Energy-saving operation method and system for draught fan of refrigeration house

Abstract

The invention relates to the technical field of refrigeration house energy-saving control and digital twinning, in particular to a refrigeration house fan energy-saving operation method and system, and the method achieves the whole-field temperature deduction of a sensor-free area by constructing a CFD reference model and simulating and predicting the three-dimensional transient airflow and temperature distribution in a refrigeration house. Model parameters are calibrated through measured data, and the order of the model is reduced by adopting intrinsic orthogonal decomposition and Galerkin projection, so that the calculation amount is remarkably reduced, and online optimization is supported. And in combination with a data assimilation algorithm, the prediction result of the reduced-order model is continuously corrected, and the adaptability to environment change and system aging is improved. The optimization control stage takes minimization of the total energy consumption of a fan as a target, solves an optimal fan control sequence under the hard constraint that the whole-field temperature does not exceed the cargo safety upper limit and does not exceed the cargo safety lower limit, and adopts a rolling time domain mode for execution to realize collaborative optimization of safety and energy conservation.

Description

Technical Field

[0001] This application relates to the fields of energy-saving control and digital twin technology for cold storage, and in particular to a method and system for energy-saving operation of cold storage fans. Background Technology

[0002] Cold storage facilities are crucial for ensuring the quality and safety of temperature-sensitive goods such as food and medicine during storage and distribution. Their core function is to maintain the internal temperature within a set range and ensure the uniformity of temperature distribution as much as possible. To achieve effective air circulation and temperature control, cold storage facilities are typically equipped with air-cooling fan systems that use forced convection to transfer cooling energy to all areas within the storage facility.

[0003] Currently, most cold storage facilities rely on temperature sensors installed at specific locations within the facility to control the operation of their fans. A typical control method involves starting the fan when the sensor detects a temperature above a set upper limit and stopping the fan when it falls below a lower limit; this is known as ON / OFF control.

[0004] However, the aforementioned traditional control methods have the following problems: First, the airflow organization and temperature distribution inside cold storage exhibit significant spatial non-uniformity. Influenced by factors such as fan layout, goods stacking, storage structure, and personnel entry and exit, localized overheating or cold air accumulation is prone to occur. Existing control strategies rely solely on data from a few fixed-location sensors, failing to comprehensively reflect the temperature field distribution within the three-dimensional space of the storage. This results in a lack of global control decisions, easily leading to some areas exceeding temperature limits while other areas experience overcooling. Although some research has attempted to use computational fluid dynamics (CFD) to simulate the airflow and temperature field inside cold storage to assist in system design and optimization, the computational demands of CFD models are enormous, making it difficult to meet the requirements of real-time control. Furthermore, how to accurately estimate the overall state and, based on this, perform energy-saving optimization control remains a challenge in the field of cold storage automation. Summary of the Invention

[0005] In view of the above-mentioned technical problems, the purpose of this application is to provide an energy-saving operation method and system for cold storage fans, which aims to solve at least one of the aforementioned technical problems.

[0006] In a first aspect, embodiments of this application provide an energy-saving operation method for a cold storage fan, the method comprising:

[0007] S1. Construct a CFD benchmark model for simulating and predicting the three-dimensional transient airflow and temperature distribution within the target cold storage;

[0008] S2. Based on the measured data, the model parameters of the CFD benchmark model are calibrated to obtain the calibrated CFD benchmark model.

[0009] S3. The calibrated CFD baseline model is reduced in order by using the method of intrinsic orthogonal decomposition combined with Galerkin projection to obtain a reduced-order model.

[0010] S4. Read the real-time temperature data of the sparse sensor network at a fixed period, use the real-time temperature data as the observation value, and continuously correct the full-field temperature distribution predicted by the reduced-order model through the data assimilation algorithm to generate the corrected optimal state estimate.

[0011] S5. In each control cycle, the corrected optimal state estimate is used as the initial state of the system. Based on the initial state, a constrained dynamic optimization problem is constructed within the set future prediction time domain. Based on the optimization algorithm, the fan action sequence that optimizes the objective function is searched among all feasible solutions that satisfy the temperature constraint. After executing the first action of the optimal fan action sequence, the process returns to step S4 for the next cycle of cyclic optimization. The decision variable of the constrained dynamic optimization problem is the fan action sequence divided by discrete time steps within a future period. The objective function is to minimize the total power consumption of the fans within the prediction time domain. The temperature constraint is: based on the reduced-order model prediction, the temperature of any unit in the cold storage must not exceed the upper limit of cargo safety or fall below the lower limit of cargo safety within the entire prediction time domain.

[0012] In one embodiment, the objective function is specifically:

[0013]

[0014] Where J is the objective function value, representing the total power consumption in the prediction time domain, and N... f Here, H represents the total number of fans involved in the control of the cold storage, H represents the prediction time domain length (i.e., the number of future discretized time steps), k is the index of the discrete time step within the prediction time domain, t represents the actual time point of the current control cycle, Δt represents the time length of each time step, and u... i (t+k) represents the control action variable of the i-th wind turbine at time t+k, u i ∈{0,1}, 0 represents the fan being turned off, 1 represents the fan being turned on, P rated,i This represents the rated power of the i-th fan.

