Dynamic evaluation method for teapot ladle pouring flow stability

Abstract

The invention provides a dynamic evaluation method for teapot ladle pouring flow stability, which comprises the following steps: deploying a multi-point temperature probe and a flow monitor in a teapot ladle pouring system, collecting radial temperature distribution data and real-time flow values from the wall of a teapot ladle to the center, and obtaining initial temperature field distribution and flow change records; identifying a temperature gradient layered region according to the initial temperature field distribution, extracting a gradient peak position and a gradient amplitude of each layer, and arranging and marking gradient features of each layer according to radial distances to obtain a radial gradient distribution diagram; performing time sequence contrast analysis according to the thickness of the viscosity layer in each area and the flow change record, and comparing the variation of the thickness of the viscosity layer near the ladle wall at different moments to obtain the time sequence distribution of the thickness of the viscosity layer; and dynamically adjusting a judgment threshold value of flow attenuation prediction by adopting the viscosity layer thickness time sequence distribution, and identifying the flow attenuation amplitude according to the viscosity layer thickening rate to obtain a flow attenuation prediction result.

Description

Technical Field

[0001] This invention relates to the field of information technology, and in particular to a dynamic evaluation method for the stability of teapot pouring flow rate. Background Technology

[0002] In industrial production, the stability of molten steel pouring flow rate directly affects product quality and production efficiency, and is a key factor in ensuring the safety and consistency of the casting process. Research in this area not only impacts the precision of material processing but also plays a significant role in reducing production defects and improving economic efficiency. Especially in high-temperature environments, ensuring the stability of molten steel during flow has become a focus of attention for many industries. However, existing methods for addressing flow stability often overlook the profound impact of uneven temperature distribution. Many solutions focus more on overall temperature control but fail to fully consider the interference of local temperature differences on the flow characteristics of molten steel. This neglect leads to inaccurate flow control in complex environments, especially during long-term operations, where problems gradually emerge and affect production results. In detail, the unevenness of temperature distribution is the core technical factor affecting flow stability. The viscosity of molten steel changes with temperature; higher temperatures result in lower viscosity and faster flow, while lower temperatures result in higher viscosity and slower flow. When there is a significant temperature difference from the center to the edge of the container, the flow velocity of molten steel in different areas will stratify. This stratification makes the overall flow unstable, and may even lead to local blockages or abrupt changes. More problematic is that the rapid heat dissipation at the container walls causes a sharp drop in the temperature of the molten steel at the edges, leading to a rapid increase in viscosity and the formation of a thick, viscous boundary layer. This further impedes the flow around the edges and exacerbates the velocity difference between the center and the edges. Considering actual casting scenarios, such as when molten steel is poured from a large ladle into the mold, the high-temperature molten steel in the center rushes out rapidly, while the low-temperature molten steel near the wall remains almost stagnant, causing the outlet flow rate to fluctuate and making it impossible to maintain a stable curve. This inconsistency directly disrupts the uniformity of filling within the mold cavity, easily leading to casting defects such as porosity and inclusions. During the transition from converter tapping to ladle, an initial temperature gradient already exists. If pouring continues for several minutes, edge cooling further intensifies stratification, making the flow instability problem even more prominent. Another typical scenario is a multi-flow casting system. When molten steel is diverted from the main ladle to multiple molds, temperature unevenness amplifies the stratification effect, increasing the flow rate differences between the branches and causing uneven casting wall thickness and potential surface cracks between molds. Therefore, accurately identifying and addressing the flow instability of molten steel caused by viscosity stratification under uneven temperature distribution has become a key issue in improving the reliability of the casting process. Summary of the Invention

[0003] This invention provides a dynamic evaluation method for the stability of teapot pouring flow rate, mainly including:

[0004] By deploying multiple temperature probes and flow monitoring instruments in the teapot pouring system, radial temperature distribution data and real-time flow values ​​from the teapot wall to the center are collected to obtain the initial temperature field distribution and flow change records.

[0005] Based on the initial temperature field distribution, identify the temperature gradient stratification regions, extract the gradient peak position and gradient amplitude of each layer, and arrange and label the gradient features of each layer according to radial distance to obtain a radial gradient distribution map.

[0006] The location of the velocity stratification boundary is evaluated using a radial gradient distribution map. The upper and lower boundaries of the viscosity layer in each region are then determined based on the location of the velocity stratification boundary, and the thickness of the viscosity layer in each region is obtained.

[0007] A time-series comparative analysis was conducted based on the records of viscosity layer thickness and flow rate changes in each region. By comparing the changes in viscosity layer thickness near the wall at different times, the time-series distribution of viscosity layer thickness was obtained.

[0008] The threshold for predicting flow attenuation is dynamically adjusted by using the temporal distribution of viscosity layer thickness, and the magnitude of flow attenuation is identified based on the viscosity layer thickening rate to obtain the flow attenuation prediction result.

[0009] The flow rate change record and the radial gradient distribution map are processed based on the flow rate attenuation prediction results. The predicted flow rate fluctuation is compared with a preset stability threshold to evaluate whether it exceeds the stability threshold. When it exceeds the threshold, a compensation signal is generated to obtain the optimized teapot pouring flow rate stability score.

[0010] Furthermore, by deploying multiple temperature probes and flow monitoring instruments in the teapot pouring system, radial temperature distribution data and real-time flow values ​​from the teapot wall to the center are collected, resulting in initial temperature field distribution and flow rate change records, including:

[0011] A thermocouple array is arranged along the radial path from the teapot bag's inner diameter to the center. An electromagnetic flowmeter is installed below the outlet to obtain a radial temperature measurement point sequence. Temperature data is collected from the thermocouples at each point in the radial temperature measurement point sequence, and the instantaneous flow rate signal output by the electromagnetic flowmeter is read synchronously. The temperature values ​​at each measurement point are time-series aligned with the corresponding flow rate values ​​based on the timestamps to obtain a temperature and flow rate time series dataset. A radial temperature distribution curve is constructed using the temperature values ​​of each radial measurement point at the same time in the temperature and flow rate time series dataset. All local temperature gradients are arranged radially to form an initial temperature field distribution. The flow rate values ​​in the time series dataset are arranged in chronological order to obtain a flow rate change record.

