Aqueous film forming foam extinguishing agent performance characterization method based on molecular dynamics simulation

Abstract

The invention discloses an aqueous film-forming foam extinguishing agent performance characterization method based on molecular dynamics simulation. According to the method, an air-AFFF-comburent three-phase model and an air-AFFF-enhanced component-comburent four-phase model are established, molecular dynamics simulation is carried out under the conditions of different temperatures and the like, and microcosmic indexes such as comburent molecular diffusion, an interface structure and a water film / hydration layer are extracted; and characterizing and evaluating the barrier effect of the AFFF and the compound system thereof on the diffusion of the fuel molecules.

Description

Technical Field

[0001] This invention belongs to the field of fire extinguishing agent performance calculation and simulation technology, and relates to a method for characterizing the performance of aqueous film-forming foam fire extinguishing agents based on molecular dynamics simulation. Background Technology

[0002] Aqueous film-forming foam (AFFF) extinguishing agents are widely used in liquid fuel fires. Their extinguishing mechanism involves forming a water film / foam film on the fuel surface, reducing interfacial tension, inhibiting fuel vapor escape, and providing thermal and oxygen insulation. In practical applications, to further improve the film-forming properties, anti-liquidation properties, and foam stability of AFFF, a formulation optimization approach of "surfactant + additives" is often adopted. However, the foam system is in a thermodynamic metastable state and is significantly affected by liquid film drainage, interfacial disturbances, etc. Some additives may also have adverse effects on the foam under inappropriate conditions. Therefore, effective screening and evaluation methods are required.

[0003] Reference 1 (Schaefer T, Dlugogorski BZ, Kennedy E M. Vapour Suppression of n-Heptane With Fire Fighting Foams Using Laboratory Flux Chamber[C] / / AOFST 7.2007.) constructed an experimental flux chamber, placed 10 mm deep n-heptane, and covered with different types of foam layers. The concentration of n-heptane in the outlet gas stream was measured using gas chromatography-flame ionization detector (GC-FID), and the mass flux of n-heptane through the foam layer was calculated based on this. The vapor breakthrough time of n-heptane for different types of foam layers (when the flux exceeds 1×10⁻⁶) was compared. -6 kg·m -2 ·s -1 (Sampling time), it was found that the vapor breakthrough time of AFFF without fluorinated surfactants was lower than that of fluorinated AFFF, thus its sealing ability for n-heptane vapor was the strongest. However, existing macroscopic experiments (such as measuring vapor breakthrough time) can reflect the sealing ability of foam for fuel vapor, but they usually have problems such as high experimental cost, strong variable coupling, difficulty in revealing the mechanism at the molecular scale, and low formulation screening efficiency.

[0004] Molecular dynamics simulation is a computational method based on classical mechanics to numerically solve for the motion of atoms and molecules. This method obtains information on the structural evolution and dynamic behavior of the system at the microscopic scale by constructing a molecular system model, selecting an appropriate force field, and solving the particle motion equations through integration. Compared with macroscopic experimental methods, molecular dynamics simulation can reveal the adsorption, arrangement, and interaction mechanisms of surfactants at the interface at the molecular level, thus offering unique advantages in studying the stability of foam liquid films, cross-interface transport of fuel molecules, and steam sealing mechanisms. Summary of the Invention

[0005] The purpose of this invention is to provide a method for characterizing the performance of aqueous film-forming foam (AFFF) fire extinguishing agents based on molecular dynamics simulation. By establishing a three-phase model of air-AFFF-combustion material and a four-phase model of air-AFFF-enhancing component-combustion material, and conducting molecular dynamics simulations under different temperature and other conditions, the method extracts indicators such as the diffusion coefficient of combustion material molecules, component density distribution, radial distribution function, water film thickness, and surfactant tail chain tilt angle. This characterizes the blocking effect of AFFF and its compound system on fuel molecule diffusion, thereby achieving a closed loop of "model construction - structure optimization - dynamics simulation - result analysis - formulation optimization" to improve formulation development efficiency and enhance the ability to explain mechanisms.

[0006] To achieve the above objectives, the present invention adopts the following technical solution:

[0007] A method for characterizing the performance of aqueous film-forming foam fire extinguishing agents based on molecular dynamics simulations includes the following steps:

[0008] S1. Use the Amorphous Cell module in Materials Studio to create an air box, an AFFF box, and a combustible material box respectively. Use Build Layers to merge the three boxes into an air-AFFF-combustible material model.

[0009] S2. The Forcite module in Materials Studio is used to optimize the structure of the air-AFFF-combustion model based on the COMPASS II molecular force field. After each step of the optimization process, the Smart algorithm of the Forcite module is used to perform geometric optimization to obtain the optimized structure model of air-AFFF-combustion with the lowest energy.

[0010] S3. Using the Dynamics task under the Forcite module of Materials Studio software, three sets of temperature variable simulations were performed. The optimized structure model of air-AFFF-combustion material was placed in the canonical ensemble (NVT) for molecular dynamics simulation. The convergence criterion of the system was that the temperature and energy curves of the system fluctuated within 5%. After the simulation system reached equilibrium, the trajectory file after equilibrium was used for result confirmation and discussion analysis.

