| 1. |
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- 2019
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Journal article (peer-reviewed)
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| 2. |
- Hu, Mengqiang, et al.
(author)
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Comparing calculation methods of state transfer matrix in Markov chain models for indoor contaminant transport
- 2022
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In: Building and Environment. - : Elsevier BV. - 0360-1323 .- 1873-684X. ; 207, s. 108515-
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Journal article (peer-reviewed)abstract
- Fast and accurate prediction of indoor airborne contaminant distribution is of great significance to the safety and health of occupants. Several Markov chain models have been developed and proved to be the potential solutions. However, there is a lack of comparison in terms of accuracy, computing cost, and robustness among these models, which limits their practical application. To this end, this study compared the performance of three Markov chain models, in which the state transfer matrix was calculated based on different principles, i.e., Markov chain model with flux-based method, with Lagrangian tracking, and with set theory approach. The investigation was conducted in a 2D ventilated cavity and a two-zone ventilated chamber. The simulation by Eulerian model for contaminant and experimental data were used as the benchmarks for the 2D and 3D cases, respectively. It is revealed that all three Markov chain models can provide a correct prediction. In the 2D case, the Markov chain model with set theory approach is the most accurate, followed by Lagrangian tracking. In the 3D case, the accuracy of Markov chain models with flux-based method and Lagrangian tracking is comparable. The Markov chain model with Lagrangian tracking is the fastest, and the time step size in this model can be relatively large. Finally, the selection guideline is given for the application of Markov chain models in different scenarios.
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| 3. |
- Dai, Ting, et al.
(author)
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Assessment of Fast Fluid Dynamics with Different Turbulence Models for Simulating Airflow and Pollutant Dispersion Around Buildings
- 2023
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In: Proceedings of the 5th International Conference on Building Energy and Environment. - : Springer Nature. ; , s. 51-59
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Conference paper (peer-reviewed)abstract
- Fast fluid dynamics (FFD) could provide efficient airflow and concentration simulation. The commonly used turbulence model in FFD was RNG k- ε turbulence model which solved two transport equations to obtain eddy viscosity. To improve computing speed, this investigation implemented no turbulence model, Smagorinsky model and dynamic Smagorinsky model which calculated eddy viscosity without solving equation in FFD in an open-source program, OpenFOAM. By simulating single-building case and comparing with experiment and CFD, this study assessed accuracy and efficiency of FFD with those turbulence models. Compared with CFD, FFD improved computing speed without reducing accuracy. The simulation of FFD without turbulence model was fast but inaccurate. FFD with Smagorinsky model increased computing speed while ensuring the same accuracy as RNG k- ε turbulence model. FFD with dynamic Smagorinsky model provided accurate results with high efficiency. This investigation suggested FFD with dynamic Smagorinsky model for outdoor airflow and pollutant dispersion studies.
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| 4. |
- Dai, Ting, et al.
(author)
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Evaluation of fast fluid dynamics with different turbulence models for predicting outdoor airflow and pollutant dispersion
- 2022
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In: Sustainable cities and society. - : Elsevier BV. - 2210-6707. ; 77, s. 103583-
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Journal article (peer-reviewed)abstract
- Fast fluid dynamics (FFD) could provide informative and efficient airflow and concentration simulation. The commonly used turbulence model in FFD was Re-Normalization Group (RNG) k-epsilon turbulence model which solved two transport equations to obtain eddy viscosity. To reduce this part of time and further improve computing speed, this investigation implemented no turbulence model, Smagorinsky model and dynamic Smagorinsky model which calculated eddy viscosity without solving equation in FFD in an open-source program, OpenFOAM. By simulating several outdoor cases of varying complexity and comparing with experiment and CFD, this study assessed the accuracy and computing efficiency of FFD with four turbulence models. Compared with CFD, FFD greatly improved the computing speed without reducing accuracy. The simulation of FFD without turbulence model was fast but inaccurate. FFD with Smagorinsky model increased the computing speed while ensuring the same accuracy as RNG k-epsilon turbulence model. FFD with dynamic Smagorinsky model provided accurate results with high efficiency. Computation errors arose mainly from inaccurate prediction of turbulence dispersion. The computing cost was associated with the number of transport equations and calculation method of model coefficient. This investigation recommended the use of FFD with dynamic Smagorinsky model for outdoor airflow and pollutant dispersion studies.
