Automatic Detection and Control of Hazardous Plumes in Wall-Bounded Flow Systems.

Recent advances in technology and computational power have made the once theoretical concept of a real-time detection and control system, for the purpose of reducing risk from the deliberate or unintentional release of a hazardous plume, a practical reality. Such a system utilizes strategically placed sensor arrays and actuators in order to first detect the release of a hazardous chemical, and subsequently to determine the actions required by the actuators to effectively and expediently mitigate the plume. This basic framework is applicable to a number of real-world scenarios that can be described as wall-bounded, fluid-based systems, such as airport terminals, aqueducts, tall buildings and passenger tunnels. The present research develops the theoretical and numerical framework for the automatic detection and control algorithm, which requires two main steps: a source inversion phase in order to trace the history of the plume and determine its original properties, followed by a boundary control phase in which the recovered source information is used to predict the propagation of the plume in time and space and thereby determine a control strategy to be performed in order to effectively mitigate it. Both the source inversion and boundary control phases can be formulated in terms of numerical optimization, and hence in this work a coupled CFD-optimization model has been developed using open source software. The CFD model implemented in this research is a RANS (Reynolds-Averaged Navier-Stokes) based finite-volume model developed using the OpenFOAM CFD library. The CFD model has been linked to an external optimization software suite (DAKOTA). The resulting model is used to simulate the source inversion and boundary control components of the automatic detection and control algorithm and to investigate the effect of various parameters on their accuracy, reliability and expediency. The results indicate that gradient-based optimization methods are successful for the boundary control phase; however the source inversion problem is complicated by issues arising from the discretization of the optimization parameters and ill-posedness. Hybrid optimization approaches offer some benefits with regards to the former problem, yet ill-posedness remains a significant challenge with respect to the source inversion process.PHDCivil EngineeringUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttp://deepblue.lib.umich.edu/bitstream/2027.42/102288/1/awarnoc_1.pd

Marine Tar Residues: a Review

Abstract Marine tar residues originate from natural and anthropogenic oil releases into the ocean environment and are formed after liquid petroleum is transformed by weathering, sedimentation, and other processes. Tar balls, tar mats, and tar patties are common examples of marine tar residues and can range in size from millimeters in diameter (tar balls) to several meters in length and width (tar mats). These residues can remain in the ocean envi-ronment indefinitely, decomposing or becoming buried in the sea floor. However, in many cases, they are transported ashore via currents and waves where they pose a concern to coastal recreation activities, the seafood industry and may have negative effects on wildlife. This review summarizes the current state of knowledge on marine tar residue formation, transport, degradation, and distribution. Methods of detection and removal of marine tar residues and their possible ecological effects are discussed, in addition to topics of marine tar research that warrant further investigation. Emphasis is placed on ben-thic tar residues, with a focus on the remnants of the Deepwater Horizon oil spill in particular, which are still affecting the northern Gulf of Mexico shores years after the leaking submarine well was capped

Marine Tar Residues: A Review

Marine tar residues originate from natural and anthropogenic oil releases into the ocean environment and are formed after liquid petroleum is transformed by weathering, sedimentation, and other processes. Tar balls, tar mats, and tar patties are common examples of marine tar residues and can range in size from millimeters in diameter (tar balls) to several meters in length and width (tar mats). These residues can remain in the ocean environment indefinitely, decomposing or becoming buried in the sea floor. However, in many cases, they are transported ashore via currents and waves where they pose a concern to coastal recreation activities, the seafood industry and may have negative effects on wildlife. This review summarizes the current state of knowledge on marine tar residue formation, transport, degradation, and distribution. Methods of detection and removal of marine tar residues and their possible ecological effects are discussed, in addition to topics of marine tar research that warrant further investigation. Emphasis is placed on benthic tar residues, with a focus on the remnants of the Deepwater Horizon oil spill in particular, which are still affecting the northern Gulf of Mexico shores years after the leaking submarine well was capped

Initial Findings on the Feasibility of Real-Time Feedback Control of a Hazardous Contaminant Released Into Channel Flow by Means of a Laboratory-Scale Physical Prototype

The threat of accidental or deliberate toxic chemicals released into public spaces is a significant concern to public safety. The real-time detection and mitigation of such hazardous contaminants has the potential to minimize harm and save lives. We develop a computational fluid dynamics (CFD) flow control model with the capability of detecting and mitigating such contaminants. Furthermore, we develop a physical prototype to then test the computer model. The physical prototype is in its final stages of construction. Its current state, along with preliminary examples of the flow control model are presented throughout this paper

Breaking Down the Computational Barriers to Real‐Time Urban Flood Forecasting

Flooding impacts are on the rise globally, and concentrated in urban areas. Currently, there are no operational systems to forecast flooding at spatial resolutions that can facilitate emergency preparedness and response actions mitigating flood impacts. We present a framework for real‐time flood modeling and uncertainty quantification that combines the physics of fluid motion with advances in probabilistic methods. The framework overcomes the prohibitive computational demands of high‐fidelity modeling in real‐time by using a probabilistic learning method relying on surrogate models that are trained prior to a flood event. This shifts the overwhelming burden of computation to the trivial problem of data storage, and enables forecasting of both flood hazard and its uncertainty at scales that are vital for time‐critical decision‐making before and during extreme events. The framework has the potential to improve flood prediction and analysis and can be extended to other hazard assessments requiring intense high‐fidelity computations in real‐time.Plain Language SummaryCurrently, we cannot forecast flooding depths and extent in real‐time at a high level of detail in urban areas. This is the result of two key issues: detailed and accurate flood modeling requires a lot of computing power for large areas such as a city, and uncertainty in precipitation forecasts is high. We present an innovative flood forecasting method that resolves flood characteristics with enough detail to inform emergency response efforts such as timely road closures and evacuation. This is achieved by performing complex analysis of information on flooding impacts well before a future storm event, which subsequently allows much faster predictions when flooding actually happens. This approach completely changes the demand for required resources, replacing the nearly impossible burden of computation in real‐time with the easy problem of data storage, feasible even with a low‐end computer. Example results for Hurricane Harvey flooding in Houston, TX, show that predictions of both flood hazard and uncertainty work well over different areas of the city. This approach has the potential to provide timely and detailed information for emergency response efforts to help save lives and reduce other negative impacts during major flood events and other natural hazards.Key PointsThere is presently no means to forecast urban flooding at high resolution due to prohibitive computational demands and data uncertaintiesProposed framework combines high‐fidelity modeling and probabilistic learning to forecast flood attributes with uncertainty in real‐timeThe framework can be extended to other real‐time hazard forecasting, requiring high‐fidelity simulations of extreme computational demandPeer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/170850/1/2021GL093585-sup-0001-Supporting_Information_SI-S01.pdfhttp://deepblue.lib.umich.edu/bitstream/2027.42/170850/2/grl63104_am.pdfhttp://deepblue.lib.umich.edu/bitstream/2027.42/170850/3/grl63104.pd