Heat pump drying experiment method based on 1D-3D joint simulation

Abstract

The invention discloses a heat pump drying experiment method based on 1D-3D joint simulation, particularly relates to the crossing field of heat energy engineering and simulation modeling, and aims to solve the problem of insufficient precision caused by disconnection of a heat pump system and cavity flow field simulation in a heat pump drying process in washing equipment such as a dish-washing machine. The method comprises the following steps: establishing a heat pump system one-dimensional dynamic model and a cavity three-dimensional flow field model, dynamically setting one-dimensionally output air supply temperature and humidity parameters as a three-dimensional inlet boundary, and meanwhile, feeding back three-dimensional air return parameters to a one-dimensional evaporator adjusting boundary to form closed-loop coupling iteration, so as to obtain a three-dimensional flow field model; dynamic high-precision simulation of the whole drying process is achieved, the structure optimization efficiency and the energy consumption evaluation reliability are effectively improved, part of physical tests are replaced, and the development cost is reduced.

Description

TECHNICAL FIELD

[0001] The present application relates to the field of heat energy engineering and simulation modeling, more particularly, the present application relates to a heat pump drying experimental method based on 1D-3D joint simulation. BACKGROUND

[0002] With the improvement of energy saving and environmental protection requirements, heat pump drying technology is increasingly widely used in washing equipment (such as dishwashers). The heat pump system can realize efficient dehumidification by recovering the latent heat on the evaporator side, and the energy consumption is reduced by more than 40% compared with the traditional electric heating drying method. However, the heat pump drying process involves multi-physical field strong coupling problems such as refrigerant circulation, cavity air flow organization, heat and moisture exchange, and its performance optimization highly depends on accurate system-level simulation. The current industry generally adopts the combination of experimental verification and single-dimensional simulation, which is difficult to comprehensively predict the drying efficiency and dewing risk in the research and development stage, resulting in long product development cycle and high trial production cost. The existing technology has the following deficiencies:

[0003] 1. Spatial limitation of one-dimensional system simulation

[0004] Although the existing 1D modeling method (such as heat pump circuit simulation based on AMESim) can calculate the system-level energy flow and temperature distribution, it completely ignores the three-dimensional air flow characteristics inside the washing cavity. This method cannot capture key phenomena such as flow dead zone caused by basket shielding and local humidity accumulation, resulting in simulation results that cannot guide the optimization design of the cavity flow field, and the actual drying uniformity prediction is seriously distorted.

[0005] 2. Boundary distortion defect of three-dimensional flow field simulation

[0006] Although the traditional 3D-CFD technology (such as STAR-CCM+ cavity simulation) can analyze the local flow field details, its inlet boundary conditions (air supply temperature / humidity / flow rate) need to be manually preset as fixed values. This method breaks the internal relationship between the dynamic operating characteristics of the heat pump system (such as compressor variable frequency regulation, refrigerant capacity attenuation) and the cavity air flow, resulting in simulation boundary conditions deviating from the physical real state, and the energy consumption prediction error is significant.

[0007] 3. Lack of multi-physical field dynamic coupling mechanism

[0008] The drying process of the dishwasher has obvious time-varying characteristics: the difference between the initial high humidity dehumidification, the middle stage steady heat exchange, and the final low temperature anti-dewing stage is huge. The existing single-dimensional simulation framework cannot build a real-time feedback loop between the heat pump system and the cavity flow field, neither can it simulate the closed-loop regulation of the return air parameters on the compressor frequency, nor can it evaluate the structural adaptability in different drying stages, resulting in a lack of theoretical support for system matching design.

[0009] Therefore, in view of the above problems, a heat pump drying experimental method based on 1D-3D joint simulation is proposed. SUMMARY

[0010] In order to overcome the above-mentioned defects of the prior art, embodiments of the present application provide a heat pump drying experiment method based on 1D-3D joint simulation to solve the problems raised in the above background.

[0011] To achieve the above object, the present application provides the following technical scheme: a heat pump drying experiment method based on 1D-3D joint simulation, a two-way coupling architecture of a one-dimensional dynamic simulation model of a heat pump system and a three-dimensional air flow field simulation model of a washing cavity is established, dynamic parameter linkage is realized through a real-time data interaction interface: the variation range of the supply air temperature, the variation range of the humidity and the fluctuation range of the mass flow rate calculated by the one-dimensional model based on the operating state of the refrigeration circuit are set as the inlet boundary conditions of the three-dimensional model, at the same time, the return air temperature or return air humidity parameters monitored by the three-dimensional model at specific positions of the cavity are fed back to the evaporator inlet boundary adjustment module of the one-dimensional model, closed-loop coupling calculation is carried out in the time iteration sequence covering the whole drying process, and the heat and humidity dynamic response process of the heat pump system and the cavity flow field is simulated.

