METHOD AND SYSTEM FOR LIFETIME ANALYSIS OF A FIELD DEVICE THROUGHED BY A FLUID CARRYING SOLID PARTICLES WITH ITSELF

Description

Field of the invention

[0001] The invention relates to a method and a system for lifetime analysis of a field device through which a fluid flows, wherein the fluid carries solid particles.

[0002] The behavior of particles entrained in the fluid, such as foreign bodies, can contribute significantly to wear in field devices. Therefore, it is desirable to consider this behavior when assessing the service life or determining the scheduled maintenance cycles of field devices.

[0003] For processes with a high oxygen content in the fluid, potential ignition processes in the installed field devices (e.g., valves) must also be considered. Seals, which may be made of PTFE, for example, are highly flammable in a concentrated oxygen atmosphere. Depending on the oxygen content and process conditions, metallic components may also be affected.

[0004] Particle impacts play a crucial role in media that are not ultrapure. The ignition mechanism is that particles or, for example, valve components heat up locally, i.e., at the point of impact, to such an extent that ignition occurs. In addition to particle velocity and size, component properties such as thermal conduction in the material and wall thickness also play a crucial role.

[0005] The European Industrial Gases Association (EIGA) has defined process limits specifically for this mechanism, compliance with which is intended to prevent ignition. A permissible local flow velocity is defined depending on the medium pressure. If this limit is met, stainless steel, for example, can be used as the material for the field device; otherwise, an approved exempt material (e.g., Monel) must be used. A more precise assessment of the behavior of solid particles in the fluid would therefore be particularly helpful in this regard. State of the art

[0006] Publications EP 2 812 613 B1 and EP 2 898 241 B1 describe methods and devices for assessing the condition and estimating the service life of seals, respectively. EP 2 898 214 B1 specifically relates to rotary valves. However, the focus here is on the influence of movements during operating cycles, e.g., the opening and closing of a valve. Properties of a flowing fluid, and in particular the influence of solid particles entrained in the fluid, are not considered.

[0007] Publication EP 3 889 553 A1 discloses a method for detecting cavitation on a control element using ultrasound, as well as a corresponding device. Cavitation can lead to increasing damage to the surface of, for example, a valve cone, a valve seat, or adjacent areas. However, particles in the fluid flowing through the control element are not addressed.

[0008] Publication DE 10 2017 101 567 A1 describes a method for simulating the impact of particles or radiation on a physical object. The simulation uses a distribution function of the impact location and / or velocity of the individual particles or radiation units. Such simulation methods are used, for example, to predict possible damage, abrasion, material deposition, and / or other consequences of exposing such an object to particles or radiation.

[0009] The publication US 2020 / 0386652 A1 shows a method for detecting critical vibration states in fluid control systems based on different process parameters. This method also includes a particle simulation method ( Particle Image Velocimetry ) is used. High-speed cameras are used to capture the particles.

[0010] Publication DE 10 2016 205 454 A1 describes a method for determining and / or predicting the operational condition of at least one inlet valve of a steam turbine, as well as a method for operating a system with a steam turbine. In particular, the method also determines the expected service life of the inlet valve. Reference data for the method can be created using simulations based on computational fluid mechanics and / or the finite element method.

[0011] Publication DE 10 2018 218 360 A1 discloses a system and a corresponding method for detecting particle impacts on a sample body. The system enables the recording of both the strength of the impact pulses and the respective impact location, which should improve the attribution of particle impacts to erosive consequences for the sample body. This should be possible with a reasonable overall effort.

[0012] The publications CN 113 567 108 A, CN 112 014 255 A, US 6 837 681 B2 and MA VERSPUI ET AL.: "Simulation model for the erosion of brittle materials", WEAR, Vol. 233-235, December 29, 1999 (1999-12-29), pages 436-443 disclose examples of systems for measuring and evaluating wear of a device. Task

[0013] The object of the invention is to provide a method and a system that better takes into account the influence of solid particles in the fluid on the service life or wear of field devices. Solution

[0014] This problem is solved by the subject matter of the independent claims. Advantageous developments of the subject matter of the independent claims are characterized in the subclaims. The wording of all claims is hereby incorporated by reference into this description.

[0015] The use of the singular shall not exclude the plural, and this shall also apply in the reverse sense unless otherwise disclosed.

[0016] Individual method steps are described in more detail below. In a preferred variant of the invention, the steps are carried out in the order specified. However, the steps do not necessarily have to be carried out in the order specified, and the method to be described may also include additional, unmentioned steps.

