An optimization method and system for temperature control measures in a concrete dam pouring bin

Abstract

The present invention discloses a method and system for optimizing temperature control measures in a concrete dam pouring bin, including the following steps: Step 1, according to the actual temperature control construction experience and construction conditions of the concrete dam project, draw up the temperature control parameters to be optimized and their value ranges; Step 2, based on the temperature control parameters to be optimized and their value ranges drawn up in Step 1, and according to the concrete temperature change process control curve, establish an objective function for optimizing temperature control measures; Step 3, establish a hybrid optimization algorithm IABAP based on the particle swarm optimization (PSO) and the artificial bee colony (ABC); Step 4, based on IABAP and temperature field simulation, establish a model for optimizing the temperature control measures of the concrete in the pouring bin, and optimize the temperature control measure parameters. The present invention slightly adjusts the optimized temperature control measures according to the actual engineering situation, and then guides the subsequent actual temperature control and crack prevention, so as to realize the refined real-time temperature control of the concrete pouring bin.

Description

Technical Field

[0001] The present invention relates to the technical field of concrete pouring bins, and particularly relates to a method and system for optimizing temperature control measures in a concrete dam pouring bin. Background Art

[0002] Due to the large pouring volume and rapid temperature rise in a concrete dam pouring bin, temperature control and crack prevention are extremely important. The use of intelligent temperature control technology has become an important technical means for the refinement of temperature control and the prevention of temperature cracks in a concrete dam pouring bin. Intelligent temperature control mainly includes four links: "monitoring, analysis, control, and early warning". Real-time monitoring of the true temperature inside the concrete pouring bin, as a key link in intelligent temperature control, can provide basic data support for the analysis of the dam temperature field. The prediction of the concrete temperature curve of the pouring bin can provide guidance for the dynamic adjustment of on-site temperature control measures. The combined optimization of multiple temperature control measure factors can reduce the risk of dam cracking. The Particle Swarm Optimization (PSO) is a swarm intelligence algorithm that simulates the foraging behavior of bird flocks. Its optimal search system consists of a group of particles, and each particle has three indicators: position, velocity, and fitness value. The particle swarm collaboratively searches in the solution space by following the globally optimal particle in the group. The Artificial Bee Colony algorithm (ABC) is a swarm intelligence algorithm that simulates the foraging behavior of bee colonies, and searches for high-quality nectar sources through scout bees, leading bees, and follower bees. Existing studies have shown that the PSO and ABC algorithms have deficiencies in terms of local search performance, convergence speed, parameter selection, etc. In terms of temperature control measures for concrete dams, the focus is mainly on the sensitivity analysis of individual temperature control factors, lacking reliability. Therefore, an integrated optimization algorithm (An Integrated Algorithm Based on Artificial bee colony and Particle swarm optimization, IABAP) can be established by combining PSO and ABC to overcome the deficiencies of single intelligent optimization algorithms and apply it to the combined optimization of temperature control measures.

[0003] Starting from improving the combined optimization ability of temperature control measures in a concrete pouring bin, a hybrid intelligent optimization algorithm is established. Taking thermal parameters as known conditions reduces the difficulty of combined optimization of multiple factors. Selecting the concrete temperature control curve as the evaluation index for intelligent optimization of temperature control measures for intelligent optimization, and providing personalized temperature control measure design for the concrete in the proposed pouring bin. Summary of the Invention

[0004] The present invention proposes an intelligent optimization method for temperature control measures in concrete dam pouring bins based on the IABAP hybrid algorithm. By selecting a more reasonable temperature change curve of mass concrete as the optimization index for temperature control measures, the internal tensile stress change of concrete is improved, and the generation of temperature cracks is avoided.

[0005] To achieve the above object, the technical solution of the method of the present invention is as follows:

[0006] An optimization method for temperature control measures in concrete dam pouring bins, comprising the following steps:

[0007] Step 1, according to the actual temperature control construction experience and construction conditions of the concrete dam project, draw up the temperature control parameters to be optimized and their value ranges;

[0008] Step 2, based on the temperature control parameters to be optimized and their value ranges drawn up in Step 1, and according to the concrete temperature change process control curve, establish an optimization objective function for temperature control measures;

[0009] Step 3, establish a hybrid optimization algorithm IABAP based on the particle swarm optimization (PSO) and artificial bee colony (ABC);

[0010] Step 4, based on IABAP and temperature field simulation, establish an optimization model for temperature control measures of concrete in the pouring bin, and optimize the temperature control measure parameters.

[0011] Further, the temperature control parameters to be optimized drawn up in Step 1 include the concrete pouring temperature T p , the water injection temperature T w , the water injection flow rate Q, and the starting time t of water injection.

[0012] Further, the optimization objective function E(x) for temperature control measures in Step 2 is:

[0013]

[0014] wherein, T′ i , T i respectively represent the temperature calculated value and the control value at the i-th moment; m is the number of vectors of the temperature time series at the center point of the pouring bin.