[0012] Further, the one-dimensional dynamic simulation model includes compressor variable frequency characteristic simulation, condenser phase change heat transfer calculation and expansion valve dynamic throttling effect, and the output parameters include the continuous variation interval of the supply air temperature from the initial stage to the final stage of drying, the nonlinear decay interval of the relative humidity in the system dehumidification period, and the dynamic variation range of the mass flow rate caused by the fan speed fluctuation.

[0013] Further, the return air parameters are collected by a virtual sensor network in the three-dimensional model, including the average value of the temperature field at the return air outlet area at the bottom of the cavity or the peak value of the humidity gradient in the dense area of the basket, which triggers the evaporator inlet enthalpy compensation algorithm in the one-dimensional model to realize the adaptive matching of the heat pump refrigerating capacity and the actual heat load of the cavity.

[0014] Further, the time iteration sequence adopts a variable step control strategy: in the initial stage of drying, a second-level step is used to capture the condensation transient in the temperature and humidity mutation period, in the steady-state drying stage, a minute-level step is used to optimize the calculation resources, and in the final dehumidification stage, a high-precision step is restored to monitor the local dew condensation critical point of the basket gap.

[0015] Further, the three-dimensional air flow field simulation model integrates the porosity distribution model and the surface wetting effect of the basket geometry structure, analyzes the air flow separation vortex structure and the thermal mass exchange boundary layer around the basket through a multi-scale grid division technology, and outputs the transient relative humidity distribution cloud map and the flow velocity vector field of each position point in the cavity.

[0016] Further, the closed-loop coupling calculation includes a heat pump control system dynamic response logic: when the return air humidity continues to be higher than the set threshold range, the one-dimensional model activates the compressor frequency increase command and the expansion valve opening shrink strategy; when the bowl basket area temperature difference is monitored to be out of limit, the three-dimensional model triggers the air duct guide vane angle optimization algorithm.

[0017] Further, based on the interference effect quantitative analysis of the porosity distribution model, the air flow uniformity index and the drying energy efficiency ratio under different combinations of bowl basket layer spacing schemes and air deflector inclination angles are compared, and a structure optimization scheme including a bowl basket porosity gradient distribution function and an air duct rectification surface curvature optimization parameter set is generated.

[0018] Further, system verification is performed by dynamic coupling simulation instead of physical test: the dehumidification efficiency attenuation curve of the heat pump under different environmental temperature and humidity conditions is simulated, the condensation risk distribution thermodynamic map at the top of the cavity under high humidity working conditions is predicted, and a verification report including an energy consumption evaluation matrix and a structure improvement priority is output.

[0019] Further, the washing equipment is specifically a dishwasher with a top air supply and bottom air return structure, the three-dimensional model includes a dynamic porosity database under the bowl basket loading state, and the one-dimensional model adapts to the phase change hysteresis characteristics of the refrigerant under high humidity environment and the air volume pulsation effect caused by the low frequency vibration of the compressor.

[0020] Technical effects and advantages of the present application:

[0021] Compared with the prior art, the scheme innovatively combines the dual advantages of the 1D system model and the 3D fluid simulation model, and realizes multi-scale coupling analysis from macroscopic system performance to microscopic local environment in a unified framework. The 1D model accurately depicts the trend of the key dynamic parameters (temperature, humidity, energy efficiency output) of the heat pump unit changing with time, and the 3D model finely analyzes the local characteristics of the heat and humidity distribution and the air flow characteristics (such as dead zone, vortex) in the drying cavity. This combined modeling method has four core values: first, through real-time bidirectional data coupling, the dynamic boundary conditions (such as supply air temperature and humidity) of the heat pump operation are automatically imported into the 3D simulation, completely abandoning the simplifying assumption of artificially setting constant boundary conditions in traditional CFD, and significantly improving the simulation credibility; second, supporting transient evolution simulation of the whole drying process (initial heating, constant humidity, and final cooling), which can not only capture the device-level energy efficiency fluctuation law, but also present the spatio-temporal evolution process of the heat and humidity distribution in the cavity, providing dynamic basis for system energy saving design and process optimization; third, the heat-humidity-flow multi-physical field coupling mechanism is established, which forms a closed loop feedback between the air side flow characteristics and the device operation state, and synchronously reveals the correlation mechanism of macroscopic performance fluctuation and local flow abnormality; fourth, the high-fidelity digital twin platform established can replace the physical prototype test, complete the air duct structure adjustment, air flow organization optimization and control strategy verification in the virtual environment, compress the development cycle by more than 40%, and greatly reduce the research and development cost and trial risk. The technical system finally realizes the whole-chain closed-loop simulation from the device operation state to the local environment response, and forms a predictive analysis tool with engineering practice guiding value. BRIEF DESCRIPTION OF DRAWINGS

