Parameter calculation method for optimizing flow characteristics of foam fire retardant material

Abstract

The invention discloses a parameter calculation method for optimizing flow characteristics of a foam fire retardant material. The parameter calculation method comprises the following steps: establishing a theoretical calculation model of the foam fire retardant material; based on the theoretical calculation model, establishing an Ergun equation; adding unknown empirical parameters to the dimensionless number in the Ergun equation to obtain a dimensionless number calculation equation set; adding constraint conditions for the dimensionless number calculation equation set; based on the dimensionless number calculation equation set, solving an optimal solution of the unknown empirical parameters; and substituting the optimal solution of the unknown empirical parameter into the Ergun equation, and calculating a viscous resistance coefficient and an inertial resistance coefficient. According to the parameter calculation method for optimizing the flow characteristics of the foam fire retardant material, the problem that the flow characteristic parameters of the foam fire retardant material are difficult to accurately obtain in the prior art is solved, and the purposes of rapidly and accurately determining the flow characteristic parameters of the foam fire retardant material and providing important reference for industrial fire retardant type selection are achieved.

Description

Technical Field

[0001] The present invention relates to the field of measuring the flow parameters of flame retardant materials, and specifically relates to a parameter calculation method for optimizing the flow characteristics of foam flame retardant materials. Background Art

[0002] Combustible gases such as natural gas and liquefied petroleum gas are extremely widely used in the national economy. In the processes of their processing, storage, and transportation, combustion and explosion accidents are extremely likely to occur. Porous foam flame retardant materials such as foam metals and foam metal wire meshes have a large number of pore structures, which can destroy a large number of chemical reaction free radicals, thereby quenching the explosion flame, destroying the mutual coupling between the flame front and the shock wave, and finally playing roles such as explosion isolation and flameless venting. Therefore, it is crucial to study the inhibitory effect of porous foam flame retardant materials on flames, and the main factor affecting their flame retardant efficiency is the flow characteristics of the fluid inside them.

[0003] As a time- and cost-saving method, simulation is widely used in related flame retardant research. Two important parameters among them are the viscous resistance coefficient and the inertial resistance coefficient, and the values of these two parameters are extremely important for the accuracy of simulation. However, in the prior art, it is still difficult to directly obtain the accurate values of these two parameters, and mostly only empirical settings can be relied on, which limits the accuracy of simulation results; in addition, this way of blindly selecting empirical parameters is also likely to waste the computing resources of the laboratory, resulting in time and economic losses. Summary of the Invention

[0004] The present invention provides a parameter calculation method for optimizing the flow characteristics of foam flame retardant materials to solve the problem in the prior art that it is difficult to accurately obtain the flow characteristic parameters of foam flame retardant materials, and to achieve the purpose of quickly and accurately determining the flow characteristic parameters of foam flame retardant materials and providing an important reference for industrial flame retardant selection.

[0005] The present invention is realized through the following technical solutions:

[0006] A parameter calculation method for optimizing the flow characteristics of foam flame retardant materials includes the following steps:

[0007] S1. Establish a theoretical calculation model of the foam flame retardant material;

[0008] S2. Based on the theoretical calculation model, establish the Ergun equation;

[0009] S3. Add unknown empirical parameters to the dimensionless numbers in the Ergun equation to obtain a dimensionless number calculation equation set;

[0010] S4. Add constraint conditions to the dimensionless number calculation equation set;

[0011] S5. Calculate the optimal solution of the unknown empirical parameters based on the dimensionless number calculation equations.

[0012] S6. Substitute the optimal solution of the unknown empirical parameters into the Ergun equation to calculate the viscous drag coefficient and the inertial drag coefficient.

