title: Ð Ð°Ð·ÑÐ°Ð±Ð¾ÑÐºÐ° Ð¼ÐµÑÐ¾Ð´Ð¸ÐºÐ¸ ÑÐ¾Ð·Ð´Ð°Ð½Ð¸Ñ ÑÑÑÑÐ¾Ð³Ð°ÑÐ½ÑÑ Ð¼Ð¾Ð´ÐµÐ»ÐµÐ¹ Ð¼ÐµÑÐ¾Ð´Ð°Ð¼Ð¸ Ð¼Ð°ÑÐ¸Ð½Ð½Ð¾Ð³Ð¾ Ð¾Ð±ÑÑÐµÐ½Ð¸Ñ Ð½Ð° Ð¾ÑÐ½Ð¾Ð²Ðµ Ð´Ð°Ð½Ð½ÑÑ Ð¾ ÑÑÑÐ±ÑÐ»ÐµÐ½ÑÐ½Ð¾Ð¼ ÑÐµÑÐµÐ½Ð¸Ð¸ Ð² ÐºÐ°Ð½Ð°Ð»Ðµ Ñ ÐºÑÑÑÑÐ¼ ÑÐ°Ð·Ð²Ð¾ÑÐ¾ÑÐ¾Ð¼
title: Ð²ÑÐ¿ÑÑÐºÐ½Ð°Ñ ÐºÐ²Ð°Ð»Ð¸ÑÐ¸ÐºÐ°ÑÐ¸Ð¾Ð½Ð½Ð°Ñ ÑÐ°Ð±Ð¾ÑÐ° Ð±Ð°ÐºÐ°Ð»Ð°Ð²ÑÐ°
titleType: Subtitle
title: Development of a technique for creating surrogate models using machine learning methods based on data on turbulent flow in a channel with a steep turn
Creators: name: ÐÑÑÐ°Ð½Ð¾Ð², ÐÐ°Ð½Ð¸Ð»
Publisher: name: Ð¡Ð°Ð½ÐºÑ-ÐÐµÑÐµÑÐ±ÑÑÐ³ÑÐºÐ¸Ð¹ Ð¿Ð¾Ð»Ð¸ÑÐµÑÐ½Ð¸ÑÐµÑÐºÐ¸Ð¹ ÑÐ½Ð¸Ð²ÐµÑÑÐ¸ÑÐµÑ ÐÐµÑÑÐ° ÐÐµÐ»Ð¸ÐºÐ¾Ð³Ð¾
Year: 2024
Subjects: ÑÐ¸ÑÐ»ÐµÐ½Ð½Ð¾Ðµ Ð¼Ð¾Ð´ÐµÐ»Ð¸ÑÐ¾Ð²Ð°Ð½Ð¸Ðµ
ÑÑÑÐ±ÑÐ»ÐµÐ½ÑÐ½Ð¾Ðµ ÑÐµÑÐµÐ½Ð¸Ðµ
ÐºÐ°Ð½Ð°Ð» Ñ ÐºÑÑÑÑÐ¼ ÑÐ°Ð·Ð²Ð¾ÑÐ¾ÑÐ¾Ð¼
Ð¼Ð°ÑÐ¸Ð½Ð½Ð¾Ðµ Ð¾Ð±ÑÑÐµÐ½Ð¸Ðµ
ÑÑÑÑÐ¾Ð³Ð°ÑÐ½Ð¾Ðµ Ð¼Ð¾Ð´ÐµÐ»Ð¸ÑÐ¾Ð²Ð°Ð½Ð¸Ðµ
Ð°ÐºÑÐ¸Ð²Ð½Ð¾Ðµ Ð¾Ð±ÑÑÐµÐ½Ð¸Ðµ
numerical simulation
turbulent flow
U-bend channel
machine learning
surrogate modeling
active learning

description: ÐÐ°Ð½Ð½Ð°Ñ ÑÐ°Ð±Ð¾ÑÐ° Ð¿Ð¾ÑÐ²ÑÑÐµÐ½Ð° Ð¸ÑÑÐ»ÐµÐ´Ð¾Ð²Ð°Ð½Ð¸Ñ, ÑÐ°Ð·ÑÐ°Ð±Ð¾ÑÐºÐµ Ð¸ ÑÐµÑÑÐ¸ÑÐ¾Ð²Ð°Ð½Ð¸Ñ Ð¼ÐµÑÐ¾Ð´Ð¸Ðº Ð¿ÑÐ¸Ð¼ÐµÐ½ÐµÐ½Ð¸Ñ Ð¼ÐµÑÐ¾Ð´Ð¾Ð² Ð¼Ð°ÑÐ¸Ð½Ð½Ð¾Ð³Ð¾ Ð¸, Ð² ÑÐ°ÑÑÐ½Ð¾ÑÑÐ¸, Ð°ÐºÑÐ¸Ð²Ð½Ð¾Ð³Ð¾ Ð¾Ð±ÑÑÐµÐ½Ð¸Ñ Ð´Ð»Ñ ÑÐ¾Ð·Ð´Ð°Ð½Ð¸Ñ ÑÑÑÑÐ¾Ð³Ð°ÑÐ½ÑÑ Ð¼Ð¾Ð´ÐµÐ»ÐµÐ¹ ÑÐ¸Ð·Ð¸ÑÐµÑÐºÐ¸Ñ Ð¿ÑÐ¾ÑÐµÑÑÐ¾Ð² Ð½Ð° Ð¿ÑÐ¸Ð¼ÐµÑÐµ ÑÑÑÐ±ÑÐ»ÐµÐ½ÑÐ½Ð¾Ð³Ð¾ ÑÐµÑÐµÐ½Ð¸Ñ Ð² ÐºÐ°Ð½Ð°Ð»Ðµ Ñ ÐºÑÑÑÑÐ¼ Ð¿Ð¾Ð²Ð¾ÑÐ¾ÑÐ¾Ð¼. ÐÐ°Ð´Ð°ÑÐ¸, ÐºÐ¾ÑÐ¾ÑÑÐµ ÑÐµÑÐ°Ð»Ð¸ÑÑ Ð² ÑÐ¾Ð´Ðµ Ð¸ÑÑÐ»ÐµÐ´Ð¾Ð²Ð°Ð½Ð¸Ñ: 1. ÐÐ¾Ð´ÑÐ¾Ð±Ð½ÑÐ¹ Ð°Ð½Ð°Ð»Ð¸Ð· Ð¸Ð¼ÐµÑÑÐµÐ¹ÑÑ Ð½Ð°ÑÑÐ½Ð¾Ð¹ Ð»Ð¸ÑÐµÑÐ°ÑÑÑÑ Ð¿Ð¾ ÑÐµÐ¼Ðµ Ð¸ÑÐ¿Ð¾Ð»ÑÐ·Ð¾Ð²Ð°Ð½Ð¸Ñ Ð¼Ð¾Ð´ÐµÐ»ÐµÐ¹ Ð¼Ð°ÑÐ¸Ð½Ð½Ð¾Ð³Ð¾ Ð¾Ð±ÑÑÐµÐ½Ð¸Ñ Ð¸ Ð¼ÐµÑÐ¾Ð´Ð¾Ð² Ð°ÐºÑÐ¸Ð²Ð½Ð¾Ð³Ð¾ Ð¾Ð±ÑÑÐµÐ½Ð¸Ñ Ð² ÐºÐ°ÑÐµÑÑÐ²Ðµ ÑÐ¾Ð·Ð´Ð°Ð½Ð¸Ñ ÑÑÑÑÐ¾Ð³Ð°ÑÐ½ÑÑ Ð¼Ð¾Ð´ÐµÐ»ÐµÐ¹ Ð´Ð»Ñ ÑÐ¸Ð·Ð¸ÑÐµÑÐºÐ¸Ñ Ð·Ð°Ð´Ð°Ñ, Ð² ÑÐ°ÑÑÐ½Ð¾ÑÑÐ¸, Ð´Ð»Ñ Ð¿ÑÐµÐ´ÑÐºÐ°Ð·Ð°Ð½Ð¸Ñ Ð¸Ð½ÑÐµÐ³ÑÐ°Ð»ÑÐ½ÑÑ ÑÐ°ÑÐ°ÐºÑÐµÑÐ¸ÑÑÐ¸Ðº Ð³Ð¸Ð´ÑÐ¾Ð´Ð¸Ð½Ð°Ð¼Ð¸ÑÐµÑÐºÐ¸Ñ ÑÐµÑÐµÐ½Ð¸Ð¹. 2. Ð Ð°Ð·ÑÐ°Ð±Ð¾ÑÐºÐ° Ð½Ð° ÑÐ·ÑÐºÐµ Python Ñ Ð¸ÑÐ¿Ð¾Ð»ÑÐ·Ð¾Ð²Ð°Ð½Ð¸ÐµÐ¼ ÑÐ¸ÑÐ¾ÐºÐ¾Ð³Ð¾ Ð½Ð°Ð±Ð¾ÑÐ° Ð¾ÑÐºÑÑÑÑÑ Ð±Ð¸Ð±Ð»Ð¸Ð¾ÑÐµÐº ÑÐµÐ°Ð»Ð¸Ð·Ð°ÑÐ¸Ð¹ Ð¾ÑÐ½Ð¾Ð²Ð½ÑÑ Ð¼Ð¾Ð´ÐµÐ»ÐµÐ¹ Ð¼Ð°ÑÐ¸Ð½Ð½Ð¾Ð³Ð¾ Ð¾Ð±ÑÑÐµÐ½Ð¸Ñ Ð¸ Ð¼ÐµÑÐ¾Ð´Ð¾Ð² Ð°ÐºÑÐ¸Ð²Ð½Ð¾Ð³Ð¾ Ð¾Ð±ÑÑÐµÐ½Ð¸Ñ, Ð° ÑÐ°ÐºÐ¶Ðµ Ð¸Ñ Ð¾Ð±ÑÑÐµÐ½Ð¸Ðµ Ñ Ð²ÑÑÐ¾ÐºÐ¾Ð¹ ÑÑÐµÐ¿ÐµÐ½ÑÑ Ð°Ð²ÑÐ¾Ð¼Ð°ÑÐ¸Ð·Ð°ÑÐ¸Ð¸ Ð¸ Ð½ÐµÐ·Ð°Ð²Ð¸ÑÐ¸Ð¼Ð¾ÑÑÑÑ Ð¾Ñ ÐºÐ¾Ð½ÐºÑÐµÑÐ½Ð¾Ð¹ ÑÐ¸Ð·Ð¸ÑÐµÑÐºÐ¾Ð¹ Ð·Ð°Ð´Ð°ÑÐ¸. 