CN102213606B

CN102213606B - Mirror image flow detection method and virtual flowmeter

Info

Publication number: CN102213606B
Application number: CN 201110088578
Authority: CN
Inventors: 胡狄辛
Original assignee: CISDI Engineering Co Ltd
Current assignee: CISDI Engineering Co Ltd
Priority date: 2011-04-08
Filing date: 2011-04-08
Publication date: 2013-08-14
Anticipated expiration: 2031-04-08
Also published as: CN102213606A

Abstract

The invention belongs to the technical field of fluid flow, and particularly relates to a mirror image flow detection method, which comprises the following steps of: measuring pressure at front and rear ends of a pipeline and the flow of fluid in the pipeline; performing self-learning of flowability characteristic coefficients of the pipeline; calculating a mirror image flow value of the fluid in the pipeline according to the obtained flowability characteristic coefficient domain and the pressure at the front and rear ends of the pipeline; and when determining that the flowability characteristic of a specified pipe section is not changed suddenly and that a flow value of the actual fluid in the monitored pipeline deviates and even is lost, selecting the mirror image flow value as a result to be outputted. The invention also discloses a virtual flowmeter, which comprises at least two pressure sensors, a self-learning module and a virtual flowmeter module. The virtual flowmeter realizes the embedding of a mirror image algorithm function, and achieves the redundant effect that the mirror image flow value becomes a shadow behind the fluid flow value gradually by the self-learning.

Description

Image flow detection method and virtual flow meter

技术领域 technical field

本发明流体计量技术领域，具体涉及一种镜像流量检测方法及虚拟流量计。The technical field of fluid measurement of the present invention, in particular relates to a mirror image flow detection method and a virtual flowmeter.

背景技术 Background technique

随着技术的进步，流量的检测形式种类众多。常用的有(节流)差压式流量计，如孔板、喷嘴等；容积式流量计，如椭圆齿轮、旋转活塞式等；速度式流量计，如叶轮式、插入式等；振动式流量计，如旋涡、旋进等；还有电磁流量计、超声波流量计、质量流量计。甚至还有压力式流量计，声音式流量计等等。With the advancement of technology, there are many types of traffic detection forms. Commonly used are (throttling) differential pressure flowmeters, such as orifice plates, nozzles, etc.; positive displacement flowmeters, such as oval gears, rotary piston types, etc.; Meters, such as vortex, precession, etc.; there are also electromagnetic flowmeters, ultrasonic flowmeters, and mass flowmeters. There are even pressure flow meters, sound flow meters and more.

为方便区别，将上述流量计统称为由硬件设备构成的“设备流量计”，测量结果输出为流体流量值。For the convenience of distinction, the above-mentioned flowmeters are collectively referred to as "equipment flowmeters" composed of hardware devices, and the measurement results are output as fluid flow values.

设备流量计之所以种类繁多，其主要原因在于流体的物理、化学特性非常繁杂，每种设备流量计仅能对应一种或几种适应的工况。如从流体介质分有气体、液体、气固混合体；从流速分有高速、低速、蠕动；还有的含颗粒物要磨损、含油脂要粘结、特种化学物品会凝固；有的流体导电或绝缘等等。The main reason for the wide variety of equipment flowmeters is that the physical and chemical characteristics of the fluid are very complicated, and each equipment flowmeter can only correspond to one or several suitable working conditions. For example, from the fluid medium, there are gas, liquid, and gas-solid mixture; from the flow velocity, there are high-speed, low-speed, and peristalsis; there are also particles that need to be worn, grease that needs to be bonded, and special chemicals that will solidify; some fluids are conductive or Insulation and more.

设计设备流量计前，都必须分析预判断流体的物理、化学特性和工作环境，才能正确选用符合工况的流量计检测形式。然而，当流体的物理、化学特性发生质的变化，或者长期使用后设备流量计检测元件被磨损、粘结等自身工作性能出现变化，必定导致流量检测值出现偏移，甚至无法正常工作。Before designing an equipment flowmeter, it is necessary to analyze and pre-judge the physical and chemical characteristics of the fluid and the working environment in order to correctly select the flowmeter detection form that meets the working conditions. However, when the physical and chemical properties of the fluid change qualitatively, or the flowmeter detection element of the equipment is worn or bonded after long-term use, its working performance changes, which will inevitably lead to a deviation in the flow detection value, or even malfunction.

如电磁流量计是直接测量管道内流体流速V作为流量Q测量依据，借助于导体在磁场中切割磁力线运动时在其两端产生感应电势的原理。由于只能测量导电液体，因此对于含有大量气泡的液体或电导率很低的液体不能测量。举例来讲，高炉冷却壁通常使用软水或纯水作为冷却介质，由于电磁流量计的最大好处在于无阻损、精度高等，通常尽量推荐采用。在设计过程电导率前期参数研判和大部分实际应用中，都能满足电磁流量计的技术要求。但有时为了改善逐渐恶化的水质而需要加药，剂量投放过大造成电导率低下；或冷却壁局部过热造成大量蒸汽气泡混入冷却水中，都使电磁流量计暂时失去流量检测功能。For example, the electromagnetic flowmeter directly measures the flow velocity V of the fluid in the pipeline as the basis for the measurement of the flow Q, and relies on the principle that the conductor generates an induced potential at both ends when the conductor cuts the magnetic force line in the magnetic field and moves. Since only conductive liquids can be measured, it cannot be measured for liquids containing a large number of air bubbles or liquids with very low conductivity. For example, blast furnace staves usually use soft water or pure water as the cooling medium. Since the biggest advantage of electromagnetic flowmeters is that they have no resistance loss and high precision, they are usually recommended as much as possible. In the early stage parameter research and judgment of conductivity in the design process and most practical applications, it can meet the technical requirements of electromagnetic flowmeters. But sometimes in order to improve the deteriorating water quality, dosing is required, and the dosage is too large to cause low conductivity; or local overheating of the cooling wall causes a large number of steam bubbles to mix into the cooling water, which temporarily loses the flow detection function of the electromagnetic flowmeter.

发明内容 Contents of the invention

有鉴于此，为了解决上述问题，本发明公开了一种镜像流量检测方法，在工程实际应用中，设备流量计受外界或内在因素影响时，当判断流量检测值出现偏差甚至突然丢失后，可利用预先收集到的动态流动性特征信息，根据压力差的变化情况推算出一个流量替代值，继续维持流量检测功能的问题。In view of this, in order to solve the above problems, the present invention discloses a mirror image flow detection method. In practical engineering applications, when the equipment flowmeter is affected by external or internal factors, when the flow detection value is judged to be deviated or even suddenly lost, it can be Using the pre-collected dynamic fluidity characteristic information, calculate a flow substitute value according to the change of pressure difference, and continue to maintain the problem of flow detection function.

