Failure analysis of a separator under various thermal loading: A numerical study

Effectiveness of area and dedicated water deluge in protecting objects impacted by crude oil/gas jet fires on offshore installations

The evaluation of ignition probabilities is a fundamental step within the calculation of hazardous phenomena frequencies associated to a loss of containment of a flammable product (explosion, pool fire, jet fire). An error on this step can substantially change quantitative risk analysis conclusions.It is important to keep in mind that quantitative risk analysis are increasingly used to design facilities. In other words, facilities (equipment, structures and buildings) are designed to withstand accidental loads up to down a certain frequency. Three main approaches are commonly used for ignition probabilities calculation: tabulated values ??from databases, semi-quantitative methods or more or less sophisticated mathematical models.Nowadays, time dependent models are implemented to predict the evolution of the ignition probability as function of time. These models are particularly useful when combined with Computational Fluid Dynamics (CFD) calculations to provide flammable gas volume build up as function of time. Thus, it is possible to predict the evolution of the delayed ignition probability as function of time. Main interest of these models is to propose a discretization by time intervals of the delayed ignition probability. This approach is particularly relevant for performing explosion risk analysis.

Nowadays, the statistical studies have revealed that major accidents (MA) are frequent in diverse industries, which has originated the development of strategies and normative focussed in foreseeing and preventing these. Thus, the process safety is in continuous improvement. The experimental studies in this field result in situations of high risks and are usually expensive. Therefore, the implementation of developments as the computational fluid dynamics (CFD) techniques is now applied, and has proven to be advantageous. In this work, CFD models for pool and jet fires are presented, as these kinds of fires are usually involved in major accidents. The results of the CFD models show orders of magnitude and behaviors in good agreement with experimental observations found in literature. The outputs of the simulations showed values of around 500 and 1400 K for the pool fires; while the jet fires predictions were of temperatures around 500 and 1050 K. Furthermore, the information obtained by these models can be used in order to develop safety plans to diminish risks in the facilities designs, safe zones and emergency exit routes.

Fire-wind interaction is a crucial factor in various fire scenarios, including large-scale wildfires, building fires, and tunnel fires. Understanding the dynamics of fire propagation and smoke dispersion under the influence of wind is essential for the built environment including health and well-being. Computational Fluid Dynamics (CFD) simulations play a pivotal role in analyzing fire behavior in these scenarios. However, the reliability of CFD models depends heavily on proper validation techniques, ensuring that simulated results accurately represent real-world fire-wind interactions. This review aims to systematically explore CFD validation methodologies for fire-wind interaction, comparing different models, validation approaches, and their applicability in various fire scenarios. The discussion focuses on how validation enhances the accuracy and reliability of CFD simulations, enabling their practical application in fire and smoke assessments for the built environment.

Modelling of fire risks in an offshore facility

Coal use for generation of electricity is used extensively world-wide accounting for 40% of total power generation. Even with reductions in use over the last 10 years, coal still accounts for 20% of total electrical generation in the United States. An often-overlooked aspect of Pulverized Coal (PC) combustion is the erosion and abrasion of the coal injection nozzles. Currently there are over 300 active PC boilers in the US and over 1000 worldwide, with each boiler having 20–40 high alloy cast injectors. Due to the high velocity of PC injection and associated elevated rates of metal loss, these nozzles require constant replacement. Replacement and costs associated with loss of revenue, required scaffolding and casting can be a significant part of Operation and Maintenance (O&M) of a PC boiler. In addition to the constant requirement for thousands of replacement injection nozzles every year, combustion performance, NOx reduction, carbon conversion and general boiler efficiency will be impacted by hardware that is out of specification, if not replaced in a “timely” manner. Significant research in the 1980’s [1] provided some insight into the loss-of-metal process during PC injection, but limitations of existing hardware and software prevented more than an empirical methodology to be developed. In parallel with the literature work and research specifically for PC coal erosion rates, generalized efforts were employed and reported [6–9]. Meng [4] summarized model development for solid particles transported by a liquid or gas as highly empirical with little commonality between the models developed by the various researchers. Meng also made specific recommendations for less empiricism in model development methodology. Although there are several state-of-the-art empirical models [6, 8 & 9] more recently, semi-mechanistic models have been developed to predict solid particle erosion (e.g. Arabnejad et al., [17]) and have been successfully applied to sand erosion and abrasion in pipelines. In the current study, this method is being applied to PC injection nozzles coupled to detailed computational fluid dynamics (CFD) simulations. The intent is to quantify nozzle material loss rates, due to impacting coal particles, as a function of geometry, local velocities, and coal properties. The method used is utilizing CFD to model flow of particles and their impingement velocity with the PC nozzles. Then erosion models that are a function of impingement speed, angle, frequency and materials properties to examine erosion rates. The insight gained from the modeling will allow improved nozzle design, increased duty life, more cost-effective supply, and elevated injection velocity. In particular, low NOx coal combustion can be critically dependent on utilization of elevated injection velocities, which previous empirical models discourage. This paper reports on the application of the erosion equations and methods developed at the Erosion/Corrosion Research Center of The University of Tulsa for predicting solid particle erosion of a PC injection nozzle that shows details of erosion patterns and parameters that are responsible for elevated erosion tendencies in the field. RJM-International is familiar with the nozzle from various applications that are associated with Low NOx operation. The advantages of utilizing semi-mechanistic erosion equations and models coupled with CFD simulations as compared to previous empirical methods are discussed. Shortcomings of applying the existing coal erosion model is also reported along with “next steps” required to successfully apply the method to PC injection nozzle designs for much higher combustion efficiencies than existing ones.

