{"created":"2024-01-18T08:49:46.874503+00:00","id":2000192,"links":{},"metadata":{"_buckets":{"deposit":"779f1447-9d39-4cab-a3b6-ecc3bac14c50"},"_deposit":{"created_by":27,"id":"2000192","owners":[27],"pid":{"revision_id":0,"type":"depid","value":"2000192"},"status":"published"},"_oai":{"id":"oai:jaxa.repo.nii.ac.jp:02000192","sets":["1887:1893","9:1090:1695564675201:1704792027113"]},"author_link":[],"control_number":"2000192","item_1692929429513":{"attribute_name":"抄録","attribute_value_mlt":[{"subitem_description":"統計的最適化(SPO)では，モンテカルロ・シミュレーション(MCS)を利用して評価関数となる失敗確率を得て，それを最小化する．MCS では様々な不確定パラメータを同時に非線形システムに組み込むため，最適化されたシステムはこれらの不確定パラメータに対してロバストとなる．その一方で，SPOでは繰り返しMCS計算を実行する必要があるため，計算負荷が極めて大きくなる．この計算負荷を緩和するため，MCSにおけるシミュレーションの評価回数を最適化の過程で変化させており，計算が進行してMCS結果が改善されるにつれて，評価回数を増加させている．本稿の目的は，最適化の過程におけるシミュレーション評価回数を，より合理的な方法で抑制し，計算負荷を軽減することである．現状のアルゴリズムでは，評価回数が突然大きく増加することがあり，その結果として全体の計算負荷が増大する事象が発生している．開発期間に制約がある実際の設計現場などにおいては，この状況は望ましいとはいえない．そこでシミュレーション評価回数の増加ロジックをより合理的な方法として見直し，計算負荷の抑制を試みた．またロジック修正の効果を，過去の飛行実験で用いられた現実的なシミュレーション・モデルを用いて検証した.","subitem_description_language":"ja","subitem_description_type":"Other"},{"subitem_description":"Stochastic Parameter Optimization (SPO) method utilizes Monte Carlo Simulation (MCS) to obtain the probability of failure that should be minimized. Since MCS incorporates various uncertain parameters into a nonlinear system, the optimized system can become robust against the uncertainties. On the other hand, SPO requires great amounts of computational resources because MCS must be repeatedly performed during the optimization process. In order to alleviate the computational burden, the number of simulations for each MCS is increased as the SPO proceeds and the MCS result improves. The objective of the number update is to reduce the computational cost when the probability of failure is relatively high. In the current algorithm, the number of MCS sometimes becomes unexpectedly large, and as a result more computational time is required. It might be inconvenient in the actual development process where design term is often limited. So, the algorithm for increasing the number of MCS is modified to evaluate the system appropriately at any time during the optimization process. The effects of the modification are confirmed using a real experimental flight model that was used in the past flight test.","subitem_description_language":"en","subitem_description_type":"Other"}]},"item_1692929640552":{"attribute_name":"内容記述","attribute_value_mlt":[{"subitem_description":"形態: カラー図版あり","subitem_description_language":"ja","subitem_description_type":"Other"},{"subitem_description":"Physical  characteristics: Original contains color illustrations","subitem_description_language":"en","subitem_description_type":"Other"}]},"item_1693474141533":{"attribute_name":"資料番号(Local)","attribute_value_mlt":[{"subitem_relation_type_id":{"subitem_relation_type_id_text":"資料番号: AA2330009000","subitem_relation_type_select":"Local"}}]},"item_1694684272217":{"attribute_name":"日付","attribute_value_mlt":[{"subitem_date_issued_datetime":"2023-10-24","subitem_date_issued_type":"Submitted"}]},"item_1695282775758":{"attribute_name":"著者所属","attribute_value_mlt":[{"subitem_text_language":"ja","subitem_text_value":"宇宙航空研究開発機構航空技術部門航空環境適合イノベーションハブ (JAXA)"},{"subitem_text_language":"en","subitem_text_value":"Aviation Environmental Sustainability Innovation Hub, Aviation Technology Directorate, Japan Aerospace Exploration Agency (JAXA)"}]},"item_3_biblio_info_10":{"attribute_name":"書誌情報","attribute_value_mlt":[{"bibliographicIssueDates":{"bibliographicIssueDate":"2024-01-19","bibliographicIssueDateType":"Issued"},"bibliographicNumberOfPages":"15","bibliographicVolumeNumber":"JAXA-RM-23-003","bibliographic_titles":[{"bibliographic_title":"JAXA