JP2002006076A - System for predicting diffusion of radioactive substance - Google Patents
System for predicting diffusion of radioactive substanceInfo
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- JP2002006076A JP2002006076A JP2000187504A JP2000187504A JP2002006076A JP 2002006076 A JP2002006076 A JP 2002006076A JP 2000187504 A JP2000187504 A JP 2000187504A JP 2000187504 A JP2000187504 A JP 2000187504A JP 2002006076 A JP2002006076 A JP 2002006076A
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- calculation
- radioactive material
- diffusion
- data
- radioactive
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- Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
- Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
- Y02E—REDUCTION OF GREENHOUSE GAS [GHG] EMISSIONS, RELATED TO ENERGY GENERATION, TRANSMISSION OR DISTRIBUTION
- Y02E30/00—Energy generation of nuclear origin
- Y02E30/30—Nuclear fission reactors
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- Monitoring And Testing Of Nuclear Reactors (AREA)
Abstract
Description
【０００１】[0001]
【発明の属する技術分野】本発明は、大気中に放出され
た放射性物質の濃度や線量を予測する放射性物質拡散予
測システムに関する。BACKGROUND OF THE INVENTION 1. Field of the Invention The present invention relates to a radioactive substance diffusion prediction system for predicting the concentration and dose of radioactive substances released into the atmosphere.
【０００２】[0002]
【従来の技術】原子力発電所等から大気中に放射性物質
が放出されるような万一の緊急時に、放出された放射性
物質の移流拡散の状況と予測線量当量を計算するシステ
ムとして、SPEEDI(System for Prediction of Environm
ental Emergency Dose Information)システムが知られ
ている。このSPEEDIシステムについては、「緊急時環境
放射線モニタリング指針」(原子力安全委員会，平成４
年)に記載されている。2. Description of the Related Art SPEEDI (System) is a system for calculating the advection-diffusion status and predicted dose equivalent of released radioactive materials in the event of emergency, such as when radioactive materials are released into the atmosphere from a nuclear power plant. for Prediction of Environm
ental Emergency Dose Information) system is known. This SPEEDI system is described in the "Emergency Environmental Radiation Monitoring Guidelines" (Nuclear Safety Commission, 1994
Year).
【０００３】SPEEDIシステムは、平常時には、各地方公
共団体のテレメータシステムの気象観測情報と気象庁の
アメダス情報を１時間毎に受信し、６時間先までの風向
・風速の統計的予測等を行う。In normal times, the SPEEDI system receives weather observation information of the telemeter system of each local government and AMeDAS information of the Meteorological Agency every hour, and performs statistical prediction of wind direction and speed up to six hours ahead.
【０００４】そして緊急時には、平常時に予測した気象
情報，地形情報及び放出源情報（ファックスで送信され
てくる施設名や発生時刻等）に基づいて、最長６時間先
までの風速場，大気中の放射性物質の濃度及び予測線量
当量を計算する。In an emergency, based on weather information, terrain information, and emission source information (facsimile transmitted facility name, occurrence time, etc.) predicted in normal times, the wind speed field up to 6 hours ahead, Calculate the concentration of radioactive material and the estimated dose equivalent.
【０００５】[0005]
【発明が解決しようとする課題】上述した従来技術で
は、放出された放射性物質の挙動は大気の挙動と全く同
一として計算が行われる。しかしながら、放出される放
射性物質には直径が１０μｍ以上ある大粒径の物質が含
まれることもあり、このような大粒径の物質は大気と比
較して挙動が遅いために大気の流線から外れてしまう。
また、大粒径の物質は湿分中では粒径が成長するが、上
記従来技術では粒径の成長について考慮されていない。
そのため、上述の従来技術では放射性物質の濃度や線量
当量を正確に計算できない可能性がある。In the above-described prior art, the calculation is performed on the assumption that the behavior of the released radioactive material is exactly the same as the behavior of the atmosphere. However, released radioactive substances may include large-diameter substances with a diameter of 10 μm or more. It will come off.
In addition, a substance having a large particle size grows in moisture, but the prior art does not consider the growth of the particle size.