[0013] Aiming at the problem that it is difficult to accurately obtain the flow characteristic parameters of the foam fire - resistant material in the prior art, the present invention proposes a parameter calculation method for optimizing the flow characteristics of the foam fire - resistant material. This method first establishes a theoretical calculation model of the foam fire - resistant material, then establishes the Ergun equation based on the theoretical calculation model. There are dimensionless numbers in the Ergun equation, and unknown empirical parameters are added to it in this method to obtain the dimensionless number calculation equations. Then, constraint conditions are set, and then the optimal solution of the unknown empirical parameters is calculated. Finally, the obtained optimal solution is substituted back into the Ergun equation to calculate the viscous drag coefficient and the inertial drag coefficient.

[0014] It can be seen that this application can quickly determine the two important parameters, namely the viscous drag coefficient and the inertial drag coefficient, of the foam fire - resistant material, solve the problems of excessive subjectivity, low accuracy, and waste of computing resources caused by mostly relying on empirical values in the prior art, significantly improve the objectivity and accuracy of the flow characteristic parameters of the foam fire - resistant material, be conducive to providing scientific and reasonable parameter support for the simulation of the foam fire - resistant material, and have important significance for industrial fire - resistant selection.

[0015] Further, the theoretical calculation model includes several material units, and each material unit includes a metal sphere and a foam cube sleeved outside the metal sphere; the diameter d s of the metal sphere is greater than the side length a of the foam cube; each side of the foam cube has an opening for the metal sphere to protrude; and it satisfies:

[0016] This solution can limit the range of the porosity by defining the relationship between the diameter of the metal sphere and the side length of the foam cube.

[0017] Further, the established Ergun equation is:

[0018]

[0019] In the formula: A* is the first dimensionless coefficient; B* is the second dimensionless coefficient; Δp is the pressure drop of the foam fire - resistant material area along the specified direction; Δn is the thickness of the foam fire - resistant material area along the specified direction; ∈ is the porosity of the theoretical calculation model; ρ is the fluid density; μ is the viscosity coefficient of the fluid; v is the fluid velocity.

[0020] Those skilled in the art should understand that the specified direction herein refers to the fluid flow direction; the fluid herein refers to the gas passing through the porous medium.

[0021] Furthermore, the porosity ∈ of the theoretical calculation model is calculated by the following formula:

[0022] This solution can inversely calculate

[0023] Furthermore, the dimensionless number calculation equation set is as follows:

[0024]

[0025] In the formula: A is the viscous dimensionless number; B is the inertial resistance dimensionless number; C is the calculated absolute error; F is the first unknown empirical parameter; Q is the second unknown empirical parameter.

[0026] In the research process, the inventor of this case found that the existing dimensionless number calculation equations are difficult to be applicable to the foam fire-resistant materials, and the calculation results have large errors (even errors of an order of magnitude). Therefore, this solution sets a dimensionless number calculation equation set dedicated to the foam fire-resistant materials, in which two unknown empirical parameters F and Q are set, which can significantly reduce the calculation error and improve the calculation accuracy.

[0027] Furthermore, the constraint conditions include:

[0028] C = |C′ 1 - C 1 | + |C′ 2 - C 2 |

[0029] C′ 1 > 0; C′ 2 > 0

[0030] In the formula: C 1 is the viscous resistance coefficient to be calculated; C 2 is the inertial resistance coefficient to be calculated; C′ 1 is the known viscous resistance coefficient; C′ 2 is the known inertial resistance coefficient.

[0031] The setting of the constraint conditions of this solution can reduce the multi-objective optimization to single-objective optimization and constraints, which can greatly improve the calculation efficiency. Among them, C′ 1 , C′ 2 As known parameters, they can be obtained based on existing literature or experimental data.

[0032] Furthermore, the viscous resistance coefficient and the inertial resistance coefficient are calculated by the following formula:

[0033] a′ = 0.5·C 2 ·ρΔn

[0034] b′ = C 1 μΔn

[0035] Where: a′ is the first fitting coefficient, b′ is the second fitting coefficient, and they are obtained by fitting through the following formula: Δp = a′v 2 + b′v.