本发明的目的是这样实现的：镜像流量检测方法，包括如下步骤：The purpose of the present invention is achieved like this: mirror image traffic detection method, comprises the steps:

1)测取管道前后两端的压力和管道内流体流量；根据下式，自学习获得管道流动性特征系数域θ：1) Measure the pressure at the front and rear ends of the pipeline and the fluid flow in the pipeline; according to the following formula, obtain the pipeline fluidity characteristic coefficient field θ by self-study:

$Q Q = = θ θ [\begin{matrix} A A \\ B B \\ C C \\ D D. \\ \cdot &Center Dot; \cdot \cdot \cdot &Center Dot; \end{matrix}] {Σ Σ}_{i i = = 00}^{m m} {((ΔP ΔP - - ΔH ΔH))}^{\frac{i i}{22}};;$

上式中，Q为管道内流体流量值；i为取值数；M为解释变量的个数；

为流动性特征系数域，其中包含指定管道特性(1，d，ε)与流体特征(ρ，η)；ΔP-ΔH为指定管道前后两端的压力差；In the above formula, Q is the fluid flow value in the pipeline; i is the number of values; M is the number of explanatory variables;

is the fluidity characteristic coefficient field, which includes the specified pipeline characteristics (1, d, ε) and fluid characteristics (ρ, η); ΔP-ΔH is the pressure difference between the front and rear ends of the specified pipeline;

2)根据流动性特征系数域和测得的管道前后两端的压力，由下式获得管道内流体镜像流量值q′：2) According to the fluidity characteristic coefficient domain and the measured pressure at both ends of the pipeline, the mirror image flow value q′ of the fluid in the pipeline is obtained by the following formula:

$q q' ' = = θ θ [\begin{matrix} A A \\ B B \\ C C \\ D D. \\ \cdot \cdot \cdot \cdot \cdot \cdot \end{matrix}] {Σ Σ}_{i i = = 00}^{m m} {((ΔP ΔP - - ΔH ΔH))}^{\frac{i i}{22}};;$

进一步，还包括如下步骤：Further, the following steps are also included:

3)当测得的管道内流体流量值发生偏差时，将测得的管道内流体流量值和镜像流量值相比较中的大值作为流量值输出；3) When the measured fluid flow value in the pipeline deviates, the larger value in the comparison between the measured fluid flow value in the pipeline and the mirror image flow value is output as the flow value;

重复执行步骤1、2、3)，动态获得管道内流体流量值和镜像流量值，并构成冗余关系；Repeat steps 1, 2, and 3) to dynamically obtain the fluid flow value and mirror image flow value in the pipeline, and form a redundant relationship;

进一步，步骤3)中，通过以下步骤判断测得的管道内流体流量值是否发生偏差：Further, in step 3), it is judged whether the measured fluid flow value in the pipeline deviates through the following steps:

当步骤3)所得的镜像流量值q′大于或等于1.06倍管道内流体流量值Q，且ΔP-ΔH变化范围在10％以内，且管段上游断面总水头压力P_前及管段下游断面总水头压力P_后变化范围也在10％以内，则判断管道内流体流量值发生偏差。When the mirror image flow value q' obtained in step 3) is greater than or equal to 1.06 times the fluid flow value Q in the pipeline, and the variation range of ΔP-ΔH is within 10%, and the total head pressure of the upstream section of the pipe section _before P and the total head pressure of the downstream section of the pipe section _If the change range of P is also within 10%, it is judged that the fluid flow value in the pipeline deviates.

本发明还公开一种虚拟流量计，包括：The invention also discloses a virtual flowmeter, comprising:

至少两个压力传感器，用于测量管道前后两端的压力；At least two pressure sensors for measuring the pressure at the front and rear ends of the pipeline;

自学习模块，接收管道前后两端的压力和管道内流体流量值数据，根据下式，自学习获得管道流动性特征系数域：The self-learning module receives the pressure at the front and rear ends of the pipeline and the data of the fluid flow value in the pipeline, and obtains the domain of the pipeline fluidity characteristic coefficient by self-learning according to the following formula:

$Q Q = = θ θ [\begin{matrix} A A \\ B B \\ C C \\ D D. \\ \cdot \cdot \cdot \cdot \cdot \cdot \end{matrix}] {Σ Σ}_{i i = = 00}^{m m} {((ΔP ΔP - - ΔH ΔH))}^{\frac{i i}{22}};;$

虚拟流量计算模块，用于根据流动性特征系数域和测得的管道前后两端的压力，由下式获得管道内流体的镜像流量值q′：The virtual flow calculation module is used to obtain the mirror image flow value q' of the fluid in the pipeline according to the fluidity characteristic coefficient field and the measured pressure at the front and rear ends of the pipeline by the following formula:

上式中，i为取值数；M为解释变量的个数；

为流动性特征系数域，其中包含指定管道特性(1，d，ε)与流体特征(ρ，η)；ΔP-ΔH为指定管道前后两端的压力差。In the above formula, i is the number of values; M is the number of explanatory variables;

is the fluidity characteristic coefficient field, which includes the specified pipeline characteristics (1, d, ε) and fluid characteristics (ρ, η); ΔP-ΔH is the pressure difference between the front and rear ends of the specified pipeline.

进一步，还包括：Further, it also includes:

测量值读取模块，用于同步获取管道内的流体流量值、前后两端的压力值；The measured value reading module is used to synchronously acquire the fluid flow value in the pipeline and the pressure values at the front and rear ends;

存储模块，用于存储该指定管道的流动性特征系数域；A storage module, configured to store the fluidity characteristic coefficient field of the designated pipeline;

进一步，还包括：Further, it also includes:

输出流量值切换模块，用以判断当指定管段流动性特征没有出现突变时，被监测的管道内流体流量值是否有偏差，如有，则停止自学习，将测得的管道内流体流量值Q和管道内流体镜像流量值q′相比较中的大值作为流量结果值输出。The output flow value switching module is used to judge whether there is any deviation in the fluid flow value in the monitored pipeline when there is no sudden change in the fluidity characteristics of the specified pipe section. If so, stop self-learning and convert the measured fluid flow value Q Compared with the mirror image flow value q' of the fluid in the pipeline, the larger value is output as the flow result value.

本发明的有益效果是：本发明对经典流体理论的数理构造分析后，成功地进行了延伸，形成了一种全新的、通用的流量计算方法；体现ΔP-ΔH、Q、θ三者关系的优化表达式，计算更为简单和精确，验证计算非常令人满意。其一步式算法，特别适合计算机运算。可将一台任何形式的设备流量计打造成具有流体流量值与镜像流量值并存的流量计，可采用独立的嵌入式控制器或直接植入设备流量计中，甚至提供一段标准产品软件，易于实现。The beneficial effects of the present invention are: after analyzing the mathematical structure of classical fluid theory, the present invention successfully extends and forms a brand-new and universal flow calculation method; The expression is optimized, the calculation is simpler and more accurate, and the verification calculation is very satisfactory. Its one-step algorithm is especially suitable for computer operations. An equipment flowmeter of any form can be made into a flowmeter with coexistence of fluid flow value and mirror image flow value. It can adopt an independent embedded controller or be directly embedded in the equipment flowmeter, and even provide a piece of standard product software, which is easy to accomplish.

适用于各种形式的设备流量计上。实现镜像算法功能嵌入，起到镜像流量值通过自学习后逐步成为流体流量值背后影子的冗余效果。即使前台设备流量计损坏或失效，找寻到流体规律的镜像流量值仍然在后台计算并及时替代输出。Applicable to various forms of equipment flow meters. Realize the function embedding of the mirror image algorithm, and achieve the redundant effect that the mirror flow value gradually becomes the shadow behind the fluid flow value after self-learning. Even if the flow meter of the foreground equipment is damaged or fails, the mirrored flow value found by the fluid law is still calculated in the background and replaced in time.