Therefore, there is a possibility that the concentration and the dose equivalent of the radioactive substance cannot be accurately calculated by the above-described conventional technology.
【０００６】更に上述の従来技術では、放出源を点源と
しているが、高温・高圧の熱流体の放出に伴う放射性雲
は直径が１００ｍ程度の半球の形状であり、この放出源
の相違によって放射性物質の濃度や線量当量の計算に誤
差が生じる可能性がある。Further, in the above-mentioned prior art, the emission source is a point source. However, the radioactive cloud accompanying the release of a high-temperature and high-pressure thermal fluid has a hemispherical shape with a diameter of about 100 m. There may be errors in the calculation of substance concentrations and dose equivalents.
【０００７】本発明の目的は、放射性物質の濃度や線量
当量の予測精度をより向上させることにある。An object of the present invention is to further improve the accuracy of predicting the concentration and dose equivalent of a radioactive substance.
【０００８】[0008]
【課題を解決するための手段】上記目的を達成するため
に、本発明では、大気中に放出された放射性物質の拡散
状況を予測して任意の地点における放射性物質の濃度及
び線量当量を予測する放射性物質拡散予測システムにお
いて、放射性物質の粒径に基づいて沈降する速度を評価
し、それに基づいて沈着場所を算出する。In order to achieve the above object, according to the present invention, the concentration of radioactive material and the dose equivalent at an arbitrary point are predicted by predicting the state of diffusion of radioactive material released into the atmosphere. In the radioactive material diffusion prediction system, the rate of sedimentation is evaluated based on the particle size of the radioactive material, and the deposition site is calculated based on the rate.
【０００９】具体的には、以下の計算アルゴリズムを用
いる。Specifically, the following calculation algorithm is used.
【００１０】粒子の沈降速度Ｕｐは、（数１）で表わさ
れるストークスの法則に従う球形粒子の終端速度Ｗｐを
用いて与える。 The sedimentation velocity Up of a particle is given by using the terminal velocity Wp of a spherical particle in accordance with Stokes' law expressed by (Equation 1).
【００１１】ここで、ρp ，ｄは粒子の密度及び直径、
ρg，μgは気体の密度及び粘性係数、ｇは重力加速度で
ある。Where ρ p , d is the density and diameter of the particles,
ρ g and μ g are the density and viscosity coefficient of the gas, and g is the gravitational acceleration.
【００１２】湿分中における粒子成長については、吸湿
性の粒子に対する湿度効果として、（数２）を適用す
る。 With respect to the particle growth in moisture, (Equation 2) is applied as the humidity effect on the hygroscopic particles.
【００１３】ここで、ｄe は平衡粒径、ｄ0 は初期粒
径、ρ0 は粒子の密度、ρw は水の密度、ｉはvan't Ho
ff因子、Ｍw は水の分子量、Ｍs は粒子の分子量、ＲＨ
は相対湿度である。湿分中における粒子成長は相対湿度
９５％以上で顕著に表れるため、計算においては大気中
に拡散した吸湿性の粒子が高湿度領域に入った際に粒径
の成長が考慮される。その際の粒径は（数２）より求め
られ、粒子と水の混合物の密度を用いて（数１）から沈
降速度が評価される。Where de is the equilibrium particle size, d 0 is the initial particle size, ρ 0 is the particle density, ρ w is the water density, and i is van't Ho.
ff factor, M w is the molecular weight of the water, the molecular weight of the M s particle, RH
Is the relative humidity. Since the growth of particles in moisture is remarkable at a relative humidity of 95% or more, the calculation takes into account the growth of the particle size when the hygroscopic particles diffused into the atmosphere enter a high humidity region. The particle diameter at that time is obtained from (Equation 2), and the sedimentation velocity is evaluated from (Equation 1) using the density of the mixture of particles and water.
【００１４】[0014]
【発明の実施の形態】本発明の好適な一実施例である放
射性物質拡散予測システムについて図面を用いて説明す
る。DESCRIPTION OF THE PREFERRED EMBODIMENTS A radioactive material diffusion prediction system according to a preferred embodiment of the present invention will be described with reference to the drawings.