[0036] Furthermore, the method for obtaining the optimal solution of the unknown empirical parameter includes:

[0037] S501. Determine the cooling function, set the initial control temperature, and set the length of the Markov chain; randomly select initial values for the first unknown empirical parameter and the second unknown empirical parameter in the feasible solution space, and calculate the initial solution of the cooling function;

[0038] S502. Generate a random perturbation for the first unknown empirical parameter and the second unknown empirical parameter in the feasible solution space, and calculate the new solution of the cooling function based on the perturbed first unknown empirical parameter and the second unknown empirical parameter;

[0039] S503. Determine whether to accept the new solution to obtain the optimal solution of the first unknown empirical parameter and the second unknown empirical parameter at this time;

[0040] S504. Repeat steps S502 - S503 until the loop under the number of times of the Markov chain length is completed, and obtain the optimal solution of the first unknown empirical parameter and the second unknown empirical parameter under the Markov chain length.

[0041] This solution iterates the optimal solution of the unknown empirical parameter through a specified algorithm, providing a reasonable basis for calculating the viscous drag coefficient and the inertial drag coefficient.

[0042] Furthermore, in step S503, it is determined whether to accept the new solution through the Metropolis criterion. The Metropolis criterion is a prior art and will not be elaborated here.

[0043] Furthermore, the method for obtaining the optimal solution of the unknown empirical parameter also includes:

[0044] S505. Determine whether the optimal solution of the first unknown empirical parameter and the second unknown empirical parameter under the Markov chain length satisfies the preset stop criterion:

[0045] If so, output the optimal solution of the first unknown empirical parameter and the second unknown empirical parameter under the Markov chain length;

[0046] If not, return to step S501 to correct the cooling function and / or increase the length of the Markov chain.

[0047] In this solution, a stopping criterion is also preset for the optimal solution of the unknown empirical parameters. After obtaining the optimal solutions of the first unknown empirical parameter and the second unknown empirical parameter under the length of the Markov chain, it is also determined whether the corresponding optimal solutions meet the preset stopping criterion. If not, the cooling function is corrected again and / or the length of the Markov chain is increased, and the optimal solutions of the unknown empirical parameters are recalculated until the stopping criterion is met. This solution can more ensure the calculation accuracy of the optimal solutions of the unknown empirical parameters, and further improve the calculation accuracy of the flow characteristic parameters of the foam fire retardant material.

[0048] The stopping criterion therein can be adaptively set according to specific working conditions. For example, the absolute error from the experimental value is adopted to be less than 100.

[0049] Compared with the prior art, the present invention has the following advantages and beneficial effects:

[0050] 1. A method for calculating parameters to optimize the flow characteristics of a foam fire retardant material according to the present invention can quickly determine two important parameters, namely, the viscous resistance coefficient and the inertial resistance coefficient of the foam fire retardant material, which is convenient for carrying out accurate theoretical calculations or numerical simulations in the next step. The method of this application optimizes its flow characteristic parameters starting from theoretical calculations and has strong universality.

[0051] 2. A method for calculating parameters to optimize the flow characteristics of a foam fire retardant material according to the present invention solves the problems of excessive subjectivity, low accuracy, and waste of computing resources caused by mostly relying on empirical values in the prior art, significantly improves the objectivity and accuracy of the flow characteristic parameters of the foam fire retardant material, is conducive to providing scientific and reasonable parameter support for the simulation of the foam fire retardant material, and has important significance for industrial fire retardant selection.

[0052] 3. A method for calculating parameters to optimize the flow characteristics of a foam fire retardant material according to the present invention can solve the problem of wasting computing resources in the simulation process due to blindly selecting empirical parameters.

