Numerical Design Of Experiments Research Articles

Topology-based potentials and the study of the competition between protein folding and aggregation

Topology-based or Go-type potentials have been shown to be very useful in the understanding of the relations between the structure of the native state of a protein and some of its folding characteristics. A different question is whether they can also make such a contribution when the aggregation process of misfolded or partially folded structures is under study. In this work, in spite of the obvious trend of these simulation models toward the native state, we show that there are some aspects about aggregation that can be addressed by topology-based potentials: the role of the thermodynamic characteristics of the transition on preventing the aggregation process, or the larger propensity of highly symmetric protein structures to form domain swapped dimers. In a second part of this work, we use the possibilities of computer simulation as a design of numerical experiments to analyze the fundamental role of intermediate states in the aggregation process of globular proteins.

Optimisation du procédé de pliage pour la mise en forme de pièces de sécurité automobile

In this work, the process optimisation of security parts manufacturing is simulated by a finite element modelling including a material damage formulation. Objective functions, stress and damage values occurring in the part at the end of a bending operation, are analysed by means of numerical Design of Experiments and Response Surface Methodology. The process optimisation is carried out by global minimisation and by means of Evolution Strategies. A multi-objective optimisation is carried out in order to identify the best choice of material and process parameters giving a reliable part-life.

Towards the Optimal Design of Numerical Experiments

This paper addresses the problem of the optimal design of numerical experiments for the construction of nonlinear surrogate models. We describe a new method, called learner disagreement from experiment resampling (LDR), which borrows ideas from active learning and from resampling methods: the analysis of the divergence of the predictions provided by a population of models, constructed by resampling, allows an iterative determination of the point of input space, where a numerical experiment should be performed in order to improve the accuracy of the predictor. The LDR method is illustrated on neural network models with bootstrap resampling, and on orthogonal polynomials with leave-one-out resampling. Other methods of experimental design such as random selection and D-optimal selection are investigated on the same benchmark problems.

Numerical Design of Experiment for Sensitivity Analysis—Application to Skin Burn Injury Prediction

Temperature evolution and skin burn process resulting from a laser radiation exposure are investigated in this paper. Transient temperature in skin is numerically estimated using a 1-D multilayered model based on Penne's equation. The degree of burn injury is numerically evaluated by using an Arrhenius-type function. Unfortunately, most of the mathematical model parameters are not well defined in literature. Thus, a sensitivity analysis has been performed in order to evaluate the effect of each parameters inaccuracy on temperature estimation and on burn injuries prediction (according to several authors' characterization). Investigated parameters uncertainties that crucially invalidate the thermal model are as follows: epidermis and dermis volumetric heat, extinction coefficient, and skin thickness of the affected area. Considering the damage prediction, the activation energy is a key parameter for the validation of an efficient predictive tool.

Study on residual stress of laser shock processing based on numerical simulation and orthogonal experimental design

Laser shock processing (LSP) is a competitive alternative surface enhancement process. Shock pressure with high magnitude and short duration induced by LSP is one key parameter to induce a residual stress field in the material. Finite element method (FEM) simulation based on the orthogonal experimental design is adopted to analyse the interaction of different shock pressure profiles with the material. It can give a relatively accurate description of the effect of shock pressure profile on the residual stress field induced in the material with a representative coverage of all parameters by less calculation cost. The analysis results show that laser shock conditions should be selected carefully to ensure a favourable shock pressure to obtain an optimised residual stress field. The simulation on the overlapping laser shock is also presented and indicates that it is an effective method to settle the residual stress drop on the top surface and can obtain a much more favourable residual compressive stress field to enhance the fatigue resistance of the material.

Development of a permittivity extraction method for ultra low k dielectrics integrated in advanced interconnects

An extraction method to determine the permittivity of ultra low k (ULK) dielectrics on real integrated structures is presented. It is a two-step method based on a comparison between measured and simulated capacitance. A best-estimate value of the k ULK value is first extracted with optimization software coupled to capacitance extraction software. Secondly, uncertainties on material and process parameters are considered to determine an error margin on the best-estimate extracted k value. The uncertainty on the best-estimate value is approximated by a function of the uncertainties on material and process variables. This function is calculated using a multi-linear approximation model and a numerical design of experiments. The same method is applied for the extraction of a ULK material k value ( k ULK) value and an effective k value ( k eff) but with two different simulation structures. In the simulation structure used for k eff extraction, an equivalent dielectric layer including the ULK layer, the etch-stop and capping layers is used. This method was applied to metal 1 single damascene structures. First results of extraction are presented for two different ULK dielectrics. With the estimated uncertainty used for the parameters in this work, the uncertainties obtained for the best-estimate value of k ULK and k effective are significant. Due to the linearity of the model, the method is still applicable with different values for parameters uncertainty. An analysis work will be realized to improve the parameters uncertainty estimation. Future work will also include extraction of ULK permittivity for more complex structures like double damascene structures.