附图说明 Description of drawings

为了使本发明的目的、技术方案和优点更加清楚，下面将结合附图对本发明作进一步的详细描述：In order to make the purpose of the present invention, technical solutions and advantages clearer, the present invention will be described in further detail below in conjunction with accompanying drawing:

图1流体循环系统中虚拟流量计检测点设置流程图；Fig. 1 Flow chart of virtual flow meter detection point setting in fluid circulation system;

图2长距离输送系统中虚拟流量计检测点设置流程图；Fig. 2 Flowchart of virtual flow meter detection point setting in long-distance conveying system;

图3流动性特征系数域对应各流量区段的关系示意图；Fig. 3 is a schematic diagram of the relationship between the fluidity characteristic coefficient domain corresponding to each flow section;

图4实验1中流体流量值与镜像流量值的拟合关系度曲线；The fitting relationship curve between the fluid flow value and the image flow value in Fig. 4 experiment 1;

图5实验2中流体流量值与镜像流量值的拟合关系度曲线；The fitting relationship curve between the fluid flow value and the image flow value in Fig. 5 experiment 2;

图6实验3中流体流量值与镜像流量值的拟合关系度曲线；The fitting relationship curve between the fluid flow value and the image flow value in Fig. 6 experiment 3;

图7实验4中流体流量值与镜像流量值的拟合关系度曲线。Fig. 7 The fitting relationship curve between the fluid flow value and the mirror image flow value in Experiment 4.

具体实施方式 Detailed ways

常态下，存在前后压差ΔP-ΔH是造成流体沿管道流动的起因，对应流量Q的大小与流体输送管道特性θ(管径、长度和摩擦阻力系数、密度、粘度等)有互动。ΔP-ΔH、Q、θ三者相互联系也互相制约，构成互为表达的函数关系。如范宁(Fanning)公式ΔP_f＝(λL/d+∑ζ)ρ/2(4/πd²)²Q²。Under normal conditions, the existence of front and rear pressure difference ΔP-ΔH is the cause of fluid flow along the pipeline, and the corresponding flow rate Q interacts with the characteristics θ of the fluid delivery pipeline (pipe diameter, length, frictional resistance coefficient, density, viscosity, etc.). ΔP-ΔH, Q, θ are interconnected and restrict each other, forming a functional relationship that expresses each other. For example, Fanning's formula ΔP _f =(λL/d+Σζ)ρ/2(4/πd ² ) ² Q ² .

本实施例提供一种镜像流量检测方法及虚拟流量计，可依据在已经知道流体流量Q与前后压差ΔP-ΔH，以及两者波动变化区段关系后，通过自学习的方式求得管道的动态流动性特征系数

域；提出了当设备流量计出现偏差甚至突然丢失检测功能时，依据已知前后压差ΔP-ΔH和已经获取的流动性特征系数

域，反向推算当时环境下的虚拟流量q′并可切换输出，称为全时域、不间断的一种基于镜像流量检测方法的虚拟流量计输出流量值。This embodiment provides a mirror image flow detection method and a virtual flow meter, which can be obtained by self-learning after knowing the fluid flow Q, the front and rear pressure difference ΔP-ΔH, and the relationship between the two fluctuation ranges. dynamic fluidity characteristic coefficient

domain; it is proposed that when the equipment flowmeter deviates or even suddenly loses the detection function, based on the known pressure difference ΔP-ΔH and the obtained fluidity characteristic coefficient

Domain, reversely calculate the virtual flow q' in the current environment and switch the output, called a full-time domain, uninterrupted virtual flow meter output flow value based on the mirror flow detection method.

镜像流量检测方法，包括如下步骤：A method for detecting mirrored traffic, comprising the steps of:

$q q' ' = = θ θ [\begin{matrix} A A \\ B B \\ C C \\ D D. \\ \cdot &Center Dot; \cdot &Center Dot; \cdot &Center Dot; \end{matrix}] {Σ Σ}_{i i = = 00}^{m m} {((ΔP ΔP - - ΔH ΔH))}^{\frac{i i}{22}} . .$

重复执行步骤1、2、3)，动态获得管道内流体流量值和镜像流量值，并构成冗余关系。Steps 1, 2, and 3) are repeated to dynamically obtain the fluid flow value in the pipeline and the mirror image flow value, and form a redundant relationship.

$Q Q = = θ θ [\begin{matrix} A A \\ B B \\ C C \\ D D. \\ \cdot \cdot \cdot &Center Dot; \cdot &Center Dot; \end{matrix}] {Σ Σ}_{i i = = 00}^{m m} {((ΔP ΔP - - ΔH ΔH))}^{\frac{i i}{22}};;$

$q q' ' = = θ θ [\begin{matrix} A A \\ B B \\ C C \\ D D. \\ \cdot &Center Dot; \cdot \cdot \cdot \cdot \end{matrix}] {Σ Σ}_{i i = = 00}^{m m} {((ΔP ΔP - - ΔH ΔH))}^{\frac{i i}{22}};;$

上式中，i为取值数；M为解释变量的个数；

进一步，还包括：Further, it also includes:

存储模块，用于存储该指定管道的流动性特征系数域。The storage module is used for storing the fluidity characteristic coefficient field of the designated pipeline.

进一步，还包括：Further, it also includes:

图1是流体循环系统中虚拟流量计检测点设置流程图。常用于冷却水闭循环流程，多台流体输送泵1、2、3、4轮换工作，通过给水总管将流体送到冷却设备附近后，设有一个环状管以分配各支管冷却给水量；通过支管对子系统完成冷却后，又进入环管汇集冷却水，以回水总管形式将水循环使用。给水总管上设有总管设备流量计FT1，并将总管出口压力PT1作为前压力P_前1；借助于给水环管上的环管压力PT2，既作为总管后压力P_后2同时也作为支管前压力P_前21、P_前22、P_前23、P_前24，支管后压力来源于出口压力PT3对应P_后31、P_后32、P_后33、P_后34，每个支管上设有支管设备流量计FT11、FT12、FT13、FT14。而给水环管后所设置的手动阀HV1、HV2、HV3、HV4用于分配各支管冷却给水量，一旦按需调整到位后基本保持不变，这里可以当成固定阻损元件加以考虑。Fig. 1 is a flow chart of virtual flow meter detection point setting in fluid circulation system. It is often used in the closed circulation process of cooling water. Multiple fluid delivery pumps 1, 2, 3, and 4 work alternately. After the fluid is sent to the vicinity of the cooling equipment through the main water supply pipe, an annular pipe is provided to distribute the cooling water supply of each branch pipe; through After the branch pipe completes the cooling of the subsystem, it enters the ring pipe to collect cooling water, and the water is recycled in the form of the return water main pipe. The main pipe equipment flowmeter FT1 is arranged on the water supply main pipe, and the outlet pressure PT1 of the main pipe is used as the front pressure P _{front 1} ; with the help of the ring pipe pressure PT2 on the water supply ring pipe, it is not only used as the post main pipe pressure P _{rear 2} but also as the branch pipe front pressure P _{front 21} , P _{front 22} , P _{front 23} , P _{front 24} , the pressure behind the branch pipe comes from the outlet pressure PT3, corresponding to P _{after 31} , P _{after 32} , P _{after 33} , P _{after 34} , each branch is equipped with branch equipment flow Count FT11, FT12, FT13, FT14. The manual valves HV1, HV2, HV3, and HV4 set behind the water supply ring are used to distribute the cooling water supply of each branch pipe. Once adjusted in place as required, they will basically remain unchanged. Here, they can be considered as fixed resistance components.

