JP3925638B2 - Flow pattern discrimination method for gas-liquid two-phase flow - Google Patents
Flow pattern discrimination method for gas-liquid two-phase flow Download PDFInfo
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- 239000007788 liquid Substances 0.000 title claims description 59
- 230000005514 two-phase flow Effects 0.000 title claims description 58
- 238000012850 discrimination method Methods 0.000 title claims description 13
- 238000004458 analytical method Methods 0.000 claims description 20
- 238000000034 method Methods 0.000 claims description 15
- 239000007921 spray Substances 0.000 claims description 8
- 239000002893 slag Substances 0.000 claims description 7
- 241000628997 Flos Species 0.000 claims description 5
- 230000000739 chaotic effect Effects 0.000 claims description 5
- 238000010586 diagram Methods 0.000 description 5
- 238000005259 measurement Methods 0.000 description 3
- 238000005291 chaos (dynamical) Methods 0.000 description 2
- 230000000694 effects Effects 0.000 description 2
- 238000005070 sampling Methods 0.000 description 2
- 238000007619 statistical method Methods 0.000 description 2
- 238000003889 chemical engineering Methods 0.000 description 1
- 230000001788 irregular Effects 0.000 description 1
- 238000003672 processing method Methods 0.000 description 1
- 230000003068 static effect Effects 0.000 description 1
- 238000012546 transfer Methods 0.000 description 1
- 230000000007 visual effect Effects 0.000 description 1
- 239000011800 void material Substances 0.000 description 1
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Description
【０００１】
【発明の属する技術分野】
本発明は、気液二相流のフローパターン判別方法に関する。
【０００２】
【従来の技術】
垂直上向きの気液二相流は、気体と液体の流量比により、図３に示すように、例えば、気泡流、スラグ流、チャーン流（フロス流）、環状噴霧流、噴霧流の異なるフローパターンとなる。このフローパターンは、圧力損失、伝熱特性、安定性に大きく影響する。従って、フローパターンの判別方法は、特に原子力やボイラーにおいて、その安定運転上、非常に重要となる。
【０００３】
気液二相流のフローパターンを判別する２つの方法が、従来から知られている。第１の方法は、目視観察で得られた相分布の幾何学的構造に基づくものである。しかしこの方法は主観的であり一般産業への適用は困難である。別の方法は、圧力変動、ボイド率、速度、温度等の計測データを処理し、気液二相流のダイナミック特性に基づきフローパターンを判別するものである。このデータ処理方法としては、フーリエ分析法や統計分析法が知られており、例えば以下の文献に開示されている。
【０００４】
（文献１）Ｊ．Ｄｒａｈｏｓ ａｎｄ Ｊ．Ｃｅｒｍａｋ，"Ｄｉａｇｎｏｓｔｉｃ ｏｆ Ｇａｓ-ｌｉｑｕｉｄ Ｆｌｏｗ Ｐａｔｔｅｒｎ ｉｎ Ｃｈｅｍｉｃａｌ Ｅｎｇｉｎｅｅｒｉｎｇ Ｓｙｓｔｅｍ"，Ｃｈｅｍ．Ｅｎｇ．Ｐｒｏｃｅｓｓ．， Ｖｏｌ．２６，ｐｐ．１４７-１６４（１９８９）
（文献２） Ｇ．Ｍａｔｓｕｉ，"Ｉｄｅｎｔｉｆｉｃａｔｉｏｎ ｏｆ Ｆｌｏｗ Ｒｅｇｉｍｅｓ ｉｎ Ｖｅｒｔｉｃａｌ Ｇａｓ-ｌｉｑｕｉｄ Ｔｗｏ-ｐｈａｓｅ Ｆｌｏｗ Ｕｓｉｎｇ Ｄｉｆｆｅｎｔｉａｌ Ｐｒｅｓｓｕｒｅ Ｆｌｕｃｔｕａｔｉｏｎｓ"，Ｉｎｔ．Ｊ．Ｍｕｌｔｉｐｈａｓｅ Ｆｌｏｗ Ｖｏｌ．１０，Ｎｏ．６，ｐｐ．７１１-７２０（１９８４）
【０００５】
