CN111709197A - SPH inflow boundary processing method based on Riemann invariant - Google Patents

SPH inflow boundary processing method based on Riemann invariant Download PDF

Info

Publication number
CN111709197A
CN111709197A CN202010552480.8A CN202010552480A CN111709197A CN 111709197 A CN111709197 A CN 111709197A CN 202010552480 A CN202010552480 A CN 202010552480A CN 111709197 A CN111709197 A CN 111709197A
Authority
CN
China
Prior art keywords
particle
inflow
boundary
particles
input
Prior art date
Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
Granted
Application number
CN202010552480.8A
Other languages
Chinese (zh)
Other versions
CN111709197B (en
Inventor
林川
Current Assignee (The listed assignees may be inaccurate. Google has not performed a legal analysis and makes no representation or warranty as to the accuracy of the list.)
Fuzhou University
Original Assignee
Fuzhou University
Priority date (The priority date is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the date listed.)
Filing date
Publication date
Application filed by Fuzhou University filed Critical Fuzhou University
Priority to CN202010552480.8A priority Critical patent/CN111709197B/en
Publication of CN111709197A publication Critical patent/CN111709197A/en
Application granted granted Critical
Publication of CN111709197B publication Critical patent/CN111709197B/en
Active legal-status Critical Current
Anticipated expiration legal-status Critical

Links

Images

Classifications

    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F30/00—Computer-aided design [CAD]
    • G06F30/20—Design optimisation, verification or simulation
    • G06F30/28—Design optimisation, verification or simulation using fluid dynamics, e.g. using Navier-Stokes equations or computational fluid dynamics [CFD]
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F17/00—Digital computing or data processing equipment or methods, specially adapted for specific functions
    • G06F17/10—Complex mathematical operations
    • G06F17/11—Complex mathematical operations for solving equations, e.g. nonlinear equations, general mathematical optimization problems

Landscapes

  • Engineering & Computer Science (AREA)
  • Physics & Mathematics (AREA)
  • General Physics & Mathematics (AREA)
  • Mathematical Physics (AREA)
  • Theoretical Computer Science (AREA)
  • Pure & Applied Mathematics (AREA)
  • Mathematical Analysis (AREA)
  • Mathematical Optimization (AREA)
  • Computational Mathematics (AREA)
  • General Engineering & Computer Science (AREA)
  • Algebra (AREA)
  • Data Mining & Analysis (AREA)
  • Fluid Mechanics (AREA)
  • Computer Hardware Design (AREA)
  • Evolutionary Computation (AREA)
  • Geometry (AREA)
  • Computing Systems (AREA)
  • Operations Research (AREA)
  • Databases & Information Systems (AREA)
  • Software Systems (AREA)
  • Management, Administration, Business Operations System, And Electronic Commerce (AREA)

Abstract

The invention discloses an SPH inflow boundary processing method based on Riemann invariants, which comprises the following steps: step S1: constructing an input particle pool containing SPH particles as an applied inflow boundary, and constructing a particle recovery system; step S2: circulating each inflow boundary, and judging whether each inflow boundary reaches the input particle standard; step S3: performing particle input on an entry domain boundary which reaches an input particle standard, and performing particle input by utilizing a Riemann invariant after judging the flow state; step S4: updating the inflow particle information list, and updating a control list and a control variable in the particle recovery system; step S5: updating a particle action information linked list; step S6: and judging whether each input boundary loop is completed or not. Which enables accurate and efficient simulation of the inflow boundary. The method provides a more effective method for accurately constructing the model of the debris flow development process in the landslide disaster.

