heat source input power Q and gas initial pressure P0, the range of the thermoacoustic cavity length is 150–400 mm, and the range of the thermoacoustic cavity cross-sectional area is 300–800 mm 2 The thermal conductivity of the regenerator filler ranges from 0.1 to 3.0 W / m·K, the driving frequency ranges from 100 to 500 Hz, the heat source input power ranges from 10 to 150 W, and the initial gas pressure ranges from 0.5 to 2.0 MPa.

[0066] Each variable is real-coded (GA), with the precision set to 2 decimal places. Each individual chromosome is a 6-dimensional vector, for example, X i =[L tc , A tc ,λ r ,f,Q,P0].

[0067] Initialize the population, each individual in the population corresponds to a set of configuration parameter combinations; set the initial population size to N = 100, that is, generate 100 different initial parameter combinations, each group as an individual, forming the population β0,

[0068] S12: Fitness calculation; for each individual X i , calculate the output of the three sub-models under their corresponding configuration parameters, input the three sub-models in sequence, calculate their corresponding output values, and the outputs are the thermal acoustic cavity acoustic work density P s , heat recovery efficiency η r and pressure drop loss ΔP; the fitness is evaluated using the coupling performance function F constructed above, and the calculation formula is as follows:

[0069]

[0070] Among them, ω1, ω2, and ω3 can be flexibly set according to cooling / power generation conditions. s,max ,η r,max , ΔP max is the normalized reference value, which is obtained from the statistics of the data set.

[0071] Fitness value f i The calculation formula is:

[0072] f i =-F(X i );

[0073] Among them, the fitness value f i The larger the value, the better the performance.

[0074] S13: Evolutionary operation; performing selection, crossover, and mutation operations to generate a new generation of population; multiple rounds of iteration to converge to multiple local optimal areas.

[0075] Specifically, based on the current generation population β t , perform standard genetic evolution process to generate the next generation population β t+1 ,like Figure 3 As shown, the standard genetic evolution process includes the following steps:

[0076] S131: Selection: Use tournament selection to randomly select two individuals in each round and choose the one with higher fitness to enhance global search ability and avoid premature maturation;

[0077] S132: Crossover: Use simulated binary crossover (SBX, Simulated Binary Crossover); where the crossover probability P c Set to 0.9 to maintain population diversity.

[0078] S133: Mutation: Use non-uniform mutation, mutation probability P m Set to 0.1, the amplitude of variation decreases as the number of generations increases.

[0079] S134: Retention: The top 5% of individuals in fitness are retained in each generation and passed directly to the next generation.

[0080] The evolution is performed for G = 100 generations, or when the optimal fitness does not exceed the stopping threshold ∈ = 10 -4 Stop after 10 consecutive generations.

[0081] S14: Construction of the optimal configuration set; in the population after convergence, multiple configuration solutions with excellent performance are identified by multi-point sampling of the fitness distribution of the coupling performance function, and representative configuration points are extracted using a clustering method to construct an optimal configuration set that adapts to different working conditions.

[0082] Specifically, when the evolution stops, all individuals with fitness greater than 90% quantile are screened from the final population to form a high-performance subset β e .

[0083] like Figure 4 As shown, the above clustering methods include:

[0084] S141: Calculate all β e The Euclidean distance between individuals is used to construct a distance matrix.

[0085] S142: Applying a K-means clustering algorithm to divide the clusters into k=4-8 clusters; wherein the optimal k is automatically selected based on the silhouette coefficient.

[0086] S143: Select the individual with the highest fitness from each category to form the final optimal configuration set β0.

[0087] In an example, if 5 categories are generated after clustering, 5 groups of configuration parameters representing the optimal operating conditions under different working conditions are finally obtained. Each group of configuration points X * j It includes a complete 6-dimensional parameter vector and is stored in the control system for subsequent operation matching and deviation control.

[0088] To achieve state self-identification and control response of the thermoacoustic-Stirling coupled system during dynamic operation, this example provides an operation deviation identification method. This method collects operating data under the current operating conditions and performs a multi-dimensional comparison of the collected operating data with the optimal configuration set obtained by previous optimization to identify the deviation vector between the current state and the ideal operating point, thereby providing a basis for subsequent phase control and performance self-optimization.

