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Results for “Evolutionary convergence”

Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

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At least 19 records
All sourcesNASA NTRSDOE OSTI
NASA NTRS2002

Do convergent developmental mechanisms underlie convergent phenotypes?

Convergence is a pervasive evolutionary process, affecting many aspects of phenotype and even genotype. Relatively little is known about convergence in developmental processes, however, nor about the degree to which convergence in development underlies convergence in anatomy. A switch in the ecology of sea urchins from feeding to nonfeeding larvae illustrates how convergence in development can be associated with convergence in anatomy. Comparisons to more distantly related taxa, however, suggest that this association may be limited to relatively close phylogenetic comparisons. Similarities in gene expression during development provide another window into the association between convergence in developmental processes and convergence in anatomy. Several well-studied transcription factors exhibit likely cases of convergent gene expression in distantly related animal phyla. Convergence in regulatory gene expression domains is probably more common than generally acknowledged, and can arise for several different reasons. Copyright 2002 S. Karger AG, Basel.

NASA Discipline Evolutionary Biology↗
NASA NTRS2017

Towards a Universal Biology: Is the Origin and Evolution of Life Predictable?

The origin and evolution of life seems an unpredictable oddity, based on the quirks of contingency. Celebrated by the late Stephen Jay Gould in several books, "evolution by contingency" has all the adventure of a thriller, but lacks the predictive power of the physical sciences. Not necessarily so, replied Simon Conway Morris, for convergence reassures us that certain evolutionary responses are replicable. The outcome of this debate is critical to Astrobiology. How can we understand where we came from on Earth without prophesy? Further, we cannot design a rational strategy for the search for life elsewhere - or to understand what the future will hold for life on Earth and beyond - without extrapolating from pre-biotic chemistry and evolution. There are several indirect approaches to understanding, and thus describing, what life must be. These include philosophical approaches to defining life (is there even a satisfactory definition of life?), using what we know of physics, chemistry and life to imagine alternate scenarios, using different approaches that life takes as pseudoreplicates (e.g., ribosomal vs non-ribosomal protein synthesis), and experimental approaches to understand the art of the possible. Given that: (1) Life is a process based on physical components rather than simply an object; (2). Life is likely based on organic carbon and needs a solvent for chemistry, most likely water, and (3) Looking for convergence in terrestrial evolution we can predict certain tendencies, if not quite "laws", that provide predictive power. Biological history must obey the laws of physics and chemistry, the principles of natural selection, the constraints of an evolutionary past, genetics, and developmental biology. This amalgam creates a surprising amount of predictive power in the broad outline. Critical is the apparent prevalence of organic chemistry, and uniformity in the universe of the laws of chemistry and physics. Instructive is the widespread occurrence of convergent or parallel evolution, which suggests that under certain conditions similar solutions are arrived at independently.

Universal biology; life everywhere↗
NASA NTRSDate not supplied

Recent Progress in OVERFLOW Convergence Improvements

Improvements have been made to the implicit symmetric successive overrelaxation algorithm in the OVERFLOW 2.3 structured, overset grid, computational fluid dynamics flow solver. These improvements, consisting of implicit boundary conditions, improved flux Jacobian linearizations, and CFL number ramping, are a series of evolutionary changes to the linear solver that have resulted in increased nonlinear convergence rates and faster time to solution. A series of test cases are presented that demonstrate the effect of the changes through comparison with the original SSOR path and other linear solver implementations within OVERFLOW.

Joseph M Derlaga↗
NASA NTRS1983

Evolutionary effects of helium diffusion in population II stars

Evolutionary calculations continuing until well past turnoff are presented for models of low-mass Population II stars which take into account the effects of the diffusion of helium relative to hydrogen. Evolutionary tracks, cluster isochrones and hydrogen distributions were obtained for stellar masses in the range 0.75 to 1.01 solar masses, both in the presence and absence of diffusion. It is found that for a star of a given mass, diffusion speeds up the evolutionary process on the main sequence, although after turnoff evolution is slowed with respect to the case without diffusion. As the stars ascend the red giant branch, their outer regions are remixed so that evidence of helium diffusion is erased, and the evolutionary tracks of the models with and without diffusion converge. Thus, if the age of a globular cluster is determined from the absolute magnitude at turnoff or from fitting isochrones, diffusion results in a 25% reduction in the derived age at a turnoff magnitude of 4.23, and a 14% reduction at a turnoff magnitude of 3.45.

