Optimization Analysis of Kitchen Cooking Environment for Air Conditioning Range Hood Based on Thermal Comfort and PM₁₀ Concentration

Abstract

For the issues of the high temperatures and pollutant accumulation generated during kitchen cooking, this paper proposes a kitchen comfort analysis method based on the air conditioning range hood. The method comprehensively considers the thermal comfort and pollutant concentration in the kitchen and systematically investigates the influence mechanism of the air conditioning range hood’s structural parameters on kitchen comfort. Firstly, the reliability of the simulation model was verified through a comparative analysis of experimental tests and simulation data. Secondly, the temperature field, relative humidity, PM₁₀ concentration, and Predicted Mean Vote (PMV) distribution in the kitchen were analyzed before and after air conditioning activation, confirming its positive effects and limitations. Finally, the optimal structural parameter configuration of the air conditioning range hood was explored in depth by combining orthogonal experiments with Computational Fluid Dynamics (CFD) simulations. The results show that the range hood’s exhaust airflow rate is the dominant factor affecting the PM₁₀ concentration distribution, while the initial diffusion velocity of oil fumes has the most significant impact on reducing the kitchen’s PMV value. When the range hood’s exhaust airflow rate is 15 m³/min, the initial diffusion speed of oil fumes is 0.6 m/s, the air conditioning supply temperature is 20 °C, and the comprehensive evaluation index of kitchen comfort reaches its optimum. Under these conditions, the volume-averaged PMV value in the kitchen is 0.36, which is a decrease of 34.56%, and the spatially averaged PM₁₀ concentration is 41.04 μg, which is a decrease of 69.49%.

1. Introduction

In recent years, with the development of the social economy and significant improvements in residents’ living standards, people have increasingly higher demands for indoor environmental comfort and demonstrate a stronger willingness to invest resources in optimizing the indoor environmental quality. As a frequently used functional space in residential buildings, the kitchen’s environmental quality shows significant correlations with the health conditions of cooking personnel [1,2,3]. Studies indicate that during typical cooking operations, such as stir-frying, the instantaneous thermal load of heat source equipment like stoves and ovens can exceed 3.5 kW [4,5]. However, the average area of modern residential kitchens is usually less than 6 m², and with limited ventilation efficiency, localized high-temperature zones can easily form. Long-term exposures to high temperatures may lead to a series of health hazards, including dizziness, nausea, dehydration, and even heatstroke, which is particularly significant in summer or enclosed spaces with poor ventilation conditions. The hot and humid environment not only significantly reduces the comfort of the cooking process but also has a serious impact on physically sensitive populations, such as the elderly and children [6]. Furthermore, pollutants such as PM₁₀ and VOCs released during cooking can cause a sharp rise in indoor concentrations within a short period, leading to a rapid deterioration of the kitchen air quality. Therefore, exploring effective methods to mitigate high-temperature environments and pollutant concentrations during cooking has become an important research topic in the fields of environmental and health studies [7,8].

Recently, research on optimizing indoor environmental comfort has primarily focused on the application and performance enhancement of heating, ventilation, and air conditioning (HVAC) systems [9]. Traditional approaches to kitchen environmental control have focused on standalone ventilation systems or air conditioning for thermal regulation. While air conditioning systems effectively lower temperatures and humidity, their role in pollutant mitigation is less understood. Air conditioning-induced airflow can influence PM₁₀ dispersion by altering local velocity fields, potentially enhancing or disrupting the range hood efficiency. For instance, an improperly configured air conditioning supply may disperse pollutants into occupant zones, whereas a coordinated airflow could synergize with exhaust systems to improve capture rates [10,11]. Additionally, optimizing ventilation strategies and the rational use of auxiliary air supply devices (e.g., fans) have been proven to be effective in improving thermal comfort and reducing energy consumption. In the field of kitchen ventilation technology, Liu et al. [12] developed a novel ventilation system that combines an air curtain above gas stoves with air-conditioned supply air introduced through under-cabinet openings, and the measured capture efficiency of the new system was 96.2–97.1%. Zhang et al. [13] systematically reviewed existing residential kitchen ventilation technologies, highlighting the distinct advantages of coordinated applications between organized makeup air methods and high-efficiency range hoods. Andrey et al. [14] used CFD simulations to compare traditional mixed ventilation and thermal displacement systems in kitchens, finding that introducing unconditioned summer air can raise temperatures by 10 °F (5.5 °C), potentially reducing staff productivity by 30%. Huang et al. [15] developed a flow field modeling approach for analyzing kitchen environments with air conditioning range hoods, demonstrating an over 95% alignment accuracy at all four measurement locations. Ma et al. [16] innovatively combined mechanical ventilation Trombe wall technology with central air conditioning and air circulation systems, effectively reducing the building’s thermal load by about 5% (around 250 kWh). Wu et al. [17] developed a personalized kitchen ventilation system for summer use, demonstrating through tests that supplying 280 m³/h of 21 °C air significantly improved the thermal comfort in conditions with a 30 °C ambient temperature, 80% humidity, and 4.5 kW localized heat. Studies demonstrate that a proper fresh air supply is crucial for optimizing the indoor airflow organization, while natural makeup air methods can achieve comparable effects [18,19,20]. Yang et al. [21] developed a novel kitchen ventilation system based on an under-cabinet air supply, further confirming its potential for energy-saving applications. The CE of the range hood was raised by over 15% as compared to the traditional natural makeup air supply through an open window. Chu et al. [22] employed Fluent software to simulate the effects of various air supply parameters (e.g., angle, velocity, temperature, and outlet height) on atrium temperature fields and airflow velocity fields, thereby determining optimal air supply configurations.