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| 5. |
- Lai, Dayi, et al.
(author)
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A comprehensive review of thermal comfort studies in urban open spaces
- 2020
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In: Science of the Total Environment. - : Elsevier BV. - 0048-9697 .- 1879-1026. ; 742
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Research review (peer-reviewed)abstract
- Urban open spaces provide various benefits to large populations in cities. Since thermally comfortable urban open spaces improve the quality of urban living, an increasing number of studies have been conducted to extend the existing knowledge of outdoor thermal comfort. This paper comprehensively reviews current outdoor thermal comfort studies, including benchmarks, data collection methods, and models of outdoor thermal comfort. Because outdoor thermal comfort is a complex issue influenced by various factors, a conceptual framework is proposed which includes physical, physiological and psychological factors as direct influences: and behavioral, personal, social, cultural factors, as well as thermal history, site, and alliesthesia, as indirect influences. These direct and indirect factors are further decomposed and reviewed, and the interactions among various factors are discussed. This review provides researchers with a systematic and comprehensive understanding of outdoor thermal comfort, and can also guide designers and planners in creating thermally comfortable urban open spaces.
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| 6. |
- Liu, K., et al.
(author)
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A machine learning approach to predict outdoor thermal comfort using local skin temperatures
- 2020
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In: Sustainable cities and society. - : Elsevier. - 2210-6707. ; 59
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Journal article (peer-reviewed)abstract
- Under the warming climate, providing thermal comfort to large urban population in city open spaces has become an important research topic. However, because of its dynamic and complex nature, the outdoor thermal comfort is difficult to predict. Skin temperature of human body may contain useful information of outdoor thermal comfort. In this paper, a Support Vector Machine (SVM) model was developed to predict the cool discomfort, comfort, and warm discomfort in outdoor environments using local skin temperatures and thermal load as inputs. In this study, the performances of models using different inputs were compared with each other. The results revealed that when using single local skin temperature as input, the skin temperature of exposed body parts exhibited the highest prediction accuracy (66 %–70 %), while that of abdomen or thorax was the lowest (42 %–58 %). The prediction accuracy increased by 1 %–5 % when the thermal load was added as an extra input feature, while that could be improved by 4%–7% when using skin temperature of two body parts as inputs. This study demonstrated that human outdoor thermal state can be captured with reasonable accuracy by monitoring skin temperatures from two local body parts.
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| 7. |
- Xu, Weidong, 1988-, et al.
(author)
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Rational molecular passivation for high-performance perovskite light-emitting diodes
- 2019
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In: Nature Photonics. - : Springer Nature Publishing AG. - 1749-4885 .- 1749-4893. ; 13:6, s. 418-424
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Journal article (peer-reviewed)abstract
- A major efficiency limit for solution-processed perovskite optoelectronic devices, for example light-emitting diodes, is trap-mediated non-radiative losses. Defect passivation using organic molecules has been identified as an attractive approach to tackle this issue. However, implementation of this approach has been hindered by a lack of deep understanding of how the molecular structures influence the effectiveness of passivation. We show that the so far largely ignored hydrogen bonds play a critical role in affecting the passivation. By weakening the hydrogen bonding between the passivating functional moieties and the organic cation featuring in the perovskite, we significantly enhance the interaction with defect sites and minimize non-radiative recombination losses. Consequently, we achieve exceptionally high-performance near-infrared perovskite light-emitting diodes with a record external quantum efficiency of 21.6%. In addition, our passivated perovskite light-emitting diodes maintain a high external quantum efficiency of 20.1% and a wall-plug efficiency of 11.0% at a high current density of 200 mA cm−2, making them more attractive than the most efficient organic and quantum-dot light-emitting diodes at high excitations.
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| 8. |
- Leebens-Mack, James H., et al.
