TY - THES AB - Most real-world flows are multiphase, yet we know little about them compared to their single-phase counterparts. Multiphase flows are more difficult to investigate as their dynamics occur in large parameter space and involve complex phenomena such as preferential concentration, turbulence modulation, non-Newtonian rheology, etc. Over the last few decades, experiments in particle-laden flows have taken a back seat in favour of ever-improving computational resources. However, computers are still not powerful enough to simulate a real-world fluid with millions of finite-size particles. Experiments are essential not only because they offer a reliable way to investigate real-world multiphase flows but also because they serve to validate numerical studies and steer the research in a relevant direction. In this work, we have experimentally investigated particle-laden flows in pipes, and in particular, examined the effect of particles on the laminar-turbulent transition and the drag scaling in turbulent flows. For particle-laden pipe flows, an earlier study [Matas et al., 2003] reported how the sub-critical (i.e., hysteretic) transition that occurs via localised turbulent structures called puffs is affected by the addition of particles. In this study, in addition to this known transition, we found a super-critical transition to a globally fluctuating state with increasing particle concentration. At the same time, the Newtonian-type transition via puffs is delayed to larger Reynolds numbers. At an even higher concentration, only the globally fluctuating state is found. The dynamics of particle-laden flows are hence determined by two competing instabilities that give rise to three flow regimes: Newtonian-type turbulence at low, a particle-induced globally fluctuating state at high, and a coexistence state at intermediate concentrations. The effect of particles on turbulent drag is ambiguous, with studies reporting drag reduction, no net change, and even drag increase. The ambiguity arises because, in addition to particle concentration, particle shape, size, and density also affect the net drag. Even similar particles might affect the flow dissimilarly in different Reynolds number and concentration ranges. In the present study, we explored a wide range of both Reynolds number and concentration, using spherical as well as cylindrical particles. We found that the spherical particles do not reduce drag while the cylindrical particles are drag-reducing within a specific Reynolds number interval. The interval strongly depends on the particle concentration and the relative size of the pipe and particles. Within this interval, the magnitude of drag reduction reaches a maximum. These drag reduction maxima appear to fall onto a distinct power-law curve irrespective of the pipe diameter and particle concentration, and this curve can be considered as the maximum drag reduction asymptote for a given fibre shape. Such an asymptote is well known for polymeric flows but had not been identified for particle-laden flows prior to this work. AU - Agrawal, Nishchal ID - 9728 KW - Drag Reduction KW - Transition to Turbulence KW - Multiphase Flows KW - particle Laden Flows KW - Complex Flows KW - Experiments KW - Fluid Dynamics SN - 2663-337X TI - Transition to turbulence and drag reduction in particle-laden pipe flows ER - TY - CONF AB - We propose a neural information processing system obtained by re-purposing the function of a biological neural circuit model to govern simulated and real-world control tasks. Inspired by the structure of the nervous system of the soil-worm, C. elegans, we introduce ordinary neural circuits (ONCs), defined as the model of biological neural circuits reparameterized for the control of alternative tasks. We first demonstrate that ONCs realize networks with higher maximum flow compared to arbitrary wired networks. We then learn instances of ONCs to control a series of robotic tasks, including the autonomous parking of a real-world rover robot. For reconfiguration of the purpose of the neural circuit, we adopt a search-based optimization algorithm. Ordinary neural circuits perform on par and, in some cases, significantly surpass the performance of contemporary deep learning models. ONC networks are compact, 77% sparser than their counterpart neural controllers, and their neural dynamics are fully interpretable at the cell-level. AU - Hasani, Ramin AU - Lechner, Mathias AU - Amini, Alexander AU - Rus, Daniela AU - Grosu, Radu ID - 10673 SN - 2640-3498 T2 - Proceedings of the 37th International Conference on Machine Learning TI - A natural lottery ticket winner: Reinforcement learning with ordinary neural circuits ER - TY - CONF AB - Many systems rely on optimistic concurrent search trees for multi-core scalability. In principle, optimistic trees have a simple performance story: searches are read-only and so run in parallel, with writes to shared memory occurring only when modifying the data structure. However, this paper shows that in practice, obtaining the full performance benefits of optimistic search trees is not so simple. We focus on optimistic binary search trees (BSTs) and perform a detailed performance analysis of 10 state-of-the-art BSTs on large scale x86-64 hardware, using both microbenchmarks and an in-memory database system. We find and explain significant unexpected performance differences between BSTs with similar tree structure and search implementations, which we trace to subtle performance-degrading interactions of BSTs with systems software and hardware subsystems. We further derive a prescriptive approach to avoid this performance degradation, as well as algorithmic insights on optimistic BST design. Our work underlines the gap between the theory and practice of multi-core performance, and calls for further research to help bridge this gap. AU - Arbel-Raviv, Maya AU - Brown, Trevor A AU - Morrison, Adam ID - 7272 SN - 9781939133021 T2 - Proceedings of the 2018 USENIX Annual Technical Conference TI - Getting to the root of concurrent binary search tree performance ER - TY - CONF AB - The Price of Anarchy (PoA) is a well-established game-theoretic concept to shed light on coordination issues arising in open distributed systems. Leaving agents to selfishly optimize comes with the risk of ending up in sub-optimal states (in terms of performance and/or costs), compared to a centralized system design. However, the PoA relies on strong assumptions about agents' rationality (e.g., resources and information) and interactions, whereas in many distributed systems agents interact locally with bounded resources. They do so repeatedly over time (in contrast to "one-shot games"), and their strategies may evolve. Using a more realistic evolutionary game model, this paper introduces a realized evolutionary Price of Anarchy (ePoA). The ePoA allows an exploration of equilibrium selection in dynamic distributed systems with multiple equilibria, based on local interactions of simple memoryless agents. Considering a fundamental game related to virus propagation on networks, we present analytical bounds on the ePoA in basic network topologies and for different strategy update dynamics. In particular, deriving stationary distributions of the stochastic evolutionary process, we find that the Nash equilibria are not always the most abundant states, and that different processes can feature significant off-equilibrium behavior, leading to a significantly higher ePoA compared to the PoA studied traditionally in the literature. AU - Schmid, Laura AU - Chatterjee, Krishnendu AU - Schmid, Stefan ID - 7346 T2 - Proceedings of the 23rd International Conference on Principles of Distributed Systems TI - The evolutionary price of anarchy: Locally bounded agents in a dynamic virus game VL - 153 ER - TY - CONF AB - The monitoring of event frequencies can be used to recognize behavioral anomalies, to identify trends, and to deduce or discard hypotheses about the underlying system. For example, the performance of a web server may be monitored based on the ratio of the total count of requests from the least and most active clients. Exact frequency monitoring, however, can be prohibitively expensive; in the above example it would require as many counters as there are clients. In this paper, we propose the efficient probabilistic monitoring of common frequency properties, including the mode (i.e., the most common event) and the median of an event sequence. We define a logic to express composite frequency properties as a combination of atomic frequency properties. Our main contribution is an algorithm that, under suitable probabilistic assumptions, can be used to monitor these important frequency properties with four counters, independent of the number of different events. Our algorithm samples longer and longer subwords of an infinite event sequence. We prove the almost-sure convergence of our algorithm by generalizing ergodic theory from increasing-length prefixes to increasing-length subwords of an infinite sequence. A similar algorithm could be used to learn a connected Markov chain of a given structure from observing its outputs, to arbitrary precision, for a given confidence. AU - Ferrere, Thomas AU - Henzinger, Thomas A AU - Kragl, Bernhard ID - 7348 SN - 1868-8969 T2 - 28th EACSL Annual Conference on Computer Science Logic TI - Monitoring event frequencies VL - 152 ER -