In the existing literature, although some studies have explored the collaborative operation strategy of air conditioning systems and range hoods, these studies have obvious limitations: Firstly, most studies use a single indicator evaluation system, which cannot fully reflect the comprehensive quality of the kitchen environment. Secondly, there is a lack of in-depth analysis on the dynamic coupling relationship between the thermal environment and air quality. Furthermore, effective multi-objective optimization methods have not yet been established to solve this typical multi-physics field coupling problem. This study constructs an optimization model based on PMV-PM₁₀ dual objective functions, achieving, for the first time, the synergistic optimization of thermal comfort and air quality in kitchen environments. This comprehensive method not only breaks through the limitations of traditional single objective optimization but also provides a new multi-objective decision-making framework for kitchen environment design.

This paper first established a transient CFD numerical model of the coupled air conditioning-range hood system, with experimental tests validating the model’s validity and accuracy. Subsequently, through a comparative analysis of indoor temperature fields, humidity fields, PM₁₀ concentration distributions, and PMV indicators under both active and inactive air conditioning conditions, the research systematically evaluated the regulatory effects of air supply on kitchen environmental parameters. Finally, employing an orthogonal experimental design combined with CFD simulations, this study quantitatively investigated the influence patterns of key parameters—including the initial diffusion velocity of oil fumes, the range hood’s exhaust airflow rate, and the air conditioning supply temperature—on comprehensive kitchen comfort. The specific research flowchart is shown in Figure 1. This research not only expands the theoretical methodology for kitchen environment optimization but also provides practical technical solutions for engineering applications and societal health requirements.

2. Theoretical Analysis

2.1. Mathematical Model [23]

2.1.1. Continuity Equation

The continuity equation is constructed based on the law of the conservation of mass, and its specific expression is

$${\displaystyle \frac{\mathsf{\partial }\rho }{\mathsf{\partial }t}}+\nabla \left(\rho \overrightarrow{u}\right)=0$$

(1)

In the formula, $\rho$ is the fluid density, ${\mathrm{kg}/\mathrm{m}}^3$; $\overrightarrow{u}$ is the velocity vector, $\mathrm{m}/\mathrm{s}$.

2.1.2. Conservation Equation of Momentum

The Navier–Stokes equation is constructed based on the law of conservation of momentum, and its specific expression is

$${\displaystyle \frac{\mathsf{\partial }\overrightarrow{u}}{\mathsf{\partial }t}}+\left(\overrightarrow{u}\cdot \nabla \right)\overrightarrow{u}=f-{\displaystyle \frac{1}{\rho }}\nabla p+{\displaystyle \frac{\mu }{\rho }}{\nabla }^2\overrightarrow{u}$$

(2)

In the formula, $f$ is the external force acting on a unit volume of fluid, $\mathrm{N}$; $p$ is the pressure, $\mathrm{Pa}$; and $\mu$ is the dynamic viscosity constant, $\mathrm{N}\cdot {\mathrm{s}/\mathrm{m}}^2$.

2.1.3. Energy Conservation Equation

The core content of the energy conservation equation is that the energy change rate of fluid micro-clusters is equal to the sum of the power of external forces performing work and the input net energy. When analyzing and calculating the air flow in the kitchen, heat transfer generally exists in processes such as cooking flames, human body heat dissipation, and kitchen equipment operation heating. The forward and backward conservation of turbulent kinetic energy needs to satisfy the following energy conservation equation:

$${\displaystyle \frac{\mathsf{\partial }(\rho T)}{\mathsf{\partial }t}}+\mathrm{div}(\rho \stackrel{\rightharpoonup }{u}T)=\mathrm{div}\left({\displaystyle \frac{K}{c_p}}\mathrm{grad}T\right)+S_T$$

(3)

In the formula, $T$ is the temperature of the kitchen airflow micro-cluster, K; $K$ is the heat transfer coefficient of the kitchen airflow, ${\mathrm{W}/\mathrm{m}}^2\cdot \mathrm{k}$; $S_T$ is the viscous dissipation term; $c_p$ is the specific heat capacity at a constant pressure, $\mathrm{kJ}/\mathrm{kg}\cdot \mathrm{K}$; and $\mathrm{grad}T$ represents the temperature gradient, and its specific expansion calculation formula is as follows:

$$\mathrm{grad}T={\displaystyle \frac{\mathsf{\partial }T}{\mathsf{\partial }x}}+{\displaystyle \frac{\mathsf{\partial }T}{\mathsf{\partial }y}}+{\displaystyle \frac{\mathsf{\partial }T}{\mathsf{\partial }z}}$$

(4)

2.2. Particle Trajectory Model

This study employs the Particle Trajectory Model, a discrete phase model (DPM), to conduct numerical simulations of the PM₁₀ particulate matter. Based on the Eulerian–Lagrangian framework, this model is particularly suitable for describing the interaction mechanisms between discrete and continuous phases with volume fractions below 10–12%. By tracking the motion trajectories of numerous particles, the spatial distribution characteristics of particles within the flow field can be obtained. Compared to Eulerian models based on the continuum assumption for solid phases, the Particle Trajectory Model possesses a more theoretically sound foundation for numerical simulations of particle–fluid systems. It has demonstrated a satisfactory simulation performance in numerical studies of turbulent gas–solid two-phase flows.

To simplify the computational model, this study adopts the following assumptions:

(1) Particle–particle interactions are negligible;

(2) No coalescence or breakage occurs during the particle sedimentation;

(3) Particles are idealized as perfect spheres.

These assumptions significantly reduce the model complexity while maintaining computational accuracy.