【００１５】図１は、本実施例の放射性物質拡散予測シ
ステムの構成を示す。本実施例では、図１の破線の範囲
を１台のパソコンで実現し、外部からのデータはネット
ワークを介してオンラインでパソコンのデータベースに
取り込む。図１に示すように、平常時には、気象庁の２
日先までの２０ｋｍメッシュの気象予測データ（風向，
風速，気温，降水量、等)である気象庁ＧＰＶ(Grid Poi
nt Value）データ１５を午前９時と午後９時の１日２回
受信し、施設周辺気象予測計算モデル３にて５００ｍ以
下の詳細なメッシュで質量保存則を満たすように内挿し
て最大５１時間先までの各メッシュの風向・風速の予測
計算の処理を行う。予測結果は予測気象結果２０として
格納される。FIG. 1 shows a configuration of a radioactive material diffusion prediction system according to this embodiment. In this embodiment, the range indicated by the broken line in FIG. 1 is realized by a single personal computer, and external data is fetched online into a database of the personal computer via a network. As shown in FIG.
Weather forecast data of 20km mesh up to the sun (wind direction,
Meteorological Agency GPV (Grid Poi) for wind speed, temperature, precipitation, etc.
nt Value) Receives data 15 twice a day at 9:00 am and 9:00 pm, and interpolates with a detailed mesh of 500 m or less using a detailed mesh of 500 m or less to satisfy the law of conservation of mass in the facility surrounding weather prediction calculation model 3 for a maximum of 51 hours. The prediction calculation of the wind direction / wind speed of each mesh is performed. The prediction result is stored as the predicted weather result 20.
【００１６】一方、放射性物質が大気中に大量に放出さ
れるような緊急時には、平常時において５００ｍ以下の
詳細なメッシュで予測された気象情報，地形図データ
９，放射線モニタ等で計測された施設内放射線モニタデ
ータ１８，モニタリングポスト等で計測された施設周辺
放射線モニタデータ１９に基づいて、最長５１時間先ま
での風速場，大気中放射性物質の濃度及び予測線量当量
の計算を行う他、気象庁のAMeDASデータを用いて過去の
再現計算も行う。On the other hand, in an emergency when a large amount of radioactive material is released into the atmosphere, weather information predicted by a detailed mesh of 500 m or less in normal times, topographic map data 9, facilities measured by a radiation monitor, etc. Based on the internal radiation monitor data 18, the radiation monitor data around the facility measured by monitoring posts, etc., the wind speed field, the concentration of radioactive materials in the atmosphere and the predicted dose equivalent up to 51 hours ahead are calculated. A past reproduction calculation is also performed using AMeDAS data.
【００１７】以下、緊急時における動作について説明す
る。The operation in an emergency will be described below.
【００１８】緊急時には、まず風速場計算モデル４にお
いて風向及び風速を計算する。この計算は予測計算と再
現計算から成る。予測計算では、３次元計算領域全体を
計算用セル（直方体の小要素）に分解し、予測気象結果
２０と地形図データ９を用いて山や丘のような地形の高
低を考慮した上で質量保存則を満たすように、各セルの
最大５１時間先までの風向・風速計算を行う。一方、再
現計算では、４０分前の気象が送信される気象庁AMeDAS
データ２（地上の風向，風速，降雨等で約２０ｋｍに１
ヶ所）を１時間毎に受信し、またドップラーソーダや排
気筒の風速・風向計で計測された高所の風向・風速や露
場で計測された地上の風向・風速等の施設内気象観測デ
ータ１６及び施設周辺で計測された地上の風向・風速で
ある施設周辺気象観測データ１を受信し、これを地形メ
ッシュ上の位置（緯度，経度）と高度に合致する点での
風向・風速として取り込んで境界条件とすることで、対
象とする領域全体の質量保存則を満たすようにして過去
の風向・風速場を再現する。予測及び再現された結果
は、それぞれ３次元予測風速場１０及び３次元再現風速
場１７として保存される。In an emergency, the wind direction and wind speed are calculated by the wind speed field calculation model 4 first. This calculation consists of a prediction calculation and a reproduction calculation. In the prediction calculation, the entire three-dimensional calculation area is decomposed into calculation cells (small elements of a rectangular parallelepiped), and the mass is calculated using the predicted weather result 20 and the topographic map data 9 in consideration of the height of a terrain such as a mountain or a hill. The wind direction and the wind speed are calculated up to 51 hours ahead of each cell so as to satisfy the conservation rule. On the other hand, in the reproduction calculation, the Meteorological Agency AMeDAS where the weather 40 minutes ago is transmitted
Data 2 (1 per 20 km due to wind direction, wind speed, rainfall, etc. on the ground)
Locations) every hour, and in-facility meteorological data such as wind direction and wind speed at high altitudes measured by Doppler soda and exhaust stacks, and wind direction and wind speed on the ground measured at the dew field 16 and the weather data 1 around the facility, which is the wind direction and wind speed on the ground measured around the facility, are received and taken as the wind direction and wind speed at a point that matches the position (latitude, longitude) and altitude on the terrain mesh. By using as boundary conditions, the past wind direction / velocity field is reproduced so as to satisfy the law of conservation of mass of the entire target area. The predicted and reproduced results are stored as a three-dimensional predicted wind speed field 10 and a three-dimensional reproduced wind speed field 17, respectively.
