Dynamic regulation and control method for hydration heat of variable cross-section stack box rigid frame aqueduct
By constructing dual digital twin models and conducting real-time data interaction verification and parameter correction, the problem of real-time dynamic control of the hydration heat temperature field of variable cross-section stacked box rigid frame aqueduct was solved, achieving high-precision temperature control and improving the safety and adaptability of the construction process.
Patent Information
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Filing Date
- 2025-10-27
- Publication Date
- 2026-03-20
AI Technical Summary
Existing technologies cannot achieve real-time dynamic control of the hydration heat temperature field of variable cross-section stacked box girder aqueducts, resulting in poor adaptability of temperature control strategies in complex construction environments, lag and prediction errors, which affect the durability and safety of the structure.
By constructing a dual digital twin model that combines temperature field and environmental parameters, and through real-time data interaction verification and parameter correction, dynamic water cooling control commands are generated to achieve high-precision prediction and real-time control of the hydration heat temperature field.
It improves the temperature control accuracy and structural safety during the construction of variable cross-section stacked box girder aqueducts, reduces temperature fluctuations and crack risks, and enhances the reliability and safety of the construction process.
Smart Images
Figure CN121706322A_ABST
Abstract
Description
TECHNICAL FIELD
[0001] The present application relates to the technical field of hydraulic engineering construction, and in particular relates to a method for dynamically regulating and controlling hydration heat of a variable cross-section stacked box rigid structure aqueduct. BACKGROUND
[0002] Due to the sudden change in the wall thickness of the variable cross-section stacked box rigid structure aqueduct (such as 0.8m of the web plate and 2.5m of the flange), a local temperature peak is easily formed under the action of hydration heat, the temperature rise gradient can reach 25℃ / m, and then temperature stress concentration is induced, the risk of concrete cracking is increased, and the durability and safety of the structure are affected.
[0003] At present, the means for controlling the hydration heat temperature of the variable cross-section stacked box rigid structure aqueduct mainly include: 1. Single-point temperature measurement and manual regulation: a thermocouple is embedded in the thick-walled concrete area for temperature monitoring, and the water flow is manually adjusted according to the monitoring results to achieve cooling. Although this method can control the maximum temperature difference within 25℃ to a certain extent, it has obvious hysteresis, and the response time of water cooling often exceeds 2 hours, which cannot realize real-time dynamic regulation and control of the temperature field.
[0004] 2. Offline finite element simulation: the hydration heat process is simulated by a finite element software before construction to optimize the arrangement scheme of the cooling water pipe. However, this method does not consider the influence of dynamic environmental factors such as sunlight and wind speed during construction, and the model prediction results have a large deviation (up to ±5℃) from the actual working conditions, which is difficult to guide real-time regulation and control on site.
[0005] It can be seen that the common defects of the existing technology are: on the one hand, the temperature monitoring data is only used for alarm prompt, and cannot form a closed-loop linkage with the cooling execution system; on the other hand, the related model cannot be dynamically updated with the change of environmental parameters, and the prediction error accumulates with time; in addition, the influence of wind speed, sunlight and other multi-source environmental factors on the temperature field is not systematically integrated, resulting in poor adaptability of the temperature control strategy in the actual complex construction environment.
[0006] Therefore, it is urgent to develop a system and method capable of real-time sensing, dynamic prediction and intelligent regulation and control of the hydration heat temperature field, so as to overcome the shortcomings of the existing technology in hysteresis, adaptability and model authenticity, and improve the temperature control accuracy and structural safety during the construction process of the variable cross-section stacked box rigid structure aqueduct. SUMMARY
[0007] The technical problem to be solved by the present application is how to design a method capable of real-time sensing, dynamic prediction and intelligent regulation and control of the hydration heat temperature field.
