Variations In Input Variables Research Articles

Ecological network analyses and their use for establishing reference domain in functional assessment of an estuary

We developed a framework to use ecological network analysis for functional assessment of large aquatic ecosystems in the context of ecosystem-based management. We established a reference domain for the Neuse River Estuary, North Carolina, USA, from changes over time. Four reference network models of the trophic structure of the estuary during early and late summers of 1997 and 1998 were constructed and analyzed. The estuary has experienced various symptoms of eutrophication during the past 20 years, including summer-time hypoxia and fish kills. The networks were used to quantify indices of nominal trophic dynamics and their variation. The ratio of biomass of nekton to that of macrobenthos, derived from network construction, was used to index severity of eutrophication and to promote accessibility of ecological network analysis to environmental management. The ratio increased from early to late summer, and network metrics demonstrated a variety of responses in association with that change. Some variables from network analysis, especially related to consumers, reflected some but not all of this change. Others reflected the most severe increase in the ratio in late summer 1997 when hypoxia was most extensive. We evaluated uncertainty and the modulating effects of hierarchy by comparing variation of input biomasses with integrative response variables. Relative variation in input variables was generally greater than that of the integrative response variables as predicted by hierarchy theory. Ecological network analysis has previously served as support for ecosystem-based management of large aquatic systems with some success. However, its use can be enhanced by making it more accessible to environmental managers and policy makers. Ways to do this include promoting simple metrics from network construction and explicitly associating network analysis to concepts familiar to the management community, such as functional assessment and reference.

Tolerance allocation in spatial cam assembly for vehicle applications

This paper deals with a procedure to assess the influence of mechanical errors of both assembly constraints and feature dimensions on the performance of spatial cam mechanisms. These devices are often used in the vehicle robotised gearboxes. The proposed investigation is based on the definition of the design specification functions (requirements about position, velocity and contact sliding velocity) and on the computation of their sensitivity coefficients to the variation of input variables (dimensions, offsets and misalignments). A comparison between the sensitivity coefficients of different motion laws is also presented and discussed.

On reliability-based optimisation methods for automotive structures

To reduce the cost of prototype and physical tests, CAE analysis has been widely used to evaluate vehicle performance during the product development process. Combining CAE analysis and optimisation approach, the vehicle design process can be implemented more efficiently at affordable cost. Reliability-based design optimisation (RBDO) formulation considers variations of input variables, such as component gauges and material properties. This paper is on the application of reliability-based design optimisation of automotive structures. Several fundamental approaches aimed at simulations, optimisation and robustness analysis are discussed. They include sampling techniques and non-linear response surface methodologies, optimisation and robustness assessment, reliability-based design optimisation and robust design formulation. The major focus is on the implementation of reliability-based design optimisation methodologies in vehicle crash safety design. Some recent applications are presented to demonstrate how these approaches were used for vehicle structure design.

A robust design method using variable transformation and Gauss–Hermite integration

Robust design seeks an optimal solution where the design objective is insensitive to the variations of input variables while the design feasibility under the variations is maintained. Accurate robustness assessment for both design objective and feasibility usually requires an intensive computational effort. In this paper, an accurate robustness assessment method with a moderate computational effort is proposed. The numerical Gauss-Hermite integration technique is employed to calculate the mean and standard deviation of the objective and constraint functions. To effectively use the Gauss-Hermite integration technique, a transformation from a general random variable into a normal variable is performed. The Gauss-Hermite integration and the transformation result in concise formulas and produce an accurate approximation to the mean and standard deviation. This approach is then incorporated into the framework of robust design optimization. The design of a two-bar truss and an automobile torque arm is used to demonstrate the effectiveness of the proposed method. The results are compared with the commonly used Taylor expansion method and Monte Carlo simulation in terms of accuracy and efficiency.

Analytical Variance-Based Global Sensitivity Analysis in Simulation-Based Design Under Uncertainty

Abstract The importance of sensitivity analysis in engineering design cannot be over-emphasized. In design under uncertainty, sensitivity analysis is performed with respect to the probabilistic characteristics. Global sensitivity analysis (GSA), in particular, is used to study the impact of variations in input variables on the variation of a model output. One of the most challenging issues for GSA is the intensive computational demand for assessing the impact of probabilistic variations. Existing variance-based GSA methods are developed for general functional relationships but require a large number of samples. In this work, we develop an efficient and accurate approach to GSA that employs analytic formulations derived from metamodels. The approach is especially applicable to simulation-based design because metamodels are often created to replace expensive simulation programs, and therefore readily available to designers. In this work, we identify the needs of GSA in design under uncertainty, and then develop generalized analytical formulations that can provide GSA for a variety of metamodels commonly used in engineering applications. We show that even though the function forms of these metamodels vary significantly, they all follow the form of multivariate tensor-product basis functions for which the analytical results of univariate integrals can be constructed to calculate the multivariate integrals in GSA. The benefits of our proposed techniques are demonstrated and verified through both illustrative mathematical examples and the robust design for improving vehicle handling performance.

Modelling the effect of ecosystem change on spawning per recruit of Baltic herring

A plea for linking assessment and management to the broader ecosystem state has been made several times in the fisheries literature. The need for ecosystem considerations is obvious for Baltic herring stock which has experienced large fluctuations in growth and natural mortality rate. Biological reference points, based on stock-recruitment data, have gained importance under precautionary approach and the need for more restrictive management. An alternative method for establishing thresholds for recruitment overfishing is spawning per recruit analysis. Within this context, understanding the effects of highly variable natural mortality and growth rate on fishing mortality reference point is of interest. We used Monte Carlo simulations to investigate variation in spawning per recruit caused by varying stock attributes. Causal biological response to changing environmental conditions was created by adjusting the correlation between growth, maturity, and natural mortality. The correlation of the input variables was controlled under three models, assuming future conditions were (1) as experienced recently (empirical model), (2) random, and (3) depending upon causal linkages in the biological key processes (ecological model). The overall uncertainty was large in all models. Biological reference point (F30%SPR) was uncertain due to problem of defining maximum spawning per recruit and due to variation in input variables in SPR analysis. The concept of Fx%SPR was judged to be ambiguous. The use of causal ecological knowledge reduced uncertainty of the reference point only to a limited extent. However, relying only to the observed data appeared to be the riskiest approach. 2003 International Council for the Exploration of the Sea. Published by Elsevier Science Ltd.

A study on the causes of deviation in mechanical properties of thin steel sheets

Multi-layer perceptron (MLP) neural networks were trained using measured process data from Rautaruukki Hämeenlinna and Raahe Works. The MLPs had process parameters as inputs and the difference between the average and the actual yield strength (ΔRp02) as output. The data set consisted of various steel grades, the average Rp02 values being calculated separately for each grade. By studying the response of the MLP to changes in input variables it was possible to draw conclusions of the causes of variation in measured yield strength. It was observed that the process parameters contain sufficient information to predict the variation and that the variables responsible for the variation differ from grade to grade. A sensitivity analysis on the variations of input variables showed that the response of MLP on the variation of one variable may depend greatly on the values of the other variables.