Simulation of length-preserving motions of flexible one dimensional oObjects using optimization

During the motion of one dimensional flexible objects such as ropes, chains, etc., the assumption of constant length is realistic. Moreover,their motion appears to be naturally minimizing some abstract distance measure, wherein the disturbance at one end gradually dies down along the curve defining the object. This paper presents purely kinematic strategies for deriving length-preserving transformations of flexible objects that minimize appropriate ‘motion’. The strategies involve sequential and overall optimization of the motion derived using variational calculus. Numerical simulations are performed for the motion of a planar curve and results show stable converging behavior for single-step infinitesimal and finite perturbations 1 as well as multi-step perturbations. Additionally, our generalized approach provides different intuitive motions for various problem-specific measures of motion, one of which is shown to converge to the conventional tractrix-based solution. Simulation results for arbitrary shapes and excitations are also included.

A practical approach for cross-functional vehicle body weight optimization

The goal of optimization in vehicle design is often blurred by the myriads of requirements belonging to attributes that may not be quite related. If solutions are sought by optimizing attribute performance-related objectives separately starting with a common baseline design configuration as in a traditional design environment, it becomes an arduous task to integrate the potentially conflicting solutions into one satisfactory design. It may be thus more desirable to carry out a combined multi-disciplinary design optimization (MDO) with vehicle weight as an objective function and cross-functional attribute performance targets as constraints. For the particular case of vehicle body structure design, the initial design is likely to be arrived at taking into account styling, packaging and market-driven requirements. The problem with performing a combined cross-functional optimization is the time associated with running such CAE algorithms that can provide a single optimal solution for heterogeneous areas such as NVH and crash safety. In the present paper, a practical MDO methodology is suggested that can be applied to weight optimization of automotive body structures by specifying constraints on frequency and crash performance. Because of the reduced number of cases to be analyzed for crash safety in comparison with other MDO approaches, the present methodology can generate a single size-optimized solution without having to take recourse to empirical techniques such as response surface-based prediction of crash performance and associated successive response surface updating for convergence. An example of weight optimization of spaceframe-based BIW of an aluminum-intensive vehicle is given to illustrate the steps involved in the current optimization process.

An estimate of equilibrium sensitivity of global terrestrial carbon cycle using NCAR CCSM4

Increasing concentrations of atmospheric CO2 influence climate, terrestrial biosphere productivity and ecosystem carbon storage through its radiative, physiological and fertilization effects. In this paper, we quantify these effects for a doubling of CO2 using a low resolution configuration of the coupled model NCAR CCSM4. In contrast to previous coupled climate-carbon modeling studies, we focus on the near-equilibrium response of the terrestrial carbon cycle. For a doubling of CO2, the radiative effect on the physical climate system causes global mean surface air temperature to increase by 2.14 K, whereas the physiological and fertilization on the land biosphere effects cause a warming of 0.22 K, suggesting that these later effects increase global warming by about 10 % as found in many recent studies. The CO2-fertilization leads to total ecosystem carbon gain of 371 Gt-C (28 %) while the radiative effect causes a loss of 131 Gt-C (10 %) indicating that climate warming damps the fertilization-induced carbon uptake over land. Our model-based estimate for the maximum potential terrestrial carbon uptake resulting from a doubling of atmospheric CO2 concentration (285-570 ppm) is only 242 Gt-C. This highlights the limited storage capacity of the terrestrial carbon reservoir. We also find that the terrestrial carbon storage sensitivity to changes in CO2 and temperature have been estimated to be lower in previous transient simulations because of lags in the climate-carbon system. Our model simulations indicate that the time scale of terrestrial carbon cycle response is greater than 500 years for CO2-fertilization and about 200 years for temperature perturbations. We also find that dynamic changes in vegetation amplify the terrestrial carbon storage sensitivity relative to a static vegetation case: because of changes in tree cover, changes in total ecosystem carbon for CO2-direct and climate effects are amplified by 88 and 72 %, respectively, in simulations with dynamic vegetation when compared to static vegetation simulations.