[0008] In order to solve the above technical problems, the present application provides a method for dynamically regulating and controlling the hydration heat of a variable cross-section stacked box rigid structure aqueduct, comprising the following steps: S1. Real-time acquisition of temperature field data and environmental parameters of the cast structure; the environmental parameters include wind speed and solar radiation intensity; S2. Construct a dual digital twin model, which includes a first digital twin model based on the temperature field data and real-time temperature data of the cast structure to simulate the evolution of the internal temperature field of the cast structure; and a second digital twin model based on the environmental parameters and historical data to predict the influence of the external environment on the temperature of the cast structure. S3. Simultaneously input the temperature field data and the environmental parameters into the first digital twin model and the second digital twin model, and obtain the temperature prediction results of both. S4. The temperature prediction results of the first digital twin model and the second digital twin model are interactively verified, and when the difference in the prediction results exceeds the tolerance range, the parameter correction rule is triggered to dynamically correct the input parameters or internal model parameters of at least one model. S5. Based on the temperature prediction results after interactive verification and dynamic correction, generate water cooling control commands to cope with temperature fluctuations and environmental disturbances during construction.
[0009] Furthermore, the dual digital twin model satisfies the expression:
[0010] In the formula, For a moment The calorific value of hydration; For a moment Temperature value; The wind speed is mentioned; The solar radiation intensity is mentioned above; , , These are coefficients, representing the contributions of temperature, wind speed, and solar radiation intensity to the heat of hydration, respectively. It is a constant term, representing the hydration heat reference unaffected by external disturbances.
[0011] Furthermore, S1 specifically includes: A three-dimensional mesh is divided within the monitoring area of the cast structure to form multiple mesh units; Temperature sensors are deployed within the grid cells to collect temperature field data of the cast structure; wherein the grid density is higher in regions of abrupt changes in wall thickness and / or regions of predicted temperature peaks than in other regions.
[0012] Furthermore, S1 also includes using a multivariate Kalman filter algorithm to filter and correct the environmental parameters.
[0013] Further, the trigger parameter correction rule in S4 specifically includes: calculating the error between the predicted temperature of the first digital twin model and the predicted temperature of the second digital twin model ; ; judging whether the error ΔT is greater than a preset error threshold ; if > , the weight of the environmental parameter in the second digital twin model and / or the material thermophysical parameter in the first digital twin model are corrected through an iterative optimization algorithm or a predefined correction strategy.
[0014] Further, the parameter correction rule satisfies the expression:
[0015] wherein, is the corrected parameter; is the original parameter in the first digital twin model and / or the second digital twin model; is the correction amount; is the learning rate, indicating the amplitude of correction.
[0016] Further, S5 specifically includes: comparing the corrected temperature prediction result with the real-time monitored temperature value to calculate the temperature difference; when the temperature difference exceeds a preset control threshold, calculating the adjusted water flow rate, which satisfies the formula:
[0017] wherein, is the adjusted water flow rate; is the current water flow rate; , , are respectively proportional coefficient, integral coefficient and differential coefficient; generating the water flow cooling control instruction according to the .
[0018] Compared with the prior art, the embodiments of the present application have the following beneficial effects: The application effectively overcomes the prediction deviation problem caused by model distortion, environmental disturbance and other factors in traditional single model prediction by constructing a double digital twin model comprising a first digital twin model based on a structural temperature field and a second digital twin model based on environmental parameters, and introducing an interactive verification and parameter correction mechanism of the prediction results, realizes high-precision, self-correcting real-time prediction of the hydration heat temperature field evolution trend, and improves the safety and reliability of the variable cross-section stacked box rigid structure aqueduct construction process. BRIEF DESCRIPTION OF DRAWINGS
[0019] In order to more clearly illustrate the technical solutions in the embodiments of the present application, the drawings needed in the embodiment description will be briefly introduced. Obviously, the drawings in the following description are only some embodiments of the present application, and other drawings can be obtained by those skilled in the art without creative labor.