[0017] To solve this problem, a method for lifetime analysis of a field device is proposed. A fluid flows through the field device, carrying solid particles, such as contaminants, abrasion debris, etc. The method comprises the following steps: Determining the flow rate (i.e. the number per unit of time) and the speed of the solid particles. This can be done, for example, using suitable sensors that do not necessarily have to be arranged directly on or in the field device. Determining the speed of the solid particles, at least partly using numerical fluid mechanics. This involves numerically determining how the fluid flow carries the entrained solid particles to the inner walls of the field device and what speed they would have at these points. If necessary, simplifying assumptions are made about the mass and size of the solid particles. In the simplest case, for example, point particles with a fixed mass can be considered. Calculating the impact energy of the solid particles on part of the field device from their determined speeds. Determining a forecast for the service life and / or wear of the field device.To determine the prognosis, a digital model is used to predict wear on the field device. This model takes into account the calculated impact energies of the solid particles. This means that higher impact energies can be used to assume greater wear. This can relate to the expected spatial occurrence of wear phenomena, i.e. which components of the field device are particularly affected by wear, as well as, for example, how wear depends on the flow velocity of the fluid. For lower impact energies, it can also be taken into account, for example, that solid particles may accumulate. The model can, for example, use extrapolation to provide a forecast of when maintenance and / or replacement of components of the field device might be necessary to ensure continued trouble-free operation.In one variant, the model's assumptions are selected so that the model predicts wear that occurs somewhat faster than in reality, in order to provide a safety buffer. The calculated forecast for the service life and / or wear of the field device is output. This can be done, for example, via a display or in the form of a written notification, which can be sent to a responsible person, for example, as an email, SMS, or any other text message, or can be sent to a printer, for example. If necessary, maintenance appointments can also be scheduled automatically.

[0018] This method allows for better consideration of how solid particles entrained in the fluid affect the wear and tear or service life of a field device. In particular, it also considers the impact of changes in the solid particle content and changes in fluid flow. This results in more reliable estimates of required maintenance measures, which can save costs and, for example, facilitate uninterrupted operation.

[0019] Documentation and further processing of the results are facilitated if the method includes the further step of creating a data carrier, on which the forecast for the service life and / or wear of the field device and / or parameters of the model are stored.

[0020] Different types of wear can be taken into account by measuring the flow rate and velocities of the solid particles both upstream and downstream of the field device and comparing the upstream and downstream flow rates.

[0021] As a result of the measurement, boundary conditions for computational fluid dynamics are available, which can be used for the detailed calculation of the velocity of the solid particles on their path through the field device. As a result of the velocity calculation, the velocity of the particles is known not only before and after the field device, but also in individual regions within the field device.

[0022] The model for predicting wear on the field device takes into account material deposits in the field device when the flow rate of solid particles determined downstream of the field device is smaller than the flow rate of solid particles determined upstream. The model for predicting wear on the field device takes into account material removal in the field device when the flow rate of solid particles determined downstream of the field device is greater than the flow rate of solid particles determined upstream. The flow rates and velocities of the solid particles in the fluid can be determined in the inlet or inlet area as well as in the outlet or outflow area of ​​the field device.

[0023] The predicted lifetime of the field device becomes more accurate if the model also considers the size and / or surface properties and / or composition of the solid particles. The type of particle, its composition (i.e., the material of which it is made), and especially properties such as material hardness and particle shape can play a role in the interaction between the particle and the field device. For example, harder particles with a sharper edge are more likely to cause material removal than softer or more brittle particles or particles with a more rounded shape. The particle material influences the impact energy through its density.

[0024] Data for this purpose can also come from laboratory tests or other external sources.

[0025] Predictions regarding the field device's lifetime are further improved if the model also takes into account the impact angle and / or impact location of the solid particles. This information is obtained from numerical flow simulation. If the flow simulation is complex and accurate enough, and the computing unit sufficiently powerful, even the effects of turbulence can be considered.

[0026] This consideration of the impact angle and / or impact location is particularly advantageous when the fluid contains oxygen. This allows monitoring to ensure that no point on the field device experiences excessive impact energies that could potentially lead to reaching an ignition temperature. This is more accurate and reliable than monitoring only the fluid flow velocity.

[0027] Preferably, the model also considers the surface properties of the components inside the field device, especially material and hardness. These have a significant influence on the effect of particle impacts.