The motion equation for individual particles is derived based on Newton’s second law [24,25]:

$${\displaystyle \frac{d{u}_{pi}}{d\tau }}={\displaystyle \sum F_i}$$

(5)

The equilibrium equation of the particle force (in the x direction) is

$${\displaystyle \frac{d{u}_{pi}}{d\tau }}={\displaystyle \frac{C_{D}R{e}_p}{24{\tau }_p}}\left(u_i-u_{pi}\right)+g_i{\displaystyle \frac{{\rho }_p-\rho }{{\rho }_p}}+F_i$$

(6)

In the formula, ${\rho }_p$, $\rho$ represents the density of particles and fluids, respectively, ${\mathrm{kg}/\mathrm{m}}^3$; ${\tau }_p$ is the particle relaxation time, $\mathrm{s}$; $u_{pi}$ is the component of particle velocity in the $i$ direction, $\mathrm{m}/\mathrm{s}$; $u_i$ is the component of fluid velocity in the $i$ direction, $\mathrm{m}/\mathrm{s}$; $g_i$ is the component of gravitational acceleration in the $i$ direction, ${\mathrm{m}/\mathrm{s}}^2$; $F_i$ represents other forms of force; and $C_D$ is the drag coefficient.

$$C_D={\displaystyle \frac{24}{R{e}_p}}\left(1+b_{1}R{e}_p^{b_2}\right)+{\displaystyle \frac{b_{3}R{e}_p}{b_4+R{e}_p}}$$

(7)

In the formula, $b_i$ is a constant; $\mu$ is the viscosity coefficient of fluid molecules, $\mathrm{kg}/\mathrm{m}\cdot \mathrm{s}$; and $R{e}_p$ is the particle Reynolds number (the relative Reynolds number).

2.3. PMV Index

As a quantitative indicator for evaluating human thermal sensation, the PMV index comprehensively considers six key parameters, including the human metabolic rate, clothing thermal resistance, air flow velocity, relative humidity, air temperature, and average radiation temperature, and constructs a comprehensive thermal comfort evaluation equation. Its mathematical expression is as follows [26,27]:

$$\mathrm{PMV}=\left(0{.0303\mathrm{e}}^{-0.036M}+0.028\right)\left\{\begin{array}{l}\left(M-W\right)-3.05\times {10}^{-3}\times \left[5733-6.99\left(M-W\right)-P_a\right]-\\ 0.42\times \left[\left(M-W-58.15\right)\right]-1.72\times {10}^{-5}M\left(5867-P_a\right)-\\ 0.0014M\left(34-t_a\right)-3.96\times {10}^{-8}{f}_{cl}\times \\ \left[{\left(t_{cl}+273.15\right)}^4-{\left(\overline{t_r}+273.15\right)}^4\right]-f_{cl}{h}_c(t_{cl}-t_a)\end{array}\right.\left.\begin{array}{l}\\ \\ \\ \\ \end{array}\right\}$$

(8)

In the formula, $P_a$ is the metabolic rate, ${\mathrm{W}/\mathrm{m}}^2$; W is the heat consumed by external work (negligible), ${\mathrm{W}/\mathrm{m}}^2$; $P_a$ is the partial pressure of water vapor, Pa; $t_a$ is the air temperature, °C; and $f_{cl}$ is the coefficient of the human clothing area, which is the ratio of the external surface area of human clothing to its naked surface area.

3. Experimental Testing of Kitchen Cooking Environment

3.1. Establishment of Cooking Test Platform

The physical kitchen system employed for experimental validation serves as the foundation for the numerical model. The experimental kitchen platform equipped with an air conditioning range hood comprises key components including the following: the integrated air conditioning range hood, heating pot, cooking stove, kitchen cabinet, manikin, sink, and door. This setup accurately replicates the layout and functionality of real residential kitchens, with geometric dimensions of 3.38 m (length) × 2.855 m (width) × 2.85 m (height). The specific experimental setup is shown in Figure 2. To facilitate observation and data collection, the kitchen test platform features transparent structures. However, to maintain the enclosed and spatially constrained characteristics of residential kitchens, the platform incorporates both a gas cooking system and an independent exhaust system. During testing, active air supply systems were deliberately excluded; instead, fresh air infiltration occurs through door gaps to maintain a slightly negative pressure environment and satisfy the design requirement of zero air recirculation.

3.2. Instrument Selection and Measurement Point Layout

This paper presents a systematic testing plan for kitchen environments with air conditioning range hoods. The kitchen experiment mainly tests four parameters: the wind speed, temperature, relative humidity, and PM₁₀ concentration. To ensure the accuracy and reliability of the test data, this study uses a particle counter LH3016 (Lighthouse, New York, NY, USA) to monitor the PM₁₀ concentration in the kitchen in real time. The Testo405i thermal anemometer (Testo AG, Southern Black Forest, Germany) is used to collect wind speed data at each measuring point, the TOPRIE TP9000 (Shenzhen Toprui Electronics Co., Ltd., Shenzhen, China) multi-channel data recorder is used to record temperature changes, and the PM6508 (Monolithic Power Systems, Fuzhou, China) digital temperature and humidity meter is used to monitor humidity data at each measuring point. In addition, the initial temperature setting of the kitchen test bench and the maintenance of the outdoor temperature are controlled through the monitoring cabinet of the thermal environment simulation (experience) research laboratory. The physical objects of each instrument and equipment are shown in Figure 3. The detailed technical specifications of all experimental instruments are listed in

Table 1. Technical parameters of each instrument.

Instrument	Measurement Parameters	Measuring Range	Accuracy
Handheld particle counter LH3016	PM0.5, PM1.0, PM2.5, PM5.0, PM10.0	0.3–10 μm	0.3 μm
TOPRIE TP9000 multi-channel data Recording	Temperature	−200~1200 °C	±0.05 °C
Testo405i thermal anemometer	Velocity	0~30 m/s	0.01 m/s
PM6508 digital temperature and humidity meter	Relative humidity	10~90%	±2.0%

.

The arrangement of monitoring points for the kitchen experiment is illustrated in Figure 4, which clearly shows the configuration of eight temperature measurement points (CH₁–CH₈), seven air velocity measurement points (V₁–V₇), two relative humidity measurement points (RH₁–RH₂), and two PM₁₀ concentration measurement points (P₁–P₂). The strategic placement of these measurement points was determined based on comprehensive considerations of the airflow patterns, heat source distribution, and pollutant dispersion characteristics within the kitchen environment to ensure the collected data accurately reflect the dynamic variations in all critical parameters. Through this well-designed multi-parameter monitoring system with real-time data acquisition capabilities, this study successfully captures the temporal and spatial evolution of key environmental factors, thereby providing high-quality experimental data that serve as a solid foundation for the subsequent validation and optimization of numerical simulation models.