【００１９】次に、濃度計算モデル５にて濃度計算が行
われる。濃度計算モデル５では、アルファ線，ベータ
線，ガンマ線等の施設内放射線モニタデータ１８とＩ−
１３１，Ｋｒ−８５，Ｐｕ−２４０等の核種組成比率デ
ータ２１から単位時間当りの放出源総量と組成から成る
放出条件を導出し、これを経度，緯度，高度から決まる
放出点として３次元予測風速場１０である最大５１時間
先までの風速場の結果を用い、風による移流と大気の乱
れによる放射性物質の拡散を数千個の個々の仮想粒子の
動きに置き換えて計算する。この結果から計算用セルご
とに、放射性物質の最大５１時間先までの大気中平均濃
度と地表蓄積量の予測計算を行う。その結果はそれぞれ
空間濃度分布７及び沈着量８として保存される。また、
濃度計算モデル５では、気象庁AMeDASデータ２による風
速場の計算結果である３次元再現風速場１７を用いて再
現計算も行う。ここで、沈着量の計算は前述の（数２）
で示した成長した粒径を用いて、降水量に応じた物質の
沈降や（数１）を用いた重力による沈降を計算する。こ
の計算結果として地図上に空間濃度分布や沈着量分布を
等値線図やコンター図として出力する。なお、施設周辺
放射線モニタデータ１９と核種組成比率データ２１から
も単位時間当りの放出源総量と組成から成る放出条件を
導出して、この放出条件を放出点として上述と同様に計
算してもよい。このように、本実施例では、核種組成比
率データ２１と施設内放射線モニタデータ１８や施設周
辺放射線モニタデータ１９とに基づいて、核種がもつエ
ネルギとモニタ値を比較することで、総量を推定して計
算することができる。Next, the density calculation is performed by the density calculation model 5. In the density calculation model 5, the radiation monitor data 18 of the facility such as alpha rays, beta rays, and gamma rays and I-
From the nuclide composition ratio data 21 such as 131, Kr-85, Pu-240, etc., an emission condition composed of the total amount and composition of emission sources per unit time is derived, and this is set as an emission point determined by longitude, latitude and altitude. Using the results of the wind speed field up to 51 hours ahead, which is the field 10, the advection due to the wind and the diffusion of radioactive material due to the turbulence of the atmosphere are replaced with the movement of thousands of individual virtual particles. From this result, for each calculation cell, a prediction calculation of the average concentration of the radioactive material in the atmosphere up to 51 hours ahead and the accumulated amount on the ground surface is performed. The results are stored as a spatial density distribution 7 and a deposition amount 8, respectively. Also,
In the concentration calculation model 5, reproduction calculation is also performed using a three-dimensional reproduction wind speed field 17 which is a calculation result of the wind speed field based on the Meteorological Agency AMeDAS data 2. Here, the calculation of the amount of deposition is as described above (Equation 2).