[0011] S4. Use the Analysis task under the Forcite module of Materials Studio software to obtain the mean square displacement (MSD) curve of the combustion molecules from the equilibrium trajectory file, and calculate the diffusion coefficient D, component density distribution map, water molecule radial distribution function map, water film thickness, and surfactant molecule tail chain tilt angle.

[0012] S5. Use the Amorphous Cell module in Materials Studio to build an enhancement component box. Based on the air-AFFF-combustion model, add an enhancement component layer through the Build Layers module, ensuring that the enhancement component layer is located between the AFFF layer and the combustion product layer, to form the air-AFFF-enhanced component-combustion product model.

[0013] S6. Using the Forcite module in Materials Studio, the air-AFFF-enhancing component-combustion model is structurally optimized based on the COMPASS II molecular force field. After each step of the optimization process, the Smart algorithm of the Forcite module is used for geometric optimization to obtain the optimized air-AFFF-enhancing component-combustion model with the lowest energy.

[0014] S7. Using the Dynamics task under the Forcite module of Materials Studio software, three sets of temperature variable simulations were performed. The optimized structure model of air-AFFF-enhancing component-combustion material was placed in a canonical ensemble for molecular dynamics simulation. The system convergence criterion was that the temperature and energy curves of the system fluctuated within 5%. After the simulation system reached equilibrium, the trajectory file after equilibrium was used for result confirmation and discussion analysis.

[0015] S8. Using the Analysis task under the Forcite module of Materials Studio software on the equilibrium trajectory file obtained in S7, the mean square displacement curve of combustion molecules, diffusion coefficient D, component density distribution map, water molecule radial distribution function map, water film thickness, and surfactant molecule tail chain tilt angle are obtained.

[0016] S9. Change the concentration or molecular type of the reinforcing component, repeat S5-S8, and obtain the mean square displacement curves of combustion molecules and diffusion coefficient D, component density distribution diagram, water molecule radial distribution function diagram, water film thickness, and surfactant molecule tail chain tilt angle for different air-AFFF-reinforcing component-combustion models.

[0017] S10. Analyze and compare the effects of the concentration or molecular type of the reinforcing component on the extinguishing effect of AFFF.

[0018] Furthermore, in S1, the air box contains 100 air molecules, the AFFF box contains 82 organic molecules and 700 water molecules, and the combustible material box contains 100 combustible material molecules. The length and width of each box are 30 Å × 30 Å, and a 2 Å vacuum layer is set between each layer.

[0019] Furthermore, in S1, the air molecules in the air box consist of 21 oxygen molecules (O2) and 79 nitrogen molecules (N2); the 82 organic molecules in the AFFF box consist of 10 sodium benzoate molecules, 30 C10 sodium fatty alcohol sulfate molecules, 30 glycerol molecules, two alkyl glycoside molecules and 10 FC-1157C molecules; and the combustible material box consists of 100 n-hexane molecules.

[0020] Furthermore, in S2, during the geometry optimization process, the energy difference is set to 0.0001 kcal / mol, the RMS force standard is 0.005 kcal / mol·Å, and the number of iterations is 30,000.

[0021] Furthermore, in S3, the three sets of simulated temperatures are set to 303K, 323K, and 342K, respectively.

[0022] Furthermore, in S3, the total duration of the molecular dynamics simulation is 500 ps, ​​and the simulation step size is set to 1 fs.

[0023] Furthermore, in S4 and S8, the formula for calculating the radial distribution function is:

[0024] (1),

[0025] in: (r) represents the probability of other particles appearing at a distance r from the central particle; dN represents the number of other particles in the spherical region at a distance r from the central particle; ρ represents the system density composed of various surfactant molecular models; dr represents the thickness of the spherical region; and r represents the distance from the central particle.

[0026] Furthermore, in S4 and S8, the formulas for calculating the root mean square displacement (MSD) of the molecules and the diffusion coefficient (D) are as follows:

[0027] (2),

[0028] (3),

[0029] Where: N is the total number of diffusing molecules in the simulation system, r i (t) represents the position of particle i at time t, r i (0) is the position of particle i at the initial moment, D is the diffusion coefficient of the microparticle, and a is the linear slope of the root mean square displacement with respect to time.

[0030] Furthermore, in S4 and S8, the water film thickness is defined as the thickness of a water molecule layer with a water density between 10% and 90% of the bulk water density.

[0031] Furthermore, in S5, the reinforcing component box includes a different number of reinforcing component molecules, and the length and width dimensions of the reinforcing component box are both 30Å × 30Å; the reinforcing component molecules are one of perfluoropropyl polyacrylamide (PFPAAm), ammonium polyphosphate (APP), and SiO2 nanoparticles, and the degree of polymerization is set to 20.