【発明が解決しようとする課題】
しかし、上述した従来の判別方法は、いずれも気液二相流のフローパターンを客観的に判別するには不十分だった。すなわち、フーリエ分析法、線型システムに適したフーリエ変換に基づくものであり、統計分析法は、ランダムシステムに適した静的解析のものである。しかし、気液二相流のフローパターンは、線型システムの一面があるが、ランダムシステムの側面もあり、どちらの方法でも、その判別の正解率が低い問題点があった。
【０００６】
本発明はかかる問題点を解決するために創案されたものである。すなわち本発明の目的は、内部が目視観察できない配管内の気液二相流のフローパターンを、配管およびその流れに影響を与えることなく、外部から高い正解率（精度）で、客観的に判別することができる気液二相流のフローパターン判別方法を提供することにある。
【０００７】
【課題を解決するための手段】
カオスとは、決定論に従うシステムでありながら、決まった単純な振舞いでなく、複雑で、不規則かつ不安定なふるまいを示す。本発明の発明者等は、気液二相流のフローパターンがカオスに近似しているため、気液二相流の複雑な現象をカオス理論的に分析することによって、その現象の特性を把握できる可能性があることに着眼した。本発明はかかる新規な着眼に基づくものである。
【０００８】
すなわち、本発明によれば、内部を気液二相流が流れる配管の外部から、該配管の前記内部において気液二相流が発生し該内部から前記配管の外表面へ伝達してきた音をデータとして連続的に記録するデータ記録ステップ（Ａ）と、記録したデータをカオス分析してフラクタル次元ＤとコルモゴロフエントロピーＫを求めるカオス分析ステップ（Ｂ）と、フラクタル次元ＤとコルモゴロフエントロピーＫの大きさから気液二相流のフローパターンを判別する判別ステップ（Ｅ）と、を有し、前記カオス分析ステップ（Ｂ）では、記録した音のデータから、フラクタル次元ＤとコルモゴロフエントロピーＫを求める、ことを特徴とする気液二相流のフローパターン判別方法が提供される。
【０００９】
本発明の好ましい実施形態によれば、既知の気液二相流のフローパターンに前記データ記録ステップ（Ａ）とカオス分析ステップ（Ｂ）を適応して、フラクタル次元ＤとコルモゴロフエントロピーＫの大きさから気液二相流のフローパターンを判別する判別基準を作成する基準作成ステップ（Ｃ）を備え、前記判別ステップ（Ｅ）において、未知の気液二相流から得られたフラクタル次元ＤとコルモゴロフエントロピーＫから前記判別基準によりフローパターンを判別する。
【００１０】
前記データ記録ステップ（Ａ）において、時間列ｔ1，ｔ2，…ｔn（ｎは整数）に対応する音の時系列ｘ1，ｘ2，…ｘnを記憶し、前記カオス分析ステップ（Ｂ）において、ｍ次元相空間Ｘｊ、ｍ次元相空間Ｘｊにおける２点ずつの距離ｒm i,j、相関積分Ｃm（ｒ）およびフラクタル次元ＤとコルモゴロフエントロピＫを算出する。
【００１１】
前記気液二相流のフローパターンは、気泡流、スラグ流、チャーン流（フロス流）、環状噴霧流、噴霧流のうち少なくとも２つである。
【００１２】
上記本発明の方法によれば、気液二相流が発生する音をデータとして気液二相流のフローパターンを判別するので、発生音はマイクロフォンを使って非接触で計測でき、配管およびその流れに影響を与えることなく、配管内のフローパターンを判別できる。従ってとりわけ既設配管のフローパターン判別に有効である。
【００１３】
また、気液二相流のフローパターンは、線型システムやランダムシステムよりも、カオスに近似しているため、気液二相流の複雑な現象をカオス理論的に分析することによって、外部から高い正解率（精度）で、客観的に判別できることが実施例で確認された。
【００１４】
【発明の実施の形態】
以下、本発明の好ましい実施形態を図面を参照して説明する。なお、各図において共通する部分には同一の符号を付して使用する。
【００１５】
図１は、本発明の気液二相流のフローパターン判別方法のフロー図である。この図に示すように、本発明の方法は、データ記録ステップ（Ａ）、カオス分析ステップ（Ｂ）、基準作成ステップ（Ｃ）及び判別ステップ（Ｅ）からなる。
【００１６】
データ記録ステップ（Ａ）（Ｓ１、Ｓ４）では、内部を気液二相流が流れる配管の外部から、気液二相流が発生する音をデータとして連続的に記録する。すなわち、このデータ記録ステップ（Ａ）において、時間列ｔ1，ｔ2，…ｔn（ｎは整数）に対応する音の時系列ｘ1，ｘ2，…ｘnを記憶する。
【００１７】
カオス分析ステップ（Ｂ）（Ｓ２、Ｓ５）では、記録したデータをカオス分析してフラクタル次元ＤとコルモゴロフエントロピーＫを求める。すなわち、このカオス分析ステップ（Ｂ）において、後述するカオス理論式により、ｍ次元相空間Ｘj、ｍ次元相空間Ｘjにおける２点ずつの距離ｒm i,j、相関積分Ｃm（ｒ）およびフラクタル次元ＤとコルモゴロフエントロピＫを算出する。
【００１８】
基準作成ステップ（Ｃ）では、既知の気液二相流のフローパターンに前記データ記録ステップ（Ａ）とカオス分析ステップ（Ｂ）を適応して、フラクタル次元ＤとコルモゴロフエントロピーＫを求め、その大きさから気液二相流のフローパターンを判別する判別基準を作成する。
【００１９】
判別ステップ（Ｅ）では、未知の気液二相流から得られたフラクタル次元ＤとコルモゴロフエントロピーＫから前記判別基準によりフローパターンを判別する。なお、上述した既知の気液二相流の未知の気液二相流は、その運転条件をできる限り一致させることが好ましい。
【００２０】