[0089] The system has multiple sets of embedded high-frequency sampling sensors, which are used to collect the operating data of the system under the current working conditions. The operating data include but are not limited to temperature parameters, pressure parameters, fluid velocity, phase parameters, drive parameters, acoustic performance and thermal efficiency. Among them, temperature parameters include hot end temperature and cold end temperature to reflect the thermal driving conditions, pressure parameters reflect the average pressure and pressure drop loss of the system, fluid velocity is the average mainstream velocity of the gas, phase parameters are the phase difference between the sound pressure and the particle velocity, drive parameters are the current drive frequency, amplitude and heat source power, acoustic performance is the real-time acoustic power density estimation, and thermal efficiency is the thermal efficiency of the regenerator.

[0090] The data sampling period ΔT is set to 0.2s, and sliding average filtering is used for denoising to ensure a balance between accuracy and response time.

[0091] The system stores the optimal configuration set obtained by pre-optimization through an improved genetic algorithm. Each configuration point corresponds to a set of optimal operating data. The optimal configuration set can contain 3-10 typical operating points, and each typical operating point represents the optimal configuration point of the system under different boundary conditions.

[0092] For the operation status comparison and deviation identification algorithm, such as Figure 5 As shown, the following steps are included:

[0093] S21: Target point matching: In the optimal configuration set, one or more closest optimal configuration points are selected as reference target points based on the current actual operation data.

[0094] The multi-factor weighted distance matching method is used, and its formula is as follows:

[0095]

[0096] Where, X c,i is the current running data vector, is the jth optimal configuration point, ω i It is a normalized weight factor that can be preferentially assigned to key variables such as driving frequency, temperature difference, and cavity geometry.

[0097] Select distance D j The smallest j=j * , the corresponding configuration point As the current reference target point.

[0098] S22: Calculate the operation deviation: Calculate the operation deviation vector ΔX between the current operation state and the optimal configuration point. The formula is as follows:

[0099]

[0100] In one example, the current operating data of the system is close to working condition A. The currently collected operating data are: temperature parameter is 480K, driving frequency is 245Hz, average pressure is 1.1MPa, phase parameter is 100°, and acoustic power density is 1300W / m 3 The latest working condition A is: temperature parameter is 500K, driving frequency is 240Hz, average pressure is 1.2MPa, phase parameter is 92°, and acoustic power density is 1450W / m 3 After comparison, the deviation is calculated and the obtained value is Δφ=8°, ΔP s =-150W / m 3 , it can be seen that the current phase deviation is large and the phase control mechanism should be triggered.

[0101] In one example, if multiple optimal configuration points are identified to be close, the optimal configuration point can be determined based on the real-time coupling performance function value F. At the same time, there may be a situation where the phase deviation is small but the acoustic power or thermal efficiency deviation is large. In this case, another matching method is provided, specifically, the real-time coupling performance function value is introduced as an auxiliary indicator, such as Figure 6 As shown, the following steps are included:

[0102] S211: Evaluate the mapping residual value, the formula is as follows:

[0103]

[0104] S212: Selecting the point with the smallest mapping residual value as the reference target point;

[0105] S213: Adopting configuration points The corresponding parameter value in is used as the set value for regulation, so that the current operating state gradually approaches the set value.

[0106] In one example, if the current operating data does not match any configuration point, the current temperature difference is much lower than all points in the optimal configuration set, and all Euclidean distances D j If both exceed the maximum threshold, the reference target point cannot be matched, and the local incremental optimization method should be triggered to re-optimize the adaptation configuration point based on the current running data.

[0107] To ensure the long-term stable operation of the thermoacoustic-Stirling coupled system within the optimal acoustic power output range, this example identifies the operating deviation between the current operating state and the optimal configuration point and then performs phase control operations to adjust the phase difference between gas velocity and pressure to a preset target phase range, thereby increasing the acoustic power density and improving the heat recovery efficiency.