图2是长距离输送系统中虚拟流量计检测点设置流程图。常用于燃烧介质的区域供气管网，为了稳定压力一般设有调节阀门M。区域管网可能分别向不同用户车间提供能源，而每个车间都可能设置有监控，因此会造成上下游并存多个前后压力检测。合理地收集这些压力信息，可以有选择性地、优化构成多重冗余新型流量计，好处在于对镜像流量值还能进行再筛选，回避指定管段流动性特征的波动，可靠性再次提升。Fig. 2 is a flow chart of virtual flowmeter detection point setting in long-distance conveying system. The regional gas supply pipeline network that is often used for combustion media is generally equipped with a regulating valve M in order to stabilize the pressure. The regional pipeline network may provide energy to different user workshops, and each workshop may be equipped with monitoring, so there will be multiple front and rear pressure tests in the upstream and downstream. Reasonable collection of these pressure information can selectively and optimize the formation of multiple redundant new flowmeters. The advantage is that the mirrored flow values can be re-screened to avoid fluctuations in the fluidity characteristics of specified pipe sections, and the reliability is improved again.

以下从理论上对本实施例的方法和虚拟流量计进行探讨：经典流体理论可以理解为：从认识管段与流体特性入手，为此定义了特性描述参数(1，d，，λ，ε)等与流体特征(ρ，μ)参数等→求得雷诺数Re，离线静态(查表)判别流动状态下相互关系→推算出管道流速V(流量)。The method of this embodiment and the virtual flowmeter are discussed theoretically as follows: the classical fluid theory can be understood as: starting from the understanding of the pipe section and fluid characteristics, defining the characteristic description parameters (1, d, λ, ε) and so on Fluid characteristic (ρ, μ) parameters, etc. → get the Reynolds number Re, and judge the relationship in the flow state offline statically (look up the table) → calculate the pipeline flow velocity V (flow rate).

一种基于镜像流量检测方法的虚拟流量计的理论可以理解为①先反向推导：从管道流速V(流量)入手→通过前后压差ΔP-ΔH的变化量，动态判别流动状态→自学习出指定管段在层流、紊流、完全紊流下区段特征，全新定义为流动性特征系数

域；②再正向换算：依据从学习中获得的指定管段流动性特征系数

域→对应学习过程记录下的前后压差ΔP-ΔH的变化区段，动态调用不同流态下的流动性特征系数

和

域中具体组，适应当前流动状态→通过

换算出当前镜像流量值。The theory of a virtual flowmeter based on the mirror image flow detection method can be understood as ① reverse derivation first: starting from the pipeline flow velocity V (flow rate) → through the change of the pressure difference ΔP-ΔH before and after, dynamically distinguish the flow state → self-learning Specify the section characteristics of the pipe section under laminar flow, turbulent flow, and fully turbulent flow, which is newly defined as the fluidity characteristic coefficient

domain; ②Re-forward conversion: based on the fluidity characteristic coefficient of the specified pipe section obtained from the study

Domain→Corresponding to the change section of the front and rear pressure difference ΔP-ΔH recorded in the learning process, dynamically call the fluidity characteristic coefficient under different flow states

and

Specific groups in the domain, adapted to the current flow state → pass

Convert the current mirror flow value.

下面分别进行简单具体推导与分析：The following is a simple and specific derivation and analysis:

1、经典流体理论所有推导基于以下三个平衡方程式：1. All derivations of classical fluid theory are based on the following three balance equations:

流体在恒定管径中连续性质量方程： $V = \frac{4 Q}{π d^{2}} . . . m / s$ Continuity mass equation of fluid in constant pipe diameter: $V = \frac{4 Q}{π d^{2}} . . . m / the s$

流体在管路中能量衡算-伯努利方程： $h_{f} = \frac{p_{1} - p_{2}}{ρ} - g (z_{2} - z_{1}) . . . J / Kg$ Energy balance calculation of fluid in pipeline - Bernoulli equation: $h_{f} = \frac{p_{1} - p_{2}}{ρ} - g (z_{2} - z_{1}) . . . J / kg$

直管摩擦阻力h_f与摩擦阻力系数λ之间关系： $h_{f} = λ \frac{l}{d} \frac{V^{2}}{2} . . . J / Kg$ The relationship between the straight pipe frictional resistance h _f and the frictional resistance coefficient λ: $h_{f} = λ \frac{l}{d} V \frac{^{2}}{2} . . . J / kg$

定义雷诺数 $Re = \frac{Vdρ}{η}$ Define the Reynolds number $Re = \frac{Vdρ}{η}$

层流时摩擦系数λ：λ＝64/ReFriction coefficient λ in laminar flow: λ=64/Re

${h h}_{f f} = = \frac{6464}{Re Re} \frac{11}{d d} \frac{{V V}^{22}}{22}$

紊流时摩擦系数λ：Friction coefficient λ in turbulent flow:

$\frac{11}{\sqrt{λ λ}} = = 1.74 1.74 - - 22 log log ((\frac{22 ϵ ϵ}{d d} + + \frac{18.7 18.7}{Re Re \sqrt{λ λ}})) \frac{11}{\sqrt{λ λ}} = = 1.74 1.74 - - 22 log log ((\frac{22 ϵ ϵ}{d d} + + \frac{18.7 18.7}{Re Re \sqrt{λ λ}}))$

或 $λ = 0.1 {(\frac{ϵ}{d} + \frac{68}{Re})}^{0.23}$ or $λ = 0.1 {(\frac{ϵ}{d} + \frac{68}{Re})}^{0.23}$

或 $λ = \frac{0.3164}{{Re}^{0.25}}$ or $λ = \frac{0.3164}{{Re}^{0.25}}$

或 $λ = 0.0056 + \frac{0.500}{{Re}^{0.32}}$ 等。or $λ = 0.0056 + \frac{0.500}{{Re}^{0.32}}$ wait.

由于λ是一个除了层流时

和光滑管的柏拉修斯公式

比较简单外，其余各公式都比较复杂，用起来比较不方便。在工程计算中为了避免试差，一般是将通过实验测出的λ与Re和的关系，以

为参变量，以λ为纵坐标，以Re为横坐标，标绘在双对数坐标纸上。此图称为莫狄摩擦因数图。Since λ is a factor other than laminar flow

and the Platius formula for smooth tubes

Apart from being relatively simple, the other formulas are more complicated and inconvenient to use. In order to avoid trial and error in engineering calculations, the λ and Re sums measured through experiments are generally relationship to

As a parameter, with λ as the vertical coordinate and Re as the horizontal coordinate, it is plotted on the double-logarithmic coordinate paper. This graph is called the Moti friction factor graph.

2、重新理解后，按新理论的需求改写关系表达式：2. After re-understanding, rewrite the relational expression according to the requirements of the new theory:

为了方便自学习，对摩擦阻力h_f与摩擦阻力系数λ之间关系：

式

和

进行扩充改写成： For the convenience of self-learning, the relationship between the frictional resistance h _f and the frictional resistance coefficient λ:

Mode

and

Expanded and rewritten as:

h_f——管道特性摩擦阻力；h _f ——Pipeline characteristic friction resistance;

——流动性特征系数域，包含指定管道特性(1，d，ε)与流体特征(ρ，η)。描述特性变化缓慢的、方便设置前后压差ΔP-ΔH检测的、完全对应设备流量计的、指定有足够长度的、预先选定无泄漏管段的流动性特征。即使出现一些蠕变，通过自学习加以及时更新修正；

——Flowability characteristic coefficient field, including specified pipeline characteristics (1, d, ε) and fluid characteristics (ρ, η). Describe the fluidity characteristics of slow-changing characteristics, convenient detection of pressure difference ΔP-ΔH before and after setting, fully corresponding to equipment flowmeter, specified sufficient length, and pre-selected leak-free pipe section. Even if there are some creeps, it will be updated and corrected in time through self-learning;

Q——管道内流体流量值；Q——fluid flow value in the pipeline;

i——取值数i - the number of values

m——解释变量的个数。m - the number of explanatory variables.