また上述した気液二相流のフローパターンは、気泡流、スラグ流、チャーン流（フロス流）、環状噴霧流、噴霧流等に区分できるが、原子力やボイラーにおいて、その安定運転上重要となる少なくとも２つのフローパターンを対象とするのがよい。
【００２１】
次ぎに、フラクタル次元ＤとコルモゴロフエントロピＫの算出方法を具体的に説明する。
１．はじめに音の強さの時係列ｘ1，ｘ2，…ｘnから［数１］の式（１）により、ｍ次元相空間Ｘjに転移させ、ｍ次元相空間Ｘjの点を作る。
２．次いで、ｍ次元相空間Ｘjにおける２点ずつの距離ｒm i,jを式（２）により計算する。
３．次に相関積分Ｃm（ｒ）を式（３）により計算する。ただし、Ｈは式（４）のＨｅａｖｉｓｉｄｅ関数であり、ｒは任意の距離である。
４．最後にフラクタル次元ＤとコルモゴロフエントロピＫを式（５）（６）により計算する。ただし、τはサンプリング周期である。
【００２２】
【数１】
【実施例】
図２は、本発明の実施例を示す図である。この図において、（Ａ）（Ｂ）は同一のデータに基づくものであり、横軸はコルモゴロフエントロピＫ、縦軸はフラクタル次元Ｄを示している。また図中菱形（◆）はスラグ流の場合、四角（■）はチャーン流の場合であり、２つの三角（▲）は未知のケースである。上述の基準作成ステップ（Ｃ）による判別基準の例として、（Ａ）では、コルモゴロフエントロピＫの大小により、（Ｂ）では楕円で囲んだ領域による判別を示している。
【００２４】
図２の例では、１つの例外を除いて、スラグ流とチャーン流のデータは主にＫの値に対してそれぞれまとまった分布を示している。さらに、未知の２ケースのデータＱに対して本発明のカオス特性解析を行ったところ、それぞれスラグ流、チャーン流の領域内に分布し、判別結果は実現象と一致した。この結果から、カオス特性解析により二相流の流動状態を判別することが可能であることが確認された。
【００２５】
上述した本発明の方法によれば、気液二相流が発生する音をデータとして気液二相流のフローパターンを判別するので、発生音はマイクロフォンを使って非接触で計測でき、配管およびその流れに影響を与えることなく、配管内のフローパターンを判別できる。従ってとりわけ既設配管のフローパターン判別に有効である。
【００２６】
また、気液二相流のフローパターンは、線型システムやランダムシステムよりも、カオスに近似しているため、気液二相流の複雑な現象をカオス理論的に分析することによって、外部から高い正解率（精度）で、客観的に判別できる。
【００２７】
なお、本発明は上述した実施形態に限定されず、本発明の要旨を逸脱しない範囲で種々に変更できることは勿論である。
【００２８】
【発明の効果】
上述したように、本発明の気液二相流のフローパターン判別方法は、内部が目視観察できない配管内の気液二相流のフローパターンを、配管およびその流れに影響を与えることなく、外部から高い正解率（精度）で、客観的に判別することができる等の優れた効果を有する。
【図面の簡単な説明】
【図１】本発明の気液二相流のフローパターン判別方法のフロー図である。
【図２】本発明の実施例を示す図である。
【図３】垂直上向きの気液二相流のフローパターンの模式図である。
【符号の説明】
ｔ1，ｔ2，…ｔn 時間列
ｘ1，ｘ2，…ｘn 音又は歪の強さの時系列
Ｘj ｍ次元相空間
ｒm i,j ｍ次元相空間Ｘjにおける２点ずつの距離
Ｃm（ｒ） 相関積分
Ｈ Ｈｅａｖｉｓｉｄｅ関数
ｒ 任意の距離
Ｄ フラクタル次元
Ｋ コルモゴロフエントロピ
τ サンプリング周期[0001]
BACKGROUND OF THE INVENTION
The present invention relates to a flow pattern discrimination method for a gas-liquid two-phase flow.
[0002]
[Prior art]
As shown in FIG. 3, the vertically upward gas-liquid two-phase flow has different flow patterns of, for example, a bubble flow, a slag flow, a churn flow (floss flow), an annular spray flow, and a spray flow, depending on the flow ratio of gas to liquid. It becomes. This flow pattern greatly affects pressure loss, heat transfer characteristics, and stability. Therefore, the flow pattern discriminating method is very important for stable operation particularly in nuclear power and boilers.
[0003]
