According to DNV GL, companies using the software are expected to be benefitted from decisions that are based on more realistic models, thus increasing efficiency and safety in the design and operation of assets where accidents could cause hazardous releases.
Cost Of Phast Software Dnv Gl
Phast is one of the world's most comprehensive process hazard analysis software tools for all stages of design and operation. The tool examines the progress of a potential incident from the initial release to far-field dispersion analysis including modelling of pool spreading and evaporation and flammable and toxic effects.
AWD is one of many highlights in the most recent release of Phast software for process hazard analysis. Some of the additional features in Phast include support for modelling releases from buried pipelines and more realistic modelling of the true nature of fireballs.
Companies using the software will benefit from decisions that are based on more realistic models, thereby increasing efficiency and safety in the design and operation of assets where accidents could cause hazardous releases.
The new method is one of many highlights in the most recent release of Phast software for process hazard analysis. Additional new features in Phast 8 include support for modelling releases from buried pipelines and more realistic modelling of the true nature of fireballs. It also improves the accuracy and speed of risk calculations. Getting results quicker means there is more time to assess the results, which improves decision-making and risk management.
Phast Lite is a user-friendly and powerful software tool with extensive modelling capabilities for hazard analysis, examining the progress of a potential incident from the initial release to far-field dispersion.
Phast Lite is a tailored version of Phast, designed to meet the needs of users who do not require the full functionality of Phast. Phast Lite uses the same discharge, dispersion and effects models as the full version of Phast, making it a powerful simulation software tool.
Operators use hazard analysis software based on failure release scenarios to predict the extent of an exclusion zone. For vapor dispersion modeling purposes, the physical process is typically divided into two phases: source term and atmospheric dispersion. The source term phase describes what occurs immediately after a release where the fluid behavior (LNG and its vapor) is mainly controlled by release conditions. After the source term phase, the atmosphere dominates the vapor dispersion behavior.
PHMSA issued advisory bulletin ADB-10-07 to provide guidance on the requirements for obtaining approval for alternate vapor dispersion software. PHMSA is currently updating the MEP and validation database. If you are interested in petitioning for alternative radiant heat flux or vapor dispersion software, contact the Office of Pipeline Safety at 202-366-4595.
Prior to the merger, both DNV and GL had independently acquired several companies in different sectors, such as Hélimax Energy (Canada), Garrad Hassan (UK), Windtest (Germany) and KEMA (Netherlands), which now contribute to DNV's expertise across several industries. In addition to providing services such as technical assessment, certification, risk management and software development, DNV also invests heavily in research.
Together with Bureau Veritas and American Bureau of Shipping, DNV is one of the three major companies in the classification business with 300 offices in 100 countries. The company is also a key player in strategic innovation and risk management for several other industries including renewable energy (particularly in wind and solar), oil and gas, electric power generation and distribution, petrochemicals, aviation, automotives, finance, food and beverage, healthcare, software and information technology.
Maros is an advanced RAM analysis software tool specifically developed for analysing reliability, availability, maintainability for the upstream oil and gas industry. It includes extensive features for modelling flow networks, maintenance strategies, typical oil and gas upstream operations, transport logistics and storage tanks.
Optimizing production and minimizing costs is a constant challenge. Key players in the oil and gas industry rely on Maros to make informed decisions about their assets. Our software development team has a history of over 30 years of working closely with super users. If you are facing a modelling challenge, most likely we have faced it and solved it.
Reliability engineering practices during the design phase aims to optimize system robustness through measures such as redundancy or diversity in the design alternatives. This allows system designs based on user requirements and alternatives to be formulated and evaluated in a more holistic approach. Maros RAM analysis software can be applied throughout the whole project asset lifecycle.
The present disclosure describes determining a Rupture Exposure Radius (RER) value based on a dispersion model. For example, in some aspects, a normalized representative number of dispersion modeling cases are utilized through available commercial software packages to develop an empirical model to efficiently determine the pipeline classification based on a RER value, reducing the thickness of the designed pipeline wall, and the number of emergency isolation valves required to present along the pipeline route.