Preface. 1 LNG Properties and Overview of Hazards. 1.1 LNG Properties. 1.2 Hazards of LNG with Respect to Public Risk. 1.3 Risk Analysis Requires Adequate Modeling. 1.4 Flammability. 1.5 Regulations in Siting Onshore LNG Import Terminals. 1.6 Regulation for Siting Offshore LNG Import Terminals. 1.7 Controversial Claims of LNG Opponents. 2 LNG Incidents and Marine History. 2.1 LNG Ship Design History. 2.2 Designs and Issues First Commercial LNG Ships. 2.3 LNG Trade History. 2.4 LNG Accident History. 2.5 Summary of LNG History and Relevant Technical Developments. 3 Current LNG Carriers. 3.1 Design Requirements. 3.2 Membrane Tanks. 3.3 Moss Spheres. 4 Risk Analysis and Risk Reduction. 4.1 Background. 4.2 Risk Analysis Process. 4.3 Frequency: Data Sources and Analysis. 4.4 Frequency: Predictive Methods. 4.5 Consequence Modeling. 4.6 Ignition Probability. 4.7 Risk Results. 4.8 Special Issues Terrorism. 4.9 Risk Reduction and Mitigation Measures for LNG. 5 LNG Discharge on Water. 5.1 Type 1 Above Water Breaches at Sea. 5.2 Type 2 At Waterline Breaches at Sea. 5.3 Type 3 Below Waterline Breaches at Sea. 5.4 Discharges from Ship's Pipework. 5.5 Cascading Failures at Sea. 5.6 Initial Discharge Rate. 5.7 Time-Dependent Discharge (Blowdown). 5.8 Vacuum Breaking and Glug-Glug Effects. 6 Risk Analysis for Onshore Terminals and Transport. 6.1 Typical Basis for LNG Receiving Terminal. 6.2 Features of LNG Receiving Terminals. 6.3 Standards for Receiving Terminal Design. 6.4 U.S. Guidelines and Regulations for Receiving Terminals. 6.5 European Regulations for LNG Receiving Terminals. 6.6 Empirical Formula for Required Land Area of Terminal. 6.7 Leak in Loading Arm or in Storage Tank. 6.8 Rollover. 6.9 LNG Land Transport Risk. 6.10 Offshore LNG Terminals. 7 LNG Pool Modeling. 7.1 Flashing and Droplet Evaporation in Jet Flow. 7.2 Pool Spread and Evaporation Modeling. 7.3 Rapid Phase Transition Explosions. 7.4 Aerosol Drop Size. 7.5 Heat Balance Terms to LNG Pool. 7.6 Nomenclature. 8 Vapor Cloud Dispersion Modeling. 8.1 Atmospheric Transport Processes. 8.2 Model Types. 8.3 LNG Dispersion Test Series. 8.4 Factors Affecting Plume Length. 8.5 Effect of Wind, Currents, and Waves on LNG Plume. 8.6 Comparison of Dispersion Model Predictions. 8.7 Descriptions of Dispersion Test Series. 8.8 Vapor Intrusion Indoors. 8.9 Theoretical Basis for Suppression of Turbulence. 9 LNG Pool Fire Modeling. 9.1 Types of Fires from LNG Facilities. 9.2 The Challenge for Pool Fire Modeling. 9.3 Pool Fire Characteristics. 9.4 Summary of LNG Fire Experiments. 9.5 Burning Rate Data and Correlations From Fire Tests. 9.6 Point Source Fire Model. 9.7 Solid Flame Models: Flame Length Correlations. 9.8 Flame Tilt Correlations. 9.9 Flame Drag Near Pools. 9.10 Sep Correlations and Smoke Shielding. 9.11 Atmospheric Transmissivity. 9.12 Trench Fires. 9.13 View Factors. 9.14 CFD Modeling. 9.15 Comparison of Model Predictions. 9.16 Fire Engulfment of LNG Carrier. 10 Other LNG Hazards. 10.1 Fire and Explosion Scenarios. 10.2 Jet Fires. 10.3 Flash Fires. 10.4 BLEVEs, Fireballs. 10.5 LNG Vapor Cloud Explosions. 10.6 Asphyxiation and Cryogenic Hazard from LNG Spills. 11 Fire Effects. 11.1 Fire Radiation Effects on Individuals. 11.2 Effects of Thermal Radiation on Property. 12 Research Needs. 12.1 Uncertainties. 12.2 Recommendations of GAO Survey. 12.3 LNG Model Evaluation Protocols (MEPs). 12.4 Special Topics. 12.5 Conclusions. References. Index.