3.3. Kitchen Experimental Testing Procedure

The kitchen experimental testing procedure was designed as follows:

(1) Pre-test Preparation: Prior to testing, the experimental environment was established according to the predefined layout requirements of the kitchen cooking laboratory. Based on the predetermined measurement point distribution, all testing instruments—including anemometers, hygrometers, and PM₁₀ concentration monitors—were installed and calibrated. Special attention was given to ensuring that the instrument accuracy met experimental requirements.

(2) Environmental Parameter Control Phase: Before initiating the test, the monitoring system of the thermal environment simulation laboratory was activated to maintain the kitchen space temperature at 35 °C. The makeup air temperature was stabilized at 35 °C through door gaps to simulate real high-temperature kitchen conditions, providing a reliable baseline for the subsequent data analysis.

(3) Test Execution Phase: Upon the completion of the preliminary preparations, the formal test commenced. The cooking surface was first heated to approximately 70 °C, followed by activating the air conditioning range hood for continuous cooking operations until readings at all measurement points stabilized. During this period, real-time data—including the air velocity, temperature, relative humidity, and pollutant concentrations—were recorded at all monitoring points to ensure data integrity and accuracy.

The specific boundary conditions during the experimental testing process are presented in

Table 2. Experimental boundary conditions.

Boundary	Parameters
Cooking Surface Temperature	Temperature: approx 70 °C; Relative humidity: 70%
Makeup Air Temperature	35 °C
Supplement Air Humidity	40%
Initial Kitchen Air Temperature	35 °C
Initial Kitchen Air Humidity	40%

.

Through this standardized testing protocol, systematic multi-parameter dynamic data under air-conditioned range hood kitchen conditions were obtained, providing high-quality experimental support for the subsequent validation and optimization of the simulation models.

4. Establishment of CFD Model for Kitchen Cooking Environment

4.1. Geometric Model of Kitchen Cooking Environment

Based on the physical test kitchen platform, this section establishes the kitchen geometric model shown in Figure 5. The model centers on the air conditioning range hood as the primary component, incorporating additional elements such as cabinets and sinks. The air conditioning system of the range hood is positioned in its upper section, with the airflow deflector set at a 55° angle to the vertical plane (i.e., the air supply angle) to simulate actual ventilation characteristics. To enhance the transient simulation efficiency and reduce model complexity, certain kitchen appliances (e.g., pots and stoves) were appropriately simplified, while the rotational effects of the range hood blades were neglected. This balanced approach maintains simulation accuracy while optimizing the computational performance.

4.2. Physical Model and Boundary Conditions

For the numerical simulation, this study employed Fluent 2021 R1 software for the CFD analysis. To accurately simulate the relative humidity distribution in the kitchen environment, the water vapor component was incorporated into the species transport model, with relative humidity derived through custom field functions. Based on the boundary conditions specified in

Table 3. Boundary parameter setting.

Key Boundary Area	Boundary Condition Type	Parameter Settings
Pot surface inlet	Velocity–inlet	Speed: 1.5 m/s Temperature: 70 °C Relative humidity: 70%
Air conditioning cooling air inlet	Mass–flow–inlet	Flow rate: 5 m3/min Temperature: 23 °C Relative humidity: 70%
Exhaust outlet of range hood	Mass–flow–outlet	Flow rate: 13 m3/min
Door gap	Pressure–inlet	Static pressure: 0 Pa Temperature: 35 °C Relative humidity: 40%

, key boundary condition spatial distributions were selected as illustrated in Figure 6. Referencing typical summer outdoor environmental parameters [28,29,30,31], the initial indoor temperature was set to 35 °C to accurately reflect pre-cooking high-temperature conditions in summer kitchens. The relative humidity was set at 40%—while this represents a lower value for humid summer conditions, it effectively characterizes the dynamic variations in humidity during cooking processes.

Furthermore, in accordance with ISO 7730:2005 [32] Ergonomics of the Thermal Environment—Analytical Determination and Interpretation of Thermal Comfort and relevant human thermal comfort studies [33], the human metabolic rate was specified as 93.04 W/m² to precisely simulate the thermal load characteristics during cooking activities.

This paper employs the DPM to simulate PM₁₀ particles, assuming them to be inert spherical particles with a diameter of 1 × 10⁻⁵ m and a density of 950 kg/m³. The emission source is simplified as a virtual heat source on the stove surface, with an injection velocity set at 1 m/s directed vertically upward from the stove surface. Particle injection positions are uniformly distributed across the source area. The mass flow rate for the particle injection is specified as 3 × 10⁻⁸ kg/s, with Fluent automatically calculating the number of particles injected per second based on their density and diameter. These particles can be captured by human faces and arms, while the gap between the door and exhaust vent serves as their escape route. All other wall surfaces are treated as particle-reflective boundaries to more accurately simulate the particle behavior [34,35].

4.3. Mesh Generation and Convergence Analysis

This paper uses a highly adaptable polyhedral mesh to partition the kitchen fluid domain. In order to reduce the influence of the number of meshes in the computational domain on the simulation results and improve computational efficiency, it is necessary to conduct a mesh convergence analysis on the model. In the kitchen model with the air conditioning range hood, under the initial conditions of an indoor temperature of 35 °C and a relative humidity of 40%, the wind speed and temperature at the center position of the air conditioning outlet were collected through simulation calculations under five different grid numbers within 20 s of the air conditioning range hood being turned on, as shown in Figure 7. The comparison shows that when the number of grids reaches 130 w, the wind speed and temperature changes at the center of the air conditioning outlet are relatively small compared to the model calculation values when the number of grids is 182 w. After further increasing the number of grids to 250 w, the changes in the wind speed and temperature became smaller. Considering the factors of saving time and costs, this article selects 1,364,903 grids as the appropriate number for the 3D model mesh partitioning. At this time, the maximum and minimum sizes of the kitchen space grid are set to 2 mm and 200 mm, respectively, and the grid quality remains above 0.4. The final grid model is shown in Figure 8.