[0012] Furthermore, based on the initial temperature field distribution, temperature gradient stratification regions are identified, the gradient peak positions and gradient amplitudes of each layer are extracted, and the gradient features of each layer are arranged and labeled according to radial distance to obtain a radial gradient distribution map, including:

[0013] The temperature gradient between adjacent measuring points is calculated based on the initial temperature field distribution data. The gradient value is then subjected to a second-order difference operation along the radial direction to obtain the gradient change rate. The inflection point where the gradient change rate changes from a positive value to a negative value is marked as the gradient peak point. The radial position coordinates and corresponding gradient amplitude values ​​of each peak point are recorded to obtain the gradient peak sequence. The minimum value of all gradient values ​​between two adjacent peak points in the gradient peak sequence is used as the layer boundary point. The radial region is divided into several temperature gradient layers based on the boundary point positions. The gradient peak positions and maximum gradient amplitude values ​​within the radial range of each layer are extracted from the layer division results. The layer data are arranged in ascending order of radial distance. The boundary point positions, peak point positions, and corresponding gradient amplitude values ​​of each layer are marked on the radial coordinate axis to obtain the radial gradient distribution map.

[0014] Furthermore, a radial gradient distribution map is used to assess the location of the velocity stratification boundary. Based on the location of the velocity stratification boundary, the upper and lower boundaries of the viscosity layer in each region are determined, and the viscosity layer thickness in each region is obtained, including:

[0015] Based on the gradient amplitude values ​​of each layer in the radial gradient distribution diagram, the temperature gradient is multiplied by the viscosity change rate of the molten steel to calculate the viscosity value of the molten steel at each radial position. If the viscosity difference between adjacent positions exceeds a preset threshold, it is marked as a viscosity abrupt change point. Connecting all abrupt change points forms a viscosity stratification boundary line, resulting in a viscosity stratification boundary set. Using the viscosity stratification boundary set to determine the range of each viscosity layer, the actual flow velocity value of each layer is calculated based on the product of the average viscosity value within each layer and the reference flow velocity. The ratio of the flow velocity difference between adjacent layers to the interlayer distance is determined as the flow velocity gradient, thus obtaining the flow velocity gradient distribution of each viscosity layer. The flow velocity gradient distribution is used to identify regions where the gradient value exceeds a preset threshold. The upper and lower bounds of the viscosity layer are determined based on the viscosity values ​​on both sides of the region. The radial position difference between the upper and lower bounds is used as the viscosity layer thickness of the region, thus obtaining viscosity layer thickness data for each region. The viscosity layer thickness data of each region is multiplied by the flow velocity gradient value of the corresponding region to obtain the flow resistance coefficient. Based on the magnitude of the flow resistance coefficient, each region is divided into three risk levels: high, medium, and low, thus obtaining the viscosity stratification risk level assessment result and the viscosity layer thickness of each region.

[0016] Furthermore, a time-series comparative analysis was conducted based on the viscosity layer thickness and flow rate changes in each region. By comparing the changes in viscosity layer thickness near the wall at different times, the time-series distribution of viscosity layer thickness was obtained, including:

[0017] By matching the timestamps in the viscosity layer thickness data and flow rate change records of each region, the viscosity layer thickness value at the same moment is associated with the corresponding flow rate value. Viscosity layer thickness data points in the vicinity of the wall are extracted at preset time intervals to obtain a thickness and flow rate time series dataset. Using the viscosity layer thickness values ​​of adjacent moments in the thickness and flow rate time series dataset, the ratio of the thickness difference to the time interval is calculated as the viscosity layer thickening rate to obtain the viscosity layer thickening rate curve. Based on the thickness change pattern reflected by the viscosity layer thickening rate curve, the viscosity layer thickness values ​​at each moment are arranged in chronological order to form a thickness data sequence that changes with time, thus obtaining the viscosity layer thickness time series distribution.

[0018] Furthermore, the threshold for predicting flow attenuation is dynamically adjusted using the temporal distribution of viscosity layer thickness. The magnitude of flow attenuation is identified based on the viscosity layer thickening rate, resulting in the flow attenuation prediction results, including:

[0019] The thickness change is calculated based on the difference between the thickness value at each moment and the initial thickness value in the viscosity layer thickness time-series distribution. The thickness change is divided by the initial thickness value to obtain the relative change rate. The relative change rate is multiplied by a preset basic judgment threshold to obtain a dynamically adjusted judgment threshold, forming an adaptive judgment threshold sequence. The viscosity layer thickening rate is segmented and marked using the adaptive judgment threshold sequence. When the thickening rate exceeds the judgment threshold at the corresponding moment, it is marked as a rapid thickening segment. The flow attenuation factor corresponding to the segment is determined based on the average thickening rate within the rapid thickening segment, resulting in a segmented attenuation factor set. The predicted attenuation is calculated by multiplying each attenuation factor in the segmented attenuation factor set by the actual flow value of the corresponding time period. The predicted flow value at future moments is obtained by subtracting the attenuation from the current flow value. The predicted flow values ​​at each moment are connected to form a flow attenuation trend curve. The flow attenuation rate is calculated based on the difference between the flow values ​​at adjacent moments in the flow attenuation trend curve, and the flow attenuation prediction result containing the predicted flow values ​​at each moment and risk markers is output.

[0020] Furthermore, based on the predicted flow attenuation results, the flow change records and the radial gradient distribution map are processed. The predicted flow fluctuations are compared with a preset stability threshold to assess whether they exceed the stability threshold. When the threshold is exceeded, a compensation signal is generated to obtain an optimized teapot pouring flow stability score, including:

[0021] Based on the predicted flow rate sequence from the flow rate attenuation prediction results, the absolute value of the flow rate difference between adjacent time points is calculated as the instantaneous fluctuation amplitude. The instantaneous fluctuation amplitude is added to the maximum gradient value at the corresponding time point in the radial gradient distribution map to obtain a comprehensive fluctuation index sequence. Each index value in the comprehensive fluctuation index sequence is compared with a preset stability threshold. If the comprehensive fluctuation index exceeds the stability threshold, the ratio of the excess value to the threshold is calculated, and compensation signals of different intensities are generated based on the ratio. The flow rate values ​​in the flow rate change record are compensated and corrected using the compensation signals. The ratio of the standard deviation of the flow rate before and after correction is calculated as the stability improvement coefficient. The stability score of the teapot pouring flow rate is determined based on the range of the stability improvement coefficient.

