Multi-physics field collaborative inversion high-temperature closed space sediment detection method and high-temperature closed space sediment detection system

Abstract

The application relates to the technical field of industrial equipment detection, and particularly relates to a high-temperature closed space deposit detection method based on multi-physical field collaborative inversion and a high-temperature closed space deposit detection system. By selecting a preferential detection area in a high-temperature closed space where deposition is prone to occur, two physical fields of ultrasonic signals and temperature field sequences fed back by the area are collected, acoustic feature vectors and thermal feature vectors corresponding to the ultrasonic signals and the temperature field sequences are respectively constructed, the acoustic feature vectors and the thermal feature vectors are input into an acoustic-thermal coupling forward model for inversion, and thus a deposition layer thickness distribution result of the preferential detection area is obtained. Since the acoustic feature is highly sensitive to thickness and interface scattering, and the thermal feature is highly sensitive to thickness and thermal diffusion / thermal capacity, the combination of the two can form a "double constraint" on the parameter space, thereby enhancing the robustness under the conditions of high temperature, window pollution and material uncertainty.

Description

Technical Field

[0001] This application relates to the field of industrial equipment testing technology, and in particular to a method and system for detecting sediments in high-temperature confined spaces using multi-physics field collaborative inversion. Background Technology

[0002] In industrial equipment, the gradual formation of deposits such as nodules, coke, and scale on the inner walls of the equipment during operation is a common problem. This can easily lead to decreased heat transfer efficiency, increased flow resistance, localized overheating, and even safety accidents. Traditional methods involve periodic shutdowns for inspection or experience-based single-point sampling. However, because deposits typically exhibit characteristics of "local initial formation, rapid growth, and spatial non-uniformity," traditional methods suffer from drawbacks such as large blind spots, low efficiency, and inability to provide real-time early warnings.

[0003] In recent years, non-contact online detection methods such as laser ultrasound and infrared thermography have attracted attention. Laser ultrasound utilizes pulsed lasers to thermoelastically excite ultrasonic waves on the material surface, achieving non-contact excitation; air coupling or laser interferometry can achieve non-contact reception. Infrared thermography can acquire the temperature distribution of a surface non-contactly.

[0004] However, in practical applications in high-temperature confined spaces, the single-physics-field inversion method faces significant limitations: the amplitude of the ultrasonic signal is not only related to the sediment thickness and interface, but is also highly susceptible to the effects of laser energy fluctuations, decreased transmittance due to contamination of the optical window, attenuation of sound waves propagating in complex paths, and changes in receiver coupling efficiency. Relying solely on the arrival time or amplitude of the sound wave for inversion results in unstable results, which are prone to drift or misjudgment. Furthermore, the temperature field sequence obtained by infrared thermometry is affected by various factors such as surface emissivity (which varies with sediment composition, roughness, and temperature), strong environmental radiation reflection under high-temperature backgrounds, changes in infrared window transmittance, and convective heat transfer inside the equipment. Relying solely on the thermal diffusion law to invert the thickness is greatly affected by these uncertainties, resulting in poor robustness of the inversion results.

[0005] In view of this, this application proposes a method for sediment detection in high-temperature confined spaces using multi-physics field collaborative inversion, aiming to achieve reliable non-contact online detection of sediments. Summary of the Invention

[0006] The main objective of this application is to provide a method for detecting sediments in high-temperature confined spaces using multi-physics field collaborative inversion, aiming to solve the problem of how to reliably perform non-contact online detection of sediments.

[0007] To achieve the above objectives, this application provides a method for sediment detection in high-temperature confined spaces using multiphysics-based collaborative inversion, the method comprising: Acquire the ultrasonic signal and temperature field sequence of the priority detection area in the high-temperature confined space under test, which are collected synchronously; Determine the acoustic feature vector based on the ultrasonic signal. And determine the thermal characteristic vector based on the temperature field sequence. ,in: ; ; In the formula, The round-trip time of the sound wave. This is the logarithmic magnitude of the ratio of the peak values ​​of the envelope between the reflected and transmitted waves. It is the logarithmic magnitude of the ratio of the baseline transmission peak value to the transmission envelope peak value; The average temperature rise peak, The decay time constant, For effective thermal diffusivity, , They are respectively Time and The radius of thermal diffusion at any given time, The inflection point time of the thermal response curve; The acoustic feature vector and the thermal eigenvector The input is fed into the acoustic-thermal coupling forward model for inversion. The feature extraction and inversion steps are repeated for multiple spatial sampling points in the priority detection area to obtain the deposition layer thickness of each sampling point. The thickness of each sampling point is then spatially interpolated or reconstructed to obtain the deposition layer thickness distribution.

[0008] Optionally, the step of determining the priority detection region includes: Obtain the wall temperature of the high-temperature enclosed space to be tested. Wall shear stress and near-wall concentration The acquisition includes: acquisition through simulation calculations based on the flow-heat-mass transfer model, and / or acquisition through sensor measurements; Based on the wall temperature T w (x), wall shear stress τ w (x), near-wall concentration S(x), to determine the deposition risk index R(x) for each region in the high-temperature confined space to be tested: ; In the formula, , , The wall temperature T w (x), wall shear stress τ w (x), normalized value of near-wall concentration S(x); , , Let be the weighting coefficient, satisfying ; The region where the deposition risk index R(x) is greater than the preset risk threshold is identified as the priority detection region.

