Modeling the Cladding Leak Detection Shop of a Nuclear Reactor's Module

Customer:




Rosatom State Atomic Energy Corporation (Rosatom) is a state holding which incorporates more than 360 enterprises in the nuclear field. It includes all nondefense nuclear ventures, enterprises from the nuclear weapon sector, research organizations, and nuclear icebreaker fleets. Rosatom is a leading organization in the nuclear industry. It holds second position in world uranium stocks, fifth in mining, and fourth position in world nuclear energy production. It controls 17% of the world nuclear fuel market and 40% of the world market of enrichment services.


Model developers: Yuriy Podvalny, Denis Gerasimov.

Problem:




When designing the fuel cladding leak detection shop, developers needed to collect the data and system parameters in cases of ruptured fuel elements production.


The cladding leak detection shop is part of an automated line of fuel assembly production. Leak control is based on heating the fuel element groups. While warming up, defective units eject the control gas, which is detected by a leak locator. The defected group is divided into two parts. Each part is screened in the same way until the leaking fuel element is found.


System engineers needed to define the dependence between annual output (production), input storage volume, and fuel element group size, to charge into furnace or different spoilage rate. In addition, they needed to define the amount of dead fuel elements assemblies due to system downtime when input storage is full.

Solution:




Since leakage rate has a stochastic nature, developers built a simulation model of a cladding leak detection shop and tested different scenarios and system operation algorithms, running multiple experiments on the model.


The model, built in AnyLogic, simulates two algorithms of leak detection shop operation. The algorithms are based on different approaches for the detection of a leaking fuel element when the fuel element group is screened and leakage is detected.


First algorithm: when leakage is detected, half of the fuel elements group is uncharged from furnace #1 to furnace #2. Both groups are heated and examined for leaking fuel elements. The qualified group is charged to the output storage. The defective group is again divided into two groups and examined for the leaking fuel element, and so on.


Modeling Nuclear Fuel Production
Simulation Model Screenshot

If both groups appear to be defective, half of each group is charged to the output storage and the examination of the rest of the halves begins. The fuel elements groups in the output storage are inspected after examination of the first halves is completed. The input storage picks up incoming assemblies with scheduled frequency. Thus, the ruptured group is "uncoiled" by two furnaces. A new group of fuel elements is not charged until the ruptured element is found. In case leakage is not detected, the furnaces operate in course, with the second furnace waiting for the first one to complete the examination of the fuel elements group.


Second algorithm: the furnaces operate independently. The ruptured group is "uncoiled" by a furnace in isolation from another until the leaking element is found. A new group of fuel elements is not charged into the furnace until the previous group is completely examined. This approach allows two furnaces to operate simultaneously.


Model user can vary the following parameters of algorithms:

  • Leakage frequency
  • Size of fuel elements group to charge into the furnace for examination 
  • Input storage size

In the course of the research, every combination of value parameters was run on the model about 100 times for "one year" in terms of simulation model time.

Results:




  • Production rate of operation algorithms was tested for different leakage frequency cases.
  • The size of the fuel elements group to charge into a furnace was defined to provide the maximum production rate for the given leakage frequency.
  • Simulation brought out the possible loss reduction due to down time if the volume of input storage is increased.
  • Leak detector production stats were received for various leak frequencies and different parameters. Each time, the model was run for 100 "simulation" years.
  • Simulation spotted the dependence between maximum volume of input storage and the size of the fuel elements group to charge into the furnace. Numerical values were received to express this dependence for various leak frequency.

Conclusion:




At the stage of designing the fuel cladding leak detection shop, experiments with the real system required lots of financial and time expenses. Analysis of data collected during simulation allowed the engineers to define the optimal design parameters to provide maximum production.


The customer is planning to use the simulation model for testing possible changes in the production line, like adding a new furnace. Users can vary parameters by editing data in the model interface. The simulation model will serve as a decision support tool for the equipment buyer for many years.