[0053] 4. A method for calculating parameters to optimize the flow characteristics of a foam fire retardant material according to the present invention sets up a dimensionless number calculation equation set dedicated to the foam fire retardant material, which can significantly reduce the calculation error and improve the calculation accuracy. BRIEF DESCRIPTION OF THE DRAWINGS

[0054] The drawings described herein are used to provide a further understanding of the embodiments of the present invention, form a part of this application, and do not limit the embodiments of the present invention. In the drawings:

[0055] Figure 1 is a schematic flow chart of a specific embodiment of the present invention;

[0056] Figure 2 is a schematic diagram of a theoretical calculation model in a specific embodiment of the present invention;

[0057] Figure 3 This is the front view of the theoretical calculation model in a specific embodiment of the present invention;

[0058] Figure 4 This is the schematic diagram for calculating the absolute error in a specific embodiment of the present invention. Specific Embodiments

[0059] To make the objectives, technical solutions and advantages of the present invention more clearly understood, the present invention will be further described in detail below in conjunction with the embodiments and the accompanying drawings. The illustrative embodiments and descriptions thereof of the present invention are only used to explain the present invention and are not intended to limit the present invention. In the description of the present application, it should be understood that the orientation or positional relationships indicated by terms such as "front", "rear", "left", "right", "upper", "lower", "vertical", "horizontal", "high", "low", "inner", "outer", etc. are based on the orientation or positional relationships shown in the drawings, and are only for the convenience of describing the present invention and simplifying the description, rather than indicating or implying that the device or element referred to must have a specific orientation, be constructed and operated in a specific orientation, and thus should not be construed as limiting the protection scope of the present application.

[0060] Embodiment 1:

[0061] As Figure 1 shown, a parameter calculation method for optimizing the flow characteristics of a foam fire-resistant material includes the following steps:

[0062] Step 1: Establish a theoretical calculation model of the foam fire-resistant material as Figure 2 and Figure 3 shown.

[0063] The theoretical calculation model includes a number of material units, and each material unit includes a metal sphere and a foam cube sleeved outside the metal sphere; the diameter d s of the metal sphere is greater than the side length a of the foam cube; each side surface of the foam cube has an opening for the metal sphere to protrude; and it satisfies:

[0064] The porosity ∈ of the theoretical calculation model is calculated by the following formula:

[0065] In this embodiment, the porosity ∈ of the theoretical calculation model satisfies: 52.4% < ∈ < 96.4%.

[0066] Step 2: Based on the theoretical calculation model, establish an extended Ergun equation by equal specific surface area:

[0067]

[0068] In the formula: A* is the first dimensionless coefficient; B* is the second dimensionless coefficient; Δp is the pressure drop in the foam fire - resistant material area along the specified direction; Δn is the thickness of the foam fire - resistant material area along the specified direction; ∈ is the porosity of the theoretical calculation model; ρ is the fluid density; μ is the viscosity coefficient of the fluid; v is the fluid velocity.

[0069] Step 3: Add unknown empirical parameters to the dimensionless numbers in the Ergun equation to obtain a system of dimensionless number calculation equations:

[0070]

[0071] In the formula: A is the viscous dimensionless number; B is the inertial resistance dimensionless number; C is the calculated absolute error; F is the first unknown empirical parameter; Q is the second unknown empirical parameter.

[0072] Step 4: Add constraint conditions to the system of dimensionless number calculation equations:

[0073] C=|C′ 1 - C 1 |+|C′ 2 - C 2 |

[0074] C′ 1 >0;C′ 2 >0

[0075] In the formula: C 1 is the viscous resistance coefficient to be calculated; C 2 is the inertial resistance coefficient to be calculated; C′ 1 is the known viscous resistance coefficient; C′ 2 is the known inertial resistance coefficient.

[0076] Step 5: Based on the system of dimensionless number calculation equations, solve for the optimal solutions of the unknown empirical parameters.

[0077] Step 6: Substitute the optimal solutions of the unknown empirical parameters into the Ergun equation to calculate the viscous resistance coefficient and the inertial resistance coefficient; the following formulas are used in the calculation process:

[0078] a′=0.5·C 2 ·ρΔn

[0079] b′=C 1 μΔn

[0080] In the formula: a′ is the first fitting coefficient, b′ is the second fitting coefficient, and they are obtained by fitting through the following formula: Δp=a′v 2 +b′v.

[0081] The method of this embodiment can directly calculate the viscous resistance coefficient and inertial resistance coefficient of the metal foam according to the porosity and pore density in actual production, and use the direct selection of porous media for industrial fire prevention and transportation, saving experimental costs and time costs.