[0009] Optionally, the acoustic-thermal coupling forward model satisfies or is constructed based on at least the following physical constraints: (1) Normal thermal boundary condition function of sediment surface: ; In the formula, The heat flux on the sediment surface. The convective heat transfer coefficient is... The temperature of the gas inside the cavity. r For surface emissivity, The Stefan-Boltzmann constant, This represents the thermal conductivity of the deposited layer material. Indicates temperature Along the outer normal direction of the sediment surface The normal temperature gradient, where T is the transient temperature inside the deposition layer / substrate. The equivalent radiation temperature of the surrounding environment on the sediment surface; (2) Temperature field sequence Satisfactory transient heat conduction function: ; In the formula, c represents the density of the deposited layer material, and c represents the specific heat capacity of the material. Represents the temperature field Regarding time Partial derivative / transient rate of change Indicates the thermal conductivity of a material. Represents the gradient of the temperature field. The term represents the volumetric heat source / internal heat source, indicating the heat source intensity per unit volume. (3) The propagation function of ultrasonic waves excited by thermal strain caused by temperature rise and through thermoelastic coupling: ; ; In the formula, For stress tensor, For strain tensor, The coefficient of thermal expansion is The elastic constant matrix, For displacement vectors, Let I represent the temperature rise between two adjacent time points, and let I be the second-order unit tensor.

[0010] Optionally, the objective function of the acoustic-thermal coupling forward model is expressed as:

[0011] In the formula, This is a predicted value for sediment thickness; For acoustic feature vectors with respect to thickness Forward mapping function, For thermal eigenvectors with respect to thickness The forward mapping function; The acoustic weight matrix, This is the thermal weighting matrix; The regularization coefficient is used. This is the prior value for thickness.

[0012] Optionally, the acoustic weight matrix and the thermal weight matrix Based on deviation value Adjustments will be made: ; ; In the formula, They are respectively and about , a monotonic function, where and and Inversely proportional; in, ; In the formula, This is an acoustic thickness estimate obtained by single-physics field inversion of sediment layer thickness using acoustic characteristics. To obtain the thermal thickness estimate by performing single-physics field inversion on the thickness of the sediment layer using thermal characteristics.

[0013] Optionally, the heat flux on the surface of the deposit Including the first heat flux and the second heat flux, wherein: When point source excitation is used for temperature field sequence acquisition, the first heat flux is taken as the heat flux on the sediment surface. The expression is: ; In the formula, Here, E is the absorptivity, E is the laser pulse energy, and r is the radial distance from the center of the laser spot. The effective radius of the laser spot. It is a pulse time function; When using line source excitation for temperature field sequence acquisition, the second heat flux is taken as the heat flux on the sediment surface. The expression is: ; In the formula, x is the spatial coordinate along the length of the line source. Let L be the spatial coordinates along the width direction of the line source, L be the line length, and Π(ζ) be the rectangular window function. When Π(ζ)=1, otherwise Π(ζ)=0; Among them, the pulse time function To take rise time into account With decay time Normalization function: .

[0014] Optionally, acoustic feature vectors middle: The round-trip time of the sound wave The expression is: ; In the formula, For the preset direct surface wave time window The time corresponding to the peak value of the inner envelope Time window for reflected surface waves The time corresponding to the peak value of the inner envelope; in, ; ; The acquired ultrasound signal is processed by bandpass filtering to obtain the signal. Its expression is:

[0015] In the formula, for Hilbert transform

[0016] The logarithmic magnitude of the ratio of the peak values ​​of the envelope between the reflected wave and the transmitted wave. And the logarithmic magnitude of the ratio of the baseline transmission peak value to the transmission envelope peak value. The expressions are as follows: ; In the formula, Peak value of the reflected wave envelope after distance compensation ; Peak value of the transmitted wave envelope after distance compensation ; The baseline transmission peak value; in, ; ; In the formula, For effective attenuation coefficient, The geometric diffusion index, This represents the distance the ultrasound travels.

[0017] Optionally, thermal eigenvectors middle: Peak average temperature rise The expression is: ; In the formula, For time windows The average temperature change obtained below; in, ; In the formula, This represents the currently acquired temperature field sequence. , compared with the reference time Temperature field sequence The difference, i.e., the temperature rise field; Priority testing areas; decay time constant The expression is: ; In the formula, This represents the theoretical temperature rise at the initial fitting time. This represents the time constant variable to be optimized. The fitting time window represents the temperature rise decay phase; thermal diffusion radius The expression is: ; In the formula, Indicates the temperature rise field The area; Effective thermal diffusivity The expression is: ; in, and For two different moments within a preset time window, and ; inflection point time of thermal response curve The expression is: .

[0018] Furthermore, to achieve the above objectives, this application also provides a sediment detection system for high-temperature confined spaces, applied to the sediment detection method for high-temperature confined spaces using the multi-physics field co-inversion method described above. The high-temperature confined space sediment detection system includes: The simulation positioning unit is used to determine the priority detection area based on the structural and operating parameters of the high-temperature confined space to be tested. A laser excitation unit is used to emit pulsed laser light into the priority detection area through an optical window to excite ultrasonic waves using the thermoelastic effect. The laser excitation unit includes two excitation modes: point source excitation and line source excitation, and has adjustable excitation radius or linewidth, rise time, and output energy. A non-contact ultrasonic receiving unit is used to receive the ultrasonic signal fed back by a pulsed laser from the high-temperature sealed space to be tested, and to extract the acoustic feature parameters from the ultrasonic signal. The infrared thermal imaging unit is used to acquire temperature field sequences of the laser-excited region through a high-temperature resistant infrared window and extract thermal characteristic parameters from the temperature field sequences. The integrated control and data processing center is electrically connected to the simulation positioning unit, laser excitation unit, non-contact ultrasonic receiving unit, and infrared thermal imaging unit, respectively. It is used to synchronously control the timing of laser triggering, ultrasonic signal acquisition, and infrared image acquisition, and to perform acoustic-thermal dual-physics field collaborative inversion based on the extracted acoustic and thermal characteristic parameters, and output the deposition layer thickness distribution results in real time.

