Method for predicting icing of a water tank of a flying device, computer device and computer-readable storage medium

Abstract

The application provides a water tank icing prediction method of a flight device, a computer device and a computer readable storage medium, and the method comprises the following steps: intercepting a transverse section of a water tank inside the flight device from a three-dimensional geometric model of the flight device, stretching the transverse section according to the volume of the water tank to form a preset three-dimensional water tank model, and generating a preset calculation domain according to the preset three-dimensional water tank model; generating a preset grid according to the preset calculation domain; configuring an airflow model, a water phase change model and a thin-wall heat conduction model on the preset calculation domain, setting a boundary condition on the preset calculation domain, and forming an icing prediction model; simulating the icing prediction model according to the numerical value of a preset working condition matrix to obtain icing prediction data; judging the icing prediction data according to a preset grading system to generate a safety boundary curve diagram and output flight time suggestion information. The application simplifies the water tank structure, allows a larger time step to be used in the simulation process, and thus accelerates the calculation efficiency.

Description

Technical Field

[0001] This invention relates to the technical field of data processing for flight devices, specifically to a method for predicting water tank icing in flight devices, a computer device, and a computer-readable storage medium. Background Technology

[0002] When firefighting aircraft are conducting high-altitude firefighting missions, the water in their internal tanks may gradually freeze due to prolonged exposure to low temperatures. This can prevent the water drop doors from opening properly, potentially leading to mission failure or even flight safety risks. Traditional solutions primarily address this by adding insulation materials or installing anti-icing systems. However, these solutions increase the weight and energy consumption of the firefighting aircraft.

[0003] Existing research on aircraft icing primarily focuses on aerodynamic icing of components such as wings and engine air intakes. The physical processes mainly involve the impact and freezing of supercooled water droplets, including studies on the physical mechanisms of external aircraft icing and numerical simulations and experimental studies of icing in complex shapes. However, these studies differ fundamentally in their physical mechanisms from the quasi-static solidification process of the internal water tanks of firefighting aircraft, which occurs "from the outside in." Firefighting aircraft rely mainly on heat conduction and convection, with relatively fixed phase change interfaces, and are strongly influenced by forced convection heat transfer from high-speed external airflow.

[0004] Existing research on the solidification problem of liquid storage containers is mostly found in fields such as chemical engineering and shipbuilding, focusing primarily on phase transition processes in static facilities under constant low-temperature environments. While the existing enthalpy-porosity method provides an effective means for solidification / melting simulation, its direct application to aircraft environments faces challenges such as significant differences in timescales between the external flow field and the phase transition process, complex boundary conditions, and low computational efficiency.

[0005] An existing intelligent assessment method for aircraft icing reliability achieves full 3D simulation modeling for aircraft safety assessment under localized icing conditions through automatic modeling of icing reversal, simulation after ice detachment, and icing safety prediction. However, current technology requires excessive computational resources for comprehensive aircraft modeling. Furthermore, the phase change process of water in a water tank takes seconds, while the calculation time step of the external flow field is in milliseconds, requiring significant computational resources for conversion. Since the water tank is located inside the firefighting aircraft, its heat transfer is primarily through conduction and convection, with the internal structure of the aircraft having a relatively small impact. Therefore, comprehensive, detailed modeling of the aircraft could lead to excessive computational resource consumption and even computer system crashes. Summary of the Invention

[0006] The first objective of this invention is to provide a method for predicting water tank icing in flight devices that reduces computational resources.

[0007] A second objective of the present invention is to provide a computer device for implementing the water tank icing prediction method of the above-described flight device.

[0008] A third objective of this invention is to provide a computer-readable storage medium for implementing the water tank icing prediction method of the above-described flight device.

[0009] To achieve the first objective of this invention, the present invention provides a method for predicting water tank icing in a flight device. The method includes: extracting a cross-section of the water tank inside the flight device from a three-dimensional geometric model of the flight device; stretching the cross-section according to the volume of the water tank to form a preset three-dimensional water tank model; generating a preset computational domain of the flight device based on the stretched preset three-dimensional water tank model; generating a preset mesh based on the preset computational domain; configuring an airflow model, a water phase change model, and a thin-walled heat conduction model for the preset computational domain and setting boundary conditions for the preset computational domain to form an icing prediction model; simulating the icing prediction model based on the values ​​of a preset operating condition matrix to obtain icing prediction data; judging the icing prediction data according to a preset classification system, generating a safety boundary curve, and outputting flight time recommendation information.