[0022] Fig. 1 The combined simulation system architecture of the application.

[0023] Fig. 2 The dynamic coupling workflow diagram of the application.

[0024] Fig. 3 The structure optimization verification flowchart of the application. DETAILED DESCRIPTION

[0025] The technical solutions in the embodiments of the application will be clearly and completely described below with reference to the drawings in the embodiments of the application. Figs. 1-3 The technical solutions in the embodiments of the application will be clearly and completely described below with reference to the drawings in the embodiments of the application.

[0026] Application scenario: structure performance verification in the initial design stage of a new product

[0027] Step 1: Building 1D heat pump system model

[0028] The high-fidelity refrigeration cycle system built in the AMESim simulation platform accurately reproduces the actual working characteristics of the heat pump drying device through the coupling of four core physical models:

[0029] 1. Variable frequency compressor dynamic response model

[0030] Input characteristics: Embedded measured motor speed-power mapping curve (speed range 800-4500 rpm corresponding to power interval 150-800 W), supporting variable frequency speed regulation strategy for quantitative analysis of energy consumption.

[0031] Output characteristics: Real-time output exhaust temperature fluctuation (characterizing overheating risk) and refrigerant mass flow dynamic change (affecting system cooling capacity), providing basis for system stability diagnosis.

[0032] 2. Condenser multiphase flow heat transfer model

[0033] Structural parameterization: According to the typical design of copper tube fin heat exchanger, set the variable range of heat transfer coefficient to 80-120 W / m 2 ·K (covering different wind speeds and dirt working conditions).

[0034] Heat transfer decomposition: Decoupling calculation of the proportion of sensible heat (temperature drop dominant) and latent heat (phase change dominant) released during refrigerant condensation, accurately quantifying air side temperature rise and dehumidification effect, supporting energy efficiency optimization.

[0035] 3. Linear control model of expansion valve flow

[0036] Opening-flow characteristics: Establish a linear proportional relationship between valve opening (10% micro-opening to 100% full opening) and refrigerant flow (0.5-2.5 kg / min), simulate the continuous adjustment ability of the valve to system cooling capacity.

[0037] Engineering value: Provide pre-characteristic verification for the PID control algorithm design of electronic expansion valve (EEV).

[0038] 4. Evaporator intelligent dehumidification model

[0039] Humidity feedback mechanism: Based on the real-time input of return air humidity sensor, dynamically adjust the effective heat exchange area utilization rate (40% basic mode to 95% peak dehumidification).

[0040] Anti-frosting strategy: Reduce evaporative area utilization rate in low humidity conditions, increase tube wall temperature above dew point, avoid heat transfer deterioration caused by frosting.

[0041] Step 2: Build 3D cavity flow field model

[0042] In STAR-CCM+ simulation platform, for the high-precision modeling of the complex heat and humidity flow environment inside the dishwasher, the balance between physical reality and computational efficiency is achieved through systematic engineering decisions:

[0043] 1. Intelligent simplification of geometric model

[0044] Three types of key structures are retained after importing the original CAD:

[0045] Air outlet grille structure (porosity 60%, directly affecting air flow organization form).

[0046] Adjustable basket structure (layer spacing 80-120mm, supporting simulation of different dish loading conditions).

[0047] Return air passage topology (guiding steam condensation path).

[0048] Remove screw holes, labels and other non-flow-related features to shorten the grid division time by 30% while ensuring the accuracy of the flow field.

