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All sourcesNASA NTRSDOE OSTI
NASA NTRS1994

Multidisciplinary design optimization using genetic algorithms

Multidisciplinary design optimization (MDO) is an important step in the conceptual design and evaluation of launch vehicles since it can have a significant impact on performance and life cycle cost. The objective is to search the system design space to determine values of design variables that optimize the performance characteristic subject to system constraints. Gradient-based optimization routines have been used extensively for aerospace design optimization. However, one limitation of gradient based optimizers is their need for gradient information. Therefore, design problems which include discrete variables can not be studied. Such problems are common in launch vehicle design. For example, the number of engines and material choices must be integer values or assume only a few discrete values. In this study, genetic algorithms are investigated as an approach to MDO problems involving discrete variables and discontinuous domains. Optimization by genetic algorithms (GA) uses a search procedure which is fundamentally different from those gradient based methods. Genetic algorithms seek to find good solutions in an efficient and timely manner rather than finding the best solution. GA are designed to mimic evolutionary selection. A population of candidate designs is evaluated at each iteration, and each individual's probability of reproduction (existence in the next generation) depends on its fitness value (related to the value of the objective function). Progress toward the optimum is achieved by the crossover and mutation operations. GA is attractive since it uses only objective function values in the search process, so gradient calculations are avoided. Hence, GA are able to deal with discrete variables. Studies report success in the use of GA for aircraft design optimization studies, trajectory analysis, space structure design and control systems design. In these studies reliable convergence was achieved, but the number of function evaluations was large compared with efficient gradient methods. Applicaiton of GA is underway for a cost optimization study for a launch-vehicle fuel-tank and structural design of a wing. The strengths and limitations of GA for launch vehicle design optimization is studied.

Unal, Resit↗
NASA NTRS1987

Gibberellins and gravitropism in maize shoots: endogenous gibberellin-like substances and movement and metabolism of [3H]Gibberellin A20

[3H]Gibberellin A20 (GA20) of high specific radioactivity (49.9 gigabecquerel per millimole) was applied equilaterally in a ring of microdrops to the internodal pulvinus of shoots of 3-week-old gravistimulated and vertical normal maize (Zea mays L.), and to a pleiogravitropic (prostrate) maize mutant, lazy (la). All plants converted the [3H]GA20 to [3H]GA1- and [3H]GA29-like metabolites as well as to several metabolites with the partitioning and chromatographic behavior of glucosyl conjugates of [3H]GA1, [3H]GA29, and [3H]GA8. The tentative identification of these putative [3H]GA glucosyl conjugates was further supported by the release of the free [3H]GA moiety after cleavage with cellulase. Within 12 hours of the [3H]GA20 feed, there was a significantly higher proportion of total radioactivity in lower than in upper halves of internode and leaf sheath pulvini in gravistimulated normal maize. Further, there was a significantly higher proportion of putative free GA metabolites of [3H]GA20, especially [3H]GA1, in the lower halves of normal maize relative to upper halves. The differential localization of the metabolites between upper and lower halves was not apparent in the pleiogravitropic mutant, la. Endogenous GA-like substances were also examined in gravistimulated maize shoots. Forty-eight hours after gravistimulation of 3-week-old maize seedlings, endogenous free GA-like substances in upper and lower leaf sheath and internode pulvini halves were extracted, chromatographed, and bioassayed using the "Tanginbozu" dwarf rice microdrop assay. Lower halves contained consistently higher total levels of GA-like activity. The qualitative elution profile of GA-like substances differed consistently, upper halves containing principally a GA20-like substance and lower halves containing principally a GA20-like substance and lower halves containing mainly GA1-like and GA19-like substances. Gibberellins A1 (10 nanograms per gram) and A20 (5 nanograms per gram) were identified from these lower leaf sheath pulvini by capillary gas chromatography-selected ion monitoring. Results from all of these experiments are consistent with a role for GAs in the differential shoot growth that follows gravitropism, although the results do not eliminate the possibility that the redistribution of GAs results from the gravitropic response.