Description

SPH inflow boundary processing method based on Riemann invariant
Technical Field
The invention relates to the field of engineering application of hydromechanics, in particular to an SPH inflow boundary processing method based on Riemann invariants.
Background
The old girth (LoWai) village is located in the southwest of the new kingdom of hong kong. No. 8/20 in 2005 caused two landslides in the northern region of the old age area (LoWai) due to heavy rainfall. The landslide mass blocks the catchment channel at the toe of the slope, and along with the gradual rise of the water level of the catchment channel, a large amount of flood flows out of two overflow weirs arranged on the catchment channel and flows into a downstream natural river channel. Because the river course has a larger gradient and faster water flow, serious erosion effect occurs. A large mass of soil and rock lumps is entrained by the flood water to gradually form two debris flows and join at a downstream 226 m. Finally stopping on the old roads about 800m downstream of the weir and forming a stack at several places.
The development process of debris flow in landslide disasters belongs to inflow boundary problem in fluid mechanics, and the treatment process is quite complicated. However, since the simulation and analysis of the process are of great significance for the prediction and early warning of landslide disasters, a reliable method is needed for processing.
In the common inflow problem in the field of computational fluid mechanics, the traditional processing method may generate virtual numerical value oscillation at an inflow boundary because the characteristic that the Riemannian invariant is constant along a characteristic line under the slow flow condition is not considered, and the accuracy of computation and the robustness of a computation program are influenced.
The Smooth Particle Hydrodynamics (SPH) method is a typical meshless method. The SPH method utilizes a series of dispersed particles to realize a numerical approximation process, and is a method without grid particles under a Lagrange framework.
Disclosure of Invention
Aiming at the problems, the invention provides an SPH inflow boundary processing method based on Riemann invariant, which overcomes the problems of local grid distortion and grid reconstruction in the traditional grid method and overcomes the adverse effect of virtual numerical value oscillation possibly occurring in the inflow boundary processing of the traditional SPH method. According to the invention, by establishing the SPH inflow boundary processing method based on the Riemannian invariant and taking the constant property of the Riemannian invariant along the characteristic line into consideration of the influence of the flow field change in the domain on the boundary, the virtual numerical value shock at the inflow boundary is effectively reduced, the robustness of a calculation program is improved, and by constructing a set of particle recovery system, the occupation amount of the inflow boundary calculation process on the memory is reduced, the calculation efficiency is improved, and the accurate and efficient simulation of the inflow boundary is realized. The method provides a more effective method for accurately constructing the model of the debris flow development process in the landslide disaster.
In order to achieve the purpose, the invention specifically adopts the following technical scheme:
an SPH inflow boundary processing method based on Riemann invariants is characterized by comprising the following steps:
step S1: constructing an input particle pool containing SPH particles as an applied inflow boundary, and constructing a particle recovery system to prepare for subsequent particle recovery;
step S2: circulating each inflow boundary, and judging whether each inflow boundary reaches the input particle standard; the method comprises the steps of establishing cyclic judgment of flow boundary by flow boundary, and realizing application of a plurality of flow boundaries;
step S3: performing particle input on an entry domain boundary which reaches an input particle standard, and performing particle input by utilizing a Riemann invariant after judging the flow state, wherein the number of input particles is equal to that of particles in the width direction of the particle pool each time;
step S4: updating the inflow particle information list, and updating a control list and a control variable in the particle recovery system;
step S5: updating a particle action information linked list to prepare for iterative solution of a subsequent control equation;
step S6: and judging whether each input boundary cycle is finished, if not, returning to the step S2 to judge the next inflow boundary, and if so, outputting an inflow boundary application result.
Preferably, in step S1, the SPH particles are virtual particles for implementing the smoothing process of applying and calculating the domain boundary to the boundary condition of the inflow particle, and the particle pool extending into the flow boundary has a thickness greater than the maximum supported domain radius of the input particle.
Preferably, in step S2, the input particle criteria are: the closest distance of the intra-domain particle to the boundary is greater than the inter-particle distance of the pool of particles.
Preferably, in step S3, the method is performed according to the Froude number
Figure BDA0002543122460000021
And judging the flow state, wherein v is the flow velocity, g is the gravity acceleration and h is the water depth.
Preferably, in step S3, the particle input using the riemann invariant after determining the flow state specifically includes the following steps:
step S31: flow state judgment: according to the Froude number
Figure BDA0002543122460000022
Judging the flow state, wherein v is the flow velocity, g is the gravity acceleration and h is the water depth;
when Froude number FrWhen the flow rate is more than 1, the water flow is a rapid flow, the influence domain of the calculation domain points to the downstream, the boundary condition is directly applied without considering the influence of the flow field in the domain, the boundary condition can be directly applied to give the flow rate and the depth information of the input particles, and the application of the inflow boundary is completed;
when Froude number FrWhen the flow rate is less than 1, the water flow is slow flow, and at the moment, according to the characteristic line theory, the influence of the flow field in the domain needs to be considered at the boundary. And keeping the constant along the characteristic line according to the Riemann invariant to realize the consideration of the boundary on the flow field change in the domain. For a fluid control equation in the form of depth integration, Riemann's first and second invariants R of a domain are calculated(1)、R(2)The expression is as follows:
Figure BDA0002543122460000031
Figure BDA0002543122460000032
