Indoor air quality evaluation in naturally cross-ventilated buildings for education using age of air

Natural ventilation (NV) is a strategy of bioclimatic design to promote hygrothermal comfort and indoor air quality (IAQ). Nowadays, COVID-19 pandemic highlights the review of ventilation standards. In Mexico, the IAQ standard states a minimum of 6 ACH for educational buildings. ACH considers NV as an ideal piston flow and does not provide information of indoor airflow distribution. In this work, new age of air associated parameters are proposed, considering the indoor airflow distribution: the air renovation per hour (ARH) and the renovation parameter R. An isolated educational building located in a rural region is studied. Four window configurations of cross-ventilation are considered. All configurations have one windward window located at bottom. The configurations axial and upward have one leeward window at bottom and top, respectively. While, configurations corner and upward corner have one lateral side window at bottom and top, respectively. A CFD model of the educational building is validated with experiments. The axial configuration has the best performance according to ACH, nevertheless has the worst performance according to ARH and R. The results show that NV evaluation using ACH can lead to wrong decisions. An improvement of NV standard with the age of air associated parameters is recommended.

Spatial-Aware Network for Hyperspectral Image Classification

Deep learning is now receiving widespread attention in hyperspectral image (HSI) classification. However, due to the imbalance between a huge number of weights and limited training samples, many problems and difficulties have arisen from the use of deep learning methods in HSI classification. To handle this issue, an efficient deep learning-based HSI classification method, namely, spatial-aware network (SANet) has been proposed in this paper. The main idea of SANet is to exploit discriminative spectral-spatial features by incorporating prior domain knowledge into the deep architecture, where edge-preserving side window filters are used as the convolution kernels. Thus, SANet has a small number of parameters to optimize. This makes it fit for small sample sizes. Furthermore, SANet is able not only to aware local spatial structures using side window filtering framework, but also to learn discriminative features making use of the hierarchical architecture and limited label information. The experimental results on four widely used HSI data sets demonstrate that our proposed SANet significantly outperforms many state-of-the-art approaches when only a small number of training samples are available.

Star Sensor Denoising Algorithm Based on Edge Protection

Single-pixel noise commonly appearing in a star sensor can cause an unexpected error in centroid extraction. To overcome this problem, this paper proposes a star image denoising algorithm, named Improved Gaussian Side Window Filtering (IGSWF). Firstly, the IGSWF algorithm uses four special triangular Gaussian subtemplates for edge protection. Secondly, it exploits a reconstruction function based on the characteristic of stars and noise. The proposed IGSWF algorithm was successfully verified through simulations and evaluated in a star sensor. The experimental results indicated that the IGSWF algorithm performed better in preserving the shape of stars and eliminating the single-pixel noise and the centroid estimation error (CEE) value after using the IGSWF algorithm was eight times smaller than the original value, six times smaller than that after traditional window filtering, and three times smaller than that after the side window filtering.

Investigation into effects of side-window rubber sealer on cabin interi-or noise due to external flow disturbances of vehicle

Electric vehicles' rapid commercialization increases the relative importance of wind noise, especially for cabin interior noise. In this study, systematic numerical methods are developed to assess the wind noise insulation performance of side-window rubber seals in a design stage. First, the simplified automotive cabin model (SACM) is constructed to test the rubber seals' sound insulation performance due to external flow disturbance generated by jet flow. The pressure signals due to the jet flow are measured inside and outside the SACM. The difference between the two signals is used as sound insulation performance criteria, so-called insertion loss (IL). Second, a numerical methodology is developed to predict the IL. The surface pressure field on the side window due to jet flow is predicted by using the high-accurate Lattice Boltzmann Method. The predicted surface pressure fluctuations are applied as input load causing side-window vibration. The interior sound is then computed by using the calculated window vibration as input. The validity of numerical methods is confirmed by comparing the predicted results with the measured ones. Finally, the present methods' ability as a design tool is confirmed by comparing the IL of the pad-added rubber seal with that of the regular seal.

Comparison of hydrodynamic and acoustic pressure on automotive front side window

The unsteady flow in the front side window region of the vehicle can generate hydrodynamic and acoustic pressure on the front side window, which can influence the interior sound field. The hydrodynamic pressure on the front side window was achieved by the incompressible wall-modeled large-scale eddy simulation (WMLES) or improved delayed detached eddy simulation (IDDES), and the hybrid computational aeroacoustics (CAA) method based on acoustic perturbation equations (APE) was employed to achieve the acoustic pressure on the front side window. The numerical results of both hydrodynamic and acoustic pressure ware validated by the wind tunnel experiment, especially the corrected force analysis technique (CFAT) is employed to validate the acoustic pressure. The comparison of hydrodynamic and acoustic pressure on the front side window was performed by the Dynamic Mode Decomposition (DMD). Results show that the hydrodynamic pressure regionally distributes on the front side window and most energy concentrates on area interacted with the side mirror wake, while the acoustic pressure distributes uniformly on the front side window acting as a diffusion field and the energy disperses in frequency region.

THE EFFECT OF OPENING DISTRIBUTION AREA MODIFICATION ON HORIZONTAL LIGHT PIPE DAYLIGHT PERFORMANCE

A deep-plan office building design limits daylight access on the workspace distant from the side window. Horizontal Light Pipe (HLP) is one of the light transport systems that can deliver daylight to these areas. The research aim was to explain and evaluate the effect of HLP’s opening distribution area on daylight performance at deep plan-private office space. The research method was experimental with simulation as a tool. Daylight level and distribution of the base case, HLP with an opening distribution area of 6.6 m2 were compared with the case, HLP with an opening distribution area of 3.41 m2. The results showed that both cases distributed daylight uniformly. A 50% reduction of HLP’s opening distribution area, from 6.6 m2 to 3.41 m2 improved the average Daylight Factor as big as 6.42%. HLP with a smaller opening distribution area can be applied as the main source of daylight on deep-plan office spaces