3、流动性特征系数域的分析、获取与应用：3. Analysis, acquisition and application of fluidity characteristic coefficient domain:

1)关系表达式

中，Q可以通过安装的管道上设备流量计获取。1) Relational expression

Among them, Q can be obtained through the equipment flowmeter installed on the pipeline.

2)还有阻力损失表现为流体势能的降低，2) There is also resistance loss manifested as a decrease in fluid potential energy,

$\frac{{p p}_{11}}{ρ ρ} + + \frac{{u u}_{11}^{22}}{22} + + {h h}_{e e} = = \frac{{p p}_{22}}{ρ ρ} + + \frac{{u u}_{22}^{22}}{22} + + {h h}_{f f}$

h_e(无外加机械能)，u₁＝u₂(等径)h _e (no external mechanical energy), u ₁ = u ₂ (equal diameter)

${h h}_{f f} = = \frac{{p p}_{11} - - {p p}_{22}}{ρ ρ} = = ((\frac{{ρ ρ}_{11}}{ρ ρ} + + g g {z z}_{11})) - - ((\frac{{p p}_{22}}{ρ ρ} + + g g {z z}_{22}))$

由此式可知，对于通常的管路，无论是直管阻力或是局部阻力，也不论是层流或是紊流，阻力损失均主要表现为流体势能的降低，既

该式同时表明，只有水平管道(z₁＝z₂)，才能以

代替

表达h_f。在工程中，可以直接写成：It can be seen from the formula that, for ordinary pipelines, whether it is straight pipe resistance or local resistance, and whether it is laminar flow or turbulent flow, the resistance loss is mainly manifested in the decrease of fluid potential energy, both

The formula also shows that only the horizontal pipeline (z ₁ =z ₂ ) can

replace

Express h _f . In engineering, it can be directly written as:

ΔP-ΔH＝h_f＝(P_前-P_后)-ΔHΔP-ΔH=h _f =(P _before -P _after )-ΔH

得到由摩擦阻力h_f特性决定的阻力损失等于前后压差ΔP-ΔH。It is obtained that the resistance loss determined by the characteristic of frictional resistance h _f is equal to the pressure difference between front and rear ΔP-ΔH.

ΔP-ΔH——换算成相当水平指定管段后的前后压力差检测值；ΔP-ΔH——converted into the detection value of the pressure difference before and after the specified pipe section at an equivalent level;

ΔP——为管段上下游两断面总水头压力差J/m³或Pa；可以通过设置前后压力检测获取。ΔP——is the total water head pressure difference J/m ³ or Pa between the upstream and downstream sections of the pipe section; it can be obtained by measuring the pressure before and after setting.

ΔH——是静扬程即实际水位高程差J/m³或Pa；可以通过测量获取。ΔH——is the static head, that is, the actual water level elevation difference J/m ³ or Pa; it can be obtained by measurement.

P_前——为管段上游断面总水头压力J/m³或Pa。 _Pfront ——is the total head pressure of the upstream section of the pipe section in J/m ³ or Pa.

P_后——为管段下游断面总水头压力J/m³或Pa。 _Pafter ——the total head pressure of the downstream section of the pipe section in J/m ³ or Pa.

因此

也可表征为镜像流量值镜像算法一般表达式：therefore

It can also be characterized as a general expression of the mirroring algorithm for mirroring traffic values:

$q q' ' = = θ θ [\begin{matrix} A A \\ B B \\ C C \\ D D. \\ \cdot \cdot \cdot &Center Dot; \cdot &Center Dot; \end{matrix}] {Σ Σ}_{i i = = 00}^{m m} {((ΔP ΔP - - ΔH ΔH))}^{\frac{i i}{22}} . .$

至此通过计算，可以获取不同m值下流动性特征系数，即A、B、C、D......(共i+1个)。So far, through calculation, the fluidity characteristic coefficients under different m values can be obtained, that is, A, B, C, D... (a total of i+1).

3)由公式：

得出：

即

3) By the formula:

inferred:

Right now

根据长期实验和结合经验得出的

和

看出λ与Q的

和

次方相关，那么

即

Based on long-term experiments and combined experience

and

It can be seen that the λ and Q

and

power correlation, then

Right now

按照数学原理，当采用更高一次的方程式可很好拟合低次方特征曲线的理论，将关系表达式可以直接写成：

According to the mathematical principle, when using a higher-order equation that can well fit the theory of a low-order characteristic curve, the relational expression can be directly written as:

根据对经典流体理论的数理构造分析过程简单理解为：某阶段管道当量长度L、管道内径d、管道实时粗糙度ε变化量很小时，以及流体的密度ρ、粘度η变化量也很小时；当ΔP-ΔH波动在各自工作区段内，流量Q与

呈现三次方关系，并通过流动性特征系数域跟随表达ΔP-ΔH、Q、θ三者相互联系也互相制约关系，构成互为表达的函数。According to the mathematical structure analysis process of the classical fluid theory, it is simply understood that: at a certain stage, the equivalent length L of the pipeline, the inner diameter d of the pipeline, and the real-time roughness ε of the pipeline have very small changes, and the changes of the density ρ and viscosity η of the fluid are also small; ΔP-ΔH fluctuates in their respective working sections, flow Q and

It presents a cubic relationship, and follows and expresses ΔP-ΔH, Q, and θ through the field of fluidity characteristic coefficients, and they are interconnected and restrict each other, forming a function of mutual expression.

总结讲，具体依据关系式，自学习出指定管段在层流、紊流、完全紊流下区段特征，全新定义为流动性特征系数

域的组成参数，实现了一种基于镜像流量检测方法的虚拟流量计的反向推导功能。In summary, based on Relational formula, self-learning the section characteristics of the specified pipe section under laminar flow, turbulent flow, and complete turbulent flow, which is newly defined as the fluidity characteristic coefficient

Composition parameters of the domain realize a reverse derivation function of a virtual flowmeter based on the image flow detection method.