Parameter analysis of brake squeal using finite element method

Brake Squeal is a friction induced instability phenomenon known to be one of the most annoying noise for drivers. This paper focuses on the mode coupling aspect of brake squeal by means of a multi parametric analysis. The study is based on a Finite Element model of the whole brake corner. A complex eigenvalue analysis is undertaken, with a modal projection technique, to detect the stable and unstable modes. Following this process, the brake stability is assessed as a function of the friction coefficient. The results highlight accurately the modecoupling phenomenon also referred to as coalescence. Then, the emphasis is put on the disc Young modulus variability by launching a numerical design of experiment. Finally, the brake robustness is displayed as functions of the friction coefficient and of the disc Young modulus.

Running Classroom Experiments in a Large Enrollment Economics Course

During the last two decades we have seen the emergence of numerous experimental designs especially suited for teaching in economics. Nevertheless, for the most part, most of these experiments are suited only for classes with small enrollment. This is unfortunate for two reasons. First, most students in college universities take their first (and sometimes more advanced) economics courses in a classroom with at least 100 students or more. Second, large enrollment classes create a set of dynamics impossible to replicate in their much smaller counterparts. While some have suggested ways of adjusting experiments for large classes, for the most part these adjustments always involve engaging only a small portion of the large class while the rest of the students are merely passive observers. In this article I will document my experience in administering one popular experiment, the double-auction, in a large enrollment class at the University of Illinois at Urbana-Champaign (UIUC). Since it is designed to be used in a large class where all students participate, this version of the experiment requires very little in terms of materials, technology, and data collection. This case study shows that by making certain adjustments to this popular experiment it is possible to create an activity that can be applied to any class size. Furthermore, by creating some dynamics impossible to replicate in the small classroom, the experiment in a large class has the potential of presenting a new pedagogical experience for students and instructors.

Probabilistic approach to corrosion risk due to carbonation via an adaptive response surface method

A study about a probabilistic approach to corrosion risk of reinforcements embedded in concrete due to carbonation is presented. The carbonation model is based on a single non-linear diffusion equation of the carbon dioxide. A global balance relationship between the carbon dioxide partial pressure and the solid calcium content in the hydrates of concrete is used in order to express the sink term of the equation and to render the solving easily tractable in a classical finite element analysis. The performance function invoked in the probabilistic approach is the deviation between the carbonation depth, i.e. the output of the carbonation model, and the concrete cover. The Hasofer–Lind reliability index is determined by the Rackwitz–Fiessler algorithm in which the performance function is replaced by a quadratic response surface in order to reduce computational cost and gain accuracy. An adaptive building of the numerical experimental design is proposed: points efficiently positioned with respect to the design point are re-used in the new iteration of the experimental design. In the case of explicit performance functions frequently reported in the literature comparisons with surface response techniques previously developed reveal the interest of the proposed technique. A practical application to a concrete girder shows that the reliability index decreases significantly with time.

Uncertainty in Finite Element Modeling and Failure Analysis: A Metrology-Based Approach

