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Phân Tích Thống Kê và Thông Minh Hành Vi Độ Nhớt của Nanolubricant Lai MgO-MWCNT (25–75%)/10W40 Sử Dụng Mô Hình Mạng Nơ-ron Nhân Tạo và Phương Pháp Bề Mặt Phản Ứng
Tóm tắt
Nghiên cứu này xem xét độ nhớt của nanolubricant lai MWCNT-MgO (25–75%)/10W40 trong khoảng tỷ lệ thể tích rắn (SVF) từ 0–1%, khoảng nhiệt độ từ 5–55 °C và khoảng tốc độ cắt từ 6665 đến 11,997 s−1 bằng cách sử dụng mô hình mạng nơ-ron nhân tạo và phương pháp bề mặt phản ứng. Nghiên cứu này được thực hiện nhằm giảm chi phí cho các nghiên cứu thực nghiệm thông qua việc sử dụng các phương pháp thông minh. Cấu trúc tối ưu nhất cho mạng nơ-ron nhân tạo đa lớp đã được lựa chọn thông qua việc nghiên cứu khoảng 400 cấu trúc khác nhau với số lượng nơ-ron khác nhau trong mỗi lớp, các hàm truyền tải khác nhau, v.v. Nghiên cứu này cũng trình bày một mối tương quan mới dựa trên toán học sử dụng phương pháp bề mặt phản ứng để ước lượng độ nhớt của nanolubricant hiện tại, lần đầu tiên. Giá trị R2 của mối tương quan toán học đề xuất và dữ liệu được dự đoán bởi mạng nơ-ron lần lượt là 0.9321 và 0.9999, có thể được coi là chính xác tốt. Cuối cùng, các kết quả của ANN và mối tương quan đề xuất đã được so sánh với các kết quả thực nghiệm, và sai số dự đoán cao nhất xảy ra ở nhiệt độ 35 °C và SVF 1%. Dựa trên kết quả ở tất cả các điều kiện, sự phù hợp của dữ liệu thực nghiệm với kết quả dự đoán của mạng nơ-ron cao hơn so với mối tương quan đề xuất.
Từ khóa
#độ nhớt #MWCNT-MgO #nanolubricant lai #mạng nơ-ron nhân tạo #phương pháp bề mặt phản ứng #mô hình hóa thông minhTài liệu tham khảo
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