인문학
사회과학
자연과학
공학
의약학
농수해양학
예술체육학
복합학
DBpia AI
AI 에이전트
AI 아이디어
AI 리더
AI 뷰어
Citeasy
크롬 서지관리 다운로드
지원사업
학술연구/단체지원/교육 등 연구자 활동을 지속하도록 DBpia가 지원하고 있어요.
커뮤니티
연구자들이 자신의 연구와 전문성을 널리 알리고, 새로운 협력의 기회를 만들 수 있는 네트워킹 공간이에요.
피인용 0
초록·키워드
Accurate prediction of excitation forces is essential for ensuring the structural integrity of dynamic systems. However, direct measurement of these forces is often impractical, prompting the development of indirect measurement techniques. This study introduces a novel method for indirectly predicting excitation forces using the Component Mode Synthesis (CMS) technique. The proposed approach derives excitation forces through equations of motion based on a CMS-based reduced-order model.
By utilizing system-level responses from only a minimal number of measurement points, the method achieves high prediction accuracy while significantly reducing computational complexity. Validation of the approach was performed using Finite Element Method (FEM) simulations and experimental testing. The results demonstrated strong agreement between theoretical and predicted values across various conditions, confirming the reliability and accuracy of the proposed method. This research presents a practical and efficient methodology for excitation force identification in dynamic systems, with promising potential for application across a wide range of industrial domains.
By utilizing system-level responses from only a minimal number of measurement points, the method achieves high prediction accuracy while significantly reducing computational complexity. Validation of the approach was performed using Finite Element Method (FEM) simulations and experimental testing. The results demonstrated strong agreement between theoretical and predicted values across various conditions, confirming the reliability and accuracy of the proposed method. This research presents a practical and efficient methodology for excitation force identification in dynamic systems, with promising potential for application across a wide range of industrial domains.
본문·목차
인공지능 문자 인식 모델을 통해 추출된 텍스트로, 일부 오타나 오류가 포함될 수 있으나 지속적으로 개선 중입니다.
오류를 발견하셨다면 해당 부분을 드래그한 후 ' 를 통해 신고해주세요.
오류를 발견하셨다면 해당 부분을 드래그한 후 ' 를 통해 신고해주세요.
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