[0015] Further, the construction method of the hybrid optimization algorithm IABAP in Step 3 includes:

[0016] Step 3.1. Divide the population into two sub-groups, one sub-group evolves according to PSO, and the other evolves according to ABC, and initialize the two sub-groups of the artificial bee colony ABC and the particle swarm PSO;

[0017] Step 3.2. The two sub-groups evolve in parallel and exchange information according to a given probability at the same time.

[0018] During the ABC evolution process, scout bees obtain information from the particle swarm with a specified probability. The scout bees randomly select a particle position in the particle swarm as the position of the new nectar source. The higher the particle fitness, the more likely it is to be selected. On the other hand, during the PSO evolution process, swarm information is obtained with a specified small probability, and a nectar source position with a high fitness value is randomly selected from the swarm to guide the update of the particle velocity. When the particle swarm meets the given probability, the particle velocity update rule is:

[0019] v id = ω×v id + c1r1(p id - x id ) + c2r2(p gd - x id ) + c3r3(p ad - x id ) (2)

[0020] In the formula, c1, c2, and c3 are learning factors, all of which are non - negative constants; r1, r2, and r3 are random numbers between [0, 1]; p ad is the d - dimensional nectar source position randomly selected from the ABC subgroup. Denote the d - dimensional best position searched by the i - th particle so far as p id , p gd is the d - dimensional historical best position searched by the entire particle swarm;

[0021] If the ABC executes the scout bee search process and meets the given probability, the new solution generated by the scout bee is randomly obtained from the PSO. Otherwise, it is obtained by the following formula:

[0022] x id = L d + rand(0, 1)(U d - L d ) (3)

[0023] In the formula, U d and L d are the upper and lower bounds of the solution space of x i respectively;

[0024] During the PSO evolution process, if the particle meets the given small probability, the velocity and position are updated according to formulas (2) and (3). Otherwise, they are updated according to formulas (4) and (5);

[0025]

[0026]

[0027] In the formula, k is the particle iteration number; d ∈ {1, 2,..., D}; ω is the inertia weight; The partial label is the velocity and position of particle i in the d-th dimension at the k-th iteration, v id ∈[-v max ,v max ;

[0028] Step 3.3. Respectively count the fitness of the two subgroups and compare the fitness values to obtain the global optimal fitness value of the algorithm;

[0029] Step 3.4. Determine whether the algorithm reaches the set maximum number of iterations Maxcycle. If the number of iterations is reached, record the best solution; otherwise, return to Steps 3.1 - 3.2.

[0030] Furthermore, in Step 4, an optimal model for the temperature control measures of the concrete in the pouring bin is established based on IABAP and temperature field simulation, and the optimized temperature control measure parameters include:

[0031] Step 4.1: Use the IABAP intelligent optimization algorithm to generate multiple parameter value samples of temperature control measures within the value range of the temperature control parameters to be optimized, and form several value sets of the parameters to be optimized:

[0032] x = {T p , T w , Q, t} (6)

[0033] In Equation (6), T p is the constructed value of the pouring temperature in the value set x; T w is the constructed value of the water temperature during water circulation in the value combination x; Q is the constructed value of the water flow rate during water circulation in the value combination x; t is the constructed value of the starting time of water circulation in the value combination x;

[0034] Step 4.2: Use ANSYS finite element software to perform temperature field simulation of the pouring bin for each randomly generated value set of the parameters to be optimized, and predict the concrete temperature change curve; apply the concrete hydration heat, ambient air temperature, and the influence of water circulation cooling at different time periods to the temperature field of the pouring bin, and calculate the corresponding concrete predicted temperature time series values for the value set x;

[0035] Step 4.3: Consider each value combination x of the parameters to be optimized as a feasible solution to the optimization problem. The IABAP intelligent optimization algorithm compares the fitness values of the solutions by substituting x into the optimization objective function of the temperature control measures to calculate the error E(x). The smaller the fitness value, the better x. Thus, obtain the minimum value of the objective function obtained by performing temperature field simulation on the pouring bin of the concrete dam using the value combination of the temperature control parameters. where {T1, T2, T3,..., T m} and {T′1, T′2, T′3,..., T′ mThey represent the controlled temperature time series values and the predicted temperature time series values using the optimal temperature control parameter combination x respectively. According to the actual engineering situation, the selected temperature control parameter combination x is rounded.

[0036] Further, in step 4.2, more than 2 continuous placement bays finite element models are established in ANSYS. Temperature curve prediction is carried out for the placement bays except the bottommost placement bay. Mesh division is performed on the finite element models, and the concrete materials and element properties are assigned values. The concrete thermal parameter values are substituted into the temperature field simulation as known conditions.

[0037] Further, initial conditions and boundary conditions are set for the simulation model. The initial condition is the placement temperature structure value in the value set x, and the boundary conditions are the ambient air temperature and cooling water injection. The side and bottom surfaces of the transverse joints of the placement bay are adiabatic boundaries, and the top surface and the upstream and downstream surfaces of the placement bay are the third kind of heat transfer boundary conditions.