[0049] 2. Hierarchical grid strategy

[0050] Core flow area of the basket: polyhedral grid (basic size 5mm) is used, which takes into account the adaptability of complex curved surfaces and the stability of calculation, and its isotropic property is significantly better than that of tetrahedral grid.

[0051] Boundary layer refinement: 3 layers of boundary layer grid are generated on the surface of the basket / cavity wall (growth rate 1.2), which accurately captures the phase change of steam condensation and wall shear effect, Y + <5 to meet the requirements of LES model.

[0052] Dynamic scale control: the total grid size is dynamically adjusted according to the number of basket layers (80-120 million), combined with automatic encryption function to realize 1mm level resolution in key areas (jet area of spray arm).

[0053] 3. Deep coupling of physical mechanisms

[0054] Turbulence model: transient large eddy simulation (LES) is selected to analyze the vortex shedding in the wake area of the basket and the transient diffusion of steam plume (time step 0.001s).

[0055] Multiphase flow model: through the Euler multiphase flow framework, the interaction mechanism of air phase (main phase) and water vapor phase (secondary phase) is simultaneously tracked.

[0056] Humidity diffusion mechanism: enable Species Transport model to dynamically calculate the mass diffusion driven by steam concentration gradient, and cooperate with condensation model to predict the dew point distribution in the cavity.

[0057] Step 3: Establish a two-way data interface

[0058] To realize the efficient co-simulation of 1D system model and 3D fluid simulation model, a cross-platform dynamic data engine based on Python is developed to build a two-way closed-loop parameter transfer channel:

[0059] 1.1D→3D supply air parameter real-time driving

[0060] The 1D system model (such as the AMESim model of heat pump drying device) reads the key parameters of supply air in real time at a high-frequency sampling period of 100 ms: temperature value (T1 range: 30-85℃), relative humidity (RH1 range: 10%-95%), and volume flow (Q1 range: 10-50 m 3 / min). Through dynamic boundary reloading technology, the updated parameters are immediately written into the inlet boundary file of 3D fluid domain (such as STAR-CCM+ drying chamber model) to drive the synchronous refresh of inlet fan, temperature and humidity boundary conditions, ensuring that the 3D simulation input reflects the real running state of heat pump in real time.

[0061] 2.3D→1D return air parameter feedback closed loop

[0062] A multi-physics monitoring section is set at the end of the 3D model return air channel to collect three-dimensional flow field data at a period of 500 ms:

[0063] Temperature field statistics: extract the area-weighted average temperature (T2 accuracy ±0.5℃) of the section.

[0064] Humidity field analysis: calculate the average relative humidity (RH2 accuracy ±0.5%) based on the steam mass fraction distribution.

[0065] Through cross-process communication protocols (such as TCP / IP Socket), the monitoring values are fed back to the evaporator inlet parameter interface of 1D system in real time to form a dynamic closed-loop control of heat and moisture exchange.

[0066] Step 4: Dynamic coupling calculation

[0067] Variable step iteration strategy is adopted:

[0068] Initial drying period (0-10 min): time step 0.1 s, capturing the rapid evaporation of condensed water leading to humidity drop (from 95% to 70%).

[0069] Steady state period (10-40 min): step length expands to 5 s, monitoring the flow rate distribution in the basket area (target value 0.8-1.2 m / s).

[0070] Steady state period (10-40 min): step length expands to 5 s, monitoring the flow rate distribution in the basket area (target value 0.8-1.2 m / s).

[0071] ​End stage (40-60min): Step back 0.2s, monitor the gradient of the dish surface humidity (threshold ≤0.5% / mm).

[0072] Step 5: Result analysis and verification

[0073] To quantitatively evaluate the comprehensive performance of the drying system, a three-dimensional evaluation system covering process quality, energy efficiency performance and safety risk is established:

[0074] 1. Drying uniformity index

[0075] Definition: Measure the maximum deviation of relative humidity between the top and bottom areas of the drying chamber, with a threshold of ≤15% (e.g. 65% at the top vs. 50% at the bottom).

[0076] Evaluation significance:

[0077] Higher than 15% will lead to excessive moisture content dispersion in the same batch of products (e.g. partial carbonization / mold growth of medicinal materials).

[0078] Mapping design defects: unreasonable air flow organization, heat and moisture stratification not broken.

[0079] Optimization measures: According to the simulation cloud map to guide the angle adjustment of the deflector, the original 25% deviation is reduced to 8%.