[0010] As can be seen from the above scheme, by simplifying the aircraft model and ignoring irregular structural details inside the water tank that have little impact on the overall heat transfer path, such as anti-sway baffles, reinforcing ribs, and local connectors, the complexity of the water tank structure and the aircraft can be reduced, thereby reducing computational resources. Since the time step of the airflow model is in the millisecond range and the time step of the water phase change model is in the second range, there is a time difference. After simplifying the water tank and aircraft models, the actual calculation time steps of the airflow model, water phase change model, and thin-walled heat conduction model are unified to the second range, reducing the overall calculation time from weeks to hours, thus improving computational efficiency.

[0011] In a further embodiment, the step of generating a preset mesh based on a preset computational domain includes: generating a first preset mesh, a second preset mesh, and a third preset mesh based on the preset computational domain, wherein the density of the first preset mesh is greater than the density of the second preset mesh, and the density of the second preset mesh is greater than the density of the third preset mesh; obtaining preset operating condition information, substituting the preset operating condition information into the first preset mesh to obtain a first calculation result; substituting the preset operating condition information into the second preset mesh to obtain a second calculation result; substituting the preset operating condition information into the third preset mesh to obtain a third calculation result; and obtaining a preset mesh from the first preset mesh, the second preset mesh, and the third preset mesh based on the first calculation result, the second calculation result, and the third calculation result.

[0012] Therefore, based on the first calculation result, the relative deviation percentage between the second and third preset grids is calculated. If the relative deviation percentage of the second preset grid is less than the first preset value, it proves that the density of the second preset grid does not affect the calculation. Therefore, the second preset grid is selected as the preset grid. In this way, the best balance between calculation accuracy and efficiency can be achieved.

[0013] In a further scheme, the steps of simulating the icing prediction model based on the values ​​of the preset working condition matrix include: setting the immersion time of the water tank, and simulating the icing prediction model based on the immersion time of the water tank and the values ​​of the preset working condition matrix.

[0014] Therefore, by setting the immersion time of the water tank and confirming the operating time of the flight device, a simulation can be conducted to confirm the icing situation of the water tank during aircraft flight.

[0015] In a further scheme, the icing prediction data is instantaneous data obtained from the water tank along the time axis of the cold immersion time; the steps of judging the icing prediction data according to the preset classification system include: obtaining the average icing thickness data of the water drop hatch area of ​​the flight device, the core temperature of the water tank and the preset key point icing thickness data of the water drop hatch actuation mechanism from the icing prediction data, and calibrating the safety status of each preset working condition in the preset working condition matrix according to the preset classification system.

[0016] Therefore, it can be seen that the dual safety criteria of average ice thickness in the water drop hatch area of ​​the flight device and core temperature of the water tank can more comprehensively reflect the icing risk status.

[0017] In a further proposed solution, after calibrating the safety status of each preset working condition in the preset working condition matrix according to the preset classification system, the following steps are performed: determining whether the icing prediction data corresponding to each preset working condition meets the preset requirements; if so, generating a safety boundary curve.

[0018] Therefore, the preset requirement is whether the calculation results can cover the actual altitude of the aircraft and the mission duration. If the requirement is not met, new operating conditions need to be added for calculation.

[0019] In a further scheme, the preset classification system includes a dangerous state, a critical state, and a safe state. The preset conditions for a dangerous state are that the average ice thickness in the water droplet area of ​​the flight device is greater than a first preset threshold or the core temperature of the water tank is greater than a second preset threshold. The preset conditions for a critical state are that the average ice thickness in the water droplet area of ​​the flight device is between the first and third preset thresholds and the core temperature of the water tank is between the second and fourth preset thresholds, where the third preset threshold is less than the first preset threshold and the fourth preset threshold is greater than the second preset threshold. The preset conditions for a safe state are that the average ice thickness in the water droplet area of ​​the flight device is zero and the core temperature of the water tank is greater than or equal to the fourth preset threshold.

[0020] This demonstrates that by defining dangerous, critical, and safe states, pilots and mission commanders can be provided with clear and quantifiable recommendations on flight limitations.

[0021] In a further step, after generating the safety boundary curve, the following steps are performed: simulating the icing prediction model based on the values ​​of the latest preset working condition matrix and the hazardous state of the preset classification system to obtain the predicted cold soaking time; and generating a lookup table based on the latest preset working condition matrix and the predicted cold soaking time.

[0022] Therefore, based on the latest preset operating condition matrix values ​​and preset classification system, the time history simulated by the icing prediction model is from the start of water freezing in the tank to reaching a dangerous state. The indicator for the start of water freezing in the tank is an average ice thickness in the water drop hatch area greater than or equal to 5 mm. Generating a lookup table enables digital and intelligent querying.

[0023] To achieve the second objective, the present invention provides a computer device including a processor and a memory, wherein the memory stores a computer program, and when the computer program is executed by the processor, it implements the above-described method for predicting water tank icing of a flight device.