[0019] This application has at least the following beneficial effects: By selecting a priority detection area prone to deposition in a high-temperature confined space, two physical fields—ultrasonic signals and temperature field sequences—are collected from this area. Acoustic and thermal feature vectors are constructed for each, respectively. These vectors are then input into a co-acoustic and co-thermal forward model for inversion, yielding the deposition layer thickness distribution in the priority detection area. Since acoustic features are highly sensitive to thickness and interface scattering, while thermal features are highly sensitive to thickness and thermal diffusion / heat capacity, their combination creates a "double constraint" on the parameter space, thereby enhancing robustness under conditions of high temperature, window contamination, and material uncertainty. Attached Figure Description

[0020] Figure 1 This is a schematic flowchart of the sediment detection method for high-temperature confined spaces using multiphysics field collaborative inversion, as described in an embodiment of this application. Figure 2 This is a schematic diagram of the surface velocity field of the high-temperature closed reactor involved in the embodiments of this application; Figure 3 This is a schematic diagram of the temperature field of the high-temperature closed reactor involved in the embodiments of this application; Figure 4 This is a schematic diagram illustrating the deposition risk of a high-temperature closed reactor involved in an embodiment of this application; Figure 5 This is a diagram illustrating the architecture of a sediment detection system in a high-temperature confined space, as described in an embodiment of this application. The realization of the purpose, functional features and advantages of this application will be further explained in conjunction with the embodiments and with reference to the accompanying drawings. Detailed Implementation

[0021] To better understand the above technical solutions, exemplary embodiments of this disclosure will be described in more detail below with reference to the accompanying drawings. While exemplary embodiments of this disclosure are shown in the drawings, it should be understood that this disclosure can be implemented in various forms and should not be limited to the embodiments set forth herein. Rather, these embodiments are provided so that this disclosure will be thorough and complete, and will fully convey the scope of this disclosure to those skilled in the art. Example 1

[0022] Reference Figure 1 This embodiment provides a method for detecting sediments in a high-temperature confined space using multi-physics field collaborative inversion. The method includes the following steps: S10, acquire the ultrasonic signal and temperature field sequence of the priority detection area in the high-temperature closed space to be tested, which are collected synchronously; In this embodiment, acoustic and thermal physical field data are first collected simultaneously in the part of the high-temperature enclosed space to be tested, which is the priority detection area.

[0023] Priority detection areas are characterized as areas where deposition is more likely to occur in the high-temperature, enclosed space to be tested, thus saving detection resources.

[0024] Further, and optionally, the determination of the priority detection area may include the following steps: S11, Collect the wall temperature T of the high-temperature sealed space to be tested. w (x), wall shear stress τ w (x), near-wall concentration S(x); In some alternative implementations, the wall temperature T w (x), wall shear stress τ w The near-wall concentration S(x) can be collected using the CFD / CHT method.

[0025] S12, based on wall temperature T w (x), wall shear stress τ w (x), near-wall concentration S(x), to determine the deposition risk index R(x) for each region in the high-temperature confined space to be tested: ; In the formula, , , The wall temperature T w (x), wall shear stress τ w (x), normalized value of near-wall concentration S(x); , , Let be the weighting coefficient, satisfying ; S13, the region where the deposition risk index R(x) is greater than the preset risk threshold is determined as the priority detection region.

[0026] In some alternative implementations, the ultrasonic signal can be sent to the priority detection area of ​​the high-temperature enclosed space to be tested by a laser excitation unit. The laser acts on the surface of the deposit to generate a thermoelastic effect, thereby exciting the ultrasonic signal to return.

[0027] Optionally, the unit that acquires the returned ultrasonic signal can be a laser interferometer and / or an air-coupled ultrasonic sensor.

[0028] A temperature field sequence refers to a sequence of multiple transient temperature fields combined according to the acquisition time sequence. In some optional implementations, the laser-excited region is synchronously imaged through another independent high-temperature resistant infrared window to obtain the temperature field sequence T(x,y,t) obtained from the evolution of the transient temperature field on the surface of the deposit after excitation.

[0029] S20, determine the acoustic feature vector based on the ultrasonic signal. And determine the thermal characteristic vector based on the temperature field sequence. ,in: ; ; In the formula, The round-trip time of the sound wave. This is the logarithmic magnitude of the ratio of the peak values ​​of the envelope between the reflected and transmitted waves. It is the logarithmic magnitude of the ratio of the baseline transmission peak value to the transmission envelope peak value; The average temperature rise peak, The decay time constant, For effective thermal diffusivity, , They are respectively Time and The radius of thermal diffusion at any given time, The inflection point time of the thermal response curve; In this embodiment, after acquiring the ultrasonic signal and temperature field sequence of the priority detection area, feature extraction is performed on the ultrasonic signal and temperature field sequence to obtain acoustic feature vectors. and thermal eigenvectors .

