تحقیقات موتور

تحقیقات موتور

شبیه‏‌سازی‏ تأثیر زمان‌بندی متغیر دریچه‌ها بر مصرف سوخت و آلایندگی خام در موتورهای پرخوران احتراق جرقه‌‏ای تزریق درگاهی به کمک یادگیری رایانه‌‏ای

نوع مقاله : مقاله پژوهشی

نویسندگان
شرکت تحقیق، طراحی و تولید موتور ایران خودرو (ایپکو)، تهران، ایران
10.22034/er.2026.2076402.1112
چکیده
با تداوم نقش موتورهای احتراق داخلی در سامانه‏ حمل‌ونقل، نیاز به روش‌های کارآمد برای نگاشت بهینه عملکرد و آلایندگی آن‌ها بیش از پیش احساس می‌شود. روش‌های نگاشت مبتنی بر آزمایش صرف، زمان‌بر و پرهزینه هستند و قادر به کشف تعاملات پیچیده و غیرخطی بین متغیرهای مؤثر نیستند. این پژوهش با به‌ کارگیری یک راه‌حل ترکیبی طرح ‌آزمون و یادگیری رایانه‏‌ای، به شبیه‏‌سازی‏ مصرف سوخت ویژه ترمزی و آلاینده‏‌های HC و NOx در یک موتور بنزینی پرخوران پرداخته است. دو روش پیشرفته با استفاده از یک طرح‌آزمون بهینه ‌شده مبتنی بر روش‏ فدوروف آموزش داده شدند و عملکرد آن‌ها در شرایط مختلف حجم داده (از ۱۲.۵% تا ۵۰% از فضای طراحی) مقایسه گردید. در شرایط داده‌ های محدود (۲۵% از داده ها)، هر دو روش‏ به دلیل مقاومت ذاتی در برابر بیش‌برازش، عملکرد ‌نسبتاً پایداری در پیش‌بینی آلاینده‌های HC و NOx نشان دادند. با این حال، خطای پیش‌بینی این متغیرها در مقایسه با مصرف سوخت همچنان قابل توجه بوده که لزوم توسعه بیشتر روش‏ و اعتبارسنجی تجربی نتایج را نشان می‌دهد. با افزایش داده به ۵۰%، هر دو روش‏ به دقت قابل قبولی دست یافتند. مقادیر میانگین R2 برای BSFC، NOx و HC بترتیب به 99%، 96% و 90 % رسید. این رویکرد نشان داد که می‌توان با تنها ۵۰% از داده‌های آزمایشی، به دقت مطلوب در نگاشت دست یافت که به معنای کاهش ۵۰ درصدی زمان و هزینه‌های آزمایش است.
کلیدواژه‌ها

عنوان مقاله English

Machine learning-driven modeling of the effect of variable valve timing on fuel consumption and emission reduction in a turbocharged port-injection engine

نویسندگان English

Faraz Javanshayani
AmirHossein Hamad
AmirHossein Parivar
Mohammad Nejat
Irankhodro Powertrain Company (IPCo), Tehran, Iran
چکیده English

The persistent use of internal combustion engines in the transportation sector underscores a growing need for efficient methods to optimize their performance and emissions calibration. Conventional calibration methods, which rely heavily on experimentation, are often time-consuming, costly, and incapable of capturing the complex, non-linear interactions among critical parameters. This study presents a hybrid methodology integrating Design of Experiments (DOE) and Machine Learning (ML) to model brake specific fuel consumption (BSFC) alongside HC and NOx emissions in a turbocharged gasoline engine. Two advanced ML algorithms were trained using an optimized DOE based on the Fedorov algorithm. Their predictive performance was systematically evaluated and compared across varying data volumes, ranging from 12.5% to 50% of the design space. Under limited data conditions (25% of the data), both models demonstrated stable performance for predicting HC and NOx emissions, attributable to their inherent robustness against overfitting. Nevertheless, the prediction errors for these pollutants remained significant compared to those for BSFC, highlighting the necessity for further model refinement and experimental validation. When the data volume was increased to 50%, both models achieved high predictive accuracy, with mean R2 values of 99% for BSFC, 96% for NOx, and 90% for HC. This approach demonstrates that a desired calibration accuracy can be achieved using only 50% of the experimental data, thereby potentially reducing testing time and associated costs by half.

کلیدواژه‌ها English

Machine Learning
Design of Experiments
Engine Calibration
Fuel Economy
Emissions
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  • تاریخ دریافت 24 آبان 1404
  • تاریخ بازنگری 09 آذر 1404
  • تاریخ پذیرش 08 اردیبهشت 1405