[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"item-2504":3},{"id":4,"title":5,"url":6,"summary":7,"summary_zh":8,"content":9,"source_name":10,"source_url":6,"published_at":11,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":15,"score_detail":16,"sources":24,"tags":26,"view_count":32,"doi":33,"paper":34,"created_at":51},2504,"Computational fluid dynamics (CFD) simulations of deep winter greenhouses using passive solar heating and an underground heat storage system","https:\u002F\u002Fdoi.org\u002F10.1016\u002Fj.atech.2026.102569","Deep Winter Greenhouses (DWGs) are energy-efficient structures designed to support year-round crop production in cold climates using passive solar heating and underground heat storage systems. However, excessive heat builds up in the summer and even during the winter months. Previous studies have applied photovoltaic (PV)-powered ventilation and computational fluid dynamics (CFD) modeling mainly to conventional greenhouse types. This study evaluated the feasibility and effectiveness of integrating a photovoltaic (PV)-powered ventilation system into DWGs using computational fluid dynamics (CFD) simulations and techno-economic analysis across three North Central US states: Minnesota, Wisconsin, and Illinois. CFD models were developed using a tetrahedral mesh with 12.1 million elements. They were validated using field measurements collected under both summer and winter conditions. The grid convergence index between the selected and finest meshes was below 5%.. Results showed that the PV-powered ventilation system reduced crop-zone summer temperatures by 4.9 to 5.8°C under extreme conditions, providing optimal conditions for a broader range of crops. In winter, daytime temperatures remained above 6.0°C even under extreme cold conditions, while a reduced-order transient analysis indicated that nighttime temperatures fell below freezing under extreme conditions, with ventilation lowering them by only 0.8 to 1.4°C. The System Advisor Model (SAM) indicated that the PV system met the annual energy demand of 845 kWh, with a payback period under six years. By coupling CFD-derived microclimate simulations with SAM-based techno-economic evaluation, this study provides quantitative information for designing self-sustaining ventilation systems for DWGs and other cold-climate greenhouses.","深冬温室（Deep Winter Greenhouses, DWGs）是一种节能型设施，旨在利用被动式太阳能加热和地下储热系统，在寒冷气候条件下支持全年作物生产。然而，夏季乃至冬季月份中会出现热量过度积聚的问题。以往研究主要将光伏（PV）驱动的通风系统和计算流体动力学（CFD）建模应用于常规温室类型。本研究通过计算流体动力学（CFD）模拟和技术经济分析，评估了在美国中北部三个州——明尼苏达州、威斯康星州和伊利诺伊州——将光伏（PV）驱动通风系统集成到深冬温室中的可行性和有效性。CFD模型采用包含1210万个单元的四面体网格构建，并利用夏季和冬季条件下采集的实地测量数据进行了验证。所选网格与最细网格之间的网格收敛指数低于5%。结果表明，在极端条件下，光伏驱动通风系统使作物区夏季温度降低了4.9至5.8°C，为更广泛的作物提供了适宜条件。冬季，即使在极端寒冷条件下，白天温度仍保持在6.0°C以上，而降阶瞬态分析表明，极端条件下夜间温度降至冰点以下，通风仅使其降低了0.8至1.4°C。系统顾问模型（SAM）表明，光伏系统满足了845 kWh的年能源需求，投资回收期低于六年。通过将CFD得出的微气候模拟与基于SAM的技术经济评估相结合，本研究为设计深冬温室及其他寒冷气候温室的自维持通风系统提供了定量信息。",null,"Smart Agricultural Technology","2026-09-12T00:00:00Z","论文",10,false,77,{"impact":17,"substance":18,"depth":19,"authority":20,"freshness":21,"relevant":22,"comment":23},15,22,18,13,9,1,"该研究将CFD微气候模拟与光伏通风系统技术经济分析结合，为寒冷地区深冬温室提供自持通风设计依据，方法新颖、数据扎实，对设施农业智能化有参考价值。",[25],{"name":10,"url":6},[27,28,29,30,31],"智慧农业","设施农业","光伏农业","计算流体力学","温室环境调控",0,"10.1016\u002Fj.atech.2026.102569",{"doi":33,"openalex_id":35,"authors":36,"venue":10,"cited_by_count":32,"oa_url":43,"card":44,"direction":48,"ingested_from":50},"W7212375345",[37,40],{"name":38,"orcid":39},"Yoonhong Yi","https:\u002F\u002Forcid.org\u002F0009-0007-8000-793X",{"name":41,"orcid":42},"Neslihan Akdeniz","https:\u002F\u002Forcid.org\u002F0000-0002-0215-0219","https:\u002F\u002Fwww.sciencedirect.com\u002Fscience\u002Farticle\u002Fpii\u002FS277237552600794X\u002Fpdf",{"tldr":45,"method":46,"finding":47,"direction":48,"opportunity":49},"用CFD模拟评估光伏通风系统在深冬温室中的降温效果与经济可行性。","CFD模拟（四面体网格1210万单元）结合技术经济分析（SAM模型）。","夏季作物区降温4.9-5.8°C，冬季夜间仍低于冰点，光伏系统投资回收期不到6年。","智慧农业 \u002F 农业物联网","可研究光伏通风与地下储热协同优化，提升冬季夜间保温能力，拓展寒冷地区温室应用。","openalex","2026-09-15T23:30:03.583589Z"]