[0030] In this embodiment, acoustic feature vectors are... The composition is limited by: the round-trip time of the sound wave The logarithmic amplitude of the ratio of the peak values ​​of the envelope between the reflected wave and the transmitted wave. The logarithmic magnitude of the ratio of the baseline transmission peak value to the transmission envelope peak value. .

[0031] The following provides a further explanation of the origins of the three parameters: Optionally, the round-trip time of the sound wave The expression is: ; In the formula, For the preset direct surface wave time window The time corresponding to the peak value of the inner envelope Time window for reflected surface waves The time corresponding to the peak value of the inner envelope; in, ; ;

[0032] In the formula, for Hilbert transform; Optionally, the logarithmic magnitude of the ratio of the peak values ​​of the envelopes between the reflected wave and the transmitted wave. And the logarithmic magnitude of the ratio of the baseline transmission peak value to the transmission envelope peak value. The expressions are as follows:

[0033] In the formula, Peak value of the reflected wave envelope after distance compensation ; Peak value of the transmitted wave envelope after distance compensation ; The baseline transmission peak value; in, ; ; In the formula, For effective attenuation coefficient, The geometric diffusion index, This represents the distance the ultrasound travels.

[0034] It should be noted that the purpose of distance compensation is to take into account the ultrasonic propagation distance. The effects of geometric diffusion and material attenuation on amplitude are considered, therefore distance compensation is performed for both.

[0035] On the other hand, in this embodiment, the thermal feature vector The composition is defined as follows: average temperature rise peak decay time constant Thermal diffusion radius at two different times and the inflection point time of the thermal response curve .

[0036] The following provides a further explanation of the origins of the four parameters: Optionally, the peak average temperature rise The expression is: ; In the formula, For time windows The average temperature change obtained below; in, ; In the formula, This represents the currently acquired temperature field sequence. , compared with the reference time Temperature field sequence The difference, i.e., the temperature rise field; Priority testing areas; Furthermore, and optionally, the temperature field Based on radiance Infrared window transmittance Emissivity of the surface being measured Background reflected radiance and path radiation terms Inversion yields: ; In the formula, That is, the temperature field .

[0037] Optionally, during the temperature rise decay phase, an exponential model can be used for fitting. The decay time constant is obtained. The expression is: ; In the formula, This represents the theoretical temperature rise at the initial fitting time. This represents the time constant variable to be optimized. The fitting time window represents the temperature rise decay phase; It should be noted that the decay time constant Characterize the spatial properties of thermal diffusion.

[0038] Optionally, thermal diffusion radius The expression is: ; In the formula, Indicates the temperature rise field The area; Furthermore, and optionally, within a given time interval, the thermal diffusion radius is assumed to approximately satisfy: ; Thus, from different times The estimated effective thermal diffusivity is calculated as follows: ; Optionally, the inflection point time of the thermal response curve That is about The second derivative of is expressed as: .

[0039] S30, the acoustic feature vector and the thermal eigenvector The input is fed into the acoustic-thermal coupling forward model for inversion. The feature extraction and inversion steps are repeated for multiple spatial sampling points in the priority detection area to obtain the deposition layer thickness of each sampling point. The thickness of each sampling point is then spatially interpolated or reconstructed to obtain the deposition layer thickness distribution.

[0040] In this embodiment, after obtaining the acoustic feature vector and the thermal feature vector, the two are used as inputs to the acoustic-thermal coupled forward model constructed in the pre-offline stage for joint inversion, thereby determining the deposition layer thickness distribution results of the priority detection area.

[0041] In this embodiment, the acoustic-thermal coupling forward model can be a numerical solution model based on the heat conduction equation and the thermoelastic equation; in some optional embodiments, samples can also be generated using an offline finite element parameter library to train a deep learning surrogate model to approximate the forward mapping function. , This enables real-time inversion; the acoustic-thermal coupling forward model is a mathematical model that incorporates a deep learning architecture.

[0042] In some alternative implementations, the estimated sediment thickness h^ obtained from the inversion is reconstructed by interpolation according to spatial location to form the sediment thickness distribution result in the measured high-temperature closed space.

[0043] Optionally, the sediment thickness distribution results can be displayed in the form of a visualization, and combined with the simulation positioning results to show the actual sediment development in high-risk areas. The detection results of the same measuring point at different times can be recorded and updated to form a trend curve of sediment thickness changing over time, which can be used for online monitoring and early warning. When the thickness or growth rate exceeds the preset threshold, maintenance or cleaning suggestions can be sent to the superior system.

[0044] In the technical solution provided in this embodiment, a priority detection area prone to deposition is selected in the high-temperature confined space to be tested. Two physical fields are collected: ultrasonic signals and temperature field sequences from this area. Acoustic and thermal feature vectors are constructed for each, respectively. These vectors are then input into a co-acoustic and co-thermal forward model for inversion, thereby obtaining the deposition layer thickness distribution in the priority detection area. Since acoustic features are highly sensitive to thickness and interface scattering, while thermal features are highly sensitive to thickness and thermal diffusion / heat capacity, and provide redundant information about the two-dimensional temperature field, their combination creates a "double constraint" on the parameter space, significantly reducing the ambiguity of the inversion and enhancing robustness under conditions of high temperature, window contamination, and material uncertainty. Example 2

[0045] Based on the first embodiment, this embodiment provides a mathematical modeling part of an acoustic-thermal coupling forward model.