Two methods for discriminating the flow pattern of a gas-liquid two-phase flow are conventionally known. The first method is based on the geometric structure of the phase distribution obtained by visual observation. However, this method is subjective and difficult to apply to general industries. Another method is to process measurement data such as pressure fluctuation, void ratio, velocity, temperature, etc., and determine the flow pattern based on the dynamic characteristics of the gas-liquid two-phase flow. As this data processing method, a Fourier analysis method and a statistical analysis method are known and disclosed in, for example, the following documents.
[0004]
(Reference 1) J. Org. Drahos and J.M. Cermak, "Diagnostic of Gas-liquid Flow Pattern in Chemical Engineering System", Chem. Eng. Process. , Vol. 26, pp. 147-164 (1989)
(Reference 2) Matsui, “Identification of Flow Regimes in Vertical Gas-liquid Two-phase Flow Using Differential Pressure Fluctuations”, Int. J. et al. Multiphase Flow Vol. 10, no. 6, pp. 711-720 (1984)
[0005]
[Problems to be solved by the invention]
However, any of the conventional discrimination methods described above is insufficient to objectively discriminate the flow pattern of the gas-liquid two-phase flow. That is, it is based on Fourier analysis and Fourier transform suitable for linear systems, and statistical analysis is based on static analysis suitable for random systems. However, the flow pattern of gas-liquid two-phase flow has one aspect of a linear system, but also has a random system aspect, and both methods have a problem that the accuracy rate of the discrimination is low.
[0006]
The present invention has been made to solve such problems. In other words, the object of the present invention is to objectively discriminate the flow pattern of gas-liquid two-phase flow in a pipe whose inside cannot be visually observed without affecting the pipe and its flow with a high accuracy rate (accuracy) from the outside. Another object of the present invention is to provide a gas-liquid two-phase flow pattern discrimination method that can be used.
[0007]
[Means for Solving the Problems]
Chaos is a system that follows determinism, but it shows complex, irregular, and unstable behavior, not simple behavior. The inventors of the present invention grasp the characteristics of the phenomenon by analyzing the complicated phenomenon of gas-liquid two-phase flow chaos theoretically because the flow pattern of gas-liquid two-phase flow approximates to chaos. Focused on the possibilities. The present invention is based on such novel attention.
