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The existing approach in aircraft wing design optimizes the structure for strength and buckling design criteria followed by a flutter stability check, which leads to design that may not be optimal. This study proposes the flutter stability as an integral of weight optimization by a two-step procedure. Step 1 comprises series of flutter and static analyses of finite element (FE) models with different weapon configurations and aerodynamic loads, which give limiting values of wing tip deflection and wing twist as flutter constraints. Step 2 focuses on weight optimization of the laminate for strength and stiffness design parameters along with the flutter constraints. The algorithm minimizes structural weight by ply drop, which is based on evaluation of the fitness value of laminates generated by a stochastic operator. The ply drop design algorithm is applied to optimize the FE model of a fourth-generation fighter aircraft wing box with three representative aerodynamic loads and three weapon configurations. The application evolved optimal laminate with seamless and symmetric plies, which has 35% lesser weight compared with the initial model having quasi-isotropic laminates for given strength and flutter stability design requirement. The application of genetic algorithm (GA) to get an initial laminate instead of quasi-isotropic laminates further improved the design by 2.5% weight reduction.

The objective of VRNPR is to extract vehicle license plate information from number plate of vehicles. As the traffic control and vehicle proprietor recognizable proof is a significant issue in every country, it is important to develop such a device that automatically detects those vehicle owners who violates traffic rules and drives fast [1]. There are many VRNPR systems are present but there is challenging factor like accuracy of extraction, speed of vehicles, lightening condition, quality of images [2]. In this paper, different methods of VRNPR and emerging technologies are used to get accurate result. The important work is the detection and recognition of the number plate which is accomplished by the Convolution Neural Network (CNN). Reason to choose CNN is the high accuracy of around 90% even with relatively small training size [4]. We categorize many VRNPR techniques as per their features they used in each stage and compare them in terms of their advantages and disadvantages, accuracy and processing speed.

The ply-drop (PD) is termination of specific plies at rib-axis for getting tapered laminates. The present optimization study aims to achieve minimum weight tapered wing panels laminates by PD followed by ply-migrations (PM). The PM are required for ply-continuity (blending) and achieving smooth external aerodynamic surface . A genetic-algorithm mutation operator and fitness based search algorithm is developed in the present study for the optimization. The laminate weight minimization has been achieved as goal of multi-objective optimization (MOO), by utilizing excess design margins of Tsai-Wu first ply failure-index (FI) and wing tip lateral deflection . The finite-element (FE) model of laminate is a set of discrete laminates (chromosomes) between ribs with continuity by virtue of ply-orientations . To select best fit laminate, ply orientations were randomly selected and perturbed for thickness during optimization. The fitness function for evaluating chromosomes is a composite function of multi-objective design requirements and design constraints. The algorithm submits orientation/thickness combinations to ABAQUS/CAE by python-script for function evaluation. The application of algorithm over an initially assumed quasi-isotropic laminate of uniform thickness showed 57% weight reduction for a fighter aircraft’s wing panel. The optimization process is automated making PD practically viable in the design process itself.

The present study aims to minimize the weight of multi-laminate aerospace structures by a classical Genetic Algorithm (GA) interfaced with a CAE solver. The structural weight minimization is a multi-objective optimization problem subjected to fulfilling of strength and stiffness design requirements as well. The desired fitness function connects the multi-objective design requirements to form a single-objective function by using carefully chosen scaling factors and a weight vector to get a near optimal solution. The scaling factors normalize and the weight vector prioritizes the objective functions. The weight vector selection was based on a posteriori articulation, after obtaining a series of Pareto fronts by 3D hull plot of strength, stiffness and assembly weight data points. During the optimization, the algorithm does an intelligent laminate selection based on static strength and alters the ply orientations and thickness of laminae for faster convergence. The study further brings out the influence of mutation percentage on convergence. The optimization procedure on a transport aircraft wing torsion box has showed 29% weight reduction compared to an initial quasi-isotropic laminated structure and 54% with respect to the metallic structure.

