Coupling Phase Change Materials and Model Predictive Controls for Energy Efficiency and Thermal Comfort in Canadian Buildings
耦合相变材料和模型预测控制以提高加拿大建筑的能源效率和热舒适度
基本信息
- 批准号:RGPIN-2017-05481
- 负责人:
- 金额:$ 3.35万
- 依托单位:
- 依托单位国家:加拿大
- 项目类别:Discovery Grants Program - Individual
- 财政年份:2022
- 资助国家:加拿大
- 起止时间:2022-01-01 至 2023-12-31
- 项目状态:已结题
- 来源:
- 关键词:
项目摘要
Envelope thermal performance requirements are becoming more stringent and the most common compliance approach is to add more insulation. In many cases this may not be practical due to space constraints and greater use of glazing façade systems. An alternative approach is to increase building envelope's thermal storage capacity by incorporating Phase Change Materials. PCMs are innovative products able to store and release large quantities of heat per unit of mass through a phase change from liquid to solid and back near typical room temperatures. The idea is that PCM absorbs part of a building's heat load during the daytime as it melts and releases this heat during the cooler night-time by returning to its solid phase. In the summer, the released heat would be flushed out using natural ventilation or fans, while in the winter it would reduce the heating requirements. However, successful and cost effective application of PCMs in buildings depends on careful selection of many factors including: working temperature range, material thickness, location within the space, and location within the building component. Additionally, the PCM material should be able to completely discharge its absorbed energy during the night to be fully effective. This often requires changes in the operation of heating, ventilation and air-conditioning (HVAC) systems. Model predictive control (MPC) is a multivariable control algorithm that can be used to override conventional HVAC control strategies and provide optimal conditions for a complete phase transition of PCMs in 24-hr. MPC considers thermal response of building systems and future projected climatic conditions to predict the behavior of the systems and apply the appropriate control inputs in advance. Therefore, coupling PCM and MPC-based systems could result in energy savings and thermal comfort improvement in buildings. There is wide interest in PCM and MPC-based building systems by researchers, the construction industry, energy policy makers and home owners. The global PCM market is estimated to grow from $460 million in 2013 to $1.5 billion by 2019. However, the thermo-physical properties of PCM-enhanced materials and their effective building integration have not been fully understood. Further research on development of more robust and reliable algorithms is needed to address challenges that inhibit commercial feasibility of MPC-based technologies. The objective of the proposed research program is to develop optimal design strategies and solutions for integrating PCM and MPC-based systems into buildings that will promote and support changes in the national building codes and the wider use of these technologies. In this 5-year cycle, five students will be trained through a series of innovative laboratory experiments, field studies, and modeling exercises to become Canada's future experts in latent heat and advanced HVAC control technologies.