[0008]
That is, according to the present invention, the sound transmitted from the inside of the pipe through which the gas-liquid two-phase flow flows to the outer surface of the pipe is generated. Data recording step (A) for continuously recording data, chaotic analysis step (B) for analyzing fractal dimension D and Kolmogolf entropy K by analyzing the recorded data, and magnitude of fractal dimension D and Kolmogolf entropy K includes a determination step of determining a flow pattern of the gas-liquid two-phase flow (E), from said at chaotic analysis step (B), data or these recorded sounds, obtains the fractal dimension D and Kolmogorov entropy K A flow pattern discrimination method for a gas-liquid two-phase flow is provided.
[0009]
According to a preferred embodiment of the present invention, the data recording step (A) and the chaos analysis step (B) are applied to a known gas-liquid two-phase flow pattern, and the magnitude of the fractal dimension D and the Kolmogorov entropy K is applied. A reference creation step (C) for creating a discrimination standard for discriminating the flow pattern of the gas-liquid two-phase flow from the fractal dimension D and Kolmogorov obtained from the unknown gas-liquid two-phase flow in the discrimination step (E) The flow pattern is discriminated from the entropy K according to the discrimination criterion.
[0010]
In the data recording step (A), sound time series x 1 , x 2 ,... X n corresponding to the time sequences t 1 , t 2 ,... T n (n is an integer) are stored, and the chaos analyzing step ( In B), m-dimensional phase space Xj, distance r m i, j of two points in m-dimensional phase space Xj, correlation integral C m (r), fractal dimension D, and Kolmogorov entropy K are calculated.
[0011]
The flow pattern of the gas-liquid two-phase flow is at least two of a bubble flow, a slag flow, a churn flow (floss flow), an annular spray flow, and a spray flow.
[0012]
According to the method of the present invention, since it is determined flow pattern of the gas-liquid two-phase flow sound gas-liquid two-phase flow is generated as data, generating sound can the measurement without contact with the microphone, piping In addition, the flow pattern in the pipe can be determined without affecting the flow. Therefore, it is particularly effective for discriminating the flow pattern of existing piping.
[0013]
In addition, the flow pattern of gas-liquid two-phase flow is more similar to chaos than linear systems and random systems, so by analyzing the complicated phenomenon of gas-liquid two-phase flow from chaos theory, it is high from the outside. It was confirmed in the examples that the correct answer rate (accuracy) can be objectively discriminated.
[0014]
DETAILED DESCRIPTION OF THE INVENTION
Hereinafter, preferred embodiments of the present invention will be described with reference to the drawings. In addition, the same code | symbol is attached | subjected and used for the common part in each figure.
[0015]
FIG. 1 is a flow diagram of a gas-liquid two-phase flow pattern discrimination method according to the present invention. As shown in this figure, the method of the present invention comprises a data recording step (A), a chaos analysis step (B), a reference creation step (C), and a discrimination step (E).
[0016]
In the data recording step (A) (S1, S4), sound generated by the gas-liquid two-phase flow is continuously recorded as data from the outside of the pipe through which the gas-liquid two-phase flow flows. That is, in the data recording step (A), the time sequence t 1, t 2, ... t n (n is an integer) time series x 1 sound corresponding to, x 2, stores ... x n.
[0017]
In the chaos analysis step (B) (S2, S5), the recorded data is subjected to chaos analysis to obtain the fractal dimension D and the Kolmogolf entropy K. That is, in this chaos analysis step (B), the distance r m i, j between the two points in the m-dimensional phase space X j and the m-dimensional phase space X j , the correlation integral C m (r) and Fractal dimension D and Kolmogorov entropy K are calculated.
[0018]
In the reference creation step (C), the data recording step (A) and the chaos analysis step (B) are applied to a known gas-liquid two-phase flow pattern to obtain the fractal dimension D and the Kolmogorov entropy K, Then, a discrimination criterion for discriminating the flow pattern of the gas-liquid two-phase flow is created.
[0019]
In the discrimination step (E), the flow pattern is discriminated based on the discrimination criterion from the fractal dimension D and Kolmogolf entropy K obtained from the unknown gas-liquid two-phase flow. The unknown gas-liquid two-phase flow of the above-described known gas-liquid two-phase flow preferably has the same operating conditions as possible.
[0020]
The gas-liquid two-phase flow pattern described above can be divided into bubbly flow, slag flow, churn flow (floss flow), annular spray flow, spray flow, etc., but is important for stable operation in nuclear power and boilers. It is preferable to target at least two flow patterns.