[0038] On the other hand, the present invention provides a temperature control measure optimization system for the placement bays of concrete dams, including:

[0039] Module 1, which is used to draw up the temperature control parameters to be optimized and their value ranges according to the actual temperature control construction experience and construction conditions of the concrete dam project;

[0040] Module 2, which is used to establish an optimization objective function for temperature control measures based on the temperature control parameters to be optimized and their value ranges drawn up in Module 1 and according to the concrete temperature change process control curve;

[0041] Module 3, which is used to establish a hybrid optimization algorithm IABAP based on the particle swarm optimization (PSO) and artificial bee colony (ABC);

[0042] Module 4, which is used to establish an optimization model for the temperature control measures of the concrete in the placement bay based on IABAP and temperature field simulation, and optimize the temperature control measure parameters;

[0043] The temperature control measure optimization system for the placement bays of concrete dams is used to execute the steps in the temperature control measure optimization method for the placement bays of concrete dams.

[0044] Compared with the prior art, the present invention has the following beneficial effects:

[0045] (1) Aiming at the deficiencies of single intelligent optimization algorithms such as PSO and ABC, the present invention establishes a hybrid optimization algorithm IABAP by exchanging information and evolving in parallel with a given probability in two sub-groups of PSO and ABC, improving the global optimization ability of the algorithm.

[0046] (2) The temperature control curve is a key factor for the intelligent temperature control and crack prevention of concrete dams. Excessive heating rates and maximum temperatures can cause large temperature stresses in the concrete placement bins, thus affecting the crack prevention safety during the construction period of concrete dams. Currently, there is little research on the curve prediction of the temperature of concrete dams. In this invention, the concrete temperature control curve is selected as an evaluation index for the intelligent optimization of temperature control measures to select reasonable temperature control measures.

[0047] (3) Based on IABAP and temperature field simulation, an optimization model for the temperature control measures of the concrete in the placement bin is established. By predicting the development of the temperature curve of the bin to be placed and comparing it with the temperature control curve, intelligent optimization of temperature control measures such as cooling water circulation is carried out. Through the deterministic feedback simulation method and intelligent optimization algorithm, the economy of the temperature control measures is also optimized on the basis of ensuring the design quality of the temperature control and crack prevention of the concrete dam. Description of the Drawings

[0048] Figure 1 It is the flow chart of the IABAP intelligent optimization algorithm of the present invention;

[0049] Figure 2 It is the flow chart of the optimization of the temperature control measures of the present invention;

[0050] Figure 3 It is the control curve of the temperature change process of the embodiment of the present invention;

[0051] Figure 4 It is the finite element model of the #035 - 039 placement bins of the embodiment of the present invention;

[0052] Figure 5 It is the information of the cooling water circulation and ambient air temperature of the #036 - 039 placement bins of the embodiment of the present invention;

[0053] Figure 6 It is the comparison chart of the actual temperature control measures and the predicted temperature curve after the adjustment of the temperature control measures of the embodiment of the present invention;

[0054] Figure 7 It is the comparison chart of the stress time - history curves of the center points of the placement bins before and after the adjustment of the temperature control measures in the embodiment of the present invention; Detailed Embodiment

[0055] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without creative efforts fall within the protection scope of the present invention.

[0056] Embodiment 1

[0057] The following will describe in detail an embodiment of an intelligent optimization method for temperature control measures in a concrete dam pouring bin based on the IABAP hybrid algorithm of the present invention. The main purpose of this embodiment is to establish a hybrid optimization algorithm IABAP that combines the particle swarm optimization (PSO) and the artificial bee colony (ABC), and establish an objective function for optimizing temperature control measures based on the concrete temperature change process control curve; establish an intelligent optimization model for temperature control measures in the pouring bin based on IABAP and temperature field simulation, and optimize the reasonable pouring temperature, water flow temperature, water flow rate, and starting time of water flow of a certain concrete dam C35 concrete pouring bin to achieve refined real-time temperature control of the concrete pouring bin. As Figure 1 shown, the embodiment includes the following specific steps: Figure 2

[0058] Step 1, according to the actual temperature control construction experience and construction conditions of the concrete dam project, draw up the temperature control parameters to be optimized and their value ranges; specifically including:

[0059] Draw up the range of temperature control parameters to be optimized. The amount of concrete in the concrete dam pouring bin is large, and the early heating rate is fast. If the cooling water pipe is not filled with water in time after the concrete is opened, the temperature of the concrete in the pouring bin will change violently. Therefore, the concrete pouring temperature T p w p w 、water flow temperature T

[0060] 、water flow rate Q, and starting time of water flow t are selected as the temperature control parameters to be optimized. The present invention selects the temperature at the center point of the pouring bin for prediction and feedback, and mainly analyzes the maximum temperature and heating rate at the center point of the proposed pouring bin. During the construction process of the concrete dam pouring bin, although the boundary conditions of the temperature field in the pouring bin are a continuously changing process, the temperature at the center point of the pouring bin is relatively insensitive to boundary conditions such as ambient temperature and surface insulation.