[0046] In this embodiment, the laser pulse acts on the deposit surface through a high-temperature optical window, generating a thermoelastic effect that causes a local transient temperature rise and elastic wave excitation. Considering convective and radiative heat transfer within the high-temperature cavity, the normal thermal boundary condition of the deposit surface can be written as: ; In the formula, The heat flux on the sediment surface. The convective heat transfer coefficient is... The temperature of the gas inside the cavity. For surface emissivity, The Stefan-Boltzmann constant, This represents the thermal conductivity of the deposited layer material. This represents the normal temperature gradient T along the direction n normal to the outer surface of the sediment, where T is the transient temperature inside the sediment layer. The equivalent radiation temperature of the surrounding environment on the sediment surface; Temperature field inside the measured area Satisfies the transient heat conduction equation: ; In the formula, c represents the density of the deposited layer material, and c represents the specific heat capacity of the material. This represents the partial derivative of the temperature field T with respect to time t / transient rate of change. Indicates the thermal conductivity of a material. Represents the gradient of the temperature field. Indicates the intensity of the heat source per unit volume; Temperature rise induces thermal strain, which in turn excites ultrasonic wave propagation via thermoelastic coupling. Its constitutive and dynamic equations are given by the following formula: ; ; In the formula, For stress tensor, For strain tensor, The coefficient of thermal expansion is The elastic constant matrix, For displacement vectors, Let I represent the temperature rise between two adjacent time points, and let I be the second-order unit tensor.

[0047] The above formulas constitute the mathematical modeling part of the acoustic-thermal coupling forward model, which can be used in the offline stage to build a two-dimensional / three-dimensional parameter library or train a surrogate model.

[0048] Furthermore, and optionally, in this embodiment, for the deep learning architecture of the acoustic-thermal coupling forward model, we designed a weighted joint least squares function as the objective function, and applied it to the deposition layer thickness according to the following formula. Perform a collaborative inversion: ; In the formula, This is a predicted value for sediment thickness; For acoustic feature vectors with respect to thickness Forward mapping function, For thermal eigenvectors with respect to thickness The forward mapping function; The acoustic weight matrix, This is the thermal weighting matrix; The regularization coefficient is used. This is the prior value for thickness. Example 3

[0049] Based on any of the above embodiments, as an optional embodiment, this embodiment sets two different laser excitation modes: point source excitation and line source excitation. When rapid preliminary screening of a large area is required, the line source excitation mode is adopted. In this mode, the heat flux is uniformly distributed along the length direction, and the sound field and thermal field along the line can be generated in one excitation, thereby improving scanning efficiency.

[0050] In areas of suspected thickening or points requiring precise measurement, the system switches to point source excitation mode to achieve higher spatial resolution and signal-to-noise ratio through a Gaussian spatially distributed focused spot.

[0051] That is, the expression for the heat flux in the normal thermal boundary condition function of the sediment surface is different under different modes. Therefore, in this embodiment, the heat flux of the sediment surface is... Including the first heat flux and the second heat flux, wherein: When point source excitation is used for temperature field sequence acquisition, the first heat flux is taken as the heat flux on the sediment surface. The expression is: ; In the formula, Here, E is the absorptivity, E is the laser pulse energy, and r is the radial distance from the center of the laser spot. The effective radius of the laser spot. It is a pulse time function; When using line source excitation for temperature field sequence acquisition, the second heat flux is taken as the heat flux on the sediment surface. The expression is: ; In the formula, x is the spatial coordinate along the length of the line source. Let L be the spatial coordinate along the width direction of the line source, and L be the line length. For rectangular window functions; Among them, the pulse time function To take rise time into account With decay time Normalization function: . Example 4

[0052] Based on any of the above embodiments, as an optional embodiment, in order to ensure the accuracy of the sedimentary layer thickness distribution results obtained by joint inversion of the two physical fields, this embodiment uses acoustic features and thermal features respectively to perform single-physical-field inversion of the sedimentary layer thickness to obtain an acoustic thickness estimate. and thermal thickness estimates Calculate the deviation between the two: ; in, ; In the formula, The coefficients obtained from calibration based on the finite element parameter library, and Let be the thermal diffusivity of the sedimentary layer, where The effective thermal diffusivity characteristic quantity is obtained by inversion from the infrared temperature rise field. These are the thermal diffusivity parameters of the sedimentary layer from the parameter library; the two can be approximately correlated under calibration conditions. This is the inflection point time.

[0053] Based on the magnitude of the deviation For the weight matrix and Perform adaptive adjustments: ; ; in, For about is a monotonic function. Where, and and Inversely proportional to the result of the inversion of a physical field, when the result of the inversion of a physical field is significantly inconsistent with that of another physical field, the weight of the latter is automatically reduced to avoid erroneous information dominating the joint inversion; if necessary, repeated measurements of the measurement point can be triggered or it can be marked as low-confidence data. Example 5

[0054] Based on any of the above embodiments, this embodiment provides a method involving specific numerical calculations to further illustrate the method for detecting sediments in high-temperature confined spaces using multi-physics field collaborative inversion in the embodiments of this application. It should be understood that the structural and numerical data involved in this embodiment are not intended to limit the embodiments of this application.

