Scholarly Works, Aerospace and Ocean Engineering
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Browsing Scholarly Works, Aerospace and Ocean Engineering by Content Type "Article"
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- Harmful algal blooms and toxic air: The economic value of improved forecastsMoeltner, Klaus; Fanara, Tracy; Foroutan, Hosein; Hanlon, Regina; Lovko, Vince; Ross, Shane D.; Schmale, David G. III (2021-02)The adverse economic impacts of harmful algal blooms can be mitigated via tailored forecasting methods. Adequate provision of these services requires knowledge of the losses avoided, or, in other words, the economic benefits they generate. The latter can be difficult to measure for broader population segments, especially if forecasting services or features do not yet exist. We illustrate how Stated Preference tools and Choice Experiments are well-suited for this case. Using as example forecasts of respiratory irritation levels associated with airborne toxins caused by Florida red tide, we show that 24-hour predictions of spatially and temporally refined air quality conditions are valued highly by the underlying population. This reflects the numerous channels and magnitude of red tide impacts on locals' life and activities, which are also highlighted by our study. Our approach is broadly applicable to any type of air quality impediment with risk of human exposure.
- Neural network based pore flow field prediction in porous media using super resolutionZhou, Xu-Hui X.; McClure, James; Chen, Cheng; Xiao, Heng (2021)Previous works have demonstrated using the geometry of the microstructure of porous media to predict the ow velocity fields therein based on neural networks. However, such schemes are purely based on geometric information without accounting for the physical constraints on the velocity fields such as that due to mass conservation. In this work, we propose using a super-resolution technique to enhance the velocity field prediction by utilizing coarse-mesh velocity fields, which are often available inexpensively but carry important physical constraints. We apply our method to predict velocity fields in complex porous media. The results demonstrate that incorporating the coarse-mesh flow field significantly improves the prediction accuracy of the fine-mesh flow field as compared to predictions that rely on geometric information alone. This study highlights the merits of including coarse-mesh flow field with physical constraints embedded in it.
- Two-Level Weight Optimization of Composite Laminates Using Integer ProgrammingBorwankar, Pranav; Zhao, Wei; Kapania, Rakesh K.; Bansal, Manish (American Institute of Aeronautics and Astronautics, 2022-11-01)Optimization of composite laminates requires the satisfaction of constraints where the design ply thicknesses and orientations can only take discrete values prescribed by the manufacturers. Heuristics such as particle swarm or genetic algorithms are inefficient in such cases because they provide suboptimal solutions when the number of design variables is large. They also are computationally expensive in handling the combinatorial nature of the problem. In contrast, with the help of binary decision variables, mixed integer programming can be adopted to optimize such laminates efficiently. This paper presents an approach to reformulate lamination parameters and failure constraints as functions of binary decision variables. The buckling load maximization for a simply supported laminated plate is initially demonstrated using integer linear programming. Next, the laminate weight is minimized by varying the number of plies for a given external bi-axial compressive load and subjected to buckling and material failure constraints. A variation of laminate weight minimization is demonstrated by fixing the number of plies and assuming discrete changes in ply thicknesses. This is achieved using a sequential two-level optimization for laminates having uniform ply thickness. Finally, a scalability study is performed to evaluate the performance of mixed integer programming for different problem sizes. It is demonstrated that all three formulations with integer programming achieve significant performance gain and robustness over standard heuristic solvers.