Numerical simulation model of shipborne ultra-low temperature quick-freezing library and application thereof

Abstract

The application relates to the fields of refrigeration engineering and food processing technology, and discloses a numerical simulation model of a shipborne ultra-low-temperature quick-freezing library and application, which comprises a basic parameter acquisition module, a numerical simulation calculation module, an experimental verification feedback module, a quality comprehensive evaluation module and a process regulation and optimization module. The model acquires the geometric structure of the quick-freezing library and the thermal physical property parameters of objects to be frozen, establishes a three-dimensional transient numerical model to calculate the distribution of a flow field and a temperature field, corrects a simulation boundary condition by using measured temperature data and microscopic organization form images, analyzes the change trend of physical and chemical indexes and establishes a correlation with the time through a maximum ice crystal generation zone. Based on the flow field analysis and the quality evaluation result, the application optimizes the air flow organization structure of the quick-freezing library, configures a guide vane group and a non-uniform inter-shelf spacing, and generates a control strategy of a variable-frequency air speed in stages. The application realizes the collaborative improvement of quick-frozen quality and energy efficiency by combining macroscopic simulation with microscopic quality feedback.

Description

Technical Field

[0001] This invention relates to the fields of cryogenic engineering and food processing technology, specifically to a numerical simulation model and application of a shipborne ultra-low temperature quick-freezing warehouse. Background Technology

[0002] With the increasing global demand for seafood, the catch volume of deep-sea fishing continues to rise, making shipboard freezing and preservation technology a crucial link in ensuring the quality and circulation of aquatic products. Sea-caught shrimp, a typical seafood rich in high-quality protein and minerals, has high water content and a delicate structure in its muscle tissue, making it highly susceptible to the effects of ice crystal morphology during freezing. If the freezing process is not properly controlled, large ice crystals forming inside the cells can rupture the cell membranes, leading to juice loss, softening, and flavor deterioration after thawing, severely impacting the product's economic value and palatability.

[0003] Currently, quick-freezing operations for shipborne seafood mainly employ plate freezing or air-cooling techniques. While traditional plate freezing offers good uniformity in contact heat transfer, its overall heat exchange efficiency is limited by contact thermal resistance and thermal conductivity, resulting in a relatively slow freezing speed. This makes it difficult for thick stacks of seafood to quickly pass through the maximum ice crystal formation zone, thus limiting the protection of the microstructure of high-quality sea-caught shrimp. In contrast, ultra-low temperature air-cooling quick-freezing technology, utilizing a powerful refrigeration system and circulating fans, has the potential to achieve extremely rapid freezing, effectively inhibiting microbial activity and reducing juice loss.

[0004] However, the shipboard operating environment is unique. Limited by the physical space and energy supply of the ship's cabins, quick-freezing equipment is often compact in structure and complex in operation. In practical applications, the airflow organization inside the ultra-low temperature quick-freezing warehouse is easily affected by the stacking method of the shelves and the air supply parameters, resulting in uneven flow field distribution and heat retention in some areas, causing significant differences in the frozen quality of the same batch of products. Although computational fluid dynamics technology has been applied in the fields of microwave heating, drying, and sterilization of land-based foods, numerical simulation studies of ultra-low temperature quick-freezing processes under shipboard dynamic environments are relatively scarce. Existing studies are mostly limited to the prediction of single physical fields and lack a comprehensive evaluation mechanism that correlates the simulation of macroscopic flow fields and temperature fields with the microstructural damage and physicochemical deterioration of seafood, making it difficult to achieve precise control and energy efficiency optimization of the quick-freezing process under the limited shipboard conditions. Summary of the Invention

[0005] To address the shortcomings of existing technologies, this invention provides a numerical simulation model and application for a shipborne ultra-low temperature quick-freezing warehouse, which solves the problems of uneven freezing, significant damage to the microstructure of seafood, and high operating energy consumption caused by uneven airflow organization and lack of microscopic quality feedback mechanism in the shipborne environment.

[0006] To achieve the above objectives, the present invention is implemented through the following technical solution: a numerical simulation model of a shipborne ultra-low temperature quick-freezing warehouse and its application.

[0007] The first aspect of this invention provides a numerical simulation model of a shipborne cryogenic quick-freezing warehouse.

[0008] The numerical simulation model includes a basic parameter acquisition module, a numerical simulation calculation module, an experimental verification and feedback module, a comprehensive quality evaluation module, and a process control and optimization module.

[0009] The basic parameter acquisition module is used to acquire various physical parameters and geometric dimensions involved in the quick-freezing process. The acquired data includes the geometric structural parameters of the quick-freezing storage, air supply system parameters, thermal property parameters of the insulation material, and structural parameters of the quick-freezing shelves and pallets. Simultaneously, it acquires the thermophysical properties of the object to be frozen, including density, thermal conductivity, and specific heat capacity.

[0010] The numerical simulation module is connected to the basic parameter acquisition module and is used to establish a three-dimensional transient numerical model to perform coupled calculations of fluid mechanics and heat transfer. Based on the continuous medium assumption, this module constructs a set of governing equations by simultaneously solving the mass conservation equation, momentum conservation equation, and energy conservation equation. To simulate a forced convection environment, this module introduces a standard k-epsilon two-equation turbulence model into the governing equations, simulating the airflow field within the reservoir by solving the turbulent kinetic energy transport equation and the turbulent dissipation rate transport equation. To handle phase change heat transfer during the freezing process, this module uses the enthalpy method to handle the latent heat of solid-liquid phase change. Specifically, it introduces a source term characterizing the absorption and release of latent heat of phase change into the energy conservation equation, calculates the change in liquid fraction to characterize the freezing process, and outputs flow field distribution data, temperature field distribution data, and the center temperature change curve of the object to be frozen.

[0011] The experimental verification feedback module is used to construct a closed-loop verification mechanism between simulation and actual measurement. This module includes temperature sensors and microstructure observation equipment deployed at different spatial locations within the blast freezer. The temperature sensors collect center temperature data of the object to be frozen at different shelf levels. The microstructure observation equipment uses scanning electron microscopy to acquire microscopic morphological images of the muscle tissue of the object to be frozen after gradient dehydration and critical point drying. By observing the integrity of muscle fibers and the porosity caused by ice crystals, the impact of freezing rate on tissue structure is evaluated. This module compares the monitoring data with the output results of the numerical simulation calculation module and adjusts the boundary condition parameters of the numerical simulation calculation module based on the comparison results.