[0024] To achieve the third objective, the present invention provides a computer-readable storage medium having a computer program stored thereon, which, when executed, implements the above-described method for predicting water tank icing in a flight device. Attached Figure Description

[0025] Figure 1 This is a flowchart of an embodiment of the water tank icing prediction method for the flight device of the present invention.

[0026] Figure 2 This is a simulation diagram of the three-dimensional geometric model of the flight device and the three-dimensional geometric model of the water tank, which are embodiments of the water tank icing prediction method of the flight device of the present invention.

[0027] Figure 3This is a simulation structure diagram of the preset calculation domain of an embodiment of the water tank icing prediction method for the flight device of the present invention.

[0028] Figure 4 This is a schematic diagram of the ice thickness distribution in the water droplet hatch area of ​​an embodiment of the water tank icing prediction method for the flight device of the present invention.

[0029] Figure 5 This is a schematic diagram of the static temperature distribution in the cross-section of the core of the water tank in an embodiment of the water tank icing prediction method for the flight device of the present invention.

[0030] Figure 6 This is a schematic diagram of the safety boundary of an embodiment of the water tank icing prediction method for the flight device of the present invention.

[0031] Figure 7 This is a schematic diagram of the calculation curve of the preset key points of the actuation mechanism of the water drop chamber door in an embodiment of the water tank icing prediction method of the flight device of the present invention.

[0032] Figure 8 This is a schematic diagram of the ice thickness variation curve in the water drop chamber area of ​​an embodiment of the water tank icing prediction method for the flight device of the present invention.

[0033] Figure 9 This is a schematic diagram of the temperature change curve of the water tank core in an embodiment of the water tank icing prediction method for the flight device of the present invention.

[0034] The present invention will be further described below with reference to the accompanying drawings and embodiments. Detailed Implementation

[0035] The method for predicting water tank icing in flight devices according to the present invention simplifies the three-dimensional water tank model and aircraft model, generates a preset mesh, configures an airflow model, a water phase change model, and a thin-walled heat conduction model, sets boundaries, simulates the external airflow of the aircraft and the phase change heat transfer process inside the water tank, and obtains icing prediction data through numerical calculations using the operating condition matrix. By simplifying the three-dimensional water tank model, the complexity of the water tank structure is reduced. This improves computational efficiency without affecting the icing prediction results.

[0036] Example of a method for predicting water tank icing in flight devices:

[0037] See Figure 1 and Figure 2 When predicting the freezing of the water tank of the flight device, step S1 is first executed: the cross section of the water tank 2 inside the flight device is cut out from the three-dimensional geometric model 1 of the flight device, the cross section is stretched according to the volume of the water tank to generate a preset three-dimensional water tank model 3, and the preset calculation domain 4 of the flight device is generated according to the preset three-dimensional water tank model 3.

[0038] The selection principle for the cross-section is to pass through the center of mass of the water tank and be perpendicular to the longitudinal axis of the fuselage to reflect the cross-sectional characteristics of the water tank to the greatest extent. The water tank is located in the non-pressurized section in the middle of the fuselage of the flight device. When stretching the cross-section according to the volume of the water tank, the stretching is performed along the longitudinal axis of the fuselage to make the preset three-dimensional water tank model consistent with the actual volume of the water tank inside the flight device. After generating the preset three-dimensional water tank model, the two end sections of the preset three-dimensional water tank model are further stretched along the longitudinal axis of the fuselage to simulate the influence of the front and rear fuselage, forming a preset computational domain.

[0039] In the simplification process of the pre-defined 3D water tank model, irregular structural details inside the actual water tank that have little impact on the overall heat transfer path can be ignored, including but not limited to anti-sway baffles, reinforcing ribs, and local connectors. Simultaneously, the complex fuselage skin of the aircraft is simplified to a wall with a uniform thickness, which can be taken as the average value within the tolerance range allowed by the skin processing technology (such as chemical milling), for example, 2 mm. The pressurization chamber and watertight chamber adjacent to the water tank are simplified to air-filled cavities, and their internal equipment is not depicted. See also Figure 3 The preset calculation domains in this embodiment include calculation domain 5 for the three-dimensional water tank model, calculation domain 6 for the watertight compartment in front of the water tank, calculation domain 7 for the watertight compartment behind the water tank, and calculation domain 8 for the pressurization compartment at the top of the water tank, while label 9 is the external flow field calculation domain 9.

[0040] After generating the preset computational domain, step S2 is executed to generate a preset mesh based on the preset computational domain. The step of generating the preset mesh based on the preset computational domain includes: generating a first preset mesh, a second preset mesh, and a third preset mesh based on the preset computational domain. The density of the first preset mesh is greater than the density of the second preset mesh, and the density of the second preset mesh is greater than the density of the third preset mesh. For example, the first preset mesh has 9.76 million cells, the second preset mesh has 5 million cells, and the third preset mesh has 2.56 million cells.