[0061] According to the actual temperature control construction experience and construction conditions of the concrete dam project, draw up the value ranges of the temperature control parameters to be optimized: pouring temperature T w

[0062] ∈[5, 12] °C; water flow rate Q ∈ [20, 42] L / min; starting time of water flow t ∈ [0, 3] d; water flow temperature T

[0062] ∈[8, 16] °C.

[0061] Step 2, based on the temperature control parameters to be optimized and their value ranges determined in Step 1, and establish an objective function for optimizing temperature control measures according to the concrete temperature change process control curve;

[0062] Establish an objective function expression for optimizing temperature control measures, and transform the problem of optimizing concrete temperature control measures into an optimization problem. The temperature control curve is a key part of the intelligent temperature control and crack prevention of concrete dams. An excessive cooling rate or too high concrete temperature will have an adverse impact on the internal stress changes in the pouring bin. The present invention selects the control curve of the concrete temperature change process as the evaluation index for the intelligent optimization of temperature control measures. Combining the curve characteristics of "three stages and nine segments" with small temperature differences, early cooling, and slow cooling proposed by Academician Zhu Bofang and the technical requirements of concrete temperature control construction, the temperature change process control curve in this embodiment is as Figure 3 shown.

[0063] For a randomly selected set of temperature control measure parameters, a temperature field simulation model of the pouring bin to be constructed can be established through the three-dimensional finite element software ANSY under the condition of known thermal parameters, so as to calculate the corresponding temperature curve. The optimization model of temperature control measures selects the cumulative relative difference between the calculated value and the control value of the concrete temperature at the center of the pouring bin at a series of identical times to measure the rationality of the selected temperature control measures. Define the objective function expression as:

[0064]

[0065] In the formula, T′ i , T i respectively represent the calculated value and the control value of the temperature at the i-th moment; m is the number of vectors of the temperature time series at the center point of the pouring bin.

[0066] Step 3, establish a hybrid optimization algorithm IABAP based on the particle swarm optimization (PSO) and the artificial bee colony (ABC);

[0067] Step 3.1: Initialize two subgroups of ABC and PSO.

[0068] The population in the particle swarm algorithm is called a particle swarm, and the individuals in the particle swarm are called particles. Suppose there are N particles in a particle swarm in a D-dimensional search space. The position of the i-th particle in the search space is represented as a D-dimensional vector x i =(x i1 , x i2 ,..., x iD ), T , i∈{1, 2,..., N}, and the corresponding flight speed is represented as v i =(v i1 , v i2 ,..., v iD ). T . The position xi of each particle can be regarded as a feasible solution, and the fitness is calculated by substituting xi into the objective function to compare the quality of the solutions. Denote the best position searched by the i-th particle so far as p i =(p i1 , pi2 ,..., p iD ), the historical best position found by the entire particle swarm is denoted as p g =(p g1 , p g2 ,..., p gD ). The velocity and position of the particle are updated by equations (4) and (5) respectively.

[0069] In the ABC algorithm, the initial numbers of leading bees and following bees each account for half of the total bee colony. The leading bees search for a new nectar source in their neighborhood based on the nectar source position in memory, and the following bees select the corresponding nectar source according to the probability calculation formula for allocating following bees and search for a new nectar source in the neighborhood of the selected nectar source. The formulas for the leading bees and following bees to conduct neighborhood search are:

[0070]

[0071] In the formula, v id represents the position of the new nectar source; d is a random integer in [1, D], indicating a random one-dimensional perturbation in x i (x i1 , x i2 , …, x iD ); T is a random number in [-1, 1], representing the perturbation amplitude; j ≠ i and j ∈ {1, 2, …, SN}, representing selecting a nectar source different from nectar source i among SN nectar sources. The probability calculation formula for allocating following bees:

[0072] The probability calculation formula for allocating following bees:

[0073]

[0074] In the formula, fit i is the fitness of nectar source i.

[0075] Assume that the quality of the nectar source position x i has not improved after limit (the maximum exploitation times) cycles, then it is considered that this solution falls into a local optimum. Abandon this nectar source position, and at this time, the corresponding leading bee is transformed into a scout bee, and the scout bee randomly generates a new nectar source position in the solution space according to equation (2) for replacement.

[0076] In this embodiment, the inertia weight w, learning factors c1, c2 of PSO are 0.8, 1.2, 0.8 respectively, the population size is 40; the number of ABC nectar sources is set to 20, the maximum exploitation times of the nectar source limit = 100; the probability that ABC obtains information from PSO in each iteration process is 0.5, the probability that PSO obtains information from ABC is 0.01, the learning factor c3 = 1, and the number of iterations Maxcycle = 200.