[0012] The comprehensive quality evaluation module is used to establish the correlation between macroscopic process parameters and microscopic quality indicators. This module performs quantitative analysis on volatile basic nitrogen, salt-soluble proteins, thiobarbituric acid reactants, and pH value. Based on the rate of change of these physicochemical indicators, it quantitatively evaluates the inhibitory effect of the quick-freezing process on protein denaturation, lipid oxidation, and spoilage. This module also correlates the changing trends of these physicochemical indicators with the time data for passing through the maximum ice crystal formation zone calculated by the numerical simulation module, providing a biochemical basis for process optimization.

[0013] The process control and optimization module generates operation control strategies and structural optimization schemes for the quick-freezing equipment based on the flow field analysis results from the numerical simulation calculation module and the evaluation results from the quality comprehensive evaluation module. Regarding structural optimization, this module determines the low-velocity areas and heat retention areas within the quick-freezing warehouse shelf area based on flow field distribution data, and configures a non-uniform airflow guiding structure. A guide vane assembly is installed between the quick-freezing warehouse air outlet and the shelf air inlet side, and the installation angle of the guide vanes is determined based on the optimal airflow incident angle. Simultaneously, this module implements a non-uniform configuration for shelf spacing. While maintaining the total shelf height, it adjusts the vertical distribution of the shelf panels, making the spacing between middle shelf layers larger than that of edge shelf layers, thereby improving the heat retention phenomenon in the middle layers.

[0014] In terms of operation control, the process regulation and optimization module uses the empirical correlation between the Nusselt number and the Reynolds number to determine the wind speed control parameters, dividing the quick-freezing process into a forced convection rapid freezing stage and a temperature maintenance stage. During the forced convection rapid freezing stage, the module outputs a high-frequency drive signal to the blower inverter, driving the blower to operate at its maximum rated speed to ensure that the object to be frozen quickly passes through the maximum ice crystal formation zone. During the temperature maintenance stage, the module outputs a low-frequency drive signal to reduce the blower speed, utilizing a combination of natural convection and weak forced convection to achieve cooling and energy efficiency balance.

[0015] A second aspect of the present invention provides an application of the numerical simulation model of the aforementioned shipborne cryogenic quick-freezing warehouse.

[0016] This application involves optimizing the airflow organization structure and operational control parameters of a shipborne cryogenic freezer based on flow field distribution data and physicochemical index data. The specific application process includes: acquiring basic structural data of the shipborne freezer and thermophysical properties of the object to be frozen using a basic parameter acquisition module; constructing a three-dimensional transient numerical model of the freezer process using a numerical simulation calculation module to calculate the flow field distribution inside the freezer and the temperature field changes of the object to be frozen; conducting a freezing experiment in a physical freezer, collecting measured data of the core temperature and microstructure images of the object to be frozen to verify the accuracy of the three-dimensional transient numerical model; measuring the physicochemical index data of the object to be frozen during the storage period, and assessing the quality degradation effect based on the changing trends of the physicochemical index data; comprehensively analyzing the heat retention area in the flow field distribution and the physicochemical index data to determine the key influencing factors of the freezer process, and adjusting the airflow organization structure and operational control parameters of the freezer based on these key influencing factors.

[0017] This invention provides a numerical simulation model and application of a shipborne cryogenic quick-freezing warehouse. It offers the following advantages: 1. This invention constructs a multi-dimensional model that combines macroscopic physical field simulation with microscopic physicochemical quality evaluation. By establishing a correlation between the cooling rate distribution output by numerical simulation and the microscopic ice crystal morphology and physicochemical indicators observed by scanning electron microscopy, it overcomes the limitation of traditional methods that rely solely on temperature sensors and cannot accurately reflect the damage to the microstructure of biological tissues caused by freezing, thereby improving the biological accuracy of the model's prediction results at the food quality level.

[0018] 2. Based on flow field simulation results, this invention proposes a structural optimization scheme including guide vane groups and non-uniform shelf layer spacing. In response to the phenomenon of low flow velocity in the middle layer of densely stacked shelves, this invention effectively reduces frictional resistance and eliminates local heat retention areas by increasing the hydraulic diameter of the middle layer and optimizing the airflow injection angle, thereby effectively improving the uniformity of flow field distribution and temperature field distribution inside the quick-freezing warehouse.

[0019] 3. This invention establishes a phased variable frequency control strategy based on the time to pass through the maximum ice crystal formation zone. In the early stage of quick-freezing, high Reynolds number flow is used to enhance convective heat transfer, ensuring that the product passes through the maximum ice crystal formation zone quickly to suppress the formation of large ice crystals. In the deep freezing stage, the wind speed is reduced, which effectively reduces the system's operating energy consumption while ensuring freezing quality, and achieves synergistic optimization of quick-freezing efficiency and energy utilization. Attached Figure Description

[0020] Figure 1 This is a diagram showing the overall architecture of the system of the present invention; Figure 2 This is a graph illustrating the mesh independence verification of the present invention. Figure 3This is a graph showing the ambient temperature changes of the ultra-low temperature freezer of the present invention; Figure 4 This is a path diagram of the ultra-low temperature freezer of the present invention; Figure 5 This is a graph showing the ambient temperature variation of the plate refrigerator of the present invention; Figure 6 This is a line graph showing the effect of the frozen storage facility of the present invention on the pH of marine shrimp; Figure 7 This is a line graph showing the effect of the frozen storage facility of the present invention on TVB-N in marine shrimp; Figure 8 This is a line graph showing the effect of the frozen storage facility of the present invention on the SPS of marine shrimp. Figure 9 This is a line graph showing the effect of the frozen storage facility of the present invention on TBA in marine shrimp. Detailed Implementation

[0021] The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0022] To better understand the present invention, the above content will be described in detail below with reference to specific embodiments.