[0080] 2. System energy efficiency ratio (COP)

[0081] Calculation logic:

[0082] Numerator: Actual dehumidification capacity per hour (kg / h, calculated by humidity sensor difference × air volume integral).

[0083] Denominator: Total power consumption of the system (kWh, including compressor, fan, control module power consumption).

[0084] Industry benchmark:

[0085]

[0086] Efficiency case: Through air return heat recovery, the COP of a certain medicinal material drying machine is increased from 3.8 to 5.1.

[0087] 3. Condensation risk dynamic map

[0088] Judgment condition: Simultaneous screening of high humidity + low temperature (T<40℃) space coordinates.

[0089] Risk classification:

[0090] Risk control:

[0091] High-risk area forced injection of 45℃ dry hot air (dew risk elimination time <15 seconds).

[0092] Medium-risk area optimized insulation layer thickness (from 20mm to 35mm, dew point temperature increased by 8℃). Implement process two: structure optimization oriented process goal: evaluate the impact of different basket layout on drying efficiency

[0093] Applicable scenario: energy efficiency improvement of existing products

[0094] Step 1: Parametric geometry generation

[0095] Based on ANSYS SpaceClaim's full parametric modeling engine, a variable basket digital prototype library for high-efficiency cleaning scenarios of dishwashers is constructed. The model takes three-dimensional key design variables as the driving core, realizing agile iteration and performance verification of product structure:

[0096] 1. Core design variable definition

[0097] Adjustable number of layers: support flexible configuration from 2 layers of compact type (small tableware) to 4 layers of extended type (pot loading).

[0098] Dynamic control of layer distance: parameterized interval of layer height 70mm (wine glass holder) - 150mm (soup pot layer), step precision 1mm.

[0099] Intelligent mapping of porosity: the opening rate of the basket bottom plate / side wall is continuously adjustable between 50% (high rigidity area) - 80% (high drainage area).

[0100] 2. Automatic design scheme generation

[0101] Intelligent orthogonal combination: according to the three-factor three-level experiment method (number of layers x layer distance x porosity), automatically generate 9 typical configuration schemes, for example:

[0102]

[0103] Geometric reconstruction rule: when porosity > 70%, automatically strengthen the frame structure (thickness + 20%), to ensure mechanical stability.

[0104] 3. Engineering verification closed loop

[0105] Fluid penetration verification: 75% porosity scheme (such as scheme B) shows a 40% increase in water flow penetration efficiency in simulation.

[0106] Structural strength guarantee: when the layer distance is expanded to 150mm (such as scheme C), the deformation variable is controlled within 1mm through rib topology optimization.

[0107] Compatibility test: Verify the interference rate < 3% between standard dishes (Φ220mm) and wine glasses (H150mm) in 4-layer configuration.

[0108] Step 2: Coupling simulation automation

[0109] Tie the tool chain through the Workflow integration platform:

[0110] Automatically call 1D model to generate the current structure's air supply parameters.

[0111] Drive STAR-CCM+ to run 9 groups of 3D simulations in batches.

[0112] Extract the return air temperature standard deviation (σ_T) and humidity compliance time for each group

[0113] Step 3: Closed-loop feedback optimization

[0114] Based on Response Surface Method (RSM):

[0115] Establish an approximate model: take the basket parameters as input and the drying time t_dry as output, and fit a quadratic polynomial:

[0116] Drying time = a × number of layers + b × layer spacing + c × porosity + d × (number of layers)^2 +...

[0117] (a, b, c, d are regression coefficients, obtained by training 30 samples)

[0118] Optimization calculation: use NSGA-II algorithm to find the Pareto optimal solution set (short drying time + low energy consumption).

[0119] Step 4: Heat pump control strategy adaptation

[0120] This system constructs a hierarchical closed-loop control strategy based on multi-source sensor feedback, through the coordinated response of 1D device regulation and 3D flow field reconstruction, realizes the optimization of efficiency and stability of the drying process of the dishwasher:

[0121] 1. Humidity-driven dynamic enhancement mechanism

[0122] Trigger condition: Place high-precision humidity sensors in the dense area of the basket (fork basket, deep bowl stacking area) to monitor the internal microenvironment of the pores in real time. When the local humidity is continuously > 70% for more than 10 seconds (indicating that water evaporation is blocked).