4、接下来是正向推导：4. Next is the forward derivation:

当设备流量计由于各种原因导致流量检测出现偏差，甚至突然失去流量检测功能，经过多数据的比较、判断，流体流量值Q与镜像流量值q′可以切换输出。实际操作方式：When the equipment flowmeter has a deviation in the flow detection due to various reasons, or even suddenly loses the flow detection function, after comparing and judging multiple data, the fluid flow value Q and the mirror image flow value q' can be switched for output. Actual operation method:

依靠自学习得到A、B、C、D流动性特征系数，并往复更新提高适应能力。前台在连续获取流体流量计Q的同时，再根据检测到的ΔP-ΔH参数后台计算虚拟流量

后，进行对比。当Relying on self-learning to obtain A, B, C, D fluidity characteristic coefficients, and reciprocating updates to improve adaptability. While the foreground continuously obtains the fluid flow meter Q, it calculates the virtual flow rate in the background according to the detected ΔP-ΔH parameters

Then, compare. when

I：如果q′≥1.06Q；(Q值出现下降)I: If q'≥1.06Q; (Q value drops)

II：且本次ΔP-ΔH变化范围在10％以内；(此时

压力差处于基本正常变化区域以及波动低于流量变化量)II: And the change range of ΔP-ΔH this time is within 10%; (at this time

The pressure difference is in the basic normal variation area and the fluctuation is lower than the flow variation)

III：再判断P_前是否变化范围在10％以内；(表明下游局部阻力未发生大的调整)III: Determine whether the range of change _before P is within 10%; (indicating that the downstream local resistance has not undergone major adjustments)

IV：P_后是否变化范围在10％以内。(表明上游局部阻力未发生大的调整)IV: Whether the range of change _after P is within 10%. (Indicating that no major adjustments have taken place in the upstream local resistance)

如果四个条件满足，说明在排除了所有外界干扰后，如果流体流量值Q还出现大幅度下降，应该认为是设备流量计受各种原因干扰导致Q出现偏差甚至突然丢失，则q′和Q相比的大值选定作为输出流量值。并停止自学习流动性特征系数，将P_前和P_后作平均值计算。If the four conditions are satisfied, it means that after all external disturbances are excluded, if the fluid flow value Q still drops sharply, it should be considered that the equipment flowmeter is disturbed by various reasons, resulting in deviation or even a sudden loss of Q, then q′ and Q Compared with the larger value selected as the output flow value. And stop the self-learning liquidity characteristic coefficient, and calculate the average value of P _before and P _after .

当条件I：如果q′≥1.06Q被解除，即q′＜1.04Q时，以及

和

恢复到输出设备流量计检测Q值状态。When condition I: if q'≥1.06Q is lifted, ie q'<1.04Q, and

and

Return to the output device flow meter detection Q value state.

5、关于θ流动性特征系数域对应层流、紊流、完全紊流工作区段下的自学习划分办法：5. About the self-study division method under the laminar flow, turbulent flow, and complete turbulent flow working sections corresponding to the θ fluidity characteristic coefficient field:

流动性特征系数域包含指定管道特性(1，d，ε)与流体特征(ρ，η)。其中通过自学习得到系数A、B、C、D数值表达，它们包含了特定管段所有流动性特征。拆分成层流时，Aa、Ba、Ca、Da；紊流时，Ab、Bb、Cb、Db；完全紊流时，Ac、Bc、Cc、Dc；流量区段分组构成。The fluidity characteristic coefficient domain includes specified pipeline characteristics (1, d, ε) and fluid characteristics (ρ, η). Among them, the numerical expressions of coefficients A, B, C, and D are obtained through self-learning, and they contain all the fluidity characteristics of a specific pipe segment. When split into laminar flow, Aa, Ba, Ca, Da; when turbulent flow, Ab, Bb, Cb, Db; when fully turbulent flow, Ac, Bc, Cc, Dc; the flow section is composed of groups.

结合经典流体理论看，管道特性是随雷诺数

变化区间而改变，因此推断流动性特征系数域也将会跟随雷诺数变化区间而跳动。通过实验首先明确

确实跟随雷诺数Re变化区间而跳动，其次不完全遵守经典判别：层流Re≤2320；紊流3000＜Re≤10⁵；完全紊流，阻力平方区Re＞10⁵的通常雷诺数变化区间划分原则。Combining with the classical fluid theory, the pipeline characteristics vary with the Reynolds number

Therefore, it is inferred that the domain of the characteristic coefficient of fluidity will also jump along with the range of the Reynolds number. Through experiments, it is first clear

It does jump with the range of Reynolds number Re change, and secondly, it does not fully comply with the classical discrimination: laminar flow Re≤2320; turbulent flow 3000<Re≤10 ⁵ ; complete turbulent flow, resistance square area Re>10 ⁵ usually divided into Reynolds number change intervals in principle.

图3是流动性特征系数域对应各流量区段的关系示意图。将流体流量与压力差之间的关系进行了图解，以ΔP-ΔH为纵坐标，以Q为横坐标。Fig. 3 is a schematic diagram of the relationship between the fluidity characteristic coefficient field and each flow section. The relationship between the fluid flow rate and the pressure difference is illustrated graphically, with ΔP-ΔH as the ordinate and Q as the abscissa.

1)层流区：压差越大，流体流速越小的曲线关系；图3左侧区段。1) Laminar flow region: the larger the pressure difference, the smaller the fluid velocity; the left section of Figure 3.

2)紊流区：压差越大，流体流速越大的曲线关系；图3中间区段。2) Turbulent flow area: the larger the pressure difference, the larger the fluid velocity; the middle section of Figure 3.

3)完全紊流，阻力平方区：压差越大，流体流速越大的近似直线关系；图3右侧区段。3) Completely turbulent flow, resistance square area: the larger the pressure difference, the greater the approximate linear relationship of the fluid velocity; the right section of Figure 3.

从图中看出，实际上分为三个不同流量区段，可以采用数学方法来简单化定量区别处理，不再需要借助雷诺数Re来进行流态定性判断。此时只存在数学意义上的划分。It can be seen from the figure that it is actually divided into three different flow sections, and mathematical methods can be used to simplify the quantitative distinction processing, and it is no longer necessary to use the Reynolds number Re for qualitative judgment of the flow state. At this point there is only a division in the mathematical sense.

在解不唯一的情况下，在一组拟合曲线中选取哪一根曲线为“最好”呢？最好的标准是最小二乘法原理，就是使误差的平方和δ达到最小。以及拟合优度的指标R²值最大，由双参数共同判断。In the case that the solution is not unique, which curve is selected as the "best" in a set of fitting curves? The best standard is the principle of the least squares method, which is to minimize the sum of squares δ of the errors. And the goodness of fit index ^R2 has the largest value, which is jointly judged by the two parameters.

具体实施时：采样步长推荐ΔP-ΔH为1％，自学习系统至少由高、中、低、备用四条多线段拟合曲线组合构成。极端情况，可以继续开辟。In specific implementation: the recommended sampling step size ΔP-ΔH is 1%, and the self-learning system is composed of at least four multi-line fitting curve combinations of high, medium, low and spare. In extreme cases, you can continue to develop.

采样自然数ΔP-ΔH，与采样因变量Q在曲线拟合过程中出现“误差的平方和δ变大，以及拟合优度的指标R²值变小”，并继续三步以上，就可能从三个不同流量区段中的一个认为跃入到另外一个流量区段，此时采用数学方式来操作拟合曲线跳转处理。Sampling the natural number ΔP-ΔH, and the sampling dependent variable Q in the curve fitting process, "the sum of the squares of the error δ becomes larger, and the value of the goodness of fit index ^R2 becomes smaller", and continues for more than three steps, it is possible to start from One of the three different flow ranges is considered to jump into another flow range, and at this time, a mathematical method is used to operate the fitting curve jump processing.