[0055] (1) Simulation positioning This detection system is installed outside the pipeline section to be tested. Two windows are opened on the pipeline wall: an optical quartz window (for laser incidence) with water cooling and air curtain protection, and an infrared zinc selenide window (for thermal imaging). The laser, laser interferometer, infrared thermal imager, and control center are located in a safe area outside the pipeline. By installing this detection system outside the pipeline section and combining it with design and construction parameters, parameters such as pipeline dimensions, medium flow velocity, temperature, and composition are obtained, and a CFD model is established. Simulation calculations obtain the spatial distribution of wall temperature, shear stress, and supersaturation of scale at various points on the inner wall of the pipeline. Weights w are set. T =0.4,w τ =0.3,w S =0.3, calculate the risk index R(x). For example... Figures 2 to 4 The diagrams show the surface velocity field, temperature field, and deposition risk of a high-temperature closed reactor. In the diagrams, the red areas are identified high-risk areas, which are designated as priority detection areas. Priority detection areas are identified at abrupt changes in flow velocity and temperature on the reactor wall, and a scanning path that can cover these priority detection areas is determined.

[0056] (2) Incentives and Data Collection The laser was controlled to move along the scanning path. First, a rapid scan was performed using line source mode (line length L = 20 mm), with the infrared thermal imager simultaneously recording the temperature field. At a certain location, the infrared image showed an abnormally asymmetrical temperature rise distribution, suspected to be localized thickening. The laser was then switched to point source mode (spot radius r0 = 1 mm) at that location, emitting a laser pulse with a pulse width of 10 ns. Simultaneously, a laser interferometer was triggered to acquire the ultrasonic signal s(t) at that point. The infrared thermal imager acquired the temperature field sequence T(x,y,t) of the area surrounding the excitation point at a rate of 500 frames per second, with an acquisition time of approximately 1 second.

[0057] (3) Feature extraction (3.1) Acoustic characteristics: The acquired ultrasonic signal s(t) was bandpass filtered from 0.5MHz to 5MHz, and the Hilbert envelope e(t) was calculated. The time t of the direct surface wave envelope peak was identified. d =5.2μs, the time t of the first obvious reflected wave envelope peak r =8.7μs, then Δt SR =3.5μs. The Rayleigh wave velocity v of the pipe substrate is known. R =2900m / s, which can be used to preliminarily estimate the distance to the reflection source. Extract the peak value A of the reflected wave. r And the peak value A of the transmitted wave (direct wave) t After distance compensation, Γ is calculated. A =0.82,Γ T =0.18. This constitutes y. a =[3.5μs,0.82,0.18,…]^T.

[0058] (3.2) Thermal characteristics: Radiometric correction is performed on the infrared image to obtain the temperature rise field ΔT(x,y,t). A region of interest (ROI) centered on the excitation point is taken, and the average temperature rise curve ΔT̄(t) is calculated. ΔT is then obtained. peak =10.8K. An exponential fit is performed on the latter part of the curve to obtain τ. th =0.43s. Calculate the thermal diffusion radius, r at t1=50ms and t2=200ms respectively. th (t1)=0.65mm,r th (t2) = 0.85 mm. Analyze the ln(ΔT̄(t)) ~ ln(t) curve to find the inflection point time t. k =0.32s. From r th (t1), r th (t2) Calculation yields a eff (Unit: m² / s), constituting y t=[10.8K,0.43s,5.0×10^-7m2 / s,0.65mm,0.85mm,0.32s,…]^T.

[0059] (4) Cooperative inversion y a and y t Input data is processed at the data processing center. The center pre-stores a 2D axisymmetric finite element parameter library for the "steel substrate-fouling" structure. This library stores theoretical eigenvectors g under different combinations of parameters such as fouling thickness h, thermal conductivity k, heat capacity ρc, and sound velocity v. a and g t The weighted least squares algorithm is used to search the parameter library for the combination of (h,k,ρc,v) that minimizes the objective function. The optimal solution is obtained. In contrast, if only acoustic features y are used... a Inversion yields thickness values. If only thermal characteristics y are used t Inversion yields thickness values. The collaborative inversion result of 10.4 mm falls between the two values ​​and is closer to the actual thickness of 10.6 mm in the subsequent shutdown verification, with an error of 0.2 mm (far less than the ±1.8 mm target of the example), verifying the effectiveness of the collaborative inversion.

[0060] (5) Output and monitoring The system recorded a thickness of 10.4 mm at this point and indicated a low thermal conductivity, suggesting the scale layer might be relatively loose. The laser was then controlled to continue scanning along the path, ultimately generating a scale thickness distribution map for that section of the pipe. The system was set to automatically perform a detection every 4 hours, continuously monitoring the thickness at key points and generating a thickness-time trend curve for early warning.

