Experimental and Numerical Modeling of Wildfire Spread via Fire Spotting

Wildfire spread via fire spotting phenomenon has three major stages, namely formation and break-off of firebrands from vegetative structures, lofting and transport of them through the ambient velocity field, and finally deposition of firebrands upon landing and ignition of spot fires. This dissertation develops novel models in different areas related to fire spotting phenomenon and integrates them to improve understanding of the firebrand flight through a multiphysics model. In this regard, a mechanical break-off model for the formation of cylindrical firebrands from coniferous trees is proposed; And by geometric scaling analysis, it is shown that the firebrand surface area scales on the mass raised to the 2/3rds power. By applying a non-linear regression model to the available experimental data on firebrands, a predictive statistical model for estimating mass and shape distribution of firebrands is proposed, that can be used as realistic input into the current fire spotting models. Further, the aerodynamic behavior of the cylindrical firebrands is characterized by conducting free-fall experiments where it is shown that the governing equations of the transport are highly sensitive to the initial conditions of the release. On this matter, near field dynamics of highly buoyant bent-over plumes are thoroughly characterized and, it is shown, analytically, that the steep trajectories of wildfire plumes necessitate for the inclusion of the boundary layer shearing effects in the mathematical models of the velocity field. Moreover, for the first time, the most extensive large scale wind tunnel experiments of the lofting and downwind transport of non-combusting model firebrands is conducted. It is found that the normalized landing location of firebrands with their maximum rise height have similar probability density functions (PDF) regardless of the aspect ratio. This implies that unlike previous studies the lofting and transport cannot be decoupled. Given the wind tunnel experiment results, a highly scalable coupled stochastic parametric model for firebrand flight is developed by synthesizing OpenFOAM and MATLAB solutions. This model couples the fine resolution time-varying Large Eddy Simulation (LES) resolved velocity field of the jets/plumes in non-uniform cross-flow boundary layers with the fully deterministic 3D 6-D.O.F. firebrand transport model. Comparisons between the experiments and corresponding numerical simulations with this model show very good agreement in estimating the average statistics of the flight. Also, it is shown that the transport equations are highly sensitive to the spatial and temporal variations in the ambient velocity field

The progressive collapse behavior of precast floor-to-floor connections using longitudinal and transverse ties

This paper involves a fundamental study of a numerical method for progressive collapse resistance design of floor-to-floor joints in precast cross-wall structures. It presents a 3D numerical study of a floor-to-floor system with longitudinal and transverse ties. The model is also used to derive the post-bond behavior and the mechanism of forming catenary action concerning the bond behavior in precast cross-wall structures. The obtained results indicated the adequacy and applicability of the code specifications in British Standard, Euro Codes, and DoD 2013. Discrepancies in the tie-force between the numerical results and codified specifications have suggested an inappropriate use of the current TF method, hence, an improved model based on the numerical results has been proposed to address this concern. To the authors’ best knowledge, this is the first numerical study to investigate the behavior of floor-to-floor joints following the removal of wall support in typical precast cross-wall structures when considering bar fracture and pull-out failure mode

A deep learning approach to downscale geostationary satellite imagery for decision support in high impact wildfires

Scarcity in wildland fire progression data as well as considerable uncertainties in forecasts demand improved methods to monitor fire spread in real time. However, there exists at present no scalable solution to acquire consistent information about active forest fires that is both spatially and temporally explicit. To overcome this limitation, we propose a statistical downscaling scheme based on deep learning that leverages multi-source Remote Sensing (RS) data. Our system relies on a U-Net Convolutional Neural Network (CNN) to downscale Geostationary (GEO) satellite multispectral imagery and continuously monitor active fire progression with a spatial resolution similar to Low Earth Orbit (LEO) sensors. In order to achieve this, the model trains on LEO RS products, land use information, vegetation properties, and terrain data. The practical implementation has been optimized to use cloud compute clusters, software containers and multi-step parallel pipelines in order to facilitate real time operational deployment. The performance of the model was validated in five wildfires selected from among the most destructive that occurred in California in 2017 and 2018. These results demonstrate the effectiveness of the proposed methodology in monitoring fire progression with high spatiotemporal resolution, which can be instrumental for decision support during the first hours of wildfires that may quickly become large and dangerous. Additionally, the proposed methodology can be leveraged to collect detailed quantitative data about real-scale wildfire behaviour, thus supporting the development and validation of fire spread models

Scheduling of Air Conditioning and Thermal Energy Storage Systems Considering Demand Response Programs