[0015] The temperature constraint condition is specifically as follows:

[0016]

[0017] T(x,t+k|t) represents the optimal state estimate based on the current time t. Under the reduced-order model prediction, it represents the predicted temperature at position x at the future time t+k. x represents any coordinate position in the three-dimensional space inside the cold storage, and Ω represents the entire spatial region of the air domain inside the cold storage. Indicates the maximum safe temperature that the goods are allowed to withstand. This indicates the minimum safe temperature that the goods can be kept at, and it is determined by the type of goods.

[0018] In one embodiment, the step of using intrinsic orthogonal decomposition combined with Galerkin projection to reduce the order of the calibrated CFD baseline model to obtain a reduced-order model includes:

[0019] S31. Based on the calibrated CFD benchmark model, numerical simulations are performed under various typical cold storage operating conditions. Temperature distribution data of the entire field is collected at multiple times to form a temperature field snapshot set. The typical operating conditions include different fan start-up combinations, different cargo stacking methods, and different door opening frequencies.

[0020] S32. Preprocess the data in the temperature field snapshot set, calculate the average temperature field, and subtract the average temperature field from each temperature field snapshot to obtain a mean-free snapshot matrix.

[0021] S33. Perform eigenorthogonal decomposition on the snapshot matrix to extract the first r dominant spatial mode functions;

[0022] S34. Substitute the eigenorthogonal decomposition expansion of the temperature field into the energy partial differential equation describing the heat transfer process in the cold storage to obtain the residual equation.

[0023] S35. Using each dominant spatial mode function as a test function, the residual equation is subjected to Galerkin projection on the cold storage spatial domain. By weighted integration and utilizing modal orthogonality, the dynamic equation of the principal mode coefficients is derived.

[0024] S36. The dominant spatial mode function and the dynamic equation are combined to form a reduced-order model, which is used to predict the spatiotemporal evolution of the temperature field inside the cold storage.

[0025] In one embodiment, the step of calibrating the model parameters of the CFD benchmark model based on measured data to obtain a calibrated CFD benchmark model includes:

[0026] A high-density sensor network was temporarily deployed inside the target cold storage to collect measured data of the three-dimensional spatial temperature field under typical cold storage operating conditions.

[0027] The measured data of the three-dimensional temperature field are compared with the simulation results of the CFD benchmark model under the same working conditions to construct a target error function, which is the mean square error between the simulated temperature field and the actual temperature field.

[0028] With the goal of minimizing the target error function, the key parameters in the CFD benchmark model are iteratively adjusted. The key parameters include the air convection heat transfer coefficient, the equivalent thermal conductivity of the cold storage enclosure structure, and the fan outlet velocity distribution characteristics.

[0029] When the target error function converges and the simulation results meet the preset accuracy threshold, the model calibration is completed, the high-density sensor network is removed, and the calibrated CFD benchmark model is output.

[0030] In one embodiment, the step of constructing a CFD benchmark model for simulating and predicting three-dimensional transient airflow and temperature distribution within a target cold storage facility includes:

[0031] Obtain the geometric structural parameters, thermal property parameters of the enclosure structure, cargo stacking layout, and parameters and installation location information of the air cooler of the target cold storage, and establish a three-dimensional geometric and physical parameterized model;

[0032] Based on the laws of conservation of mass, momentum and energy, and combined with a turbulence model suitable for low-temperature environments and a thermal buoyancy effect correction term, a three-dimensional transient control equation set describing the air flow and heat transfer process in the cold storage is constructed.

[0033] Based on the geometric structure of the three-dimensional geometric and physical parameterized model, a mesh is generated to produce a computational mesh, and local refinement is performed on key areas;

[0034] Based on the physical parameters configured in the three-dimensional geometric and physical parameterization model, initial conditions and boundary conditions are set; the initial conditions include the initial temperature field and velocity field; the boundary conditions include the heat flow of the enclosure structure, the air velocity and temperature of the air cooler, and the heat source of the cargo area.

[0035] Based on the computational grid, the three-dimensional transient control equations are discretely solved using the finite volume method. The initial and boundary conditions are used as inputs, and the numerical solution describing the airflow and heat transfer process in the cold storage is obtained through iterative calculation using a pressure-velocity coupling algorithm, thus forming a CFD benchmark model.

[0036] In one embodiment, the data assimilation algorithm employs ensemble Kalman filtering.

[0037] Secondly, embodiments of this application provide an energy-saving operation system for cold storage fans, the system comprising:

[0038] The building blocks are used to construct a CFD benchmark model for simulating and predicting three-dimensional transient airflow and temperature distribution within a target cold storage facility.

[0039] The calibration module is used to calibrate the model parameters of the CFD benchmark model based on measured data to obtain the calibrated CFD benchmark model.