NASA Discipline Plant Biology↗
NASA NTRS1985

Growth and graviresponsiveness of primary roots of Zea mays seedlings deficient in abscisic acid and gibberellic acid

The objective of this research was to determine if gibberellic acid (GA) and/or abscisic acid (ABA) are necessary for graviresponsiveness by primary roots of Zea mays. To accomplish this objective we measured the growth and graviresponsiveness of primary roots of seedlings in which the synthesis of ABA and GA was inhibited collectively and individually by genetic and chemical means. Roots of seedlings treated with Fluridone (an inhibitor of ABA biosynthesis) and Ancymidol (an inhibitor of GA biosynthesis) were characterized by slower growth rates but not significantly different gravicultures as compared to untreated controls. Gravicurvatures of primary roots of d-5 mutants (having undetectable levels of GA) and vp-9 mutants (having undectable levels of ABA) were not significantly different from those of wild-type seedlings. Roots of seedlings in which the biosynthesis of ABA and GA was collectively inhibited were characterized by gravicurvatures not significantly different for those of controls. These results (1) indicate that drastic reductions in the amount of ABA and GA in Z. mays seedlings do not significantly alter root graviresponsiveness, (2) suggest that neither ABA nor GA is necessary for root gravicurvature, and (3) indicate that root gravicurvature is not necessarily proportional to root elongation.

Zea mays/drug effects/growth & development/metabol↗
NASA NTRS2006

Genetic Algorithm for Optimization: Preprocessor and Algorithm

Genetic algorithm (GA) inspired by Darwin's theory of evolution and employed to solve optimization problems - unconstrained or constrained - uses an evolutionary process. A GA has several parameters such the population size, search space, crossover and mutation probabilities, and fitness criterion. These parameters are not universally known/determined a priori for all problems. Depending on the problem at hand, these parameters need to be decided such that the resulting GA performs the best. We present here a preprocessor that achieves just that, i.e., it determines, for a specified problem, the foregoing parameters so that the consequent GA is a best for the problem. We stress also the need for such a preprocessor both for quality (error) and for cost (complexity) to produce the solution. The preprocessor includes, as its first step, making use of all the information such as that of nature/character of the function/system, search space, physical/laboratory experimentation (if already done/available), and the physical environment. It also includes the information that can be generated through any means - deterministic/nondeterministic/graphics. Instead of attempting a solution of the problem straightway through a GA without having/using the information/knowledge of the character of the system, we would do consciously a much better job of producing a solution by using the information generated/created in the very first step of the preprocessor. We, therefore, unstintingly advocate the use of a preprocessor to solve a real-world optimization problem including NP-complete ones before using the statistically most appropriate GA. We also include such a GA for unconstrained function optimization problems.

Sen, S. K.↗
NASA NTRS1987

Anisotropic transport in modulation-doped quantum-well structures

Anisotropic electron transport has been observed in GaAs modulation-doped quantum wells grown by molecular-beam epitaxy on a thick (001) Al(0.3)Ga(0.7)As buffer grown at 620 C. Thicker quantum wells (150, 200, and 300 A) show progressively less anisotropy, which vanishes for a 300-A quantum well. The degree of anisotropy is also reduced or eliminated by suspending growth of the Al(0.3)Ga(0.7)As for a period of 300 s prior to growing the GaAs quantum well. Growing the Al(0.3)Ga(0.7)As buffer at higher temperatures (680 C) also reduces the degree of anisotropy. Higher two-dimensional electron gas sheet densities result in less anisotropy.The anisotropy is eliminated by replacing the thick Al(0.3)Ga(0.7)As buffer with a periodic multilayer structure comprising 15 A of GaAs and 200 A of Al(0.3)Ga(0.7)As. The degree of anisotropy is related to the thickness and growth parameters of the Al(0.3)Ga(0.7)As layer grown just prior to the growth of the GaAs.