4.4. Verification of Numerical Simulation Results

Figure 9 presents a comparative analysis of the measured data and simulation results of the air conditioning range hood model at various monitoring points under a cooking condition of 180 s. The results showed that the numerical simulation results of the temperature field, velocity field, humidity field, and PM₁₀ concentration field at each monitoring point showed a good consistency with the experimental measurement values, and the relative errors of each parameter were controlled within 10%. The error range meets the requirements for model accuracy in most engineering applications and scientific research, thus verifying that the CFD numerical model established in this paper can accurately characterize the multi-physics field coupling characteristics of the kitchen cooking environment.

Table 4. RMSE values for temperature, velocity, humidity, and PM₁₀ concentration.

	Temperature	Velocity	Humidity	PM10 Concentration
Average test value	28.04 °C	3.09 m/s	52.40%	66.48 μg
RMSE	1.23 °C	0.05 m/s	2.45%	3.72 μg

shows the root mean square error (RMSE) values of the temperature, wind speed, humidity, and PM₁₀ concentration measurement points. Specifically, the RMSE between the simulated and experimental values of the temperature, wind speed, humidity, and PM₁₀ concentration are 1.23 °C, 0.05 m/s, 2.45%, and 3.72 μg, respectively. Meanwhile, the average experimental values of the temperature, wind speed, humidity, and PM₁₀ concentration were 28.04 °C, 3.09 m/s, 52.40%, and 66.48 μg, respectively. It is worth noting that the RMSE values of the temperature, wind speed, and humidity are significantly lower than their respective average experimental values, indicating that the deviation between simulated and experimental values is much smaller in magnitude than the typical range of variables, thus verifying the overall high accuracy of the model and the correlation between experimental and simulated values.

5. Quantitative Analysis of Kitchen Comfort Before and After Air Conditioning Operation

To investigate the impact of the air conditioning cooling airflow on the thermal comfort of the kitchen environment, this study employs the previously established research methodology to conduct numerical simulations of the cooking process under two operating conditions: with the air conditioning system turned off and turned on. The analysis focuses on the environmental parameters at the 180 s mark of the cooking process. By comparing the spatial and temporal variations in the temperature distribution, relative humidity distribution, PM₁₀ particle concentration, and PMV index on the horizontal cross-section at Z = 0.83 m, this paper systematically evaluates the regulatory effect of air conditioning operations on the micro-environmental quality of the kitchen.

5.1. Temperature Field Analysis

Figure 10 shows the temperature contour maps on the Z = 0.83 m cross-section at 180 s of cooking under two operating conditions: air conditioning off and on. When the air conditioning is off, the initial kitchen temperature has already reached 35 °C. As cooking progresses, cooking fumes that are not fully captured by the range hood carry substantial heat upward, further increasing the ambient temperature. The temperature distribution exhibits a distinct vertical stratification: above the cooking pot, the rising thermal plume raises temperatures to around 40 °C, while the lower region remains close to the initial 35 °C. At this stage, the kitchen exhibits a significant temperature gradient, with an average temperature of 39.49 °C. After 180 s of air conditioning operation, the temperature distribution changes markedly. Two cooling airflows are effectively delivered from the air conditioner outlets, establishing a well-organized airflow pattern that reduces temperatures in most areas, including the human activity zone, to approximately 29 °C. The average kitchen temperature drops to 30.88 °C, which is 8.61 °C lower than when the air conditioning is off, with a noticeable improvement in the temperature uniformity. This comparative analysis demonstrates that the air conditioning system can effectively regulate the kitchen’s thermal environment, significantly enhancing the thermal comfort during cooking.

5.2. Relative Humidity Analysis

Figure 11 presents the relative humidity contour maps on the Z = 0.83 m cross-section of the kitchen under both air conditioning off and on conditions. When the air conditioning is off, the kitchen space divides into two distinct humidity layers, exhibiting an uneven humidity distribution. Notably, the human head level coincides precisely with this stratification boundary, making the humidity non-uniformity particularly perceptible. After 180 s of air conditioning operation, most areas of the kitchen maintain a relatively uniform humidity level of around 60%. This observation further confirms the air conditioning system’s effective regulation of the humidity distribution.

5.3. PM₁₀ Particulate Matter Concentration Analysis

Figure 12 displays the PM₁₀ concentration distribution on the Z = 0.83 m cross-section at 180 s of cooking under both air conditioning off and on conditions. Under the air conditioning off condition, PM₁₀ particles rise with the thermal convection to the upper kitchen space by 180 s, then gradually settle under gravity, ultimately forming distinct particle clusters in the central region with localized high-concentration accumulation characteristics. When the air conditioning system operates for 180 s, the disturbance from the cooling airflow significantly alters PM₁₀ distribution patterns. The originally clustered particles transition to a relatively uniform dispersion state, exhibiting an expanded spatial distribution but reduced concentration gradients. Based on an assumed initial background PM₁₀ concentration of 15 μg/m³ in the kitchen, the quantitative analysis reveals the average PM₁₀ concentration decreases from 73.88 μg (AC off) to 69.36 μg (AC on), representing an 8.4% reduction. While demonstrating some improvement, this mitigation effect remains limited.

Notably, the PM₁₀ dispersion occurs under both conditions, primarily because cooking-generated particles possess an initial momentum that prevents their complete capture by the range hood’s negative pressure. These particles ascend along the right side of the cookware, move across the range hood casing toward the AC vent area, and subsequently disperse throughout the space. This phenomenon suggests two key optimization directions for future improvements: (1) enhancing the capture efficiency of the range hood’s exhaust system and (2) reducing the initial dispersion velocity of cooking-generated particles, which would fundamentally improve the kitchen air quality.