Probabilistic real-time natural gas jet fire consequence modeling of offshore platforms by hybrid deep learning approach

Optimization methods have been widely applied to the aerodynamic design of gas turbine blades. While applying optimization to high-fidelity computational fluid dynamics (CFD) simulations has proven capable of improving engineering design performance, a challenge has been overcoming the prolonged run-time due to the computationally expensive CFD runs. Reduced-order models and, more recently, machine learning methods have been increasingly used in gas turbine studies to predict performance metrics and operational characteristics, model turbulence, and optimize designs. The application of machine learning methods allows for utilizing existing knowledge and datasets from different sources, such as previous experiments, CFD, low-fidelity simulations, 1D or system-level studies. The present study investigates inserting a machine learning model that utilizes such data into a high-fidelity CFD driven optimization process, and hence effectively reduces the number of required evaluations of the CFD model. Artificial Neural Network (ANN) models were trained on data from over three thousand two-dimensional (2D) CFD analyses of turbine blade cross-sections. The trained ANN models were then used as surrogates in a nested optimization process alongside a full three-dimensional Navier–Stokes CFD simulation. The much lower evaluation cost of the ANN model allows for tens of thousands of design evaluations to guide the search of the best blade profiles to be used in the more expensive, high-fidelity CFD runs, improving the progress of the optimization while reducing the required computation time. It is estimated that the current workflow achieves a five-fold reduction in computational time in comparison to an optimization process that is based on three-dimensional (3D) CFD simulations alone. The methodology is demonstrated on the NASA/General Electric Energy Efficient Engine (E3) high pressure turbine blade and found Pareto front designs with improved blade efficiency and power over the baseline. Quantitative analysis of the optimization data reveals that some design parameters in the present study are more influential than others, such as the lean angle and tip scaling factor. Examining the optimized designs also provides insight into the physics, showing that the optimized designs have a lower amount of pressure drop near the trailing edge, but have an earlier onset of pressure drop on the suction side surface when compared to the baseline design, contributing to the observed improvements in efficiency and power.