[0021]
Next, a method for calculating the fractal dimension D and the Kolmogorov entropy K will be described in detail.
1. First , the sound intensity time series x 1 , x 2 ,..., X n is transferred to the m-dimensional phase space X j by the equation (1) of [Equation 1] to create a point in the m-dimensional phase space X j .
2. Next, the distance r m i, j between the two points in the m-dimensional phase space X j is calculated by the equation (2).
3. Next, the correlation integral C m (r) is calculated by the equation (3). However, H is a Heaviside function of Formula (4), and r is an arbitrary distance.
4). Finally, the fractal dimension D and Kolmogorov entropy K are calculated by the equations (5) and (6). Where τ is a sampling period.
[0022]
[Expression 1]
【Example】
FIG. 2 is a diagram showing an embodiment of the present invention. In this figure, (A) and (B) are based on the same data, the horizontal axis indicates Kolmogorov entropy K, and the vertical axis indicates the fractal dimension D. In the figure, rhombuses (♦) indicate slug flow, squares (■) indicate churn flow, and two triangles (▲) indicate unknown cases. As an example of the discrimination criterion in the above-described criterion creation step (C), (A) shows discrimination based on the size of Kolmogorov entropy K, and (B) shows discrimination based on an area surrounded by an ellipse.
[0024]
In the example of FIG. 2, with one exception, the slag flow and churn flow data mainly show a distribution with respect to the value of K. Furthermore, when the chaos characteristic analysis of the present invention was performed on the data Q of two unknown cases, they were distributed in the slag flow and churn flow regions, respectively, and the discrimination results were consistent with the actual phenomenon. From this result, it was confirmed that it was possible to discriminate the flow state of two-phase flow by chaotic characteristic analysis.
[0025]
According to the method of the present invention described above, since it is determined flow pattern of the gas-liquid two-phase flow sound gas-liquid two-phase flow is generated as data, the generated sound can the measurement without contact with the microphone, distribution The flow pattern in the pipe can be determined without affecting the pipe and its flow. Therefore, it is particularly effective for discriminating the flow pattern of existing piping.
[0026]
In addition, the flow pattern of gas-liquid two-phase flow is more similar to chaos than linear systems and random systems, so by analyzing the complicated phenomenon of gas-liquid two-phase flow from chaos theory, it is high from the outside. The correct answer rate (accuracy) can be objectively determined.
[0027]
In addition, this invention is not limited to embodiment mentioned above, Of course, it can change variously in the range which does not deviate from the summary of this invention.
[0028]
【The invention's effect】
As described above, the gas-liquid two-phase flow pattern discrimination method according to the present invention can be applied to a gas-liquid two-phase flow pattern in a pipe whose inside cannot be visually observed without affecting the pipe and its flow. Therefore, it has an excellent effect that it can be objectively discriminated with a high accuracy rate (accuracy).
[Brief description of the drawings]
FIG. 1 is a flow diagram of a gas-liquid two-phase flow pattern discrimination method according to the present invention.
FIG. 2 is a diagram showing an embodiment of the present invention.
FIG. 3 is a schematic diagram of a flow pattern of a gas-liquid two-phase flow vertically upward.
[Explanation of symbols]
t 1 , t 2 ,... t n time sequence x 1 , x 2 ,... x n sound or distortion intensity time series X j m-dimensional
Claims (7)
記録したデータをカオス分析してフラクタル次元ＤとコルモゴロフエントロピーＫを求めるカオス分析ステップ（Ｂ）と、
フラクタル次元ＤとコルモゴロフエントロピーＫの大きさから気液二相流のフローパターンを判別する判別ステップ（Ｅ）と、を有し、
前記カオス分析ステップ（Ｂ）では、記録した音のデータから、フラクタル次元ＤとコルモゴロフエントロピーＫを求める、ことを特徴とする気液二相流のフローパターン判別方法。Data that continuously records the sound transmitted from the inside to the outer surface of the pipe from the outside of the pipe through which the gas-liquid two-phase flow flows. Recording step (A);
A chaos analysis step (B) for analyzing the recorded data to obtain a fractal dimension D and a Kolmogorov entropy K;
A discriminating step (E) for discriminating the flow pattern of the gas-liquid two-phase flow from the magnitude of the fractal dimension D and the Kolmogorov entropy K,
Wherein the chaotic analysis step (B), data or these recorded sounds, obtains the fractal dimension D and Kolmogorov entropy K, flow pattern identification method of the gas-liquid two-phase flow, characterized in that.