[0032] Furthermore, in S10, the MSD curve and diffusion coefficient D of fuel molecules are used to characterize the migration ability of fuel molecules near the AFFF interface film. A lower MSD growth rate and diffusion coefficient D indicate that the cross-interface transport of fuel molecules is limited, thus indicating that the AFFF has a stronger shielding and sealing ability for fuel vapor. The component density distribution is used to reveal the enrichment degree of water molecules in the system and the degree of obstruction to fuel molecule diffusion. If fuel molecules are mainly confined to the fuel side, and the aqueous phase and surfactant form a continuous enrichment layer at the interface, it indicates that the interface film has a strong blocking effect on the diffusion of fuel molecules, thus corresponding to better vapor sealing ability. The RDF of water molecules reflects the local order and molecular aggregation characteristics of the aqueous phase at the interface. If the AFFF system exhibits more obvious water molecule structural characteristics near the interface, and Corresponding to a lower fuel diffusion coefficient and a more stable film thickness, it is believed that a more effective aqueous barrier is formed to inhibit fuel molecule penetration. The water film thickness directly reflects the scale of the liquid phase barrier formed by the AFFF on the fuel surface. A larger effective film thickness means a longer fuel molecule diffusion path and a higher interfacial mass transfer resistance, thus corresponding to better vapor sealing capability. If there is also a small film thickness fluctuation, it indicates that the interfacial film has better continuity and stability. The tail chain tilt angle of the surfactant is used to characterize the orientation order and density of the interfacial adsorption layer. If the tail chain tilt angle is more concentrated and the interfacial arrangement is more ordered, it indicates that the surfactant has formed a more complete adsorption layer at the fuel-water interface. This helps to reduce the interfacial free volume and inhibit the cross-interfacial migration of fuel molecules, thereby improving the vapor sealing capability of the AFFF.

[0033] Compared with the prior art, the present invention has at least the following beneficial effects:

[0034] (1) Establish a repeatable nanoscale characterization system: Through standardized air-AFFF-combustion model and air-AFFF-enhancing component-combustion model, the calculable characterization of the combustion migration barrier effect can be realized.

[0035] (2) Introduce the “simulation-experiment trend consistency” verification path: using the consistency between the diffusion coefficient change trend and the macro steam concentration change trend as a rationality test is conducive to establishing a correspondence between micro-simulation and macro-phenomena.

[0036] (3) Multidimensional indicators support mechanism explanation and formulation optimization: In addition to outputting the diffusion coefficient, it can also output structural / interface information such as density distribution, RDF, water film thickness and tail chain tilt angle, providing a basis for the mechanism analysis of the reinforcing components and formulation optimization.

[0037] (4) Improve the efficiency of formula screening: It is suitable for rapid comparison and evaluation of different types and concentration conditions of reinforcing components, thereby improving the efficiency of new formula screening. Attached Figure Description

[0038] Figure 1 This is a block diagram of the overall structure of the performance characterization system used for screening new formulations in this embodiment;

[0039] Figure 2 This is a flowchart of the performance characterization method of the embodiment (S1~S10).

[0040] Figure 3 This is a schematic diagram of the three-phase simulation system of air-AFFF-n-hexane in Example 1;

[0041] Figure 4 This is a graph showing the mean square displacement (MSD) of n-hexane molecules at different aqueous solution layer thicknesses and / or temperatures in Example 1, where a is 30°C, b is 50°C, and c is 69°C.

[0042] Figure 5 This is a graph showing the radial distribution function (RDF) of water molecules at the head group of surfactant molecules at different temperatures in Example 1;

[0043] Figure 6 This is a graph showing the change in the formation energy of the aqueous solution layer-n-hexane layer interface at different temperatures in Example 1;

[0044] Figure 7 This is a diagram showing the tilt angle distribution of the tail chain groups of surfactant molecules at different temperatures in Example 1;

[0045] Figure 8 This is a graph showing the mean square displacement (MSD) curves of n-hexane molecules in the PFPAAm system at different concentrations in Example 2;

[0046] Figure 9 This is a density distribution diagram of each component of the PFPAAm system along the Z-axis in Example 2;

[0047] Figure 10 This is a radial distribution function (RDF) graph of water molecules in the PFPAAm system with different concentrations in Example 2;

[0048] Figure 11 This is a graph showing the mean square displacement (MSD) curves of n-hexane molecules in the APP system at different concentrations in Example 3;

[0049] Figure 12 This is a radial distribution function (RDF) graph of water molecules in the APP system with different concentrations in Example 3;

[0050] Figure 13 This is a graph showing the mean square displacement (MSD) curves of n-hexane molecules in different concentrations of SiO2 in Example 4;

[0051] Figure 14 This is a density distribution diagram of each component in the SiO2 system along the Z-axis in Example 4;

[0052] Figure 15 This is a radial distribution function (RDF) diagram of water molecules in the SiO2 system with different concentrations in Example 4. Detailed Implementation

[0053] The present invention will be further described below with reference to the accompanying drawings and embodiments. It should be understood that the embodiments described herein are for illustrative purposes only and are not intended to limit the scope of protection of the present invention.

[0054] The method for characterizing the performance of aqueous film-forming foam fire extinguishing agents based on molecular dynamics simulation of the present invention includes the following steps:

[0055] S1. Use the Amorphous Cell module in Materials Studio to create an air box, an AFFF box, and a combustible material box respectively. Use Build Layers to merge the three boxes into an air-AFFF-combustible material model.

[0056] S2. The Forcite module in Materials Studio is used to optimize the structure of the air-AFFF-combustion model based on the COMPASS II molecular force field. After each step of the optimization process, the Smart algorithm of the Forcite module is used to perform geometric optimization to obtain the optimized structure model of air-AFFF-combustion with the lowest energy.