[0022] The technical solutions provided by the embodiments of the present invention may include the following beneficial effects:

[0023] This invention discloses a dynamic evaluation method for the flow rate stability of teapot pouring. Addressing the challenges of viscosity stratification, flow rate decay, and insufficient stability in teapot pouring due to heat dissipation, the method collects radial temperature distribution and flow rate data in real time to construct an initial temperature field and radial gradient distribution map. This accurately identifies temperature gradient stratification regions and viscosity layer thickness changes. Combined with time-series analysis, the method dynamically evaluates the viscosity layer thickening rate and flow rate decay, generating prediction results and comparing them with a stability threshold. When the threshold is exceeded, a compensation signal is automatically generated to adjust heating or insulation measures. Through multi-dimensional correlation analysis and dynamic control of temperature, flow rate, and viscosity, this invention significantly improves the flow rate stability of the pouring system, reduces the risk of uneven flow rate caused by viscosity stratification, and provides an efficient and intelligent optimization solution for the molten steel pouring process. Ultimately, it achieves high flow rate stability and improved production efficiency in teapot pouring. Attached Figure Description

[0024] Figure 1 This is a flowchart of a dynamic evaluation method for the stability of teapot pouring flow rate according to the present invention.

[0025] Figure 2 This is a schematic diagram of a dynamic evaluation method for the stability of teapot pouring flow rate according to the present invention.

[0026] Figure 3 This is another schematic diagram of a dynamic evaluation method for the stability of teapot pouring flow rate according to the present invention. Detailed Implementation

[0027] To make the objectives, technical solutions, and advantages of this invention clearer, the invention will be described in detail below with reference to the accompanying drawings and specific embodiments.

[0028] like Figures 1-3 This embodiment of a dynamic evaluation method for the stability of teapot pouring flow rate may specifically include:

[0029] Step S101: By deploying multiple temperature probes and flow monitoring instruments in the teapot pouring system, radial temperature distribution data and real-time flow values ​​from the teapot wall to the center are collected to obtain the initial temperature field distribution and flow change records.

[0030] Based on the inner diameter of the teapot-shaped steel container, a K-type thermocouple array is arranged along the radial path from the container wall to the center according to a logarithmic interval. Thermocouple probes are installed from the inner surface of the container wall at increasing intervals to the center. An electromagnetic flowmeter is installed at a predetermined distance below the steel outlet to obtain a radial temperature measurement point sequence. Temperature data within a predetermined time window after molten steel injection is collected using thermocouples at each point in the radial temperature measurement point sequence. The temperature values ​​at each measurement point are recorded according to a predetermined sampling frequency. Simultaneously, the instantaneous flow rate signal output by the electromagnetic flowmeter is read. The temperature values ​​at each measurement point are time-series aligned with the corresponding flow rate values ​​based on timestamps to obtain a temperature and flow rate time-series dataset. A radial temperature distribution curve is constructed using the temperature values ​​of each radial measurement point at the same moment in the temperature and flow rate time-series dataset. The ratio of the temperature difference between adjacent measurement points to the distance between measurement points is calculated as a local temperature gradient. All local temperature gradients are arranged radially to form an initial temperature field distribution. The flow rate values ​​in the time-series dataset are arranged chronologically to obtain a flow rate change record.

[0031] In one embodiment, the K-type thermocouple array is arranged with a logarithmic interval. Starting from the inner wall of the teapot-shaped container, the first measuring point is 10 mm from the inner wall, and the spacing of subsequent measuring points increases by a factor of 1.5, successively to 15 mm, 22 mm, and 33 mm, until the center of the container. This logarithmic interval arrangement results in dense measuring points near the container wall where temperature changes are drastic, while the measuring points in the relatively uniform central area are sparse, effectively capturing radial temperature gradient changes. When the electromagnetic flowmeter is installed below the steel outlet, considering the stability of the molten steel flow, a distance of 300 mm from the outlet is selected as the measurement reference point. At this location, the molten steel has formed a stable flow stream, avoiding interference from outlet turbulence on the flow measurement.

[0032] Specifically, during temperature data acquisition, each thermocouple probe synchronously records temperature values ​​at a sampling frequency of 10 Hz, while the electromagnetic flowmeter synchronously outputs a 4-20 mA current signal corresponding to the instantaneous flow rate. The analog signal is converted to a digital signal via a data acquisition card. Each data point is accompanied by a microsecond-level timestamp. During time-series alignment, the temperature and flow rates of all measuring points at the same moment are paired according to the timestamps, forming a complete temperature and flow time-series dataset. This ensures the accurate temporal correspondence between temperature distribution and flow rate changes in subsequent analysis. The radial temperature distribution curve is obtained by arranging the temperature values ​​of each measuring point at the same moment according to radial distance and performing cubic spline interpolation, presenting a continuous temperature change trend from the wall to the center.

[0033] Preferably, the calculation of the local temperature gradient adopts the finite difference method between adjacent measuring points. The temperature difference between two adjacent measuring points is divided by the actual distance between the measuring points to obtain the average temperature gradient value of the interval. When the gradient value exceeds the preset threshold, it indicates that there is a significant temperature stratification phenomenon in the region. All gradient values ​​are arranged radially to form a complete initial temperature field distribution map. At the same time, the flow rate value during the acquisition period is stored in time sequence as a flow rate change record.

[0034] Step S102: Identify the temperature gradient layered regions based on the initial temperature field distribution, extract the gradient peak position and gradient amplitude of each layer, and arrange and label the gradient features of each layer according to radial distance to obtain a radial gradient distribution map.

[0035] The temperature gradient values ​​between adjacent measuring points are calculated based on the initial temperature field distribution data. The gradient values ​​are then subjected to a second-order difference operation along the radial direction to obtain the gradient rate of change. The inflection point where the gradient rate of change changes from positive to negative is marked as the gradient peak point. The radial position coordinates and corresponding gradient amplitude values ​​of each peak point are recorded to obtain a gradient peak sequence. The minimum value of all gradient values ​​between any two adjacent peak points in the gradient peak sequence is used as the layer boundary point. Based on the boundary point position, the radial region is divided into several temperature gradient layers. Each gradient layer is numbered in order from the wall to the center to determine the radial range of each layer, obtaining the layer division result. Using the radial range of each layer in the layer division result, the gradient peak position and maximum gradient amplitude within that range are extracted. The layer data are arranged in ascending order of radial distance. The boundary point position, peak point position, and corresponding gradient amplitude value of each layer are marked on the radial coordinate axis to obtain a radial gradient distribution map. This map, generated using the above marking method, is used to visualize the distribution and peak characteristics of the temperature gradient layers.