[0123] Execution action:

[0124] Send a compressor frequency increase command (variable frequency signal + 5Hz, corresponding to power increase ≈150W) to the heat pump 1D control model.

[0125] Synchronous activation of the air supply temperature compensation program (boost setpoint 3-5℃).

[0126] Engineering implication: compress the intensive zone drying cycle from 35 minutes to 28 minutes, avoiding water spots on the dishes.

[0127] 2. Temperature difference guided air flow reconstruction strategy

[0128] Criterion definition: calculate the temperature difference between the top and bottom of the return air channel (reflecting the degree of thermal air stratification), when ΔT > 8℃ for 30 seconds (indicating insufficient mixing of hot and humid air).

[0129] Action performed:

[0130] Drive the 3D air duct's array of guide vanes to dynamically adjust (deflection angle 5°-25° adjusted progressively).

[0131] Angle calculation logic: deflection angle = min(25°, max(5°, 2.5 x ΔT)) (example: ΔT = 10℃ → angle = 25°).

[0132] Physical effect: disrupt the thermal air stratification phenomenon, reducing the top / bottom temperature difference to ≤ 3℃.

[0133] Step 5: Physical prototype verification

[0134] 3D-printed optimized bowl basket structure, measured comparison:

[0135]

[0136] Implementation process three: extreme working condition verification process Goal: predict the system reliability in high humidity environments

[0137] Applicable scenario: adaptability testing of products for export to tropical regions

[0138] Step 1: Environmental condition loading

[0139] To verify the stability of the drying system in harsh environments, three sets of limit parameter combinations beyond the conventional design baseline were injected into the 1D simulation model to simulate the superimposed challenges of high humidity, manufacturing deviations, and extreme heat load working conditions: 1. Extreme heat load working conditions

[0140] Environmental temperature: jump from the conventional design baseline of 30℃ to 38℃ (simulate the scorching hot working conditions in equatorial regions).

[0141] System impact:

[0142] Condenser heat dissipation efficiency decayed by 35% (forced air cooling limit working condition).

[0143] Compressor discharge temperature breakthrough 110℃ safety red line (trigger overheat protection probability rise).

[0144] 2. Super-saturation wet load shock

[0145] Initial chamber humidity: steeply increase from 70% of standard working condition to 95% (simulate high moisture content scenario of foodstuff after heavy rain).

[0146] System impact:

[0147] Evaporator transient dehumidification load surges 300%.

[0148] Return air passage condensate generation rate reaches 5 times of normal value (risk of overflow).

[0149] 3. Refrigerant charge tolerance pressure test

[0150] Charge fluctuation: randomly jump within ±10% of the nominal value (cover production line filling error and micro-leakage scenario).

[0151] Critical failure mode:

[0152] 10% overcharge: liquid refrigerant backflow compressor causes the risk of liquid knock.

[0153] 10% undercharge: evaporator outlet superheat loss of control makes system COP plummet 40%.

[0154] Step 2: Fault mode simulation

[0155] Accurately reproduce the core failure mode of the drying system through 1D-3D full coupling simulation platform, and implement targeted improvement based on multi-physical field linkage mechanism:

[0156] 1. Condensate backflow pathology modeling

[0157] Trigger mechanism: when the evaporator wall temperature (3D model local monitoring) is lower than the dew point temperature (calculated by return air humidity in real time) > 2℃ for 10 seconds.

[0158] Simulation implementation:

[0159] 3D model dynamic loading: automatically generate liquid water film layer on low temperature wall (thickness range 0.1-0.5mm, increase with temperature difference gradient).

[0160] Multiphase flow interaction: water film absorbs steam in air flow to thicken the film layer, then drops under the action of gravity, finally triggers the return air duct liquid water detection sensor.

[0161] Failure consequences:

[0162] Thermal resistance multiplication: 0.5mm water film makes the evaporator heat transfer efficiency decay by 35%.

[0163] Electrical short: probability of water droplet intrusion into the electrical control box rises to 17% / year.

[0164] 2. Heat pump oscillation start-stop working condition reproduction

[0165] Controller logic defect: preset ±3℃ temperature hysteresis band in 1D control model.

[0166] Start-stop boundary:

[0167] Start condition: cavity temperature > set value + 3℃ (e.g. start at 73℃).

[0168] Stop condition: cavity temperature < set value - 3℃ (e.g. stop at 67℃).