The processing area of offshore platform has high probability of leakage of hydrocarbons. Liquefied Natural Gas (LNG) is one of the most common hydrocarbon produced in offshore platforms. Leakage of LNG can cause pool fire, jet fire, flash fire or fire ball. Thermal radiations due to fire is the major source of damage to workers on board. But due to fire, various combustion product toxic gases are also produced that have both acute and chronic health effects. These toxic gases can cause incapacitation, increased heart rate, vomiting and even death. Predicting the human injury due to thermal radiations and concentration of toxic gases are the key issues. A risk based approach takes in to consideration the duration a worker spent on different location of offshore platform and also it has the additive ability to evaluate overall risk due to fire radiation and toxic gases. Grid based approach helps in better visualization of risk posed by fire radiation and combustion product toxic gases at different locations of platform. The current study proposed an integrated consequence modelling approach for fire and combustion product toxic gases using risk based and grid based approaches. The integrated accident is modelled using Computational Fluid Dynamics (CFD) code Fire Dynamics Simulator (FDS). The results showed that risk posed by thermal radiation is confined on sub cellar deck (lower deck) but estimated risk due to combustion product gas (carbon monoxide) on cellar deck (upper deck) has significant value that needs to be considered. The current approach would be useful for emergency preparedness plans and safety measures designs for offshore platforms.

Recently, image-based computational fluid dynamic (CFD) simulations have been proposed to investigate the local hemodynamics inside human cerebral aneurysms. It was suggested that the knowledge of the computed three-dimensional flow fields can be used to assist clinical risk assessment and treatment decision making. Therefore, it was desired to know the reliability of CFD for cerebral blood flow simulation, and be able to provide clinical feedback. However, the validations were not yet comprehensive as they lack either patient-specific boundary conditions (BCs) required for CFD simulations or quantitative comparison methods. In this study, based on a recently proposed in-vitro quantitative CFD evaluation approach via virtual angiography, the CFD evaluation was extended from phantom to patient studies. In contrast to previous work, patient-specific blood flow rates obtained by transcranial color coded Doppler ultrasound measurements were used to impose CFD BCs. Virtual angiograms (VAs) were constructed which resemble clinically acquired angiograms (AAs). Quantitative measures were defined to thoroughly evaluate the correspondence of the detailed flow features between the AAs and the VAs, and thus, the reliability of CFD simulations. The proposed simulation pipeline provided a comprehensive validation method of CFD simulation for reproducing cerebral blood flow, with a focus on the aneurysm region. Six patient cases were tested and close similarities were found in terms of spatial and temporal variations of contrast agent (CA) distribution between AAs and VAs. For patient #1 to #5, discrepancies of less than 11% were found for the relative root mean square errors in time intensity curve comparisons from characteristic vasculature positions. For patient #6, where the CA concentration curve at vessel inlet cannot be directly extracted from the AAs and given as a BC, deviations about 20% were found. As a conclusion, the reliability of the CFD simulations was well confirmed. Besides, it was shown that the accuracy of CFD simulations was closely related to the input BCs.

Fire safety engineering involves the mechanisms of conduction, convection, and radiation. In engineering structures of industrial applications associated with hydrocarbons, fire is defined as a combustible vapor or gas that combines with an oxidizer in a combustion process that manifests in the evolution of light, heat, and flame. Within the risk assessment and management framework (as described in Chap. 16), the impacts of elevated temperatures generated by fire are the primary concerns regarding the structural loads that result from hazards. Fire events usually occur due to the release of hydrocarbons from flanges, valves, seals, vessels, or nozzles with ignition from various kinds of sparks. Pool fires are associated with released oils and jet fires occur as a result of leaking gases. This chapter presents the modeling practices of gas cloud temperature analysis in fires. Fire curves must be defined to predict gas cloud temperatures over time. Empirical formulations of fire curves are available for industry practices. In this chapter, the modeling techniques for refined computational fluid dynamics (CFD) simulations are presented to compute the gas cloud temperature versus time history. As gas cloud temperatures are transferred into structures, heat transfer analysis must be undertaken to determine the temperatures in structures for the crashworthiness analysis (as described in Chap. 12).

Study of fire and explosion hazards of alternative fuel vehicles in tunnels

API Standard 521 new alternative method to evaluate fire relief for pressure relief device sizing and depressuring system design

Bench test and computational fluid dynamics (CFD ) simulation of the air pump loss on the 380 hydraulic retarder are completed in this paper . It turns out that the CFD simulation has nice reliability from the contrast of the air pump loss between the test and the simulation . And on this foundation , CFD simulation of two kinds of retarders with different turbulent structures is done . The contrast of the results of the simulation give the effects of different turbulent structures on cutting down the are pump loss .