The high penetration rate of renewable energy sources (RESs) in smart energy systems has both threat and opportunity consequences. On the positive side, it is inevitable that RESs are beneficial with respect to conventional energy resources from the environmental aspects. On the negative side, the RESs are a great source of uncertainty, which will make challenges for the system operators to cope with. To tackle the issues of the negative side, there are several methods to deal with intermittent RESs, such as electrical and thermal energy storage systems (TESSs). In fact, pairing RESs to electrical energy storage systems (ESSs) has favorable economic opportunities for the facility owners and power grid operators (PGO), simultaneously. Moreover, the application of demand-side management approaches, such as demand response programs (DRPs) on flexible loads, specifically thermal loads, is an effective solution through the system operation. To this end, in this work, an air conditioning system (A/C system) with a TESS has been studied as a way of volatility compensation of the wind farm forecast-errors (WFFEs). Additionally, the WFFEs are investigated from multiple visions to assist the dispatch of the storage facilities. The operation design is presented for the A/C systems in both day-ahead and real-time operations based on the specifications of WFFEs. Analyzing the output results, the main aims of the work, in terms of applying DRPs and make-up of WFFEs to the scheduling of A/C system and TESS, will be evaluated. The dispatched cooling and base loads show the superiority of the proposed method, which has a smoother curve compared to the original curve. Further, the WFFEs application has proved and demonstrated a way better function than the other uncertainty management techniques by committing and compensating the forecast errors of cooling loads

Does the Anti-Mullerian Hormone Decline Rate Improve the Prediction of Age at Menopause?

publishedVersio

Über Pankreaszyste

Wir haben in unserer Klinik 3 Fälle von Pankreaszyste beobachtet. Sie waren alle Pseudzyste und haben sich als Species gastrocolica entwickelt uud wurden nach Gussenbauer operiert und geheilt entlassen. I-Fall: Ein 8 jähriges Mädchen bekam einen faustgrossen Tumor einen Monat nach Bauchtrauma. Es wurde in 33 Tagen nach der Operation geheilt entlassen. Der Zysteninhalt war klar und gelbbräunlich. In der Zystenflüssigkeit wurden Lipase und Diastase nachgewiesen. II-Fall: Ein 46 jähriger Mann konnte nicht veranlassendes Moment angeben. Es wurde zuerst als Magenbeschwerde und dann als Hydronephrose diagnostiert. Er wurde in 39 Tagen nach der Operation geheilt entlassen. In der klaren gelbbräunlichen Zystenflüssigkeit wurden Lipase und Tripsin nachgewiesen. III-Fall: Ein 38 jahrige Frau bemerkte einen kindskopfgrossen Tumor nach der Entbindung Die Zystenwand wurde operativ exzidiert und ergab sich histologisch als Pseudozyste. In 93 Tagen nach der Operation wurde sie geheilt entlassen. In der gelbbraunlichen Flüssigkeit des Inhaltes wurden Trypsin, Lipase und Diastase nachgewiesen

Efficiency evaluation of cement production companies using nonhomogeneous network DEA