wherein the content of the first and second substances,
Figure BDA0002543122460000033
is the depth average flow rate. For inflow boundary conditions, the influence of the flow field in the domain on the boundary needs to be considered, and a Riemann second invariant R is utilized(2)To perform subsequent calculations. In the iterative solution process, setting that the flow field condition of the calculation domain of the nth step is obtained, and further obtaining the result of the step n + 1;
therefore, step S32 is executed;
step S32: at the nth time step, the flow velocity of the input particle is calculated without applying boundary processing
Figure BDA0002543122460000034
And depth h*Thereby obtaining a Riemann second invariant R(2)*(provenance invariant) is:
Figure BDA0002543122460000035
step S33: keeping the Riemann invariants unchanged along the direction of the characteristic line, and obtaining the Riemann second invariants of the boundary particles at the time step of n +1 according to the transfer sequence as follows:
Figure BDA0002543122460000036
wherein the content of the first and second substances,
Figure BDA0002543122460000037
for the inflow velocity at time step n +1,
Figure BDA0002543122460000038
the depth of inflow at time step n + 1;
step S34, obtaining the inflow velocity and the inflow depth after the boundary processing of the particles according to the known inflow depth or the known inflow velocity:
determining the inflow velocity for a known inflow depth:
Figure BDA0002543122460000039
determining the inflow depth for the known inflow velocity:
Figure BDA0002543122460000041
preferably, in step S4, the inflow particle information list includes: a depth list rho (:), an initial depth list rho0(:), a quality list mass (:), an initial quality list mass0(:), a particle type list itype (: a smooth length list hsml (: a flow rate list u (:);
the control list includes: whether the list in the computation domain if _ out _ domain (:), the list of available particles list _ avail _ nodes (:);
the control variables include: total number of particles npoin, Total number of particles in the boundary particle pool np _ batg _ Total, and Total number of available particles n _ avail _ nodes.
In the initial stage of calculation, because the domain-entering particles are not present, there are: the available number of particles is the Total number of particles-the Total number of particles in the boundary particle pool (n _ avail _ nodes is npoin-np _ batg _ Total).
In order to realize effective management of the memory and avoid excessive memory occupation under the condition of continuous inflow, the particle recovery system for realizing the reuse of the out-of-domain particles by utilizing the control list and the control variables comprises the following steps:
and storing the intra-domain list of the particles of the last time step. And assigning the intra-domain list if _ out _ domain (of the previous time step) to the list if _ out _ domain _ LAST, and updating the intra-domain list if _ out _ domain (of the current time step) according to the particle coordinates of the current time step and the calculation domain range, wherein the list number corresponds to the SPH particle number, the element value 1 represents that the particle is out of the domain, and the element value 0 represents that the element is in the domain.
And judging the out-of-range particles in the current time step. Find out the particles with value 0 in the table if _ out _ domain _ LAST and value 1 in the table if _ out _ domain, these particles are the particles in the current time step domain, that is, the particles that can be recycled next.
Collecting out-domain particles, updating the available particle list and finishing particle recovery. The front end of the list of available particles _ available _ nodes is the available particle number, and the back end is an empty memory location and is assigned with 0. And (4) placing the domain-out particle numbers collected in the step (II) into the positions of the list _ avail _ nodes after the available particles are stored, and increasing the total number n _ avail _ nodes of the available particles according to the number of the collected particles.
And fourthly, using the recovered particles. And performing particle input on the input domain boundary reaching the input particle standard, extracting the available particles in the available particle list _ avail _ nodes from back to front according to the number of the particles required to be input, and marking 0 at the extraction position. And simultaneously, subtracting the number of input particles from the total number n _ avail _ nodes of the available particles to complete particle input.
Preferably, in step S5, the process of updating the linked list of particle action information is as follows: and rapidly searching the interaction particle pairs by adopting a particle grid algorithm (PIC), and updating an action particle action information linked list (linked _ list) after the inflow particles are input.
The invention and the preferable scheme thereof provide an SPH inflow boundary processing method based on Riemann invariants, aiming at the problem of virtual numerical value oscillation possibly occurring in the inflow boundary processing of the current SPH method. By utilizing the constant property of Riemann invariant along the characteristic line and considering the influence of the flow field change in the domain on the boundary by arranging the boundary particle pool, the virtual numerical value oscillation at the inflow boundary is effectively reduced, and the robustness of the calculation program is improved. By constructing a set of particle recovery system, the method avoids a large amount of occupation of the memory when the particles are input by continuous inflow, realizes the reutilization of the particles out of the domain by utilizing a series of control lists and the dynamic update of the control parameters, reduces the occupation amount of the memory in the inflow boundary calculation process, improves the calculation efficiency and realizes the accurate and efficient simulation of the SPH inflow boundary. The method provides a more effective method for accurately constructing the model of the debris flow development process in the landslide disaster.
Drawings
FIG. 1 is a schematic overall flow chart of a method according to an embodiment of the present invention;
FIG. 2 is a schematic view of an open channel flow algorithm in accordance with an embodiment of the present invention;
FIG. 3 shows the change of the available particle list before and after the particle recovery process in the open channel flow calculation example according to the present invention;
FIG. 4 is a diagram illustrating a table if _ out _ domain showing whether a particle is in a domain in an initial state according to an embodiment of the present invention;
FIG. 5 is a diagram illustrating a list of particles available in an initial state according to an embodiment of the present invention.