The sedimentation of a substance corresponding to the amount of precipitation and the sedimentation caused by gravity using (Equation 1) are calculated using the grown particle diameter indicated by. As a result of the calculation, the spatial density distribution and the deposition amount distribution are output as an iso-contour diagram and a contour diagram on a map. In addition, the emission condition consisting of the total amount and composition of emission sources per unit time may be derived from the radiation monitor data 19 around the facility and the nuclide composition ratio data 21, and the emission condition may be calculated in the same manner as described above using the emission condition as an emission point. . As described above, in this embodiment, the total amount is estimated by comparing the energy of the nuclide with the monitor value based on the nuclide composition ratio data 21 and the in-facility radiation monitor data 18 and the perimeter radiation monitor data 19. Can be calculated.
【００２０】次に、本実施例では、線量計算モデル６に
て線量を計算する。濃度計算の結果をもとに、核種の半
減期やエネルギ等のデータである核種物理定数データ２
２を用いて地上における最大５１時間先までの吸収線量
率，外部被曝による実効線量当量，吸入による甲状腺線
量当量などの予測計算を行うと共に、再現計算も行う。
得られた結果は、外部被曝線量１２及び内部被曝線量１
３として保存されると共に、地図上に外部被曝線量や内
部被曝線量の等値線図やコンター図として出力される。
なお、線量計算モデル６においては、各計算用セルの内
部で放射性物質の平均濃度を均一と仮定し、核種組成比
率データ２１を考慮し、評価地点で最大３０核種による
線量率などの予測計算を行う。Next, in this embodiment, the dose is calculated by the dose calculation model 6. Based on the result of the concentration calculation, nuclide physical constant data 2 which is data on nuclide half-life, energy, etc.
2 is used to perform a prediction calculation of an absorbed dose rate up to 51 hours ahead on the ground, an effective dose equivalent due to external exposure, a thyroid dose equivalent due to inhalation, and a reproduction calculation.
The results obtained were: external dose 12 and internal dose 1
3 and is output as a contour map or contour diagram of the external exposure dose and the internal exposure dose on a map.
In addition, in the dose calculation model 6, it is assumed that the average concentration of the radioactive material is uniform inside each calculation cell, and in consideration of the nuclide composition ratio data 21, the prediction calculation such as the dose rate of up to 30 nuclides is performed at the evaluation point. Do.
【００２１】図２は図１の濃度計算モデル５における計
算の流れを示す。まず、図１の風速場計算モデル４の計
算結果である３次元予測風速場１０又は３次元再現風速
場１７と地形図データ９を用いて、図２の大気拡散解析
用空気メッシュ生成Ｓ１１で３次元空間のメッシュを生
成する。FIG. 2 shows the flow of calculation in the density calculation model 5 of FIG. First, using the three-dimensional predicted wind field 10 or the three-dimensional reproduced wind field 17 and the topographic map data 9 which are the calculation results of the wind field calculation model 4 in FIG. Generate a mesh in dimensional space.