[0023] Please see the appendix Figure 1 This invention provides a numerical simulation model and application for a shipborne cryogenic quick-freezing warehouse, comprising: a basic parameter acquisition module, a numerical simulation calculation module, an experimental verification feedback module, a comprehensive quality evaluation module, and a process control and optimization module. These modules are connected via a data bus or communication interface to achieve data transmission and interaction.

[0024] The basic parameter acquisition module is used to obtain various physical parameters and geometric dimensions involved in the quick-freezing process. The data collected by this module includes the geometric structural parameters of the shipborne quick-freezing storage facility, the thermophysical properties of the insulation materials, and the geometric dimensions and material properties of the quick-freezing racks and pallets. Simultaneously, the module is also responsible for acquiring the thermophysical properties of the object to be frozen (such as wild-caught shrimp), including at least density, thermal conductivity, and specific heat capacity. This basic data provides boundary and initial conditions for subsequent modeling and calculations.

[0025] The numerical simulation module connects to the basic parameter acquisition module, receiving the aforementioned parameters and establishing a three-dimensional transient numerical model. Internally, the numerical simulation module performs coupled calculations of fluid mechanics and heat transfer. First, it constructs a three-dimensional physical model based on the actual geometric dimensions of the freezer, then meshes this model to generate a discretized computational domain. The module then constructs a set of governing equations based on the laws of conservation of mass, momentum, and energy. For the phase change heat transfer phenomenon during the freeze-freezing process, the module uses the enthalpy method to handle the latent heat of solid-liquid phase change, characterizing the freezing process by calculating the change in liquid fraction. For the flow field characteristics under forced convection, the module uses a turbulence model to simulate the flow behavior of cold air between shelves. The module iteratively solves the governing equations, outputting flow field distribution data, temperature field distribution data, and the center temperature change curve of the object to be frozen within the freezer.

[0026] The experimental verification feedback module is used to collect monitoring data of the quick-freezing process in a real physical environment to verify the accuracy of the output results of the numerical simulation calculation module. The module includes temperature sensors and microstructure observation equipment placed in different spatial locations within the quick-freezing warehouse. The temperature sensors collect center temperature data of the object to be frozen at different shelf levels at preset time intervals. The microstructure observation equipment uses scanning electron microscopy to acquire microscopic morphological images of the muscle tissue of the object to be frozen, evaluating the impact of freezing rate on tissue structure by observing the integrity of muscle fibers and the porosity caused by ice crystals. The experimental verification feedback module compares the measured temperature data with the simulated temperature data output by the numerical simulation calculation module. If the error exceeds a preset threshold, the boundary condition parameters of the numerical simulation calculation module are adjusted.

[0027] The comprehensive quality evaluation module analyzes the physicochemical properties of the object to be frozen under different quick-freezing processes and storage periods. It performs quantitative analysis of volatile basic nitrogen, salt-soluble proteins, thiobarbituric acid reactants, and pH value. The module records the trends of these physicochemical properties over storage time and correlates these trends with the time data for passing through the maximum ice crystal formation zone calculated by the numerical simulation module. Based on the rate of change of these physicochemical properties, the module quantitatively evaluates the inhibitory effect of quick-freezing processes on protein denaturation, lipid oxidation, and spoilage.

[0028] The process control and optimization module generates an operation control strategy for the quick-freezing equipment based on the flow field analysis results from the numerical simulation calculation module and the evaluation results from the quality comprehensive evaluation module. According to the low-velocity or heat retention regions in the flow field distribution, the process control and optimization module calculates the optimal layer spacing parameters for the quick-freezing racks or the installation position parameters for the guide vanes. Simultaneously, the process control and optimization module sets the air velocity parameters for the air supply system and the temperature parameters for the refrigeration system based on the time requirement for passing through the maximum ice crystal formation zone.

[0029] Methods for optimizing the quick-freezing process and evaluating the quality of shipborne seafood include the following steps: S1. Obtain the basic structural data of the shipborne quick-freezing storage facility and the thermophysical property parameters of the object to be frozen; the basic structural data includes the geometric dimensions of the quick-freezing storage facility, the parameters of the air supply system, and the properties of the insulation material; the thermophysical property parameters include the density of the object to be frozen. Thermal conductivity and specific heat capacity ; S2. Based on the basic structural data and thermophysical property parameters, a three-dimensional transient numerical model of the quick-freezing process is constructed. Using computational fluid dynamics, the mass conservation equation, momentum conservation equation and energy conservation equation are solved simultaneously. Combined with the enthalpy phase transition model and turbulence model, the flow field distribution inside the quick-freezing chamber and the temperature field change data of the object to be frozen are calculated. S3. Perform a freezing experiment in a physical quick-freezing chamber, and collect measured data of the core temperature and microstructure images of the object to be frozen; compare the measured core temperature data with the calculation results of the three-dimensional transient numerical model to verify the accuracy of the numerical model. S4. Measure the physicochemical index data of the object to be frozen during the storage period; the physicochemical index data include pH value, volatile basic nitrogen content, salt-soluble protein content and thiobarbituric acid reactant value; evaluate the inhibitory effect of different quick-freezing methods on quality deterioration based on the changing trend of the physicochemical index data. S5. Analyze the heat retention area and physicochemical index data in the flow field distribution to determine the key influencing factors of the quick-freezing process; adjust the airflow organization structure or operation control parameters of the quick-freezing warehouse based on the key influencing factors.

[0030] In step S1, obtaining the basic structural data of the shipborne quick-freezing storage facility and the thermophysical property parameters of the object to be frozen is a prerequisite for constructing a high-precision numerical model. This step is implemented through a combination of on-site surveying and physical property testing, and specifically includes the following steps: S101. Establish a database of geometric parameters for quick-freezing storage structures.

[0031] The external and internal structural dimensions of the shipborne cryogenic freezer were collected and entered. In one specific embodiment, the physical model of the freezer was defined as a rectangular sealed chamber with a length, width, and height of 4m, 2m, and 2.2m, respectively. The walls of the freezer were constructed of polyurethane insulation material, with an insulation layer thickness of 10cm. Simultaneously, the geometric characteristics of the air supply system were determined, including two blower assemblies distributed on each side of the freezer, with a blower diameter of 50cm and an axial length of 15cm. The dimensions of the freezer door were entered as 2m × 1m. These geometric parameters defined the fluid domain boundary for the computational fluid dynamics simulation.