Stringfellow, G. S.↗
NASA NTRS1989

The Deinococcus-Thermus phylum and the effect of rRNA composition on phylogenetic tree construction

Through comparative analysis of 16S ribosomal RNA sequences, it can be shown that two seemingly dissimilar types of eubacteria Deinococcus and the ubiquitous hot spring organism Thermus are distantly but specifically related to one another. This confirms an earlier report based upon 16S rRNA oligonucleotide cataloging studies (Hensel et al., 1986). Their two lineages form a distinctive grouping within the eubacteria that deserved the taxonomic status of a phylum. The (partial) sequence of T. aquaticus rRNA appears relatively close to those of other thermophilic eubacteria. e.g. Thermotoga maritima and Thermomicrobium roseum. However, this closeness does not reflect a true evolutionary closeness; rather it is due to a "thermophilic convergence", the result of unusually high G+C composition in the rRNAs of thermophilic bacteria. Unless such compositional biases are taken into account, the branching order and root of phylogenetic trees can be incorrectly inferred.

Non-NASA Center↗
NASA NTRS2016

Converging Supergranular Flows and the Formation of Coronal Plumes

Earlier studies have suggested that coronal plumes are energized by magnetic reconnection between unipolar flux concentrations and nearby bipoles, even though magnetograms sometimes show very little minority-polarity flux near the footpoints of plumes. Here we use high-resolution extreme-ultraviolet (EUV) images and magnetograms from the Solar Dynamics Observatory (SDO) to clarify the relationship between plume emission and the underlying photospheric field. We find that plumes form where unipolar network elements inside coronal holes converge to form dense clumps, and fade as the clumps disperse again. The converging flows also carry internetwork fields of both polarities. Although the minority-polarity flux is sometimes barely visible in the magnetograms, the corresponding EUV images almost invariably show loop-like features in the core of the plumes, with the fine structure changing on timescales of minutes or less. We conclude that the SDO observations are consistent with a model in which plume emission originates from interchange reconnection in converging flows, with the plume lifetime being determined by the approximately 1-day evolutionary timescale of the supergranular network. Furthermore, the presence of large EUV bright points and/or ephemeral regions is not a necessary precondition for the formation of plumes, which can be energized even by the weak, mixed-polarity internetwork fields swept up by converging flows.

Sun: corona↗
NASA NTRS1993

Microjansky source counts and spectral indices at 8.44 GHz

Deep images were made with 10 arcsec resolution at 8.44 GHz with microjansky sensitivity of two regions previously imaged at several lower frequencies. The differential microjansky source count fits a power-law distribution with slope 2.3 +/- 0.2. This slope is roughly constant between 1.41 and 8.44 GHz and between S(v) = about 4 and X(v) = 1000 microJy. Unless the majority of radio sources at microJy levels are local, their average luminosity and/or space density probably has evolved with cosmic epoch in order to sustain the steep and nearly Euclidean value of the slope over such a large range in flux density. The 8.44 GHz counts probably converge at less than the 1 microjansky level if the evolutionary properties of these weak sources are similar to those of luminous gE radio galaxies and quasars. The median angular size of sources detected at 8.44 GHz at microjansky levels is 2.6 +/- 1.4 arcsec, and 40 percent of the source are larger than 5 arcsec.

Windhorst, Rogier A.↗
NASA NTRS2006

Single- and Multiple-Objective Optimization with Differential Evolution and Neural Networks

