Investigation of Adjoint Based Shape Optimization Techniques in NASCART-GT using Automatic Reverse Differentiation

Automated shape optimization involves making suitable modifications to a geometry that can lead to significant improvements in aerodynamic performance. Currently available mid-fdelity Aerodynamic Optimizers cannot be utilized in the late stages of the design process for performing minor, but consequential, tweaks in geometry. Automated shape optimization involves making suitable modifications to a geometry that can lead to significant improvements in aerodynamic performance. Currently available mid-fidelity Aerodynamic Optimizers cannot be utilized in the late stages of the design process for performing minor, but consequential, tweaks in geometry. High-fidelity shape optimization techniques are explored which, even though computationally demanding, are invaluable since they can account for realistic effects like turbulence and viscocity. The high computational costs associated with the optimization have been avoided by using an indirect optimization approach, which was used to dcouple the effect of the flow field variables on the gradients involved. The main challenge while performing the optimization was to maintain low sensitivity to the number of input design variables. This necessitated the use of Reverse Automatic differentiation tools to generate the gradient. All efforts have been made to keep computational costs to a minimum, thereby enabling hi-fidelity optimization to be used even in the initial design stages. A preliminary roadmap has been laid out for an initial implementation of optimization algorithms using the adjoint approach, into the high fidelity CFD code NASCART-GT.High-fidelity shape optimization techniques are explored which, even though computationally demanding, are invaluable since they can account for realistic effects like turbulence and viscocity. The high computational costs associated with the optimization have been avoided by using an indirect optimization approach, which was used to dcouple the effect of the flow field variables on the gradients involved. The main challenge while performing the optimization was to maintain low sensitivity to the number of input design variables. This necessitated the use of Reverse Automatic differentiation tools to generate the gradient. All efforts have been made to keep computational costs to a minimum, thereby enabling hi-fidelity optimization to be used even in the initial design stages. A preliminary roadmap has been laid out for an initial implementation of optimization algorithms using the adjoint approach, into the high fidelity CFD code NASCART-GT.Ruffin, Stephen - Faculty Mentor ; Feron, Eric - Committee Member/Second Reader ; Sankar, Lakshmi - Committee Member/Second Reade

Efficient collective swimming by harnessing vortices through deep reinforcement learning

Fish in schooling formations navigate complex flow-fields replete with mechanical energy in the vortex wakes of their companions. Their schooling behaviour has been associated with evolutionary advantages including collective energy savings. How fish harvest energy from their complex fluid environment and the underlying physical mechanisms governing energy-extraction during collective swimming, is still unknown. Here we show that fish can improve their sustained propulsive efficiency by actively following, and judiciously intercepting, vortices in the wake of other swimmers. This swimming strategy leads to collective energy-savings and is revealed through the first ever combination of deep reinforcement learning with high-fidelity flow simulations. We find that a `smart-swimmer' can adapt its position and body deformation to synchronise with the momentum of the oncoming vortices, improving its average swimming-efficiency at no cost to the leader. The results show that fish may harvest energy deposited in vortices produced by their peers, and support the conjecture that swimming in formation is energetically advantageous. Moreover, this study demonstrates that deep reinforcement learning can produce navigation algorithms for complex flow-fields, with promising implications for energy savings in autonomous robotic swarms.Comment: 26 pages, 14 figure

Subfilter scalar-flux vector orientation in homogeneous isotropic turbulence

The geometric orientation of the subfilter-scale scalar-flux vector is examined in homogeneous isotropic turbulence. Vector orientation is determined using the eigenframe of the resolved strain-rate tensor. The Schmidt number is kept sufficiently large so as to leave the velocity field, and hence the strain-rate tensor, unaltered by filtering in the viscous-convective subrange. Strong preferential alignment is observed for the case of Gaussian and box filters, whereas the sharp-spectral filter leads to close to a random orientation. The orientation angle obtained with the Gaussian and box filters is largely independent of the filter width and the Schmidt number. It is shown that the alignment direction observed numerically using these two filters is predicted very well by the tensor-diffusivity model. Moreover, preferred alignment of the scalar gradient vector in the eigenframe is shown to mitigate any probable issues of negative diffusivity in the tensor-diffusivity model. Consequentially, the model might not suffer from solution instability when used for large eddy simulations of scalar transport in homogeneous isotropic turbulence. Further a priori tests indicate poor alignment of the Smagorinsky and stretched vortex model predictions with the exact subfilter flux. Finally, strong filter dependence of subfilter scalar-flux orientation suggests that explicit filtering may be preferable to implicit filtering in large eddy simulations