5.4. PMV Index Analysis

Figure 13 presents a comparative analysis of PMV contours at the Z = 0.83 m cross-section during cooking (180 s) under air conditioning off and on conditions. The results reveal that with the air conditioning off, the kitchen’s overall PMV approaches the critical threshold of 3.0, indicating a severe thermal discomfort primarily caused by high ambient temperatures. In contrast, the air conditioning operation significantly improves the PMV distribution, maintaining values around 1.0 in occupant activity zones—achieving optimal comfort levels. The quantitative analysis demonstrates that the space-averaged PMV decreases from 2.94 (AC off) to 1.19 (AC on), representing a substantial 59.5% reduction. This conclusively validates the air conditioning system’s remarkable effectiveness in enhancing the kitchen thermal comfort.

6. Optimization Analysis of Kitchen Cooking Environment Comfort

Based on the analysis results in Section 5, it can be concluded that although the air conditioning’s cooling air can significantly improve the kitchen thermal environment, its effect on reducing the PM₁₀ particulate matter concentration is limited and further optimization is needed. According to the research direction proposed in Section 5.3, this study intends to adopt the following optimization measures: Firstly, by adjusting the range hood’s exhaust airflow rate to improve its capture efficiency. Secondly, controlling the initial diffusion velocity of oil fumes can improve the capture rate of PM₁₀ particles. At the same time, the impact mechanism of the air conditioning supply temperature on the kitchen thermal comfort and pollutant distribution will also be investigated. To avoid the inefficiency of traditional single variable methods, this study innovatively adopts a combination of an orthogonal experimental design and a CFD simulation to systematically investigate the synergistic effects of the three key parameters on kitchen environment quality and quantitatively analyze the significance level of each parameter, providing a scientific basis for kitchen environment optimization.

6.1. Orthogonal Optimization Design of Key Parameters for Air Conditioning Range Hood

This paper adopted a three-factor three-level orthogonal experimental design method, selecting the initial diffusion velocity of oil fumes (0.6, 0.8, and 1.0 m/s), the range hood’s exhaust airflow rate (11, 13, 15 m³/min), and the air conditioning supply temperature (20, 22, 24 °C) as key parameter variables to construct an L9 (3³) orthogonal experimental scheme. As shown in

Table 5. Orthogonal experiment parameter table.

Number	The Initial Diffusion Velocity of Oil Fumes (m/s)	The Range Hood’s Exhaust Airflow Rate (m3/min)	Air Conditioning Supply Temperature (°C)
1	0.6	11	20
2	0.6	13	24
3	0.6	15	22
4	0.8	11	24
5	0.8	13	22
6	0.8	15	20
7	1	11	22
8	1	13	20
9	1	15	24

, this experimental design systematically examines the impact and interaction of various factors on the kitchen environmental quality by scientifically arranging nine sets of parameter combinations. While ensuring the comprehensiveness of the experiment, it significantly improves the research efficiency and provides a reliable basis for the subsequent parameter optimization.

6.2. Comfort Optimization Based on PM₁₀ Concentration Range Analysis

This section is based on an orthogonal experimental design, and CFD simulation calculations were carried out for nine working conditions, and the distribution characteristics of the average PM₁₀ concentration in the kitchen volume under each working condition were systematically analyzed.

Table 6. The analysis of the range of the average PM₁₀ concentration in the kitchen volume under various operating conditions.

Number	The Initial Diffusion Velocity of Oil Fumes (m/s)	The Range Hood’s Exhaust Airflow Rate (m3/min)	Air Conditioning Supply Temperature (°C)	The Volume-Averaged PM10 Concentration (μg)
1	0.6	11	20	61.13
2	0.6	13	24	53.74
3	0.6	15	22	40.31
4	0.8	11	24	71.35
5	0.8	13	22	62.71
6	0.8	15	20	53.31
7	1	11	22	72.52
8	1	13	20	64.75
9	1	15	24	62.36
K (0.6)	51.73	-	-	-
K (0.8)	62.46	-	-	-
K (1.0)	66.54	-	-	-
K (11)	-	68.33	-	-
K (13)	-	60.40	-	-
K (15)	-	51.99	-	-
K (20)	-	-	59.73	-
K (22)	-	-	58.51	-
K (24)	-	-	62.48	-
R	14.81	16.34	3.97	-
Optimal level	0.6	15	22	-

shows the calculation results of the volume-averaged PM₁₀ concentration in the kitchen under different parameter combinations, while Figure 14 presents the PM₁₀ concentration field distribution at two characteristic cross-sections, Z = 0.83 m and X = 0.216 m, after a cooking process of 180 s. According to the research results in Section 5.3, the influence of the air conditioning supply parameters on the PM₁₀ concentration is relatively limited. Through a horizontal comparative analysis, it was found that under the condition of a fixed initial diffusion velocity of the oil fumes (for each row of operating conditions), as the exhaust air volume of the range hood increased from 11 m³/min to 15 m³/min, the PM₁₀ concentration in the two characteristic sections showed a significant decrease trend. This indicates that increasing the range hood’s exhaust airflow rate can effectively improve the oil fume capture efficiency. The longitudinal comparison results show that when the exhaust air volume of the range hood is kept constant (in each operating condition), the concentration of PM₁₀ increases significantly with the initial diffusion velocity of oil fumes increasing from 0.6 m/s to 1.0 m/s, confirming the importance of controlling the initial diffusion velocity of oil fumes for improving air quality. The comprehensive optimization analysis shows that operating condition 3 exhibits the best pollutant control effect, which is completely consistent with the influence law of the above parameters and provides a clear direction for optimizing the kitchen ventilation system.