Genetic and evolutionary algorithms have been applied to solve numerous problems in engineering design where they have been used primarily as optimization procedures. These methods have an advantage over conventional gradient-based search procedures became they are capable of finding global optima of multi-modal functions and searching design spaces with disjoint feasible regions. They are also robust in the presence of noisy data. Another desirable feature of these methods is that they can efficiently use distributed and parallel computing resources since multiple function evaluations (flow simulations in aerodynamics design) can be performed simultaneously and independently on ultiple processors. For these reasons genetic and evolutionary algorithms are being used more frequently in design optimization. Examples include airfoil and wing design and compressor and turbine airfoil design. They are also finding increasing use in multiple-objective and multidisciplinary optimization. This lecture will focus on an evolutionary method that is a relatively new member to the general class of evolutionary methods called differential evolution (DE). This method is easy to use and program and it requires relatively few user-specified constants. These constants are easily determined for a wide class of problems. Fine-tuning the constants will off course yield the solution to the optimization problem at hand more rapidly. DE can be efficiently implemented on parallel computers and can be used for continuous, discrete and mixed discrete/continuous optimization problems. It does not require the objective function to be continuous and is noise tolerant. DE and applications to single and multiple-objective optimization will be included in the presentation and lecture notes. A method for aerodynamic design optimization that is based on neural networks will also be included as a part of this lecture. The method offers advantages over traditional optimization methods. It is more flexible than other methods in dealing with design in the context of both steady and unsteady flows, partial and complete data sets, combined experimental and numerical data, inclusion of various constraints and rules of thumb, and other issues that characterize the aerodynamic design process. Neural networks provide a natural framework within which a succession of numerical solutions of increasing fidelity, incorporating more realistic flow physics, can be represented and utilized for optimization. Neural networks also offer an excellent framework for multiple-objective and multi-disciplinary design optimization. Simulation tools from various disciplines can be integrated within this framework and rapid trade-off studies involving one or many disciplines can be performed. The prospect of combining neural network based optimization methods and evolutionary algorithms to obtain a hybrid method with the best properties of both methods will be included in this presentation. Achieving solution diversity and accurate convergence to the exact Pareto front in multiple objective optimization usually requires a significant computational effort with evolutionary algorithms. In this lecture we will also explore the possibility of using neural networks to obtain estimates of the Pareto optimal front using non-dominated solutions generated by DE as training data. Neural network estimators have the potential advantage of reducing the number of function evaluations required to obtain solution accuracy and diversity, thus reducing cost to design.

Rai, Man Mohan↗
NASA NTRS2003

Aerodynamic Shape Optimization Using Evolutionary Algorithms

A method for aerodynamic shape optimization based on an evolutionary algorithm approach is presented and demonstrated. Results are presented for a number of model problems to access the effect of algorithm parameters on convergence efficiency and reliability. A transonic viscous airfoil optimization problem, both single and two-objective variations, is used as the basis for a preliminary comparison with an adjoint-gradient optimizer. The evolutionary algorithm is coupled with a transonic full potential flow solver and is used to optimize the inviscid flow about transonic wings including multi-objective and multi-discipline solutions that lead to the generation of pareto fronts. The results indicate that the evolutionary algorithm approach is easy to implement, flexible in application and extremely reliable.

Holst, Terry L.↗
NASA NTRS2003

Aerodynamic Shape Optimization using an Evolutionary Algorithm

A method for aerodynamic shape optimization based on an evolutionary algorithm approach is presented and demonstrated. Results are presented for a number of model problems to access the effect of algorithm parameters on convergence efficiency and reliability. A transonic viscous airfoil optimization problem-both single and two-objective variations is used as the basis for a preliminary comparison with an adjoint-gradient optimizer. The evolutionary algorithm is coupled with a transonic full potential flow solver and is used to optimize the inviscid flow about transonic wings including multi-objective and multi-discipline solutions that lead to the generation of pareto fronts. The results indicate that the evolutionary algorithm approach is easy to implement, flexible in application and extremely reliable.

Hoist, Terry L.↗
NASA NTRS2006

Efficient Agent-Based Models for Non-Genomic Evolution

Modeling dynamical systems composed of aggregations of primitive proteins is critical to the field of astrobiological science involving early evolutionary structures and the origins of life. Unfortunately traditional non-multi-agent methods either require oversimplified models or are slow to converge to adequate solutions. This paper shows how to address these deficiencies by modeling the protein aggregations through a utility based multi-agent system. In this method each agent controls the properties of a set of proteins assigned to that agent. Some of these properties determine the dynamics of the system, such as the ability for some proteins to join or split other proteins, while additional properties determine the aggregation s fitness as a viable primitive cell. We show that over a wide range of starting conditions, there are mechanisins that allow protein aggregations to achieve high values of overall fitness. In addition through the use of agent-specific utilities that remain aligned with the overall global utility, we are able to reach these conclusions with 50 times fewer learning steps.