[0020] Figure 1 The flowchart disclosed by the present application is shown in the figure. DETAILED DESCRIPTION
[0021] In order to enable those skilled in the art to better understand the present application, the technical solutions in the embodiments of the present application will be described clearly and completely with reference to the drawings in the embodiments of the present application. Obviously, the described embodiments are only some embodiments of the present application, not all. Based on the embodiments in the present application, all other embodiments obtained by those skilled in the art without creative labor are within the scope of protection of the present application.
[0022] The present application aims to provide a variable cross-section stacked box rigid structure aqueduct hydration heat dynamic regulation method to solve the prediction deviation problem caused by model distortion, environmental disturbance and other factors in traditional single model prediction.
[0023] Please refer to Figure 1 The method mainly comprises the following steps: S1, real-time acquisition of temperature field data and environmental parameters of the cast structure. The environmental parameters include wind speed and solar intensity.
[0024] Specifically, a three-dimensional gridding method is used to divide the monitoring area into a plurality of small cubic units, that is, three-dimensional gridding is performed in the monitoring area of the cast structure to form a plurality of grid units. Temperature sensors are arranged in each grid unit to ensure that the monitoring system covers all key positions. The temperature field data of the cast structure is collected by the temperature sensors. The grid density in the wall thickness mutation area and / or the estimated temperature peak area is higher than that in other areas.
[0025] In addition, a multivariate Kalman filter algorithm is used to filter and correct the environmental parameters: First, set the system state , the expression is , wherein, is the state transition matrix, is the control input matrix, is the control input (such as the collected environmental parameters), is the noise.
[0026] Next, set the measurement equation , wherein, is the measurement value (such as the temperature value of the sensor), is the measurement matrix, is the measurement noise.
[0027] Further, use the Kalman filter algorithm for estimation, update the state and the covariance matrix. Wherein, the formula of Kalman gain is , wherein, is the Kalman gain, is the covariance matrix, is the measurement noise covariance matrix.
[0028] S2, construct a double digital twin model, the double digital twin model includes a first digital twin model for simulating the evolution of the internal temperature field of the pouring structure based on the temperature field data of the pouring structure and the real-time temperature data; and a second digital twin model for predicting the influence of the external environment on the temperature of the pouring structure based on the environmental parameters and historical data.
[0029] In the scheme, the double digital twin model satisfies the expression:
[0030] , wherein, is the hydration heat value at time ; is the temperature value at time ; is the wind speed; is the solar radiation intensity; , , are coefficients, respectively representing the contribution of temperature, wind speed and solar radiation intensity to hydration heat, which will be adjusted according to the real-time measured environmental data; is a constant term, representing the hydration heat benchmark not affected by external disturbance.
[0031] In a specific example, when the pouring area is under strong sunlight, the solar radiation intensity will significantly affect the surface temperature of the concrete, causing the accelerated release of hydration heat. At this time, the This will increase the risk of hydration heat. We need to adjust the hydration heat prediction to ensure that it is consistent with the actual environment, and then take appropriate cooling strategies.
[0032] If the wind speed is high, the equation... The calculation will be adjusted based on the wind speed value. Taking into account the accelerating effect of wind speed on the evaporation of cement moisture, the prediction of the hydration reaction will be slowed down to avoid excessive release of hydration heat.
[0033] The dual digital twin model is the core technology of this solution, aiming to improve the accuracy of temperature control and hydration heat prediction by mutually verifying and correcting each other through two independent models. These two models focus on different aspects, specifically: The first digital twin model simulates and predicts the evolution of the temperature field inside the structure, especially under the influence of hydration heat. Due to differences in the thickness and material of concrete sections, the temperature distribution exhibits significant non-uniformity. This model needs to consider the following factors: a. Heat conduction equation:
[0034] In the formula, It is a location and time Temperature; It is the second spatial derivative of temperature, representing the change in the temperature field; It is the thermal diffusivity, which represents the thermal conductivity. It is the source of heat of hydration, which is usually related to the thermochemical reactions of concrete.
[0035] b. Hydration heat model: The heat of hydration of concrete is the main driving factor for changes in the temperature field. (Heat release during hydration) The generation of heat is influenced by various factors, including the cement composition, temperature, and humidity of the concrete. By introducing the heat of hydration equation, the generation of heat can be accurately simulated.
