python を使用した放射線リスク評価のためのホットスポット健康物理コードの自動化

Najeeb N M Maglas¹, Merouane Najar², Zhao Qiang²

¹College of Nuclear Science and Technology, Harbin Engineering University, Harbin, 150001, China; Thamar University, Republic of Yemen, Faculty of Science, Department of Physics, Yemen; National Atomic Energy Commission-Yemen (NATEC), Sana'a, Yemen.

Applied radiation and isotopes : including data, instrumentation and methods for use in agriculture, industry and medicine

|September 3, 2025

まとめ

この研究は,放射線リスク評価のためのホットスポット健康物理コードを強化するための Python 自動化フレームワークを導入します. 原子炉の自動化により原子炉の効率と精度が向上します

科学分野:

健康物理学
コンピュータ科学
環境科学

背景:

ホットスポット健康物理コードは,核事故時の放射線リスク評価に不可欠です.
パラメータの変動のある複雑なシナリオのホットスポット限度効率のマニュアル入力と分析.

研究の目的:

ホットスポット健康物理コードの Python ベースの自動化フレームワークを開発する.
手動データ処理と分析の限界に対処し,効率と精度を向上させる.

主な方法:

ホットスポットパラメータの調整 (放射性核酸濃度,天気,地形) を自動化する Python フレームワークを開発しました.
リアルタイムで組織化されたデータセットの生成を実施しました.
統合された自動ビジュアライゼーションと効率的なデータ抽出.

主要な成果:

ホットスポットの主要パラメータのダイナミック調整を達成しました.
リアルタイムのデータ生成と組織を可能にします.
放射線リスク評価の精度向上と分析時間の短縮

結論:

ホットスポットの自動化は放射線リスク評価の能力を大幅に改善します
フレームワークは,さまざまな緊急事態に対して,より高い精度,柔軟性,迅速な意思決定を提供します.
原子力事故と緊急事態への対応のための方法論を進めている.

キーワード:

緊急対応ガウスの羽根モデルホットスポットコードの自動化原子力安全 Python プログラミング放射線リスク評価

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