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We introduce a closure model for coarse-grained kinetic theories of polar active fluids. Based on a quasi-equilibrium approximation of the particle distribution function, the model closely captures important analytical properties of the kinetic theory, including its linear stability and the balance of entropy production and dissipation. Nonlinear simulations show the model reproduces the qualitative behavior and nonequilibrium statistics of the kinetic theory, unlike commonly used closure models. We use the closure model to simulate highly turbulent suspensions in both two and three dimensions in which we observe complex multiscale dynamics, including large concentration fluctuations and a proliferation of polar and nematic defects.

We examine the interactions between actively rotating proteins moving in a membrane. Experimental evidence suggests that such rotor proteins, like the ATP synthases of the inner mitochondrial membrane, can arrange themselves into lattices. We show that crystallization is possible through a combination of hydrodynamic and repulsive interactions between the rotor proteins. In particular, hydrodynamic interactions induce rotational motion of the rotor protein assembly that, in the presence of repulsion, drives the system into a hexagonal lattice. The entire crystal rotates with an angular velocity which increases with motor density and decreases with lattice diameter - larger and sparser arrays rotate at a slower pace. The rotational interactions allow ensembles of proteins to sample configurations and reach an ordered steady state, which are inaccessible to the quenched nonrotational system. Rotational interactions thus act as a sort of temperature that removes disorder, except that actual thermal diffusion leads to expansion and loss of order. In contrast, the rotational interactions are bounded in space. Hence, once an ordered state is reached, it is maintained at all times., 5 pages, 4 figures

This paper presents a constraint-aware decentralized approach to construction with teams of robots. We present an extension to existing work on a distributed controller for robotic construction of simple structures. Our previous work described a set of adaptive algorithms for constructing truss structures given a target geometry using continuous and graph-based equal-mass partitioning [1], [2]. Using this work as a foundation, we present an algorithm which performs construction tasks and conforms to physical constraints while considering those constraints to parallelize tasks. This is accomplished by defining a mass function which reflects the priority of part placement and prevents physically impossible states. This mass function generates a set of pointmasses in ℝn, and we present a novel algorithm for finding a locally optimal, equal-mass, convex tessellation of such a set., Boeing Company, National Science Foundation (U.S.)., National Science Foundation (U.S.). Office of Emerging Frontiers in Research and Innovation (Grant #0735953), United States. Army Research Office. Multidisciplinary University Research Initiative. Swarms of Autonomous Robots and Mobile Sensors Project (Grant number N0014-09-1051), United States. Army Research Office. Multidisciplinary University Research Initiative. Scalable (Grant number 544252)

Multi-Attribute Tradespace Exploration (MATE) for Survivability is introduced as a general methodology for survivability analysis and demonstrated through an application to a satellite radar system. MATE for Survivability applies decision theory to the parametric modeling of thousands of design alternatives across representative distributions of disturbance environments. Survivability considerations are incorporated into the existing MATE process (i.e., a solution-generating and decision-making framework that applies decision theory to model-based design) by applying empirically-validated survivability design principles and value-based survivability metrics to concept generation and concept evaluation activities, respectively. MATE for Survivability consists of eight iterative phases: (1) define system value proposition, (2) generate concepts, (3) specify disturbances, (4) apply survivability principles, (5) model baseline system performance, (6) model impact of disturbances on dynamic system performance, (7) apply survivability metrics, and (8) select designs for further analysis. The application of MATE for Survivability to satellite radar demonstrates the importance of incorporating survivability considerations into conceptual design for identifying inherently survivable architectures that efficiently balance competing performance metrics of lifecycle cost, mission utility, and operational survivability.

Multi-Attribute Tradespace Exploration for Survivability is a system design and analysis methodology that incorporates survivability considerations into the tradespace exploration process (i.e., a solution-generating and decision-making framework that applies decision theory to model-based design). During the concept generation phase of tradespace exploration, the methodology applies seventeen empirically validated survivability design principles spanning susceptibility reduction, vulnerability reduction, and resilience enhancement. During subsequent concept evaluation, the methodology adds value-based survivability metrics to traditional architectural evaluation criteria of mission utility and lifecycle cost. Applied to a satellite radar mission, the methodology allowed operational survivability to be statistically evaluated across representative distributions of naturally occurring disturbances in the space environment and for survivability to be incorporated as a decision factor earlier in the design process. Constellations in the illustrative example are shown to be the most survivable, mitigating disturbances architecturally, rather than through additive features., Massachusetts Institute of Technology (Systems Engineering Advancement Research Initiative (SEAri)), Massachusetts Institute of Technology. Program on Emerging Technologies