The aerospace industry is on a perpetual drive to optimize structures with developments in modeling and analysis capabilities. The traditionally used laminates with 0 ° , 90 ° , ± 45 ° orientations are called Legacy QUAD Laminates (LQL). The recent discovery of Trace of an orthotropic stress tensor A allows reduction of design variables by the replacement of LQL with equivalent stiffness DD 1 (double-double) laminate having self-repeating orientations in a set of ± ϕ / ± ψ . The optimized LQL structures are with mid-plane symmetry, excessive ply-migrations, and variability in ply-orientations, which make the manufacturing process cumbersome. On the other hand, DD-laminates are simplified, thinner and free from mid-plane symmetry, thus enable ten-fold reduction in production resources. The present study, an artificial intelligent (AI) genetic-algorithm based stochastic optimizer replaces LQL with DD-laminates, which follows DD-drop design for mass optimization. The optimization algorithm works with unit-circle failure, buckling mode, and wing-tip deflection design criteria and derives optimal-wing with lowest mass, well suited for design requirements in multiple design load-case. The application of algorithm shows 68–70% mass reduction to an initial full-length ply wing-box model of LQL. The minimization of ply-migrations by D/DD-drop optimization yields structures with better resistance for delamination and well suited for automated production.

A redesign of canard control-surface of an advanced all-metallic fighter aircraft was carried out by using carbon fibre composite (CFC) for ribs and panels. In this study ply-orientations of CFC structure are optimized using a Genetic-Algorithm (GA) with an objective function to have minimum failure index (FI) according to Tsai-Wu failure criterion. The redesigned CFC structure was sufficiently strong to withstand aerodynamic loads from stress and deflection points of view. Now, in the present work CFC canard structure has been studied for its buckling strength in comparison to existing metallic design. In this study, the existing metallic design was found to be weak in buckling. Upon a detailed investigation, it was revealed that there are reported failures in the vicinity of zones where initial buckling modes are excited as predicted by the finite element based buckling analysis. In view of buckling failures, the redesigned CFC structure is sufficiently reinforced with stringers at specific locations. After providing reinforcements against buckling, the twist and the camber variations of the airfoil are checked and compared with existing structure data. Finally, the modal analysis has been carried out to compare the variation in excitation frequency due to material change. The CFC structure thus redesigned is safe from buckling and aerodynamic aspects as well.

The minimization of weight and maximization of payload is an ever challenging design procedure for air vehicles. The present study has been carried out with an objective to redesign control surface of an advanced all-metallic fighter aircraft. In this study, the structure made up of high strength aluminum, titanium and ferrous alloys has been attempted to replace by carbon fiber composite (CFC) skin, ribs and stiffeners. This study presents an approach towards development of a methodology for optimization of first-ply failure index (FI) in unidirectional fibrous laminates using Genetic-Algorithms (GA) under quasi-static loading. The GAs, by the application of its operators like reproduction, cross-over, mutation and elitist strategy, optimize the ply-orientations in laminates so as to have minimum FI of Tsai-Wu first-ply failure criterion. The GA optimization procedure has been implemented in MATLAB and interfaced with commercial software ABAQUS using python scripting. FI calculations have been carried out in ABAQUS with user material subroutine (UMAT). The GA's application gave reasonably well-optimized ply-orientations combination at a faster convergence rate. However, the final optimized sequence of ply-orientations is obtained by tweaking the sequences given by GA's based on industrial practices and experience, whenever needed. The present study of conversion of an all metallic structure to partial CFC structure has led to 12% of weight reduction. Therefore, the approach proposed here motivates designer to use CFC with a confidence.