[0036] Second-order difference operations are implemented by performing two difference operations on adjacent gradient values. The first difference yields the change in gradient, and the second difference yields the rate of change of the gradient, reflecting the concavity and convexity of the gradient curve. When the second-order difference value changes from positive to negative, it indicates that the gradient curve has reached a local maximum. In radial temperature field analysis, second-order difference operations obtain the gradient rate of change data by calculating the difference between adjacent gradient values. The specific calculation process is as follows: first, obtain the temperature gradient values ​​G1, G2, and G3 at radial positions r1, r2, and r3, and then calculate the first-order difference value. and Finally, the second-order difference is calculated as follows: When the second-order difference is positive, it indicates that the gradient value is accelerating; when the second-order difference is negative, it indicates that the gradient value is decelerating or decreasing. The gradient peak point identification process starts from the wall and proceeds radially point by point. For the i-th measurement point, its gradient change rate is calculated as the difference between the gradient differences of the two preceding and following points, i.e., the gradient difference between point i+1 and point i minus the gradient difference between point i and point i-1. The turning point where this value changes from positive to negative is the peak point. The radial position coordinates of this point are recorded, such as 120 mm from the wall, along with the corresponding gradient amplitude value, such as 18℃ / 100 mm, forming a gradient peak sequence containing information on multiple peak points. In one implementation, the layer boundary point is determined by traversing all gradient values ​​between adjacent peak points, finding the minimum value as the boundary between two gradient layers. This method ensures that each gradient layer maintains relatively uniform temperature change characteristics.

[0037] In one possible implementation, the layer boundary points are determined using a minimum search algorithm. This algorithm compares all gradient values ​​point by point within the radial interval between two adjacent gradient peak points to identify the location of the minimum gradient magnitude. Layer boundary points typically correspond to regions with relatively gentle temperature changes, representing natural boundaries between different temperature gradient layers.

[0038] For example, when the first peak point is located at a radial position of 30 mm and the second peak point is located at a radial position of 80 mm, the algorithm searches for the minimum gradient within the 30 mm to 80 mm interval. Assuming the minimum gradient is found at 55 mm, this position is marked as the layer boundary point. Optionally, local analysis is performed for each divided temperature gradient layer. The algorithm searches for gradient peak points within the radial range of each layer, extracting the maximum gradient magnitude and its corresponding radial coordinates within that layer. This process identifies temperature change characteristic points within each layer, reflecting the most significant temperature gradient characteristics in that region. Simultaneously, the algorithm arranges the layer data in ascending order of radial distance to ensure spatial continuity and logical consistency of the data.

[0039] Preferably, the layers are numbered according to their physical location. The gradient layer closest to the wall is numbered as the first layer, and the numbering increases sequentially towards the center. The radial range of each layer is determined by the position of its two boundary points. For example, the range of the first layer is 0-85 mm, the second layer is 85-180 mm, and the third layer is 180-300 mm. This numbering method facilitates the rapid location of high-risk areas during subsequent viscosity stratification risk assessment.

[0040] For example, in the process of constructing a radial gradient distribution map, the boundary point positions of each layer are first marked on the radial coordinate axis and represented by vertical dashed lines. Then, the peak point positions are marked within each layer and represented by solid circles. The gradient magnitude values ​​are marked next to the circles to form a complete radial gradient distribution map, which intuitively shows the layered structure of the temperature gradient of molten steel.

[0041] Step S103: The radial gradient distribution map is used to assess the viscosity stratification risk level, analyze the velocity stratification phenomenon caused by the viscosity difference of molten steel in areas with large gradient amplitude, and mark the upper and lower boundaries of the viscosity layer in each area according to the location of the velocity stratification boundary to obtain the viscosity layer thickness in each area.

[0042] Based on the gradient amplitude values ​​of each layer in the radial gradient distribution map generated by the aforementioned temperature field simulation, the temperature gradient is multiplied by the viscosity change rate of the molten steel to calculate the viscosity value of the molten steel at each radial position. The viscosity-temperature relationship is expressed by the formula... Let μ be the viscosity, μ0 be the reference viscosity, k be the temperature sensitivity coefficient, and ΔT be the temperature difference. The viscosity field is calculated by integrating over radial positions. If the viscosity difference between adjacent positions exceeds a preset threshold, it is marked as a viscosity abrupt change point. Connecting all abrupt change points forms the viscosity stratification boundary line, resulting in a viscosity stratification boundary set. Using the viscosity stratification boundary set, the actual flow velocity of each viscosity layer is calculated by dividing the reference flow velocity by the average viscosity value within each layer. The ratio of the velocity difference between adjacent layers to the interlayer distance is determined as the velocity gradient, obtaining the velocity gradient distribution for each viscosity layer. Regions with abrupt velocity changes are identified by the locations where the gradient value exceeds the preset threshold. The upper and lower bound viscosity values ​​of the viscosity layer are determined based on the viscosity values ​​on both sides of this region. The radial position difference between the upper and lower bounds is taken as the viscosity layer thickness for this region, obtaining viscosity layer thickness data for each region. The flow resistance coefficient is obtained by multiplying the viscosity layer thickness data of each region by the corresponding velocity gradient value. Based on the magnitude of the flow resistance coefficient, each region is divided into high, medium, and low risk levels, obtaining the viscosity stratification risk level assessment result and the viscosity layer thickness for each region.

[0043] In one implementation, the viscosity value is calculated based on the physical relationship between temperature gradient and viscosity change rate. The viscosity of molten steel increases exponentially with decreasing temperature; for example, when the temperature drops from 1550℃ to 1450℃, the viscosity can increase from 0.005 Pa·s to 0.015 Pa·s. This non-linear relationship is reflected by the gradient amplitude value in the radial gradient distribution diagram. Specifically, the temperature gradient value at each radial position is extracted, in units of ℃ / mm, and multiplied by the empirical viscosity change rate coefficient of 0.0001 Pa·s·℃. -1 ·mm -1The viscosity increment at that location is obtained and then added to the reference viscosity to obtain the actual viscosity value. The identification of viscosity abrupt change points is based on the viscosity difference between adjacent locations. When the difference exceeds a preset threshold of 0.003 Pa·s, it indicates that there is obvious viscosity stratification at that location, which needs to be marked as an abrupt change point. These abrupt change points usually appear in regions where the temperature gradient changes drastically, such as the cooling boundary layer near the wall.