Optimization of thermoacoustic refrigerator using response surface methodology

Thermoacoustic refrigerator (TAR) converts acoustic waves into heat without any moving parts. The study presented here aims to optimize the parameters like frequency, stack position, stack length, and plate spacing involving in designing TAR using the Response Surface Methodology (RSM). A mathematical model is developed using the RSM based on the results obtained from DeltaEC software. For desired temperature difference of 40 K, optimized parameters suggested by the RSM are the frequency 254 Hz, stack position 0.108 m, stack length 0.08 m, and plate spacing 0.0005 m. The experiments were conducted with optimized parameters and simulations were performed using the Design Environment for Low-amplitude ThermoAcoustic Energy Conversion (DeltaEC) which showed similar results.

Feature weighting for clustering by particle swarm optimization

Clustering has been the most popular method for data exploration. Clustering is partitioning the data set into sub-partitions based on some measures say the distance measure, each partition has its own significant information. There are a number of algorithms explored for this purpose, one such algorithm is the Particle Swarm Optimization(PSO) which is a population based heuristic search technique derived from swarm intelligence. In this paper we present an improved version of the Particle Swarm Optimization where, each feature of the data set is given significance accordingly by adding some random weights, which also minimizes the distortions in the dataset if any. The performance of the above proposed algorithm is evaluated using some benchmark datasets from Machine Learning Repository. The experimental results shows that our proposed methodology performs significantly better than the previously performed experiments.

Topology optimization-based design of a compliant aircraft compliant aircraft wing for morphing leading and trailing edges

In this paper, we present a methodology for designing a compliant aircraft wing, which can morph from a given airfoil shape to another given shape under the actuation of internal forces and can offer sufficient stiffness in both configurations under the respective aerodynamic loads. The least square error in displacements, Fourier descriptors, geometric moments, and moment invariants are studied to compare candidate shapes and to pose the optimization problem. Their relative merits and demerits are discussed in this paper. The `frame finite element ground structure' approach is used for topology optimization and the resulting solutions are converted to continuum solutions. The introduction of a notch-like feature is the key to the success of the design. It not only gives a good match for the target morphed shape for the leading and trailing edges but also minimizes the extension of the flexible skin that is to be put on the airfoil frame. Even though linear small-displacement elastic analysis is used in optimization, the obtained designs are analysed for large displacement behavior. The methodology developed here is not restricted to aircraft wings; it can be used to solve any shape-morphing requirement in flexible structures and compliant mechanisms.

Splicing Functions and Global Dependency on Fission Yeast Slu7 Reveal Diversity in Spliceosome Assembly

The multiple short introns in Schizosaccharomyces pombe genes with degenerate cis sequences and atypically positioned polypyrimidine tracts make an interesting model to investigate canonical and alternative roles for conserved splicing factors. Here we report functions and interactions of the S. pombe slu7(+) (spslu7(+)) gene product, known from Saccharomyces cerevisiae and human in vitro reactions to assemble into spliceosomes after the first catalytic reaction and to dictate 3' splice site choice during the second reaction. By using a missense mutant of this essential S. pombe factor, we detected a range of global splicing derangements that were validated in assays for the splicing status of diverse candidate introns. We ascribe widespread, intron-specific SpSlu7 functions and have deduced several features, including the branch nucleotide-to-3' splice site distance, intron length, and the impact of its A/U content at the 5' end on the intron's dependence on SpSlu7. The data imply dynamic substrate-splicing factor relationships in multiintron transcripts. Interestingly, the unexpected early splicing arrest in spslu7-2 revealed a role before catalysis. We detected a salt-stable association with U5 snRNP and observed genetic interactions with spprp1(+), a homolog of human U5-102k factor. These observations together point to an altered recruitment and dependence on SpSlu7, suggesting its role in facilitating transitions that promote catalysis, and highlight the diversity in spliceosome assembly.

Wet and dry spell characteristics of global tropical rainfall

In this study, we analyze satellite-based daily rainfall observations to compare and contrast the wet and dry spell characteristics of tropical rainfall. Defining a wet (dry) spell as the number of consecutive rainy (nonrainy) days, we find that the distributions of wet spells appear to exhibit universality in the following sense. While both ocean and land regions with high seasonal rainfall accumulation (humid regions; e. g., India, Amazon, Pacific Ocean) show a predominance of 2-4 day wet spells, those regions with low seasonal rainfall accumulation (arid regions; e. g., South Atlantic, South Australia) exhibit a wet spell duration distribution that is essentially exponential in nature, with a peak at 1 day. The behavior that we observed for wet spells is reversed for the dry spell characteristics. In other words, the main contribution to the dry part of the season, in terms of the number of nonrainy days, appears to come from 3-4 day dry spells in the arid regions, as opposed to 1 day dry spells in the humid regions. The total rainfall accumulated in each wet spell has also been analyzed, and we find that the major contribution to seasonal rainfall for arid regions comes from 1-5 day wet spells; however, for humid regions, this contribution comes from wet spells of duration as long as 30 days. We also explore the role of chance as well as the influence of organized convection in determining some of the observed features.

