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Turbulent Flow: Problem Solving01:09

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Carbonation is a process used to dissolve carbon dioxide gas in a liquid, commonly used in the production of carbonated beverages. Achieving efficient carbonation requires careful control of temperature, pressure, and flow conditions. By adjusting these parameters, carbonation efficiency can be maximized, producing a higher concentration of CO2 in the liquid.
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In the growing field of wind energy, incorporating wind turbine models into transient stability analysis is essential. Induction and synchronous machines are the primary models used, with induction machines being prevalent due to their simplicity and reliability.
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Assessing safety in wind-exposed installations is crucial to preventing potential failures. This example explores the calculation and design adjustments needed to mount a circular disc on a building facade, where wind forces are a primary concern. A 4-meter diameter disc was initially designed as an aesthetic feature facing winds at a velocity of 25 meters per second, with an air density of 1.25 kilograms per cubic meter. Given these conditions, the drag force on the disc was determined using...
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Maxwell-Boltzmann Distribution: Problem Solving01:20

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Individual molecules in a gas move in random directions, but a gas containing numerous molecules has a predictable distribution of molecular speeds, which is known as the Maxwell-Boltzmann distribution, f(v).
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Laminar flow occurs when a fluid moves smoothly in parallel layers with minimal mixing and turbulence. In fluid mechanics, ensuring laminar flow within a pipe is essential for precise control of flow characteristics, especially in engineering applications. The key factor in determining whether flow remains laminar is the Reynolds number, a dimensionless quantity that depends on the fluid's velocity, density, viscosity, and the pipe's diameter. A Reynolds number of 2100 or lower...
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Granulación de información difusa basada en la predicción de la velocidad del viento con optimización multiobjetivo

Chi Zhang1, Jianzhou Wang2, Zhiwu Li1

  • 1Institute of Systems Engineering, Macau University of Science and Technology, Macau, 999078, China.

Scientific reports
|December 29, 2025
PubMed
Resumen
Este resumen es generado por máquina.

La predicción precisa de la energía eólica es crucial para la integración de energías renovables. Un nuevo marco de granulación de información difusa y optimización multiobjetivo mejora la precisión y eficiencia de la predicción de la velocidad del viento.

Palabras clave:
Granulación de información difusaAprendizaje automáticoConjunto de modelosOptimizador de banco de atún multiobjetivoPredicción de la velocidad del viento

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Área de la Ciencia:

  • Sistemas de Energía
  • Inteligencia Artificial
  • Energía Renovable

Sus antecedentes:

  • La predicción precisa de la energía eólica es vital para la integración de energías renovables y el apoyo a la descarbonización.
  • La naturaleza estocástica de los recursos eólicos presenta desafíos para la predicción a corto y medio plazo, lo que provoca incertidumbres operativas.
  • Los modelos de pronóstico existentes a menudo luchan por lograr una alta precisión constante, lo que impulsa la necesidad de avances metodológicos.

Objetivo del estudio:

  • Introducir un marco de pronóstico multiescala novedoso para mejorar la predicción de la energía eólica.
  • Abordar las limitaciones en las técnicas de pronóstico actuales mejorando la precisión y la robustez.
  • Gestionar eficazmente el equilibrio entre la precisión de la predicción y la eficiencia computacional.

Principales métodos:

  • Integración de la granulación de información difusa para capturar características intrínsecas y reducir la complejidad de los datos de la volátil velocidad del viento.
  • Utilización de una estrategia de optimización multiobjetivo con múltiples redes neuronales que emplean diversos principios predictivos.
  • Integración adaptativa de las salidas de la red neuronal a través de algoritmos de optimización heurística para mejorar la predicción.

Principales resultados:

  • El marco propuesto demuestra un rendimiento sobresaliente en el conjunto de datos del parque eólico de Penglai.
  • La granulación de información difusa mitiga eficazmente la interferencia del ruido para los modelos de aprendizaje profundo.
  • El algoritmo de optimización colaborativa equilibra con éxito la precisión de la predicción y la eficiencia computacional.

Conclusiones:

  • El novedoso marco de pronóstico multiescala mejora significativamente la precisión y la robustez de la predicción de la energía eólica.
  • La granulación de información difusa es eficaz para manejar datos complejos y volátiles de velocidad del viento.
  • El enfoque desarrollado ofrece una solución viable para mejorar la confiabilidad de los sistemas de energía eólica.