[0040] The order reduction module is used to reduce the order of the calibrated CFD baseline model by using the method of intrinsic orthogonal decomposition combined with Galerkin projection to obtain a reduced-order model.

[0041] The correction module is used to read the real-time temperature data of the sparse sensor network at a fixed period, use the real-time temperature data as the observation value, and continuously correct the full-field temperature distribution predicted by the reduced-order model through a data assimilation algorithm to generate the corrected optimal state estimate.

[0042] The execution module is used in each control cycle to construct a constrained dynamic optimization problem within a set future prediction time domain, using the corrected optimal state estimate as the initial state of the system. Based on the initial state, it searches for the optimal fan action sequence among all feasible solutions that satisfy the temperature constraint, using an optimization algorithm. After executing the first action of the optimal fan action sequence, it returns to the step correction module for cyclic optimization in the next cycle. The decision variable of the constrained dynamic optimization problem is the fan action sequence divided into discrete time steps within a future period. The objective function is to minimize the total power consumption of the fans within the prediction time domain. The temperature constraint is: based on the reduced-order model prediction, the temperature of any unit in the cold storage must not exceed the upper limit of cargo safety or fall below the lower limit of cargo safety throughout the entire prediction time domain.

[0043] This application's embodiments construct a CFD (Computational Fluid Dynamics) benchmark model for simulating and predicting the three-dimensional transient airflow and temperature distribution within a target cold storage facility. This allows for the estimation of the entire temperature distribution even in sensorless areas, laying the foundation for subsequent fan operation control based on the overall temperature distribution. The model parameters of the CFD benchmark model are calibrated using measured temperature data, enabling the model to more accurately reflect the thermodynamic characteristics of the actual cold storage facility. An intrinsic orthogonal decomposition combined with Galerkin projection is used to reduce the order of the calibrated CFD benchmark model, reducing the high-dimensional CFD model to a state-space model containing only a few dominant modes. This significantly reduces the computational load and provides computational feasibility for subsequent online optimization control. A data assimilation algorithm continuously corrects the overall temperature distribution predicted by the reduced-order model, enabling the system to adapt to deviations caused by uncertainties such as cold storage aging and environmental changes, while also providing reliable initial conditions for optimized control. During the online optimization control phase, the objective function is to minimize the total power consumption of the fans. Under the hard constraint that the temperature of any unit within the cold storage must not exceed the upper limit or fall below the lower limit of cargo safety throughout the entire prediction time domain, the optimal fan action sequence is solved to minimize energy consumption while ensuring cargo safety. The optimal control sequence for a future period is planned in advance for each control cycle, and the first step of this sequence is executed continuously. This proactive control strategy enables better energy-saving performance while ensuring safety. Attached Figure Description

[0044] To more clearly illustrate the technical solution of this application, the drawings used in the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of this application. For those skilled in the art, other drawings can be obtained from these drawings without creative effort.

[0045] Figure 1 This is a flowchart illustrating the energy-saving operation method for cold storage fans provided in the embodiments of this application.

[0046] Figure 2 This is a schematic diagram of the structure of the energy-saving operation system for cold storage fans provided in the embodiments of this application. Detailed Implementation

[0047] To make the objectives, technical solutions, and advantages of this application clearer, the following detailed description is provided in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative and not intended to limit the scope of this application.

[0048] Those skilled in the art will understand that, unless specifically stated otherwise, the singular forms “a,” “an,” “the,” and “the” used herein may also include the plural forms. It should be further understood that the word “comprising” as used in the specification of this application means the presence of features, integers, steps, etc.

[0049] Operations, elements, modules, and / or components, but do not preclude the presence or addition of one or more other features, integers, steps, operations, elements, modules, components, and / or groups thereof. It should be understood that when we say an element is “connected” or “coupled” to another element, it can be directly connected or coupled to the other element, or there may be intermediate elements. Furthermore, the term “connected” or “coupled” as used herein can include wireless connections or wireless coupling. The word “and / or” as used herein includes all or any modules and all combinations thereof of one or more associated listed items.

[0050] It will be understood by those skilled in the art that, unless otherwise defined, all terms used herein (including technical and scientific terms) have the same meaning as commonly understood by one of ordinary skill in the art to which this application pertains. It should also be understood that terms such as those defined in general dictionaries should be understood to have the same meaning as in the context of the prior art, and should not be interpreted in an idealized or overly formal sense unless specifically defined as herein.

[0051] Please see Figure 1 This application provides an energy-saving operation method for cold storage fans, the method comprising:

[0052] S1. Construct a CFD benchmark model for simulating and predicting the three-dimensional transient airflow and temperature distribution within the target cold storage;

[0053] S2. Based on the measured data, the model parameters of the CFD benchmark model are calibrated to obtain the calibrated CFD benchmark model.

[0054] S3. The calibrated CFD baseline model is reduced in order by using the method of intrinsic orthogonal decomposition combined with Galerkin projection to obtain a reduced-order model.