Statistical analysis has become an important technique to accurately factor in the effect of process variations in circuit behavior. Statistical analysis techniques depend on the generation of compact, fast, accurate and robust models that capture some specific aspect of the circuit behavior. The process of characterizing the circuit behavior to generate variation-aware models for standard cells has a large runtime penalty (l00x of nominal model generation). This runtime explosion is primarily due to the additional numbers of simulations required to capture the effects of within-die (WID) variations. We look at the techniques used for capturing WID effects in model generation and present some techniques to reduce the runtime of statistical delay and leakage characterization significantly. We show that our technique can speed up timing model generation by l0x and leakage model generation by approximately 2x.

To reduce pessimism in cross talk analysis, a key technique that is employed is the use of timing windows. However, timing windows are associated with corners, hence the use of a single corner timing windows during analysis can lead to optimism. Alternately if the timings windows from best and worst corners are combined to create a wider window, it can lead to excessive pessimism. We propose an approach based on parametrized statistical timing analysis modeling to overcome the shortcomings of the existing approaches, and to account for process variations in the defnition of timing windows. We show that this approach can overcome both the issues in the current approaches.

With decreasing process nodes and increasing design density, crosstalk analysis is a must for getting design closure in the UDSM era. In addition to this, while crosstalk analysis is complex in itself, the new process nodes are showing increasing variations of process parameters for devices and interconnect. This in turn adds more complexity to crosstalk analysis. Standard techniques of factoring in the effects of process variations (corner-based analysis) is particularly ineffective for crosstalk analysis, so we need to look at techniques of statistical analysis of crosstalk in a manner similar to that used for timing. We look at a basic infrastructure for doing statistical crosstalk analysis - and look at how it can incorporate the effects of variations in cell variations and on aggressor slew. We also look at aggressor window clustering as a technique to reduce pessimism in crosstalk - and see how this technique can be modified to take in the effect of process variations. We lay the theoretical framework for these techniques in this paper, and show the results of a prototype implementation on real designs. We show that using this framework and techniques shows a close correlation with Monte-Carlo simulations.

With increasing complexity of designs and reducing process nodes, the number of devices in designs has been rapidly rising. This has a dramatic impact on analysis of these designs, as the software and hardware are unable to analysis such designs "flat". The authors discuss a method for partitioning designs so that crosstalk analysis can be done on these smaller partitions and show that the technique cleanly divides the netlist into independent problem statements that can be analyzed parallely. The authors also show that the results of such independent runs can be merged and that the final report of the analysis is accurate (as compared to the analysis on the complete design). The authors show that the technique provides for dramatic reductions in runtime, and that the memory requirements of the design are also partitioned across the machines in approximately the same ratio as the size of the partitions themselves

In SOC designs, it is common to have multiple clocks with some of them capable of operating at different frequencies. Crosstalk noise analysis uses switching information of nets in the form of timing windows to identify simultaneously switching aggressors. When clock frequencies change, the switching overlap relationship among nets also changes. Hence it is important to perform crosstalk analysis at all frequencies at which the chip may operate. In this paper we propose an approach to perform crosstalk analysis across multiple frequencies concurrently. We first present a method to obtain the worst crosstalk noise across all frequencies for each stage. Then we consider glitch propagation effects and build an efficient approach to analyze for crosstalk failures at all frequencies. We also discuss a method to guarantee functionality for a range of frequencies.

This paper targets at reducing the crosstalk noise closure time by filtering the set of false violations. We propose two approaches to reduce the pessimism in the crosstalk noise analysis. The first approach uses the arrival time windows for glitch propagation. The second approach uses the critical window of the sequential cell pins to identify the violations. We have presented some results that show the reduction in number of violation with new approach.

Crosstalk Noise analysis uses timing windows information for aggressor selection. Since switching activity changes across different operating modes of the chip, timing windows are also mode-specific. Large SOC designs often have multiple modes of operation, and to perform noise analysis at each mode will involve very high run times. In this paper, we propose an efficient method to perform noise analysis at multiple modes. The design is analyzed without using timing windows to identify a superset of noise nets. At each mode, coupling and propagated glitches are re-evaluated using mode specific timing windows and constraints. The method yields realistic results with low analysis time.