State-of-the-art in aerodynamic shape optimisation methods

Aerodynamic optimisation has become an indispensable component for any aerodynamic design over the past 60 years, with applications to aircraft, cars, trains, bridges, wind turbines, internal pipe flows, and cavities, among others, and is thus relevant in many facets of technology. With advancements in computational power, automated design optimisation procedures have become more competent, however, there is an ambiguity and bias throughout the literature with regards to relative performance of optimisation architectures and employed algorithms. This paper provides a well-balanced critical review of the dominant optimisation approaches that have been integrated with aerodynamic theory for the purpose of shape optimisation. A total of 229 papers, published in more than 120 journals and conference proceedings, have been classified into 6 different optimisation algorithm approaches. The material cited includes some of the most well-established authors and publications in the field of aerodynamic optimisation. This paper aims to eliminate bias toward certain algorithms by analysing the limitations, drawbacks, and the benefits of the most utilised optimisation approaches. This review provides comprehensive but straightforward insight for non-specialists and reference detailing the current state for specialist practitioners

Rational Reprogramming of Fungal Polyketide First Ring Cyclization

Resorcylic acid lactones (RAL) and dihydroxyphenylacetic acid lactones (DAL) represent important pharmacophores with heat shock response and immune system modulatory activities. The biosynthesis of these fungal polyketides involves a pair of collaborating iterative polyketide synthases (iPKSs): a highly reducing iPKS (hrPKS) whose product is further elaborated by a nonreducing iPKS (nrPKS) to yield a 1,3-benzenediol moiety bridged by a macrolactone. Biosynthesis of unreduced polyketides requires the sequestration and programmed cyclization of highly reactive poly-β-ketoacyl intermediates to channel these uncommitted, pluripotent substrates towards defined subsets of the polyketide structural space. Catalyzed by product template (PT) domains of the fungal nrPKSs and discrete aromatase/cyclase enzymes in bacteria, regiospecific first-ring aldol cyclizations result in characteristically different polyketide folding modes. However, a few fungal polyketides, including the DAL dehydrocurvularin, derive from a folding event that is analogous to the bacterial folding mode. The structural basis of such a drastic difference in the way a PT domain acts has not been investigated until now. We report here that the fungal versus the bacterial folding mode difference is portable upon creating hybrid enzymes, and structurally characterize the resulting unnatural products. Using structure-guided active site engineering, we unravel structural contributions to regiospecific aldol condensations, and show that reshaping the cyclization chamber of a PT domain by only three selected point mutations is sufficient to reprogram the dehydrocurvularin nrPKS to produce polyketides with a fungal fold. Such rational control of first ring cyclizations will facilitate efforts towards the engineered biosynthesis of novel chemical diversity from natural unreduced polyketides

The Self-Organization of Interaction Networks for Nature-Inspired Optimization

Over the last decade, significant progress has been made in understanding complex biological systems, however there have been few attempts at incorporating this knowledge into nature inspired optimization algorithms. In this paper, we present a first attempt at incorporating some of the basic structural properties of complex biological systems which are believed to be necessary preconditions for system qualities such as robustness. In particular, we focus on two important conditions missing in Evolutionary Algorithm populations; a self-organized definition of locality and interaction epistasis. We demonstrate that these two features, when combined, provide algorithm behaviors not observed in the canonical Evolutionary Algorithm or in Evolutionary Algorithms with structured populations such as the Cellular Genetic Algorithm. The most noticeable change in algorithm behavior is an unprecedented capacity for sustainable coexistence of genetically distinct individuals within a single population. This capacity for sustained genetic diversity is not imposed on the population but instead emerges as a natural consequence of the dynamics of the system

Structural Drift: The Population Dynamics of Sequential Learning

We introduce a theory of sequential causal inference in which learners in a chain estimate a structural model from their upstream teacher and then pass samples from the model to their downstream student. It extends the population dynamics of genetic drift, recasting Kimura's selectively neutral theory as a special case of a generalized drift process using structured populations with memory. We examine the diffusion and fixation properties of several drift processes and propose applications to learning, inference, and evolution. We also demonstrate how the organization of drift process space controls fidelity, facilitates innovations, and leads to information loss in sequential learning with and without memory.Comment: 15 pages, 9 figures; http://csc.ucdavis.edu/~cmg/compmech/pubs/sdrift.ht

Forest Stand Structure and Primary Production in relation to Ecosystem Development, Disturbance, and Canopy Composition

Temperate forests are complex ecosystems that sequester carbon (C) in biomass. C storage is related to ecosystem-scale forest structure, changing over succession, disturbance, and with community composition. We quantified ecosystem biological and physical structure in two forest chronosequences varying in disturbance intensity, and three late successional functional types to examine how multiple structural expressions relate to ecosystem C cycling. We quantified C cycling as wood net primary production (NPP), ecosystem structure as Simpson’s Index, and physical structure as leaf quantity (LAI) and arrangement (rugosity), examining how wood NPP-structure relates to light distribution and use-efficiency. Relationships between structural attributes of biodiversity, LAI, and rugosity differed. Development of rugosity was conserved regardless of disturbance and composition, suggesting optimization of vegetation arrangement over succession. LAI and rugosity showed significant positive productivity trends over succession, particularly within deciduous broadleaf forests, suggesting these measures of structure contain complementary, not redundant, information related to C cycling

The Self-Organization of Interaction Networks for Nature-Inspired Optimization

Over the last decade, significant progress has been made in understanding complex biological systems, however there have been few attempts at incorporating this knowledge into nature inspired optimization algorithms. In this paper, we present a first attempt at incorporating some of the basic structural properties of complex biological systems which are believed to be necessary preconditions for system qualities such as robustness. In particular, we focus on two important conditions missing in Evolutionary Algorithm populations; a self-organized definition of locality and interaction epistasis. We demonstrate that these two features, when combined, provide algorithm behaviors not observed in the canonical Evolutionary Algorithm or in Evolutionary Algorithms with structured populations such as the Cellular Genetic Algorithm. The most noticeable change in algorithm behavior is an unprecedented capacity for sustainable coexistence of genetically distinct individuals within a single population. This capacity for sustained genetic diversity is not imposed on the population but instead emerges as a natural consequence of the dynamics of the system

Improved dynamical particle swarm optimization method for structural dynamics

A methodology to the multiobjective structural design of buildings based on an improved particle swarm optimization algorithm is presented, which has proved to be very efficient and robust in nonlinear problems and when the optimization objectives are in conflict. In particular, the behaviour of the particle swarm optimization (PSO) classical algorithm is improved by dynamically adding autoadaptive mechanisms that enhance the exploration/exploitation trade-off and diversity of the proposed algorithm, avoiding getting trapped in local minima. A novel integrated optimization system was developed, called DI-PSO, to solve this problem which is able to control and even improve the structural behaviour under seismic excitations. In order to demonstrate the effectiveness of the proposed approach, the methodology is tested against some benchmark problems. Then a 3-story-building model is optimized under different objective cases, concluding that the improved multiobjective optimization methodology using DI-PSO is more efficient as compared with those designs obtained using single optimization.Peer ReviewedPostprint (published version