To further investigate the significant impact of the above three parameters on reducing PM₁₀ concentrations in the kitchen, this study conducted a range analysis on the PM₁₀ concentration under various operating conditions. Table 6 lists the relevant parameters for the range analysis, where the K value in Table 6 represents the arithmetic mean of each parameter (corresponding column) at different levels, and the range R is the maximum difference between each K value. The range analysis results indicate that the exhaust volume of the range hood (with the highest R value) has the most significant impact, followed by the initial diffusion velocity of the oil fumes on the pot surface, while the air conditioning supply temperature has the smallest impact (with a smaller R value), and its contribution to reducing the PM₁₀ concentration is relatively limited. According to the data in Table 6, the optimal parameter combination for the PM₁₀ concentration is as follows: an exhaust air volume of the range hood at 15 m³/min, an initial diffusion velocity of oil fumes at 0.6 m/s, and an air conditioning supply temperature at 22 °C. Traditional studies (e.g., Aslam et al. [36] on PM_2.5 and PM₁₀ monitoring in Faisalabad, Pakistan) primarily focus on the macroscopic characterization of pollution, which, while revealing the alarming situation of PM₁₀ concentrations exceeding WHO standards by 16-fold, lack proactive control strategies. In contrast, this study targets the micro-environment of kitchens, reducing pollutant emissions by controlling the initial dispersion velocity of cooking fumes (0.6 m/s) rather than relying on end-of-pipe treatments. This ‘source control’ concept fundamentally mitigates PM₁₀ accumulation, representing a paradigm shift from passive monitoring to active regulation.

6.3. Comfort Optimization Based on PMV Index Range Analysis

This section presents a CFD numerical simulation study of nine different working conditions to systematically investigate the distribution characteristics of the PMV index in kitchen spaces. As shown in

Table 7. An analysis of the range of the average PMV in the kitchen volume under various operating conditions.

Number	The Initial Diffusion Velocity of Oil Fumes (m/s)	The Range Hood’s Exhaust Airflow Rate (m3/min)	Air Conditioning Supply Temperature (°C)	The Volume-Averaged PMV
1	0.6	11	20	0.43
2	0.6	13	24	1.03
3	0.6	15	22	0.40
4	0.8	11	24	1.89
5	0.8	13	22	1.18
6	0.8	15	20	0.51
7	1	11	22	2.07
8	1	13	20	1.36
9	1	15	24	1.99
K (0.6)	0.62	-	-	-
K (0.8)	1.19	-	-	-
K (1.0)	1.80	-	-	-
K (11)	-	1.46	-	-
K (13)	-	1.19	-	-
K (15)	-	0.97	-	-
K (20)	-	-	0.77	-
K (22)	-	-	1.22	-
K (24)	-	-	1.64	-
R	1.18	0.49	0.87	-
Optimal level	0.6	15	20	-

, this study quantitatively compares the volume-averaged PMV values in the kitchen space under different parameter combinations, while Figure 15 displays the spatial distribution contours of the PMV on two characteristic cross-sections (Z = 0.83 m and X = 0.216 m) after 180 s of cooking.

Although the contour analysis failed to reveal distinct variation patterns, this study identified a significant positive correlation between the kitchen PM₁₀ concentration and PMV values: when the PM₁₀ concentration decreased, the PMV value correspondingly reduced, indicating improved thermal comfort. This phenomenon can be attributed to the following mechanisms: Firstly, reducing the initial dispersion velocity of cooking fumes significantly enhanced the range hood’s capture efficiency of PM₁₀ particles, thereby decreasing the heat transferred indoors through fume diffusion. Secondly, increasing the range hood’s exhaust flow rate also improved the PM₁₀ particle capture. Additionally, this study found a direct relationship between the supply air temperature and PMV values—lowering the supply air temperature effectively reduced indoor temperatures, consequently improving the PMV index and enhancing thermal comfort.

To further investigate the significance of these three parameters on kitchen thermal comfort, this study conducted a range analysis on the PMV values under various working conditions. Table 7 presents the relevant parameters for the range analysis. The results demonstrate that the initial dispersion velocity of cooking fumes (with the largest R-value) has the most significant impact on kitchen thermal comfort, followed by the air conditioning supply air angle, with the range hood exhaust flow rate being the least influential. Based on the data in Table 7, the optimal combination for improving thermal comfort is as follows: a range hood exhaust flow rate of 15 m³/min, an initial fume dispersion velocity of 0.6 m/s, and an air conditioning supply temperature of 20 °C. The dynamic coupling law between the thermal environment and pollutant diffusion was revealed through CFD models. For example, reducing the initial velocity of oil fumes can reduce the sensible heat carried by the thermal plume, thereby indirectly improving the PMV index. This discovery fills the gap in the existing literature [37,38,39] on the mechanism of multi-physics field interactions.

6.4. Comfort Optimization Based on Range Analysis of Comprehensive Evaluation Values

In a practical parameter selection, it is impossible to optimize the thermal comfort and PM₁₀ concentration separately. Therefore, this study proposes a weighted comprehensive evaluation method to systematically assess the impact of each parameter on the kitchen environmental quality. Using the equal-weight method, both the PM₁₀ concentration and PMV index are assigned the same 50% weighting coefficient to reflect their equal importance.

To eliminate dimensional effects and ensure comparability between indicators, dimensionless reference values of 50 μg for the PM₁₀ concentration and one for the PMV are established. Subsequently, a comprehensive evaluation value model is constructed as follows:

$$CEV=0.5\times ({\displaystyle \frac{C_{PM10}}{50}})+0.5\times ({\displaystyle \frac{PMV}{1}})$$

(9)

Finally, based on this method, the comprehensive evaluation values of the kitchen environment under nine working conditions were calculated and summarized in

Table 8. Analysis of CEV range for comprehensive evaluation of kitchen comfort under various operating conditions.