3. ÐÑÐ¾Ð²ÐµÐ´ÐµÐ½Ð¸Ðµ Ñ Ð¸ÑÐ¿Ð¾Ð»ÑÐ·Ð¾Ð²Ð°Ð½Ð¸ÐµÐ¼ CFD-ÐºÐ¾Ð´Ð° SINF/Flag-S Ð¼Ð½Ð¾Ð³Ð¾Ð¿Ð°ÑÐ°Ð¼ÐµÑÑÐ¸ÑÐµÑÐºÐ¸Ñ ÑÐ°ÑÑÐµÑÐ¾Ð² ÑÐµÑÐµÐ½Ð¸Ñ Ð² Ð¾Ð±ÑÐ°ÑÐ½Ð¾Ð¼ ÐºÐ°Ð½Ð°Ð»Ðµ ÑÑÑÐ¿ÐµÐ½Ð¸ ÑÐµÐ½ÑÑÐ¾Ð±ÐµÐ¶Ð½Ð¾Ð³Ð¾ ÐºÐ¾Ð¼Ð¿ÑÐµÑÑÐ¾ÑÐ°. 4. Ð Ð°Ð·ÑÐ°Ð±Ð¾ÑÐºÐ° Ð½Ð° ÑÐ·ÑÐºÐµ Python ÑÐºÑÐ¸Ð¿ÑÐ¾Ð² Ð´Ð»Ñ Ð¿ÑÐµÐ´Ð²Ð°ÑÐ¸ÑÐµÐ»ÑÐ½Ð¾Ð¹ Ð¾Ð±ÑÐ°Ð±Ð¾ÑÐºÐ¸ Ð´Ð°Ð½Ð½ÑÑ Ð¸ Ð¾Ð±ÑÑÐµÐ½Ð¸Ñ Ð¼Ð¾Ð´ÐµÐ»ÐµÐ¹ Ð¼Ð°ÑÐ¸Ð½Ð½Ð¾Ð³Ð¾ Ð¾Ð±ÑÑÐµÐ½Ð¸Ñ Ñ Ð¿ÑÐ¸Ð¼ÐµÐ½ÐµÐ½Ð¸ÐµÐ¼ ÑÑÑÐ°ÑÐµÐ³Ð¸Ð¹ Ð°ÐºÑÐ¸Ð²Ð½Ð¾Ð³Ð¾ Ð¾Ð±ÑÑÐµÐ½Ð¸Ñ. 5. ÐÑÐµÐ½ÐºÐ° ÐºÐ°ÑÐµÑÑÐ²Ð° ÑÐ°Ð±Ð¾ÑÑ ÑÐ°Ð·ÑÐ°Ð±Ð¾ÑÐ°Ð½Ð½Ð¾Ð¹ Ð¼ÐµÑÐ¾Ð´Ð¸ÐºÐ¸ ÑÐ¾Ð·Ð´Ð°Ð½Ð¸Ñ ÑÑÑÑÐ¾Ð³Ð°ÑÐ½ÑÑ Ð¼Ð¾Ð´ÐµÐ»ÐµÐ¹ Ñ Ð¸ÑÐ¿Ð¾Ð»ÑÐ·Ð¾Ð²Ð°Ð½Ð¸ÐµÐ¼ Ð¼ÐµÑÐ¾Ð´Ð¾Ð² Ð°ÐºÑÐ¸Ð²Ð½Ð¾Ð³Ð¾ Ð¾Ð±ÑÑÐµÐ½Ð¸Ñ. ÐÑÐ½Ð¾Ð²Ð½ÑÐ¼ ÑÐµÐ·ÑÐ»ÑÑÐ°ÑÐ¾Ð¼ Ð²ÑÐ¿Ð¾Ð»Ð½ÐµÐ½Ð¸Ñ ÑÐ°Ð±Ð¾ÑÑ ÑÐ²Ð»ÑÐµÑÑÑ Ð¼ÐµÑÐ¾Ð´Ð¸ÐºÐ° ÑÐ¾Ð·Ð´Ð°Ð½Ð¸Ñ ÑÑÑÑÐ¾Ð³Ð°ÑÐ½ÑÑ Ð¼Ð¾Ð´ÐµÐ»ÐµÐ¹, ÑÐ¿Ð¾ÑÐ¾Ð±Ð½Ð°Ñ Ð´Ð°ÑÑ ÑÑÑÐµÑÑÐ²ÐµÐ½Ð½ÑÐ¹ Ð²ÑÐ¸Ð³ÑÑÑ Ð²Ð¾ Ð²ÑÐµÐ¼ÐµÐ½Ð¸ Ð¿ÑÐ¸ ÑÐ¾Ð¿Ð¾ÑÑÐ°Ð²Ð¸Ð¼Ð¾Ð¼ ÐºÐ°ÑÐµÑÑÐ²Ðµ Ð¿ÑÐµÐ´ÑÐºÐ°Ð·Ð°Ð½Ð¸Ð¹ Ð¿Ð¾ ÑÑÐ°Ð²Ð½ÐµÐ½Ð¸Ñ Ñ Ð¿ÑÐ¾Ð²ÐµÐ´ÐµÐ½Ð¸ÐµÐ¼ ÑÐ¸ÑÐ»ÐµÐ½Ð½ÑÑ ÑÐ°ÑÑÐµÑÐ¾Ð².
description: The given work is devoted to the research, development and testing of methodologies for applying machine learning methods and, in particular, active learning to create surrogate models of physical processes using the task of turbulent flow in a channel with a sharp turn. The tasks that have been solved during the research: 1. A detailed analysis of the available scientific literature on the use of machine learning models and active learning methods as surrogate models for physical problems, in particular for predicting the integral characteristics of hydrodynamic flows. 2. Development on Python using a wide range of open libraries of implementations of basic machine learning models and active learning methods, as well as their training with a high degree of automation and independence from a specific physical task. 3. Carrying out multiparametric calculations of the flow in the return channel of the centrifugal compressor stage using the SINF/Flag-S CFD code. 4. Development on Python scripts for preprocessing data and training machine learning models using active learning strategies. 5. Evaluation of the quality of the obtained methodology for creating surrogate models using active learning methods. The main result of the work is the methodology for creating surrogate models that can give a significant gain in time with comparable prediction quality compared to numerical calculations.