Detailed Description
The invention is further explained below with reference to the drawings and the embodiments.
In order to make the objects, features and advantages of the present invention more obvious and understandable, the technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings in the embodiments of the present invention, and it is obvious that the embodiments described below are only a part of the embodiments of the present invention, and not all of the embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
The technical scheme of the invention is further explained by the specific implementation mode in combination with the attached drawings.
The invention provides an SPH inflow boundary processing method based on Riemann invariants, aiming at solving the problem of virtual numerical value oscillation possibly occurring in inflow boundary processing of the existing SPH method. By utilizing the constant property of Riemann invariant along the characteristic line and considering the influence of the flow field change in the domain on the boundary by arranging the boundary particle pool, the virtual numerical value oscillation at the inflow boundary is effectively reduced, and the robustness of the calculation program is improved. By constructing a set of particle recovery system, a large amount of memory occupation when the particles are input by continuous inflow is avoided, the reuse of the memory space of the particles in the out-of-area is realized, and the calculation efficiency is improved.
The specific implementation flow chart is shown in fig. 1. Mainly comprises the following 6 steps:
step 1: circulating each inflow boundary, and constructing an input particle pool as a virtual particle applied to the inflow boundary;
step 2: circulating each inflow boundary, and judging whether each inflow boundary reaches the standard of input particles;
and step 3: for the entry domain boundary which reaches the input particle standard, judging the flow state, and then inputting particles by utilizing a Riemann invariant, wherein the particles with the number of particles in the width direction of the particle pool are input each time;
and 4, step 4: updating the inflow particle information list, and constructing a set of particle recovery system to update the available particle list, the control list of whether the particles are in the domain and the like and the control variable;
and 5: updating the particle action information linked list to prepare for iterative solution of a subsequent control equation;
step 6: and (3) checking whether each input boundary cycle is finished or not, if not, repeating the step (2) to judge the next inflow boundary, and if so, outputting an inflow boundary application result.
Selecting an open channel flow algorithm (schematic example figure 2) with a free surface, wherein the open channel flow algorithm has an inflow boundary and an outflow boundary, and the two sides of the channel are non-permeable boundaries, and the detailed description is provided according to a method flow (figure 1):
step 1: and circulating each inflow boundary, and constructing an input particle pool as a virtual particle applied to the inflow boundary. The example comprises one inflow boundary, 12 input pool particles are constructed to be virtual particles applied to the inflow boundary, and variable information carried by the pool particles is given according to boundary conditions.
Step 2: and circulating each inflow boundary, and judging whether each inflow boundary reaches the standard of input particles. Judging the particles in the calculation domain, wherein two possibilities exist, if one calculation does not have the particles in the domain, the particles are directly input, and an initial state value is obtained by interpolation according to the interaction relation between the input particles and the pool particles, so that the inflow boundary application process is completed; in another case, when the calculation is already started, the nth time step is used for calculating the result (n >1) of the (n + 1) th time step, and whether the input particle standard is reached needs to be judged, the method comprises the following steps:
s2.1: all particles in the domain are calculated over the nth time step, finding the particle a closest to the inflow boundary (see fig. 2).
S2.2: and comparing whether the distance between the particle a and the boundary meets the domain entry condition, namely the distance is larger than the inter-particle distance of the particle pool. If the time step is smaller than the threshold value, the domain entry condition is not met, the particles are not input in the time step, and the boundary processing is finished; if the number is larger than the threshold value, the entering-domain condition is met for particle input.
S2.3: and arranging initial positions of input particles, and shifting the particle distance length of the particle pool in the vertical direction of the boundary according to the positions of a row of particles extending along the boundary in the particle pool to obtain the initial positions of the input particles.
And step 3: and judging the flow state of the input domain boundary which reaches the input particle standard, and then inputting the particles by utilizing the Riemann invariant. Comprises the following steps:
and S3.1, judging the flow state. According to the Froude number
Figure BDA0002543122460000071
And judging the flow state, wherein v is the flow velocity, g is the gravity acceleration and h is the water depth. When Froude number FrThe water flow is a rapid flow, the influence of the flow field in the domain is not needed to be considered when the influence domain of the calculation domain points to the downstream, boundary conditions are directly applied to give flow velocity and depth information of input particles, and the application of an inflow boundary is completed. When Froude number FrThe water flow less than 1 is slow flow, and at the moment, according to the characteristic line theory, the influence of the flow field in the domain on the input particles needs to be considered.
S3.2 when the water flow is slow flow, first, the flow velocity of the input particles under the condition of not applying boundary processing is calculated
Figure BDA0002543122460000072
And depth h*. From this, the Riemann invariant R of the domain can be obtained(2)*(i.e., the Riemann's second invariant, represented here for simplicity by the out-of-field Riemann's invariant) is:
Figure BDA0002543122460000073
s3.3, keeping the Riemann invariants unchanged along the direction of the characteristic line according to the Riemann invariants, and obtaining the Riemann second invariants of the boundary particles at the time step of n +1 according to the transfer sequence as follows:
Figure BDA0002543122460000074
and S3.4, respectively obtaining the inflow velocity and the inflow depth after the boundary processing of the particles according to the known inflow depth or the known inflow velocity.