[0032] S102. Define the structural parameters of the internal quick-freezing racks and storage units.

[0033] Obtain the specific dimensions of the blast freezer rack located at the geometric center of the blast freezer. Set the overall rack dimensions to 3m long, 1.2m wide, and 2m high. The rack adopts a multi-layer structure, containing a total of 10 storage layers, with a layer spacing of 20cm. This layer spacing directly relates to the flow resistance characteristics in the flow field simulation. Further define the dimensions of the shrimp trays placed on the rack, setting the length, width, and height of a single shrimp tray to 0.4m, 0.6m, and 0.05m, respectively. In subsequent modeling, this shrimp tray area is defined as a porous medium region or an equivalent solid region.

[0034] S103. Measure and record the thermophysical parameters of the quick-freezing device components.

[0035] Based on the materials of each component of the quick-freezing storage, enter the corresponding density. Thermal conductivity and specific heat capacity The specific parameter settings are as follows: Quick-freezing rack (aluminum): Density thermal conductivity Specific heat capacity Warehouse insulation layer (polyurethane material): density thermal conductivity Specific heat capacity Shrimp tray (polyethylene material): density thermal conductivity Specific heat capacity .

[0036] The above parameters are used as solution coefficients for the solid heat conduction equation in the numerical simulation calculation module.

[0037] S104. Determine the equivalent thermophysical properties of the object to be frozen.

[0038] For the wild-caught shrimp in this embodiment, considering the complexity of the organism's structure, it is treated as a homogeneous continuous medium for parameter calibration. The basic physical properties of the wild-caught shrimp are recorded as follows: density. thermal conductivity Specific heat capacity In the simulation, thermal conductivity and specific heat capacity can be further defined as functions of temperature, especially in the temperature range involving the release of latent heat of phase change, to accurately reflect the changes in thermal inertia during the freezing process.

[0039] refer to Figure 2 In step S2, the specific implementation process of constructing and calculating the three-dimensional transient numerical model of the quick-freezing process is mainly achieved through a computational fluid dynamics software platform. This process transforms the physical problem into a mathematical problem and solves it using numerical methods. Specifically, it includes the following steps: S201. Construct a three-dimensional physical model and perform geometric simplification.

[0040] When constructing the 3D physical model, the computational domain is defined based on the actual physical dimensions of the shipborne quick-freezing chamber. As a specific example, the physical model of the quick-freezing chamber is set as a closed cavity with a length of 4m, a width of 2m, and a height of 2.2m. For the complex geometry inside the quick-freezing chamber, equivalent simplification is performed while ensuring the realism of the flow field characteristics. Specifically, the 3D geometry of the fan is simplified to surface fan boundary conditions, ignoring the rotational details of the fan blades and retaining only the pressure jumps or velocity boundary characteristics at its inlet and outlet. For the object to be frozen (such as wild-caught shrimp), its accumulation state in the shrimp tray is equivalent to a 3D cuboid prism, and it is assumed that the interior of the cuboid prism is an isotropic homogeneous medium. This simplification ignores the tiny gaps between individual shrimp and the difference in thermal properties between the shrimp shell and the shrimp meat, thus simplifying the discontinuous porous medium heat transfer problem into a continuous medium unsteady-state heat conduction problem, effectively reducing the order of magnitude of the computational grid and improving the convergence speed.

[0041] S202. Perform mesh generation and independence verification on the computational domain.

[0042] The aforementioned three-dimensional physical model was discretized using a mesh generation tool. Considering the regular structure of the blast freezer, a structured hexahedral mesh was preferred to reduce numerical truncation errors. In areas with drastic flow field changes, particularly at air inlets, outlets, and gaps between shelf layers, local mesh refinement was performed to accurately capture changes in velocity and temperature gradients. The quality of mesh generation directly determines the accuracy of the calculation results; therefore, mesh independence verification is necessary. Specifically, multiple sets of meshes with different node densities were generated, and trial calculations were performed under the same boundary conditions, monitoring temperature changes at the center point of the computational domain. When the temperature difference calculated by two adjacent sets of meshes was less than a preset convergence threshold (e.g., error less than 1%), the mesh with fewer nodes was selected as the final computational mesh to achieve a balance between computational accuracy and computational resource consumption.

[0043] S203. Establish the governing equations for fluid flow and heat transfer.

[0044] The numerical simulation module is based on the continuous medium assumption and uses the simultaneous mass conservation equation, momentum conservation equation and energy conservation equation to describe the air flow and heat exchange process in the quick-freezing chamber.

[0045] The mass conservation equation (continuity equation) is expressed as: ; in, This represents the divergence operator, which describes the spatial variation of the velocity field. This is the fluid velocity vector.

[0046] The momentum conservation equation describes the motion of viscous fluids and is expressed as: ; in, Density; For time; This is the convective acceleration term, reflecting inertial effects; It is the fluid pressure; Dynamic viscosity characterizes viscous resistance; This refers to the viscous diffusion term in the velocity field. Let be the gravitational acceleration vector. This equation takes into account the effect of gravity on natural convection.

[0047] The energy conservation equation describes the change in the temperature field and is expressed as: ; in, For specific heat capacity, For temperature, Thermal conductivity, This is the phase transition source term.

[0048] S204. Introduce a turbulence model to solve the forced convection field.

[0049] Given that forced circulation is achieved using fans within the shipborne quick-freezing chamber, resulting in a high Reynolds number turbulent flow field, a standard flow model is introduced. A two-equation turbulence model is used to close the aforementioned set of governing equations. This model solves for turbulent kinetic energy. and turbulent dissipation rate The transport equation is used to simulate the effect of turbulent viscosity on the flow field.

[0050] Turbulent kinetic energy The transport equation is: ; Turbulent dissipation rate The transport equation is: ; In the above equation, This represents the turbulent kinetic energy generation term caused by the average velocity gradient; For turbulent viscosity, it passes through Calculated; and , respectively, represent the Prandtl number of turbulent kinetic energy and dissipation rate; , and These are empirical model constants; in this embodiment, each empirical constant takes the value of... , , turbulent Prandtl number , For the standard In models, these constants are typically taken as standard values ​​recognized in the field.