前記判別ステップ（Ｅ）において、未知の気液二相流から得られたフラクタル次元ＤとコルモゴロフエントロピーＫから前記判別基準によりフローパターンを判別する、ことを特徴とする請求項１乃至３のいずれかに記載の気液二相流のフローパターン判別方法。Applying the data recording step (A) and the chaos analysis step (B) to the known gas-liquid two-phase flow pattern, the flow pattern of the gas-liquid two-phase flow is calculated from the magnitude of the fractal dimension D and the Kolmogorov entropy K. A standard creation step (C) for creating a standard for discrimination;
The flow pattern is discriminated by the discrimination criterion from the fractal dimension D and Kolmogolf entropy K obtained from the unknown gas-liquid two-phase flow in the discrimination step (E). The flow pattern discrimination method of the gas-liquid two-phase flow described in 1.
前記カオス分析ステップ（Ｂ）において、ｍ次元相空間Ｘj、ｍ次元相空間Ｘjにおける２点ずつの距離ｒm i,j、相関積分Ｃm（ｒ）およびフラクタル次元ＤとコルモゴロフエントロピＫを算出する、ことを特徴とする請求項１乃至４のいずれかに記載の気液二相流のフローパターン判別方法。In the data recording step (A), the time sequence t 1, t 2, ... t n (n is an integer) time series x 1 sound corresponding to, x 2, stores ... x n,
Wherein in Chaos Analysis step (B), an m-dimensional phase space X j, the distance r m i of each two points in an m-dimensional phase space X j, j, the correlation integral C m (r) and the fractal dimension D and Kolmogorov entropy K The flow pattern discrimination method for gas-liquid two-phase flow according to claim 1, wherein the flow pattern is calculated.
記録したデータをカオス分析してフラクタル次元ＤとコルモゴロフエントロピーＫを求めるカオス分析ステップ（Ｂ）と、
フラクタル次元ＤとコルモゴロフエントロピーＫの大きさから気液二相流のフローパターンを判別する判別ステップ（Ｅ）と、を有し、
前記カオス分析ステップ（Ｂ）では、記録した音のデータから、フラクタル次元ＤとコルモゴロフエントロピーＫを求め、
前記データ記録ステップ（Ａ）において、時間列ｔ1，ｔ2，…ｔn（ｎは整数）に対応する音の時系列ｘ1，ｘ2，…ｘnを記憶し、
前記カオス分析ステップ（Ｂ）において、ｍ次元相空間Ｘj、ｍ次元相空間Ｘjにおける２点ずつの距離ｒm i,j、相関積分Ｃm（ｒ）およびフラクタル次元ＤとコルモゴロフエントロピＫを算出し、
前記気液二相流のフローパターンは、スラグ流とチャーン流（フロス流）である、ことを特徴とする気液二相流のフローパターン判別方法。Data that continuously records the sound transmitted from the inside to the outer surface of the pipe from the outside of the pipe through which the gas-liquid two-phase flow flows. Recording step (A);
A chaos analysis step (B) for analyzing the recorded data to obtain a fractal dimension D and a Kolmogorov entropy K;
A discriminating step (E) for discriminating the flow pattern of the gas-liquid two-phase flow from the magnitude of the fractal dimension D and the Kolmogorov entropy K,
Wherein the chaotic analysis step (B), data or these recorded sound, determine the fractal dimension D and Kolmogorov entropy K,
In the data recording step (A), the time sequence t 1, t 2, ... t n (n is an integer) time series x 1 sound corresponding to, x 2, stores ... x n,
Wherein in Chaos Analysis step (B), an m-dimensional phase space X j, the distance r m i of each two points in an m-dimensional phase space X j, j, the correlation integral C m (r) and the fractal dimension D and Kolmogorov entropy K Calculate
A flow pattern discrimination method for a gas-liquid two-phase flow, wherein the flow pattern of the gas-liquid two-phase flow is a slag flow and a churn flow (floss flow).
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