【００２２】次に、放出源から放出される物質の初期化
Ｓ１２を行う。この初期化では、よう素，希ガスの他，
粒子状核種の放出も想定される場合には、粒子状核種の
データベース３１から、放出が予想される核種の密度，
粒径を選択する。放射性物質は排気筒又は建屋から直接
放出される形態があり、これはファックス等で伝送され
てくる情報を見て、ユーザが以下の通り手入力する。放
出される箇所が排気筒である場合には、フィルタを通過
した後の核種が放出されるので、粒径は１μｍとする
が、建屋から直接放出される場合には、核種はそのまま
放出されるので、核種の物理的な粒径を選択する。ま
た、配管破断のような形態がファックス等で伝送されて
くる場合には、高温，高圧の熱流体も同時に放出される
ので、この熱流体が断熱膨張して熱平衡状態で決まる半
球形状の放射性雲の直径を用いた体積源をデータベース
３２から選択する。更に、放射線源がきのこ雲形状の場
合は、それを体積源データベース３２から選択する。こ
れらの体積源については、その形状の表面及び内部に均
一に仮想粒子を配置して、これを時刻ｔ＝０として初期
化する。この初期化を終了後、図２の流れに沿って計算
する。まず、粒子番号ｎ＝１について、時刻ｔの粒子ｎ
＝１の位置における風速・風速変動等を内挿し、風速変
動，気象条件に依存する拡散速度を計算する。次に、降
雨や霧等の影響を考慮して、（数２）による物質の成長
を計算し、次に（数１）により沈降速度を計算する。以
上より、時刻がΔｔ進んだ時の粒子ｎ＝１の位置を（数
３）より求める。Next, initialization S12 of the substance emitted from the emission source is performed. In this initialization, in addition to iodine and rare gas,
If the release of particulate nuclides is also assumed, the density of nuclides that are expected to be released from the database 31 of particulate nuclides,
Select the particle size. There is a form in which the radioactive material is directly emitted from the exhaust stack or the building, and this is manually input by a user as described below by looking at information transmitted by facsimile or the like. If the emission site is the exhaust stack, the nuclide after passing through the filter is emitted, so the particle size is 1 μm. So choose the physical particle size of the nuclide. When a form such as a pipe break is transmitted by facsimile or the like, a high-temperature, high-pressure heat fluid is also released at the same time, and the heat fluid is adiabatic expanded and a hemispherical radioactive cloud determined by a thermal equilibrium state. Is selected from the database 32 using the diameter of. Further, when the radiation source has a mushroom cloud shape, it is selected from the volume source database 32. With respect to these volume sources, virtual particles are uniformly arranged on the surface and inside of the shape, and initialized at time t = 0. After the initialization is completed, calculation is performed according to the flow of FIG. First, for the particle number n = 1, the particle n at time t
The wind speed and the wind speed fluctuation at the position of = 1 are interpolated, and the diffusion speed depending on the wind speed fluctuation and weather conditions is calculated. Next, taking into account the effects of rainfall, fog, etc., the growth of the substance is calculated by (Equation 2), and then the sedimentation velocity is calculated by (Equation 1). From the above, the position of the particle n = 1 when the time advances by Δt is obtained from (Equation 3).
【００２３】 Ｘ(ｔ＋Δｔ)＝Ｘ(ｔ)＋Δｔ(Ｕw＋Ｕd＋Ｕp) …（数３） なお、（数３）においてＵw は風速、Ｕd は乱流変動又
は大気安定度、Ｕp は沈降速度である。次にｎ＝２につ
いて同様に計算を行い、ｎが数千個まで計算を繰り返
す。そして、時刻ｔにおける濃度分布の計算が終了した
後、これをファイルに出力し、次に時刻Δｔ後のｔ＋Δ
ｔでの計算を行うというように、逐次計算を進める。そ
して、求めたい時間までの最終的な計算結果として、核
種毎の空間濃度や沈着量を地図上の分布図として出力す
る。本実施例では、気象データは気象庁AMeDASデータ２
の他、気象庁ＧＰＶ（Grid Point Value）データ１５を
用いるので、約２日先までの予測計算ができる。更に、
施設内気象観測データ１６や施設周辺気象観測データ１
を用いるので気象計算の精度が向上する。そして、計算
結果をＣＲＴ画面に表示する。このＣＲＴ画面には、空
間濃度，地表沈着量を表示すると共に、地図上にも表示
する。また、同時に体積源の形状も地図上に表示する。X (t + Δt) = X (t) + Δt (U w + U d + Up ) (Equation 3) In Equation (3), U w is wind speed, U d is turbulent flow fluctuation or atmospheric stability, and U p is the sedimentation velocity. Next, the same calculation is performed for n = 2, and the calculation is repeated until n is several thousand. Then, after the calculation of the density distribution at time t is completed, this is output to a file, and then t + Δ after time Δt
The calculation is performed sequentially, such as performing the calculation at t. Then, as a final calculation result up to the desired time, the spatial concentration and deposition amount of each nuclide are output as a distribution map on a map. In this embodiment, the meteorological data is the Meteorological Agency AMeDAS data 2
In addition, since the Meteorological Agency GPV (Grid Point Value) data 15 is used, a prediction calculation up to about two days ahead can be performed. Furthermore,
In-facility weather observation data 16 and perimeter weather observation data 1
, The accuracy of weather calculation is improved. Then, the calculation result is displayed on the CRT screen. This CRT screen displays the spatial density and the amount of surface deposition, as well as on a map. At the same time, the shape of the volume source is also displayed on the map.