[0108] In thermoacoustic systems, the phase difference between the gas particle velocity and the sound pressure has a significant impact on the acoustic energy conversion efficiency. Under ideal operating conditions, the phase difference remains within an optimal range, namely the target phase range. For example, if the phase difference caused by load disturbances or temperature fluctuations deviates from the target phase range, it will lead to a decrease in the regenerator efficiency or weaken the acoustic energy conversion capability. In this example, the system parameters will be adjusted to return the phase difference to the target phase range.

[0109] like Figure 7 As shown in FIG, this example uses a method for obtaining the instantaneous phase difference between gas velocity and pressure, including the following steps:

[0110] S31: piezoelectric sensors and hot wire anemometers or velocity sensors are respectively arranged to detect sound pressure and gas particle velocity;

[0111] S32: extracting the phase angle of the main frequency component of the collected periodic signal using fast Fourier transform;

[0112] S33: Calculate the phase difference value.

[0113] The system can update the phase at a frequency of 1s to filter out high-frequency noise.

[0114] Furthermore, to adjust the phase difference, the system's adjustable parameters include drive frequency, drive amplitude, input power, and cavity length. The drive frequency is set by the sound source control circuit; increasing the frequency increases phase lag. The drive amplitude is adjusted by the sound source amplitude; increasing the amplitude changes the waveform shape. The input power is set by the heat source controller; increasing the heat input improves the excitation response. The cavity length is adjusted through an adjustable structure to change the standing wave distribution, thereby adjusting the phase difference. In actual control, drive frequency and input power are preferred as the adjustment variables to avoid system perturbations caused by structural intervention.

[0115] In addition, the structural parameters of the thermoacoustic cavity also include the cross-sectional area and resonant frequency of the thermoacoustic cavity, and the above structural parameters can be used to adjust the phase difference.

[0116] Specifically, the phase difference is compared with the set target phase interval, and the control parameters are adjusted according to the phase difference so that the phase difference is within the target phase interval; wherein, the phase difference deviation value is calculated, that is, the difference between the phase difference and the maximum or minimum value of the target phase interval. If the deviation value of the phase difference from the maximum value of the target phase interval is greater than zero, it means that the phase lag is too large, and the driving frequency or the input power can be reduced; if the deviation value of the phase difference from the minimum value of the target phase interval is less than zero, it means that the phase lag is insufficient, and the driving frequency or the heat input can be increased.

[0117] To ensure that the thermoacoustic-Stirling coupled system continuously operates in the high-efficiency conversion region, this experimental example provides an adaptive maintenance mechanism based on phase trend analysis. By sensing the phase change trend between gas velocity and pressure, the control parameters are autonomously adjusted to keep the phase difference within the target phase range for a long time, thereby achieving the simultaneous maximization of acoustic power density and heat recovery efficiency.

[0118] like Figure 8 As shown, the adaptive retention mechanism includes the following steps:

[0119] S34: Continuously monitor the real-time phase difference between gas velocity and pressure.

[0120] S35: Based on the phase change trend within the sliding time window, the current control effect is judged; in each control cycle, the phase difference of the last N cycles is recorded to form a sliding window sequence, and the mean, standard deviation and trend gradient are calculated on this basis. Among them, the mean is the center position of the current phase operation, the standard deviation represents the phase fluctuation intensity, and the trend gradient can show whether it is stable / deviation direction. Based on the above three indicators, it is judged whether it is currently stable in the target phase range and whether there is a risk of continued deviation.

[0121] S36: If the phase change trend is detected to exceed a second preset threshold, the control parameters are adjusted. To reflect the strength and directionality of the phase change trend, the phase change amplitude and the average phase change rate are calculated. The second preset threshold includes thresholds corresponding to the change amplitude and the average phase change rate. When the change amplitude or the average change rate exceeds the corresponding threshold, the current phase fluctuation trend is considered abnormal. Alternatively, when both the change amplitude and the average change rate exceed the corresponding threshold, the current phase fluctuation trend is considered abnormal. When the current phase fluctuation trend is abnormal, the trend direction is determined, which includes rising or falling, and the parameter adaptive adjustment stage is entered. For example, in a window of N = 10, if the phase difference gradually increases from 91° to 98° in the past 10 seconds, the phase difference is 7° and is greater than the corresponding threshold, then adjustment is triggered. If the trend is rising, the driving frequency is reduced or the input power is reduced; if the trend is falling, the driving frequency is increased or the input power is increased.