[0057] S3. Using the Dynamics task under the Forcite module of Materials Studio software, three sets of temperature variable simulations were performed. The optimized structure model of air-AFFF-combustion material was placed in the canonical ensemble for molecular dynamics simulation. The system convergence criterion was that the temperature and energy curves of the system fluctuated within 5%. After the simulation system reached equilibrium, the trajectory file after equilibrium was used for result confirmation and discussion analysis.

[0058] S4. Use the Analysis task under the Forcite module of Materials Studio software to obtain the MSD curve of the combustion molecules from the equilibrium trajectory file, and calculate the diffusion coefficient D, component density distribution map, water molecule radial distribution function map, water film thickness, and surfactant molecule tail chain tilt angle.

[0059] S5. Use the Amorphous Cell module in Materials Studio to build an enhancement component box. Based on the air-AFFF-combustion model, add an enhancement component layer through the Build Layers module, ensuring that the enhancement component layer is located between the AFFF layer and the combustion product layer, to form the air-AFFF-enhanced component-combustion product model.

[0060] S6. Using the Forcite module in Materials Studio, the air-AFFF-enhancing component-combustion model is structurally optimized based on the COMPASS II molecular force field. After each step of the optimization process, the Smart algorithm of the Forcite module is used for geometric optimization to obtain the optimized air-AFFF-enhancing component-combustion model with the lowest energy.

[0061] S7. Using the Dynamics task under the Forcite module of Materials Studio software, three sets of temperature variable simulations were performed. The optimized structure model of air-AFFF-enhancing component-combustion material was placed in a canonical ensemble for molecular dynamics simulation. The system convergence criterion was that the temperature and energy curves of the system fluctuated within 5%. After the simulation system reached equilibrium, the trajectory file after equilibrium was used for result confirmation and discussion analysis.

[0062] S8. Using the Analysis task under the Forcite module of Materials Studio software on the equilibrium trajectory file obtained in S7, the mean square displacement curve of combustion molecules, diffusion coefficient D, component density distribution map, water molecule radial distribution function map, water film thickness, and surfactant molecule tail chain tilt angle are obtained.

[0063] S9. Change the concentration or molecular type of the reinforcing component, repeat S5-S8, and obtain the MSD curves of the combustion molecules, diffusion coefficient D, component density distribution map, water molecule radial distribution function map, water film thickness, and surfactant molecule tail chain tilt angle for different air-AFFF-reinforcing component-combustion models.

[0064] S10. Analyze and compare the effects of the concentration or molecular type of the reinforcing component on the extinguishing effect of AFFF.

[0065] Furthermore, in S1, the air box contains 100 air molecules, the AFFF box contains 82 organic molecules and 700 water molecules, and the combustible material box contains 100 combustible material molecules. The length and width of each box are 30 Å × 30 Å, and a 2 Å vacuum layer is set between each layer.

[0066] Furthermore, in S1, the air molecules in the air box are composed of O2 and N2, with the ratio set according to the ratio in real air, distributing 100 air molecules into 79 nitrogen molecules and 21 oxygen molecules; the 82 organic molecules in the AFFF box are composed of 10 sodium benzoate molecules, 30 C10 sodium fatty alcohol sulfate molecules, 30 glycerol molecules, two alkyl glycoside molecules and 10 FC-1157C molecules; the combustible material box is composed of 100 n-hexane molecules.

[0067] Furthermore, in S2, during the geometry optimization process, the energy difference is set to 0.0001 kcal / mol, the RMS force standard is 0.005 kcal / mol·Å, and the number of iterations is 30,000.

[0068] Furthermore, in S3, the three sets of simulated temperatures are set to 303K, 323K, and 342K, respectively.

[0069] Furthermore, in S3, the total duration of the molecular dynamics simulation is 500 ps, ​​and the simulation step size is set to 1 fs.

[0070] Furthermore, in S4 and S8, the formula for calculating the radial distribution function is:

[0071] (1),

[0072] in: (r) represents the probability of other particles appearing at a distance r from the central particle; dN represents the number of other particles in the spherical region at a distance r from the central particle; ρ represents the system density composed of various surfactant molecular models; dr represents the thickness of the spherical region; and r represents the distance from the central particle.

[0073] Furthermore, in S4 and S8, the formulas for calculating the root mean square displacement (MSD) of the molecules and the diffusion coefficient (D) are as follows:

[0074] (2),

[0075] (3),

[0076] Where: N is the total number of diffusing molecules in the simulation system, r i (t) represents the position of particle i at time t, r i (0) is the position of particle i at the initial moment, D is the diffusion coefficient of the microparticle, and a is the linear slope of the root mean square displacement with respect to time.

[0077] Furthermore, in S4 and S8, the water film thickness is defined as the thickness of a water molecule layer with a water density between 10% and 90% of the bulk water density.

[0078] Furthermore, in S5, the reinforcing component box includes a different number of reinforcing component molecules, and the length and width dimensions of the reinforcing component box are both 30Å × 30Å; the reinforcing component molecules are one of perfluoropropyl polyacrylamide, ammonium polyphosphate, and SiO2 nanoparticles, and the degree of polymerization is set to 20.