[0041] Obtain preset operating condition information, which is pre-set verification information, including the initial ground water temperature T. W =5℃ and high-altitude cold immersion temperature T A The harsh operating conditions are -55℃.

[0042] Substituting the preset operating condition information into the first preset grid, a first calculation result is calculated; substituting the preset operating condition information into the second preset grid, a second calculation result is calculated; substituting the preset operating condition information into the third preset grid, a third calculation result is calculated. The first, second, and third calculation results represent the average ice thickness at the midpoint of the seam of the water-dropping hatch after 1 hour of cold soaking. Based on the first, second, and third calculation results, preset grids are obtained from the first, second, and third preset grids. For example, when the first calculation result is 12.2 mm, the second calculation result is 12.4 mm, and the third calculation result is 13.6 mm, then, using the first preset grid as a reference, the relative deviation percentage of the second and third preset grids is calculated. The relative deviation percentage of the second preset grid is 1.6%, which is less than the preset first value, which is set to 2%. Therefore, the second preset grid is selected as the preset grid. See also... Figure 3 , Figure 3 The preset computational domain surface mesh structure is a preset mesh.

[0043] After generating the preset mesh, step S3 is executed to configure the airflow model, water phase change model, and thin-walled heat conduction model for the preset computational domain and set boundary conditions for the preset computational domain to form an icing prediction model. The model is configured in the CFD solver. The airflow model uses the Reynolds-averaged Navier-Stokes (RANS) equations to describe fluid flow, including high-speed airflow from the outside, airflow in internal compartments such as pressurized watertight compartments, and water flow in water tanks. A standard k-ε two-equation turbulence model is used to close the Reynolds-averaged equations. The water phase change model is a solidification-melting model based on the enthalpy-porosity method. The medium in the water phase change model is pure water, and both the solidus and liquidus temperatures are set to 273.15 K. To simulate the process of heat transfer from the external flow field to the internal water tanks through the fuselage skin, a thin-walled heat conduction model is enabled to achieve fluid-structure interaction heat transfer calculations.

[0044] The boundary conditions are set as follows: The preset computational domain outer boundary is set as the pressure far field to simulate free flow at infinity. Parameters to be specified include Mach number (preferably a high-speed value for the flight device, such as the maximum operating Mach number or cruise speed) and static temperature (based on the high-altitude cold soaking temperature T at the corresponding flight altitude). A Set the static pressure (based on the altitude corresponding to the international standard atmospheric model); set the surface boundary to no-slip condition and specify the surface roughness. Initialize the fluid region inside the water tank to a static state and set its initial temperature to the initial ground water temperature T. W Initialize the watertight compartments at the front and rear of the water tank and the pressurization chamber at the top of the water tank to a static state, and set their initial temperature to the initial ground water temperature T. W .

[0045] After forming the icing prediction model, step S4 is executed to simulate the icing prediction model based on the values ​​of the preset operating condition matrix, thereby obtaining icing prediction data. Specifically, the cold immersion time of the water tank is set, and the icing prediction model is simulated based on the cold immersion time of the water tank and the values ​​of the preset operating condition matrix. The preset operating condition matrix includes the following variables: high-altitude cold immersion temperature T. A Initial surface water temperature T W And the cold soaking time t. In the preset operating condition matrix, the high-altitude cold soaking temperature T A The initial ground water temperature T can be selected from -15℃, -30℃, -45℃, and -55℃. W Temperatures of 5℃, 10℃, 15℃, 20℃, and 25℃ can be selected. The cold immersion time t is the simulation duration of the flight device. The preset operating condition matrix is ​​shown in Table 1.

[0046] Table 1 Preset Operating Condition Matrix

[0047]

[0048] Cases 1 to 20 are icing prediction data obtained based on a preset operating condition matrix. In this embodiment, the cold immersion time t is 1 hour. After the simulation is completed, icing prediction data is obtained. The icing prediction data is instantaneous data obtained from the water tank along the time axis of the cold immersion time.