初始从中段开始，按1％步长采集ΔP-ΔH及Q进行曲线拟合，自学习得到第一组流量区段数据如Aa、Ba、Ca、Da(指数学意义上的分区)；越过本流量区段，跳转后将前面的三步值移入新段(根据方向进入高或低段)再学习第二组流量区段数据如Ab、Bb、Cb、Db。又一次满足拟合曲线跳转处理条件时，则调用备用段如Ac、Bc、Cc、Dc。保证上下运动学习都预备有三段(从中、高、到备用段，或中、低、再到备用段。)Initially start from the middle section, collect ΔP-ΔH and Q according to 1% step size for curve fitting, and obtain the first set of flow section data such as Aa, Ba, Ca, Da (referring to the division in the mathematical sense) through self-learning; In the traffic section, after the jump, move the previous three-step values into the new section (enter the high or low section according to the direction) and then learn the second set of traffic section data such as Ab, Bb, Cb, Db. When the fitting curve jump processing condition is satisfied again, the spare segments such as Ac, Bc, Cc, and Dc are called. Make sure that there are three stages for up and down movement learning (from medium, high, to spare, or medium, low, and then to spare.)

由于各流量区段采样自然数取值个数不定，只能计算平均平方误差量为

通过数据分析δ值不应该大于0.02为合理值，即当δ小于0.02时认为拟合运算正常，否则判断为异常。它是基于拟合精度的硬性指标。Since the number of natural numbers sampled in each flow section is uncertain, the average square error can only be calculated as

Through data analysis, the δ value should not be greater than 0.02, which is a reasonable value, that is, when δ is less than 0.02, the fitting operation is considered normal, otherwise it is judged as abnormal. It is a hard metric based on fitting accuracy.

R²作为检验回归方程与样本值拟合优度的指标：R²(0≤R²≤1)越大，表示回归方程与样本拟合的越好；反之回归方程与样本值拟合较差。拟合优度的指标，用于说明非直线相关的多变量之间的紧密程度。

或其中，n是样本观测值的个数，k是解释变量的个数(k＝m＝3)。R ² is used as an index to test the goodness of fit between the regression equation and the sample value: the larger the R ² (0≤R ² ≤1), the better the fit between the regression equation and the sample; otherwise, the poorer the fit between the regression equation and the sample value . An indicator of goodness-of-fit that illustrates how closely related multivariates are not linearly related.

or Among them, n is the number of sample observations, and k is the number of explanatory variables (k=m=3).

拟合优度的指标R²值减少超过1％，即0.99R² _前≥R² _后则计数开始，持续三次则操作拟合曲线跳转。以表格的方式例举四组有代表性的样本计算结果：The goodness-of-fit index R ² value decreases by more than 1%, that is, counting starts _when the value of R ² _before ≥ R ² is 0.99, and the operation fitting curve jumps if it continues for three times. Four sets of representative sample calculation results are exemplified in the form of a table:

实验1Experiment 1

图4是实验1中流体流量值与镜像流量值的拟合关系度曲线。兰线是流体流量值Q的实测曲线，红线表示镜像流量值q′的镜像曲线。图中流体处于层流状态，实验Re≤1172，流量与前后压差ΔP-ΔH均较小。对比后发现两条曲线不是非常润滑，最大误差有-0.99596％。分析其原因是由于流量绝对值过小，存在观测误差。纵坐标：流量范围，横坐标：样本观测值的个数。Fig. 4 is a fitting relationship curve between the fluid flow value and the mirror image flow value in Experiment 1. The blue line is the measured curve of the fluid flow value Q, and the red line is the mirror image curve of the mirror flow value q′. The fluid in the figure is in a laminar flow state, the experiment Re≤1172, the flow rate and the pressure difference ΔP-ΔH before and after are both small. After comparison, it is found that the two curves are not very smooth, and the maximum error is -0.99596%. It is analyzed that the reason is that the absolute value of the flow rate is too small and there is an observation error. Vertical axis: flow range, horizontal axis: number of sample observations.

实验2Experiment 2

图5是实验2中流体流量值与镜像流量值的拟合关系度曲线。图中流体前段处于紊流区19938＜Re≤73359，后段处于完全紊流区Re＞73359，流量与前后压差ΔP-ΔH变化大。对比后发现两者拟合较令人满意，最大误差有0.10445％。Fig. 5 is a fitting relationship curve between the fluid flow value and the mirror image flow value in Experiment 2. In the figure, the front section of the fluid is in the turbulent flow zone 19938<Re≤73359, and the back section is in the complete turbulent flow zone Re>73359, and the flow rate and the front-back pressure difference ΔP-ΔH change greatly. After comparison, it is found that the fitting of the two is satisfactory, and the maximum error is 0.10445%.

实验3Experiment 3

图6是实验3中流体流量值与镜像流量值的拟合关系度曲线。图中流体前段处于紊流区19938＜Re≤73359，后段处于完全紊流区Re＞73359，流量与前后压差ΔP-ΔH变化较大。对比后发现两者拟合令人满意，最大误差有-0.00000834393％，可以忽略。Fig. 6 is a fitting relationship curve between the fluid flow value and the mirror image flow value in Experiment 3. In the figure, the front section of the fluid is in the turbulent flow zone 19938<Re≤73359, and the back section is in the complete turbulent flow zone Re>73359, and the flow rate and the front-back pressure difference ΔP-ΔH change greatly. After comparison, it is found that the fitting of the two is satisfactory, and the maximum error is -0.00000834393%, which can be ignored.

实验4Experiment 4

图7是实验4中流体流量值与镜像流量值的拟合关系度曲线。图中流体处于紊流区6687＜Re≤65352，流量与前后压差ΔP-ΔH变化大。对比后发现两条曲线不是非常润滑，拟合较令人满意，最大误差有-0.58668％。Fig. 7 is a fitting relationship curve between the fluid flow value and the mirror image flow value in Experiment 4. In the figure, the fluid is in the turbulent flow zone 6687<Re≤65352, and the flow rate and the front-back pressure difference ΔP-ΔH change greatly. After comparison, it is found that the two curves are not very smooth, and the fitting is satisfactory, with a maximum error of -0.58668%.

其它状况描述与数据分析：Other status description and data analysis:

根据不同的流态及管道条件，前后共积累了7组实验数据，上面是从中抽取的4组实验数据。According to different flow patterns and pipeline conditions, a total of 7 sets of experimental data have been accumulated before and after, and the above are 4 sets of experimental data extracted from them.

依据

镜像流量值算法优化表达式，对所有的实验数据进行了处理与分析，经过一年多的比对，从“使误差的平方和δ达到最小，以及拟合优度的指标R²值最大”双参数共同判断自学习拟合准确度上看，结果可信。in accordance with

The mirror image flow value algorithm optimizes the expression, processes and analyzes all the experimental data, and after more than a year of comparison, from "minimizing the square sum of errors δ, and maximizing the ^R2 value of the goodness of fit index" Judging the accuracy of the self-learning fitting together with two parameters, the result is credible.

其中与镜像流量值镜像算法优化表达式进行过比对的算法有：第一种降阶算法

阶数下降，弯曲度降低，需要拟合的预备线段数量上升，平均平方误差量δ大幅度变大，拟合优度指标R²减小约1～5％。第二种简化算法

和

验证δ效果略好于第一种降阶算法，只是R²减小不明显。Among them, the algorithms that have been compared with the optimization expression of the mirror flow value mirror algorithm are: the first order reduction algorithm

The order decreases, the curvature decreases, the number of preparatory line segments to be fitted increases, the average square error δ increases significantly, and the goodness-of-fit index R ² decreases by about 1-5%. The second simplified algorithm

and

The verification δ effect is slightly better than the first order reduction algorithm, but the reduction of R ² is not obvious.

第三种升阶算法

拟合计算工作量显著加大，阶数上升，弯曲度提高，贴合采样值能力加强，平均平方误差量δ变得更小，拟合优度指标R²相当。The third step-up algorithm

The fitting calculation workload is significantly increased, the order is increased, the curvature is increased, the ability to fit the sampled values is strengthened, the average square error δ becomes smaller, and the goodness of fit index R ² is equivalent.