Radulescu, D. C.↗
NASA NTRS1989

Chronology and complexity of early lunar crust

The petrology and chronology of early lunar crust is examined using the least equivocal of the available petrographic and age data on lunar rock samples, and the possible processes which produced the lunar crust are discussed. The results suggest that the lunar anorthositic crust was formed by about 120 Ma after the primary accretion of the moon at 4.56 Ga. At least some members of the diverse Mg-suites of rocks, such as norites, troctolites, and dunites, crystallized within a very few 100s of Ma after 4.56 Ga. A trace-element-rich material (KREEP) was formed by about 4.3 Ga ago, and this residue was subsequently reworked in melting and impact processes such that most samples which contain it have ages around 3.9-4.0 Ga. The findings also suggest that the onset of ferrous mare basalt volcanism began about 4.33 Ga, much earlier than was once assumed, and was still in process before the end of the most intense period of bombardment (3.9-4.0 Ga ago).

Dasch, E. J.↗
NASA NTRS1993

A Darwinian approach to control-structure design

Genetic algorithms (GA's), as introduced by Holland (1975), are one form of directed random search. The form of direction is based on Darwin's 'survival of the fittest' theories. GA's are radically different from the more traditional design optimization techniques. GA's work with a coding of the design variables, as opposed to working with the design variables directly. The search is conducted from a population of designs (i.e., from a large number of points in the design space), unlike the traditional algorithms which search from a single design point. The GA requires only objective function information, as opposed to gradient or other auxiliary information. Finally, the GA is based on probabilistic transition rules, as opposed to deterministic rules. These features allow the GA to attack problems with local-global minima, discontinuous design spaces and mixed variable problems, all in a single, consistent framework.

Zimmerman, David C.↗
NASA NTRS1994

Mineralogy and Ar-39 - Ar-40 of an old pristine basalt: Thermal history of the HED parent body

Previous investigations of mineral chemistry and Rb-Sr and Sm-Nd ages indicated that clast,84 from eucrite Yamato 75011 had preserved the pristine nature of its initial crystallization during an early stage of the HED parent body. Microscale mineralogy and Ar-39-Ar-40 ages of this clast, however, revealed local disturbance of microtextures and partially reset ages. This evidence suggests that, in addition to initial crystallization and rapid cooling, the Y75011,84 clast experienced shock deformation, reheating of short duration at higher temperature, and brecciation. These characteristics suggest two or more impact events. Fe-rich olivine filling fractures in pyroxene may have been introduced during the accompanying shock fracturing. The inferred Ar-39-Ar-40 degassing ages for Y75011 matrix and clast, 84 are 3.94 +/- 0.04 Ga and 3.98 +/- 0.03 Ga, respectively. The suggested degassing age for a clast from Y790020, believed to be paired with Y75011, is approximately 4.03 Ga, but could be younger. We consider it likely that all three samples experienced a common degassing event 3.95 +/- 0.05 Ga ago, but we cannot rule out two or more events spaced over a approximately 0.1 Ga interval. Higher temperature extractions of the two clast samples show significantly older apparent ages up to approximately 4.5 Ga and suggest that the time/temperature regime of this event was not sufficient to degas Ar totally. Most likely, the K-Ar ages were reset by thermal metamorphism associated with one or more impact events associated with shock fracturing, formation of Fe-rich olivine veins, and/or meteorite brecciation. The pyroxene annealing that commonly occurs in many eucrites is likely to be a much earlier process than the impact-produced textural changes and reset K-Ar ages observed in these meteorites. The existence of mineralogical and chronological evidence for metamorphism in an otherwise pristine eucrite suggests that the HED parent body experienced an extensive degree of early cratering.

Takeda, Hiroshi↗
NASA NTRS2001

General Aviation Aircraft Reliability Study

This reliability study was performed in order to provide the aviation community with an estimate of Complex General Aviation (GA) Aircraft System reliability. To successfully improve the safety and reliability for the next generation of GA aircraft, a study of current GA aircraft attributes was prudent. This was accomplished by benchmarking the reliability of operational Complex GA Aircraft Systems. Specifically, Complex GA Aircraft System reliability was estimated using data obtained from the logbooks of a random sample of the Complex GA Aircraft population.