[0028] If pressure sensors are provided upstream and downstream of the field device, and the model also takes into account the fluid pressure, particularly the pressure drop during flow through the field device, a correction for particle erosion or particle deposition can be determined. The greater the particle deposition in the field device, the greater the pressure difference between the upstream and downstream pressure sensors.

[0029] Furthermore, it is preferred that a temperature sensor be provided to measure the fluid temperature. The model also considers temperature and / or viscosity and / or other characteristics of the fluid. This also increases the accuracy of predictions regarding the service life of the field device, as it takes into account that most reactions that lead to increased wear, for example, occur more rapidly at elevated temperatures. Depending on the fluid, however, the opposite may also apply: the solids content increases at lower temperatures, e.g., because the freezing temperature of fluid components is reached.

[0030] The model preferably takes into account the spatial shape of the field device, e.g., in the form of CAD data and / or digital blueprints, to determine the most likely locations on the field device where deposits can form. This allows for the highest possible accuracy in determining these locations, since the exact shape and size of all considered parts of the field device are available. In particular, the exact arrangement of the internal surfaces of the field device is known.

[0031] In the method immediately mentioned above, it is particularly preferred that the model calculates material removal or deposition in the field device based on the temporal progression of the flow rate and the calculated impact energies of the solid particles. Changes in the spatial shape of the field device resulting from the calculated material removal or deposition are subsequently taken into account in the flow simulation. With the changed spatial shape, a different flow, and in particular a different particle distribution, will emerge. This results in a feedback system. This system adapts to changing conditions and thus provides a forecast of the service life of the field device that becomes more accurate over time.

[0032] An even more accurate prediction of the service life of the field device can be achieved if wear caused by impact and / or deposition and / or removal of solid particles in the field device is simulated using the finite element method.

[0033] More complex interactions, as well as possibly unknown relationships, between the different parameters can be taken into account if the procedure includes the following additional steps: Fluid and / or environmental data are correlated. The correlations are statistically evaluated. The statistical evaluation is used to calculate the current deposition or removal of solid particles. A forecast for the service life and / or wear of the field device is generated from the current deposition or removal of solid particles.

[0034] An example of this could be a high ambient temperature being transferred into the interior of the field device. The temperature influences the particle loading, or rather, the concentration of particles in the fluid. Thus, the particle flow itself is affected. Velocity, impact angle, and impact location change due to the temperature change.

[0035] The ongoing computational effort required can be reduced by building a collection from a plurality of simulation results for the flow of solid particles through the field device and the associated conditions (e.g., flow rate and velocities of the solid particles) upstream and downstream of the field device. For the existing conditions upstream and downstream of the field device, the flow of solid particles through the field device is obtained by taking a simulation result from the collection for conditions that match within a specified tolerance. Results for the current situation at the field device can be subsequently calculated by comparing them with the existing results and, if necessary,Interpolation between or extrapolation from it can be obtained, which requires significantly less computational effort than if a complete simulation using numerical fluid mechanics is required for each instantaneous situation.

[0036] Based on a number of fully calculated simulation results for the flow of solid particles, a simplified parameterized model for the flow of solid particles in the field device can be developed. Reduced-Order-Model (ROM) enables the prediction of flow conditions and the associated particle distribution with significantly reduced computational effort. Even during operation, it could potentially be used to detect whether the field device is operating under hazardous process conditions at a given time.

[0037] For this purpose, various measurement data from the system, such as pressure, temperature, other fluid data, or even particle loading and / or particle size, are taken into account. Even under changing conditions (e.g., start-up and shutdown processes, changes in process variables, or general, time-varying processes), it can then always be ensured that the operation of the field device under critical conditions does not go unnoticed and can be interrupted in the event of danger.

[0038] For processes involving oxygen-containing fluids, it is advantageous to use the calculated impact energies to identify points and / or components of the field device that are exposed to an increased risk of ignition when exposed to oxygen-containing fluids. This is evident in the fact that the determined impact energies and, for example, the thermal conductivity properties of the material of the field device components can lead to local heating that lies within a specified safety margin of an ignition temperature. If such points and / or components are identified, these components can be replaced with ones made of safer material (e.g., Monel) or with components reinforced at critical points, in addition to other safety measures.This method can also be used to identify components that—perhaps contrary to previous practice—are not critical with regard to the temperatures reached and therefore do not need to be manufactured from special materials in the future. The design of the field device can be optimized in this way.