The method comprises the following steps of solving the inflow velocity situation of the known inflow depth:
Figure BDA0002543122460000081
solving the inflow depth situation of the known inflow speed comprises the following steps:
Figure BDA0002543122460000082
this completes the application of the input particles and boundary conditions based on the riemann invariants.
And 4, step 4: and updating the inflow particle information list, firstly constructing a set of particle recovery system by a program in the initial calculation stage, and updating control lists such as an available particle list, an intra-domain list and the like and control variables in the subsequent calculation according to the particle input condition of each time step. Updating the inflow particle information list includes: a depth list rho (:), an initial depth list rho0(:), a quality list mass (:), an initial quality list mass0(:), a particle type list itype (: a smooth length list hsml (: a flow rate list u (:); the control list includes: whether the intra-domain list if _ out _ domain (:), the available particle list _ avail _ nodes (: whether the list and the available particle list in the initial state are in the intra-domain are schematically shown in fig. 4 and 5); the control variables include: total number of particles npoin, Total number of particles in the boundary particle pool np _ batg _ Total, and Total number of available particles n _ avail _ nodes.
In order to realize effective management of the memory and avoid excessive memory occupation under the condition of continuous inflow, the particle recovery system for realizing the reuse of the out-of-domain particles by utilizing the control list and the control variables comprises the following steps:
s4.1, storing whether the particle in the last time step is in the domain list. And assigning the intra-domain list if _ out _ domain (of the previous time step) to the list if _ out _ domain _ LAST, and updating the intra-domain list if _ out _ domain (of the current time step) according to the particle coordinates of the current time step and the calculation domain range, wherein the list number corresponds to the SPH particle number, the element value 1 represents that the particle is out of the domain, and the element value 0 represents that the element is in the domain.
And S4.2, judging the out-of-range particles in the current time step. Find out the particles with value 0 in the table if _ out _ domain _ LAST and value 1 in the table if _ out _ domain, these particles are the particles in the current time step domain, that is, the particles that can be recycled next. For example, in this example, the b-particle is found to be the current time step out-of-domain particle by comparing the in-domain list of previous and subsequent time steps (see fig. 2).
S4.3, collecting out-domain particles, updating the available particle list and completing particle recovery. The front end of the list of available particles _ available _ nodes is the number of the available particles, and the back end is the empty memory location and is assigned with 0 (see fig. 3 for a schematic diagram of the list of available particles in the initial stage of calculation in the pre-recycling state). And (4) placing the domain-out particle number collected according to the step (S4.2) to a position after the available particles are stored in the available particle list _ avail _ nodes, and increasing the total number n _ avail _ nodes of the available particles according to the number of the collected particles. Taking b-particles as an example, the variation process of the list of available particles list _ avail _ nodes before and after being collected is shown in FIG. 3
S4.4 use the recovered particles. And performing particle input on the input domain boundary reaching the input particle standard, extracting the available particles in the available particle list _ avail _ nodes from back to front according to the number of the particles required to be input, and marking 0 at the extraction position. And simultaneously, subtracting the number of input particles from the total number n _ avail _ nodes of the available particles to complete particle input. Taking particle b as an example, if the particle needs to be imported after recovery, it is extracted from the list of available particles.
And 5: updating the particle action information linked list provides for iterative solution of subsequent control equations. And fast searching for the interaction particle pairs is realized by adopting a particle grid algorithm (PIC), and the update of an action particle action information linked list (linked _ list) after the inflow particles are input is completed.
Step 6: and (3) checking whether each input boundary cycle is finished or not, if not, repeating the step (2) to judge the next inflow boundary, and if so, outputting an inflow boundary application result.
For the development of debris flow like old village debris flow from a state of almost pure water, the process that the water body gradually carries the soil body and even some stones can be divided into two stages through the continuous erosion effect along the way: 1. high-sand Flows (Hyper-centralized Flows) or Debris Flows (Debris waters); 2. debris flow (Debris Flows).
In the simulation of the debris flow development process, the triggered inflow boundary can simulate the overflow process of water flow through a particle pool, and inflow particles are utilized to simulate the water flow and the sand and stone carrying condition of water under the erosion action. The method of the embodiment of the invention can solve the problem of how to set inflow boundary conditions of a plurality of overflows in the development process of the debris flow in the landslide accident similar to the old village surrounding, thereby providing help for realizing the simulation of an accurate accident model and having important significance for disaster prevention and control work.
It will be evident to those skilled in the art that the invention is not limited to the details of the foregoing illustrative embodiments, and that the present invention may be embodied in other specific forms without departing from the spirit or essential attributes thereof. The present embodiments are therefore to be considered in all respects as illustrative and not restrictive, the scope of the invention being indicated by the appended claims rather than by the foregoing description, and all changes which come within the meaning and range of equivalency of the claims are therefore intended to be embraced therein. Any reference sign in a claim should not be construed as limiting the claim concerned.
The above description is only a preferred embodiment of the present invention, and all equivalent changes and modifications made in accordance with the claims of the present invention should be covered by the present invention.