Cement production in Iran takes place across various geographical locations, each characterized by distinct weather conditions. The technology employed in cement production varies depending on the availability of raw materials, fuel sources, and essential resources like water. Consequently, diverse inputs and outputs assume significance in each production technology, resulting in non-homogeneity among cement factories. Despite these differences, all these facilities are engaged in cement production, warranting a comparative analysis of their efficiency. This study examines the operational processes of five different cement production technologies—dry, semi-dry, humid, semi-humid, and wet slurry—across four companies comprising a total of nine factories. The study evaluates their efficiency between 2017 and 2020 using network data envelopment analysis under non-homogeneous conditions across three modeling stages. An important aspect of this study is its focus on the entire supply chain, from raw materials to the final product. Although the raw materials employed vary among different cement production technologies, the end product remains largely consistent.IntroductionIn certain real-world scenarios, even with similar production technologies, the assumption of homogeneous decision-making units may not hold true. Practical applications often involve supply chain structures that differ significantly from others. For instance, some supply chains may, at certain stages, eject intermediate products to meet specific needs, a phenomenon not universal to all supply chains, resulting in non-homogeneous chains. The cement industry, including Iran, constitutes one of the pivotal economic sectors. Therefore, mitigating shortcomings, including resource and material waste reduction, can have a substantial impact on this industry and consequently on the broader economy. Due to varying climatic conditions, cement production employs diverse technologies, primarily categorized as dry or wet processes. This study investigates the operational processes of five different cement production methods—dry, semi-dry, humid, semi-humid, and wet slurry—across four companies with a total of nine factories. Their performance between 2017 and 2020 is evaluated using network DEA under non-homogeneous conditions, encompassing three modeling stages.Materials and MethodsIn novel approaches, DEA is utilized to assess the performance of network decision-making units. The models typically assume homogeneity among decision-making units, which may not always align with real-world conditions. Practical situations often violate assumptions of unit homogeneity and uniformity in input and output parameters. Consequently, it is imperative to present and employ methods and models capable of accommodating non-homogeneous units. This study employs a scientific library research approach and practical purposive data collection to gather relevant information. This information informs specific adjustments to operational processes. Consequently, the development of a robust system for evaluating supply chain performance becomes essential. The study utilizes common models to evaluate efficiency under non-homogeneous conditions. Classification of operational processes and related data, followed by modeling using Lingo software, is employed in this research.Discussion and Result:This article consists of two parts. Initially, it introduces the fundamental performance evaluation model and subsequently delves into the three-stage model of data envelopment analysis (DEA) within the supply chain context. In the second part, the production processes of Portland cement are examined, covering dry, semi-dry, humid, semi-humid, and wet slurry processes. The proposed approach assesses the performance of four cement production companies over a four-year period. Efficiency calculations for nine factories are conducted in three stages:The first stage consists of three steps as follows:First step: Input and output parameters used across the entire production process are categorized based on the different production methods.Second step: Processes utilizing similar production steps, as determined in the first stage, are grouped into four categories.Third step: Efficiency assessments for factories sharing similar production stages from the previous step are conducted, resulting in the identification of nine categories.Second stage: The efficiency of each category, characterized by a common feature from the previous step, is calculated.Third stage: To determine the overall efficiency of each factory, the efficiencies of individual processes are multiplied.ConclusionsThe results indicate that the fourth cement production company exhibits the highest efficiency, while the first company has the lowest efficiency. Notably, the lowest efficiency for the years 2017 to 2020 was recorded by the first company in 2020, while the fourth company achieved the highest efficiency in the same year. Among the factories, the lowest efficiency was observed in 2017 for the first company's five semi-dry factories, the fourth company's four semi-humid factories in 2018, the fourth company's nine wet slurry factories in 2018, the third company's seven semi-humid factories in 2020, and the fourth company's four semi-humid factories in 2020, which recorded the highest efficiency. Further examination and identification of suitable solutions to enhance efficiency in cases with lower efficiency levels can follow this study

In Vitro Cytotoxicity Of Folate-Silica-Gold Nanorods On Mouse Acute Lymphoblastic Leukemia And Spermatogonial Cells

Objective The purpose of this study was to evaluate in vitro cytotoxicity of gold nanorods (GNRs) on the viability of spermatogonial cells (SSCs) and mouse acute lymphoblastic leukemia cells (EL4s). Materials And Methods In this experimental study, SSCs were isolated from the neonate mice, following enzymatic digestion and differential plating. GNRs were synthesized, then modified by silica and finally conjugated with folic acid to form F-Si-GNRs. Different doses of F-Si-GNRs (25, 50, 75, 100, 125 and 140 µM) were used on SSCs and EL4s. MTT (3-(4,5-dimethylthiazol-2-yl)-2,5-diphenyltetrazolium bromide) proliferation assay was performed to examine the GNRs toxicity. Flow cytometry was used to confirm the identity of the EL4s and SSCs. Also, the identity and functionality of SSCs were determined by the expression of specific spermatogonial genes and transplantation into recipient testes. Apoptosis was determined by flow cytometry using an annexin V/propidium iodide (PI) kit. Results Flow cytometry showed that SSCs and EL4s were positive for Plzf and H-2kb, respectively. The viability percentage of SSCs and EL4s that were treated with 25, 50, 75, 100, 125 and 140 µM of F-Si-GNRs was 65.33 ± 3.51%, 60 ± 3.6%, 51.33 ± 3.51%, 49 ± 3%, 30.66 ± 2.08% and 16.33 ± 2.51% for SSCs and 57.66 ± 0.57%, 54.66 ± 1.5%, 39.66 ± 1.52%, 12.33 ± 2.51%, 10 ± 1% and 5.66 ± 1.15% for EL4s respectively. The results of the MTT assay indicated that 100 µM is the optimal dose to reach the highest and lowest level of cell death in EL4s and in SSCs, respectively. Conclusion Cell death increased with increasing concentrations of F-Si-GNRs. Following utilization of F-Si-GNRs, there was a significant difference in the extent of apoptosis between cancer cells and SSCs