Pettit, Duane↗
NASA NTRS2004

A Study of Penalty Function Methods for Constraint Handling with Genetic Algorithm

COMETBOARDS (Comparative Evaluation Testbed of Optimization and Analysis Routines for Design of Structures) is a design optimization test bed that can evaluate the performance of several different optimization algorithms. A few of these optimization algorithms are the sequence of unconstrained minimization techniques (SUMT), sequential linear programming (SLP) and the sequential quadratic programming techniques (SQP). A genetic algorithm (GA) is a search technique that is based on the principles of natural selection or "survival of the fittest". Instead of using gradient information, the GA uses the objective function directly in the search. The GA searches the solution space by maintaining a population of potential solutions. Then, using evolving operations such as recombination, mutation and selection, the GA creates successive generations of solutions that will evolve and take on the positive characteristics of their parents and thus gradually approach optimal or near-optimal solutions. By using the objective function directly in the search, genetic algorithms can be effectively applied in non-convex, highly nonlinear, complex problems. The genetic algorithm is not guaranteed to find the global optimum, but it is less likely to get trapped at a local optimum than traditional gradient-based search methods when the objective function is not smooth and generally well behaved. The purpose of this research is to assist in the integration of genetic algorithm (GA) into COMETBOARDS. COMETBOARDS cast the design of structures as a constrained nonlinear optimization problem. One method used to solve constrained optimization problem with a GA to convert the constrained optimization problem into an unconstrained optimization problem by developing a penalty function that penalizes infeasible solutions. There have been several suggested penalty function in the literature each with there own strengths and weaknesses. A statistical analysis of some suggested penalty functions is performed in this study. Also, a response surface approach to robust design is used to develop a new penalty function approach. This new penalty function approach is then compared with the other existing penalty functions.

Ortiz, Francisco↗
NASA NTRS2011

Sm-Nd Isotopic Studies of Ureilite Novo Urei

Ureilites are ultramafic (harzburgitic) achondrites composed predominantly of olivine and pyroxenes, abundant carbon (graphites and shock-produced diamonds), some metal and sulfides. These rocks probably represent ultramafic mantles of differentiated parent asteroidal bodies. Age determinations of these rocks by Rb-Sr and Sm-Nd methods have been difficult because of their extremely low abundances of these parent-daughter elements. Nevertheless, Sm-Nd isochron ages were reported for Kenna, Goalpara, MET 78008 and PCA 82506 yielding ages of 3.74+/-0.02 Ga, approx.3.7 Ga, 4.09+/-0.08 Ga, 4.23+/-0.06 Ga, respectively [1-4]. These "young" Sm-Nd ages may represent secondary metasomatism events [1] related to impacts [5], as indicated by the similarly young Ar-39-Ar-40 degassing ages of 3.3-4.1 Ga for ureilites Kenna, Novo Urei and Havero [6]. Alternatively, it has been suggested that these rocks may have been contaminated with terrestrial crustal materials and the isochrons do not have any age significance [2,7]. Indications of old approx.4.56 Ga ages for ureilites were reported from the U-Pb and Sm-Nd model ages for MET 78008 [8]. More reliable evidences for old formation ages of ureilites were reported recently using the short-lived chronometers Hf-182-W-182, Al-26-Mg-26 and Mn-53-Cr-53. The deficits of 182W in ureilites suggest the metal-silicate segregation occurred very early, approx.1-2 Ma after CAI [9]. The Al-26-Mg-26 and Mn-53-Cr-53 studies for a feldspathic lithology [10] and the Mn-53-Cr-53 for olivine- and pyroxene-dominant lithologies [11] in ureilites revealed that they crystallized approx.5.4 Ma after CAI, i.e., at 4563.8+/-0.5 Ma relative to D.Orbigny. In this report, we present Sm-Nd isotopic data for a relatively fresh ureilite, Novo Urei, a rare ureilite fall (1886). We compare these data to Sm-Nd data for other ureilites, and discuss Novo Urei's petrogenesis