[0039] It is also advantageous if safety measures are initiated when calculated impact energies exceed a specified threshold. This could, for example, be a visual and / or acoustic warning of critical operating conditions, and / or the throttling of an inflow, or possibly even the triggering of a process interruption.

[0040] The problem is further solved by a system for analyzing the service life of a field device through which a fluid flows, wherein the fluid carries solid particles with it. The system has sensor means for determining the flow rate and speed of the solid particles, as well as a computing unit with a digital model for predicting wear on the field device. The computing unit calculates the speeds of the solid particles using numerical fluid mechanics and uses this to determine their impact energies on a part of the field device. The model takes the impact energies of the solid particles into account. The computing unit uses the model to determine a forecast for the service life and / or wear of the field device. The system also has output means for outputting the determined forecast for the service life and / or wear of the field device.

[0041] This system allows for better consideration of how solid particles entrained in the fluid affect the wear and tear of a field device. In particular, it also considers the impact of changes in the solid particle content and changes in fluid flow. This results in more reliable estimates of required maintenance measures, which can save costs and, for example, facilitate uninterrupted operation.

[0042] By arranging at least one sensor means for determining the flow rate and speed of the solid particles upstream of the field device, data can be recorded on the solid particles that are transported into the field device by the fluid flow.

[0043] By arranging at least one sensor means for determining the flow rate and speed of the solid particles downstream of the field device, data can be recorded on the solid particles that are carried out of the field device by the fluid flow.

[0044] When using particle sensors in both the inlet and outlet areas, the following conclusions can be drawn: If the measured particle density in the inlet is higher than in the outlet, material buildup on specific components inside the field device can be expected. If the field device is a control valve, the flow rate could be reduced, meaning the desired KV value is no longer achieved. Such material buildup could also lead to sticking or jamming of the control valve's actuator, which would no longer ensure safe operation. Using CFD simulations and, for example, the CAD data of the control valve, the locations where material buildup is particularly likely can be determined.

[0045] If additional pressure sensors are present in both the inlet and outlet areas, the predicted material deposit can be compared with the change in pressure differences: The greater the material deposit in the control valve, the greater the pressure difference between the measuring points in the inlet and outlet areas. This effect can be used to verify the material deposit predicted based on the measurements from the particle sensors and to further improve the forecasts using correction factors. Additional externally obtained data, e.g., from laboratory tests, can also be used to more precisely determine the relationship between material deposits in the valve and changes in the pressure difference between the inlet and outlet.

[0046] If, however, the particle density in the inlet is lower than in the outlet, an abrasive effect from the solid particles in the fluid can be expected. Certain areas in the field device therefore lose material from their surface. If the field device is a control valve, for example, and significant particle movement is detected in the outlet when the valve element is closed, tight closure is likely no longer guaranteed. A constant inlet pressure with the valve element closed, but increasing particle movement in the outlet over time, can result in greater wear on the valve seat, preventing tight closure. Such wear would also be noticeable in the pressure difference between the inlet and outlet.

[0047] The sensors used to determine the flow rate and velocity of solid particles are preferably selected from ultrasonic sensors, optical sensors, inductive sensors, and capacitive sensors. Ultrasonic sensors can, for example, detect particle density from the attenuation of an ultrasonic signal. Optical sensors are particularly well-suited for determining particle size (from reflection and scattering). Inductive and capacitive sensors, on the other hand, are particularly suitable for detecting metal particles.

[0048] A combination of ultrasonic and optical sensors is also conceivable. Unlike optical sensors, ultrasonic sensors can more accurately determine the total density of solid particles in the fluid.

[0049] It is also advantageous to have at least one electromagnet to determine the content of magnetic solid particles in the fluid. These particles can be attracted by the electromagnet, so that, for example, the weight or volume of a collection of these particles can be periodically determined. It is advisable to either periodically remove such particle accumulations from the electromagnet or to release them again by briefly deactivating the electromagnet so that they can be flushed away by the fluid.

[0050] Electromagnets can be placed in the inlet and / or outlet areas of the field device. At least magnetic metal particles, which arise, for example, due to wear in the process plant, can be separated using magnets. The density of such particles adhering to the magnet can be measured, for example, inductively. A comparison between the particle flow rate in the inlet and outlet areas reveals whether such wear is occurring on the field device. Using the electromagnet, the metal particles can be periodically collected and / or released again.