[0055] S4. Read the real-time temperature data of the sparse sensor network at a fixed period, use the real-time temperature data as the observation value, and continuously correct the full-field temperature distribution predicted by the reduced-order model through the data assimilation algorithm to generate the corrected optimal state estimate.

[0056] S5. In each control cycle, the corrected optimal state estimate is used as the initial state of the system. Based on the initial state, a constrained dynamic optimization problem is constructed within the set future prediction time domain. Based on the optimization algorithm, the fan action sequence that optimizes the objective function is searched among all feasible solutions that satisfy the temperature constraint. After executing the first action of the optimal fan action sequence, the process returns to step S4 for the next cycle of cyclic optimization. The decision variable of the constrained dynamic optimization problem is the fan action sequence divided by discrete time steps within a future period. The objective function is to minimize the total power consumption of the fans within the prediction time domain. The temperature constraint is: based on the reduced-order model prediction, the temperature of any unit in the cold storage must not exceed the upper limit of cargo safety or fall below the lower limit of cargo safety within the entire prediction time domain.

[0057] In this embodiment, a CFD (Computational Fluid Dynamics) benchmark model is constructed to simulate and predict the three-dimensional transient airflow and temperature distribution within the target cold storage. This allows for the estimation of the entire temperature distribution even in sensorless areas, laying the foundation for subsequent fan operation control based on the overall temperature distribution. The model parameters of the CFD benchmark model are calibrated using measured temperature data, enabling the model to more accurately reflect the thermodynamic characteristics of the actual cold storage. The eigenorthogonal decomposition combined with Galerkin projection is used to reduce the order of the calibrated CFD benchmark model, reducing the high-dimensional CFD model to a state-space model containing only a few dominant modes. This significantly reduces the computational load and provides computational feasibility for subsequent online optimization control. A data assimilation algorithm continuously corrects the overall temperature distribution predicted by the reduced-order model, enabling the system to adapt to deviations caused by uncertainties such as cold storage aging and environmental changes, while providing reliable initial conditions for optimized control. During the online optimization control phase, the objective function is to minimize the total power consumption of the fans. Under the hard constraint that the temperature of any unit within the cold storage must not exceed the upper limit or fall below the lower limit of cargo safety throughout the entire prediction time domain, the optimal fan action sequence is solved to minimize energy consumption while ensuring cargo safety. The optimal control sequence for a future period is planned in advance for each control cycle, and the first step of this sequence is executed continuously. This proactive control strategy enables better energy-saving performance while ensuring safety.

[0058] In step S5, the optimization algorithm can be a sequential quadratic programming algorithm, interior point method, etc.

[0059] In one embodiment, the objective function is specifically:

[0060]

[0061] Where J is the objective function value, representing the total power consumption in the prediction time domain, and N... f Here, H represents the total number of fans involved in the control of the cold storage, H represents the prediction time domain length (i.e., the number of future discretized time steps), k is the index of the discrete time step within the prediction time domain, t represents the actual time point of the current control cycle, Δt represents the time length of each time step, and u... i (t+k) represents the control action variable of the i-th wind turbine at time t+k, u i ∈{0,1}, 0 represents the fan being turned off, 1 represents the fan being turned on, P rated,i This represents the rated power of the i-th fan.

[0062] The temperature constraint condition is specifically as follows:

[0063]

[0064] T(x,t+k|t) represents the optimal state estimate based on the current time t. Under the reduced-order model prediction, it represents the predicted temperature at position x at the future time t+k. x represents any coordinate position in the three-dimensional space inside the cold storage, and Ω represents the entire spatial region of the air domain inside the cold storage. Indicates the maximum safe temperature that the goods are allowed to withstand. This indicates the minimum safe temperature that the goods can be kept at, and it is determined by the type of goods.

[0065] In one embodiment, the step of using intrinsic orthogonal decomposition combined with Galerkin projection to reduce the order of the calibrated CFD baseline model to obtain a reduced-order model includes:

[0066] S31. Based on the calibrated CFD benchmark model, numerical simulations are performed under various typical cold storage operating conditions. Temperature distribution data of the entire field is collected at multiple times to form a temperature field snapshot set. The typical operating conditions include different fan start-up combinations, different cargo stacking methods, and different door opening frequencies.

[0067] S32. Preprocess the data in the temperature field snapshot set, calculate the average temperature field, and subtract the average temperature field from each temperature field snapshot to obtain a mean-free snapshot matrix.

[0068] S33. Perform eigenorthogonal decomposition on the snapshot matrix to extract the first r dominant spatial mode functions;

[0069] S34. Substitute the eigenorthogonal decomposition expansion of the temperature field into the energy partial differential equation describing the heat transfer process in the cold storage to obtain the residual equation.

[0070] S35. Using each dominant spatial mode function as a test function, the residual equation is subjected to Galerkin projection on the cold storage spatial domain. By weighted integration and utilizing modal orthogonality, the dynamic equation of the principal mode coefficients is derived.

