Simulation by Slepian method of plastic displacements of Gaussian process excited multistory shear frame

The object of study is a stationary Gaussian white noise excited multi-degree-of-freedom (MDOF) linear elastic, ideal plastic, linearly damped, statically determinate oscillator with several potential elements of ideal plastic yielding. Specifically the study is exemplified for a plane multistory shear frame with rigid traverses where all the connecting columns except the columns in one or more of the bottom floors have finite symmetrical yield limits. The white noise excitation acts on the mass of the first floor making the movement of the elastic bottom floors simulate a ground motion that interacts with the structure above the bottom floors. The method of study is so-called Slepian model simulation and is in principle the same for other statically determinate MDOF elasto-plastic oscillators of the considered type. The method is fast as compared to direct simulation and provides results that give good insight in the behavior of the plastic displacement response process.     

Creating geometrically robust designs for highly sensitive problems using topology optimization

Resonance and wave-propagation problems are known to be highly sensitive towards parameter variations. This paper discusses topology optimization formulations for creating designs that perform robustly under spatial variations for acoustic cavity problems. For several structural problems, robust topology optimization methods have already proven their worth. However, it is shown that direct application of such methods is not suitable for the acoustic problem under consideration. A new double filter approach is suggested which makes robust optimization for spatial variations possible. Its effect and limitations are discussed. In addition, a known explicit penalization approach is considered for comparison. For near-uniform spatial variations it is shown that highly robust designs can be obtained using the double filter approach. It is finally demonstrated that taking non-uniform variations into account further improves the robustness of the designs.     

On the similarities between micro/nano lithography and topology optimization projection methods

The aim of this paper is to incorporate a model for micro/nano lithography production processes in topology optimization. The production process turns out to provide a physical analogy for projection filters in topology optimization. Blueprints supplied by the designers cannot be directly used as inputs to lithographic processes due to the proximity effect which causes rounding of sharp corners and geometric interaction of closely spaced design elements. Therefore, topology optimization is applied as a tool for proximity effect correction. Furthermore, it is demonstrated that the robust projection filter can be used to account for uncertainties due to lithographic production processes which results in manufacturable blueprint designs and eliminates the need for subsequent corrections.     

Parallel framework for topology optimization using the method of moving asymptotes

The complexity of problems attacked in topology optimization has increased dramatically during the past decade. Examples include fully coupled multiphysics problems in thermo-elasticity, fluid-structure interaction, Micro-Electro Mechanical System (MEMS) design and large-scale three dimensional problems. The only feasible way to obtain a solution within a reasonable amount of time is to use parallel computations in order to speed up the solution process. The focus of this article is on a fully parallel topology optimization framework implemented in C++, with emphasis on utilizing well tested and simple to implement linear solvers and optimization algorithms. However, to ensure generality, the code is developed to be easily extendable in terms of physical models as well as in terms of solution methods, without compromising the parallel scalability. The widely used Method of Moving Asymptotes optimization algorithm is parallelized and included as a fundamental part of the code. The capabilities of the presented approaches are demonstrated on topology optimization of a Stokes flow problem with target outflow constraints as well as the minimum compliance problem with a volume constraint from linear elasticity.     

On projection methods,convergence and robust formulations in topology optimization

Mesh convergence and manufacturability of topology optimized designs have previously mainly been assured using density or sensitivity based filtering techniques. The drawback of these techniques has been gray transition regions between solid and void parts, but this problem has recently been alleviated using various projection methods. In this paper we show that simple projection methods do not ensure local mesh-convergence and propose a modified robust topology optimization formulation based on erosion, intermediate and dilation projections that ensures both global and local mesh-convergence.     

Robust topology optimization accounting for spatially varying manufacturing errors

This paper presents a robust approach for the design of macro-, micro-, or nano-structures by means of topology optimization, accounting for spatially varying manufacturing errors. The focus is on structures produced by milling or etching; in this case over- or under-etching may cause parts of the structure to become thinner or thicker than intended. This type of error is modeled by means of a projection technique: a density filter is applied, followed by a Heaviside projection, using a low projection threshold to simulate under-etching and a high projection threshold to simulate over-etching. In order to simulate the spatial variation of the manufacturing error, the projection threshold is represented by a (non-Gaussian) random field. The random field is obtained as a memoryless transformation of an underlying Gaussian field, which is discretized by means of an EOLE expansion. The robust optimization problem is formulated in a probabilistic way: the objective function is defined as a weighted sum of the mean value and the standard deviation of the structural performance. The optimization problem is solved by means of a Monte Carlo method: in each iteration of the optimization scheme, a Monte Carlo simulation is performed, considering 100 random realizations of the manufacturing error. A more thorough Monte Carlo simulation with 10000 realizations is performed to verify the results obtained for the final design. The proposed methodology is successfully applied to two test problems: the design of a compliant mechanism and a heat conduction problem.     

Dynamic Data Driven Simulations in Stochastic Environments

To improve the predictions in dynamic data driven simulations (DDDAS) for subsurface problems, we propose the permeability update based on observed measurements. Based on measurement errors and a priori information about the permeability field, such as covariance of permeability field and its values at the measurement locations, the permeability field is sampled. This sampling problem is highly nonlinear and Markov chain Monte Carlo (MCMC) method is used. We show that using the sampled realizations of the permeability field, the predictions can be significantly improved and the uncertainties can be assessed for this highly nonlinear problem.     

Office-based surgery and anesthesia: where are we now?

At the time of publication of this journal, the Center for Urinary Control in Memphis, Tennessee, has performed eight office-based surgical procedures using anesthesia. Patients were aged between 56 and 80 years, and all were ASA I-III class. The procedures involved either cystoscopy with collagen injection into the bladder neck or cystoscopy with indigo laser treatment of the prostate. All patients were prepared by the anesthesiologist prior to the day of surgery, and appropriate laboratory tests were ordered and evaluated. All patients did very well, the surgical field was quiet, and each procedure went very smoothly. Each patient tolerated anesthesia without any adverse effect, was recovered in an adjacent examination room, and was extremely satisfied upon discharge from the office. Anesthesia was induced with Propofol (Diprivan) and sublimaze, maintained with a Propofol infusion and oxygen. The surgeon performed a periprostatic block on the patient having the Indigo laser treatment.     

Direct preparation of ionic potassium—coal adducts

The possibility for a direct preparation of ionic potassium—coal adduct is shown, from finely ground low-volatile bituminous coal (89.2% C) and metallic potassium with stirring at 120 °C and a vacuum of 13 mPa. The treatment of the adduct dispersed in THF with CH₃l results in reductive methylation of the coal, similar to that of the ‘coal anion’, obtained in THF during its interaction with potassium—naphthalene.     

Chloroform-extractable material from bituminous coals

Material soluble in chloroform has been extracted from bright bituminous coals of two different ranks

1. (1) following preheating to the temperatures of maximal plasticity;

2. (2) after treatment with sodium in liquid ammonia;

3. (3) by fractionating the pyridine extracts;

4. (4) after preliminary acetylation with acetic anhydride in the presence of pyridine.

The extracts were characterized by elemental analysis, average molecular weights, and n.m.r. and i.r. spectroscopy. The spectroscopic data afforded quantitative information on the distribution of hydrogen between various structural groups and some statistically averaged structural parameters. The results are discussed with regard to the mechanism of formation of the chloroform-extractable material during the various preliminary treatments of the coals.