[0079] Furthermore, in S10, the MSD curve and diffusion coefficient D of fuel molecules are used to characterize the migration ability of fuel molecules near the AFFF interface film. A lower MSD growth rate and diffusion coefficient D indicate that the cross-interface transport of fuel molecules is limited, thus indicating that the AFFF has a stronger shielding and sealing ability for fuel vapor. The component density distribution is used to reveal the enrichment degree of water molecules in the system and the degree of obstruction to fuel molecule diffusion. If fuel molecules are mainly confined to the fuel side, and the aqueous phase and surfactant form a continuous enrichment layer at the interface, it indicates that the interface film has a strong blocking effect on the diffusion of fuel molecules, thus corresponding to better vapor sealing ability. The RDF of water molecules reflects the local order and molecular aggregation characteristics of the aqueous phase at the interface. If the AFFF system exhibits more obvious water molecule structural characteristics near the interface, and Corresponding to a lower fuel diffusion coefficient and a more stable film thickness, it is believed that a more effective aqueous barrier is formed to inhibit fuel molecule penetration. The water film thickness directly reflects the scale of the liquid phase barrier formed by the AFFF on the fuel surface. A larger effective film thickness means a longer fuel molecule diffusion path and a higher interfacial mass transfer resistance, thus corresponding to better vapor sealing capability. If there is also a small film thickness fluctuation, it indicates that the interfacial film has better continuity and stability. The tail chain tilt angle of the surfactant is used to characterize the orientation order and density of the interfacial adsorption layer. If the tail chain tilt angle is more concentrated and the interfacial arrangement is more ordered, it indicates that the surfactant has formed a more complete adsorption layer at the fuel-water interface. This helps to reduce the interfacial free volume and inhibit the cross-interfacial migration of fuel molecules, thereby improving the vapor sealing capability of the AFFF.

[0080] Example 1: Suppression simulation and characterization of the AFFF-n-hexane system under different temperature and liquid film thickness conditions.

[0081] 1. A three-phase model simulation system was constructed, consisting of an air layer-AFFF layer-n-hexane layer. The combustion chamber consisted of 100 n-hexane molecules, the air chamber contained 79 nitrogen molecules and 21 oxygen molecules, and the AFFF chamber contained 700 water molecules and 82 organic molecules. All three chambers were 30 Å × 30 Å in length and width. A 2 Å vacuum layer was added between the layers, and the air-AFFF-n-hexane model was obtained by merging them using Build Layers. Figure 3 As shown.

[0082] 2. In the Forcite module, the COMPASS II force field is used for structural optimization, and the Smart algorithm is used to continue geometric optimization after each step. The energy difference threshold is 0.0001 kcal / mol, the RMS force standard is 0.005 kcal / (mol·Å), and the maximum number of iterations is 30,000.

[0083] 3. Molecular dynamics simulations were performed in the NVT ensemble with a step size of 1 fs and a total duration of 500 ps. Convergence was determined by controlling temperature and energy fluctuations within 5%. After system equilibrium, the trajectory of the last 150 ps was used for statistical analysis. To verify the influence of temperature and thickness, temperature and thickness variable groups were set with temperatures of 30℃, 50℃, and 69℃, and AFFF thicknesses of 2cm, 3cm, 4cm, 5cm, and 6cm.

[0084] 4. Calculate the MSD of n-hexane molecules and obtain the diffusion coefficient D, and output the density distribution, RDF of water molecules, interface formation energy, and other indicators. Results are as follows: Figures 4-7 As shown, where Figure 4 This is a graph showing the mean square displacement (MSD) curves of n-hexane molecules at different aqueous solution layer thicknesses and / or temperatures in Example 1. Figure 5 This is a graph showing the radial distribution function (RDF) of water molecules at the head group of the surfactant molecule at different temperatures in Example 1. Figure 6 This is a graph showing the change in the formation energy of the aqueous solution-n-hexane layer interface at different temperatures in Example 1. Figure 7 This is a distribution diagram of the tail chain tilt angle of surfactant molecules at different temperatures under a 4cm AFFF thickness in Example 1. The results show that when the AFFF thickness is fixed, D increases significantly with increasing temperature; at 30℃, the value of D is 2.32 × 10⁻⁶ for a 4cm AFFF thickness. -6 cm 2 / s, 4.61×10 at 50℃ -6 cm 2 / s, 7.95×10 at 69℃ -6 cm 2 / s; Under constant temperature conditions, increasing the AFFF thickness can reduce D: at 30℃, D for 2-6cm decreases from 3.86 to 1.57 (×10). -6 cm 2 / s), and at 30℃, the thickness is further optimized to be 4cm (D=2.32×10). -6 cm 2 / s); at 50℃, the D value for 2-6cm decreases from 5.90 to 3.65, and a thickness of ≥5cm is preferred, further preferred to be 5cm (D=3.90×10). -6 cm 2 / s); at 69℃, the D value corresponding to 2-6cm decreases from 9.91 to 5.44, and the preferred thickness is ≥5cm, further preferably 5cm (D=6.04×10). -6 cm 2 Based on this, it was determined that increased temperature weakens the inhibition ability, while at higher temperatures, increasing the thickness of the AFFF can achieve a better blocking effect.

[0085] Table 1. Fuel Molecular Diffusion Coefficient

[0086]

[0087] Example 2: Suppression simulation and characterization of the AFFF / perfluoropropyl polyacrylamide (PFPAAm)-n-hexane complex system.