More Case Studies

  • 自動生産ラインのプランニングおよび最適化
    ドイツCentrotherm Photovoltaics AG 社は、太陽光発電、半導体およびマイクロエレクトロニクス業界への、技術および設備を提供するグローバルサプライヤーです。同社は、コストを最小限に抑え、スループットと信頼性を最大限にするため、最良の自動化生産ラインおよび工場構成を確認する必要がありました。
  • GE Manufacturing Plant Uses AnyLogic for Real Time Decision Support
    In 2012, GE opened a new battery manufacturing plant in conjunction with the launch of an innovative energy storage business. GE’s exciting opportunity brought on many new challenges, such as increasing production throughput and yield under evolving processes and uncertainties, and reducing manufacturing costs in order to gain market share. The GE Global Research Center sought out a powerful and flexible tool to analyze, not just the specific process, but the manufacturing system as a whole.
  • AnyLogicでキャパシティー分析
    NASSCOは、現在の生産および可能性のある新しい仕事の両方に関して、きわめて詳細でかつ正確なキャパシティー分析を提供するために、The Large Scale Computer Simulation Modeling System for Shipbuilding (LSMSe)と呼ばれる特注の分析システムを利用します。
  • トンネル掘削機でのトンネル建設シミュレーション
    トンネル掘削機の休止による1時間のコストは、通常高いのが実情です。また、プロジェクト・マネージャーは、工事現場において作業遅れを回避するために最善を尽くさなければなりません。 Ruhr University Bochum in Germanyで開発されたシミュレーション・プロジェクトの目標は、可能性のある金銭上のロスを最小化するために、トンネル建設プロセスでのボトルネックを究明することができるシミュレーション・モデルを作成することでした。
  • 船舶産業における生産計画
    イタリアは歴史的に、世界に誇るヨットやスーパーヨットを生産す る国として知られています。多数の有名なブランドを市場に浸 透させるために、原価管理とライトサイジングは製品とプロセス・ イノベーションと同様に重要です。豪華ヨットの製造工程は複 雑であり、また完成品と技量の質は落とすことができません。 ヨットの製造工程は膨大な時間および労働力を必要とし、多く の生産タスク、非常に熟練した手仕事が必要です。
  • 造船所はオーダー履行能力を検証するため、生産設備やその分布を可視化
    アドミラルティ造船所(Admiralty Shipyards JSC)は、ディーゼル潜水艦の大口注文に直面し、現在の生産設備で受注に十分対応できるか、できない場合は2016年までにどれだけ生産できるかを評価しなければなりませんでした。アドミラルティ造船所JSCはまた、受注に対応するために新たな生産設備が必要になるかを検証します。
  • アイスクリーム製造 シミュレート:制限認識および生産計画の最適化
    ウルグアイで最大手の乳製品製造会社Conaprole社は、生産ライン5本を稼働させ、ラインごとに異なる5種類の包装形態で、150SKU(Stock Keeping Unit)種類を超える商品を生産します。需要と供給のバランスを保つために彼らの計画を再構築して重要な商品の在庫切れを回避することが経営陣の挑戦になっていました。さらに、生産設備利用の最適化も求めていました。
  • Analysis of Management Strategies for the Aircraft Production Ramp-up
    The Airbus Group joined the European Union ARUM (Adaptive Production Management) project, which is focused on creating an IT solution for risk reduction, decision-making, and planning during new product ramp-ups. The project is aimed mainly at aircraft and shipbuilding industries. Simulation was chosen as a part of the ARUM solution, because it would allow the participants to reproduce the real production facility experience.
  • Simple Simulation Model Helps Intel Avoid Production Plant Downtime
    Intel factories used a particular type of equipment that often broke down, which caused capacity constraints. These expensive parts were used in critical factory operations, and the repairs took significant time, so it was necessary to have extra spare parts on hand to avoid downtimes. Broken parts caused constraints at some of the factories while other factories over purchased spares.
  • Improving Mining Outbound Logistics with Agent-Based Simulation Modeling
    One of the largest resource companies in the world, with over $80 billion in sales, decided to enter a new market. It was planning to build a new potash mine with 90% of the resources exported. They wanted to design a reliable supply chain, with a high speed of supply replenishing, and the ability to recover from natural disasters and man-made crises benefiting from such volatility. Amalgama and Goldratt companies contracted this project to design the potash mining operations and a full supply chain of outbound logistics.