[0051] A less preferred alternative is a permanent magnet instead of an electromagnet. This would require periodic mechanical cleaning, as accumulated metal particles cannot be released. The actual detection of the metal particles can also be achieved capacitively, as an alternative to inductive detection.

[0052] The object is further achieved by a system as just described, with means suitable for carrying out the steps of the method as described above.

[0053] The object is also achieved by a computer program comprising instructions which cause the above-mentioned system to carry out the method steps described above.

[0054] The task is finally solved by a computer-readable medium on which the aforementioned computer program is stored.

[0055] Further details and features will become apparent from the following description of a preferred embodiment in conjunction with the figures. The respective features can be implemented individually or in combination with one another. The possibilities for achieving the object are not limited to the embodiment.

[0056] An exemplary embodiment is shown schematically in the figures. Identical reference numerals in the individual figures denote identical or functionally equivalent elements, or elements that correspond to one another in terms of their functions. In detail: Fig. 1 shows a schematic sectional view through a pipeline with a field device with upstream and downstream sensors; and Fig. 2 shows a representation of particle trajectories on a field device, the particle trajectories being determined using numerical flow simulation.

[0057] Fig. 1 shows a pipeline 100 with a control valve as the field device 110. Transmitters or sensors for measuring the transmission of an ultrasonic signal through the fluid are arranged both in the inlet 120 to the field device and in the outlet 125 from the field device. In this specific exemplary embodiment, these devices for measuring the transmission of an ultrasonic signal are implemented by an ultrasonic transmitter / receiver 130, 135 and an oppositely arranged ultrasonic transmitter / receiver 140, 145, which can measure the flow rate and velocity of the solid particles in a known manner. Of course, other configurations for measuring the transmission of an ultrasonic signal through the fluid are also conceivable.

[0058] Solid particles 150 entrained in the fluid attenuate the transmission of the ultrasonic signal 160 in the inlet 120 of the field device and the ultrasonic signal 165 in the outlet 125 of the field device. Depending on the attenuation of the ultrasonic signals 160, 165, the content of solid particles 150 in the fluid can be determined in a known manner. Fig. 1 In particular, the present arrangement also makes it possible to determine whether the content of solid particles differs between inlet 120 and outlet 125, which indicates particle deposition or particle removal inside the field device 110.

[0059] The ultrasonic sensor(s) are preferably calibrated with a pure test fluid, i.e., one without particle influence. Ultrasonic sensors can be used to determine both the flow velocity of the fluid and the density of solid particles. The signal strength of an ultrasonic transmitter is compared with the signal strength received by a receiver. The more solid particles in the fluid, the more the ultrasonic signal is attenuated. In contrast, ultrasound propagates with little attenuation in fluids without solid particles.

[0060] Fig. 2shows an example of three exemplary particle trajectories on a field device 200, where the particle trajectories were determined using numerical flow simulation. The field device 200 is a control valve with a valve body 210 and an actuator 220. The actual valve seat with the valve cone is located at position 230, but has been omitted for clarity.

[0061] Here, it can be seen that different velocities and impact angles occur at different locations. The resulting impact energies of the particles on the inner wall of the field device lead to different and varying degrees of wear. The greatest directional changes occur in the inlet area 240 and the outlet area 250 of the valve body 210, as well as in between. The lowest particle velocities also occur in the inlet area 240 of the valve body 210. Thus, for example, deposits are most likely to form in the area around position 260, which could potentially lead to a narrowing of the valve's flow opening or prevent it from closing tightly.

[0062] The highest particle velocities, however, occur in the valve outlet 270. The impacts into the wall there also usually occur at shallower angles. Accordingly, material removal is most likely in this area, which, in the worst case, could lead to a reduction in wall thickness. Furthermore, due to the higher impact energy of the particles, relatively high local heating can be expected at the impact points. Corrosion reactions or even ignition processes are therefore most likely in this area. Depending on the application, special materials or particularly intensive monitoring can be provided for such areas. Lifespan models

[0063] A particularly simple service life model for a field device can be obtained, for example, by taking a known maintenance interval as the starting value. This is then assigned a factor that takes into account the currently measured flow rate of solid particles in the fluid. More solid particles mean more wear and thus a shorter service life. The expected particle size can also be taken into account, with larger particles - if their other properties remain unchanged - typically causing greater wear. Likewise, an average value of the impact energies determined using numerical flow simulation can be added as a factor. Here, too, a higher value means greater wear.