[0077] Step 3.2: The two subgroups evolve in parallel and exchange information according to a given probability.

[0078] During the ABC evolution process, scout bees obtain information from the particle swarm with a specified probability. The scout bees randomly select a particle position in the particle swarm as the new nectar source position. The higher the particle fitness, the more likely it is to be selected. On the other hand, during the PSO evolution process, information from the bee swarm is obtained with a specified small probability, and a nectar source position with a high fitness value is randomly selected from the bee swarm to guide the update of the particle velocity. When the particle swarm meets the given probability, the particle velocity update rule is:

[0079] v id = ω×v id + c1r1(p id - x id ) + c2r2(p gd - x id ) + c3r3(p ad - x id ) (2)

[0080] In Equation (2), c1, c2, and c3 are learning factors, all of which are non-negative constants; r1, r2, and r3 are random numbers between [0, 1]; p ad is the d-dimensional nectar source position randomly selected from the ABC subgroup. Denote the d-dimensional best position searched by the i-th particle so far as p id , and denote the d-dimensional historical best position searched by the entire particle swarm as p gd .

[0081] If the ABC performs the scout bee search process and meets the given probability, the new solution generated by the scout bee is randomly obtained from the PSO; otherwise, it is obtained through Equation (3).

[0082] x id = L d + rand(0, 1)(U d - L d ) (3)

[0083] In Equation (3), U d and L d are the upper and lower bounds of the solution space of x i , respectively.

[0084]

[0085]

[0086] where k is the number of particle iterations; c1 and c2 are learning factors, both non - negative constants; r1 and r2 are random numbers between [0, 1]; d ∈ {1, 2,..., D}; ω is the inertia weight; x id The superscripts indicate the velocity and position of particle i in the d - th dimension at the k - th iteration, v id ∈[-v max ,v max .

[0087] During the PSO evolution process, if the particle meets a given small probability, update the velocity and position according to Equations (2) and (3), otherwise update according to Equations (4) and (5).

[0088] Step 3.3: Calculate the fitness of each of the two subgroups respectively and compare the fitness values to obtain the global optimal fitness value of the algorithm;

[0089] Step 3.4: Determine whether the algorithm has reached the set maximum number of iterations Maxcycle. If the iteration number is reached, record the best solution, otherwise return to Steps 3.1 - 3.2.

[0090] To verify that the improved IABAP has a stronger optimization ability, the present invention selects four 30 - dimensional standard performance test functions, namely Rastrigin, Sphere, Rosenbrock, and Ackely, to conduct optimization performance tests on the PSO, GA (Genetic Algorithm), ABC commonly used in the temperature control field of concrete dams and the IABAP algorithm established in the present invention respectively. The theoretical optimal values of the test functions are all 0. In GA, the mutation probability and crossover probability are 0.05 and 0.8 respectively, and the population size is 40.

[0091] Table 1 Test function expressions

[0092]

[0093] Optimize the 4 standard functions respectively to obtain the mean and standard deviation SD of each function. After running independently 10 times, take the average as the final test result. The test results are shown in Table 2. Obviously, the ABC algorithm has better performance than PSO and GA. The average value and standard deviation of the 10 - time optimal solutions obtained by using IABAP have higher accuracy and better robustness than the other three algorithms, reflecting that the algorithm improved by combining ABC and PSO has better optimization ability. In addition, IABAP also performs better in the optimization process of the complex Rosenbrock function.

[0094] Table 2 Comparison of the performance of intelligent optimization algorithms

[0095]

[0096]

[0097] Step 4: Based on IABAP and temperature field simulation, establish an optimized model for the temperature control measures of the concrete in the pouring bin, and optimize the parameters of the temperature control measures. It includes the following sub-steps:

[0098] Step 4.1: Use the IABAP intelligent optimization algorithm to generate multiple parameter value samples of the temperature control measures within the value range of the temperature control parameters to be optimized, and form several value sets of the parameters to be optimized:

[0099] x = {T p , T w , Q, t} (6)

[0100] In formula (6), T p is the constructed value of the pouring temperature in the value set x; T w is the constructed value of the water temperature for water circulation in the value combination x; Q is the constructed value of the water flow rate for water circulation in the value combination x; t is the constructed value of the starting time of water circulation in the value combination x.

[0101] Step 4.2: Use the ANSYS finite element software to perform temperature field simulation of the pouring bin for each randomly generated value set of the parameters to be optimized, and predict the concrete temperature change curve.

[0102] Select five C35 continuous concrete pouring bins #035 - 039 of a certain concrete dam for intelligent optimization of temperature control measures. As Figure 4 shown, establish a finite element model for 5 bins, with a total of 41,760 elements and 45,725 nodes. The division uses hexahedral eight-node isoparametric elements. The concrete density ρ is 2553 kg / m 3 , the specific heat c is 0.873 kJ / (kg·°C), and the thermal diffusivity α is 0.073 m 2 / d. Each pouring bin consists of 6 layers with a thickness of 0.5 m, and the bin thickness is 3 m. The distributed optical fiber for monitoring the concrete temperature change is buried 0.2 m above the second layer of each bin. The horizontal spacing of the polyethylene cooling water pipes is 1.5 m, and the vertical spacing is 1.5 m, which are buried in the first layer and the fourth layer.