[0071] S36. The dominant spatial mode function and the dynamic equation are combined to form a reduced-order model, which is used to predict the spatiotemporal evolution of the temperature field inside the cold storage.

[0072] This application combines Proper Orthogonal Decomposition (POD) with Galerkin projection to achieve efficient reduced-order modeling of the temperature field in a cold storage facility. Temperature field snapshots are collected under various typical operating conditions, and dominant spatial mode functions are extracted to form a low-dimensional orthogonal basis that accurately characterizes the system's dynamic properties. By substituting the POD expansion into the energy partial differential equation describing the heat transfer process in the cold storage facility and performing Galerkin projection, the ordinary differential dynamic equations of the principal mode coefficients are derived, thus establishing a reduced-order model driven by a few state variables. This model significantly reduces the computational complexity of the original CFD model, greatly improving the speed of overall temperature prediction while maintaining high accuracy and meeting real-time requirements.

[0073] In step S31, the dominant spatial mode function refers to a set of optimal spatial basis functions extracted from a large amount of temperature field snapshot data using the Proper Orthogonal Decomposition (POD) method, which can efficiently reconstruct the main features of the original high-dimensional temperature field with the fewest possible functions. Let the mean-removed snapshot matrix be: X = [θ1(x), θ2(x), ..., θ N [x]; where θ i (x) is the result of subtracting the average temperature field from the temperature field snapshot at the i-th time step, where x ∈ Ω, x represents the spatial location, and Ω is the spatial domain of the target cold storage. N is the number of snapshots. Then the dominant spatial mode function... The solution to the following optimization problem is: Find a set of uniformly orthogonal functions within the set of all possible orthogonal functions. To maximize their ability to capture the energy (i.e., variance) of the snapshot set. That is, to solve: in, This indicates that the temperature field θ(x) in the mode The projection onto the surface. Finally, the first r corresponding to the largest eigenvalues ​​are selected. As the dominant spatial mode function.

[0074] The first r dominant spatial mode functions are obtained through orthogonal decomposition. These are functions of spatial location x, representing the dominant spatial structure of the temperature field. In the reduced-order model, the temperature field T(x,t) at any time t is approximated as a linear combination of these dominant mode functions:

[0075]

[0076] Where T(x,t) represents the temperature at location x in the cold storage at time t. For the average temperature field, a j (t) represents the coefficient of the j-th dominant mode, indicating the activation level of this mode at time t, φ j (x) is the j-th dominant spatial mode function, and r represents the number of modes retained.

[0077] In step S34, the energy partial differential equation describing the heat transfer process in the cold storage is:

[0078]

[0079] in, This is the convective term, representing airflow, such as heat transport from fan-driven airflow and natural convection; where u(x,t) is the velocity field. It is the diffusion term, α T It is the thermal diffusivity, S T These are internal heat sources, such as heat generated by fan motors, heat from the respiration of goods, heat dissipation from lighting or personnel, and heat intrusion caused by opening doors.

[0080] In step S35, the modal coefficients α are obtained by Galerkin projection. j (t) How the set of ordinary differential equations evolves over time, i.e. the dynamic equations of the principal mode coefficients;

[0081]

[0082] in, F represents the time derivative of the j-th modal coefficient. j denoted by , which represents the nonlinear function derived from the energy equation, the first r dominant spatial modes, boundary conditions, and system input; u(t) represents the system input vector, which includes the fan start / stop status, cargo stacking parameters, and door opening frequency, etc.

[0083] It should be noted that the optimal state estimation in step S4 refers to the modal coefficients in the reduced-order model. After obtaining the optimal modal coefficients at the current moment, they are substituted into formulas (2) and (1) in turn to obtain the reconstructed temperature field.

[0084] In one embodiment, the step of calibrating the model parameters of the CFD benchmark model based on measured data to obtain a calibrated CFD benchmark model includes:

[0085] A high-density sensor network was temporarily deployed inside the target cold storage to collect measured data of the three-dimensional spatial temperature field under typical cold storage operating conditions.

[0086] The measured data of the three-dimensional temperature field are compared with the simulation results of the CFD benchmark model under the same working conditions to construct a target error function, which is the mean square error between the simulated temperature field and the actual temperature field.

[0087] With the goal of minimizing the target error function, the key parameters in the CFD benchmark model are iteratively adjusted. The key parameters include the air convection heat transfer coefficient, the equivalent thermal conductivity of the cold storage enclosure structure, and the fan outlet velocity distribution characteristics.

[0088] When the target error function converges and the simulation results meet the preset accuracy threshold, the model calibration is completed, the high-density sensor network is removed, and the calibrated CFD benchmark model is output.