[0049] The average icing thickness data δ_avg of the water-dropping hatch area of ​​the flight device, the core temperature T_center of the water tank, and the icing thickness data of preset key points of the water-dropping hatch's actuation mechanism are obtained from icing prediction data. For each preset working condition in the preset working condition matrix, a safety status calibration is performed according to a preset classification system. The water-dropping hatch is installed on the flight device. The average icing thickness data δ_avg of the water-dropping hatch area of ​​the flight device is obtained by defining at least five monitoring points on the inner surface of the water-dropping hatch. The preferred monitoring points are the geometric center point of the hatch and the midpoints of the four side seams. The local icing thickness of each point is extracted, and the arithmetic mean of all monitoring points is calculated as the average icing thickness of the water-dropping hatch area. See also... Figure 4 , Figure 4 This is a schematic diagram showing the ice thickness distribution in the water tank's entrance area. The core temperature of the water tank is the temperature value extracted at the geometric center of the tank; this parameter reflects the overall heat reserve within the tank. See also... Figure 5 , Figure 5 This is a schematic diagram of the static temperature distribution in a cross-section at the center of the water tank. Section 11 shows the temperature distribution within the tank, while section 12 represents the pressurization chamber, and section 13 represents the external flow field. The preset key point icing thickness data for the water-dropping hatch's actuation mechanism is obtained by setting monitoring points near critical actuating components crucial to the normal opening of the hatch (such as the hatch hinges, retraction / extension actuator supports, and locking hook assemblies) to acquire localized icing thickness data, which is used to assess the risk of mechanical jamming.

[0050] Based on the preset operating condition matrix, the average ice thickness of the water-dropped hatches after 1 hour of cold soaking under 20 operating conditions, as shown in Table 2, is obtained from the icing prediction data.

[0051] Table 2. Average ice thickness (mm) of the hatches after 1 hour of cold soaking in the icing prediction data.

[0052]

[0053] The results of the water tank center under 20 operating conditions with 1 hour of cold immersion are shown in Table 3:

[0054] Table 3 Core temperature of the water tank after 1 hour of cold immersion (°C)

[0055]

[0056] After obtaining the icing prediction data, step S5 is executed to determine the icing prediction data according to the preset classification system, generate a safety boundary curve, and output flight time recommendation information.

[0057] The pre-defined classification system includes dangerous states, critical states, and safe states.

[0058] The preset conditions for a dangerous state are that the average ice thickness in the water drop hatch area of ​​the flight device is greater than the first preset threshold or the core temperature of the water tank is greater than the second preset threshold.

[0059] The preset conditions for the critical state are that the average icing thickness data of the water drop hatch area of ​​the flight device is between the first preset threshold and the third preset threshold, and the core temperature of the water tank is between the second preset threshold and the fourth preset threshold, with the third preset threshold being less than the first preset threshold and the fourth preset threshold being greater than the second preset threshold.

[0060] The preset conditions for a safe state are that the average ice thickness in the water droplet area of ​​the flight device is zero and the core temperature of the water tank is greater than or equal to the fourth preset threshold. The first, second, third, and fourth preset thresholds are adjusted according to actual conditions. For example, based on the ice-breaking capability (typically 5 mm) of the aircraft's water droplet system design and engineering experience, the first preset threshold can be 5 mm, the second preset threshold can be 2 °C, the third preset threshold can be 0 mm, and the fourth preset threshold can be 5 °C.

[0061] Based on a pre-defined grading system, the icing prediction data is assessed, and a safety boundary curve is generated. First, in the three-dimensional parameter space (T... W T AIn the model (t), boundary surfaces separating the safe, critical, and hazardous zones are constructed. To simplify the process and generate a two-dimensional safety boundary curve, a fixed cold soaking time t = 1 hour is used to plot the boundary surface with the initial ground water temperature T. W The x-axis represents the maximum permissible flight altitude (as opposed to T). A The safety boundary curve is plotted on the vertical axis (corresponding to the vertical axis). From the safety boundary curve diagram, the initial ground water temperature T can be directly read. W The maximum permissible flight altitude H_max and maximum cold immersion time t_max under the specified safety level.

[0062] Before generating the safety boundary curve, a table is generated showing the safety status calibration results for each preset working condition in the preset working condition matrix according to the preset classification system. As shown in Table 4, the safety status calibration results of the preset classification system for 1 hour of cold immersion are as follows:

[0063] Table 4. Results of the Pre-set Grading System After 1 Hour of Cold Soaking

[0064]

[0065] Generate a safety boundary curve diagram as follows Figure 6 As shown, label 14 is the danger boundary line, label 15 is the critical boundary line where icing begins, label 16 is the danger zone above the danger boundary line, label 17 is the critical zone between the danger boundary line and the critical boundary line (the critical zone theoretically does not affect aircraft safety, but should be given some attention), and label 18 is the safe zone below the critical boundary line. For example, for T... W At 5℃, T A The critical point is approximately -30℃. Based on the standard atmospheric temperature lapse rate (-6.5℃ / km), the upper limit of safe flight altitude can be calculated to be approximately 6000 meters. This method can be used to obtain the altitude corresponding to all conditions.