[0044] Preferably, the viscosity stratification boundary line is formed by connecting all marked abrupt change points, exhibiting a curved shape that gradually transitions from the wall to the center, reflecting the viscosity distribution characteristics inside the molten steel.

[0045] Specifically, the flow velocity calculation considers the hindering effect of viscosity on flow. During the teapot pouring process, the baseline flow velocity at the tapping spout is typically 2.5 m / s. The actual flow velocity changes as the molten steel passes through different viscosity layers. The relationship between the average viscosity value within each layer and the baseline flow velocity follows the viscous flow laws in fluid mechanics. The flow velocity in the high-temperature layer with a viscosity of 0.005 Pa·s is close to the baseline value, while the flow velocity in the low-temperature layer with a viscosity of 0.015 Pa·s can drop to 1.5 m / s. By substituting the average viscosity value of each layer into the flow velocity calculation formula... Where v0 is the reference flow velocity, μ0 is the reference viscosity, and μ is the actual viscosity, the actual flow velocity values ​​of each layer are obtained. The flow velocity gradient distribution is obtained by calculating the ratio of the flow velocity difference between adjacent viscosity layers to the interlayer distance; this gradient value reflects the rate of change of flow velocity in the radial direction.

[0046] For example, the identification of regions with abrupt changes in flow velocity is achieved based on a threshold judgment of the flow velocity gradient value; when the gradient value exceeds 10 seconds... -1 This indicates the presence of strong velocity shear in the region. In actual teapot pouring, this shear zone mainly appears within 50-100 mm of the teapot wall. Within this region, a significant velocity difference forms between the high-viscosity boundary layer and the low-viscosity main fluid, leading to flow instability. The identification process involves traversing the velocity gradient distribution data and filtering out continuous intervals where the gradient value exceeds a threshold. These intervals represent regions of rapid velocity change, where the viscosity difference between the two sides typically exceeds 0.008 Pa·s.

[0047] In one possible implementation, the viscosity layer thickness is calculated by the radial position difference corresponding to the determined upper and lower bound viscosity values. The upper bound is the viscosity value of the high viscosity side of the shear region, and the lower bound is the viscosity value of the low viscosity side. The position difference between the two on the radial coordinate is the viscosity layer thickness of that region.

[0048] For example, the flow resistance coefficient is calculated by multiplying the viscosity layer thickness by the velocity gradient to obtain a dimensionless resistance coefficient, which comprehensively reflects the degree to which viscosity stratification hinders flow. (Example: 30 mm thickness, velocity gradient 15 s). -1The viscosity layer has a flow resistance coefficient of 450, indicating that this region strongly impedes the flow of molten steel and is prone to flow fluctuations during casting. Risk level assessment is based on the magnitude of the flow resistance coefficient: areas with a coefficient exceeding 400 are classified as high-risk, those between 200 and 400 as medium-risk, and those below 200 as low-risk. This classification method allows operators to quickly identify areas requiring special attention and promptly implement temperature compensation or flow regulation measures to ensure the stability of the casting process.

[0049] Step S104: Perform time-series comparative analysis based on the viscosity layer thickness and flow rate change records of each region, compare the changes in viscosity layer thickness near the wall at different times, and obtain the time-series distribution of viscosity layer thickness.

[0050] By matching the viscosity layer thickness data of each region with the timestamps in the flow rate change records, the viscosity layer thickness value at the same moment is associated with the corresponding flow rate value. Viscosity layer thickness data points in the vicinity of the wall are extracted at preset time intervals to obtain a thickness and flow rate time-series dataset. Using the viscosity layer thickness values ​​of adjacent moments in the thickness and flow rate time-series dataset, the viscosity layer thickening rate is calculated, where the time difference is derived from the timestamps in the flow rate change records, assumed to be 1 second. If the viscosity layer thickening rate is positive, based on the physical principle that heat dissipation causes a local temperature decrease and fluid viscosity increase, it is determined that there is a thickening phenomenon caused by heat dissipation in that region, obtaining a viscosity layer thickening rate curve. Based on the thickness change pattern reflected by the viscosity layer thickening rate curve, the viscosity layer thickness values ​​at each moment are arranged in chronological order to form a thickness data sequence that changes over time, obtaining the viscosity layer thickness time-series distribution.

[0051] In one implementation, time-series data matching is achieved by establishing a time index table. Each data point in the viscosity layer thickness data and flow rate change record is marked with a timestamp accurate to the second. A sliding time window method is used for matching, with the window width set to 1 second. When the difference between the thickness data timestamp and the flow rate data timestamp is less than 0.5 seconds, they are considered to be data from the same time. After the matching is completed, an associated dataset containing three dimensions of time, thickness, and flow rate is formed.

[0052] Preferably, the area near the casing wall is defined as an annular area within 100 mm of the inner surface of the casing wall. The viscosity layer thickness value of this area is extracted from the associated dataset at a preset time interval of 5 seconds to ensure that the dynamic process of thickness change is captured.

[0053] Specifically, the viscosity layer thickening rate is calculated using the finite difference method. For the thickness values ​​h1 and h2 at times t1 and t2, the thickening rate is calculated as follows: The unit is millimeters per second (mm / s). In the actual teapot pouring process, heat dissipation from the teapot wall causes a continuous decrease in the boundary layer temperature, resulting in a corresponding increase in viscosity. This manifests as a continuous increase in the thickness of the viscosity layer. When the thickening rate at three consecutive time points is positive and within the range of 0.1-0.5 mm / s, it is determined that a stable heat dissipation thickening phenomenon exists in this region, and the pouring parameters are adjusted accordingly in subsequent predictions. The viscosity layer thickening rate curve is formed by connecting the thickening rate values ​​at each time point, showing the rate variation over time.