Number	The Initial Diffusion Velocity of Oil Fumes (m/s)	The Range Hood’s Exhaust Airflow Rate (m3/min)	Air Conditioning Supply Temperature (°C)	CEV
1	0.6	11	20	1.65
2	0.6	13	24	2.11
3	0.6	15	22	1.21
4	0.8	11	24	3.32
5	0.8	13	22	2.43
6	0.8	15	20	1.58
7	1	11	22	3.52
8	1	13	20	2.66
9	1	15	24	3.24
K (0.6)	1.6545	-	-	-
K (0.8)	2.4425	-	-	-
K (1.0)	3.1375	-	-	-
K (11)	-	2.83	-	-
K (13)	-	2.398	-	-
K (15)	-	2.0065	-	-
K (20)	-	-	1.961	-
K (22)	-	-	2.3869	-
K (24)	-	-	2.886	-
R	1.483	0.8235	0.925	-
Optimal level	0.6	15	20	-

, effectively solving the problem of balancing indicators in multi-objective optimization and providing a quantitative basis for optimizing kitchen environment parameters.

Similarly, a range analysis was conducted on the comprehensive evaluation values of kitchen comfort under nine working conditions, and the results showed that there were significant differences in the impact of various influencing factors on the overall comfort: the initial diffusion speed of oil fumes showed the strongest significant impact (with the highest range R value), followed by the air conditioning supply temperature, and finally the exhaust volume of the range hood. After the multiple factor optimization analysis, the optimal parameter combination was determined as follows: an initial diffusion velocity of oil fumes of 0.6 m/s, an air conditioning supply temperature of 20 °C, and an exhaust air volume for the range hood of 15 m³/min. This combination can maximize the comprehensive comfort index of the kitchen environment. To verify whether the comprehensive evaluation value (CEV) under the optimal parameter combination condition is better than the optimal condition among the nine orthogonal conditions, this paper performed calculations using the same simulation calculation method. The average PM₁₀ concentration in the kitchen volume was 41.04 μg, the average PMV in the kitchen volume was 0.36, and the comprehensive evaluation value was calculated as 1.1808, which is better than condition 3 with the highest comprehensive evaluation value among the nine orthogonal conditions. This verifies the correctness and feasibility of the orthogonal optimization. Compared to the initial operating conditions of the air conditioning in the fifth section, the average PM₁₀ concentration in the kitchen volume decreased by 34.56%, and the average PMV value in the kitchen volume decreased by 69.49%. Unlike traditional single indicator evaluation systems that only focus on the thermal discomfort index or pollutant concentration [40,41], this study first constructed a PMV-PM₁₀ dual objective optimization model, achieving a synergistic regulation of thermal comfort and air quality. Compared with passive measures, such as the natural ventilation and building shading proposed by Ehsan et al. [39], the active control strategy in this study (such as adjusting the exhaust volume to 15 m³/min and reducing the oil fume diffusion speed to 0.6 m/s) can simultaneously reduce the spatial average PMV value by 34.56% and the PM₀ concentration by 69.49%, breaking through the limitations of optimizing a single environmental parameter.

7. Conclusions

This study established a validated simulation model for kitchen cooking environments integrating air conditioning and range hood systems through combined experimental testing and numerical simulations. Through systematic benchmarking, the model quantitatively analyzed the positive impacts and limitations of air conditioning operations on kitchen comfort across four key parameters: temperature, humidity, PM₁₀ concentrations, and the PMV index. Furthermore, by integrating CFD with an orthogonal experimental design, this research investigated the optimal structural configuration of the air conditioning range hood system and evaluated the significance of various parameters on the thermal comfort and pollutant concentration. The main findings are as follows:

(1) After activating the air conditioning system, the volume-averaged kitchen temperature decreased significantly from 39.49 °C to 30.88 °C, while the volume-averaged PMV index dropped from 2.94 to 1.19 (a 59.5% reduction), demonstrating a substantial improvement in thermal comfort. However, the volume-averaged PM₁₀ concentration only showed an 8.4% reduction (from 73.88 μg to 69.36 μg), indicating the necessity for the further optimization of pollutant control strategies.

(2) This study reveals that reducing the initial dispersion velocity of cooking fumes significantly enhances the particulate capture efficiency, while increasing the exhaust flow rate of the range hood effectively strengthens its negative pressure effect. The range hood’s exhaust airflow rate exhibits the most pronounced influence on the PM₁₀ reduction, showing a progressive decrease in volume-averaged PM₁₀ concentrations with increasing flow rates. Conversely, the initial diffusion velocity of oil fumes predominantly affects the PMV index, where lower dispersion speeds correspond to reduced volume-averaged PMV values.

(3) The optimal parameter combination integrating both thermal comfort and pollutant control was determined as follows: a range hood exhaust airflow rate of 15 m³/min, an initial diffusion velocity for oil fumes of 0.6 m/s, and an air conditioning supply temperature of 20 °C. This configuration yielded the lowest CEVs, with the volume-averaged PMV index reaching 0.36 (a 34.56% reduction) and volume-averaged PM₁₀ concentration decreasing to 41.04 μg (a 69.49% reduction), both achieving excellent performance levels.

This study innovatively establishes a PMV-PM₁₀ dual-objective optimization model, achieving, for the first time, the synergistic optimization of thermal comfort and air quality in kitchen environments. Breaking through the limitations of traditional single-objective research, it not only fills the theoretical gap in understanding the coupling mechanism between thermal environments and pollutants, but also establishes a scientific multi-objective decision-making framework for kitchen environment designs. The research findings demonstrate significant practical value: by optimizing the coordinated control strategy of air conditioning and range hoods, it can effectively maintain thermal comfort (PMV: −0.5 to +0.5) while controlling the PM₁₀ concentration (<50 μg). This provides residential and commercial kitchens with an environmental control solution that balances both comfort and health, bearing important theoretical and practical significance for improving the indoor environmental quality and safeguarding residents’ health.