Gupta, Nachi↗
NASA NTRS1984

25S ribosomal RNA homologies of basidiomycetous yeasts: taxonomic and phylogenetic implications

Genera, families, and possibly orders of basidiomycetous yeasts can be defined by 25S rRNA homology and correlated phenotypic characters. The teleomorphic genera Filobasidium, Leucosporidium, and Rhodosporidium have greater than 96 relative binding percent (rb%) intrageneric 25S rRNA homology and significant intergeneric separation from each other and from Filobasidiella. The anamorphic genus Cryptococcus can be defined by morphology (monopolar budding), colony color, and greater than 75 rb% intrageneric homology; Vanrija is heterogeneous. Agaricostilbum (Phragmobasidiomycetes, Auriculariales), Hansenula (Ascomycotera, Endomycota), Tremella (Phragmobasidiomycetes, Tremellales), and Ustilago (Ustomycota, Ustilaginales) appear equally unrelated to the Cryptococcus, Filobasidiella, and Rhodosporidium spp. used as probes. The Filobasidiaceae and Sporidiaceae, Filobasidiales and Sporidiales, form coherent homology groups which appear to have undergone convergent 25S rRNA evolution, since their relatedness is much greater than that indicated by 5S rRNA homology. Ribosomal RNA homologies do not appear to measure evolutionary distance.

Yeasts/classification/genetics↗
NASA NTRS1995

Dominant takeover regimes for genetic algorithms

The genetic algorithm (GA) is a machine-based optimization routine which connects evolutionary learning to natural genetic laws. The present work addresses the problem of obtaining the dominant takeover regimes in the GA dynamics. Estimated GA run times are computed for slow and fast convergence in the limits of high and low fitness ratios. Using Euler's device for obtaining partial sums in closed forms, the result relaxes the previously held requirements for long time limits. Analytical solution reveal that appropriately accelerated regimes can mark the ascendancy of the most fit solution. In virtually all cases, the weak (logarithmic) dependence of convergence time on problem size demonstrates the potential for the GA to solve large N-P complete problems.

Noever, David↗
NASA NTRS2004

Design Space Issues for Intrinsic Evolvable Hardware

This paper discuss the problem of increased programming time for intrinsic evolvable hardware (EHW) as the complexity of the circuit grows. We develop equations for the size of the population, n, and the number of generations required for the population to converge, ngen, based on L, the length of the programming string. We show that the processing time of the computer becomes negligible for intrinsic EHW since the selection/crossover/mutation steps are only done once per generation, suggesting there is room for use of more complex evolutionary algorithms m intrinsic EHW. F i y , we review the state of the practice and discuss the notion of a system design approach for intrinsic EHW.

Hereford, James↗
NASA NTRS2012

Three-Dimensional Structure and Evolution of Extreme-Ultraviolet Bright Points Observed by STEREO/SECCHI/EUVI

We unveil the three-dimensional structure of quiet-Sun EUV bright points and their temporal evolution by applying a triangulation method to time series of images taken by SECCHI/EUVI on board the STEREO twin spacecraft. For this study we examine the heights and lengths as the components of the three-dimensional structure of EUV bright points and their temporal evolutions. Among them we present three bright points which show three distinct changes in the height and length: decreasing, increasing, and steady. We show that the three distinct changes are consistent with the motions (converging, diverging, and shearing, respectively) of their photospheric magnetic flux concentrations. Both growth and shrinkage of the magnetic fluxes occur during their lifetimes and they are dominant in the initial and later phases, respectively. They are all multi-temperature loop systems which have hot loops (approx. 10(exp 6.2) K) overlying cooler ones (approx 10(exp 6.0) K) with cool legs (approx 10(exp 4.9) K) during their whole evolutionary histories. Our results imply that the multi-thermal loop system is a general character of EUV bright points. We conclude that EUV bright points are flaring loops formed by magnetic reconnection and their geometry may represent the reconnected magnetic field lines rather than the separator field lines.