[0088] 1. Based on the three-phase model of Example 1, a reinforcing component layer is introduced between the AFFF layer and the n-hexane layer. The reinforcing component is PFPAAm, with a degree of polymerization of 20 and concentrations of 2 μmol / L, 3 μmol / L, and 4 μmol / L, respectively. The reinforcing component layer is located between the AFFF layer and the n-hexane layer, and a 2 Å vacuum layer is set between the layers to form an air-AFFF-PFPAAm-n-hexane model.

[0089] 2. In the Forcite module, the COMPASS II force field is used to parameterize the composite system, and structural optimization and interaction settings are performed: Smart Minimizer is used for geometry optimization, with a maximum iteration step of 20,000 steps; Ewald summation is used for long-range electrostatic interactions, with an accuracy of 0.001 kcal / mol; the van der Waals interaction is performed using the Atom-based method, with a cutoff radius of 1.55 nm, and density approximation correction is applied to the cutoff external contribution; the temperature is controlled using a Nose–Hoover heat bath, with a coupling constant of 0.1 ps; the time step is 1 fs, and the trajectory is output every 1 ps; periodic boundary conditions are used in the X, Y, and Z directions.

[0090] 3. Perform molecular dynamics simulations at 500 ps in the NVT ensemble with a step size of 1 fs, and use the last 150 ps for statistical analysis. To maintain consistency with the control conditions, the temperature was set to 80℃ and the AFFF thickness was set to 4 cm.

[0091] 4. Extract MSD, diffusion coefficient D, density distribution, and water structure indices. The results are as follows: Figures 8-10 As shown, where Figure 8 This is the MSD curve of n-hexane molecules in the PFPAAm system with different concentrations in Example 2. Figure 9 This is a density distribution diagram of each component of the PFPAAm system along the Z-axis in Example 2. Figure 10 This is the RDF diagram of water molecules in the PFPAAm system at different concentrations in Example 2. The results show that at 80℃ and an AFFF thickness of 4 cm, D decreases with increasing PFPAAm concentration: 7.45 for 2 μmol / L, 5.63 for 3 μmol / L, and 4.66 (×10⁻⁶) for 4 μmol / L. -6 cm 2 ( / s). Meanwhile, the coordination number of water molecules increases with increasing concentration: 2.76 for 2 μmol / L, 3.87 for 3 μmol / L, and 4.42 for 4 μmol / L, indicating enhanced interfacial water structuring. The preferred PFPAAm concentration is ≥3 μmol / L, further preferred at 4 μmol / L to obtain a lower diffusion coefficient; considering both the addition amount and the synergistic inflection point, approximately 3 μmol / L is preferable.

[0092] Example 3: Suppression simulation and characterization of the AFFF / ammonium polyphosphate (APP)-n-hexane complex system.

[0093] 1. Based on the three-phase model of Example 1, an APP reinforcing component layer was introduced between the AFFF layer and the n-hexane layer. The degree of polymerization was set to 20, and the concentrations were set to 2 μmol / L, 3 μmol / L, and 4 μmol / L, respectively, to form an air-AFFF-APP-n-hexane model.

[0094] 2. In the Forcite module, the COMPASS II force field is used to parameterize the composite system, and structural optimization and interaction settings are performed: Smart Minimizer is used for geometry optimization, with a maximum iteration step of 20,000 steps; Ewald summation is used for long-range electrostatic interactions, with an accuracy of 0.001 kcal / mol; the van der Waals interaction is performed using the Atom-based method, with a cutoff radius of 1.55 nm, and density approximation correction is applied to the external contribution of the cutoff; the temperature is controlled using a Nose-Hoover heat bath, with a coupling constant of 0.1 ps; the time step is 1 fs, and the trajectory is output every 1 ps; periodic boundary conditions are used in the X, Y, and Z directions.

[0095] 3. Perform molecular dynamics simulations for 500 ps in the NVT ensemble with a step size of 1 fs, and take the last 150 ps for statistical analysis; set the temperature to 80℃ and the AFFF thickness to 4 cm.

[0096] 4. The analysis was performed, and the results are as follows: Figure 11 , Figure 12 As shown, where Figure 11 This is the MSD curve of n-hexane molecules in the APP system at different concentrations in Example 3. Figure 12 This is the RDF diagram of water molecules in the APP system at different concentrations in Example 3. The results show that at 80℃ and an AFFF thickness of 4 cm, D decreases with increasing APP concentration: 8.83 for 2 μmol / L, 7.54 for 3 μmol / L, and 5.78 (×10⁻⁶) for 4 μmol / L. -6 cm 2 / s). The coordination number of water molecules increases with increasing concentration: 2.23 for 2 μmol / L, 2.88 for 3 μmol / L, and 3.76 for 4 μmol / L; when the concentration is ≥3 μmol / L, RDF shows a characteristic peak of the second hydration layer, as shown in the results. Figure 13 As shown, where Figure 13 The figures show the RDF diagrams of water molecules in the APP system at different concentrations in Example 3, indicating the formation of a multilayer hydration barrier. The preferred APP concentration is ≥3 μmol / L, and more preferably 4 μmol / L to obtain a lower diffusion coefficient and a more pronounced multilayer hydration structure.

[0097] Example 4: Suppression simulation and characterization of the AFFF / SiO2 nanoparticle-n-hexane complex system.