[0036] In the formula, It is the standard heat of hydration of cement; It is a dynamic coefficient of the hydration process, which changes over time; It is the hydration rate per unit mass of cement.
[0037] c. Boundary conditions and initial conditions Boundary conditions (such as external temperature) and initial conditions (such as initial temperature) need to be set using field data or simulation models.
[0038] The output of the first digital twin model is typically the temperature distribution at each time step and each location. These temperature data can be monitored in real-time by temperature sensors and compared with the simulation model for validation.
[0039] The second digital twin model simulates the impact of external environmental factors (such as wind speed, solar radiation intensity, etc.) on the internal temperature field of the structure. These external environmental parameters significantly affect the release of hydration heat and the evolution of the temperature field during actual construction, so the second digital twin model focuses on handling the following dynamic factors: a. Environmental thermal disturbance model The second digital twin model uses the following thermal disturbance equation to simulate the impact of environmental factors on the temperature field:
[0040] In the formula, is the temperature change caused by environmental thermal disturbance; is the wind speed; is the solar radiation intensity, obtained using a radiation sensor; is the radiation absorption coefficient of the structure; is the radiation propagation coefficient, taking into account the surface reflection and heat absorption characteristics of the structure.
[0041] b. Wind speed and solar radiation intensity correction In the second digital twin model, wind speed and solar radiation intensity are important correction factors. An increase in wind speed can accelerate the evaporation of water on the concrete surface, causing changes in the hydration reaction rate and affecting the release of hydration heat. Solar radiation intensity directly affects the temperature rise on the concrete surface, thereby affecting the overall temperature control strategy.
[0042] c. Feedback and correction mechanism The second digital twin model adjusts the temperature control strategy based on real-time changes in environmental data. For example, if the wind speed is high, the water flow can be reduced to reduce the drastic changes in the temperature field, and vice versa.
[0043] S3, synchronize the temperature field data and environmental parameters to the first digital twin model and the second digital twin model, and obtain the temperature prediction results of both models.
[0044] S4, interactively verify the temperature prediction results of the first digital twin model and the second digital twin model, and when the difference between the prediction results exceeds the tolerance interval, trigger the parameter correction rule to dynamically correct the input parameters or internal model parameters of at least one model.
[0045] First, calculate the prediction temperature of the first digital twin model and the prediction temperature of the second digital twin model between the error .
[0046] Then, it is judged whether the error ΔT is greater than a preset error threshold .
[0047] If > Then, the weight of the environmental parameter in the second digital twin model and / or the material thermophysical parameter in the first digital twin model is corrected through an iterative optimization algorithm or a predefined correction strategy.
[0048] Wherein, the parameter correction rule satisfies the expression:
[0049] In the formula, is the corrected parameter; is the original parameter in the first digital twin model and / or the second digital twin model; is the correction amount; is the learning rate, indicating the amplitude of correction.
[0050] S5, based on the temperature prediction result after interactive verification and dynamic correction, a water cooling control instruction is generated to cope with the change of temperature fluctuation and environmental disturbance in the construction process.
[0051] Specifically: S51, compare the corrected temperature prediction result with the real-time monitored temperature value, and calculate the temperature difference.
[0052] S52, when the temperature difference exceeds the preset control threshold, the adjusted water flow is calculated, which satisfies the formula:
[0053] In the formula, is the adjusted water flow; is the current water flow; , , are proportional coefficient, integral coefficient and differential coefficient respectively.
[0054] S53, according to generate water cooling control instruction, ensure that the temperature control strategy and cooling strategy are always adapted to the real-time environment and temperature change, avoid the temperature fluctuation and crack risk caused by lag or external disturbance.
[0055] The mathematical model of feedback control in the present scheme is based on the classic PID control (proportional-integral-derivative control) or fuzzy control algorithm, which is not described here.