[0051] S205. Apply the enthalpy-based phase transition model to handle the latent heat of freezing.

[0052] To address the issue of water phase change occurring during the cooling process of the object to be frozen, this invention does not employ a moving mesh to track the phase interface. Instead, it uses the enthalpy method to simulate the phase change process on a fixed mesh. Within the solid region of the object to be frozen (fluid velocity vector...),... ), the source term in the energy equation Used to characterize the absorption or release of latent heat of phase change.

[0053] The energy equation and phase transition source term for the solid region are defined as follows: ; ; in, Latent heat of phase change represents the heat absorbed or released per unit mass of a substance during a phase change. is the liquid phase ratio, a dimensionless parameter used to quantify the phase state of matter within a calculation unit.

[0054] Liquid phase ratio With temperature There exists a functional mapping relationship: when temperature Above the liquidus temperature, (Completely liquid); when the temperature Below the solidus temperature, (Completely solid); When the temperature is between the solid-liquid phase lines (i.e., the temperature range corresponding to the maximum ice crystal formation zone), between 0 and 1, the substance is in a paste-like region where solid and liquid coexist. This is achieved by updating at each time step. The value of , and the introduction of the source term in the energy equation. This allows for the simulation of the hysteresis effect of latent heat release on the temperature field distribution during freezing, thus eliminating the need to explicitly track the complex ice crystal growth interface.

[0055] S206. Set boundary conditions and physical property parameters and execute the solution.

[0056] Before solving the above system of equations, specific boundary conditions need to be assigned to each boundary surface of the model. For the air inlet of the ultra-low temperature quick-freezing chamber, it is set as a velocity inlet boundary condition, specifying the air supply velocity and air supply temperature (e.g., (45℃ to 45℃); for the return air inlet, set as pressure outlet boundary condition; for the tank wall, set as adiabatic wall boundary condition or convective heat transfer wall condition with a given heat flux density.

[0057] Simultaneously, the collected material thermophysical parameters are assigned to the corresponding computational domain. This includes setting the density, thermal conductivity, and specific heat capacity of the quick-freezing rack material (such as aluminum), setting the thermal resistance characteristics of the insulation material (such as polyurethane), and setting the variable physical property parameters of the object to be frozen (such as wild-caught shrimp). For the object to be frozen, its thermal conductivity and specific heat capacity can be set as functions of temperature to more accurately reflect the changes in physical properties under frozen conditions.

[0058] After completing the above settings, the governing equations are discretized using the finite volume method, and time-step calculations are performed using an unsteady-state solver. Within each time step, the pressure and velocity fields are iteratively solved using a pressure-velocity coupled algorithm (such as SIMPLE or PISO) until the residuals of all physical quantities converge to a preset standard. Finally, the time-varying temperature and flow field data are output, as shown in the attached figure. Figure 3 —Appendix Figure 5 The specific discretization scheme selection and sub-relaxation factor setting involved in the numerical solver are well-known techniques in this field and will not be elaborated upon here.

[0059] Step S3, which involves performing a freezing experiment in a physical freezer and verifying the accuracy of the numerical model, includes not only monitoring the macroscopic physical field but also preparing and characterizing the microscopic morphology. The specific implementation process includes the following steps: S301. Set up temperature monitoring points and collect time-varying temperature data.

[0060] Within the shelf area of ​​the physical quick-freezing warehouse, representative monitoring locations are selected based on spatial geometric distribution characteristics. Specifically, at the top, middle, and bottom shelves, objects to be frozen located at their geometric centers are selected as monitoring samples. Temperature sensor probes are placed at the geometric center of the monitoring samples to acquire core temperature data. The quick-freezing equipment is started, and the temperature values ​​at each monitoring point are recorded synchronously at preset time intervals (e.g., every 20 minutes) until the freezing process is complete. The system will then collect multiple sets of measured temperature data. Simulated temperature data at the same location obtained from numerical simulation calculation in step S2 Compare the two models. Calculate the root mean square error or average relative error of the two models throughout the freezing period. If the error value is within the preset confidence interval (e.g., the error is less than 10%), the boundary condition settings and physical parameter selection of the numerical model are determined to be accurate. If the error exceeds the range, the convective heat transfer coefficient or contact thermal resistance parameter in the numerical model is corrected based on the measured data.

[0061] S302. Prepare biological tissue samples for microscopic characterization.

[0062] To verify the impact of rapid freezing on cell structure at the microscopic level, histological preparation of the frozen samples is necessary. Tiny tissue blocks are excised from the samples after rapid freezing is terminated and immediately placed in a fixative for chemical fixation to maintain the morphological stability of the tissue structure. As a specific example, a 4% phosphate-buffered formalin solution is used as the fixative, and the fixation time is set to 24 hours. This step coagulates proteins through cross-linking, preventing autolysis or putrefaction of the tissue during subsequent processing, thereby preserving the morphological imprint of ice crystals at the end of rapid freezing.

[0063] S303. Perform gradient dehydration treatment to replace tissue moisture.

[0064] Since subsequent scanning electron microscopy imaging requires a high vacuum environment, and moisture in the sample can interfere with electron beam imaging, it is essential to remove the moisture from the sample. To avoid rapid evaporation and tissue shrinkage caused by direct drying, a gradient replacement with ethanol solution is employed. Specifically, the fixed sample is sequentially immersed in a series of ethanol solutions with increasing concentrations. The concentration gradient of the ethanol solutions is set sequentially to 50%, 70%, 80%, 90%, 95%, and 100%. The sample is immersed in each concentration solution for a predetermined time (e.g., 15 minutes). By gradually increasing the ethanol concentration, the miscibility of ethanol and water is utilized to gently and completely replace the water within the tissue cells with ethanol, thereby minimizing the mechanical damage to the cell walls or muscle fibers caused by sudden changes in osmotic pressure.

[0065] S304. Perform critical point drying and prepare conductive coating.