Research and Development Memorandum","bibliographic_titleLang":"en"},{"bibliographic_title":"宇宙航空研究開発機構研究開発資料","bibliographic_titleLang":"ja"}]}]},"item_3_description_33":{"attribute_name":"レポート番号","attribute_value_mlt":[{"subitem_description":"レポート番号: RM-23-003","subitem_description_language":"ja","subitem_description_type":"Other"}]},"item_3_identifier_registration":{"attribute_name":"ID登録","attribute_value_mlt":[{"subitem_identifier_reg_text":"10.20637/0002000192","subitem_identifier_reg_type":"JaLC"}]},"item_3_publisher_8":{"attribute_name":"出版者","attribute_value_mlt":[{"subitem_publisher":"宇宙航空研究開発機構 (JAXA)","subitem_publisher_language":"ja"},{"subitem_publisher":"Japan Aerospace Exploration Agency (JAXA)","subitem_publisher_language":"en"}]},"item_3_source_id_22":{"attribute_name":"ISSN ONLINE","attribute_value_mlt":[{"subitem_source_identifier":"2433-2224","subitem_source_identifier_type":"EISSN"}]},"item_3_version_type_30":{"attribute_name":"著者版フラグ","attribute_value_mlt":[{"subitem_version_resource":"http://purl.org/coar/version/c_970fb48d4fbd8a85","subitem_version_type":"VoR"}]},"item_access_right":{"attribute_name":"アクセス権","attribute_value_mlt":[{"subitem_access_right":"open access","subitem_access_right_uri":"http://purl.org/coar/access_right/c_abf2"}]},"item_creator":{"attribute_name":"著者","attribute_type":"creator","attribute_value_mlt":[{"creatorAffiliations":[{"affiliationNames":[{"affiliationName":"宇宙航空研究開発機構 (JAXA)","affiliationNameLang":"ja"},{"affiliationName":"Japan Aerospace Exploration Agency (JAXA)","affiliationNameLang":"en"}]}],"creatorNames":[{"creatorName":"元田, 敏和","creatorNameLang":"ja"},{"creatorName":"MOTODA, Toshikazu","creatorNameLang":"en"}]}]},"item_files":{"attribute_name":"ファイル情報","attribute_type":"file","attribute_value_mlt":[{"accessrole":"open_date","date":[{"dateType":"Available","dateValue":"2024-01-19"}],"filename":"AA2330009000.pdf","format":"application/pdf","mimetype":"application/pdf","url":{"url":"https://jaxa.repo.nii.ac.jp/record/2000192/files/AA2330009000.pdf"},"version_id":"df942eb7-11be-43e2-853d-3ef7f94ca6b4"}]},"item_keyword":{"attribute_name":"キーワード","attribute_value_mlt":[{"subitem_subject":"Stochastic Parameter Optimization","subitem_subject_language":"en","subitem_subject_scheme":"Other"},{"subitem_subject":"Monte Carlo simulation","subitem_subject_language":"en","subitem_subject_scheme":"Other"},{"subitem_subject":"Confidence Interval","subitem_subject_language":"en","subitem_subject_scheme":"Other"},{"subitem_subject":"Robust System","subitem_subject_language":"en","subitem_subject_scheme":"Other"}]},"item_language":{"attribute_name":"言語","attribute_value_mlt":[{"subitem_language":"jpn"}]},"item_resource_type":{"attribute_name":"資源タイプ","attribute_value_mlt":[{"resourcetype":"technical report","resourceuri":"http://purl.org/coar/resource_type/c_18gh"}]},"item_title":"統計的最適化におけるモンテカルロ・シミュレーション評価回数更新法の見直し","item_titles":{"attribute_name":"タイトル","attribute_value_mlt":[{"subitem_title":"統計的最適化におけるモンテカルロ・シミュレーション評価回数更新法の見直し","subitem_title_language":"ja"},{"subitem_title":"Modification for Updating Number of Monte Carlo Simulations in Stochastic Parameter Optimization","subitem_title_language":"en"}]},"item_type_id":"40003","owner":"27","path":["1704792027113","1893"],"pubdate":{"attribute_name":"PubDate","attribute_value":"2024-01-19"},"publish_date":"2024-01-19","publish_status":"0","recid":"2000192","relation_version_is_last":true,"title":["統計的最適化におけるモンテカルロ・シミュレーション評価回数更新法の見直し"],"weko_creator_id":"27","weko_shared_id":-1},"updated":"2024-01-18T08:56:14.612235+00:00"}