[0089] This application embodiment acquires real three-dimensional temperature field data within the cold storage facility by deploying a high-density sensor network, providing a reliable basis for CFD model calibration. By spatiotemporally matching and comparing the measured temperature field with simulation results, the mean square error is constructed as the objective function to quantify model bias, improving the objectivity and accuracy of the calibration. An iterative optimization method is used to jointly adjust uncertain parameters such as the air convection heat transfer coefficient, the equivalent thermal conductivity of the building envelope, and the fan outlet velocity distribution, effectively correcting modeling errors caused by simplified geometry, inaccurate boundary conditions, or material properties. This process significantly improves the predictive fidelity of the CFD model for the actual thermal behavior of the cold storage facility. The calibrated model not only enhances the credibility of the digital twin system but also provides a high-precision foundation for subsequent reduced-order modeling, state estimation, and optimized control, avoiding control deviations caused by model distortion. Finally, after meeting the accuracy requirements, the temporary sensors are removed, balancing calibration effectiveness and long-term operating costs.

[0090] In one embodiment, the step of constructing a CFD benchmark model for simulating and predicting three-dimensional transient airflow and temperature distribution within a target cold storage facility includes:

[0091] Obtain the geometric structural parameters, thermal property parameters of the enclosure structure, cargo stacking layout, and parameters and installation location information of the air cooler of the target cold storage, and establish a three-dimensional geometric and physical parameterized model;

[0092] Based on the laws of conservation of mass, momentum and energy, and combined with a turbulence model suitable for low-temperature environments and a thermal buoyancy effect correction term, a three-dimensional transient control equation set describing the air flow and heat transfer process in the cold storage is constructed.

[0093] Based on the geometric structure of the three-dimensional geometric and physical parameterized model, a mesh is generated to produce a computational mesh, and local refinement is performed on key areas;

[0094] Based on the physical parameters configured in the three-dimensional geometric and physical parameterization model, initial conditions and boundary conditions are set; the initial conditions include the initial temperature field and velocity field; the boundary conditions include the heat flow of the enclosure structure, the air velocity and temperature of the air cooler, and the heat source of the cargo area.

[0095] Based on the computational grid, the three-dimensional transient control equations are discretely solved using the finite volume method. The initial and boundary conditions are used as inputs, and the numerical solution describing the airflow and heat transfer process in the cold storage is obtained through iterative calculation using a pressure-velocity coupling algorithm, thus forming a CFD benchmark model.

[0096] This application's embodiments construct a high-fidelity three-dimensional parametric physical model by integrating the geometry, thermal properties, cargo layout, and air cooler parameters (dimensions, outlet angle, etc.) and installation location of the cold storage, providing an accurate geometric and physical foundation for CFD simulation. Based on the laws of conservation of mass, momentum, and energy, and by introducing turbulence models suitable for low-temperature environments (such as k-ε or k-ωSST) and thermal buoyancy corrections (such as the Boussinesq approximation), the model realistically depicts natural convection effects such as cold air sinking and hot plume rising, improving the physical accuracy of airflow-temperature coupling simulation. By rationally dividing the mesh and locally refining key areas (such as the near field of the fan and cargo gaps), computational accuracy and efficiency are balanced. The use of the finite volume method and pressure-velocity coupling algorithms (such as SIMPLE) for transient solutions ensures numerical stability and convergence. The constructed CFD benchmark model can accurately simulate the three-dimensional transient airflow organization and temperature distribution inside the cold storage under complex operating conditions, providing a reliable digital twin foundation for subsequent model calibration, order reduction modeling, and intelligent control.

[0097] In one embodiment, the data assimilation algorithm employs ensemble Kalman filtering.

[0098] This application employs an ensemble Kalman filter (EnKF) as the data assimilation algorithm. This effectively fuses real-time temperature observation data from sparse sensors with the prediction results of a reduced-order CFD model, enabling dynamic and high-precision estimation of the overall temperature distribution within the cold storage facility. Furthermore, compared to traditional Kalman filtering, ensemble Kalman filtering eliminates the need to calculate high-dimensional covariance matrices. Combined with the reduced-order model, the computational burden is significantly reduced, making it suitable for real-time online correction and control under limited computational resources.

[0099] like Figure 2 As shown in the figure, this application embodiment also provides an energy-saving operation system for cold storage fans, the system comprising:

[0100] Module 1 is used to build a CFD benchmark model for simulating and predicting three-dimensional transient airflow and temperature distribution within a target cold storage facility.

[0101] Calibration module 2 is used to calibrate the model parameters of the CFD benchmark model based on measured data to obtain the calibrated CFD benchmark model.

[0102] The order reduction module 3 is used to reduce the order of the calibrated CFD baseline model by using the method of intrinsic orthogonal decomposition combined with Galerkin projection to obtain a reduced-order model.

[0103] The correction module 4 is used to read the real-time temperature data of the sparse sensor network at a fixed period, use the real-time temperature data as the observation value, and continuously correct the full-field temperature distribution predicted by the reduced-order model through a data assimilation algorithm to generate the corrected optimal state estimate.