[0066] Although the dehydrated sample removed water, it remained filled with ethanol. To remove the ethanol while maintaining the three-dimensional structure, a critical point drying method was employed. The sample was placed in a critical point dryer, where liquid carbon dioxide replaced the ethanol. By adjusting the temperature and pressure, the carbon dioxide was brought to a critical state, where the gas-liquid interface disappeared and surface tension was zero, thus avoiding the damaging effects of liquid surface tension on the microstructure during conventional drying processes. After drying, the sample was fixed on a sample stage, and its surface was metal-sputtered using an ion sputtering system. By depositing a nanoscale gold-palladium conductive film on the sample surface, the charge accumulation effect caused by electron beam irradiation was eliminated, and the yield of secondary electrons was improved, ensuring the signal-to-noise ratio and image sharpness.

[0067] S305. Acquire microscopic morphology images and correlate them with the freezing rate.

[0068] The prepared sample is placed in the vacuum chamber of a scanning electron microscope, and an accelerating voltage (e.g., 15 kV) is set for imaging. The magnification is adjusted to focus on the cross-section or longitudinal section of the muscle fibers. By observing the tightness of the muscle fiber arrangement, the size of the gaps between fibers, and the presence of breakage, the growth state of ice crystals during freezing is inferred. Specifically, if the image shows that the muscle fibers are neatly arranged with small gaps, it indicates that the time to pass through the maximum ice crystal formation zone is short, and the formed ice crystals are small and uniformly distributed, corresponding to the high cooling rate region in the numerical simulation. If the image shows that the muscle fibers have large-scale breakage, twisting, or huge gaps, it indicates that large ice crystals have formed, corresponding to the heat retention region or low cooling rate region in the numerical simulation. By establishing a database of the correspondence between the degree of microstructural damage and the simulated cooling rate, a biological basis is provided for the optimization of subsequent process parameters.

[0069] The specific operating procedures and equipment calibration processes for the temperature sensor, scanning electron microscope, critical point dryer, and ion sputtering instrument involved in the above steps are well-known in this field and will not be elaborated here.

[0070] refer to Figure 6 - Figure 9 In step S4, the specific implementation process of measuring the physicochemical index data of the object to be frozen during the storage period and evaluating the quality deterioration effect involves the quantitative analysis of protein denaturation, lipid oxidation and spoilage degree, and specifically includes the following steps: S401. Perform standardized pretreatment and group sampling of the samples.

[0071] To eliminate interference from the thawing process on physicochemical test results and ensure comparability between different batches of samples, a standardized pretreatment procedure was established. Samples frozen in step S3 were placed at a preset temperature (e.g., 18℃ or Long-term storage was carried out in a constant temperature environment (30℃). At preset sampling time points (e.g., every 20 days), a sufficient number of parallel samples were randomly selected from different quick-freezing process groups. The frozen samples were placed in an environment of 4℃ for slow thawing until the core temperature of the sample reached 0℃. The thawed samples were immediately dehulled and the muscle tissue was separated, and then prepared into a uniform meat paste under low temperature conditions for subsequent testing of various indicators.

[0072] S402. Measure pH value to assess the acid-base buffering capacity of muscle tissue and the accumulation of metabolic products.

[0073] Take a measured amount (e.g., 10g) of shrimp paste sample and add a measured volume (e.g., 40mL) of distilled water. Homogenize the sample at high speed using a homogenizer to disrupt cell structure and release cell sap. Transfer the homogenate to a beaker for ultrasonic treatment to promote ion release and equilibration. Subsequently, centrifuge the mixture at low temperature to remove precipitates and extract the supernatant. Measure the pH of the supernatant directly using a calibrated precision pH meter. This step, by monitoring pH changes, reflects the glycolysis and acid production process in the early stages of storage and the protein decomposition and ammonia production process in the later stages, thus indirectly characterizing endogenous enzyme activity and microbial growth status.

[0074] S403. Determine the content of volatile basic nitrogen (TVB-N) to quantify freshness and spoilage process.

[0075] Volatile basic nitrogen is a key chemical indicator for evaluating the degree of spoilage of aquatic products, and its determination is based on the principle of the semi-micro Kjeldahl nitrogen determination method. The specific procedure is as follows: A fixed amount (e.g., 20g) of sample is placed in a container, soaked in distilled water, and shaken to extract. The extract is then filtered. A certain volume of the extract is injected into the reaction chamber of the Kjeldahl nitrogen analyzer, and steam distillation is performed under alkaline conditions to allow volatile alkaline nitrogenous substances (such as ammonia and methylamine) in the sample to escape and be absorbed by boric acid solution. Finally, the absorbent is titrated with a standard concentration of hydrochloric acid solution until the indicator color changes abruptly.

[0076] Based on the titration results, the content of volatile basic nitrogen in the sample is calculated using the following formula: ; In the formula, The content of TVB-N (mg / 100g); The volume (mL) of hydrochloric acid standard solution consumed by the sample. The volume (mL) of hydrochloric acid standard solution consumed for blank treatment. This represents the actual concentration (mol / L) of the hydrochloric acid standard solution. The sample mass is (g). Where is the molar mass of nitrogen. This is the conversion factor for dilution ratio.

[0077] S404. Determine the salt-soluble protein (SSP) content to assess the degree of protein freeze denaturation.

[0078] Changes in the solubility of salt-soluble proteins are directly related to the spatial structural integrity of myofibrillar proteins. A high-ionic-strength salt solution (e.g., 5% sodium chloride solution) was used as the extraction solvent. The sample was homogenized and centrifuged, and the supernatant containing myosin and actin was collected. To eliminate interference from non-protein nitrogen, trichloroacetic acid solution was added to the supernatant to induce protein precipitation. After centrifugation, the precipitate was retained. A biuret reagent was used to react with the protein peptide bonds under alkaline conditions to form a purple-red complex. The absorbance was measured at a specific wavelength (e.g., 540 nm), and the concentration of salt-soluble proteins was calculated based on the bovine serum albumin standard curve. The rate of decrease in this index was used to characterize the extent to which ice crystal growth during freezing disrupts intermolecular hydrogen bonds and hydrophobic interactions of proteins.

[0079] Step S450: Determine the thiobarbituric acid reactive substances (TBARS) value to monitor lipid oxidation levels.