Sun: UV Radiation↗
NASA NTRS1994

Interactions between nested sunspots. 1: The formation and breakup of a delta-type sunspot

We investigate a nest of sunspots in which three ordinary bipolar pairs of sunspots are aligned collinearly. The usual spreading action of the growing regions brings two spots of leading polarity together (p-p collision) and forces the leading and trailing spots of the two interior regions to overlap inot a single penumbra (p-f collision), thus forming a delta-spot. We examine digitally processed images from the Ottawa River Solar Observatory of two related events inside the delta-spot 5 days after the p-f collision begins: the violent disruption of the f-umbra, and the formation in less than a day of an hydrogen-alpha filament. The evolutionary changes in shape, area, relative motions, and brightness that we measure for each spot in the elongated nest are more compatible with Parker's (1979a) hypothesis of a sunspot as a cluster of flux tubes held together by downdrafts than with the notion of a sunspot as a monolithic plug of magnetic flux. From chromospheric developments over the delta-spot, we show that a shearing motion along a polarity inversion is more effective than convergence for creating a chromospheric filament. We invoke the release of an instability, triggered by a sequence of processes lasting 1 day or more, to explain the disruption of the f-umbra in this delta-spot. We show that the sequence is initiated when the colliding p-f umbrae reach a critical separation around 3200 +/- 200 km. We present a descriptive model in which the reconnected magnetic fields block vertical transport of convective heat flux just beneath the photosphere. We observe the formation of an unusual type of penumbra adjacent to the f-polarity portion of this delta-spot just before its disruption. A tangential penumbral band grows out of disordered matter connected to the f-umbra. We present this as evidence for the extrusion of umbral magnetic flux by thermal plumes rising through a loosely bound umbra.

Gaizauskas, V.↗
NASA NTRS2019

A Mixed Integer Efficient Global Optimization Algorithm with Multiple Infill Strategy - Applied to a Wing Topology Optimization Problem

With the advancement in high performance computing and numerical optimization techniques,engineering design optimization problems are becoming more complex, larger scale,higher fidelity, and computationally more demanding, requiring longer run times than ever before. There exists methodologies and techniques that can address some of these challenges but very few can address all, and most are limited in the extent that these concerns can be addressed. With the goal of addressing such challenging engineering problems, we developed anew optimization framework, named AMIEGO, that combines concepts from surrogate-based optimization approaches, gradient-based numerical methods, Partial Least Squares, evolutionary algorithms, and Branch-and-Bound, providing newer capabilities that were not previouslyperceived. However, the original version of this framework, in the process of adaptive samplingto explore and exploit the design space, finds only a single sample point per iteration. The efforthere builds upon this previously developed optimization framework to include multiple infillsampling capability that combines the concept of generalized expected improvement function,unsupervised learning, and multi-objective evolutionary technique. To demonstrate, AMIEGOwith the multiple infill capability (called AMIEGO-MIMOS) solves a series of increasingly difficultengineering design optimization problems. The results reveal the performance of the newapproach is problem dependent. When applied to a ten-bar truss problem, the newly proposedmultiple infill strategy consistently leads to a better design solutions when compared to theexisting CPTV method (implemented with the context of the AMIEGO framework). On theother hand, when applied to a mixed-integer high fidelity wing topology optimization problem- MIMOS, despite showing a steeper convergence at the start, eventually leads to an inferiorsolution as compared to CPTV approach. These results also reveal that a small number ofstarting points, in general, are sufficient to lead to a good overall solution.

Mixed-integer optimization↗
NASA NTRS1999

Visual circuits of the avian telencephalon: evolutionary implications

Birds and primates are vertebrates that possess the most advanced, efficient visual systems. Although lineages leading to these two classes were separated about 300 million years ago, there are striking similarities in their underlying neural mechanisms for visual processing. This paper discusses such similarities with special emphasis on the visual circuits in the avian telencephalon. These similarities include: (1) the existence of two parallel visual pathways and their distinct telencephalic targets, (2) anatomical and functional segregation within the visual pathways, (3) laminar organization of the telencephalic targets of the pathways (e.g. striate cortex in primates), and (4) possible interactions between multiple visual areas. Additional extensive analyses are necessary to determine whether these similarities are due to inheritance from a common ancestral stock or the consequences of convergent evolution based on adaptive response to similar selective pressures. Nevertheless, such a comparison is important to identify the general and specific principles of visual processing in amniotes (reptiles, birds, and mammals). Furthermore, these principles in turn will provide a critical foundation for understanding the evolution of the brain in amniotes.

Review, Tutorial↗