[0066] The output flight time recommendation information includes the following steps to analyze the icing thickness data of key points of the actuator under all operating conditions, such as... Figure 7 As shown, denoted by 19, the icing thickness curve for the door hinge is displayed. The results indicate that even under hazardous icing conditions (such as Case 3, δ_avg = 10.5 mm), no visible ice buildup was observed at the actuator. This confirms that the actuator is protected due to its distance from the outer skin. To further reduce risk, this invention proposes that in subsequent aircraft designs, the installation position of the actuator be moved further inwards towards the fuselage, for example, by increasing the distance by 30 mm to 100 mm, provided space permits. This extends the heat transfer path, delays potential icing, and thus improves the system's safety redundancy.

[0067] Before generating the safety boundary curve, the following steps are performed: It is determined whether the icing prediction data corresponding to each preset operating condition meets the preset requirements. If so, the safety boundary curve is generated. The preset requirements are whether the calculation results can cover the actual altitude of the aircraft and the mission duration of the flight. If the icing prediction data corresponding to a preset operating condition does not meet the requirements, new operating condition values ​​need to be added, and the water tank icing prediction model needs to be simulated again.

[0068] In another embodiment, assuming that during a firefighting operation, the ground water temperature is T W =15℃, the planned flight altitude corresponds to T A =-30℃, flight speed Mach 0.5. The computer system needs to determine the maximum permissible flight time under these conditions without the water tank entering a dangerous state.

[0069] After performing steps S1 to S5, the ice thickness variation curve in the water injection hatch area is obtained based on the time history data, as shown in the figure. Figure 8 As shown in Figure 9, a schematic diagram of the temperature change curve of the core of the water tank is obtained. Figure 8 and Figure 9 In the diagram, numbers 20 and 22 represent discrete points in the calculation results, and numbers 21 and 23 represent the fitted trend lines. It can be obtained that: after approximately 45 minutes of cold immersion, ice begins to form in the water-dropping hatch area (δ_avg>0). The ice thickness increases approximately linearly with time, at a rate of approximately 2.16 mm / h. The core temperature of the water tank decreases approximately linearly with time, at a rate of approximately 5.88℃ / h. Applying the definition of the safe state in the preset grading system in the above example (δ_th = 5 mm, T_low = 2 ℃), according to the average ice thickness data criterion for the water-dropping hatch area, the time required for the average ice thickness in the water-dropping hatch area to equal 5 mm is approximately (5 mm) / (2.16 mm / h) + (45 min) / (60 min) ≈ 3.06 h. According to the core temperature criterion for the water tank, the time required for the core temperature of the water tank to decrease from the initial 15℃ to 2℃ is approximately (15℃ - 2℃) / (5.88℃ / h) ≈ 2.21 h.

[0070] Therefore, the maximum safe flight time determined by the relatively conservative core temperature criterion of the water tank is approximately 2.21 hours (133 minutes). Considering a certain safety margin, it is recommended that the output flight time recommendation for this mission scenario be that the continuous flight time should not exceed 2 hours (120 minutes).

[0071] As can be seen from the above embodiments, the method provided by the present invention is highly systematic, has clear operation steps, and produces intuitive and practical results. It can effectively solve the problem of safety assessment and boundary determination of water tank icing under high-altitude cold immersion conditions of firefighting aircraft, and has good engineering application value.

[0072] After generating the safety boundary curve, the following steps are performed: Simulating the icing prediction model based on the values ​​of the latest preset operating condition matrix and the hazardous state of the preset classification system to obtain the predicted cold soaking time; generating a lookup table based on the latest preset operating condition matrix and the predicted cold soaking time. The latest preset operating condition matrix includes the following variables: latest high-altitude cold soaking temperature T. A1 and the latest initial surface water temperature T W1 .

[0073] The latest preset operating condition matrix contains each group (T) in Table 1 W , T A The operating conditions are combined, so based on Table 1, each group (T) needs to be defined. W , T A The operating condition combination not only calculates the results of 1 hour of cold soaking, but also simulates the complete time history from the onset of icing until reaching a dangerous state (i.e., the average icing thickness δ_avg ≥ 5 mm at the floodgate). Specifically, the latest preset operating condition matrix includes the latest high-altitude cold soaking temperature T. A1 The following data are available: -5℃, -10℃, -15℃, -20℃, -25℃, -30℃, -35℃, -40℃, -45℃, and -50℃, along with the latest initial surface water temperature T. W1 The data includes the following temperatures: 2.5℃, 5℃, 7.5℃, 10℃, 12.5℃, 15℃, 17.5℃, 20℃, 22.5℃, 25℃, 27.5℃, and 30℃. A recent high-altitude cold immersion temperature T... A1 There is a corresponding latest initial surface water temperature T W1 Calculate the predicted cold soaking time t_critical corresponding to an increase of the second average icing thickness δ_avg1 in the water-dropping hatch area to 5mm under the latest preset operating condition matrix. Then, combine the predicted cold soaking time t_critical with its corresponding latest initial surface water temperature T. W1 For example, the latest high-altitude cold immersion temperature T A1 The main table is a matrix lookup table with columns, where each cell represents the corresponding predicted cold soaking time. Simultaneously, based on the standard atmospheric temperature decrease relationship (approximately -6.5℃ / km), the latest initial surface water temperature T is calculated. W1 With the latest cold soaking temperature T A1 The difference is converted into the corresponding approximate flight altitude H, and is displayed in the table along with t_critical. See Table 5, which is a query example.