[0054] For example, the formation of time-series distribution arranges the viscosity layer thickness values ​​at each time point in chronological order to form one-dimensional time series data. This series intuitively reflects the evolution of viscosity layer thickness with pouring time, providing basic data support for predicting flow rate decay.

[0055] Step S105: The threshold for predicting flow attenuation is dynamically adjusted using the temporal distribution of viscosity layer thickness. The magnitude of flow attenuation is identified based on the viscosity layer thickening rate to obtain the flow attenuation prediction result.

[0056] The thickness change is calculated based on the difference between the thickness value at each moment in the viscosity layer thickness time-series distribution and the initial thickness value. The thickness change is divided by the initial thickness value to obtain the relative change rate. This relative change rate is multiplied by a preset basic judgment threshold to obtain a dynamically adjusted judgment threshold, forming an adaptive judgment threshold sequence. The viscosity layer thickening rate is segmented using this adaptive judgment threshold sequence. When the thickening rate exceeds the judgment threshold at the corresponding moment, it is marked as a rapid thickening segment. The flow attenuation factor corresponding to this segment is determined based on the average thickening rate within the rapid thickening segment, resulting in a segmented attenuation factor set. The predicted attenuation is calculated by multiplying each attenuation factor in the segmented attenuation factor set by the actual flow value for the corresponding time period. The actual flow value is determined by flow data acquired from real-time monitoring equipment or the average flow value for the corresponding time period in historical records. The predicted flow value for future moments is obtained by subtracting the attenuation from the current flow value, where the current flow value originates from the monitoring data at the corresponding moment in the actual flow value sequence. The flow attenuation rate is calculated by dividing the difference in flow values ​​at adjacent times in the flow attenuation trend curve by the time interval, where the time interval is the time difference between adjacent times in hours. This ensures that the calculation results are consistent in dimension. If the attenuation rate exceeds the preset warning value, a risk marker is added for that time period. The output includes the predicted flow values ​​at each time and the risk marker.

[0057] In one implementation, the dynamic threshold adjustment mechanism is based on the real-time variation characteristics of the viscosity layer thickness. The initial thickness value serves as a baseline reference. As the pouring process progresses, the thickness value at each moment is compared with the initial value, forming a continuous sequence of thickness changes. In the initial stage of teapot pouring, the viscosity layer thickness is typically in the range of 20-30 mm. As heat dissipation from the teapot wall intensifies, the thickness gradually increases to 50-80 mm. This variation directly affects the stability of the flow rate. The thickness change is obtained by subtracting the initial thickness from the current thickness, reflecting the cumulative thickening of the viscosity layer. A larger change indicates a more severe heat dissipation effect, requiring a correspondingly higher judgment threshold to achieve adaptive identification of different thickening stages.

[0058] Specifically, the relative change rate is calculated by dividing the thickness change by the initial thickness value, resulting in a dimensionless relative index that eliminates the influence of differences in initial conditions between different teapot bags. When the relative change rate is 0.5, it indicates a 50% increase in thickness, at which point the risk of flow rate attenuation increases significantly. The preset basic judgment threshold is typically set at 0.2 mm / s; after multiplying by the relative change rate, the threshold is dynamically adjusted within the range of 0.2-0.6 mm / s. The adaptive judgment threshold sequence is arranged chronologically, with a corresponding judgment threshold at each moment, forming a threshold curve that changes over time.

[0059] For example, the segmentation marking process divides a continuous time series into several segments, and the thickening rate within each segment is compared with a judgment threshold at that moment. When the thickening rate consistently exceeds the judgment threshold within a certain time period, that segment is marked as a rapid thickening segment, indicating a sharp increase in the viscosity layer thickness during that time period. The average of all thickening rate values ​​within the rapid thickening segment is used to obtain the representative thickening rate of that segment, such as 0.35 mm / s. The flow attenuation factor is determined based on the representative thickening rate; the higher the rate, the larger the attenuation factor. Typically, a linear mapping relationship is used to map the rate range of 0.2-0.6 mm / s to the attenuation factor range of 0.05-0.15. Each rapid thickening segment has a corresponding attenuation factor, while the attenuation factor for non-rapid thickening segments is set to a smaller value, such as 0.02, forming a complete set of segmented attenuation factors.

[0060] Preferably, the predicted attenuation is obtained by multiplying the attenuation factor by the actual flow rate, reflecting the expected reduction in flow rate during the period. For example, if the current flow rate is 100 liters / minute and the attenuation factor is 0.1, then the predicted attenuation is 10 liters / minute.

[0061] In one possible implementation, the flow rate decay trend curve is constructed starting from the current moment, progressively predicting the flow rate values ​​at future moments. The flow rate value at the first predicted moment equals the current actual flow rate minus the predicted decay amount for the first segment. The flow rate value at the second predicted moment is the first predicted value minus the decay amount for the second segment, and so on, forming a continuous prediction sequence. This cumulative decay method reflects the cumulative effect of the continuous thickening of the viscosity layer on the flow rate. The prediction curve typically shows a gradually decreasing trend, with the rate of decrease accelerating in the later stages of casting.

[0062] For example, the flow rate decay rate is calculated by dividing the difference in predicted flow rates between adjacent time points by the time interval, with the unit being liters per minute², which intuitively reflects the acceleration of the flow rate decline. The preset warning value is set according to the casting process requirements, typically 2 liters per minute². Exceeding this value means that the flow rate is decreasing too rapidly, which may affect the quality of the casting.

[0063] Understandably, risk markers are added on a time-period basis, with each period exceeding the warning threshold marked as a high-risk period, allowing operators to prepare compensation measures in advance. The output flow attenuation prediction results include a complete sequence of predicted flow values ​​and risk period markers.

[0064] Step S106: Process the flow change record and radial gradient distribution map according to the flow attenuation prediction result, compare the predicted flow fluctuation with the preset stability threshold to evaluate whether it exceeds the stability threshold, and generate a compensation signal to adjust the heating or heat preservation measures of the pouring system when it exceeds the threshold, so as to obtain the optimized teapot pouring flow stability score.