Claims (7)

1. An SPH inflow boundary processing method based on Riemann invariants is characterized by comprising the following steps:
step S1: constructing an input particle pool containing SPH particles as an applied inflow boundary, and constructing a particle recovery system;
step S2: circulating each inflow boundary, and judging whether each inflow boundary reaches the input particle standard;
step S3: performing particle input on an entry domain boundary which reaches an input particle standard, and performing particle input by utilizing a Riemann invariant after judging the flow state, wherein the number of input particles is equal to that of particles in the width direction of the particle pool each time;
step S4: updating the inflow particle information list, and updating a control list and a control variable in the particle recovery system;
step S5: updating a particle action information linked list;
step S6: and judging whether each input boundary cycle is finished, if not, returning to the step S2 to judge the next inflow boundary, and if so, outputting an inflow boundary application result.
2. An SPH inflow boundary processing method based on Riemannian invariants, according to claim 1, wherein: in step S1, the SPH particles are virtual particles for implementing the smoothing process of applying the boundary condition of the inflow particle and calculating the domain boundary, and the particle pool extending into the flow boundary has a thickness greater than the maximum supported domain radius of the input particle.
3. An SPH inflow boundary processing method based on Riemannian invariants, according to claim 1, wherein: in step S2, the input particle criteria are: the closest distance of the intra-domain particle to the boundary is greater than the inter-particle distance of the pool of particles.
4. An SPH inflow boundary processing method based on Riemannian invariants, according to claim 1, wherein: in step S3, the method is based on the Froude number
Figure FDA0002543122450000011
And judging the flow state, wherein v is the flow velocity, g is the gravity acceleration and h is the water depth.
5. An SPH inflow boundary processing method based on Riemannian invariants, according to claim 4, wherein:
in step S3, the particle input using the riemann invariant after the flow state is determined specifically includes the steps of:
step S31: flow state judgment: according to Froude number
Figure FDA0002543122450000021
Judging the flow state, wherein v is the flow velocity, g is the gravity acceleration and h is the water depth;
when Froude number FrWhen the flow rate is larger than 1, directly applying boundary conditions to give flow rate and depth information of input particles, and finishing the application of an inflow boundary;
when Froude number FrIf < 1, go to step S32;
step S32: at the nth time step, the flow velocity of the input particle is calculated without applying boundary processing
Figure FDA0002543122450000022
And depth h*Thereby obtaining a Riemann second invariant R(2)*Comprises the following steps:
Figure FDA0002543122450000023
step S33: keeping the Riemann invariants unchanged along the direction of the characteristic line, and obtaining the Riemann second invariants of the boundary particles at the time step of n +1 according to the transfer sequence as follows:
Figure FDA0002543122450000024
wherein the content of the first and second substances,
Figure FDA0002543122450000025
for the inflow velocity at time step n +1,
Figure FDA0002543122450000026
the depth of inflow at time step n + 1;
step S34, obtaining the inflow velocity and the inflow depth after the boundary processing of the particles according to the known inflow depth or the known inflow velocity:
determining the inflow velocity for a known inflow depth:
Figure FDA0002543122450000027
determining the inflow depth for the known inflow velocity:
Figure FDA0002543122450000028
6. an SPH inflow boundary processing method based on Riemannian invariants, according to claim 1, wherein: in step S4, the inflow particle information list includes: a depth list, an initial depth list, a mass list, an initial mass list, a particle type list, a smooth length list, and a flow rate list;
the control list includes: a list of available particles and a list of whether they are within a computational domain;
the control variables include: total number of particles, total number of particles in the boundary particle pool, and total number of available particles.
7. An SPH inflow boundary processing method based on Riemannian invariants, according to claim 1, wherein: in step S5, the process of updating the linked list of particle action information is: and rapidly searching the interaction particle pairs by adopting a particle grid method, and updating the action information linked list of the interaction particles after the inflow particles are input.
CN202010552480.8A 2020-06-17 2020-06-17 SPH inflow boundary processing method based on Riemann invariant Active CN111709197B (en)