[0061] As one implementation, this application embodiment also relates to a sediment detection system for high-temperature confined spaces, applied to the multi-physics field co-inversion sediment detection method for high-temperature confined spaces as described above. The high-temperature confined space sediment detection system includes: The simulation positioning unit is used to determine the priority detection area based on the structural and operating parameters of the high-temperature confined space to be tested. A laser excitation unit is used to emit pulsed laser light into the priority detection area through an optical window to excite ultrasonic waves using the thermoelastic effect. The laser excitation unit includes two excitation modes: point source excitation and line source excitation, and has adjustable excitation radius or linewidth, rise time, and output energy. A non-contact ultrasonic receiving unit is used to receive the ultrasonic signal fed back by a pulsed laser from the high-temperature sealed space to be tested, and to extract the acoustic feature parameters from the ultrasonic signal. The infrared thermal imaging unit is used to acquire temperature field sequences of the laser-excited region through a high-temperature resistant infrared window and extract thermal characteristic parameters from the temperature field sequences. The integrated control and data processing center is electrically connected to the simulation positioning unit, laser excitation unit, non-contact ultrasonic receiving unit, and infrared thermal imaging unit, respectively. It is used to synchronously control the timing of laser triggering, ultrasonic signal acquisition, and infrared image acquisition, and to perform acoustic-thermal dual-physics field collaborative inversion based on the extracted acoustic and thermal characteristic parameters, and output the deposition layer thickness distribution results in real time.

[0062] In some optional implementations, this embodiment also provides a method such as... Figure 5 The diagram shows the architecture of a sediment detection system in a high-temperature, confined space. The upstream simulation positioning unit, also known as the simulation positioning unit, determines the risk index of each region using a flow-thermal-deposition model. Based on the risk index, it identifies priority detection areas and sends the coordinates and paths of these priority detection areas to the midstream excitation and synchronous acquisition unit (composed of a laser excitation unit, a non-contact ultrasonic receiving unit, and an infrared thermal imaging unit) as the execution unit for signal generation and acquisition.

[0063] After the collaborative inversion engine of the central control and processing center determines the thickness of the sedimentary layer, it sends the thickness distribution results to the downstream output and monitoring units in the form of a distribution map for recording. In addition, the consistency check module of the central control and processing center ensures that the acquired ultrasonic signals and temperature fields are consistent in time.

[0064] Although preferred embodiments of this application have been described, those skilled in the art, upon learning the basic inventive concept, can make other changes and modifications to these embodiments. Therefore, the appended claims are intended to be interpreted as including the preferred embodiments as well as all changes and modifications falling within the scope of this application.

[0065] Obviously, those skilled in the art can make various modifications and variations to this application without departing from the spirit and scope of this application. Therefore, if such modifications and variations fall within the scope of the claims of this application and their equivalents, this application also intends to include such modifications and variations.

Claims

1. A method for sediment detection in a high-temperature confined space using multi-physics field collaborative inversion, characterized in that, The method includes the following steps: Acquire the ultrasonic signal and temperature field sequence of the priority detection area in the high-temperature confined space under test, which are collected synchronously; Determine the acoustic feature vector based on the ultrasonic signal. And determine the thermal characteristic vector based on the temperature field sequence. ,in: ； ； In the formula, The round-trip time of the sound wave. This is the logarithmic magnitude of the ratio of the peak values ​​of the envelope between the reflected and transmitted waves. It is the logarithmic magnitude of the ratio of the baseline transmission peak value to the transmission envelope peak value; The average temperature rise peak, The decay time constant, For effective thermal diffusivity, , They are respectively Time and The radius of thermal diffusion at any given time, The inflection point time of the thermal response curve; The acoustic feature vector and the thermal eigenvector The input is fed into the acoustic-thermal coupling forward model for inversion. The feature extraction and inversion steps are repeated for multiple spatial sampling points in the priority detection area to obtain the deposition layer thickness of each sampling point. The thickness of each sampling point is then spatially interpolated or reconstructed to obtain the deposition layer thickness distribution.

2. The method for sediment detection in a high-temperature confined space using multi-physics field collaborative inversion as described in claim 1, characterized in that, The steps for determining the priority detection region include: Obtain the wall temperature of the high-temperature enclosed space to be tested. Wall shear stress and near-wall concentration The acquisition includes: acquisition through simulation calculations based on the flow-heat-mass transfer model, and / or acquisition through sensor measurements; Based on the wall temperature T w (x), wall shear stress τ w (x), near-wall concentration S(x), to determine the deposition risk index R(x) for each region in the high-temperature confined space to be tested: ； In the formula, , , The wall temperature T w (x), wall shear stress τ w (x), normalized value of near-wall concentration S(x); , , Let be the weighting coefficient, satisfying ; The region where the deposition risk index R(x) is greater than the preset risk threshold is identified as the priority detection region.

3. The method for sediment detection in a high-temperature confined space using multiphysics field collaborative inversion as described in claim 1, characterized in that, The acoustic-thermal coupling forward model must satisfy or be constructed based on the following physical constraints: (1) Normal thermal boundary condition function of sediment surface: ； In the formula, The heat flux on the sediment surface. The convective heat transfer coefficient is... The temperature of the gas inside the cavity. r For surface emissivity, The Stefan-Boltzmann constant, This represents the thermal conductivity of the deposited layer material. Indicates temperature Along the outer normal direction of the sediment surface The normal temperature gradient, where T is the transient temperature inside the deposition layer / substrate. The equivalent radiation temperature of the surrounding environment on the sediment surface; (2) Temperature field sequence Satisfactory transient heat conduction function: ； In the formula, c represents the density of the deposited layer material, and c represents the specific heat capacity of the material. Represents the temperature field Regarding time Partial derivative / transient rate of change Indicates the thermal conductivity of a material. Represents the gradient of the temperature field. The term represents the volumetric heat source / internal heat source, indicating the heat source intensity per unit volume. (3) The propagation function of ultrasonic waves excited by thermal strain caused by temperature rise and through thermoelastic coupling: ； ； In the formula, For stress tensor, For strain tensor, The coefficient of thermal expansion is The elastic constant matrix, For displacement vectors, Let I represent the temperature rise between two adjacent time points, and let I be the second-order unit tensor.