On filtering in the viscous-convective subrange for turbulent mixing of high Schmidt number passive scalars

In the present work, we investigate the possibility of performing velocity-resolved, scalar-filtered (VR-SF) numerical simulations of turbulent mixing of high Schmidt number scalars, by using a Large Eddy Simulation (LES)-type filter in the viscous-convective subrange. The only requirement for this technique is the large scale separation between the Kolmogorov and Batchelor length scales, which is a direct outcome of the high Schmidt number of the scalar. The present a priori analysis using high fidelity direct numerical simulation data leads to two main observations. First, the missing triadic interactions between (resolved) velocity and (filtered-out) scalar modes in the viscous-convective subrange do not affect directly the large scales. Second, the magnitude of the subgrid term is shown to be extremely small, which makes it particularly susceptible to numerical errors associated with the scalar transport scheme. A posteriori tests indicate that upwinded schemes, generally used for LES in complicated geometries, are sufficiently dissipative to overwhelm any contribution from the subgrid term. This renders the subgrid term superfluous, and as a result, VR-SF simulations run without subgrid scalar flux models are able to preserve large scale transport characteristics with remarkable accuracy

Optimal sensing for fish school identification

Fish schooling implies an awareness of the swimmers for their companions. In flow mediated environments, in addition to visual cues, pressure and shear sensors on the fish body are critical for providing quantitative information that assists the quantification of proximity to other swimmers. Here we examine the distribution of sensors on the surface of an artificial swimmer so that it can optimally identify a leading group of swimmers. We employ Bayesian experimental design coupled with two-dimensional Navier Stokes equations for multiple self-propelled swimmers. The follower tracks the school using information from its own surface pressure and shear stress. We demonstrate that the optimal sensor distribution of the follower is qualitatively similar to the distribution of neuromasts on fish. Our results show that it is possible to identify accurately the center of mass and even the number of the leading swimmers using surface only information

Detection and Predictability of Spatial and Temporal Patterns and Trends of Riverine Nutrient Loads in the Midwest

The deleterious effects of multiple stressors on global water resources have become more significant over the past few decades. Anthropogenic activities such as industrialization, urbanization, deforestation, and increased application of agricultural nutrients have led to a decline in overall quality of our aquatic environment. Additionally, these activities have increased greenhouse gas concentrations globally, warming the earth’s atmosphere and eventually having a detrimental effect on global water and energy balances. The global water cycle has been altered, leading to its overall intensification and an increase in frequency of extreme floods and droughts. Addressing increasing water demands coupled with declining water quality and a depletion of water resources requires new approaches in water management. In determining optimum management actions, it is critical to understand the spatial and temporal variability and trends in water quantity and quality. This research aims to improve our knowledge of anthropogenic and natural impacts on water resources by evaluating and refining the science of predicting pollutant (nutrient and sediment) loadings from medium- to large-scale watersheds. To enable these goals, this research is centered on large watersheds in the Midwestern United States, which have been some of the primary sources of nutrient and sediment loadings to downstream water bodies such as the Gulf of Mexico and Lake Erie. In total, 14 watersheds in Illinois, Indiana, Ohio, and Michigan, with extensive water quality datasets, are analyzed in different stages of this research. Most of these watersheds are predominantly agricultural with intensive row-cropped farmlands and have a network of sub-surface tile drainage systems. Pollutant loadings and associated hydrological processes have been simulated using four major modeling approaches: statistical modeling, empirical modeling, physically based modeling, and data mining methods. This report includes eight chapters. The first three chapters describe the problem and research objectives, study area, and data preparation and processing. Next, the impacts of available water quality data on concentration and load predictions and trend calculations are assessed based on traditional statistical methods and several new, improved, and modified approaches (Chapter 4). This segment emphasizes the difficulties in predicting nutrient load and concentration trends under changing climatic conditions, highlighting the importance of continuous nutrient monitoring. Next, two data mining techniques (the nearest-neighbor method and decision trees), scarcely used in hydrology, were applied to predict the missing Nitrate Nitrogen (NO3-N) concentrations for two extensively monitored watersheds in the Lake Erie basin. These predictions (Chapter 5) are important in load estimations and demonstrate the potential of data mining to produce results comparable with statistical and empirical methods presented in the previous chapter. In Chapter 6, statistical regression techniques are used to assess the role of large load events in predicting Total Suspended Solids (SS), Total Phosphorus (TP), and NO3-N annual loads. A novel constituent-specific baseflow separation technique based on mechanistic differences in nutrient and sediment loadings is proposed and applied. As a result, regression relationships between the largest annual loads and total annual loads were developed for all three constituents. An Analysis of Covariance (ANCOVA) indicated that these relationships are often statistically indistinguishable from each other when applied to watersheds with a similar land use. Then, in Chapter 7, the temporal patterns of pollutant loadings from large Midwestern watersheds are analyzed using circular statistics. Critical periods of high loadings, precipitation, and river flow were identified. While river flows and pollutant loadings are highest in late winter and early spring (e.g., March and April), rainfall totals are highest during late spring and early summer (e.g., May through August). Finally, Chapter 8 shows the results based on the physically based SWAT model. The model is calibrated for river discharge and water quality in the largest watershed in the Lake Erie basin, the Maumee River watershed. The calibrated model is used to gauge the impacts of future projected climate change from the mid-century and late-century time periods on the hydrology and water quality in the watershed. The results indicate that climate change could have a significant impact on sediment and nutrient loads, and that more detailed studies are needed to more accurately assess this impact and its confidence limits.published or submitted for publicationis peer reviewedOpe