[0064] Such an analysis can, of course, also be performed separately for each individual component of the field device. This can be further refined, down to small units of the field device's internal surface, as far as the available computing infrastructure allows. The impact energies would then be averaged only over the respective unit of the field device's internal surface under consideration.

[0065] If the flow rate and velocities of the solid particles are determined both upstream and downstream of the field device and these values ​​are compared, the model can include not only an expected lifetime value for each field device unit under consideration, but also, for example, an expected wall thickness. This value is increased if particle deposition is detected and decreased if particle erosion is detected. Deposition is more likely to occur where the impact energies of the solid particles are low, and erosion where the impact energies are high. The predicted lifetime value in such a model can be determined from when the respective expected wall thickness leaves a predetermined interval when extrapolating its instantaneous change.

[0066] A lifetime model can contain a matrix of various influencing factors. For example, the following influencing factors can be considered in the calculation: Particle count, particle velocity, particle size, particle type; impact angle, turbulence, corrected impact velocity based on flow simulations (CFD); material properties of the internal valve components, e.g., hardness and material; pressure reduction in the fluid, corrections for erosion or deposits; characteristics of the fluid; temperature, correction factors for materials inside the control valve, and viscosity of the process fluid.

[0067] All process and environmental data are preferably correlated and recorded in a matrix, then statistically evaluated, from which the current particle deposition or removal rate is calculated. Based on the development of the particle deposition or removal rate, future material removal or deposits are predicted and corrected using the measured data in the matrix, creating a self-learning system. Thus, using the measured sensor data, continuous corrections are made to the model to improve future forecasts. Situation with oxygen-containing fluids

[0068] Ignition processes in field devices pose a hazard in processes involving oxygen-containing fluids. Impacts from solid particles entrained in the fluid are crucial for such ignition processes, as their impact energy causes local heating, which may exceed ignition temperatures. To prevent this, permissible local flow velocities are defined, depending on the fluid pressure. It is known that these local flow velocities can be optimized through simulations.

[0069] However, the particle distribution and the actual particle velocity (and thus the impact energy) depend heavily on the size and density of the particles. Therefore, for oxygen-containing fluids, it is particularly advantageous to determine the particle behavior through CFD simulations, as described, and to obtain more realistic impact energies. This allows for a more accurate assessment of the situation with regard to potential ignition processes, as the following data can be obtained: Distribution and actual velocity of the particles in the field device; impact angle and location; local energy input into the component walls caused by the particle impact.

[0070] In addition to the qualitative assessment of particle movement in the flow-through field device, a more precise quantitative risk assessment of the field device can be developed, if necessary, taking into account the component materials, particle size and density, as well as the energy input. This can, for example, optimize the design of the field device, particularly with regard to whether and, if so, which components need to be reinforced or made of a special material (e.g., Monel). Such reinforcements can - depending on the accuracy of the simulation results - be limited to particularly critical points if the corresponding components can be manufactured in the optimized form. Special processes such as metal 3D printing may be required for this purpose. glossary field device

[0071] A field device is a technical device in the field of automation technology that is directly related to a production process. In automation technology, "field" refers to the area outside of control cabinets or control rooms. Field devices can therefore be both actuators (control elements, valves, pumps, etc.) and sensors (e.g., measuring transducers) in factory and process automation. Fluid

[0072] Fluid is a common term for gases and liquids. Monel

[0073] Monel (material number 2.4360, UNS N 04400, Alloy 400, Nicorros), also called Monel metal (after Ambrose Monell, † 1921), is a nickel-copper alloy. Monel consists of approximately 65% ​​nickel, 33% copper, and 2% iron with high tensile strength. It is highly resistant to atmospheric corrosion, seawater, and a variety of acids and alkaline solutions. Monel is used in marine engineering, turbine construction, equipment in the chemical and hydrocarbon industries, and for valves, pumps, and heat exchangers. (From https: / / de.wikipedia.org / wiki / Monel.) Numerical flow simulation

[0074] Computational fluid dynamics (CFD) is an established method in fluid mechanics. Its goal is to solve fluid mechanics problems approximately using numerical methods. The model equations used are typically the Navier-Stokes equations, Euler equations, Stokes equations, or potential equations.

[0075] The most comprehensive model is the Navier-Stokes equations. These are a system of second-order nonlinear partial differential equations. In particular, turbulence and hydrodynamic boundary layers can also be calculated using the Navier-Stokes equations, although this places considerable demands on computing power, memory, and numerical methods.