(author)
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One thousand plant transcriptomes and the phylogenomics of green plants
- 2019
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In: Nature. - : Nature Publishing Group. - 0028-0836 .- 1476-4687. ; 574:7780, s. 679-
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Journal article (peer-reviewed)abstract
- Green plants (Viridiplantae) include around 450,000-500,000 species(1,2) of great diversity and have important roles in terrestrial and aquatic ecosystems. Here, as part of the One Thousand Plant Transcriptomes Initiative, we sequenced the vegetative transcriptomes of 1,124 species that span the diversity of plants in a broad sense (Archaeplastida), including green plants (Viridiplantae), glaucophytes (Glaucophyta) and red algae (Rhodophyta). Our analysis provides a robust phylogenomic framework for examining the evolution of green plants. Most inferred species relationships are well supported across multiple species tree and supermatrix analyses, but discordance among plastid and nuclear gene trees at a few important nodes highlights the complexity of plant genome evolution, including polyploidy, periods of rapid speciation, and extinction. Incomplete sorting of ancestral variation, polyploidization and massive expansions of gene families punctuate the evolutionary history of green plants. Notably, we find that large expansions of gene families preceded the origins of green plants, land plants and vascular plants, whereas whole-genome duplications are inferred to have occurred repeatedly throughout the evolution of flowering plants and ferns. The increasing availability of high-quality plant genome sequences and advances in functional genomics are enabling research on genome evolution across the green tree of life.
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| 9. |
- Lei, Lei, et al.
(author)
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A building energy consumption prediction model based on rough set theory and deep learning algorithms
- 2021
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In: Energy and Buildings. - : Elsevier BV. - 0378-7788 .- 1872-6178. ; 240
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Journal article (peer-reviewed)abstract
- The efficient and accurate prediction of building energy consumption can improve the management of power systems. In this paper, the rough set theory was used to reduce the redundant influencing factors of building energy consumption and find the critical factors of building energy consumption. These key factors were then used as the input of a deep neural network with a & ldquo;deep & rdquo; architecture and powerful capabilities in extracting features. Building energy consumption is output of the deep neural network. This study collected data from 100 civil public buildings for rough set reduction, and then collected data from a laboratory building of a university in Dalian for nearly a year to train and test deep neural net-works. The test included both the short-term and medium-term predictions of building energy consump-tion. The prediction results of the deep neural network were compared with that of the back propagation neural network, Elman neural network and fuzzy neural network. The results show that the integrated rough set and deep neural network was the most accurate. The method proposed in this study could pro-vide a practical and accurate solution for building energy consumption prediction.& nbsp;
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| 10. |
- Lei, Lei, et al.
(author)
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A comprehensive evaluation method for indoor air quality of buildings based on rough sets and a wavelet neural network
- 2019
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In: Building and Environment. - : PERGAMON-ELSEVIER SCIENCE LTD. - 0360-1323 .- 1873-684X. ; 162
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Journal article (peer-reviewed)abstract
- Understanding the level of indoor air quality is very important to improve the quality of air that people breathe indoors. In this paper, a comprehensive evaluation method combining rough sets and a wavelet neural network is proposed to evaluate the indoor air quality of buildings. Through on-site inspections of the indoor air in six large shopping malls in Beijing, Wuhan and Guangzhou, raw data of the environmental parameters affecting the indoor air quality of large shopping malls are obtained. First, rough sets are used to reduce the dimension of features that affect indoor air quality by removing unimportant features, and important environmental parameters that affect indoor air quality are obtained. These important environmental parameters are used as input parameters of the wavelet neural network. Then, the structure of the wavelet neural network is determined, and an evaluation model of the indoor air quality of buildings based on rough sets and the wavelet neural network is established. Finally, the model is applied to the evaluation of indoor air quality in large shopping malls, and the back propagation neural network, fuzzy neural network and Elman neural network are introduced for comparison of the testing accuracy of the wavelet neural network in the sample testing stage. The results show that the structure of the wavelet neural network is optimized by using a rough set to reduce the redundant attributes of the data, and that the comprehensive evaluation method based on rough sets and a wavelet neural network can accurately evaluate the indoor air quality level of buildings. The results of this study have significance for and can guide the evaluation of the indoor air quality of buildings.
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