【００２４】また、本実施例では、風向と風速が急変す
る場合に、１時間前と１時間後の風向と風速を用いて評
価した移流拡散の結果は急激に変化し、実気象と著しく
異なる結果となることが予想されるので、１時間毎の気
象情報をそのまま使用しないで、以下のような内挿を行
う。具体的には、気象情報は１時間毎であることから、
１時間毎の風向・風速のベクトル量Ｕ60（ｔ1 ）と次の
１時間後のベクトル量Ｕ60（ｔ1 ＋６０）を用いて、
（数４）のような重み付を行い、時間についての内挿を
行う。 Further, in this embodiment, when the wind direction and the wind speed change suddenly, the result of the advection diffusion evaluated using the wind direction and the wind speed one hour before and one hour later changes abruptly and is significantly different from the actual weather. Since the result is expected, the following interpolation is performed without using the hourly weather information as it is. Specifically, since the weather information is every hour,
Using the vector amount U 60 (t 1 ) of the wind direction and the wind speed every hour and the vector amount U 60 (t 1 +60) one hour later,
Weighting as in (Equation 4) is performed, and time interpolation is performed.
【００２５】ここで、ｔ＝１０分，２０分，…，５０分
である。Here, t = 10 minutes, 20 minutes,..., 50 minutes.
【００２６】更に、本発明では、１０物質以上が同時に
放出される場合にも対応できるようにした。具体的に
は、放出される物質の移流拡散では、破断口から放出さ
れる箇所に応じて、その破断した配管，容器の物質の組
成比は決っている。そこで、３０の物質から成る単位量
を同時に計算して、放出された物質の風下側でのガンマ
線等の測定値と比較して、組成比はそのままでそのガン
マ線等の値が単位量の何倍に相当するかを計算すること
で放出源総量を推定できるようにした。Further, the present invention can cope with a case where ten or more substances are simultaneously released. Specifically, in the advection diffusion of the substance to be released, the composition ratio of the substance of the broken pipe and the container is determined according to the location to be released from the break. Therefore, the unit amount consisting of 30 substances is simultaneously calculated and compared with the measured value of gamma ray etc. on the lee side of the released substance, and the value of the gamma ray etc. is many times the unit amount while the composition ratio remains unchanged. It was made possible to estimate the total amount of emission sources by calculating whether or not it corresponds to
【００２７】以上説明した本実施例によれば、直径が１
０μｍ以上の大粒径の物質の拡散を計算できるので、こ
のような大粒径の物質が放出点近傍に沈着し、拡散の影
響が遠方に及ばないことが評価できる。According to the embodiment described above, the diameter is 1
Since the diffusion of a substance having a large particle diameter of 0 μm or more can be calculated, it can be evaluated that the substance having such a large particle diameter is deposited near the emission point and the influence of the diffusion does not reach far.
【００２８】更に、１００μｍ程度の雨が降る場合に
は、放出された物質が放出点近傍に沈着することから、
拡散の影響が遠方に及ばないことが評価できる。Further, in the case of rain of about 100 μm, since the released substance is deposited near the release point,
It can be evaluated that the effect of diffusion does not extend far.
【００２９】また、粒子の成長は相対湿度が９５％以上
で顕著に表れる。一方、熱流体が断熱膨張した半球状の
放射性雲は相対湿度がほぼ１００％であることから、粒
径が成長し、放出点近傍に沈着するので、拡散の影響が
遠方に及ばないことが評価できる。The growth of the particles is remarkable when the relative humidity is 95% or more. On the other hand, a semi-spherical radioactive cloud in which a thermal fluid is adiabatically expanded has a relative humidity of almost 100%, so the particle size grows and deposits near the emission point, so the effect of diffusion does not reach far. it can.
【００３０】[0030]
【発明の効果】本発明によれば、放射性物質の濃度や線
量当量の予測精度をより向上させることができる。According to the present invention, it is possible to further improve the accuracy of predicting the concentration of radioactive substances and the dose equivalent.