Priority Applications (1)

Application Number Priority Date Filing Date Title
CN202010552480.8A CN111709197B (en) 2020-06-17 2020-06-17 SPH inflow boundary processing method based on Riemann invariant

Applications Claiming Priority (1)

Application Number Priority Date Filing Date Title
CN202010552480.8A CN111709197B (en) 2020-06-17 2020-06-17 SPH inflow boundary processing method based on Riemann invariant

Publications (2)

Publication Number Publication Date
CN111709197A true CN111709197A (en) 2020-09-25
CN111709197B CN111709197B (en) 2022-07-22

Family

ID=72540883

Family Applications (1)

Application Number Title Priority Date Filing Date
CN202010552480.8A Active CN111709197B (en) 2020-06-17 2020-06-17 SPH inflow boundary processing method based on Riemann invariant

Country Status (1)

Country Link
CN (1) CN111709197B (en)

Cited By (2)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN116992796A (en) * 2023-09-27 2023-11-03 中国科学技术大学 Self-adaptive low-dissipation SPH-HLLC Riemann solver coupling algorithm
CN117870744A (en) * 2024-03-12 2024-04-12 成都理工大学 Monitoring device and method for realizing inversion of mud-rock flow dynamic parameters

Citations (4)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US20120316848A1 (en) * 2011-06-13 2012-12-13 Korea Advanced Institute Of Science And Technology Sph fluid simulation method and system for multi-level vorticity, recording medium for the same
JP2014067329A (en) * 2012-09-27 2014-04-17 Toko Inc Simulation device, simulation method and program
CN109948109A (en) * 2019-01-31 2019-06-28 天津大学 Unsteady flow in open mesh free particle simulation method containing changes of section
CN110569541A (en) * 2019-08-01 2019-12-13 天津大学 Pipeline water hammer analysis method