信封热性能要求变得越来越严格,最常见的合规性方法是增加隔热层。在许多情况下,由于空间限制和更大的使用玻璃外观系统的使用可能是不实际的。另一种方法是通过增加相变材料来增加构建信封的热存储能力。 PCM是能够通过从液体到固体的相变和典型的房间温度来存储和释放每单位质量热量的创新产品。这个想法是,PCM在白天吸收了建筑物的一部分热量,因为它可以通过返回其固体阶段在凉爽的夜间融化并释放这种热量。在夏天,释放的热量将使用自然通风或风扇冲洗,而在冬季,将减少加热要求。但是,PCM在建筑物中的成功且具有成本效益的应用取决于仔细选择许多因素,包括:工作温度范围,物质厚度,空间内的位置以及建筑物组件内的位置。此外,PCM材料应能够在夜间完全排放其吸收能量,以完全有效。这通常需要更改加热,通风和空调(HVAC)系统的操作。模型预测控制(MPC)是一种多变量控制算法,可用于覆盖常规的HVAC控制策略,并为24小时中PCM的完整相变提供最佳条件。 MPC考虑建筑系统的热响应和未来预测的杂种条件,以预测系统的行为并提前应用适当的控制输入。因此,耦合PCM和基于MPC的系统可能会导致能源节省和建筑物的热舒适度。研究人员,建筑行业,能源政策制定者和房主对基于PCM和MPC的建筑系统充满了兴趣。全球PCM市场估计将从2013年的4.6亿美元增长到2019年的15亿美元。但是,尚未完全了解PCM增强材料的热物理特性及其有效的建筑整合。需要进一步研究开发更健壮和可靠的算法,以应对抑制基于MPC技术的商业可行性的挑战。拟议的研究计划的目的是制定最佳的设计策略和解决方案,以将基于PCM和MPC的系统集成到建筑物中,以促进和支持国家建筑法规的变化以及对这些技术的更广泛使用。在这个5年的周期中,将通过一系列创新的实验室实验,现场研究和建模练习来培训五名学生,以成为加拿大未来的潜在热和高级HVAC控制技术专家。
项目成果
期刊论文数量(0)
专著数量(0)
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会议论文数量(0)
专利数量(0)
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Kavgic, Miroslava其他文献
Application and sensitivity analysis of the phase change material hysteresis method in EnergyPlus: A case study
- DOI:
10.1016/j.applthermaleng.2019.114222 - 发表时间:
2019-11-05 - 期刊:
- 影响因子:6.4
- 作者:
Al-Janabi, Ali;Kavgic, Miroslava - 通讯作者:
Kavgic, Miroslava
Thermal, microstructural and numerical analysis of hemperete-microencapsulated phase change material composites
- DOI:
10.1016/j.applthermaleng.2020.115520 - 发表时间:
2020-09-01 - 期刊:
- 影响因子:6.4
- 作者:
Abdellatef, Yaser;Kavgic, Miroslava - 通讯作者:
Kavgic, Miroslava
Kavgic, Miroslava的其他文献
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{{ truncateString('Kavgic, Miroslava', 18)}}的其他基金
Coupling Phase Change Materials and Model Predictive Controls for Energy Efficiency and Thermal Comfort in Canadian Buildings
耦合相变材料和模型预测控制以提高加拿大建筑的能源效率和热舒适度
- 批准号:
RGPIN-2017-05481 - 财政年份:2021
- 资助金额:
$ 3.35万 - 项目类别:
Discovery Grants Program - Individual
Coupling Phase Change Materials and Model Predictive Controls for Energy Efficiency and Thermal Comfort in Canadian Buildings
耦合相变材料和模型预测控制以提高加拿大建筑的能源效率和热舒适度
- 批准号:
RGPIN-2017-05481 - 财政年份:2020
- 资助金额:
$ 3.35万 - 项目类别:
Discovery Grants Program - Individual
Coupling Phase Change Materials and Model Predictive Controls for Energy Efficiency and Thermal Comfort in Canadian Buildings
耦合相变材料和模型预测控制以提高加拿大建筑的能源效率和热舒适度
- 批准号:
RGPIN-2017-05481 - 财政年份:2019
- 资助金额:
$ 3.35万 - 项目类别:
Discovery Grants Program - Individual
Coupling Phase Change Materials and Model Predictive Controls for Energy Efficiency and Thermal Comfort in Canadian Buildings
耦合相变材料和模型预测控制以提高加拿大建筑的能源效率和热舒适度
- 批准号:
RGPIN-2017-05481 - 财政年份:2018
- 资助金额:
$ 3.35万 - 项目类别:
Discovery Grants Program - Individual
Coupling Phase Change Materials and Model Predictive Controls for Energy Efficiency and Thermal Comfort in Canadian Buildings
耦合相变材料和模型预测控制以提高加拿大建筑的能源效率和热舒适度
- 批准号:
RGPIN-2017-05481 - 财政年份:2017
- 资助金额:
$ 3.35万 - 项目类别:
Discovery Grants Program - Individual
Development of a new hempcrete 'wall' formulation with advanced hygrothermal properties
开发具有先进湿热性能的新型麻混凝土“墙”配方
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521313-2017 - 财政年份:2017
- 资助金额:
$ 3.35万 - 项目类别:
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