[0103] The transverse joint surface of the finite element model of the pouring bins #035 - 039 is assumed to be an adiabatic boundary. The top surface of the pouring bin #039 and the upstream and downstream surfaces of the five bins #035 - 039 are all in contact with the air, and the third-kind boundary condition is adopted.

[0104] The third-kind heat transfer boundary condition: When the concrete is in contact with the air, it is assumed that the heat flux q passing through the concrete surface is proportional to the difference between the concrete surface temperature T(τ) and the air temperature Ta:

[0105] q = β(T(τ) - T a ) (7)

[0106] In formula (7), β is the surface heat release coefficient.

[0107] To reduce the difficulty of the combined optimization of multiple factors in temperature control measures, θ0 = 21.48 °C, n = 5.94, and β = 176.46 kJ / (m 2 ·d·°C) are substituted as known conditions for the optimization of temperature control measures into the temperature field simulation.

[0108] Zhu Bofang proposed a combined exponential formula for facilitating the calculation of the adiabatic temperature rise of concrete:

[0109]

[0110] In formula (8), θ0 is the adiabatic temperature rise of concrete, τ is the age of concrete; s, m1, and m2 are three undetermined coefficients. Experience shows that s can be taken as 0.60, and n is the temperature rise law parameter.

[0111] In the first-stage water cooling stage, cooling is often carried out in multiple gears (including two cases of non-water-cooling and water-cooling). The combined exponential formula of the adiabatic temperature rise of concrete is used to simulate the non-water-cooling situation of the pouring bin, and the equivalent heat conduction equation of pipe cooling is used to simulate the water-cooling situation. The calculation expression for the average temperature of concrete considering water-cooling is:

[0112]

[0113] In formula (9), e is the natural constant in mathematics, T wi is the water temperature of the i-th gear of water cooling; T i is the temperature of concrete at the end of the (i - 1)-th gear of water cooling and at the start of the i-th gear of water cooling; φ i (t) is the water-cooling function when the water temperature of the i-th gear is cooled; θ0 is the final adiabatic temperature rise; ψ i (t) is the water-cooling temperature rise function at the i-th gear; p i is the water-cooling parameter at the i-th gear; t i is the moment when the flow rate or water temperature changes. When the flow rate or water temperature changes, t must start from 0; t is the cooling time. Among them, the water-cooling parameter is calculated by formula (10):

[0114]

[0115] In formula (10), k, a', and D are constants; D is the diameter of the equivalent cooling cylinder; b is the radius of the equivalent cooling cylinder; S1 and S2 are the horizontal and vertical spacings of the pipes during the layout process; ρ w is the density of water; q w is the water flow rate; L is the length of the pipe; c wis the specific heat of water; a' is the equivalent thermal conductivity of polyethylene cooling water pipe; c is the outer radius of the water pipe; r0 is the inner radius of the water pipe; λ1 is the thermal conductivity of the water pipe; λ is the thermal conductivity of concrete.

[0116] The "life and death unit" in the thermal analysis module of the ANSYS finite element software is used to simulate the sequence before and after the concrete silo is poured. The water cooling information, ambient temperature and pouring temperature of the lower pouring silo are based on the actual monitoring values ​​during the construction period. The water information and pouring temperature of the pouring silo to be predicted are based on the constructed values ​​in the value set x, and the ambient temperature is based on the predicted value of the meteorological station. IABAP is used to randomly generate a combination of temperature control measure parameters as the calculation load of the pouring silo and apply it to the temperature field of the finite element model for positive analysis, so as to obtain the predicted concrete temperature time series value corresponding to the value set x. #036~039 The cooling water information and ambient temperature information of the pouring silo are as follows Figure 5 As shown, (a) is the water temperature information, (b) is the water flow information, and (c) is the ambient temperature information.

[0117] Step 4.3: Each combination of parameters to be optimized x i It can be regarded as a feasible solution to the optimization problem. The IABAP intelligent optimization algorithm calculates x i Substitute into formula (1) to calculate the error E(x i ) to compare the fitness value of the solution. The smaller the fitness value x i The better. The minimum value of the objective function is obtained by simulating the temperature field of the concrete dam to be poured using a combination of temperature control parameter values. where {T1,T2,T3,...,T m} and {T′1,T′2,T′3,...,T′ m} respectively represent the controlled temperature time series value and the predicted temperature time series value using the optimal temperature control parameter combination xi.

