인공신경망을 이용한 가변 기구 터보차저의 터빈 질량유량 모델링

국문 초록 (Abstract)

이 논문에서는 인공신경망을 이용하여 가변 기구 터보차저(VGT)의 터빈 질량유량을 추정하는 모델을 제안하고자 한다. 터빈 질량유량을 추정하기 위한 모델의 입력변수는 VGT 베인 개도량, 엔진 회전속도, 배기매니폴드 압력, 배기매니폴드 온도, 터빈 출구 압력이 사용되었으며, 터빈 입구 유효 단면적을 추정하는 부분에 인공신경망을 적용하였다. 실험을 통하여 이 논문에서 제안한 모델의 터빈 질량유량 추정 성능을 검증하였으며, 터빈 맵을 이용하여 추정한 결과와 비교를 통하여 제안한 모델의 우수성을 확인하였다.

다국어 초록 (Multilingual Abstract)

In this paper, we propose a turbine mass flow rate model for a variable geometry turbocharger (VGT) using an artificial neural network (ANN). The model predicts the turbine mass flow rate using the VGT vane position, engine rotational speed, exhaust manifold pressure, exhaust manifold temperature, and turbine outlet pressure. The ANN is used for the estimation of the effective flow area. In order to validate the results estimated by the proposed model, we have compared estimation results with engine experimental results. The results, in addition, represent improved estimation accuracy when compared with the performance using the turbine map.

목차 (Table of Contents)

• 초록
• Abstract
• 1. 서론
• 2. 터빈 질량유량 모델
• 3. 인공신경망(ANN)
• 4. 실험 환경
• 5. 모델 검증
• 6. 결론
• 후기
• 참고문헌

참고문헌 (Reference)

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