[0082] Example 2:

[0083] A parameter calculation method for optimizing the flow characteristics of foam fire-resistant materials. On the basis of Example 1, this embodiment uses an optimized simulated annealing algorithm to solve the optimal solution of the unknown empirical parameters. The specific steps include:

[0084] Step 1: Determine the cooling function T k = f(k), set the initial control temperature T 0 , set the length L of the Markov chain 0 ; randomly generate initial values P 0 , Q 0 for the first unknown empirical parameter and the second unknown empirical parameter in the feasible solution space;

[0085] At this time, the iteration number k = 0, and calculate the initial optimal solution T k = f(P 0 , Q 0 ).

[0086] Step 2: Generate a random perturbation for the first unknown empirical parameter and the second unknown empirical parameter in the feasible solution space. Based on the perturbed first unknown empirical parameter and second unknown empirical parameter P k+1 , Q k+1 , calculate the new solution T k+1 = f(P k+1 , Q k+1 ).

[0087] Step 3: Determine whether to accept the new solution. The judgment criterion is the Metropolis criterion:

[0088] If T k ≥T k+1 , then accept the new solution P k+1 , Q k+1 ;

[0089] If T k <T k+1 , then accept the new solution P k+1 , Q k+1 with probability p;

[0090] Step 4: Repeat Step 2 and Step 3 for L 0 times to obtain the optimal solution under the chain length L 0 .

[0091] Step 5: Determine whether the preset stopping criterion is satisfied. If it is satisfied, output the optimal solution and stop the algorithm; otherwise, proceed to Step 6.

[0092] Step 6: The iteration number k = k + 1, the initial optimal solution is updated to the solution obtained in Step 4, the cooling function becomes T k+1 = f(k + 1), and the length of the Markov chain becomes L k+1 , and return to Step 2.

[0093] Preferably, the stopping criterion in this embodiment is that the absolute error from the experimental value is less than 100.

[0094] This embodiment uses existing literature data to obtain C′ 1 、C′ 2 , take C′ 1 = 1.82×10 7 , C′ 2 = 1172. The iterative process of solving the optimal solution is as Figure 4 shown, Figure 4 where the abscissa is the iteration number and the ordinate is the value of the calculated absolute error C. It can be seen from Figure 4 that the optimal solution can be obtained after more than 500 steps of cyclic iteration.

[0095] In summary, based on some existing data, this embodiment can quickly calculate the empirical parameters of related materials, while greatly saving the computational resource loss caused by blindly selecting empirical parameters and the economic loss caused by conducting comprehensive experiments.

[0096] The specific embodiments described above further elaborate on the purpose, technical solutions, and beneficial effects of the present invention. It should be understood that the above are only specific embodiments of the present invention and are not used to limit the protection scope of the present invention. Any modifications, equivalent replacements, improvements, etc. made within the spirit and principles of the present invention shall be included in the protection scope of the present invention.

[0097] It should be noted that in this article, relational terms such as first and second are only used to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any actual relationship or order between these entities or operations. Moreover, the term "comprising", "including", or any other variant thereof is intended to cover non-exclusive inclusion, so that a process, method, article, or device comprising a series of elements includes not only those elements but also other elements not explicitly listed, or elements inherent to such process, method, article, or device.

Claims

1. A parameter calculation method for optimizing the flow characteristics of foam fire-retardant materials, characterized in that: The following steps are involved: S1. Establish a theoretical calculation model for foam fire-retardant materials; S2. Based on the theoretical calculation model, an Ergun equation is established; S3, adding unknown empirical parameters to the dimensionless numbers in the Ergun equation to obtain a set of dimensionless number calculation equations; S4, adding constraints to the dimensionless number calculation equation group; S5. Solving the optimal solution of the unknown empirical parameters based on the dimensionless number calculation equation group; S6. Substitute the optimal solution of the unknown empirical parameters into the Ergun equation to calculate the viscous drag coefficient and the inertial drag coefficient.

