A Review of Modelling and Simulation Methods for Flashover Prediction in Confined Space Fires

Confined space fires are common emergencies in our society. Enclosure size, ventilation, or type and quantity of fuel involved are factors that determine the fire evolution in these situations. In some cases, favourable conditions may give rise to a flashover phenomenon. However, the difficulty of handling this complicated emergency through fire services can have fatal consequences for their staff. Therefore, there is a huge demand for new methods and technologies to tackle this life-threatening emergency. Modelling and simulation techniques have been adopted to conduct research due to the complexity of obtaining a real cases database related to this phenomenon. In this paper, a review of the literature related to the modelling and simulation of enclosure fires with respect to the flashover phenomenon is carried out. Furthermore, the related literature for comparing images from thermal cameras with computed images is reviewed. Finally, the suitability of artificial intelligence (AI) techniques for flashover prediction in enclosed spaces is also surveyed.This work has been partially funded by the Spanish Government TIN2017-89069-R grant supported with Feder funds. This work was supported in part by the Spanish Ministry of Science, Innovation and Universities through the Project ECLIPSE-UA under Grant RTI2018-094283-B-C32 and the Lucentia AGI Grant

Data Asset Management and Visualization Based on Intelligent Algorithm: Taking Power Equipment Data as An Example

Data asset management is adequate in solving the problem of data silence and data idleness for enterprises. Through intelligent algorithms such as neural network, in-depth learning and block chain, and guided by business needs, it extracts, analyzes and visualizes the existing business precipitation data, and forms scattered and disordered data into valuable information to support the development of the company, so as to activate data assets. Taking the management data of electric power equipment as an example, this paper proposes a method of fusion of multiple intelligent control algorithms. The specific modules include the fusion of heterogeneous data; feature extraction of equipment asset management data based on machine learning; intelligent control of multi-objective optimization environment based on energy consumption data; BIM data visualization based on data classification-energy extraction-neural network (SVM-CART-SAE-DNN) algorithm fusion. The algorithm can effectively improve the efficiency of equipment management and enhance the security and economy of power infrastructure through intelligent control of equipment management

Mission Aware Energy Saving Strategies For Army Ground Vehicles

Fuel energy is a basic necessity for this planet and the modern technology to perform many activities on earth. On the other hand, quadrupled automotive vehicle usage by the commercial industry and military has increased fuel consumption. Military readiness of Army ground vehicles is very important for a country to protect its people and resources. Fuel energy is a major requirement for Army ground vehicles. According to a report, a department of defense has spent nearly $13.6 billion on fuel and electricity to conduct ground missions. On the contrary, energy availability on this plant is slowly decreasing. Therefore, saving energy in Army ground vehicles is very important. Army ground vehicles are embedded with numerous electronic systems to conduct missions such as silent and normal stationary surveillance missions. Increasing electrical energy consumption of these systems is influencing higher fuel consumption of the vehicle. To save energy, the vehicles can use any of the existing techniques, but they require complex, expensive, and time consuming implementations. Therefore, cheaper and simpler approaches are required. In addition, the solutions have to save energy according to mission needs and also overcome size and weight constraints of the vehicle. Existing research in the current literature do not have any mission aware approaches to save energy. This dissertation research proposes mission aware online energy saving strategies for stationary Army ground vehicles to save energy as well as to meet the electrical needs of the vehicle during surveillance missions. The research also proposes theoretical models of surveillance missions, fuzzy logic models of engine and alternator efficiency data, and fuzzy logic algorithms. Based on these models, two energy saving strategies are proposed for silent and normal surveillance type of missions. During silent mission, the engine is on and batteries power the systems. During normal surveillance mission, the engine is on, gear is on neutral position, the vehicle is stationary, and the alternator powers the systems. The proposed energy saving strategy for silent surveillance mission minimizes unnecessary battery discharges by controlling the power states of systems according to the mission needs and available battery capacity. Initial experiments show that the proposed approach saves 3% energy when compared with the baseline strategy for one scenario and 1.8% for the second scenario. The proposed energy saving strategy for normal surveillance mission operates the engine at fuel-efficient speeds to meet vehicle demand and to save fuel. The experiment and simulation uses a computerized vehicle model and a test bench to validate the approach. In comparison to vehicles with fixed high-idle engine speed increments, experiments show that the proposed strategy saves fuel energy in the range of 0-4.9% for the tested power demand range of 44-69 kW. It is hoped to implement the proposed strategies on a real Army ground vehicle to start realizing the energy savings

A top-down approach to understand the fire performance of building facades using standard test data

Designing the outer walls of a building – its facade – is a complex, multi-objective problem. While some design objectives, like weight, are easily quantified others, like fire safety, are harder to assess, making it difficult to compare facades in that objective. A move to more complex facades with novel materials has led the number of facade fires to quadruple over the last 3 decades. Current fire safety literature has no way to accurately predict the performance of a facade from its individual components, therefore the only way to test whether a facade system is safe is to run a large-scale fire test. Thousands of these tests are run internationally each year, subjecting different facades to different fire scenarios depending on the country, but data from these tests are locked away, missing an opportunity to learn from this untapped source of knowledge. This thesis taps into that source for the first time by presenting a unique database of 384 facade tests, named KRESNIK. We analysed this database using a top-down, statistical approach, and found that facades with combustible cladding and a cavity generally performed worse in these tests. These trends were investigated further in a parametric series of 20 intermediate-scale experiments on facades with different cladding and insulation materials, with and without a cavity. These experiments were analysed using a novel method to measure the heat released through visible flames in a fire from regular camera footage. We refer to this quantity as visual fire power. KRESNIK was also used to compare, for the first time, the behaviour of similar facades tested using different test standards, finding that different standards could not agree on how to rank the same facades. This work demonstrates the power of a top-down approach to tackle fire safety using data that already exists.Open Acces