[0169] Dynamic process simulation:

[0170] Oscillation period: compressor starts and stops > 8 times per hour (actual data: 3 times / hour → fault state 9 times / hour).

[0171] Energy consumption degradation: frequent start-stop reduces system COP by 22% (4.1 → 3.2)

[0172] Mechanical damage: valve piece fatigue life is shortened to 1 / 3 of the design value.

[0173] Step 3: Multi-physical field coupling analysis

[0174] To build a closed-loop simulation system covering operation safety and user experience, two core dimensions are expanded in the existing heat pump drying system model:

[0175] 1. Dynamic prediction and defense system for icing risk

[0176] Intelligent diagnosis criterion: real-time monitoring of evaporator tube wall temperature, when any area <0℃ and lasts ≥10 seconds (predicting icing critical state).

[0177] Defrost program digital reconstruction:

[0178] Reverse heat cycle activation: simulate four-way valve switching process, high-temperature exhaust gas (75-85℃) directly hits the evaporator.

[0179] Ice layer growth / melting model: dynamically calculate ice layer thickness with tube temperature changes (-5℃ growth rate 0.1mm / min → 65℃ melting rate 2mm / min).

[0180] Fault diffusion interception mechanism:

[0181]

[0182]

[0183] 2. Vibration noise transmission full-link simulation

[0184] Excitation source digital modeling:

[0185] Compressor flow pulsation (fundamental frequency 45 Hz ± 15% fluctuation) → generate pressure fluctuation time domain signal.

[0186] Refrigerant pulsation frequency (2 times rotational speed frequency) and air duct structure modal analysis automatic matching.

[0187] Noise transmission path reconstruction:

[0188] Simulate the transmission of airflow vibration in the air duct by 3D acoustic boundary element method (focus on analyzing cavity resonance peaks).

[0189] Label high noise areas (> 45 dB(A), such as backflow duct variable cross-section, guide vane back vortex area). Sound quality optimization matrix:

[0190]

[0191] Step 4: Strengthening control strategy

[0192] Deploy high humidity emergency response algorithm in heat pump drying system, eliminate the risk of condensation in real time through double actuator linkage mechanism:

[0193] 1. Intelligent decision trigger condition: continuously monitor the backflow channel humidity sensor array, when the dynamic weighted average value Last for 5 seconds (break through the safety operation threshold).

[0194] 2. Double-channel actuator cooperative control:

[0195] Compressor frequency strengthening strategy

[0196] The frequency increase amount increases linearly with the over-limit humidity: every 1% over the threshold, the compressor frequency increases by 2 Hz (example: ).

[0197] Hard protection mechanism: the maximum amplitude of frequency increase is limited to +20 Hz (to prevent motor overload).

[0198] Negative correction of expansion valve opening

[0199] The opening reduction amount is adjusted in proportion to the humidity deviation: every 1% over the threshold, the expansion valve opening is reduced by 1.5% (example: ).

[0200] Safety lower limit lock: the opening is not less than 15% (to ensure minimum refrigerant flow).

[0201] Step 5: Generate verification report

[0202] Automatic output key conclusions:

[0203] Safety boundary map: Mark the highest allowed ambient temperature and humidity combination (e.g. 38°C / 85%).

[0204] Maintenance recommendation: Recommend increasing the evaporator fin pitch to 2.5mm (original design 1.8mm).

[0205] Energy consumption benchmark: COP drops to 2.1 under extreme operating conditions (3.2 under normal operating conditions).

[0206] Finally, it should be noted that in the description of the present application, it should be noted that unless otherwise specified and limited, the terms "mounting", "connecting", "connecting" should be understood broadly, which can be mechanical connection or electrical connection, or the communication between two elements, or direct connection, "up", "down", "left", "right" and the like are only used to indicate the relative positional relationship, when the absolute position of the described object changes, the relative positional relationship may change;

[0207] Secondly: the structure involved in the disclosed embodiment of the present application is only involved in the disclosed embodiment of the present application, other structures can refer to the usual design, and in the case of no conflict, the same embodiment and different embodiments of the present application can be combined with each other;

[0208] Finally: the above only describes the preferred embodiments of the present application, and is not used to limit the present application, any modification, equivalent replacement, improvement, etc. within the spirit and principle of the present application should be included in the protection scope of the present application.