[0080] Secondary products such as malondialdehyde generated from lipid oxidation undergo a condensation reaction with thiobarbituric acid. The specific detection procedure is as follows: Take the homogenized sample solution and add a mixed colorimetric reagent containing thiobarbituric acid, trichloroacetic acid, and hydrochloric acid. Place the mixture in a boiling water bath and heat under reflux to allow the lipid peroxidation products to fully react with thiobarbituric acid to form a pink compound. After cooling, centrifuge and collect the supernatant. Measure the absorbance at a wavelength of 532 nm.

[0081] The TBARS value, expressed as milligrams of malondialdehyde per kilogram of sample, is calculated based on a standard curve of 1,1,3,3-tetraethoxypropane. This index is used to evaluate the effectiveness of different quick-freezing processes in inhibiting lipid oxidative rancidity, particularly for seafood rich in unsaturated fatty acids. Lower TBARS values ​​correspond to a faster rate of passage through the maximum ice crystal formation zone, indicating that rapid freezing effectively reduces the release of pro-oxidative factors caused by cell membrane damage.

[0082] S406. Establish an evaluation model that correlates physical and chemical indicators with simulation parameters.

[0083] Using the rate of change of various physicochemical indicators measured in the above steps as the dependent variable, and the key thermodynamic parameters obtained from the simulation calculation in step S2 (such as the time of maximum ice crystal formation and the temperature uniformity index) as independent variables, a quality evaluation correlation matrix is ​​constructed. If the TVB-N growth rate and SSP degradation rate corresponding to a certain quick-freezing process are lower than those of the control group, and are consistent with the high flow rate and high heat transfer coefficient region predicted in the simulation model, then the process parameter is determined to be the preferred parameter. This step realizes quantitative feedback from microscopic chemical changes to macroscopic process parameters, providing a decision-making basis based on quality data for subsequent process control.

[0084] The calibration and operation details of conventional analytical instruments such as precision pH meters, Kjeldahl nitrogen analyzers, homogenizers, and UV-Vis spectrophotometers involved in the above steps are well-known in this field and will not be elaborated here.

[0085] In step S5, the process of comprehensively analyzing the heat retention region and physicochemical index data in the flow field distribution, and determining key influencing factors to adjust the airflow organization structure or operation control parameters of the quick-freezing warehouse involves active intervention and time-varying control of the convective heat transfer mechanism, specifically including the following steps: S501. Optimize the configuration of non-uniform airflow organization structure based on flow field simulation results.

[0086] Based on the flow field velocity vector diagram output in step S2, the location of low-velocity areas within the blast freezer shelf area is determined. Addressing the simulation results showing that the flow velocity in the middle shelf layer is lower than that in the edge layer (thermal stagnation), a guide vane assembly is installed between the blast freezer air inlet and the shelf air inlet side. The guide vane assembly consists of multiple parallel metal plates. The installation angle of the guide vanes is set according to the optimal airflow incident angle calculated from the simulation, guiding the main axis of the airflow towards the middle shelf layer area. Furthermore, a non-uniform configuration is implemented for the shelf layer spacing. While maintaining a constant total shelf height, the vertical distribution of the shelf panels is adjusted so that the spacing between the middle shelf layers is greater than that between the edge shelf layers, thereby increasing the hydraulic diameter of the middle shelf layer area, reducing the frictional resistance of the fluid flow, and eliminating the thermal stagnation phenomenon in the middle layer.

[0087] S502. Construct a phased variable frequency wind speed control strategy based on the time-dependent maximum ice crystal formation.

[0088] Based on the conclusions drawn from steps S3 and S4, namely, rapid passage through the maximum ice crystal formation zone ( 1℃ to Temperature (5℃) is crucial for maintaining muscle fiber integrity and inhibiting the deterioration of biochemical indicators, so a control logic with time as the variable is established. The quick-freezing process is divided into a forced convection rapid freezing stage and a temperature maintenance stage.

[0089] Define the convective heat transfer coefficient With wind speed The relationship between them is described by the following empirical correlation formula: ; ; in, For Nusselt numbers; The convective heat transfer coefficient; Characteristic dimension (hydraulic diameter); The thermal conductivity of air; , , These are empirical constants related to geometry; It is the Reynolds number; air density; For air supply speed; Aerodynamic viscosity; It is a Prandtl number.

[0090] As can be seen from the above formula, increasing the air supply velocity... It can effectively increase the Reynolds number. This increases the convective heat transfer coefficient. This accelerates the freezing rate. In the initial stage of the quick-freezing process (i.e., before the core temperature of the object to be frozen drops to...),... Before 5°C, the control system sends a command to the blower frequency converter to make the blower run at its maximum rated speed and provide the maximum wind speed (e.g., above 9 m / s) to overcome the boundary layer thermal resistance and ensure that the object to be frozen passes through the maximum ice crystal formation zone in the shortest time (e.g., within 12 minutes).

[0091] S503, Adjustment of operating parameters in the later stage of implementing energy efficiency balance.

[0092] When the experimental verification feedback module or online monitoring sensor detects that the center temperature of the object to be frozen exceeds the lower limit of the maximum ice crystal formation zone (i.e., below), When the temperature reaches 5℃ and enters the deep freezing zone, the system automatically switches to the temperature maintenance phase. During this phase, most of the moisture inside the object to be frozen has crystallized, and thermal resistance becomes the main limiting factor for heat transfer. Maintaining extremely high wind speeds reduces the contribution to shortening the freezing time and increases the energy consumption of the fan and the heat load on the compressor. Therefore, the control system reduces the fan speed to a maintenance speed (e.g., 3m / s to 5m / s), utilizing a combination of natural convection and weak forced convection until the center temperature of the object to be frozen reaches the final target temperature (e.g., 5℃). 18℃ or (40℃). Through this phased control, the refrigeration energy consumption per unit of product is reduced while ensuring product quality (i.e., excellent physicochemical indicators).

[0093] S504, Process parameter correction command for generating closed-loop feedback.

[0094] The process control and optimization module extracts the physicochemical index values ​​(such as TVB-N value, SSP value, or TBARS value) measured in step S4 and uses them as feedback signals to evaluate the quick-freezing effect. If the physicochemical index of a batch of products shows a degree of quality deterioration (e.g., SSP degradation rate) higher than the preset quality standard, it indicates that the actual heat transfer efficiency of that batch has not met the requirements for suppressing microstructural damage. The system calculates the required convective heat transfer coefficient increment based on the Nusselt number correlation, and then determines the initial air supply velocity correction value or guide vane angle adjustment value required for the next batch operation, realizing closed-loop optimization of process parameters based on quality data.

