Tecplot (United States)

Predicting the behavior of individuals acting under their own motivation is a challenge shared across multiple scientific fields, from economic to ecological systems. In rivers, fish frequently change their orientation even when stimuli are unchanged, which makes understanding and predicting their movement in time-varying environments near built infrastructure particularly challenging. Cognition is central to fish movement, and our lack of understanding is costly in terms of time and resources needed to design and manage water operations infrastructure that is able to meet the multiple needs of human society while preserving valuable living resources. An open question is how best to cognitively account for the multi-modal, -attribute, -alternative, and context-dependent decision-making of fish near infrastructure. Here, we leverage agent- and individual-based modeling techniques to encode a cognitive approach to mechanistic fish movement behavior that operates at the scale in which water operations river infrastructure is engineered and managed. Our cognitive approach to mechanistic behavior modeling uses a Eulerian-Lagrangian-agent method (ELAM) to interpret and quantitatively predict fish movement and passage/entrainment near infrastructure across different and time-varying river conditions. A goal of our methodology is to leverage theory and equations that can provide an interpretable version of animal movement behavior in complex environments that requires a minimal number of parameters in order to facilitate the application to new data in real-world engineering and management design projects. We first describe concepts, theory, and mathematics applicable to animals across aquatic, terrestrial, avian, and subterranean domains. Then, we detail our application to juvenile Pacific salmonids in the Bay-Delta of California. We reproduce observations of salmon movement and passage/entrainment with one field season of measurements, year 2009, using five simulated behavior responses to 3-D hydrodynamics. Then, using the ELAM model calibrated from year 2009 data, we predict the movement and passage/entrainment of salmon for a later field season, year 2014, which included a novel engineered fish guidance boom not present in 2009. Central to the fish behavior model’s performance is the notion that individuals are attuned to more than one hydrodynamic signal and more than one timescale. We find that multi-timescale perception can disentangle multiplex hydrodynamic signals and inform the context-based behavioral choice of a fish. Simulated fish make movement decisions within a rapidly changing environment without global information, knowledge of which direction is downriver/upriver, or path integration. The key hydrodynamic stimuli are water speed, the spatial gradient in water speed, water acceleration, and fish swim bladder pressure. We find that selective tidal stream transport in the Bay-Delta is a superset of the fish-hydrodynamic behavior repertoire that reproduces salmon movement and passage in dam reservoir environments. From a cognitive movement ecology perspective, we describe how a behavior can emerge from a repertoire of multiple fish-hydrodynamic responses that are each tailored to suit the animal’s recent past experience (localized environmental context). From a movement behavior perspective, we describe how different fish swim paths can emerge from the same local hydrodynamic stimuli. Our findings demonstrate that a cognitive approach to mechanistic fish movement behavior modeling does not always require the maximum possible spatiotemporal resolution for representing the river environmental stimuli although there are concomitant tradeoffs in resolving features at different scales. From a water operations perspective, we show that a decision-support tool can successfully operate outside the calibration conditions, which is a necessary attribute for tools informing future engineering design and management actions in a world that will invariably look different than the past.

View Video Presentation: https://doi.org/10.2514/6.2023-0911.vid Higher-Order finite-element CFD methods have the potential to reduce the computational cost to achieve a desired solution error. These techniques have been an area of research for many years and are becoming more widely available in popular CFD codes. CFD visualization software is lagging behind the development of higher-order CFD analysis codes. This paper discusses a technique for visualizing isosurfaces and streamlines in higher-order element solutions with reduced memory usage. The technique recursively subdivides higher-order elements into smaller linear sub-elements where the isosurface can be extracted using standard marching-tets techniques. Memory usage is minimized by discarding unneeded sub-elements. In a previous paper this technique was demonstrated with higher-order quadratic hexahedra, tetrahedra, prism, and pyramid elements with Lagrangian polynomial basis functions. In this paper, the technique is extended to cubic elements and streamlines, and performance optimization are discussed. The results are compared to other techniques for visualization of higher-order element isosurfaces.

We use two zoom-in ΛCDM hydrodynamical simulations of massive disk galaxies to study the possible existence of fixed satellite groups showing a kinematically coherent behavior across evolution (angular momentum conservation and clustering). We identify three such groups in the two simulations, defining kinematically coherent persistent planes (KPPs) that last at least from virialization to z = 0 (more than 7 Gyr). This proves that orbital pole clustering is not necessarily set in at low redshift, representing a long-lived property of galaxy systems. KPPs are thin and oblate, represent ∼25%–40% of the total number of satellites in the system, and are roughly perpendicular to their corresponding central disk galaxies during certain periods, consistently with Milky Way z = 0 data. KPP satellite members are statistically distinguishable from satellites outside KPPs: they show higher specific orbital angular momenta, orbit more perpendicularly to the central disk galaxy, and have larger pericentric distances than the latter. We numerically prove, for the first time, that KPPs and the best-quality positional planes share the same space configuration across time, such that KPPs act as “skeletons” preventing the latter from being washed out in short timescales. In one of the satellite−host systems, we witness the late capture of a massive dwarf galaxy endowed with its own satellite system, also organized into a KPP configuration prior to its capture. We briefly explore the consequences this event has on the host’s KPP and on the possible enhancement of the asymmetry in the number of satellites rotating in one sense or the opposite within the KPP.