4. The method for sediment detection in a high-temperature confined space using multiphysics field collaborative inversion as described in claim 3, characterized in that, The objective function of the acoustic-thermal coupling forward model is expressed as follows: ； In the formula, This is a predicted value for sediment thickness; For acoustic feature vectors with respect to thickness Forward mapping function, For thermal eigenvectors with respect to thickness The forward mapping function; The acoustic weight matrix, This is the thermal weighting matrix; The regularization coefficient is used. This is the prior value for thickness.

5. The method for sediment detection in a high-temperature confined space using multiphysics field collaborative inversion as described in claim 4, characterized in that, The acoustic weight matrix and the thermal weight matrix Based on deviation value Adjustments will be made: ； ； In the formula, They are respectively and about , a monotonic function, where and and Inversely proportional; in, ; In the formula, This is an acoustic thickness estimate obtained by single-physics field inversion of sediment layer thickness using acoustic characteristics. To obtain the thermal thickness estimate by performing single-physics field inversion on the thickness of the sediment layer using thermal characteristics.

6. The method for sediment detection in a high-temperature confined space using multi-physics field collaborative inversion as described in claim 3, characterized in that, The heat flux on the surface of the sediment Including the first heat flux and the second heat flux, wherein: When point source excitation is used for temperature field sequence acquisition, the first heat flux is taken as the heat flux on the sediment surface. The expression is: ； In the formula, Here, E is the absorptivity, E is the laser pulse energy, and r is the radial distance from the center of the laser spot. The effective radius of the laser spot. It is a pulse time function; When using line source excitation for temperature field sequence acquisition, the second heat flux is taken as the heat flux on the sediment surface. The expression is: ； In the formula, x is the spatial coordinate along the length of the line source. Let L be the spatial coordinates along the width direction of the line source, L be the line length, and Π(ζ) be the rectangular window function. When Π(ζ)=1, otherwise Π(ζ)=0; Among them, the pulse time function To take rise time into account With decay time Normalization function: 。 7. The method for sediment detection in a high-temperature confined space using multiphysics field collaborative inversion as described in claim 1, characterized in that acoustic... Feature vector middle: The round-trip time of the sound wave The expression is: ； In the formula, For the preset direct surface wave time window The time corresponding to the peak value of the inner envelope Time window for reflected surface waves The time corresponding to the peak value of the inner envelope; in, ; ; The acquired ultrasound signal is processed by bandpass filtering to obtain the signal. Its expression is: ； In the formula, for Hilbert transform; The logarithmic magnitude of the ratio of the peak values ​​of the envelope between the reflected wave and the transmitted wave. And the logarithmic magnitude of the ratio of the baseline transmission peak value to the transmission envelope peak value. The expressions are as follows: ； ； In the formula, Peak value of the reflected wave envelope after distance compensation ; Peak value of the transmitted wave envelope after distance compensation ; The baseline transmission peak value; in, ; ; In the formula, For effective attenuation coefficient, The geometric diffusion index, This represents the distance the ultrasound travels.

8. The method for sediment detection in a high-temperature confined space using multiphysics field collaborative inversion as described in claim 1, characterized in that, Thermal eigenvectors middle: Peak average temperature rise The expression is: ； In the formula, For time windows The average temperature change obtained below; in, ; In the formula, This represents the currently acquired temperature field sequence. , compared with the reference time Temperature field sequence The difference, i.e., the temperature rise field; Priority testing areas; decay time constant The expression is: ； In the formula, This represents the theoretical temperature rise at the initial fitting time. This represents the time constant variable to be optimized. The fitting time window represents the temperature rise decay phase; thermal diffusion radius The expression is: ； In the formula, Indicates the temperature rise field The area; Effective thermal diffusivity The expression is: ； in, and For two different moments within a preset time window, and ; inflection point time of thermal response curve The expression is: 。 9. A sediment detection system for a high-temperature confined space, characterized in that, A sediment detection method for high-temperature confined spaces using multiphysics field co-inversion as described in any one of claims 1 to 8, wherein the sediment detection system for the high-temperature confined space comprises: The simulation positioning unit is used to determine the priority detection area based on the structural and operating parameters of the high-temperature confined space to be tested. A laser excitation unit is used to emit pulsed laser light into the priority detection area through an optical window to excite ultrasonic waves using the thermoelastic effect. The laser excitation unit includes two excitation modes: point source excitation and line source excitation, and has adjustable excitation radius or linewidth, rise time, and output energy. A non-contact ultrasonic receiving unit is used to receive the ultrasonic signal fed back by a pulsed laser from the high-temperature sealed space to be tested, and to extract the acoustic feature parameters from the ultrasonic signal. The infrared thermal imaging unit is used to acquire temperature field sequences of the laser-excited region through a high-temperature resistant infrared window and extract thermal characteristic parameters from the temperature field sequences. The integrated control and data processing center is electrically connected to the simulation positioning unit, laser excitation unit, non-contact ultrasonic receiving unit, and infrared thermal imaging unit, respectively. It is used to synchronously control the timing of laser triggering, ultrasonic signal acquisition, and infrared image acquisition, and to perform acoustic-thermal dual-physics field collaborative inversion based on the extracted acoustic and thermal characteristic parameters, and output the deposition layer thickness distribution results in real time.

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