[0076] A simpler model is the Euler equations, which, due to the neglected friction, do not represent the boundary layer and also do not represent turbulence, meaning that, for example, a flow separation cannot be simulated using this model. For this reason, much coarser grids are suitable for solving the equations meaningfully. For those parts of the flow where the boundary layer does not play a significant role, the Euler equations are very well suited.

[0077] Finally, the potential equations are especially useful when rough predictions need to be made quickly. They assume constant entropy, which means that strong shock waves cannot occur, since the entropy is even discontinuous at these. Further simplification via constant density then leads to the Laplace equation.

[0078] The most common solution methods in numerical fluid mechanics are Finite difference method (FDM) Finite volume method (FVM) Finite element method (FEM).

[0079] FEM is suitable for many problems, especially elliptic and parabolic problems in the incompressible regime, but less so for hyperbolic ones. It is characterized by robustness and solid mathematical foundations. FVM is suitable for conservation equations, especially for compressible flows. FDM is very simple and therefore primarily of theoretical interest.

[0080] Other common methods are Spectral method Lattice-Boltzmann method (LBM) Smoothed particle hydrodynamics (SPH) Boundary element method (BEM) Fast multipole method (FMM) Method of fundamental solutions (MFS) Finite point method (FPM) Moving particle semi-implicit method (MPS) Fast fluid dynamics (FFD) Particle in cell method (PIC) Vortex in cell method (VIC)

[0081] All methods are numerical approximations that must be compared with quantitative experiments for validation. With the exception of the particle-based methods, the starting point for the above-mentioned methods is the discretization of the problem using a computational grid. (Adapted from https: / / de.wikipedia.org / wiki / Numerische_Str%C3%B6mungsmechanik.) Computing unit

[0082] A computing unit is a system or device that processes data using programmable computing instructions. The terms computer, computing system, data processing system, or electronic data processing system are synonymous. Such a system is often a PC (personal computer). However, it can also be larger systems, which may consist of several interconnected computers, possibly in a client-server architecture. The networking does not necessarily have to be local ("cloud"). Embedded systems are also included, e.g., single-board computers, DSPs, FPGAs, microcontrollers, etc. Reference symbol

[0083] 100Pipe with field device 110Field device (control valve) 120Inlet to field device 125Outlet from field device 130, 135Ultrasonic transmitter 140, 145Ultrasonic receiver 150Solid particles 160, 165Ultrasonic signal 200Field device 210Valve body 220Actuator 230Position of valve seat with valve cone 240Inlet area of ​​the valve body 250Outlet area of ​​the valve body 260Position with high probability of deposits 270Outlet of the valve cited literature

[0084] cited patent literature CN 113 567 108 A CN 112 014 255 A DE 10 2016 205 454 A1 DE 10 2017 101 567 A1 DE 10 2018 218 360 A1 EP 2 812 613 B1 EP 2 898 241 B1 EP 3 889 553 A1 MA VERSPUI ET AL.: "Simulation model for the erosion of brittle materials", WEAR, Vol. 233-235, December 29, 1999 (1999-12-29), pages 436-443 US 2020 / 0386652 A1 US 6 837 681 B2

Claims

1. A Method for analysing the service life of a field device (110; 200) through which a fluid flows, the fluid carrying solid particles (150), comprising the following steps: 1.1 determining the flow rate and velocities of the solid particles, 1.1.1 wherein the velocities of the solid particles are determined at least partially by means of numerical fluid mechanics; characterised by the following steps: 1.2 calculating impact energies of the solid particles on a part of the field device from their determined velocities; 1.3 determining a prediction for the service life and / or wear of the field device; 1.3.1 wherein a digital model for predicting wear on the field device is used to determine the prediction; 1.3.2 wherein the model takes into account the flow rate and the calculated impact energies of the solid particles; and 1.4 outputting the determined prediction for the service life and / or wear of the field device.

2. A method according to the preceding claim, with the further step of creating a data carrier on which the prediction for the service life and / or the wear of the field device and / or parameters of the model are stored.