【図１】本発明の好適な一実施例である放射性物質拡散
予測システムの構成図である。FIG. 1 is a configuration diagram of a radioactive material diffusion prediction system according to a preferred embodiment of the present invention.
【図２】図１の濃度計算モデル５の計算手順を示す図で
ある。FIG. 2 is a diagram showing a calculation procedure of a density calculation model 5 of FIG.
１…施設周辺気象観測データ、２…気象庁AMeDASデー
タ、３…施設周辺気象予測計算モデル、４…風速場計算
モデル、５…濃度計算モデル、６…線量計算モデル、７
…空間濃度分布、８…沈着量、９…地形図データ、１０
…３次元予測風速場、１１…分布図出力、１２…外部被
曝線量、１３…内部被曝線量、１４…分布図出力、１５
…気象庁ＧＰＶデータ、１６…施設内気象観測データ、
１７…３次元再現風速場、１８…施設内放射線モニタデ
ータ、１９…施設周辺放射線モニタデータ、２０…予測
気象結果、２１…核種組成比率データ、２２…核種物理
定数データ。1 ... Meteorological observation data around the facility, 2 ... Meteorological Agency AMeDAS data, 3 ... Model for predicting weather around the facility, 4 ... Wind speed field calculation model, 5 ... Concentration calculation model, 6 ... Dose calculation model, 7
... spatial density distribution, 8 ... deposition amount, 9 ... topographic map data, 10
... three-dimensional predicted wind speed field, 11 ... distribution map output, 12 ... external exposure dose, 13 ... internal exposure dose, 14 ... distribution map output, 15
… Meteorological Agency GPV data, 16… Meteorological observation data in facilities,
17: three-dimensional reproduced wind field, 18: radiation monitor data in the facility, 19: radiation monitor data around the facility, 20: predicted weather result, 21: nuclide composition ratio data, 22: nuclide physical constant data.
───────────────────────────────────────────────────── フロントページの続き (72)発明者 岩重 健五 茨城県日立市大みか町七丁目２番１号 株 式会社日立製作所電力・電機開発研究所内 (72)発明者 笠野 利夫 茨城県日立市幸町三丁目２番１号 日立エ ンジニアリング株式会社内 Ｆターム(参考） 2G075 BA12 CA50 DA08 DA10 FA20 FB15 FB18 FC11 GA21 GA36 ──────────────────────────────────────────────────続 き Continuing on the front page (72) Inventor Kengo Iwashige 7-2-1, Omika-cho, Hitachi City, Ibaraki Prefecture Inside Power and Electricity Research Laboratory, Hitachi, Ltd. (72) Inventor Toshio Kasano Hitachi, Ibaraki F-term (reference) in Hitachi Engineering Co., Ltd. 2-1-1 Sachicho 2G075 BA12 CA50 DA08 DA10 FA20 FB15 FB18 FC11 GA21 GA36
Claims (2)
を予測して任意の地点における放射性物質の濃度及び線
量当量を予測する放射性物質拡散予測システムにおい
て、 放射性物質の粒径に基づいて沈降する速度を評価し、そ
れに基づいて沈着場所を算出することを特徴とする放射
性物質拡散予測システム。1. A radioactive material diffusion prediction system for predicting the radioactive material released into the atmosphere and predicting the concentration and dose equivalent of the radioactive material at an arbitrary point, wherein the sedimentation is based on the particle size of the radioactive material. A radiological diffusion prediction system, which evaluates the speed at which a radioactive material spreads and calculates a deposition site based on the speed.
雲形状或いはきのこ雲形状を選択し、選択された形状に
基づいて放射性物質の拡散状況を予測することを特徴と
する放射性物質拡散予測システム。2. A radioactive material diffusion prediction system, wherein a hemispherical radioactive cloud shape or a mushroom cloud shape is selected as a radioactive material emission source, and a diffusion state of the radioactive material is predicted based on the selected shape.
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| JP2000187504A JP3709330B2 (en) | 2000-06-19 | 2000-06-19 | Radioactive material diffusion prediction system |
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