[0095] The inverter control technology, PID regulation algorithm, and blade design specifications for fluid machinery involved in the above steps are well-known technologies in this field and will not be elaborated further here.

Claims

1. A numerical simulation model for a shipborne cryogenic quick-freezing warehouse, characterized in that, include: The basic parameter acquisition module is used to acquire the physical parameters and geometric dimensions involved in the quick-freezing process; The numerical simulation calculation module is connected to the basic parameter acquisition module and is used to establish a three-dimensional transient numerical model, perform coupled calculations of fluid mechanics and heat transfer, and output flow field distribution data, temperature field distribution data, and the center temperature change curve of the object to be frozen. The experimental verification feedback module is used to collect monitoring data of the quick-freezing process, compare the monitoring data with the output results of the numerical simulation calculation module, and correct the boundary condition parameters of the numerical simulation calculation module based on the comparison results. The quality comprehensive evaluation module is used to analyze the physicochemical indicators of the object to be frozen during the storage period, and to establish a correlation between the changing trends of the physicochemical indicators and the time data of passing through the maximum ice crystal formation zone calculated by the numerical simulation calculation module. The process control and optimization module generates an operation control strategy for the quick-freezing equipment based on the flow field analysis results from the numerical simulation calculation module and the evaluation results from the comprehensive quality evaluation module.

2. The numerical simulation model of a shipborne cryogenic quick-freezing warehouse according to claim 1, characterized in that, The numerical simulation calculation module is based on the continuous medium assumption and constructs a set of governing equations by simultaneously solving the mass conservation equation, momentum conservation equation, and energy conservation equation. The numerical simulation module introduces the standard k-epsilon two-equation turbulence model into the governing equations and simulates the forced convection field by solving the turbulent kinetic energy transport equation and the turbulent dissipation rate transport equation. The numerical simulation calculation module uses the enthalpy method to handle the latent heat of solid-liquid phase change, introduces a source term to characterize the absorption and release of latent heat of phase change into the energy conservation equation, and characterizes the freezing process by calculating the change in liquid fraction.

3. The numerical simulation model of a shipborne cryogenic quick-freezing warehouse according to claim 1, characterized in that, The data acquired by the basic parameter acquisition module includes the geometric structural parameters of the blast freezer, the air supply system parameters, the thermal properties of the insulation material, and the structural parameters of the blast freezer shelves and pallets. The thermophysical properties of the object to be frozen, acquired by the basic parameter acquisition module, include density, thermal conductivity, and specific heat capacity.

4. The numerical simulation model of a shipborne cryogenic quick-freezing warehouse according to claim 1, characterized in that, The experimental verification feedback module includes temperature sensors and microstructure observation equipment arranged in different spatial locations of the quick-freezing warehouse. The temperature sensor collects the center temperature data of the object to be frozen at different shelf levels; The microstructure observation device uses scanning electron microscopy to acquire microscopic morphological images of the muscle tissue of the object to be frozen. By observing the integrity of muscle fibers and the porosity caused by ice crystals, the effect of freezing rate on tissue structure is evaluated.

5. The numerical simulation model of a shipborne cryogenic quick-freezing warehouse according to claim 1, characterized in that, The comprehensive quality evaluation module performs quantitative analysis on volatile basic nitrogen, salt-soluble protein, thiobarbituric acid reactants, and pH value. The comprehensive quality evaluation module quantifies the inhibitory effect of quick-freezing process on protein denaturation, lipid oxidation, and spoilage based on the rate of change of physicochemical indicators.

6. The numerical simulation model of a shipborne cryogenic quick-freezing warehouse according to claim 1, characterized in that, The process control and optimization module determines the low flow rate area and the heat retention area within the quick-freezing warehouse shelf area based on the flow field distribution data. The process control and optimization module is configured with a non-uniform airflow guiding structure, and a guide vane group is set between the air outlet of the quick-freezing warehouse and the air inlet side of the shelf. The installation angle of the guide vane is determined according to the optimal incident angle of the airflow. The process control and optimization module implements a non-uniform configuration of shelf layer spacing. While keeping the total height of the shelves constant, it adjusts the vertical distribution of the shelf panels so that the layer spacing of the middle shelf is greater than that of the edge shelf.

7. The numerical simulation model of a shipborne cryogenic quick-freezing warehouse according to claim 1, characterized in that, The process control and optimization module uses the empirical correlation between Nusselt number and Reynolds number to determine wind speed control parameters. The process control and optimization module divides the quick-freezing process into a forced convection rapid freezing stage and a temperature maintenance stage. During the forced convection rapid freezing stage, the process control and optimization module outputs a high-frequency drive signal to the blower frequency converter to drive the blower to run at its maximum rated speed. During the temperature maintenance phase, the process control and optimization module outputs a low-frequency drive signal to reduce the fan speed and achieve cooling by combining natural convection and weak forced convection.

8. The numerical simulation model of a shipborne cryogenic quick-freezing warehouse according to claim 2, characterized in that, The numerical simulation calculation module constructs a three-dimensional physical model based on the actual geometric dimensions of the quick-freezing warehouse, and uses a structured hexahedral mesh to discretize the computational domain. The numerical simulation calculation module performs local mesh densification operations at the air supply vents, return air vents, and gaps between shelf layers.

9. The numerical simulation model of a shipborne cryogenic quick-freezing warehouse according to claim 4, characterized in that, The microstructure observation device performs gradient dehydration on the sample, using an ethanol solution to replace tissue water. The microstructure observation device uses the critical point drying method to replace ethanol with liquid carbon dioxide, and adjusts the temperature and pressure to make the carbon dioxide reach the critical state. The microstructure observation equipment uses an ion sputtering instrument to perform metal sputtering on the sample surface.

10. The application of the numerical simulation model of a shipborne cryogenic quick-freezing chamber as described in any one of claims 1 to 9 in optimizing the airflow organization structure and operation control parameters of the shipborne cryogenic quick-freezing chamber based on flow field distribution data and physicochemical index data.

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