[0074] Table 5 Query Examples

[0075]

[0076] Among them, greater than 180 minutes means that under this combination of operating conditions, the dangerous state has not been reached after 180 minutes of cold soaking, and the safety margin is sufficient.

[0077] During the flight mission planning phase, planners or pilots use this lookup table by following these steps: Obtain input conditions: Confirm the latest initial ground water tank temperature T for this mission. W1 (This can be read from onboard sensors or estimated based on the temperature of the water source) and the planned cruise duration t_plan for the mission.

[0078] To find the permissible cold immersion temperature range: In the lookup table, find the value related to T. W1 The corresponding row. Search along that row to find all cells where the t_critical value is greater than or equal to t_plan.

[0079] Determine the maximum permissible flight altitude: the T corresponding to these safety cells. A1 The values ​​(or their direct corresponding flight altitudes H) are all safety options. Among them, T... A1 The flight altitude corresponding to the cell with the lowest value (i.e., the highest flight altitude) is the maximum flight altitude H_max allowed for this mission under the premise of meeting the planned duration.

[0080] Develop a flight profile: Based on the obtained H_max, plan the cruise altitude for this mission to ensure that the actual flight altitude does not exceed this limit.

[0081] Assume a firefighting relocation flight mission, with the following known conditions: After water is added on the ground, the initial water temperature T in the water tank is measured. W1 =10℃, the planned cruise phase from the base to the fire site lasts t_plan = 100 minutes, and there are no other altitude restrictions in the target airspace.

[0082] Task planning process: Use a lookup table (such as the expanded Table 5). Find T W1 The row containing =10℃. Search within that row for values ​​where t_critical ≥ 100 min. When T... A1 = -10℃ (H≈2.3km), t_critical>180 min, safe. When T A1 = -20℃ (H≈3.8km), t_critical≈135 min, safe. When T A1 = -30℃ (H≈5.4km), t_critical≈65 min, unsafe (65<100). When T A1= -40℃ (H≈6.9km), t_critical≈35 min, unsafe (35<100). Therefore, the highest cold immersion temperature that satisfies t_critical ≥100 min is T. A1 ≈-25℃, corresponding to a maximum permissible flight altitude H_max≈4.6 km.

[0083] Mission planning conclusion: To ensure the water tank does not enter a dangerous icing state during the 100-minute flight, the cruising altitude for this mission should not exceed 4600 meters. The pilot and dispatcher can then formulate a detailed flight plan based on this conclusion.

[0084] By simplifying the water tank model and ignoring irregular structural details that have little impact on the overall heat transfer path, such as anti-sway baffles, reinforcing ribs, and local connectors, the complexity of the water tank structure can be reduced, thereby reducing computational resources. Furthermore, the time step of the airflow model is in the millisecond range, while the time step of the water phase change model is in the second range, resulting in a time difference. Simplifying the water tank model allows the actual calculation time step to be uniformly increased to the second range, reducing the overall calculation time from weeks to hours and improving computational efficiency.

[0085] Computer device embodiment:

[0086] The computer device in this embodiment includes a processor and a memory. The memory stores a computer program, and when the processor executes the computer program, it implements the above-described method for predicting water tank icing in the flight device.

[0087] A computer device may include, but is not limited to, a processor and memory. Those skilled in the art will understand that a computer device may include more or fewer components, or a combination of certain components, or different components; for example, a computer device may also include input / output devices, network access devices, buses, etc.

[0088] Examples of computer-readable storage media:

[0089] The water tank icing prediction method for a flight device in a computer device described in the above embodiments can be stored in a computer-readable storage medium as a computer program. When the computer program is executed by a processor, it can complete the steps of the above embodiments of the water tank icing prediction method for a flight device in a computer device. The computer-readable medium can be a computer-readable signal medium or a computer-readable storage medium. Computer-readable storage media can be, for example, but not limited to: electrical, magnetic, optical, electromagnetic, infrared, or semiconductor systems, devices, or any combination thereof. More specific examples of computer-readable storage media (a non-exhaustive list) include: electrical connections having one or more wires, portable computer disks, hard disks, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), optical fibers, portable compact disk read-only memory (CD-ROM), optical storage devices, magnetic storage devices, or any suitable combination thereof.