[0065] Based on the predicted flow rate sequence from the flow rate attenuation prediction results, the absolute value of the flow rate difference between adjacent time points is calculated as the instantaneous fluctuation amplitude. The instantaneous fluctuation amplitude and the maximum gradient value are normalized to the 0-1 interval and then added together to obtain a comprehensive fluctuation index. Each index value in the comprehensive fluctuation index sequence is compared one by one with a preset stability threshold. If the comprehensive fluctuation index exceeds the stability threshold, the ratio of the excess value to the threshold is calculated. When the ratio is less than 0.5, a low-intensity compensation signal is generated; when the ratio is between 0.5 and 1.0, a medium-intensity compensation signal is generated; and when the ratio is greater than 1.0, a high-intensity compensation signal is generated. The compensation signal includes the corresponding heating temperature increment or heat preservation time extension value. The flow rate values ​​in the flow rate change record are compensated and corrected using the compensation signal. The ratio of the standard deviation of the flow rate before and after correction is calculated as the stability improvement coefficient. The stability score of the teapot pouring flow rate is determined based on the range of the improvement coefficient: an improvement coefficient greater than 1.5 is excellent, between 1.2 and 1.5 is good, and less than 1.2 is average.

[0066] In one implementation, the instantaneous fluctuation amplitude is obtained by calculating the absolute value of the flow rate difference between adjacent moments in the predicted flow rate value sequence, reflecting the degree of instantaneous change in flow rate. The instantaneous fluctuation amplitude is added to the maximum gradient value at the corresponding moment in the radial gradient distribution map to form a comprehensive fluctuation index. This addition operation considers both flow rate fluctuation and temperature gradient as influencing factors; when both are simultaneously large, it indicates an unstable state. The preset stability threshold is set according to the casting process requirements, typically taken as an empirical upper limit of the sum of the flow rate fluctuation amplitude and the gradient value, such as 15 liters / minute plus 20°C / 100mm, i.e., a comprehensive value of 35.

[0067] Specifically, the classification of compensation signal strength levels adopts a proportional judgment method. When the comprehensive fluctuation index exceeds the stability threshold, the ratio between the excess value and the threshold is calculated. Low-intensity compensation corresponds to a heating temperature increment of 20-30℃ or an extension of the holding time of 10-15 minutes; medium-intensity compensation corresponds to a temperature increment of 30-50℃ or an extension of the holding time of 15-25 minutes; high-intensity compensation corresponds to a temperature increment exceeding 50℃ or an extension of the holding time exceeding 25 minutes. This tiered compensation mechanism avoids energy waste caused by over-adjustment while ensuring effective improvement in flow stability.

[0068] Preferably, the compensation correction is achieved by superimposing a compensation amount on the original flow value. The compensation amount is calculated based on the parameters in the compensation signal, and the fluctuation amplitude of the corrected flow curve is significantly reduced.

[0069] For example, the stability improvement coefficient is obtained by calculating the ratio of the standard deviation of the flow rate before and after correction. The standard deviation reflects the dispersion of the flow data, and a ratio greater than 1 indicates that stability has been improved. The scoring criteria divide the improvement coefficient into three levels, corresponding to excellent, good, and average ratings, providing operators with intuitive stability evaluation results.

[0070] Obviously, those skilled in the art can make various modifications and variations to the embodiments of this application without departing from the spirit and scope of the embodiments of this application. Therefore, if these modifications and variations to the embodiments of this application fall within the scope of the claims of this application and their equivalents, this application also intends to include these modifications and variations.

Claims

1. A dynamic evaluation method for the stability of teapot pouring flow rate, characterized in that, The method includes: By deploying multiple temperature probes and flow monitoring instruments in the teapot pouring system, radial temperature distribution data and real-time flow values ​​from the teapot wall to the center are collected to obtain the initial temperature field distribution and flow change records. Based on the initial temperature field distribution, identify the temperature gradient stratification regions, extract the gradient peak position and gradient amplitude of each layer, and arrange and label the gradient features of each layer according to radial distance to obtain a radial gradient distribution map. The location of the velocity stratification boundary is evaluated using a radial gradient distribution map. The upper and lower boundaries of the viscosity layer in each region are then determined based on the location of the velocity stratification boundary, and the thickness of the viscosity layer in each region is obtained. A time-series comparative analysis was conducted based on the records of viscosity layer thickness and flow rate changes in each region. By comparing the changes in viscosity layer thickness near the wall at different times, the time-series distribution of viscosity layer thickness was obtained. The threshold for predicting flow attenuation is dynamically adjusted by using the temporal distribution of viscosity layer thickness, and the magnitude of flow attenuation is identified based on the viscosity layer thickening rate to obtain the flow attenuation prediction result. The flow rate change record and the radial gradient distribution map are processed based on the flow rate attenuation prediction results. The predicted flow rate fluctuation is compared with a preset stability threshold to evaluate whether it exceeds the stability threshold. When it exceeds the threshold, a compensation signal is generated to obtain the optimized teapot pouring flow rate stability score.

2. The dynamic evaluation method for the stability of teapot pouring flow rate according to claim 1, characterized in that, The process involves deploying multiple temperature probes and flow monitoring devices in the teapot pouring system to collect radial temperature distribution data and real-time flow values ​​from the teapot wall to the center, thereby obtaining initial temperature field distribution and flow change records, including: A thermocouple array is arranged along the radial path from the teapot bag's inner diameter to the center. An electromagnetic flowmeter is installed below the outlet to obtain a radial temperature measurement point sequence. Temperature data is collected from the thermocouples at each point in the radial temperature measurement point sequence, and the instantaneous flow rate signal output by the electromagnetic flowmeter is read synchronously. The temperature values ​​at each measurement point are time-series aligned with the corresponding flow rate values ​​based on the timestamps to obtain a temperature and flow rate time series dataset. A radial temperature distribution curve is constructed using the temperature values ​​of each radial measurement point at the same time in the temperature and flow rate time series dataset. All local temperature gradients are arranged radially to form an initial temperature field distribution. The flow rate values ​​in the time series dataset are arranged in chronological order to obtain a flow rate change record.