[0104] Execution module 5 is used to construct a constrained dynamic optimization problem within a set future prediction time domain, based on the corrected optimal state estimate as the initial state of the system in each control cycle. Using an optimization algorithm, it searches for the optimal fan action sequence among all feasible solutions that satisfy the temperature constraint. After executing the first action of the optimal fan action sequence, it returns to the step correction module for cyclic optimization in the next cycle. The decision variable of the constrained dynamic optimization problem is the fan action sequence divided by discrete time steps within a future period. The objective function is to minimize the total power consumption of the fans within the prediction time domain. The temperature constraint is: based on a reduced-order model prediction, the temperature of any unit in the cold storage must not exceed the upper limit of cargo safety or fall below the lower limit of cargo safety throughout the entire prediction time domain.

[0105] Those skilled in the art will understand that all or part of the processes in the methods of the above embodiments can be implemented by a computer program instructing related hardware. The computer program can be stored in a non-volatile computer-readable storage medium. When executed, the computer program can include the processes of the embodiments of the above methods. Any references to memory, storage, databases, or other media provided in this application and in the embodiments may include non-volatile and / or volatile memory. Non-volatile memory may include read-only memory (ROM), programmable ROM (PROM), electrically programmable ROM (EPROM), electrically erasable programmable ROM (EEPROM), or flash memory. Volatile memory may include random access memory (RAM) or external cache memory. By way of illustration and not limitation, RAM is available in a variety of forms, such as static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), dual-speed SDRAM (SSRSDRAM), enhanced SDRAM (ESDRAM), synchronous link DRAM (SLDRAM), RAMbus direct RAM (RDRAM), direct memory bus dynamic RAM (DRDRAM), and memory bus dynamic RAM (RDRAM).

[0106] It should be noted that, in this document, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, apparatus, article, or method that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such process, apparatus, article, or method. Unless otherwise specified, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, apparatus, article, or method that includes that element.

[0107] The above description is only a preferred embodiment of this application and does not limit the patent scope of this application. Any equivalent structural or procedural changes made based on the content of this application's specification and drawings, or direct or indirect applications in other related technical fields, are similarly included within the patent protection scope of this application.

Claims

1. A method for energy-saving operation of a cold storage fan, characterized in that, The method includes: S1. Construct a CFD benchmark model for simulating and predicting the three-dimensional transient airflow and temperature distribution within the target cold storage; S2. Based on the measured data, the model parameters of the CFD benchmark model are calibrated to obtain the calibrated CFD benchmark model. S3. The calibrated CFD baseline model is reduced in order by using the method of intrinsic orthogonal decomposition combined with Galerkin projection to obtain a reduced-order model. S4. Read the real-time temperature data of the sparse sensor network at a fixed period, use the real-time temperature data as the observation value, and continuously correct the full-field temperature distribution predicted by the reduced-order model through the data assimilation algorithm to generate the corrected optimal state estimate. S5. In each control cycle, the corrected optimal state estimate is used as the initial state of the system. Based on the initial state, a constrained dynamic optimization problem is constructed within the set future prediction time domain. Based on the optimization algorithm, the fan action sequence that optimizes the objective function is searched among all feasible solutions that satisfy the temperature constraint. After executing the first action of the optimal fan action sequence, the process returns to step S4 for the next cycle of cyclic optimization. The decision variable of the constrained dynamic optimization problem is the fan action sequence divided by discrete time steps within a future period. The objective function is to minimize the total power consumption of the fans within the prediction time domain. The temperature constraint is: based on the reduced-order model prediction, the temperature of any unit in the cold storage must not exceed the upper limit of cargo safety or fall below the lower limit of cargo safety within the entire prediction time domain.

2. The energy-saving operation method for cold storage fans according to claim 1, characterized in that, The objective function is specifically: Where J is the objective function value, representing the total power consumption in the prediction time domain, and N... f Here, H represents the total number of fans involved in the control of the cold storage, H represents the prediction time domain length (i.e., the number of future discretized time steps), k is the index of the discrete time step within the prediction time domain, t represents the actual time point of the current control cycle, Δt represents the time length of each time step, and u... i (t+k) represents the control action variable of the i-th wind turbine at time t+k, u i ∈{0,1}, 0 represents the fan being turned off, 1 represents the fan being turned on, P rated,i This represents the rated power of the i-th fan. The temperature constraint condition is specifically as follows: T(x,t+k|t) represents the optimal state estimate based on the current time t. Under the reduced-order model prediction, it represents the predicted temperature at position x at the future time t+k. x represents any coordinate position in the three-dimensional space inside the cold storage, and Ω represents the entire spatial region of the air domain inside the cold storage. Indicates the maximum safe temperature that the goods are allowed to withstand. This indicates the minimum safe temperature that the goods can be kept at, and it is determined by the type of goods.