Claims

1. A heat pump drying experiment method based on 1D-3D joint simulation, characterized in that A two-way coupling architecture of a one-dimensional dynamic simulation model of a heat pump system and a three-dimensional air flow field simulation model of a washing cavity is established, and dynamic parameter linkage is realized through a real-time data exchange interface: the one-dimensional model calculates the supply air temperature variation range, humidity variation range and mass flow fluctuation range based on the operation state of the refrigeration circuit, and sets them as the inlet boundary conditions of the three-dimensional model; meanwhile, the return air temperature or return air humidity parameters monitored by the three-dimensional model at specific positions in the cavity are fed back to the evaporator inlet boundary adjustment module of the one-dimensional model, and closed-loop coupling calculation is performed in the time iteration sequence covering the whole drying process, to simulate the thermal and moisture dynamic response process of the heat pump system and the cavity flow field.

2. The heat pump drying experiment method based on 1D-3D combined simulation according to claim 1, characterized in that The one-dimensional dynamic simulation model comprises compressor variable frequency characteristic simulation, condenser phase change heat transfer calculation and expansion valve dynamic throttling effect, and the output parameters include the continuous variation interval of the supply air temperature in the initial and final stages of drying, the nonlinear decay interval of the relative humidity in the system dehumidification period, and the dynamic variation range of the mass flow caused by the fan speed fluctuation.

3. The heat pump drying experiment method based on 1D-3D combined simulation according to claim 1, characterized in that The return air parameters are collected by a virtual sensor network in the three-dimensional model, including the temperature field average value of the return air outlet area at the bottom of the cavity or the humidity gradient peak value of the dense area of the basket, which triggers the evaporator inlet enthalpy compensation algorithm in the one-dimensional model, to realize adaptive matching of the heat pump refrigerating capacity and the actual heat load of the cavity.

4. The heat pump drying experiment method based on 1D-3D combined simulation according to claim 1, characterized in that The time iteration sequence adopts a variable step control strategy: in the initial stage of drying, a second-level step is used to capture the condensation transient in the temperature and humidity mutation period, a minute-level step is used to optimize the calculation resources in the steady-state drying stage, and a high-precision step is used to monitor the local dew condensation critical point of the basket gap in the final dehumidification stage.

5. The heat pump drying experimental method based on 1D-3D combined simulation according to claim 1, characterized in that The three-dimensional air flow field simulation model integrates the porosity distribution model and surface wetting effect of the basket geometry, analyzes the air flow separation vortex structure and heat and mass exchange boundary layer around the basket through multi-scale grid division technology, and outputs the transient relative humidity distribution cloud map and flow velocity vector field of each position in the cavity.

6. The heat pump drying experimental method based on 1D-3D combined simulation according to claim 1, characterized in that The closed-loop coupling calculation comprises the dynamic response logic of the heat pump control system: when the return air humidity continuously exceeds the set threshold range, the compressor frequency increase command and the expansion valve opening degree contraction strategy in the one-dimensional model are activated; when the temperature difference of the basket area is monitored to be out of limit, the air duct guide vane angle optimization algorithm of the three-dimensional model is triggered.

7. The heat pump drying experiment method based on 1D-3D combined simulation according to claim 5, characterized in that Based on the interference effect quantitative analysis of the porosity distribution model, the air flow uniformity index and drying energy efficiency ratio under different basket layer spacing schemes and air deflector inclination angle combinations are compared, and the structure optimization scheme including the basket gap porosity gradient distribution function and the air duct rectification surface curvature optimization parameter set is generated.

8. The heat pump drying experimental method based on 1D-3D combined simulation according to claim 1, characterized in that System verification is performed through dynamic coupling simulation instead of physical test: the dehumidification efficiency decay curve of the heat pump under different environmental temperature and humidity conditions is simulated, the condensation risk distribution thermodynamic map of the top of the cavity under high humidity conditions is predicted, and a verification report containing energy consumption evaluation matrix and structure improvement priority is output.

9. The heat pump drying experimental method based on 1D-3D combined simulation according to any one of claims 1-8, characterized in that The washing equipment is particularly a dishwasher with a top air supply and bottom air return structure, the three-dimensional model of which comprises a dynamic porosity database under the basket loading state, and the one-dimensional model is adapted to the phase change hysteresis characteristics of the refrigerant and the air volume pulsation effect caused by the low frequency vibration of the compressor under high humidity environment.

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