Understanding Crowd Dynamics in Processions during Mass Religious Gatherings A case study of Shahi Snan in Kumbh Mela

Large people gatherings in public places exhibit crowd dynamics that are quite complex. Such mass events with high densities are fraught with potentially serious consequences if not understood and managed properly. In mass religious processions, the participants in the crowd are emotional and mostly impulsive and often crowd disasters occur due to the behaviour of the crowd. These events have great potential to cause safety hazards to the people. This paper attempts to narrate the typical situations of crowd dynamics observed in the Kumbh Mela procession-2016 and to describe the characteristics of the crowd that have not been reported in literature so far but have a significant impact on the crowd. Extreme crowd pressures resulting in individual loss control due to psychological and physiological factors, heterogeneity in the crowd, group behaviour and their induced competitiveness, unexpected behaviour exhibited due to the motivation behind participating in such procession makes it a typical crowd concentrated event to study the potentially critical dynamics of crowd. Physical and psychological forces acting on the people and their resulting dynamics of crowd in the Kumbh Mela procession 2016 lead to serpentine behaviour, which can possibly lead to crowd crushes, or any such crowd risk situations. Therefore, the characteristics of crowd participating in the Kumbh Mela procession have to be clearly understood so that it helps in better planning and well-organized movement patterns

Uncovering dynamically critical regions in near-wall turbulence using 3D Convolutional Neural Networks

Near-wall regions in wall-bounded turbulent flows experience strong intermittent events involving ejections of slow-moving fluid parcels away from the wall, and `sweeps' of faster moving fluid towards the wall. Here, we train a three-dimensional Convolutional Neural Network (CNN) to predict the intensity of ejection events that occur in Direct Numerical Simulation (DNS) of a periodic channel flow. The trained network is able to predict burst intensities accurately for flow snaphshots that are sufficiently removed from the training data so as to be temporally decorrelated. More importantly, we probe the trained network to reveal regions of the flow where the network focuses its attention in order to make a prediction. We find that these salient regions correlate very well with fluid parcels being ejected away from the wall. Moreover, the CNN is able to keep track of the salient fluid parcels as the flow evolves in time. This demonstrates that CNNs are capable of discovering dynamically critical phenomena in turbulent flows without requiring any a-priori knowledge of the underlying dynamics. Remarkably, the trained CNN is able to predict ejection intensities accurately for data at different Reynolds numbers, which highlights its ability to identify physical processes that persist across varying flow conditions. The results presented here highlight the immense potential of CNNs for discovering and analyzing nonlinear spatial correlations in turbulent flows.Comment: 10 pages, 7 figure