Singularities in optimal structural design

Singularity conditions that arise during structural optimization can seriously degrade the performance of the optimizer. The singularities are intrinsic to the formulation of the structural optimization problem and are not associated with the method of analysis. Certain conditions that give rise to singularities have been identified in earlier papers, encompassing the entire structure. Further examination revealed more complex sets of conditions in which singularities occur. Some of these singularities are local in nature, being associated with only a segment of the structure. Moreover, the likelihood that one of these local singularities may arise during an optimization procedure can be much greater than that of the global singularity identified earlier. Examples are provided of these additional forms of singularities. A framework is also given in which these singularities can be recognized. In particular, the singularities can be identified by examination of the stress displacement relations along with the compatibility conditions and/or the displacement stress relations derived in the integrated force method of structural analysis

Using monoclonal antibodies to label living root hairs: a novel tool for studying cell wall microarchitecture and dynamics in <i>Arabidopsis</i>

Background&lt;p&gt;&lt;/p&gt; The Arabidopsis root hair represents a valuable cell model for elucidating polar expansion mechanisms in plant cells and the overall biology of roots. The deposition and development of the cell wall is central to the root hair expansion apparatus. During this process, incorporation of specific wall polymers into the growing wall architecture constitutes a critical spatio-temporal event that controls hair size and growth rate and one that is closely coordinated with the cell’s endomembrane, cytoskeletal and signal transduction apparatuses.&lt;p&gt;&lt;/p&gt; Results&lt;p&gt;&lt;/p&gt; In this study, the protocol for live cell labeling of roots with monoclonal antibodies that bind to specific wall polymers is presented. This method allows for rapid assessment of root hair cell wall composition during development and assists in describing changes to cell wall composition in transgenic mutant lines. Enzymatic “unmasking” of specific polymers prior to labeling allows for refined interpretation of cell wall chemistry. Live cell immunofluorescence data may also be correlated with transmission electron microscopy-based immunogold labeling.&lt;p&gt;&lt;/p&gt; Conclusions&lt;p&gt;&lt;/p&gt; Live Arabidopsis root hairs may be labeled with cell wall polymer-specific antibodies. This methodology allows for direct visualization of cell wall dynamics throughout development in stable transgenic plant lines. It also provides an important new tool in the elucidation of the specific interactions occurring between membrane trafficking networks, cytoskeleton and the cell wall deposition/remodeling mechanism

Structural Optimization Using the Newton Modified Barrier Method

The Newton Modified Barrier Method (NMBM) is applied to structural optimization problems with large a number of design variables and constraints. This nonlinear mathematical programming algorithm was based on the Modified Barrier Function (MBF) theory and the Newton method for unconstrained optimization. The distinctive feature of the NMBM method is the rate of convergence that is due to the fact that the design remains in the Newton area after each Lagrange multiplier update. This convergence characteristic is illustrated by application to structural problems with a varying number of design variables and constraints. The results are compared with those obtained by optimality criteria (OC) methods and by the ASTROS program

Effects of buprenorphine on model development in an adjuvant-induced monoarthritis rat model

Complete Freund’s adjuvant (CFA)-induced arthritis in rats is a common animal model for studying chronic inflammatory pain. However, modelling of the disease is associated with unnecessary pain and impaired animal wellbeing, particularly in the immediate post-induction phase. Few attempts have been made to counteract these adverse effects with analgesics. The present study investigated the effect of buprenorphine on animal welfare, pain-related behaviour and model-specific parameters during the disease progression in a rat model of CFA-induced monoarthritis. The aim was to reduce or eliminate unnecessary pain in this model, in order to improve animal welfare and to avoid suffering, without compromising the quality of the model. Twenty-four male Sprague Dawley rats were injected with 20 μl of CFA into the left tibio-tarsal joint to induce monoarthritis. Rats were treated with either buprenorphine or carprofen for 15 days during the disease development, and were compared to a saline-treated CFA-injected group or a negative control group. Measurements of welfare, pain-related behaviour and clinical model-specific parameters were collected. The study was terminated after 3 weeks, ending with a histopathologic analysis. Regardless of treatment, CFA-injected rats displayed mechanical hyperalgesia and developed severe histopathological changes associated with arthritis. However, no severe effects on general welfare were found at any time. Buprenorphine treatment reduced facial pain expression scores, improved mobility, stance and lameness scores and it did not supress the CFA-induced ankle swelling, contrary to carprofen. Although buprenorphine failed to demonstrate a robust analgesic effect on the mechanical hyperalgesia in this study, it did not interfere with the development of the intended pathology

Soft computing methods in design of superalloys

Soft computing techniques of neural networks and genetic algorithms are used in the design of superalloys. The cyclic oxidation attack parameter K(sub a), generated from tests at NASA Lewis Research Center, is modeled as a function of the superalloy chemistry and test temperature using a neural network. This model is then used in conjunction with a genetic algorithm to obtain an optimized superalloy composition resulting in low K(sub a) values

Hydrodynamic attraction of swimming microorganisms by surfaces

Cells swimming in confined environments are attracted by surfaces. We measure the steady-state distribution of smooth-swimming bacteria (Escherichia coli) between two glass plates. In agreement with earlier studies, we find a strong increase of the cell concentration at the boundaries. We demonstrate theoretically that hydrodynamic interactions of the swimming cells with solid surfaces lead to their re-orientation in the direction parallel to the surfaces, as well as their attraction by the closest wall. A model is derived for the steady-state distribution of swimming cells, which compares favorably with our measurements. We exploit our data to estimate the flagellar propulsive force in swimming E. coli

Weight minimization of structural components for launch in space shuttle

Minimizing the weight of structural components of the space station launched into orbit in a space shuttle can save cost, reduce the number of space shuttle missions, and facilitate on-orbit fabrication. Traditional manual design of such components, although feasible, cannot represent a minimum weight condition. At NASA Lewis Research Center, a design capability called CometBoards (Comparative Evaluation Test Bed of Optimization and Analysis Routines for the Design of Structures) has been developed especially for the design optimization of such flight components. Two components of the space station - a spacer structure and a support system - illustrate the capability of CometBoards. These components are designed for loads and behavior constraints that arise from a variety of flight accelerations and maneuvers. The optimization process using CometBoards reduced the weights of the components by one third from those obtained with traditional manual design. This paper presents a brief overview of the design code CometBoards and a description of the space station components, their design environments, behavior limitations, and attributes of their optimum designs

Parallel Computational Environment for Substructure Optimization

Design optimization of large structural systems can be attempted through a substructure strategy when convergence difficulties are encountered. When this strategy is used, the large structure is divided into several smaller substructures and a subproblem is defined for each substructure. The solution of the large optimization problem can be obtained iteratively through repeated solutions of the modest subproblems. Substructure strategies, in sequential as well as in parallel computational modes on a Cray YMP multiprocessor computer, have been incorporated in the optimization test bed CometBoards. CometBoards is an acronym for Comparative Evaluation Test Bed of Optimization and Analysis Routines for Design of Structures. Three issues, intensive computation, convergence of the iterative process, and analytically superior optimum, were addressed in the implementation of substructure optimization into CometBoards. Coupling between subproblems as well as local and global constraint grouping are essential for convergence of the iterative process. The substructure strategy can produce an analytically superior optimum different from what can be obtained by regular optimization. For the problems solved, substructure optimization in a parallel computational mode made effective use of all assigned processors