[0098] 1. Based on the three-phase model in Example 1, a SiO2 nanoparticle reinforcing component layer was introduced between the AFFF layer and the n-hexane layer. The degree of polymerization was set to 20, and the concentrations were set to 2 μmol / L, 3 μmol / L, and 4 μmol / L, respectively, to form an air-AFFF-SiO2-n-hexane model.

[0099] 2. In the Forcite module, the COMPASS II force field is used to parameterize the composite system, and structural optimization and interaction settings are performed: Smart Minimizer is used for geometry optimization, with a maximum iteration step of 20,000 steps; Ewald summation is used for long-range electrostatic interactions, with an accuracy of 0.001 kcal / mol; the van der Waals interaction is performed using the Atom-based method, with a cutoff radius of 1.55 nm, and density approximation correction is applied to the cutoff external contribution; the temperature is controlled using a Nose–Hoover heat bath, with a coupling constant of 0.1 ps; the time step is 1 fs, and the trajectory is output every 1 ps; periodic boundary conditions are used in the X, Y, and Z directions.

[0100] 3. Perform molecular dynamics simulations for 500 ps in the NVT ensemble with a step size of 1 fs, and take the last 150 ps for statistical analysis; set the temperature to 80℃ and the AFFF thickness to 4 cm.

[0101] 4. The analysis was performed, and the results are as follows: Figures 13-15 As shown, where Figure 13 This is an MSD curve of n-hexane molecules in a SiO2 system with different concentrations in Example 4 of this invention. Figure 14 This is a density distribution diagram of each component in the SiO2 system along the Z-axis in Example 4. Figure 15 This is the RDF diagram of water molecules in the SiO2 system with different concentrations in Example 4. The results show that at 80℃ and an AFFF thickness of 4 cm, D decreases with increasing SiO2 concentration: 5.43 for 2 μmol / L, 4.84 for 3 μmol / L, and 3.99 (×10⁻⁶) for 4 μmol / L. -6 cm 2 SiO2 stably resides at the interface and forms a steric hindrance of the particle layer, while simultaneously inducing multiple hydration layers to enhance barrier properties. The coordination number of water molecules increases with increasing concentration: 3.33 for 2 μmol / L, 4.28 for 3 μmol / L, and 4.86 for 4 μmol / L. Furthermore, the number of RDF characteristic peaks increases with increasing concentration. A SiO2 concentration ≥3 μmol / L is preferred, and 4 μmol / L is further preferred to obtain the lowest diffusion coefficient and a stronger interfacial barrier.

Claims

1. A method for characterizing the performance of aqueous film-forming foam fire extinguishing agents based on molecular dynamics simulations, characterized in that, Includes the following steps: S1. Use the Amorphous Cell module in Materials Studio to create an air box, an AFFF box, and a combustible material box respectively. Use Build Layers to merge the three boxes into an air-AFFF-combustible material model. S2. The Forcite module in Materials Studio is used to optimize the structure of the air-AFFF-combustion model based on the COMPASS II molecular force field. After each step of the optimization process, the Smart algorithm of the Forcite module is used to perform geometric optimization to obtain the optimized structure model of air-AFFF-combustion with the lowest energy. S3. Using the Dynamics task under the Forcite module of Materials Studio software, three sets of temperature variable simulations were performed. The optimized structure model of air-AFFF-combustion material was placed in the canonical ensemble for molecular dynamics simulation. The system convergence criterion was that the temperature and energy curves of the system fluctuated within 5%. After the simulation system reached equilibrium, the trajectory file after equilibrium was used for result confirmation and discussion analysis. S4. Use the Analysis task under the Forcite module of Materials Studio software on the equilibrium trajectory file to obtain the mean square displacement curve of the combustion molecules, and calculate the diffusion coefficient D, component density distribution map, water molecule radial distribution function map, water film thickness, and surfactant molecule tail chain tilt angle. S5. Use the Amorphous Cell module in Materials Studio to build an enhancement component box. Based on the air-AFFF-combustion model, add an enhancement component layer through the Build Layers module, ensuring that the enhancement component layer is located between the AFFF layer and the combustion product layer, to form the air-AFFF-enhanced component-combustion product model. S6. Using the Forcite module in Materials Studio, the air-AFFF-enhancing component-combustion model is structurally optimized based on the COMPASS II molecular force field. After each step of the optimization process, the Smart algorithm of the Forcite module is used for geometric optimization to obtain the optimized air-AFFF-enhancing component-combustion model with the lowest energy. S7. Using the Dynamics task under the Forcite module of Materials Studio software, three sets of temperature variable simulations were performed. The optimized structure model of air-AFFF-enhancing component-combustion material was placed in a canonical ensemble for molecular dynamics simulation. The system convergence criterion was that the temperature and energy curves of the system fluctuated within 5%. After the simulation system reached equilibrium, the trajectory file after equilibrium was used for result confirmation and discussion analysis. S8. Using the Analysis task under the Forcite module of Materials Studio software on the equilibrium trajectory file obtained in S7, the mean square displacement curve of combustion molecules, diffusion coefficient D, component density distribution map, water molecule radial distribution function map, water film thickness, and surfactant molecule tail chain tilt angle are obtained. S9. Change the concentration or molecular type of the reinforcing component, repeat S5-S8, and obtain the mean square displacement curves of combustion molecules and diffusion coefficient D, component density distribution diagram, water molecule radial distribution function diagram, water film thickness, and surfactant molecule tail chain tilt angle for different air-AFFF-reinforcing component-combustion models. S10. Analyze and compare the effects of the concentration or molecular type of the reinforcing component on the extinguishing effect of AFFF.