结论：对经典公式

进行数理构造分析后，得到的镜像流量值算法优化表达式：

是一个恰当体现ΔP-ΔH、Q、θ三者关系的表达式，且唯一变量ΔP-ΔH。对其简化后，不能满足拟合精度的需求。对其升阶后，采样学习段拟合精度提高不明显，但向外延伸段的预测精度下降。另外采取的是数学方式描述、计算法，特别适用于计算机运算处理。Conclusion: to the classical formula

After analyzing the mathematical structure, the optimized expression of the mirror flow value algorithm is obtained:

It is an expression that properly reflects the relationship between ΔP-ΔH, Q, and θ, and the only variable ΔP-ΔH. After simplifying it, it cannot meet the requirement of fitting accuracy. After it is upgraded, the fitting accuracy of the sampling learning section is not significantly improved, but the prediction accuracy of the outward extension section decreases. In addition, it adopts mathematical description and calculation method, which is especially suitable for computer operation processing.

可以看出，一种基于镜像流量检测方法的虚拟流量计实质上归结为实时动态、在线运算模型和接入已经安装了的设备流量计、管段前后压力计的检测信号共同构成。它既可以单独配置成一套小型独立的嵌入式控制器安装在现场或控制室盘(柜)装内，也可以将本运算模型以软件产品(如功能块)的方式移到相关的自动化控制系统中运算。甚至直接设置在设备流量计二次表中成套构成机电一体化产品。It can be seen that a virtual flowmeter based on the mirror flow detection method is essentially composed of real-time dynamics, an online calculation model, and the detection signals of the installed equipment flowmeter and the pressure gauge before and after the pipe section. It can be configured as a small independent embedded controller and installed in the field or control room panel (cabinet), or the calculation model can be moved to the relevant automatic control system in the form of software products (such as function blocks) middle operation. It can even be directly set in the secondary meter of the equipment flow meter to form a complete set of mechatronics products.

以上所述仅为本发明的优选并不用于限制本发明，显然，本领域的技术人员可以对本发明进行各种改动和变型而不脱离本发明的精神和范围。这样，倘若本发明的这些修改和变型属于本发明权利要求及其等同技术的范围之内，则本发明也意图包含这些改动和变型在内。The above description is only the preference of the present invention and is not intended to limit the present invention. Obviously, those skilled in the art can make various changes and modifications to the present invention without departing from the spirit and scope of the present invention. Thus, if these modifications and variations of the present invention fall within the scope of the claims of the present invention and equivalent technologies thereof, the present invention also intends to include these modifications and variations.

Claims

1. the mirror image flow rate testing methods is characterized in that, comprises the steps:

1) measures pressure and the pipeline inner fluid flow of pipeline rear and front end; According to following formula, self study obtains the mobile characteristic coefficient of pipeline territory

θ [\begin{matrix} A \\ B \\ C \\ D \\ \cdot \cdot \cdot \end{matrix}]

:

Q = θ [\begin{matrix} A \\ B \\ C \\ D \\ \cdot \cdot \cdot \end{matrix}] Σ_{i = 0}^{m} {(ΔP - ΔH)}^{\frac{i}{2}};

In the following formula, Q is pipeline inner fluid flow value; I is the value number of explanatory variable number; M is the number of explanatory variable;

θ [\begin{matrix} A \\ B \\ C \\ D \\ \cdot \cdot \cdot \end{matrix}]

Be mobile characteristic coefficient territory, wherein comprise and specify pipe characteristic l, d, ε and characteristic of fluid ρ, η, l represent the pipeline equivalent length, and d represents that internal diameter of the pipeline, ε represent the real-time roughness of pipeline, and ρ represents the density of fluid, the viscosity that η represents fluid; Δ P-Δ H is for specifying the pressure differential of pipeline rear and front end;

2) according to the pressure of mobile characteristic coefficient territory with the pipeline rear and front end that records, obtain pipeline inner fluid mirror image flow value q ' by following formula:

q^{'} = θ [\begin{matrix} A \\ B \\ C \\ D \\ \cdot \cdot \cdot \end{matrix}] Σ_{i = 0}^{m} {(ΔP - ΔH)}^{\frac{i}{2}} .

2. mirror image flow rate testing methods as claimed in claim 1 is characterized in that: also comprise the steps:

3) when the pipeline inner fluid flow value generation deviation that records, the pipeline inner fluid flow value Q that records and the big value among the pipeline inner fluid mirror image flow value q ' are exported as flow value;

Repeated execution of steps 1), 2), 3), dynamically obtain pipeline inner fluid flow value and mirror image flow value, and constitute redundancy relationship;

In the step 3), judge by following steps whether the pipeline inner fluid flow value that records deviation takes place:

When the mirror image flow value q ' of gained more than or equal to 1.06 times of pipeline inner fluid flow value Q, and Δ P-Δ H variation range is in 10%, and pipeline section upstream section gross head pressure P _BeforeAnd pipeline section downstream section gross head pressure P _AfterVariation range also in 10%, is then judged pipeline inner fluid flow value generation deviation.

3. virtual flowmeter is characterized in that: comprising:

At least two pressure transducers are for the pressure of measuring channel rear and front end;

Self-learning module receives pressure and the pipeline inner fluid flow value data of specifying the pipeline rear and front end, and according to following formula, self study obtains the mobile characteristic coefficient of pipeline territory

θ [\begin{matrix} A \\ B \\ C \\ D \\ \cdot \cdot \cdot \end{matrix}]

:

Q = θ [\begin{matrix} A \\ B \\ C \\ D \\ \cdot \cdot \cdot \end{matrix}] Σ_{i = 0}^{m} {(ΔP - ΔH)}^{\frac{i}{2}};

θ [\begin{matrix} A \\ B \\ C \\ D \\ \cdot \cdot \cdot \end{matrix}]

Virtual flow computing module is used for being obtained the mirror image flow value q ' of pipeline inner fluid by following formula according to the pressure of mobile characteristic coefficient territory with the pipeline rear and front end that records:

q^{'} = θ [\begin{matrix} A \\ B \\ C \\ D \\ \cdot \cdot \cdot \end{matrix}] Σ_{i = 0}^{m} {(ΔP - ΔH)}^{\frac{i}{2}} .

4. virtual flowmeter as claimed in claim 3 is characterized in that: also comprise:

The measured value read module is used for obtaining synchronously the fluid flow value in the pipeline, the force value of rear and front end;

Memory module, this specifies the mobile characteristic coefficient territory of pipeline to be used for storage.

5. virtual flowmeter as claimed in claim 4 is characterized in that: also comprise:

Delivery rate value handover module, in order to judge when specifying the mobile feature of pipeline section not occur suddenling change, whether monitored pipeline inner fluid flow value has deviation, if any, then stop self study, the big value during the pipeline inner fluid flow value Q that records and pipeline inner fluid mirror image flow value q ' are compared is exported as the flow results value; Wherein, the mirror image flow value q ' of gained is more than or equal to 1.06 times of pipeline inner fluid flow value Q, and Δ P-Δ H variation range is in 10%, and pipeline section upstream section gross head pressure P _BeforeAnd pipeline section downstream section gross head pressure P _AfterVariation range also in 10%, is then judged pipeline inner fluid flow value generation deviation.