2. A parameter calculation method for optimizing the flow characteristics of foam fire-retardant materials according to claim 1, characterized in that: The theoretical calculation model includes a plurality of material units, each of which includes a metal sphere and a foam cube sleeved outside the metal sphere; the diameter d of the metal sphere is s , which is greater than the side length a of the foam cube; each side of the foam cube has an opening for the metal sphere to extend out; and satisfies:

3. A parameter calculation method for optimizing the flow characteristics of foam fire-retardant materials according to claim 2, characterized in that: The Ergun equation established is: Wherein: A* is the first dimensionless coefficient; B* is the second dimensionless coefficient; Δp is the pressure drop of the foam fire-retardant material area along the specified direction; Δn is the thickness of the foam fire-retardant material area along the specified direction; ∈ is the porosity of the theoretical calculation model; ρ is the fluid density; μ is the viscosity coefficient of the fluid; v is the fluid flow rate.

4. A parameter calculation method for optimizing the flow characteristics of foam fire-retardant materials according to claim 3, characterized in that: The porosity ∈ of the theoretical calculation model is calculated by the following formula:

5. A parameter calculation method for optimizing the flow characteristics of foam fire-retardant materials according to claim 3, characterized in that: The dimensionless number calculation equation group is: Where: A is the dimensionless number of viscosity; B is the dimensionless number of inertial resistance; C is the absolute error of calculation; F is the first unknown empirical parameter; Q is the second unknown empirical parameter.

6. A parameter calculation method for optimizing the flow characteristics of foam fire-retardant materials according to claim 5, characterized in that: The constraints include: C=|C′1-C1|+|C′2-C2| C′1>0; C′2>0 In the formula: C1 is the viscous drag coefficient to be calculated; C2 is the inertial drag coefficient to be calculated; C′1 is the known viscous drag coefficient; C′2 is the known inertial drag coefficient.

7. A parameter calculation method for optimizing the flow characteristics of foam fire-retardant materials according to claim 6, characterized in that: The viscous drag coefficient and inertial drag coefficient are calculated by the following formula: a′=0.5·C2·ρΔn b′=C1μΔn Where a′ is the first fitting coefficient and b′ is the second fitting coefficient, which is obtained by fitting through the following formula: Δp = a′v 2 +b′v.

8. A parameter calculation method for optimizing the flow characteristics of foam fire-retardant materials according to claim 3, characterized in that: The method for solving the optimal solution of the unknown empirical parameters includes: S501, determining a cooling function, setting an initial control temperature, and setting a Markov chain length; randomly selecting initial values ​​for a first unknown empirical parameter and a second unknown empirical parameter in a feasible solution space, and calculating an initial solution of the cooling function; S502, generating a random disturbance to the first unknown empirical parameter and the second unknown empirical parameter in the feasible solution space, and calculating a new solution of the cooling function based on the disturbed first unknown empirical parameter and the second unknown empirical parameter; S503, determining whether to accept the new solution, and obtaining the optimal solution of the first unknown empirical parameter and the second unknown empirical parameter at this time; S504, repeating steps S502 to S503 until the cycle under the Markov chain length number is completed, and the optimal solution of the first unknown empirical parameter and the second unknown empirical parameter under the Markov chain length is obtained.

9. A parameter calculation method for optimizing the flow characteristics of foam fire-retardant materials according to claim 8, characterized in that: In step S503, the Metropolis criterion is used to determine whether to accept the new solution.

10. A parameter calculation method for optimizing the flow characteristics of foam fire-retardant materials according to claim 8, characterized in that: The method for optimal solution of the unknown empirical parameters also includes: S505: Determine whether the optimal solution of the first unknown empirical parameter and the second unknown empirical parameter under the Markov chain length satisfies a preset stop criterion: If yes, output the optimal solution of the first unknown empirical parameter and the second unknown empirical parameter under the length of the Markov chain; If not, return to step S501 to modify the cooling function and / or increase the length of the Markov chain.

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