[0122] Among them, the adjustment of the control parameters aims to maximize the weight terms corresponding to the acoustic power density and the heat recovery efficiency in the coupling performance function, so as to achieve adaptive control of the phase difference.

[0123] To enhance the adaptability of the thermoacoustic-Stirling coupled system in dynamic environments, the system initiates an incremental optimization process when a performance deviation between the current operating state and the optimal configuration point exceeds a first preset threshold. This process constructs a perturbation variable space within the parameter neighborhood of the current optimal configuration point, guided by the current operating deviation. Local optimization is performed on key sensitive parameters to quickly obtain a local optimal configuration point that better suits the current operating conditions. This local optimal configuration point is then used to replace the current optimal configuration point, forming an optimization control closed loop.

[0124] Specifically, the incremental optimization operation includes the following steps:

[0125] The difference between the current comprehensive performance function value and the optimal configuration point value exceeds a first preset threshold. Within the neighborhood of the current optimal configuration point, a perturbation parameter space is constructed. The perturbation direction is determined by the current operating deviation. Design parameters with high sensitivity to phase difference and system performance are selected to form a perturbation variable set. The perturbation variable set includes driving frequency, input power, thermoacoustic cavity length, and regenerator filling ratio. A perturbation interval is constructed with the current deviation as the center. If the deviation direction is clear, for example, the current high frequency causes the phase to advance, the frequency perturbation range can be narrowed.

[0126] To avoid the high cost and slow response of global optimization, this example uses a local evolutionary optimization algorithm, such as a micropopulation genetic algorithm or a gradient-enhanced particle swarm optimization algorithm, to quickly search the perturbation space. Specifically, this algorithm prioritizes variables with high phase difference sensitivity; fixes low-sensitivity variables to reduce the optimization dimension; maintains the fitness function, still using the coupled performance function; and terminates the optimization when the fitness improvement rate falls below a third preset threshold or the number of iterations reaches an upper limit. After obtaining a local optimal configuration point, it is added to the optimal configuration set. If its performance consistently exceeds that of the current optimal configuration point over multiple cycles, the current optimal configuration point is replaced. If the optimization effect is not significant, the original configuration is retained and the current fluctuation is marked as tolerable.

[0127] In one embodiment of the present application, a phase control system 10 of a thermoacoustic Stirling coupling system is provided, using the method provided in the above example, such as Figure 10 As shown, the system includes a performance modeling module 11, an optimization module 12, a data acquisition and deviation identification module 13, a phase regulation module 14, an adaptive control module 15 and an evaluation and incremental optimization module 16, which are respectively used for performance modeling, optimization, operation status monitoring, phase difference regulation, adaptive control and re-optimization, and are suitable for high-performance sound-heat energy conversion scenarios such as low-temperature refrigeration and waste heat recovery.

[0128] like Figure 11 As shown, the performance modeling module 11 constructs a coupling performance function 20 based on the multi-physics domain modeling technology, integrates the thermal acoustic cavity acoustic power output model 17, the regenerator thermal conversion model 18 and the pressure drop loss model 19, and the performance modeling module 11 is configured to establish a joint performance model, which includes the thermal acoustic cavity acoustic power output model 17, the regenerator thermal conversion model 18, the pressure drop loss model 19, and the coupling performance function 20 constructed based on the thermal acoustic cavity acoustic power output model 17, the regenerator thermal conversion model 18, and the pressure drop loss model 19.