Chemical Sensor Systems and Associated Algorithms for Fire Detection: A Review

Indoor fire detection using gas chemical sensing has been a subject of investigation since the early nineties. This approach leverages the fact that, for certain types of fire, chemical volatiles appear before smoke particles do. Hence, systems based on chemical sensing can provide faster fire alarm responses than conventional smoke-based fire detectors. Moreover, since it is known that most casualties in fires are produced from toxic emissions rather than actual burns, gas-based fire detection could provide an additional level of safety to building occupants. In this line, since the 2000s, electrochemical cells for carbon monoxide sensing have been incorporated into fire detectors. Even systems relying exclusively on gas sensors have been explored as fire detectors. However, gas sensors respond to a large variety of volatiles beyond combustion products. As a result, chemical-based fire detectors require multivariate data processing techniques to ensure high sensitivity to fires and false alarm immunity. In this paper, we the survey toxic emissions produced in fires and defined standards for fire detection systems. We also review the state of the art of chemical sensor systems for fire detection and the associated signal and data processing algorithms. We also examine the experimental protocols used for the validation of the different approaches, as the complexity of the test measurements also impacts on reported sensitivity and specificity measures. All in all, further research and extensive test under different fire and nuisance scenarios are still required before gas-based fire detectors penetrate largely into the market. Nevertheless, the use of dynamic features and multivariate models that exploit sensor correlations seems imperative

Performance Based Design and Machine Learning in Structural Fire Engineering: A Case for Masonry

The volatile and extreme nature of fire makes structural fire engineering unique in that the load actions dictating design are intense but not geographically or seasonally bound. Simply, fire can break out anywhere, at any time, and for any number of reasons. Despite the apparent need, fire design of structures still relies on expensive fire tests, complex finite element simulations, and outdated procedures with little room for innovation. This thesis will make a case for adopting the principles of performance-based design and machine learning in structural fire engineering to simplify the process and promote the consideration of fire in all structural engineering applications. This thesis begins with an overview of relevant topics, providing context and a frame of reference for the coming chapters. The first section of this thesis argues for the adoption of performance-based design for the structural fire design of buildings, as obtained through a comprehensive and much needed literature review. The second half of this thesis revolves around the application of performance-based design and simple machine learning in our field. An Excel file accompanies this thesis as an easy-to-use tool to encourage the consideration of fire criteria in masonry projects, focusing not on how heat affects the material-level properties but rather on how those effects accumulate to affect the final design requirements. An outline for the development of a coding-free machine learning model capable of predicting failure of unreinforced masonry structural elements exposed to elevated temperatures including its abilities and limitations, is presented. The thesis concludes with a summary of the above information and the potential for related project scopes in the future

Noise modelling, vibro-acoustic analysis, artificial neural networks on offshore platform

PhD ThesisDue to the limitations of the present noise prediction methods used in the offshore industry, this research is aimed to develop an efficient noise prediction technique that can analyze and predict the noise level for the offshore platform environment during the design stage as practically as possible to meet the criteria for crews’ comfort against high noise level. Several studies have been carried out to improve the understanding of acoustic environment onboard offshore platform, as well as the present prediction techniques. The noise prediction methods for the offshore platform were proposed from three aspects: by empirical acoustic modeling, analytical computation or neural network method. First, through evaluating the five-selected empirical acoustic models originated from other applications and statistical energy analaysis with direct field (SEA-DF), Heerema and Hodgson model was selected for calculating the sound level in the machinery room on the offshore platform. Second, the analytical model modeled three-dimensional fully coupled structural and acoustic systems by considering of the structural coupling force and the moment at edges, and structural-acoustic interaction on the interface. Artificial spring technique was implemented to illustrate the general coupling and boundary conditions. The use of Chebyshev expansions solutions ensured the accuracy and rapid convergence of the three-dimensional problem of single room and conjugate rooms. The proposed model was validated by checking natural frequencies and responses of against the results obtained from finite element software. Third, a modified multiple generalised regression neural network (GRNN) was first proposed to predict the noise level of various compartments onboard of the offshore platform with limited samples available. By preprocessing the samples with fuzzy c-means (FCM) and principal component analysis (PCA), dominant input features can be identified before commencing the GRNN’s training process. With optimal spread variables, the newly developed tool showed comparable performance to the SEA-DF and empirical formula that requires less time and resources to solve during the early stage of the offshore platform design.Singapore Economic Development Board (EDB) for providing the funding for the research under EDB-Industrial Postgraduate Programme (IPP) with SembCorp Marine in Singapore

Advancements in Arc Fault Detection for Electrical Distribution Systems: A Comprehensive Review from Artificial Intelligence Perspective

This comprehensive review paper provides a thorough examination of current advancements and research in the field of arc fault detection for electrical distribution systems. The increasing demand for electricity, coupled with the increasing utilization of renewable energy sources, has necessitated vigilance in safeguarding electrical distribution systems against arc faults. Such faults could lead to catastrophic accidents, including fires, equipment damage, loss of human life, and other critical issues. To mitigate these risks, this review article focuses on the identification and early detection of arc faults, with a particular emphasis on the vital role of artificial intelligence (AI) in the detection and prediction of arc faults. The paper explores a wide range of methodologies for arc fault detection and highlights the superior performance of AI-based methods in accurately identifying arc faults when compared to other approaches. A thorough evaluation of existing methodologies is conducted by categorizing them into distinct groups, which provides a structured framework for understanding the current state of arc fault detection techniques. This categorization serves as a foundation for identifying the existing constraints and future research avenues in the domain of arc fault detection for electrical distribution systems. This review paper provides the state of the art in arc fault detection, aiming to enhance safety and reliability in electrical distribution systems and guide future research efforts