[0118] According to the actual situation of the project, the temperature control parameter combination x is selected i After rounding (the water supply start time is in units of 0.5 days), the optimal pouring temperature, water supply temperature, water supply flow rate and water supply start time of the pouring bin are obtained. The final results of the temperature control measures are shown in Table 3. The concrete temperature control curve, the predicted temperature curve using the actual temperature control measures and the predicted temperature curve after the temperature control measures are adjusted are shown in Table 3. Figure 6 shown.

[0119] Table 3 Optimization results of temperature control measures for casting silo

[0120]

[0121] in accordance with Figure 6As can be seen from (a)-(d), when selecting the intelligent optimal temperature control measure parameter values to simulate the temperature fields of the four pouring bins #036 - 039, comparing the predicted results of the temperature curves with those predicted using the actual temperature control measure parameter values during the construction period, it is found that they are closer to the given concrete temperature control curve and the temperature rise rate is smaller. Additionally, due to the large amount of concrete used in the pouring bins of the concrete dam, the heat generated by internal hydration heat is large and not easily dissipated, resulting in a fast early temperature rise. The optimized pouring temperature is reduced to varying degrees compared to the actual pouring temperature, and the starting time of cooling water injection is advanced, which is beneficial for controlling the tensile stress of the concrete. The relatively large difference between the predicted temperature values at some nodes of the predicted temperature curve and the control curve after adjusting the temperature control measures is because the selected temperature control measures have been slightly adjusted in combination with the on-site construction technology and conditions, and the average values of the water temperature and water flow rate for water injection are taken, without being precisely optimized every 0.5d.

[0122] By analyzing the concrete stresses calculated for the four pouring bins #036 - 039, the stress time history curves at the center points of the concrete pouring bins before and after adjusting the temperature control measures are as Figure 7 shown. After adjusting the temperature control measure parameter values, the tensile stresses at the center points of each bin have been reduced to varying degrees, indicating that selecting a reasonable temperature curve for mass concrete as the optimal index for temperature control measures can play a role in improving the change process of the internal temperature stress of the concrete.

[0123] Embodiment 2

[0124] This embodiment provides an optimization system for the temperature control measures of the pouring bins of a concrete dam, characterized by including:

[0125] Module 1, which is used to determine the temperature control parameters to be optimized and their value ranges based on the actual temperature control construction experience and construction conditions of the concrete dam project;

[0126] Module 2, which is used to establish an optimization objective function for temperature control measures based on the temperature control parameters to be optimized and their value ranges determined in Module 1 and according to the concrete temperature change process control curve;

[0127] Module 3, which is used to establish a hybrid optimization algorithm IABAP based on the particle swarm optimization (PSO) and artificial bee colony (ABC);

[0128] Module 4, which is used to establish an optimal selection model for the temperature control measures of the concrete in the pouring bin based on IABAP and temperature field simulation, and optimize the temperature control measure parameters.

[0129] As described above, it is only a preferred specific embodiment of the present invention, but the scope of protection of the present invention is not limited thereto. Any modification, equivalent replacement, improvement, etc. made by those skilled in the art within the technical scope disclosed by the present invention should be included within the scope of protection of the invention. Although the preferred embodiments of the present invention have been described, those skilled in the art can make additional changes and modifications to these embodiments once they know the basic creative concept. Therefore, the appended claims are intended to be interpreted as including the preferred embodiments as well as all changes and modifications falling within the scope of the present invention.

[0130] Obviously, those skilled in the art can make various changes and modifications to the embodiments of the present invention without departing from the spirit and scope of the embodiments of the present invention. Thus, if these modifications and variations of the embodiments of the present invention fall within the scope of the claims of the present invention and their equivalent technologies, the present invention also intends to include these changes and modifications.

[0131] Other parts not described in detail are all prior arts.

Claims

1. A method for optimizing temperature control measures in a concrete dam pouring bin, characterized in that: The steps include: Step 1: According to the actual temperature control construction experience and construction conditions of concrete dam projects, the temperature control parameters to be optimized and their value ranges are formulated; Step 2: Based on the temperature control parameters to be optimized and their value ranges proposed in step 1, an optimization objective function of temperature control measures is established according to the concrete temperature change process control curve; Step 3, establish a hybrid optimization algorithm IABAP based on particle swarm PSO and artificial bee colony ABC; include: Step 3.

1. Divide the population into two subgroups, one subgroup evolves according to PSO and the other evolves according to ABC, and initialize the artificial bee colony ABC and particle swarm PSO subgroups; Step 3.