Patent Citations (4)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US20120316848A1 (en) * 2011-06-13 2012-12-13 Korea Advanced Institute Of Science And Technology Sph fluid simulation method and system for multi-level vorticity, recording medium for the same
JP2014067329A (en) * 2012-09-27 2014-04-17 Toko Inc Simulation device, simulation method and program
CN109948109A (en) * 2019-01-31 2019-06-28 天津大学 Unsteady flow in open mesh free particle simulation method containing changes of section
CN110569541A (en) * 2019-08-01 2019-12-13 天津大学 Pipeline water hammer analysis method

Non-Patent Citations (3)

* Cited by examiner, † Cited by third party
Title
SIROTKIN, FV 等: "Implementations and tests of Godunov-type particle hydrodynamics", 《COMPUTERS & FLUIDS》 *
上官子柠: "基于黎曼解的SPH-ALE方法在自由面流动问题中的应用研究", 《中国优秀博硕士学位论文全文数据库(博士)工程科技Ⅱ辑》 *
杨秋足等: "一种基于黎曼解处理大密度比多相流SPH的改进算法", 《力学学报》 *

Cited By (3)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN116992796A (en) * 2023-09-27 2023-11-03 中国科学技术大学 Self-adaptive low-dissipation SPH-HLLC Riemann solver coupling algorithm
CN116992796B (en) * 2023-09-27 2023-12-22 中国科学技术大学 Self-adaptive low-dissipation SPH-HLLC Riemann solver coupling method
CN117870744A (en) * 2024-03-12 2024-04-12 成都理工大学 Monitoring device and method for realizing inversion of mud-rock flow dynamic parameters

Also Published As

Publication number Publication date
CN111709197B (en) 2022-07-22

Similar Documents

Publication Publication Date Title
Ghimire et al. Formulation of a fast 2D urban pluvial flood model using a cellular automata approach
CN111709197B (en) SPH inflow boundary processing method based on Riemann invariant
Avolio et al. SCIDDICA-SS 3: a new version of cellular automata model for simulating fast moving landslides
Roushangar et al. Evaluation of genetic programming-based models for simulating friction factor in alluvial channels
CN106886652A (en) Reservoir on hyperconcentration river muddy water free flow and density current coupled simulation method
Hartanto et al. Application of a coastal modelling code in fluvial environments
Khosronejad et al. Fully coupled free‐surface flow and sediment transport modelling of flash floods in a desert stream in the Mojave Desert, California
CN113705127A (en) Dam break risk prediction and emergency response method and device for tailing pond and electronic equipment
CN112227292A (en) Intelligent analysis method for water choking of open-web arch bridge based on one-dimensional hydrodynamic model
Taheri et al. Groundwater artificial recharge assessment in Kangavar Basin, a semi-arid region in the western part of Iran
Kouzehgar et al. Physical modeling into outflow hydrographs and breach characteristics of homogeneous earthfill dams failure due to overtopping
Zheng et al. A novel explicit-implicit coupled solution method of SWE for long-term river meandering process induced by dambreak
Zhang Study on flow and bed evolution in channels with spur dykes
Zhang et al. Integrating 1D and 2D hydrodynamic, sediment transport model for dam-break flow using finite volume method
KR101487846B1 (en) Method for analyzing 2D shallow water flow through assignment of wetting/drying condition using critical drying depth method
Idrees et al. Complementary modeling approach for estimating sedimentation and hydraulic flushing parameters using artificial neural networks and RESCON2 model
CN115828757A (en) Flood discharge hysteresis characteristic construction and selection method for basin water level prediction
Esmaeili et al. Three-dimensional numerical modeling of sediment flushing: Case study of Dashidaira Reservoir, Japan
Tsakiris et al. Embankment dam break: Uncertainty of outflow based on fuzzy representation of breach formation parameters
Jassam et al. Hydraulic characteristics of the lower part of Diyala River
Haun et al. 3D numerical modelling of the reservoir flushing of the Bodendorf reservoir, Austria
Ahn et al. Effect of climate change on long-term riverbed change using GSTARS model in Nakdong River, Korea
Liang et al. Simulation of dike-break processes in the Yellow River
Torres et al. Three dimensional numerical modelling of full-scale hydraulic structures
Sécher et al. COURLIS: a 1D suspension and bedload code

Legal Events

Date Code Title Description
PB01 Publication
PB01 Publication
SE01 Entry into force of request for substantive examination
SE01 Entry into force of request for substantive examination
GR01 Patent grant
GR01 Patent grant