In this paper, we first review the impact of the powerful finite element method (FEM) in structural engineering, and then address the shortcomings of FEM as a tool for risk-based decision making and incomplete-data-based failure analysis. To illustrate the main shortcoming of FEM, i.e., the computational results are point estimates based on “deterministic” models with equations containing mean values of material properties and prescribed loadings, we present the FEM solutions of two classical problems as reference benchmarks: (RB-101) The bending of a thin elastic cantilever beam due to a point load at its free end and (RB-301) the bending of a uniformly loaded square, thin, and elastic plate resting on a grillage consisting of 44 columns of ultimate strengths estimated from 5 tests. Using known solutions of those two classical problems in the literature, we first estimate the absolute errors of the results of four commercially available FEM codes (ABAQUS, ANSYS, LSDYNA, and MPAVE) by comparing the known with the FEM results of two specific parameters, namely, (a) the maximum displacement and (b) the peak stress in a coarse-meshed geometry. We then vary the mesh size and element type for each code to obtain grid convergence and to answer two questions on FEM and failure analysis in general: (Q-1) Given the results of two or more FEM solutions, how do we express uncertainty for each solution and the combined? (Q-2) Given a complex structure with a small number of tests on material properties, how do we simulate a failure scenario and predict time to collapse with confidence bounds? To answer the first question, we propose an easy-to-implement metrology-based approach, where each FEM simulation in a grid-convergence sequence is considered a “numerical experiment,” and a quantitative uncertainty is calculated for each sequence of grid convergence. To answer the second question, we propose a progressively weakening model based on a small number (e.g., 5) of tests on ultimate strength such that the failure of the weakest column of the grillage causes a load redistribution and collapse occurs only when the load redistribution leads to instability. This model satisfies the requirement of a metrology-based approach, where the time to failure is given a quantitative expression of uncertainty. We conclude that in today’s computing environment and with a precomputational “design of numerical experiments,” it is feasible to “quantify” uncertainty in FEM modeling and progressive failure analysis.

Comment on the paper: On the design of practicable numerical experiments to investigate stratospheric temperature change, by S. Hare et al. (2005)

AbstractIf stratospheric temperature trends are to be understood, coupled chemistry climate models will need to be run. Simulations with fixed ozone trends might provide a misleading indication of future temperature trends. Copyright © 2005 Royal Meteorological Society

On the design of practicable numerical experiments to investigate stratospheric temperature change

AbstractForty‐year, time‐varying GCM integrations have been run both with and without coupled chemistry. The results suggest that while computer resources preclude multi‐member ensemble experiments with coupled chemistry models to achieve statistical significance, robust results can be achieved for some atmospheric parameters by running ensemble experiments with imposed ozone changes. Copyright © 2005 Royal Meteorological Society

Design of an aluminium-based vehicle platform for front impact safety

The current paper examines the design of an aluminium-intensive small car platform for desirable front impact safety performance. A space frame-type architecture comprised of extruded aluminium members with welded joints is considered for inherent structural rigidity, and low investment in terms of tooling. A finite element model of the vehicle is employed for crash analysis using the explicit code LS-DYNA. Confidence in analysis is established at the component level by benchmarking finite element models of welded joints against experimental data, and axial crushing of aluminium tubes against published numerical results and theoretical prediction. A numerical design of experiments is conducted for arriving at a front-end design that will yield desirable safety performance during impact against a rigid barrier at 30mph (FMVSS 208 condition). For comparable new car assessment program performance at a higher speed of 35mph, a lumped parameter idealization is used to identify the principal design changes that may be necessary. The current approach of component level testing combined with finite element and lumped parameter-based simulations can be regarded as an effective and time-saving procedure in the crash safety design of new vehicles.

Resin transfer molding process optimization using numerical simulation and design of experiments approach

AbstractThe art of resin transfer molding (RTM) process optimization requires a clear understanding of how the process performance is affected by variations in some important process parameters. In this paper, maximum pressure and mold filling time of the RTM process are considered as characteristics of the process performance to evaluate the process design. The five process parameters taken into consideration are flow rate, fiber volume fraction, number of gates, gate location, and number of vents. An integrated methodology was proposed to investigate the effects of process prameters on maximum pressure and mold filling time and to find the optimum processing conditions. The method combines numerical simulation and design of experiments (DOE) approach and is applied to process design for a cylindrical composite part. Using RTM simulation, a series of numerical experiments were conducted to predict maximum pressure and mold filling time of the RTM process. A half‐fractional factorial design was conducted to identify the significant factors in the RTM process. Furthermore, the empirical models and sensitivity coefficients for maximum pressure and mold filling time were developed. Comparatively close agreements were found among the empirical approximations, numerical simulations, and actual experiments. These results were further utilized to find the optimal processing conditions for the example part.

Numerical analysis and experimental design of tunable birefringence in microstructured optical fiber

We present detailed experimental and numerical results for birefringence tuning in microstructured optical fibers. Index tunable polymer is infused into specific air-holes to obtain birefringence whose tunability is achieved by temperature tuning the polymer index. We also study the symmetry properties of the modes for different waveguide structures.

