• 文献检索
  • 文档翻译
  • 深度研究
  • 学术资讯
  • Suppr Zotero 插件Zotero 插件
  • 邀请有礼
  • 套餐&价格
  • 历史记录
应用&插件
Suppr Zotero 插件Zotero 插件浏览器插件Mac 客户端Windows 客户端微信小程序
定价
高级版会员购买积分包购买API积分包
服务
文献检索文档翻译深度研究API 文档MCP 服务
关于我们
关于 Suppr公司介绍联系我们用户协议隐私条款
关注我们

Suppr 超能文献

核心技术专利:CN118964589B侵权必究
粤ICP备2023148730 号-1Suppr @ 2026

文献检索

告别复杂PubMed语法,用中文像聊天一样搜索,搜遍4000万医学文献。AI智能推荐,让科研检索更轻松。

立即免费搜索

文件翻译

保留排版,准确专业,支持PDF/Word/PPT等文件格式,支持 12+语言互译。

免费翻译文档

深度研究

AI帮你快速写综述,25分钟生成高质量综述,智能提取关键信息,辅助科研写作。

立即免费体验

船舶热模拟中气象变量与现场变量的对比分析

Comparative Analysis of Meteorological versus In Situ Variables in Ship Thermal Simulations.

作者信息

Arce Elena, Suárez-García Andrés, López-Vázquez José Antonio, Devesa-Rey Rosa

机构信息

Polytechnic School of Engineering of Ferrol, University of A Coruña, 15403 Ferrol, Spain.

Defense University Center, Spanish Naval Academy, University of Vigo, 36920 Marín, Spain.

出版信息

Sensors (Basel). 2024 Apr 11;24(8):2454. doi: 10.3390/s24082454.

DOI:10.3390/s24082454
PMID:38676071
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC11055125/
Abstract

Thermal simulations have become increasingly popular in assessing energy efficiency and predicting thermal behaviors in various structures. Calibration of these simulations is essential for accurate predictions. A crucial aspect of this calibration involves investigating the influence of meteorological variables. This study aims to explore the impact of meteorological variables on thermal simulations, particularly focusing on ships. Using TRNSYS (TRaNsient System Simulation) software (v17), renowned for its capability to model complex energy systems within buildings, the significance of incorporating meteorological data into thermal simulations was analyzed. The investigation centered on a patrol vessel stationed in a port in Galicia, northwest Spain. To ensure accuracy, we not only utilized the vessel's dimensions but also conducted in situ temperature measurements onboard. Furthermore, a dedicated weather station was installed to capture real-time meteorological data. Data from multiple sources, including Meteonorm and MeteoGalicia, were collected for comparative analysis. By juxtaposing simulations based on meteorological variables against those relying solely on in situ measurements, we sought to discern the relative merits of each approach in enhancing the fidelity of thermal simulations.

摘要

热模拟在评估能源效率和预测各种结构的热行为方面越来越受欢迎。对这些模拟进行校准对于准确预测至关重要。这种校准的一个关键方面涉及研究气象变量的影响。本研究旨在探讨气象变量对热模拟的影响,特别是针对船舶。使用以能够对建筑物内复杂能源系统进行建模而闻名的TRNSYS(瞬态系统模拟)软件(v17),分析了将气象数据纳入热模拟的重要性。调查集中在一艘驻扎在西班牙西北部加利西亚一个港口的巡逻舰上。为确保准确性,我们不仅利用了该船的尺寸,还在船上进行了现场温度测量。此外,还安装了一个专用气象站以获取实时气象数据。收集了包括Meteonorm和MeteoGalicia在内的多个来源的数据用于比较分析。通过将基于气象变量的模拟与仅依赖现场测量的模拟并列比较,我们试图辨别每种方法在提高热模拟保真度方面的相对优点。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/afc5/11055125/d7e6a6219d9a/sensors-24-02454-g008.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/afc5/11055125/e1913a133aad/sensors-24-02454-g001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/afc5/11055125/625ccdcb6f6a/sensors-24-02454-g002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/afc5/11055125/aa42a264aa94/sensors-24-02454-g003.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/afc5/11055125/6474db7b3d6d/sensors-24-02454-g004.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/afc5/11055125/d8107f12a1e4/sensors-24-02454-g005.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/afc5/11055125/022966274153/sensors-24-02454-g006.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/afc5/11055125/0fa8125e571a/sensors-24-02454-g007.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/afc5/11055125/d7e6a6219d9a/sensors-24-02454-g008.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/afc5/11055125/e1913a133aad/sensors-24-02454-g001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/afc5/11055125/625ccdcb6f6a/sensors-24-02454-g002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/afc5/11055125/aa42a264aa94/sensors-24-02454-g003.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/afc5/11055125/6474db7b3d6d/sensors-24-02454-g004.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/afc5/11055125/d8107f12a1e4/sensors-24-02454-g005.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/afc5/11055125/022966274153/sensors-24-02454-g006.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/afc5/11055125/0fa8125e571a/sensors-24-02454-g007.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/afc5/11055125/d7e6a6219d9a/sensors-24-02454-g008.jpg