3. The dynamic evaluation method for the stability of teapot pouring flow rate according to claim 1, characterized in that, The process of identifying temperature gradient stratification regions based on the initial temperature field distribution, extracting the gradient peak position and gradient amplitude of each layer, and arranging and labeling the gradient features of each layer according to radial distance to obtain a radial gradient distribution map includes: The temperature gradient between adjacent measuring points is calculated based on the initial temperature field distribution data. The gradient value is then subjected to a second-order difference operation along the radial direction to obtain the gradient change rate. The inflection point where the gradient change rate changes from a positive value to a negative value is marked as the gradient peak point. The radial position coordinates and corresponding gradient amplitude values ​​of each peak point are recorded to obtain the gradient peak sequence. The minimum value of all gradient values ​​between two adjacent peak points in the gradient peak sequence is used as the layer boundary point. The radial region is divided into several temperature gradient layers based on the boundary point positions. The gradient peak positions and maximum gradient amplitude values ​​within the radial range of each layer are extracted from the layer division results. The layer data are arranged in ascending order of radial distance. The boundary point positions, peak point positions, and corresponding gradient amplitude values ​​of each layer are marked on the radial coordinate axis to obtain the radial gradient distribution map.

4. The dynamic evaluation method for the stability of teapot pouring flow rate according to claim 1, characterized in that, The radial gradient distribution map is used to evaluate the velocity stratification boundary position, and the upper and lower boundaries of the viscosity layer in each region are marked according to the velocity stratification boundary position to obtain the viscosity layer thickness in each region, including: Based on the gradient amplitude values ​​of each layer in the radial gradient distribution diagram, the temperature gradient is multiplied by the viscosity change rate of the molten steel to calculate the viscosity value of the molten steel at each radial position. If the viscosity difference between adjacent positions exceeds a preset threshold, it is marked as a viscosity abrupt change point. Connecting all abrupt change points forms a viscosity stratification boundary line, resulting in a viscosity stratification boundary set. Using the viscosity stratification boundary set to determine the range of each viscosity layer, the actual flow velocity value of each layer is calculated based on the product of the average viscosity value within each layer and the reference flow velocity. The ratio of the flow velocity difference between adjacent layers to the interlayer distance is determined as the flow velocity gradient, thus obtaining the flow velocity gradient distribution of each viscosity layer. The flow velocity gradient distribution is used to identify regions where the gradient value exceeds a preset threshold. The upper and lower bounds of the viscosity layer are determined based on the viscosity values ​​on both sides of the region. The radial position difference between the upper and lower bounds is used as the viscosity layer thickness of the region, thus obtaining viscosity layer thickness data for each region. The viscosity layer thickness data of each region is multiplied by the flow velocity gradient value of the corresponding region to obtain the flow resistance coefficient. Based on the magnitude of the flow resistance coefficient, each region is divided into three risk levels: high, medium, and low, thus obtaining the viscosity stratification risk level assessment result and the viscosity layer thickness of each region.

5. The dynamic evaluation method for the stability of teapot pouring flow rate according to claim 1, characterized in that, The step involves performing a time-series comparative analysis based on the viscosity layer thickness and flow rate changes in each region, comparing the changes in viscosity layer thickness near the wall at different times to obtain the time-series distribution of viscosity layer thickness, including: By matching the timestamps in the viscosity layer thickness data and flow rate change records of each region, the viscosity layer thickness value at the same moment is associated with the corresponding flow rate value. Viscosity layer thickness data points in the vicinity of the wall are extracted at preset time intervals to obtain a thickness and flow rate time series dataset. Using the viscosity layer thickness values ​​of adjacent moments in the thickness and flow rate time series dataset, the ratio of the thickness difference to the time interval is calculated as the viscosity layer thickening rate to obtain the viscosity layer thickening rate curve. Based on the thickness change pattern reflected by the viscosity layer thickening rate curve, the viscosity layer thickness values ​​at each moment are arranged in chronological order to form a thickness data sequence that changes with time, thus obtaining the viscosity layer thickness time series distribution.

6. The dynamic evaluation method for the stability of teapot pouring flow rate according to claim 1, characterized in that, The determination threshold for flow attenuation prediction is dynamically adjusted based on the temporal distribution of viscosity layer thickness. The flow attenuation magnitude is identified based on the viscosity layer thickening rate to obtain the flow attenuation prediction result, including: The thickness change is calculated based on the difference between the thickness value at each moment and the initial thickness value in the viscosity layer thickness time-series distribution. The thickness change is divided by the initial thickness value to obtain the relative change rate. The relative change rate is multiplied by a preset basic judgment threshold to obtain a dynamically adjusted judgment threshold, forming an adaptive judgment threshold sequence. The viscosity layer thickening rate is segmented and marked using the adaptive judgment threshold sequence. When the thickening rate exceeds the judgment threshold at the corresponding moment, it is marked as a rapid thickening segment. The flow attenuation factor corresponding to the segment is determined based on the average thickening rate within the rapid thickening segment, resulting in a segmented attenuation factor set. The predicted attenuation is calculated by multiplying each attenuation factor in the segmented attenuation factor set by the actual flow value of the corresponding time period. The predicted flow value at future moments is obtained by subtracting the attenuation from the current flow value. The predicted flow values ​​at each moment are connected to form a flow attenuation trend curve. The flow attenuation rate is calculated based on the difference between the flow values ​​at adjacent moments in the flow attenuation trend curve, and the flow attenuation prediction result containing the predicted flow values ​​at each moment and risk markers is output.

7. The dynamic evaluation method for the stability of teapot pouring flow rate according to claim 1, characterized in that, The process involves processing the flow change records and the radial gradient distribution map based on the flow attenuation prediction results, comparing the predicted flow fluctuations with a preset stability threshold to assess whether they exceed the stability threshold, and generating a compensation signal when the threshold is exceeded to obtain an optimized teapot pouring flow stability score, including: Based on the predicted flow rate sequence from the flow rate attenuation prediction results, the absolute value of the flow rate difference between adjacent time points is calculated as the instantaneous fluctuation amplitude. The instantaneous fluctuation amplitude is added to the maximum gradient value at the corresponding time point in the radial gradient distribution map to obtain a comprehensive fluctuation index sequence. Each index value in the comprehensive fluctuation index sequence is compared with a preset stability threshold. If the comprehensive fluctuation index exceeds the stability threshold, the ratio of the excess value to the threshold is calculated, and compensation signals of different intensities are generated based on the ratio. The flow rate values ​​in the flow rate change record are compensated and corrected using the compensation signals. The ratio of the standard deviation of the flow rate before and after correction is calculated as the stability improvement coefficient. The stability score of the teapot pouring flow rate is determined based on the range of the stability improvement coefficient.

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