Thermal micromagnetic model with a pseudo-three-dimensional junction for magnetoresistive heads

We developed a pseudo-three-dimensional (3D) thermal model in conjunction with a thermal micromagnetic model to study the performance of magnetoresistive (MR) readers. With decreasing sensor dimensions, the junction plays an important role in the overall sensor response. With an image method, we converted the 3D thermal problem to a set of coupled nonlinear differential-integral equations in three regions: sensor stack region, junction region, and lead region. Analytic methods for each region allow a speedy solution for rapid development in MR head design and performance analysis, such as numerical design of experiments and regression for design optimization, Monte Carlo analysis, and statistical analysis for product yield.

Design and analysis of numerical experiments to compare four canopy reflectance models

A method designed to study the relative effects of the input parameters of any model has been investigated with canopy reflectance (CR) models. Traditionally, sensitivity analyses are performed by changing one input parameter at a time. Such an approach is limited because it lacks strategy. A promising alternative is in the use of design of experiments, a statistical method that allows defining a structured and restricted number of simulations for which all input parameters vary simultaneously. This approach is especially helpful in multidimensional parameter spaces. It is demonstrated using four 1D radiative transfer models that are compared in direct mode. These models are combinations of the PROSPECT leaf optical properties model with the four CR models, SAIL (Scattering and Arbitrarily Inclined Leaves), KUUSK, IAPI, and NADI (New Advanced DIscrete model). The sensitivity studies were conducted in the visible/near-infrared on the following parameters: the leaf structure ( N), the chlorophyll- a and - b content ( C ab), the leaf area index (LAI), the mean leaf inclination angle ( θ l), the hot spot ( s l), and the soil brightness ( α soil). We compared simulated reflectances for a given set of measurement geometries and two wavebands of the POLDER (Polarization and Directionality of the Earth's Reflectances) spaceborne instrument. The relative effects of the biophysical parameters are assessed as well as their contribution to reflectance, allowing us to rank the most influential ones. Their interactions were also studied from the perspective of improving inversion procedures. Globally, the four models agree well in terms of computed reflectances and parameter effects, nevertheless with some discrepancies due to the implementation of different leaf angle distribution (LAD) functions.

Review: home visitation by nurses beginning prenatally and extending through infancy prevents child abuse and neglect

MacMillan HL with the Canadian Task Force on Preventive Health Care. Preventive health care, 2000 update: prevention of child maltreatment. CMAJ2000 Nov 28; 163 : 1451 –8. [OpenUrl][1][Abstract/FREE Full Text][2]...

Open‐channel flow through simulated vegetation: Suspended sediment transport modeling

A two‐equation turbulence model, based on the k‐ϵ closure scheme, was used to determine the mean flow and turbulence structure of open channels through simulated vegetation, thus providing the necessary information to estimate suspended sediment transport processes. Dimensional analysis allowed identification of the dimensionless parameters that govern suspended sediment transport in the presence of vegetation and thus helped in the design of numerical experiments to investigate the role of different flow properties, sediment characteristics, and vegetation parameters upon the transport capacity. A reduction of the averaged streamwise momentum transfer toward the bed (i.e., shear stress) induced by the vegetation was identified as the main reason for lower suspended sediment transport capacities in vegetated waterways compared to those observed in nonvegetated channels under similar flow conditions. Computed values of kinematic eddy viscosity were used to solve the sediment diffusion equation, yielding distributions of relative sediment concentration slightly in excess of the ones predicted by the Rousean formula. A power law was found to provide a very good collapse of all the numerically generated data for suspended sediment transport rates in vegetated channels.

Genetic algorithms for dynamic tuning of structures

A category of methods developed in the branch of artificial intelligence known as genetic algorithms is suggested for numerical estimation and experimental design of dynamic tuning and resonance limit state analysis in ultimate dynamic states of structures in civil engineering. The performance of a set of genetic algorithms is presented by using the application problems. The results indicate that genetic algorithms are promising tools for solving nonlinear optimization problems, even when the objective function is either discrete, nonconvex, mulitmodal or flat in shape. The applications are performed on the adoption of up-to-date vectorized numerical methods for wave propagation and ultimate transient dynamics problems and optoelectronical experimental approaches for resonance tuning of engineering structures and technologies submitted to limit state dynamic loads.