2. The two subgroups evolve in parallel and exchange information with a given probability. During the ABC evolution process, scout bees obtain information from the particle swarm with a specified probability. The scout bees randomly select a particle position in the particle swarm as the new nectar source position. The higher the particle fitness, the more likely it is to be selected. On the other hand, during the PSO evolution process, swarm information is obtained with a specified smaller probability, and a nectar source position with a high fitness value is randomly selected from the swarm to guide the particle speed update. When the particle swarm meets the given probability, the particle speed update rule is: （2） In the formula, , , are learning factors, all of which are non-negative constants; , , is a random number between [0,1]; is the first randomly selected d The location of the honey source will be i The particle searched d The best position is recorded as , The first d The best position in history; If ABC executes the scout bee search process and satisfies the given probability, the new solution generated by the scout bee is randomly obtained from PSO, otherwise the following equation is obtained: （3） In the formula, and They are Upper and lower bounds of the solution space; During the PSO evolution process, if the particle meets the given smaller probability, the speed and position are updated according to equations (2) and (3), otherwise they are updated according to equations (4) and (5); （4） （5） In the formula, is the number of particle iterations; ; is the inertia weight; , Particles In the The first iteration d The speed and position of the dimension, ; Step 3.

3. Count the fitness of the two subgroups respectively and compare the fitness values ​​to obtain the global optimal fitness value of the algorithm; Step 3.

4. Determine whether the algorithm has reached the set number of iterations Maxcycle If the number of iterations is reached, the best solution is recorded, otherwise, return to steps 3.1-3.2; Step 4: Based on IABAP and temperature field simulation, a model for optimizing temperature control measures for concrete in the casting silo is established to optimize the parameters of temperature control measures.

2. The method for optimizing temperature control measures for a concrete dam pouring bin according to claim 1, characterized in that: The temperature control parameters to be optimized in step 1 include the concrete pouring temperature , water temperature , water flow , and water supply start time .

3. The method for optimizing temperature control measures for concrete dam pouring bin according to claim 1, characterized in that: The temperature control measures in step 2 optimize the objective function for: （1） in, , Respectively represent i The calculated and controlled temperature values ​​at the moment; m is the number of time series vectors of the temperature at the center of the casting bin.

4. The method for optimizing temperature control measures for a concrete dam pouring bin according to claim 1, characterized in that: In step 4, a model for optimizing temperature control measures for concrete in the casting bin is established based on IABAP and temperature field simulation, and the parameters of the temperature control measures are optimized, including: Step 4.1: Use the IABAP intelligent optimization algorithm to generate parameter value samples of multiple temperature control measures within the value range of the temperature control parameter to be optimized, and form several value sets of the parameter to be optimized: （6） In formula (6) For value collection Medium pouring temperature construction value; For value collection The structural value of the water temperature of the middle water; For value collection The structural value of the water flow rate in the middle passage; For value collection The construction value of the start time of water flow in the middle; Step 4.2: Use ANSYS finite element software to simulate the temperature field of the pouring bin for each randomly generated set of values ​​of the optimal parameters to be selected, and predict the concrete temperature change curve; apply the concrete hydration heat, ambient temperature, and water cooling effects in different time periods to the temperature field of the pouring bin, and calculate the value set The corresponding concrete predicted temperature time series value; Step 4.3: The value set of each parameter to be optimized As a feasible solution to the optimization problem, the IABAP intelligent optimization algorithm uses Substitute the temperature control measures into the optimization objective function to calculate the error To compare the fitness value of the solution, the smaller the fitness value The better, the minimum value of the objective function obtained by simulating the temperature field of the concrete dam to be poured using the temperature control parameter value set ,in and Represent the control temperature time series value and the optimal temperature control parameter set The predicted temperature time series value is used to select the optimal temperature control parameter value set according to the actual project situation. Rounding is performed.

5. A method for optimizing temperature control measures for concrete dam pouring bin according to claim 4, characterized in that: In step 4.2, more than two continuous casting bin finite element models are established in ANSYS, the temperature curve of the casting bins other than the bottom casting bin is predicted, the finite element model is meshed, the concrete material and unit properties are assigned, and the concrete thermal parameter values ​​are substituted as known conditions into the temperature field simulation.

6. A method for optimizing temperature control measures for concrete dam pouring bin according to claim 5, characterized in that: Set initial conditions and boundary conditions for the simulation model. The initial conditions are a set of values. The casting temperature is constructed in the middle, the boundary conditions are ambient air temperature and cooling water, the side and bottom of the casting bin transverse joint are adiabatic boundaries, and the top surface and upstream and downstream surfaces of the casting bin are the third type of heat transfer boundary conditions.

7. A temperature control optimization system for a concrete dam pouring bin, characterized in that: include: Module 1 is used to formulate the optimal temperature control parameters and their value ranges based on the actual temperature control construction experience and construction conditions of concrete dam projects; Module 2 is used to establish the temperature control measure optimization objective function based on the temperature control parameters and their value ranges proposed in module 1 and the concrete temperature change process control curve; Module 3 is used to establish a hybrid optimization algorithm IABAP based on particle swarm optimization (PSO) and artificial bee colony (ABC); Module 4 is used to establish a model for optimizing temperature control measures for concrete in the casting bin based on IABAP and temperature field simulation, and to optimize the parameters of temperature control measures; The concrete dam casting bin temperature control measure optimization system is used to execute the steps in the concrete dam casting bin temperature control measure optimization method described in any one of claims 1-6.

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