3. A method according to one of the preceding claims, characterised in that< / b> 3.1 the flow rate and velocities of the solid particles are measured both upstream and downstream of the field device; 3.2 wherein the flow rates determined upstream and downstream are compared with each other; 3.3.1 wherein the model for predicting wear on the field device takes into account material deposition in the field device if the flow rate of solid particles determined downstream of the field device is less than the flow rate of solid particles determined upstream; and 3.3.2 wherein the model for predicting wear on the field device takes into account material removal in the field device if the flow rate of solid particles determined downstream of the field device is greater than the flow rate of solid particles determined upstream.

4. A method according to one of the preceding claims, characterised in that< / b> 4.1 the model additionally takes into account size and / or surface properties and / or composition of the solid particles; and / or 4.2 the model additionally takes into account impact angle and / or impact location of the solid particles; 4.2.1 wherein this information is obtained from the numerical flow simulation; and / or 4.3 the model additionally takes into account surface properties of the components inside the field device, in particular material and hardness.

5. A method according to one of the preceding claims, characterised in that< / b> 5.1 a temperature sensor for measuring the temperature of the fluid and / or pressure sensors upstream and downstream of the field device are provided; and 5.2 the model additionally takes into account temperature and / or viscosity and / or the pressure of the fluid, in particular a pressure drop during flow through the field device, and / or further characteristic data of the fluid.

6. A method according to one of the preceding claims, characterised in that the model takes into account a spatial shape of the field device in order to determine the most probable locations of the field device where deposits may form.

7. A method according to the immediately preceding claim, characterised in that 7.1 the model calculates material removal or deposition in the field device from the temporal course of the flow rate and of the calculated impact energies of the solid particles; 7.2 changes in the spatial shape of the field device resulting from the calculated material removal or the calculated material deposition are taken into account in the flow simulation; 7.3 resulting in a feedback system.

8. A method according to one of the preceding claims, characterised in that wear due to impact and / or deposition and / or removal of solid particles in the field device is simulated by means of the finite element method.

9. A method according to one of the preceding claims, comprising the following additional steps: 9.1 data of the fluid and / or environmental data are correlated; 9.2 the correlations are statistically evaluated; 9.3 an instantaneous deposition or removal of solid particles is calculated from the statistical evaluation; 9.3.1 wherein a prediction for the service life and / or wear of the field device is generated from the instantaneous deposition or removal of solid particles.

10. A method according to one of the preceding claims, characterised in that< / b> 10.1 a collection is built up from a plurality of simulation results for the flow of the solid particles through the field device and the associated conditions upstream and downstream of the field device; and 10.2 the flow of the solid particles through the field device is obtained for conditions found upstream and downstream of the field device by using a simulation result from the collection for conditions matching within a predetermined tolerance.

11. A method according to one of the preceding claims, characterised in that a simplified parameterised model for the flow of the solid particles in the field device is created from a plurality of simulation results for the flow of the solid particles.

12. A method according to one of the preceding claims, characterised in that< / b> 12.1 the calculated impact energies are used to determine locations and / or components of the field device which are exposed to an increased ignition risk in the case of oxygen-containing fluids; and / or 12.2 safety measures are initiated when calculated impact energies occur which exceed a predetermined threshold.

13. A system for analysing the service life of a field device (110; 200) through which a fluid flows, the fluid carrying solid particles (150), comprising 13.1 sensor means (130, 135, 140, 145) for determining the flow rate and velocities of the solid particles; 13.2 a computing unit having a digital model for predicting the wear on the field device; 13.2.1 wherein the computing unit calculates the velocities of the solid particles by means of numerical fluid mechanics; characterised in that 13.2.2 the computing unit calculates their impact energies on a part of the field device from the velocities of the solid particles; 13.2.3 the model takes into account the flow rate and the calculated impact energies of the solid particles; 13.2.4 the computing unit uses the model to determine a prediction for the service life and / or wear of the field device; and having 13.3 output means for outputting the determined prediction for the service life and / or wear of the field device.

14. A system according to the immediately preceding claim, characterised in that at least one sensor means (130, 135, 140, 145) for determining the flow rate and velocities of the solid particles (150) is arranged upstream and / or downstream of the field device (110; 200).

15. A system according to one of the preceding system claims, characterised in that at least one electromagnet is present in order to determine the content of magnetic solid particles (150) in the fluid.

16. A system according to one of the preceding system claims having means suitable for performing the steps of the method according to one of the method claims.

17. A computer program comprising instructions which, when the program is executed by the computer, cause the system according to the immediately preceding claim to perform the method steps according to any one of the method claims.

18. A computer-readable medium on which the computer program according to the immediately preceding claim is stored.