Integrated sustainable irrigation planning with multiobjective Fuzzy optimization approach

Multiobjective fuzzy methodology is applied to a case study of Khadakwasla complex irrigation project located near Pune city of Maharashtra State, India. Three objectives, namely, maximization of net benefits, crop production and labour employment are considered. Effect of reuse of wastewater on the planning scenario is also studied. Three membership functions, namely, nonlinear, hyperbolic and exponential are analyzed for multiobjective fuzzy optimization. In the present study, objective functions are considered as fuzzy in nature whereas inflows are considered as dependable. It is concluded that exponential and hyperbolic membership functions provided similar cropping pattern for most of the situations whereas nonlinear membership functions provided different cropping pattern. However, in all the three cases, irrigation intensities are more than the existing irrigation intensity.

Optimization of HfO2 films for high transconductance back gated graphene transistors

Hafnium dioxide (HfO2) films, deposited using electron beam evaporation, are optimized for high performance back-gated graphene transistors. Bilayer graphene is identified on HfO2/Si substrate using optical microscope and subsequently confirmed with Raman spectroscopy. Back-gated graphene transistor, with 32 nm thick HfO2 gate dielectric, has been fabricated with very high transconductance value of 60 mu S. From the hysteresis of the current-voltage characteristics, we estimate the trap density in HfO2 to be in the mid 10(11)/cm(2) range, comparable to SiO2.

A strategy-proof and budget-balanced mechanism for carbon footprint reduction by global companies

The problem addressed in this paper is concerned with an important issue faced by any green aware global company to keep its emissions within a prescribed cap. The specific problem is to allocate carbon reductions to its different divisions and supply chain partners in achieving a required target of reductions in its carbon reduction program. The problem becomes a challenging one since the divisions and supply chain partners, being autonomous, may exhibit strategic behavior. We use a standard mechanism design approach to solve this problem. While designing a mechanism for the emission reduction allocation problem, the key properties that need to be satisfied are dominant strategy incentive compatibility (DSIC) (also called strategy-proofness), strict budget balance (SBB), and allocative efficiency (AE). Mechanism design theory has shown that it is not possible to achieve the above three properties simultaneously. In the literature, a mechanism that satisfies DSIC and AE has recently been proposed in this context, keeping the budget imbalance minimal. Motivated by the observation that SBB is an important requirement, in this paper, we propose a mechanism that satisfies DSIC and SBB with slight compromise in allocative efficiency. Our experimentation with a stylized case study shows that the proposed mechanism performs satisfactorily and provides an attractive alternative mechanism for carbon footprint reduction by global companies.

Modified particle swarm optimization for pattern clustering

Data clustering is a common technique for statistical data analysis, which is used in many fields, including machine learning and data mining. Clustering is grouping of a data set or more precisely, the partitioning of a data set into subsets (clusters), so that the data in each subset (ideally) share some common trait according to some defined distance measure. In this paper we present the genetically improved version of particle swarm optimization algorithm which is a population based heuristic search technique derived from the analysis of the particle swarm intelligence and the concepts of genetic algorithms (GA). The algorithm combines the concepts of PSO such as velocity and position update rules together with the concepts of GA such as selection, crossover and mutation. The performance of the above proposed algorithm is evaluated using some benchmark datasets from Machine Learning Repository. The performance of our method is better than k-means and PSO algorithm.

Simultaneous feature selection and clustering using particle swarm optimization

Data clustering groups data so that data which are similar to each other are in the same group and data which are dissimilar to each other are in different groups. Since generally clustering is a subjective activity, it is possible to get different clusterings of the same data depending on the need. This paper attempts to find the best clustering of the data by first carrying out feature selection and using only the selected features, for clustering. A PSO (Particle Swarm Optimization)has been used for clustering but feature selection has also been carried out simultaneously. The performance of the above proposed algorithm is evaluated on some benchmark data sets. The experimental results shows the proposed methodology outperforms the previous approaches such as basic PSO and Kmeans for the clustering problem.