[0129] The optimization module 12 is configured to optimize the coupling performance function 20 using a preset optimization algorithm to obtain an optimal configuration set containing multiple optimal configuration points. The first stage is a global search, which uses an improved genetic algorithm to initialize the population, perform crossover and mutation operations, and globally search the design space to identify multiple local optimal regions. The second stage is a local refinement, which uses the center of each local region as a starting point and performs small-scale particle swarm optimization or simulated annealing optimization to improve the accuracy of each configuration point. The third stage is configuration set generation, which selects several representative configuration points based on fitness ranking and coverage to form the final optimal configuration set for reference in subsequent operations.

[0130] The data acquisition and deviation identification module 13 integrates a multi-channel sensor system. The data acquisition and deviation identification module 13 is configured to collect operating data under the current operating conditions and compare the operating data with one or more optimal configuration points in the optimal configuration set to identify the deviation between the current operating state and the optimal configuration point; wherein the collected data includes the gas pressure waveform inside the thermoacoustic cavity, the temperature difference between the two ends of the regenerator, the system pressure difference, flow rate and frequency, ambient temperature and load change data.

[0131] The phase control module 14 is configured to perform phase control operations based on the identified operating deviation to adjust the phase difference between the gas velocity and pressure to a target phase range. Specifically, the phase control module identifies the phase difference between the two based on the real-time detected pressure and velocity signals, and compares it with the target phase range. If it deviates from the target phase range, the system is restored by adjusting the control parameters, such as adjusting the driving frequency, changing the heat source input power, adjusting the thermal conductivity of the regenerator, etc.

[0132] The adaptive control module 15 is configured to monitor the phase difference in real time and adjust the control parameters according to the monitoring results, adaptively keeping the phase difference in the target phase range to maximize the acoustic power density and heat recovery efficiency; specifically, by constructing a sliding time window to monitor the phase difference fluctuation trend, when the fluctuation exceeds a second preset threshold, the frequency or power is adaptively adjusted; the control target is to maximize the high-weight items in the coupling performance function (such as acoustic power density and heat recovery efficiency).

[0133] The evaluation and incremental optimization module 16 is configured to perform a performance evaluation on the current operating state within a preset evaluation period. If the difference between the current operating state and the current optimal configuration point exceeds a first preset threshold, an incremental optimization operation is performed and the current optimal configuration point is updated.

[0134] While several embodiments of the present invention have been described, these embodiments are provided as examples and are not intended to limit the scope of the invention. These embodiments can be implemented in various other forms and can be omitted, replaced, or modified without departing from the spirit of the invention. These embodiments and their variations are included within the scope and spirit of the invention and are also included in the invention described in the claims and their equivalents.

Claims

1. A phase control method for a thermoacoustic Stirling coupling system, comprising: Establishing a joint performance model and optimizing the joint performance model using a preset optimization algorithm to obtain an optimal configuration set including multiple optimal configuration points, wherein the joint performance model includes a thermoacoustic cavity acoustic power output model, a regenerator thermal conversion model, a pressure drop loss model, and a coupling performance function; collecting operating data of the system under current operating conditions, and comparing the operating data with one or more optimal configuration points in the optimal configuration set to identify an operating deviation between the current operating state and the optimal configuration point; Based on the identified operating deviation, a phase control operation is performed to adjust the phase difference between the gas velocity and pressure to a target phase range and perform adaptive control; as well as A performance evaluation is performed within a preset evaluation period. If the operating deviation exceeds a first preset threshold, an incremental optimization operation is performed and a current optimal configuration point is updated.

2. The method according to claim 1, wherein The acoustic power output model of the thermoacoustic cavity takes the excitation frequency, hot end temperature and cavity geometric parameters as input, and the acoustic power density generated per unit time as output; The regenerator thermal conversion model takes air mass displacement, regenerator thermal conductivity and structural parameters as inputs and regenerator efficiency as output; The pressure drop loss model takes the air flow channel size, flow velocity and temperature gradient as input and outputs the total pressure loss value; The output results of the thermoacoustic cavity acoustic power output model, the regenerator thermal conversion model and the pressure drop loss model are used to construct a coupling performance function.