[0056] While embodiments of the application have been shown and described, it is to be understood that the embodiments described are merely exemplary of the principles and application of the present application. Numerous modifications and adaptions can be effected without departing from the spirit and scope of the present application, which is not limited to the exact construction and arrangement described. It is intended, therefore, to cover all modifications and adaptions that fall within the scope of the claims and their equivalents.
Claims
1. A method for dynamic control of hydration heat in a variable cross-section stacked box rigid frame aqueduct, characterized in that, Includes the following steps: S1. Real-time acquisition of temperature field data and environmental parameters of the cast structure; the environmental parameters include wind speed and solar radiation intensity; S2. Construct a dual digital twin model, which includes a first digital twin model based on the temperature field data and real-time temperature data of the cast structure to simulate the evolution of the internal temperature field of the cast structure; and a second digital twin model based on the environmental parameters and historical data to predict the influence of the external environment on the temperature of the cast structure. S3. Simultaneously input the temperature field data and the environmental parameters into the first digital twin model and the second digital twin model, and obtain the temperature prediction results of both. S4. The temperature prediction results of the first digital twin model and the second digital twin model are interactively verified, and when the difference in the prediction results exceeds the tolerance range, the parameter correction rule is triggered to dynamically correct the input parameters or internal model parameters of at least one model. S5. Based on the temperature prediction results after interactive verification and dynamic correction, generate water cooling control commands to cope with temperature fluctuations and environmental disturbances during construction.
2. The method for dynamic control of hydration heat in a variable cross-section stacked box rigid frame aqueduct according to claim 1, characterized in that, The dual digital twin model satisfies the expression: In the formula, For a moment The calorific value of hydration; For a moment Temperature value; The wind speed is mentioned; The solar radiation intensity is mentioned above; , , These are coefficients, representing the contributions of temperature, wind speed, and solar radiation intensity to the heat of hydration, respectively. It is a constant term, representing the hydration heat reference unaffected by external disturbances.
3. The method for dynamic control of hydration heat in a variable cross-section stacked box rigid frame aqueduct according to claim 1, characterized in that, S1 specifically includes: A three-dimensional mesh is divided within the monitoring area of the cast structure to form multiple mesh units; Temperature sensors are deployed within the grid cells to collect temperature field data of the cast structure; wherein the grid density is higher in regions of abrupt changes in wall thickness and / or regions of predicted temperature peaks than in other regions.
4. The method for dynamic control of hydration heat in a variable cross-section stacked box rigid frame aqueduct according to claim 1, characterized in that, S1 further includes using a multivariate Kalman filter algorithm to filter and correct the environmental parameters.
5. The method for dynamic control of hydration heat in a variable cross-section stacked box rigid frame aqueduct according to claim 1, characterized in that, The trigger parameter correction rules in S4 specifically include: Calculate the predicted temperature of the first digital twin model Temperature prediction with the second digital twin model Error between ; Determine whether the error ΔT is greater than a preset error threshold. ; like > Then, through iterative optimization algorithms or predefined correction strategies, the weights of the environmental parameters in the second digital twin model and / or the material thermophysical parameters in the first digital twin model are corrected.
6. The method for dynamic control of hydration heat in a variable cross-section stacked box rigid frame aqueduct according to claim 1 or 5, characterized in that, The parameter correction rule satisfies the expression: In the formula, These are the corrected parameters; These are the original parameters in the first digital twin model and / or the second digital twin model; This is a correction amount; The learning rate represents the magnitude of the correction.
7. The method for dynamic control of hydration heat in a variable cross-section stacked box rigid frame aqueduct according to claim 1, characterized in that, S5 specifically includes: The corrected temperature prediction result is compared with the real-time monitored temperature value to calculate the temperature difference; When the temperature difference exceeds the preset control threshold, the adjusted water flow rate is calculated, which satisfies the formula: In the formula, The adjusted water flow rate; This represents the current water flow rate; , , These are the proportional coefficient, integral coefficient, and differential coefficient, respectively. According to the above Generate the water cooling control command.