2. The method for characterizing the performance of aqueous film-forming foam fire extinguishing agent according to claim 1, characterized in that, In S1, the air box contains 100 air molecules, the AFFF box contains 82 organic molecules and 700 water molecules, and the combustible material box contains 100 combustible material molecules. The length and width of each box are 30 Å × 30 Å, and a 2 Å vacuum layer is set between each layer.

3. The method for characterizing the performance of aqueous film-forming foam fire extinguishing agent according to claim 1, characterized in that, In S1, the air molecules in the air box consist of 21 oxygen molecules and 79 nitrogen molecules; the 82 organic molecules in the AFFF box consist of 10 sodium benzoate molecules, 30 C10 sodium fatty alcohol sulfate molecules, 30 glycerol molecules, two alkyl glycoside molecules and 10 FC-1157C molecules; and the combustible material box consists of 100 n-hexane molecules.

4. The method for characterizing the performance of aqueous film-forming foam fire extinguishing agent according to claim 1, characterized in that, In S2, during the geometry optimization process, the energy difference is set to 0.0001 kcal / mol, the RMS force standard is 0.005 kcal / mol·Å, and the number of iterations is 30,000.

5. The method for characterizing the performance of aqueous film-forming foam fire extinguishing agent according to claim 1, characterized in that, In S3, the three simulation temperatures were set to 303K, 323K, and 342K, respectively; the total duration of the molecular dynamics simulation was 500ps, and the simulation step size was set to 1fs.

6. The method for characterizing the performance of aqueous film-forming foam fire extinguishing agent according to claim 1, characterized in that, In S4 and S8, the formula for calculating the radial distribution function is: （1）， in: (r) represents the probability of other particles appearing at a distance r from the central particle; dN represents the number of other particles in the spherical region at a distance r from the central particle; ρ represents the system density composed of various surfactant molecular models; dr represents the thickness of the spherical region; and r represents the distance from the central particle.

7. The method for characterizing the performance of aqueous film-forming foam fire extinguishing agent according to claim 1, characterized in that, In S4 and S8, the formulas for calculating the root mean square displacement (MSD) and diffusion coefficient (D) are as follows: （2）， （3）， Where: N is the total number of diffusing molecules in the simulation system, r i (t) represents the position of particle i at time t, r i (0) is the position of particle i at the initial moment, D is the diffusion coefficient of the microparticle, and a is the linear slope of the root mean square displacement with respect to time.

8. The method for characterizing the performance of aqueous film-forming foam fire extinguishing agent according to claim 1, characterized in that, In S4 and S8, the water film thickness is defined as the thickness of the water molecule layer where the water density is between 10% and 90% of the bulk water density.

9. The method for characterizing the performance of aqueous film-forming foam fire extinguishing agent according to claim 1, characterized in that, In S5, the reinforcing component box includes a different number of reinforcing component molecules, and the length and width dimensions of the reinforcing component box are both 30Å × 30Å; the reinforcing component molecules are one of perfluoropropyl polyacrylamide, ammonium polyphosphate, and SiO2 nanoparticles, and the degree of polymerization is set to 20.

10. The method for characterizing the performance of aqueous film-forming foam fire extinguishing agent according to claim 1, characterized in that, In S10, the MSD curve and diffusion coefficient D of fuel molecules are used to characterize the migration ability of fuel molecules near the AFFF interface film. A lower MSD growth rate and diffusion coefficient D indicate that the cross-interface transport of fuel molecules is limited, thus indicating that the AFFF has a stronger shielding and sealing ability for fuel vapor. The component density distribution is used to reveal the enrichment degree of water molecules in the system and the degree of obstruction of fuel molecule diffusion. If fuel molecules are mainly confined to the fuel side, and the aqueous phase and surfactant form a continuous enrichment layer at the interface, it indicates that the interface film has a strong blocking effect on the diffusion of fuel molecules, thus corresponding to better vapor sealing ability. The RDF of water molecules reflects the local order and molecular aggregation characteristics of the aqueous phase at the interface. If the AFFF system exhibits more obvious water molecule structure characteristics near the interface, and corresponds to a lower fuel diffusion coefficient and a more stable film thickness, it is considered that it forms an aqueous phase barrier that is more conducive to inhibiting the penetration of fuel molecules. The water film thickness directly reflects the scale of the liquid phase barrier formed by the AFFF on the fuel surface. A larger effective film thickness means a longer fuel molecule diffusion path and a higher interfacial mass transfer resistance, thus corresponding to better vapor sealing ability. If there is a small film thickness fluctuation, it indicates that the interfacial film has better continuity and stability. The tail chain tilt angle of the surfactant is used to characterize the orientation order and density of the interfacial adsorption layer. If the tail chain tilt angle distribution is more concentrated and the interfacial arrangement is more ordered, it indicates that the surfactant has formed a more complete adsorption layer at the fuel-water interface. This helps to reduce the free volume of the interface and inhibit the cross-interfacial migration of fuel molecules, thereby improving the vapor sealing capability of AFFF.

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