Study of Relationships between Ceiling Smoke Leakage Rate and Evacuation Time in the Ward

With reference to the requirements of CNS 15038 and testing principles, this study proposes a set of equipment for measuring the leakage volume of ceilings and provides detailed assembly specifications for future users. In this study, a total of 405 tests were conducted as part of a set of experiments for measuring the leakage volume of ceilings, using various ceiling materials, ceiling sizes, and construction methods, in conjunction with the principles of fluid mechanics, to propose a method for evaluating the leakage volume of ceilings of various sizes and materials. Two cases—bottom-up airflow and top-down airflow—were considered. According to our research findings, in the case of bottom-up airflow, the pressure difference, panel weight, and panel size were correlated with the leakage volume; the more significant the pressure difference, the larger the leakage volume; the heavier the panel weight, the more minor the leakage volume; and the larger the panel size, the more significant the leakage volume. On the other hand, in the case of top-down airflow, different leakage volumes were observed for different ceiling materials, even if the ceiling size was identical. On the other hand, when the ceiling material was the same, and the ceiling size was different, there was not a positive relationship between the leakage volume and a larger panel size; instead, the leakage volume observed for the largest panel was the smallest. Finally, in this study we propose a volumetric leakage assessment table for assessing a ceiling as a whole, which can be utilized by engineers in the future to calculate the smoke leakage value and to estimate the smoke fall time for ward escape designs.

DL-LA: Deep Learning Leakage Assessment

In recent years, deep learning has become an attractive ingredient to side-channel analysis (SCA) due to its potential to improve the success probability or enhance the performance of certain frequently executed tasks. One task that is commonly assisted by machine learning techniques is the profiling of a device’s leakage behavior in order to carry out a template attack. At CHES 2019, deep learning has also been applied to non-profiled scenarios for the first time, extending its reach within SCA beyond template attacks. The proposed method, called DDLA, has some tempting advantages over traditional SCA due to merits inherited from (convolutional) neural networks. Most notably, it greatly reduces the need for pre-processing steps< when the SCA traces are misaligned or when the leakage is of a multivariate nature. However, similar to traditional attack scenarios the success of this approach highly depends on the correct choice of a leakage model and the intermediate value to target. In this work we explore, for the first time in literature, whether deep learning can similarly be used as an instrument to advance another crucial (non-profiled) discipline of SCA which is inherently independent of leakage models and targeted intermediates, namely leakage assessment. In fact, given the simple classification-based nature of common leakage assessment techniques, in particular distinguishing two groups fixed-vs-random or fixed-vs-fixed, it comes as a surprise that machine learning has not been brought into this context, yet. Our contribution is the development of the first full leakage assessment methodology based on deep learning. It gives the evaluator the freedom to not worry about location, alignment and statistical order of the leakages and easily covers multivariate and horizontal patterns as well. We test our approach against a number of case studies based on FPGA, ASIC and μC implementations of the PRESENT block cipher, equipped with state-of-the-art SCA countermeasures. Our results clearly show that the proposed methodology and network structures are robust across all case studies and outperform the classical detection approaches (t-test and X2-test) in all considered scenarios.

Fluid Flow and Leakage Assessment Through an Unlined/Shotcrete Lined Pressure Tunnel: A Case from Nepal Himalaya

The use of unlined/shotcrete lined pressure tunnels and shafts are cost-effective solutions for a hydropower project and are being implemented worldwide. To implement this concept, the ground conditions at the area of concern should be favorable regarding minimum principal stress magnitude, which should be higher than hydrostatic water head acting on the tunnel periphery. In addition, the rock mass should be relatively unjointed or joints in the rock mass should be relatively tight. Among the most important issues in the design of unlined/shotcrete lined pressure tunnels is the extent of hydraulic jacking and water leakage out of the tunnel during operation. This manuscript first presents fluid flow and potential hydraulic jacking assessment of two selected locations of the headrace tunnel of Upper Tamakoshi Hydroelectric Project (UTHP) in Nepal using the UDEC. It is noted here that the 7960 m long headrace tunnel will experience a hydrostatic water head that will vary from 2.9 to 11.5 bars (0.29–1.15 MPa). The headrace tunnel is supported by sprayed concrete (shotcrete) in combination with systematic rock bolts in the tunnel walls and crown. The invert of the tunnel and few hundred meters downstream end (at surge shaft area) of the headrace tunnel is being concrete lined after the completion of all other works. The qualitative fluid flow assessment carried out using UDEC indicated considerable pressure built-up in the joint systems suggesting potential hydraulic jacking. This was especially the case at the downstream segment (downstream from chainage 7100 m) of the headrace tunnel. The manuscript further presents the quantitative results of water leakage estimation from the headrace tunnel carried out using Panthi (Panthi KK (2006) Analysis of engineering geological uncertainties related to tunnelling in Himalayan rock mass conditions. PhD Thesis, NTNU, Trondheim, Norway;Panthi, Note on estimating specific leakage using Panthi’s approach, NTNU, Trondheim, 2010;) approach. The leakage assessment carried out indicated an average specific leakage of about 2.5 l/min/m tunnel, which may result in over 210 l/s leakage from the headrace tunnel. The evaluation also indicated that the outer reach (860 m downstream segment) of the headrace tunnel after chainage 7100 m seems extremely vulnerable and over 80 l/s water leakage may occur only from this headrace tunnel segment during operation of the hydropower plant.