Gas pipelines are classified based on the population existing inside the RER zone along the pipeline route. The classification will determine the design factor; hence, the percentage of the pipe rated strength, which is the Specified Minimum Yield Stress (SMYS) that could be reached during operations. Moreover, this classification will determine the number of sectionalizing and emergency isolation valves required along the pipeline. As an example illustrated in Table 1, the higher the class, the shorter the distance required for valve spacing. As a result, pipeline class will have major impact in its capital cost. The pipeline classification can be directly affected by the RER value. For example, certain pipeline safety standard determines the pipeline classification by dividing the pipeline into segments and counting the dwellings located in these segments. The counted number is identified as the pipeline density index (PDI). The zones inside these segments are one-kilometer long and 2 times the pipeline RER value wide. The pipeline class then can be selected based on the population density index determination.
Currently, for example, certain pipelines safety standards set default RER values to be used in the pipeline design based on conservative estimates of downwind gas cloud extent. This conservative value has a great impact on both the capital cost and land utilization of the pipeline construction, especially for designing pipelines that carry sweet gases which are known to have lower RER values than mandated by certain safety standards. For example, lower RER distances result in more flexibility in route selection, lower pipeline location class and hence thinner pipeline wall, less emergency isolation valves required and longer span between sectionalizing valves resulting a high amount of savings. As sweet gas pipeline systems are hugely expanding, an efficient way of calculating RER will be highly beneficial and cost effective for the pipeline design projects.
In some implementations, these pipeline network parameters, that is the NFR value, the pipeline diameter and the pipeline pressure, are used in a regression analysis on a 95% confidence interval to determine whether there are any relations between them. Some commercial available software, such as the Microsoft Excel add-in can be used in this step. The result shows a correlation with the square root of the pressure multiplied by the parameter squared. In some implementations, it is concluded that these three parameters are interlinked and could be expressed as a function as shown in Eq. 1.
The dispersion model can be a commercially available dispersion model software that is capable of representing the extent of the flammable vapor clouds beginning with their release from the pipeline until they disperse. For example, it is required by some safety standards to trace the cloud's flammable components for sweet gas pipeline until its concentration reaches LFL at ground level or 1 m height, that is, the end point of lethal flammable concentration. In some implementations, a gas dispersion software package, for example, DNV PHAST or Shell FRED can be used to simulate the dispersion.
FIG. 2 is a graph that illustrates the change of pipeline flow rate over time when a pipeline failure occurred, according to some implementations. For the graph shown in FIG. 2, the x-axis is time in seconds (s), and the y-axis is the pipeline flow rate (fraction of initial). When a full-bore rupture failure happened, gas will release with spike high pipeline flow rate due to relatively very low atmospheric pressure and the release that comes from both the sides (that is, the upstream and the downstream) of the pipeline's rupture location. Within seconds, the flow rate will decrease until it reaches a steady state value. To determine the flow rate, the initial release rate during initial gas discharge is first determined by software. The initial peak rate will last for few seconds only and can be estimated using the equation for sonic or choked flow through an orifice. As it only lasts for few seconds, the determined peak rate is multiplied by a parameter representing the decay in the rate (A) to have a representative rate for the release duration. The value of λ depends on the size of modelled pipeline, the pressure in the line at the time of failure and the conveyed fluid. λ usually ranges from 0.2-0.5. A value of 0.33 is accepted in the industry for sweet gas as a representative yet conservative value.
FIGS. 3A-3B are graphs that illustrate the dispersion analysis result generated by the software packages, according to some implementations. Consequence modelling software that analyze and calculate the dispersion can be used in dispersion analysis. Currently, two commercial packages are used, that is, DNV PHAST and Shell FRED. Both software packages have been used for a sample calculation and almost matching results were observed, as the sample results shown in FIGS. 3A-3B, where FIG. 3A shows a sample result for the DNV PHAST, and FIG. 3B shows a sample result for Shell FRED. Only minor dissimilarities in meters are observed which is caused by the difference in each software definition of the input composition LFL content and in its built-in reporting options of maximum concentration, heights. 2ff7e9595c
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