相似文献

1
Comparative Analysis of Meteorological versus In Situ Variables in Ship Thermal Simulations.船舶热模拟中气象变量与现场变量的对比分析
Sensors (Basel). 2024 Apr 11;24(8):2454. doi: 10.3390/s24082454.
2
All-Weather Thermal Simulation Methods for Concrete Maglev Bridge Based on Structural and Meteorological Monitoring Data.基于结构与气象监测数据的混凝土磁悬浮桥梁全天候热模拟方法
Sensors (Basel). 2021 Aug 28;21(17):5789. doi: 10.3390/s21175789.
3
[Influence of weather in the incidence of acute myocardial infarction in Galicia (Spain)].[天气对西班牙加利西亚地区急性心肌梗死发病率的影响]
Med Clin (Barc). 2015 Aug 7;145(3):97-101. doi: 10.1016/j.medcli.2014.04.020. Epub 2014 Jul 26.
4
The influence of meteorological variables on CO and CH trends recorded at a semi-natural station.气象变量对半自然站记录的 CO 和 CH 趋势的影响。
J Environ Manage. 2018 Mar 1;209:37-45. doi: 10.1016/j.jenvman.2017.12.028. Epub 2017 Dec 21.
5
On the complexity of measuring forests microclimate and interpreting its relevance in habitat ecology: the example of Ixodes ricinus ticks.衡量森林小气候的复杂性及其在生境生态学中的相关性:以蓖子硬蜱为例。
Parasit Vectors. 2017 Nov 6;10(1):549. doi: 10.1186/s13071-017-2498-5.
6
Weather data analysis and building performance assessment during extreme climate events: A Canadian AMY weather file data set.极端气候事件期间的气象数据分析与建筑性能评估:加拿大AMY气象文件数据集
Data Brief. 2024 Jan 9;52:110036. doi: 10.1016/j.dib.2024.110036. eCollection 2024 Feb.
7
Evaluating the Effect of Window-to-Wall Ratios on Cooling-Energy Demand on a Typical Summer Day.评估典型夏季日窗户面积与墙面积比(窗墙比)对制冷能耗的影响。
Int J Environ Res Public Health. 2021 Aug 9;18(16):8411. doi: 10.3390/ijerph18168411.
8
Short communication: Summer on-farm environmental condition assessments in Québec tiestall farms and adaptation of temperature-humidity index calculated with local meteorological data.简报:魁北克厩舍农场夏季场内环境条件评估及利用当地气象数据计算温湿度指数的调整。
J Dairy Sci. 2019 Aug;102(8):7503-7508. doi: 10.3168/jds.2018-16159. Epub 2019 May 31.
9
Uncertainty in predictions of forest carbon dynamics: separating driver error from model error.预测森林碳动态的不确定性:区分驱动因素误差与模型误差。
Ecol Appl. 2011 Jul;21(5):1506-22. doi: 10.1890/09-1183.1.
10
Predicting hourly air pollutant levels using artificial neural networks coupled with uncertainty analysis by Monte Carlo simulations.使用人工神经网络结合蒙特卡罗模拟的不确定性分析预测逐时空气污染物水平。
Environ Sci Pollut Res Int. 2013 Jul;20(7):4777-89. doi: 10.1007/s11356-012-1451-6. Epub 2013 Jan 6.

本文引用的文献

1
Maritime Emission Monitoring: Development and Testing of a UAV-Based Real-Time Wind Sensing Mission Planner Module.海上排放监测:基于无人机的实时风感任务规划模块的开发与测试
Sensors (Basel). 2024 Feb 1;24(3):950. doi: 10.3390/s24030950.
2
Challenges in Developing Ventilation and Indoor Air Quality Standards: The Story of ASHRAE Standard 62.制定通风与室内空气质量标准面临的挑战:美国采暖、制冷与空调工程师学会标准62的故事
Build Environ. 2015;91. doi: 10.1016/j.buildenv.2015.02.026.
3
Monitoring System Analysis for Evaluating a Building's Envelope Energy Performance through Estimation of Its Heat Loss Coefficient.
通过估算建筑物的热损失系数来评估其围护结构能源性能的监测系统分析。
Sensors (Basel). 2018 Jul 20;18(7):2360. doi: 10.3390/s18072360.
4
Integrated Method for Personal Thermal Comfort Assessment and Optimization through Users' Feedback, IoT and Machine Learning: A Case Study .通过用户反馈、物联网和机器学习实现个人热舒适评估和优化的综合方法:案例研究
Sensors (Basel). 2018 May 17;18(5):1602. doi: 10.3390/s18051602.