An Ethical Framework for Artificial Intelligence and Sustainable Cities

The digital revolution has brought ethical crossroads of technology and behavior, especially in the realm of sustainable cities. The need for a comprehensive and constructive ethical framework is emerging as digital platforms encounter trouble to articulate the transformations required to accomplish the sustainable development goal (SDG) 11 (on sustainable cities), and the remainder of the related SDGs. The unequal structure of the global system leads to dynamic and systemic problems, which have a more significant impact on those that are most vulnerable. Ethical frameworks based only on the individual level are no longer sufficient as they lack the necessary articulation to provide solutions to the new systemic challenges. A new ethical vision of digitalization must comprise the understanding of the scales and complex interconnections among SDGs and the ongoing socioeconomic and industrial revolutions. Many of the current social systems are internally fragile and very sensitive to external factors and threats, which lead to unethical situations. Furthermore, the multilayered net-like social tissue generates clusters of influence and leadership that prevent communities from a proper development. Digital technology has also had an impact at the individual level, posing several risks including a more homogeneous and predictable humankind. To preserve the core of humanity, we propose an ethical framework to empower individuals centered on the cities and interconnected with the socioeconomic ecosystem and the environment through the complex relationships of the SDGs. Only by combining human-centered and collectiveness-oriented digital development will it be possible to construct new social models and interactions that are ethical. Thus, it is necessary to combine ethical principles with the digital innovation undergoing in all the dimensions of sustainability

Drag-reduction strategies in wall-bounded turbulent flows using deep reinforcement learning

In this work we compare different drag-reduction strategies that compute their actuation based on the fluctuations at a given wall-normal location in turbulent open channel flow. In order to perform this study, we implement and describe in detail the reinforcement-learning interface to a computationally-efficient, parallelized, high-fidelity solver for fluid-flow simulations. We consider opposition control (Choi, Moin, and Kim, Journal of Fluid Mechanics 262, 1994) and the policies learnt using deep reinforcement learning (DRL) based on the state of the flow at two inner-scaled locations ($y^+ = 10$ and $y^+ = 15$). By using deep deterministic policy gradient (DDPG) algorithm, we are able to discover control strategies that outperform existing control methods. This represents a first step in the exploration of the capability of DRL algorithm to discover effective drag-reduction policies using information from different locations in the flow.Comment: 6 pages, 5 figure

Intense reynolds-stress events in turbulent ducts

The aim of the present work is to investigate the role of intense Reynolds shear-stress events in the generation of the secondary flow in turbulent ducts. We consider the connected regions of flow where the product of the instantaneous fluctuations of two velocity components is higher than a threshold based on the long-time turbulence statistics, in the spirit of the three-dimensional quadrant analysis proposed by Lozano-Durán et al. (J. Fluid Mech., vol. 694, 2012, pp. 100–130). We examine both the geometrical properties of these structures and their contribution to the mean in-plane velocity components, and we perfom a comparison with turbulent channel flow at similar Reynolds number. The contribution to a certain mean quantity is defined as the ensemble average over the detected coherent structures, weighted with their own occupied volume fraction. In the core region of the duct, the contribution of intense events to the wall-normal component of the mean velocity is in very good agreement with that in the channel, despite the presence of the secondary flow in the former. Additionally, the shapes of the three-dimensional objects do not differ significantly in both flows. In the corner region of the duct, the proximity of the walls affects both the geometrical properties of the coherent structures and the contribution to the mean component of the vertical velocity. However, such contribution is less relevant than that of the complementary portion of the flow not included in such objects. Our results show that strong Reynolds shear-stress events are affected by the presence of a corner but, despite the important role of these structures in the dynamics of wall-bounded turbulent flows, their contribution to the secondary flow is relatively low, both in the core and in the corner

Coherent structures in turbulent boundary layers over an airfoil

This preliminary study is concerned with the identification of three-dimensional coherent structures, defined as intense Reynolds-stress events, in the turbulent boundary layer developing over the suction side of a NACA4412 airfoil at a Reynolds number based on the chord lenght and the incoming velocity of Rec = 200, 000. The scientific interest for such flows originates from the non-uniform adverse pressure gradient that affects the boundary-layer development. Firstly, we assess different methods to identify the turbulent-non-turbulent interface, in order to exclude the irrotational region from the analysis. Secondly, we evaluate the contribution of the considered coherent structures to the enhanced wall-normal velocity, characteristic of adverse pressure gradients. Our results show that it is necessary to limit the detection of coherent structures to the turbulent region of the domain, and that the structures reveal qualitative differences between the contributions of intense events to the wall-normal velocity in adverse-pressure-gradient and zero-pressure-gradient turbulent boundary layers