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.
초록·키워드
This study aims to predict lap time trends over the next five years in the Qualifying (Q1) sessions of the Monaco Grand Prix and Italian Grand Prix using the NeuralProphet model. Data from 2000 to 2024 were analyzed to investigate the impact of FIA technical regulation changes and advancements in vehicle performance on lap times. The results showed a continuous decrease in lap times for both circuits, with Monaco exhibiting a greater reduction due to its complex track characteristics and high-downforce setup requirements. In contrast, the Italian Monza circuit, characterized by long straights and low-downforce setups, showed a relatively smaller reduction. The potential impact of the new 1.6L V6 turbo engines and sustainable fuels, set to be introduced in 2026, was also considered. The study found similar lap time reduction patterns among teams using the same engine. Incorporating multivariate analysis, including external factors such as temperature, humidity, and tire conditions, could further enhance prediction accuracy. Future studies on diverse circuits with varying conditions, such as Mexico's high-altitude track or Singapore's humid environment, are suggested for generalization. This research expands the scope of F1 studies by leveraging the NeuralProphet model, contributing to improved race strategies, vehicle setups, and increased fan engagement, offering significant academic and practical implications.
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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