Shih, C.-Y.↗
NASA NTRS2012

Downsampling Photodetector Array with Windowing

In a photon counting detector array, each pixel in the array produces an electrical pulse when an incident photon on that pixel is detected. Detection and demodulation of an optical communication signal that modulated the intensity of the optical signal requires counting the number of photon arrivals over a given interval. As the size of photon counting photodetector arrays increases, parallel processing of all the pixels exceeds the resources available in current application-specific integrated circuit (ASIC) and gate array (GA) technology; the desire for a high fill factor in avalanche photodiode (APD) detector arrays also precludes this. Through the use of downsampling and windowing portions of the detector array, the processing is distributed between the ASIC and GA. This allows demodulation of the optical communication signal incident on a large photon counting detector array, as well as providing architecture amenable to algorithmic changes. The detector array readout ASIC functions as a parallel-to-serial converter, serializing the photodetector array output for subsequent processing. Additional downsampling functionality for each pixel is added to this ASIC. Due to the large number of pixels in the array, the readout time of the entire photodetector is greater than the time between photon arrivals; therefore, a downsampling pre-processing step is done in order to increase the time allowed for the readout to occur. Each pixel drives a small counter that is incremented at every detected photon arrival or, equivalently, the charge in a storage capacitor is incremented. At the end of a user-configurable counting period (calculated independently from the ASIC), the counters are sampled and cleared. This downsampled photon count information is then sent one counter word at a time to the GA. For a large array, processing even the downsampled pixel counts exceeds the capabilities of the GA. Windowing of the array, whereby several subsets of pixels are designated for processing, is used to further reduce the computational requirements. The grouping of the designated pixel frame as the photon count information is sent one word at a time to the GA, the aggregation of the pixels in a window can be achieved by selecting only the designated pixel counts from the serial stream of photon counts, thereby obviating the need to store the entire frame of pixel count in the gate array. The pixel count se quence from each window can then be processed, forming lower-rate pixel statistics for each window. By having this processing occur in the GA rather than in the ASIC, future changes to the processing algorithm can be readily implemented. The high-bandwidth requirements of a photon counting array combined with the properties of the optical modulation being detected by the array present a unique problem that has not been addressed by current CCD or CMOS sensor array solutions.

Patawaran, Ferze D.↗
NASA NTRS2017

Shergottite Lead Isotope Signature in Chassigny and the Nakhlites

The nakhlites/chassignites and the shergottites represent two differing suites of basaltic martian meteorites. The shergottites have ages less than or equal to 0.6 Ga and a large range of initial Sr-/Sr-86 and epsilon (Nd-143) ratios. Conversely, the nakhlites and chassignites cluster at 1.3-1.4 Ga and have a limited range of initial Sr-87/Sr-86 and epsilon (Nd-143). More importantly, the shergottites have epsilon (W-182) less than 1, whereas the nakhlites and chassignites have epsilon (W-182) approximately 3. This latter observation precludes the extraction of both meteorite groups from a single source region. However, recent Pb isotopic analyses indicate that there may have been interaction between shergottite and nakhlite/chassignite Pb reservoirs.Pb Analyses of Chassigny: Two different studies haveinvestigated 207Pb/204Pb vs. 206Pb/204Pb in Chassigny: (i)TIMS bulk-rock analyses of successive leaches and theirresidue [3]; and (ii) SIMS analysis of individual minerals[4]. The bulk-rock analyses fall along a regression of SIMSplagioclase analyses that define an errorchron that is olderthan the Solar System (4.61±0.1 Ga); i.e., these define amixing line between Chassigny’s principal Pb isotopic components(Fig. 1). Augites and olivines in Chassingy (notshown) also fall along or near the plagioclase regression [4].This agreement indicates that the whole-rock leachateslikely measure indigenous, martian Pb, not terrestrial contamination[5]. SIMS analyses of K-spars and sulfides definea separate, sub-parallel trend having higher 207Pb/206Pbvalues ([4]; Fig. 1). The good agreement between the bulkrockanalyses and the SIMS analyses of plagioclases alsoindicates that the Pb in the K-spars and sulfides cannot be amajor component of Chassigny.The depleted reservoir sampled by Chassigny plagioclaseis not the same as the solar system initial (PAT) andrequires a multi-stage origin. Here we show a two-stagemodel (Fig. 1) with a 238U/204Pb (μ) of 0.5 for 4.5-2.4 Gaand a μ of 7 for 2.4-1.4 Ga. This is not a unique model butdoes produce a Pb composition that falls on the plagioclaseregression at 1.4 Ga, the approximate igneous age of Chassigny [1]. It should be noted that low-μ single-stage modelsare not capable of producing sufficiently radiogenic 206Pb/204Pb at 1.4 Ga.Relation to Shergottites: The Chassigny K-spars and sulfides fall along a second mixing line defined by leachesand residues of depleted and intermediate shergottites [6]. This mixing line falls above the plagioclase regression.Therefore, we also interpret the radiogenic component of this mixing line to represent indigenous martian Pb. It ispossible that the depleted and intermediate shergottites and the Chassigny plagioclases sample radiogenic Pb from thethe same source, i.e., the mixing lines may intersect at high 206Pb/204Pb.Both K-spar and sulfide are late-stage phases. At the time of their crystallization, the Chassigny system appearsto have remained open to a depleted shergottite Pb reservoir. The depleted component of the shergottite mixing linecan be generated by a single-stage evolution from PAT (4.5 to 1.4 Ga) in a reservoir having a μ ~2. A similar modelfor the most depleted shergottites is also possible: μ = 1.5 for 4.5 to 0.3 Ga.Nakhlites: Nakhlite analyses plot between the shergottite and Chassigny plagioclase regressions [3]. So again,members of the nakhlite/chassignite suite show affinities to shergottite Pb.