3. The method according to claim 2, wherein: The coupling performance function is as follows: Where, P s is the acoustic power density per unit volume, η r is the thermal efficiency of the regenerator, ΔP is the system pressure drop loss, ω1, ω2, and ω3 are the weight coefficients of acoustic power output, regenerator efficiency, and pressure drop loss, respectively.

4. The method according to claim 1, wherein The preset optimization algorithm is a coupling performance optimization method based on a genetic algorithm, including: Initialize the population, where each individual in the population corresponds to a set of device configuration parameters; Using the coupling performance function as a fitness function, calculating the fitness value of each individual; Perform selection, crossover, and mutation operations to generate the next generation population; Under a preset termination condition, several configurations with the highest fitness are output as the optimal configuration set.

5. The method according to claim 4, wherein The preset optimization algorithm adopts a staged optimization method, including: Generate multiple candidate solutions in the initial population, perform multiple rounds of iterations through the selection, crossover and mutation operations in the genetic algorithm, and globally search the entire solution space to obtain multiple local optimal regions; For multiple local optimal regions obtained in the global search, local searches are performed using the current optimal configuration point as the initial solution to improve the accuracy of the optimal configuration point. The configuration point with the highest comprehensive performance score is selected from each local optimal region and used as the final optimal configuration point in the optimal configuration set.

6. The method according to claim 1, wherein The incremental optimization operation includes re-optimizing based on the range of the disturbance variable within the neighborhood of the current optimal configuration point, including: Construct a disturbance space with the current running deviation as the guiding direction; Adjust the search endpoint to a high sensitivity parameter that affects phase difference stability; Obtain a local optimal configuration point that is more suitable for the current working conditions, and add this configuration point as the updated optimal configuration point to the optimal configuration set.

7. The method according to claim 1, wherein The phase control operation includes adjusting the structural parameters of the thermoacoustic cavity, wherein the structural parameters include the length, cross-sectional area and resonant frequency of the thermoacoustic cavity.

8. The method according to claim 7, wherein the phase control operation comprises: Real-time detection of the phase difference between current gas velocity and pressure; comparing the phase difference with a target phase interval, and adjusting a control parameter according to the phase difference so that the phase difference is within the target phase interval; The control parameters include one or more of a driving frequency, a driving amplitude and a heat source input power.

9. The method according to claim 8, wherein adaptively maintaining the phase difference within the target phase interval comprises: Continuously monitor the real-time phase difference between gas velocity and pressure; Based on the phase change trend within the sliding time window, the current control effect is judged; If it is detected that the phase change trend exceeds a second preset threshold value according to the current control effect, the control parameter is adjusted; Among them, the adjustment of the control parameters aims to maximize the weight terms corresponding to the acoustic power density and the heat recovery efficiency in the coupling performance function, so as to achieve adaptive control of the phase difference.

10. A phase control system for a thermoacoustic Stirling coupling system, using the method according to any one of claims 1 to 9, wherein the system comprises: a performance modeling module configured to establish a joint performance model, the joint performance model including a thermoacoustic cavity acoustic power output model, a regenerator thermal conversion model, a pressure drop loss model, and a coupled performance function constructed based on the thermoacoustic cavity acoustic power output model, the regenerator thermal conversion model, and the pressure drop loss model; an optimization module configured to optimize the coupling performance function using a preset optimization algorithm to obtain an optimal configuration set including a plurality of optimal configuration points; a data collection and deviation identification module, configured to collect operating data under current operating conditions and compare the operating data with one or more optimal configuration points in the optimal configuration set to identify deviations between the current operating state and the optimal configuration points; a phase control module configured to perform a phase control operation according to the identified operating deviation to adjust the phase difference between the gas velocity and pressure to a target phase range; an adaptive control module configured to monitor the phase difference in real time and adjust control parameters according to the monitoring results, adaptively maintaining the phase difference within the target phase range to maximize the acoustic power density and heat recovery efficiency; The evaluation and incremental optimization module is configured to perform a performance evaluation on the current operating state within a preset evaluation period. If the difference between the current operating state and the current optimal configuration point exceeds a first preset threshold, an incremental optimization operation is performed and the current optimal configuration point is updated.

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