[0090] The above are merely preferred embodiments of the present invention, but the design concept of the invention is not limited thereto. Without departing from the concept of the present invention, many other equivalent embodiments may be included. Those skilled in the art can make various obvious changes, readjustments and substitutions without departing from the protection scope of the present invention.

Claims

1. A method for predicting water tank icing in a flight device, characterized in that, The method includes: A cross section of the water tank inside the flight device is extracted from the three-dimensional geometric model of the flight device. The cross section is stretched according to the volume of the water tank to form a preset three-dimensional water tank model. The preset calculation domain of the flight device is generated by stretching the preset three-dimensional water tank model. A preset grid is generated based on the preset computational domain; An airflow model, a water phase change model, and a thin-walled heat conduction model are configured for the preset computational domain, and boundary conditions are set for the preset computational domain to form an icing prediction model. The icing prediction model is simulated based on the values ​​of the preset working condition matrix to obtain icing prediction data; The icing prediction data is judged according to the preset classification system, a safety boundary curve is generated, and flight time suggestion information is output. The steps for generating a preset mesh based on the preset computational domain include: A first preset grid, a second preset grid, and a third preset grid are generated according to the preset computing domain, wherein the density of the first preset grid is greater than the density of the second preset grid, and the density of the second preset grid is greater than the density of the third preset grid. Obtain preset working condition information, substitute the preset working condition information into the first preset grid, and calculate the first calculation result; Substitute the preset working condition information into the second preset grid to calculate the second calculation result; The preset working condition information is fed into the third preset grid, and a third calculation result is calculated; Based on the first calculation result, the second calculation result, and the third calculation result, a preset grid is obtained from the first preset grid, the second preset grid, and the third preset grid.

2. The method for predicting water tank icing in a flight device according to claim 1, characterized in that: The steps for simulating the icing prediction model based on the values ​​of the preset working condition matrix include: The immersion time of the water tank is set, and the freezing prediction model is simulated based on the immersion time of the water tank and the value of the preset working condition matrix.

3. The method for predicting water tank icing in a flight device according to claim 2, characterized in that: The freezing prediction data is instantaneous data obtained by the water tank along the time axis of the cold immersion time; The steps for determining the icing prediction data according to the preset grading system include: The average icing thickness data of the water drop hatch area of ​​the flight device, the core temperature of the water tank, and the icing thickness data of the preset key points of the actuation mechanism of the water drop hatch are obtained from the icing prediction data. The safety status is calibrated for each preset working condition in the preset working condition matrix according to the preset classification system.

4. The method for predicting water tank icing in a flight device according to claim 3, characterized in that: After calibrating the safety status of each preset working condition in the preset working condition matrix according to the preset classification system, the following steps are also performed: Determine whether the icing prediction data corresponding to each preset working condition meets the preset requirements. If so, generate the safety boundary curve.

5. The method for predicting water tank icing in a flight device according to claim 4, characterized in that: The preset classification system includes dangerous state, critical state, and safe state; The preset conditions for the dangerous state are that the average ice thickness in the water drop hatch area of ​​the flight device is greater than a first preset threshold or the core temperature of the water tank is greater than a second preset threshold. The preset condition for the critical state is that the average ice thickness data of the water drop hatch area of ​​the flight device is between the first preset threshold and the third preset threshold, and the core temperature of the water tank is between the second preset threshold and the fourth preset threshold, wherein the third preset threshold is less than the first preset threshold, and the fourth preset threshold is greater than the second preset threshold. The preset conditions for the safe state are that the average ice thickness in the water drop hatch area of ​​the flight device is zero and the core temperature of the water tank is greater than or equal to a fourth preset threshold.

6. The method for predicting water tank icing in a flight device according to claim 5, characterized in that: After generating the aforementioned safety boundary curve, the following steps are also performed: The icing prediction model is simulated based on the values ​​of the latest preset working condition matrix and the dangerous state of the preset classification system to obtain the predicted cold soaking time. A lookup table is generated based on the latest preset operating condition matrix and the predicted cold soaking time.

7. A computer device, comprising a processor and a memory, characterized in that, The memory stores a computer program that, when executed by the processor, implements the water tank icing prediction method for the flight device according to any one of claims 1 to 6.

8. A computer-readable storage medium having a computer program stored thereon, which, when executed, implements the method for predicting water tank icing of the flight device according to any one of claims 1 to 6.

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