Jones, J. H.↗
NASA NTRS2021

Generation-based Evolutionary Tool for the Optimization of Constellations (GenETOC)

With the rapid growth in the capabilities of smaller satellites, satellite architectures that replace a single, extremely capable spacecraft with multiple, cheaper ones are gaining in popularity. Unfortunately, the orbit design process for constellations can be significantly more involved, especiallywhen the relative placement of the individual spacecraft within the constellation is not constrained by mission and/or science objectives. Optimizing a satellite constellation in the presence of multiple, competing objectives is a highly complex problem to which many traditional mathematical optimization methods cannot be applied and few tools exist to help mission designers search for promising candidate mission designs. The Generation-based Evolutionary Tool for the Optimization of Constellations (GenETOC) has been created to search for near-optimal constellation design options. GenETOC combines a modified version of the Non-dominated Sorting Genetic Algorithm II (NSGA II) with STK Components libraries (a 3rdparty .NET package created by Analytical Graphics Inc.) to create a framework that enables a mission designer to generate a simulation that models the design problem and obtain a family of potential, near-optimal solutions that can be investigated more in detail. GenETOC was developed in C# using the .NET framework with Windows Presentation Foundation (WPF) serving as the framework from which to create the graphical user interface (GUI). GenETOC user inputs can be categorized into three major data components: definition of the problem (areas of interest, satellite decision parameters, and sensor configurations), definition of performance objectives, and specification of the genetic algorithm (GA) parameters. In the problem definition component, the user is prompted to define the areas of interest against which the performance metrics will be computed, define the sensor parameters and attach them to specific spacecraft, select which satellite orbital parameters will be added to the decision space of the GA, and specify the range of desired values for each optimization parameter. For performance objectives, the user is presented with a list of available coverage and revisit performance based calculation options from which two metrics are chosen to serve as the objective functions that the GA will use to evaluate solutions during the optimization process. Finally, the definition of the GA parameters provides user control over the number of generations (number of optimization iterations), the population size (number of candidate constellations created in each generation), and the adaptive mutation and crossover threshold values (control parameters for how frequently each process occurs during the optimization). GenETOC has been extensively tested to verify the individual components of the optimization process. The GA has been tested against a suite of GA test problems to confirm convergence to the known two and three-dimensional Pareto fronts. The coverage and revisit performance metrics obtained in GenETOC are compared with STK desktop scenarios, confirming the constellations are being appropriately modeled within GenETOC simulations. A walkthrough of a simple, example problem is provided to illustrate the workings of GenETOC and to demonstrate the output available to the mission designer.

mission design↗
NASA NTRS1983

The greenstone belts of Zimbabwe

The present configuration of the greenstone belts is explained. Three ages of granite-greenstone terrain can be recognized within the Zimbabwe Archean Craton. The oldest greenstone belt remnants constitute the volcano-sedimentary Sebakwian group dated at c. 3.5 Ga minimum on the evidence from various granites and gneisses. The more extensive, main greenstone belts comprise the dominantly volcanic Bulawayan Group and dominantly sedimentary Shamvaian Group. An unconformity within the Bulawayan Group allows its subdivision into the Lower and Upper Greenstones. The Lower Greenstones possibly form part of a granite-greenstone terrain about 2.9 Ga old. The widespread Upper Greenstones and the locally developed, unconformably overlying Shamvaian Group are about 2.7 Ga old. Two suites of late granites post-date the main greenstone belts. These comprise that tonalitic Sesombi Suite at c. 2.7 Ga and the more potashrich Chilimanzi Suite at c. 2.6 Ga.