3. The energy-saving operation method for cold storage fans according to claim 1, characterized in that, The steps for reducing the order of the calibrated CFD baseline model using the method of intrinsic orthogonal decomposition combined with Galerkin projection to obtain the reduced-order model include: S31. Based on the calibrated CFD benchmark model, numerical simulations are performed under various typical cold storage operating conditions. Temperature distribution data of the entire field is collected at multiple times to form a temperature field snapshot set. The typical operating conditions include different fan start-up combinations, different cargo stacking methods, and different door opening frequencies. S32. Preprocess the data in the temperature field snapshot set, calculate the average temperature field, and subtract the average temperature field from each temperature field snapshot to obtain a mean-free snapshot matrix. S33. Perform eigenorthogonal decomposition on the snapshot matrix to extract the first r dominant spatial mode functions; S34. Substitute the eigenorthogonal decomposition expansion of the temperature field into the energy partial differential equation describing the heat transfer process in the cold storage to obtain the residual equation. S35. Using each dominant spatial mode function as a test function, the residual equation is subjected to Galerkin projection on the cold storage spatial domain. By weighted integration and utilizing modal orthogonality, the dynamic equation of the principal mode coefficients is derived. S36. The dominant spatial mode function and the dynamic equation are combined to form a reduced-order model, which is used to predict the spatiotemporal evolution of the temperature field inside the cold storage.

4. The energy-saving operation method for cold storage fans according to claim 1, characterized in that, The step of calibrating the model parameters of the CFD benchmark model based on measured data to obtain the calibrated CFD benchmark model includes: A high-density sensor network was temporarily deployed inside the target cold storage to collect measured data of the three-dimensional spatial temperature field under typical cold storage operating conditions. The measured data of the three-dimensional temperature field are compared with the simulation results of the CFD benchmark model under the same working conditions to construct a target error function, which is the mean square error between the simulated temperature field and the actual temperature field. With the goal of minimizing the target error function, the key parameters in the CFD benchmark model are iteratively adjusted. The key parameters include the air convection heat transfer coefficient, the equivalent thermal conductivity of the cold storage enclosure structure, and the fan outlet velocity distribution characteristics. When the target error function converges and the simulation results meet the preset accuracy threshold, the model calibration is completed, the high-density sensor network is removed, and the calibrated CFD benchmark model is output.

5. The energy-saving operation method for cold storage fans according to claim 1, characterized in that, The steps for constructing a CFD benchmark model for simulating and predicting three-dimensional transient airflow and temperature distribution within a target cold storage facility include: Obtain the geometric structural parameters, thermal property parameters of the enclosure structure, cargo stacking layout, and parameters and installation location information of the air cooler of the target cold storage, and establish a three-dimensional geometric and physical parameterized model; Based on the laws of conservation of mass, momentum and energy, and combined with a turbulence model suitable for low-temperature environments and a thermal buoyancy effect correction term, a three-dimensional transient control equation set describing the air flow and heat transfer process in the cold storage is constructed. Based on the geometric structure of the three-dimensional geometric and physical parameterized model, a mesh is generated to produce a computational mesh, and local refinement is performed on key areas; Based on the physical parameters configured in the three-dimensional geometric and physical parameterization model, initial conditions and boundary conditions are set; the initial conditions include the initial temperature field and velocity field; the boundary conditions include the heat flow of the enclosure structure, the air velocity and temperature of the air cooler, and the heat source of the cargo area. Based on the computational grid, the three-dimensional transient control equations are discretely solved using the finite volume method. The initial and boundary conditions are used as inputs, and the numerical solution describing the airflow and heat transfer process in the cold storage is obtained through iterative calculation using a pressure-velocity coupling algorithm, thus forming a CFD benchmark model.

6. The energy-saving operation method for cold storage fans according to claim 1, characterized in that, The data assimilation algorithm employs ensemble Kalman filtering.

7. An energy-saving operation system for cold storage fans, characterized in that, The system includes: The building blocks are used to construct a CFD benchmark model for simulating and predicting three-dimensional transient airflow and temperature distribution within a target cold storage facility. The calibration module is used to calibrate the model parameters of the CFD benchmark model based on measured data to obtain the calibrated CFD benchmark model. The order reduction module is used to reduce the order of the calibrated CFD baseline model by using the method of intrinsic orthogonal decomposition combined with Galerkin projection to obtain a reduced-order model. The correction module is used to read the real-time temperature data of the sparse sensor network at a fixed period, use the real-time temperature data as the observation value, and continuously correct the full-field temperature distribution predicted by the reduced-order model through a data assimilation algorithm to generate the corrected optimal state estimate. The execution module is used in each control cycle to construct a constrained dynamic optimization problem within a set future prediction time domain, using the corrected optimal state estimate as the initial state of the system. Based on the initial state, it searches for the optimal fan action sequence among all feasible solutions that satisfy the temperature constraint using an optimization algorithm. After executing the first action of the optimal fan action sequence, it returns to the step correction module for cyclic optimization in the next cycle. The decision variable of the constrained dynamic optimization problem is the fan action sequence divided into discrete time steps within a future period. The objective function is to minimize the total power consumption of the fans within the prediction time domain. The temperature constraint is: based on the reduced-order model prediction, the temperature of any unit in the cold storage must not exceed the upper limit of cargo safety or fall below the lower limit of cargo safety throughout the entire prediction time domain.

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