Wilson, J. F.↗
NASA NTRS1985

Late Precambrian aulacogens of the North China craton

According to tectonic styles, the Precambrian evolution history of the North China craton may be subdivided into four stages: (1) Archean consolidation in 3.5 to 2.5 Ga, (2) Early Proterozoic rifting in 2.5 to 1.8 Ga, (3) Late Precambrian aulacogen in 1.8 to 0.8 Ga and (4) Platform regime after 0.8 Ga. In the Late Precambrian aulacogen stage of the North China craton there were two main aulacogens, Yanliao and Zhongtiao (Y and Z), developed in Middle Proterozoic time with an age 1.8 to 1.0 Ga. Their NE trend caused them to meet together in the central part of the craton and build up one great Y-Z aulacogen throughout the craton.

Qian, X.↗
NASA NTRS1988

Significance of the late Archaean granulite facies terrain boundaries, Southern West Greenland

Three distinct episodes and occurrences of granulite metamorphism in West Greenland are described: (1) the oldest fragmentary granulites occur within the 3.6-Ga Amitsoq gneisses and appear to have formed 200 Ma after the continental crust in which they lie (Spatially associated rapakivi granites have zircon cores as old as 3.8 Ga, but Rb-Sr, whole-rock Pb-Pb, and all other systems give 3.6 Ga, so these granulites apparently represent a later metamorphic event); (2) 3.0-Ga granulites of the Nordlandet Peninsula NW of Godthaab, developed immediately after crustal formation in hot, dry conditions, are carbonate-free, associated with voluminous tonalite, and formed at peak metamorphic conditions of 800 C and 7 to 8 kbar (Synmetamorphic trondhjemite abounds and the activity of H2O has been indicated by Pilar to have varied greatly); and (3) 2.8-Ga granulites south of Godthaab, lie to the south of retrogressed amphibolite terranes. Prograde amphibolite-granulite transitions are clearly preserved only locally at the southern end of this block, near Bjornesund, south of Fiskenaesset. Progressively deeper parts of the crust are exposed from south to north as a major thrust fault is approached. Characteristic big hornblende pegmatites, which outcrop close to the thrust in the east, have been formed by replacement of orthopyroxene. Comparable features were not seen in South Indian granulites. It was concluded that no one mechanism accounts for the origin of all granulites in West Greenland. Various processes have interacted in different ways, and what happened in individual areas must be worked out by considering all possible processes.

Friend, C. R. L.↗
NASA NTRS1992

Improved interpretation of satellite altimeter data using genetic algorithms

Genetic algorithms (GA) are optimization techniques that are based on the mechanics of evolution and natural selection. They take advantage of the power of cumulative selection, in which successive incremental improvements in a solution structure become the basis for continued development. A GA is an iterative procedure that maintains a 'population' of 'organisms' (candidate solutions). Through successive 'generations' (iterations) the population as a whole improves in simulation of Darwin's 'survival of the fittest'. GA's have been shown to be successful where noise significantly reduces the ability of other search techniques to work effectively. Satellite altimetry provides useful information about oceanographic phenomena. It provides rapid global coverage of the oceans and is not as severely hampered by cloud cover as infrared imagery. Despite these and other benefits, several factors lead to significant difficulty in interpretation. The GA approach to the improved interpretation of satellite data involves the representation of the ocean surface model as a string of parameters or coefficients from the model. The GA searches in parallel, a population of such representations (organisms) to obtain the individual that is best suited to 'survive', that is, the fittest as measured with respect to some 'fitness' function. The fittest organism is the one that best represents the ocean surface model with respect to the altimeter data.

Messa, Kenneth↗