A test of three fitting criteria for multiresponse non-linear modeling

1991 ◽  
Vol 5 (4) ◽  
pp. 375-387 ◽  
Author(s):  
Randy J. Pell ◽  
Bruce R. Kowalski
Keyword(s):  
Linear Modeling ◽  
Non Linear

Non-Linear Modeling of Bushings and Cab Mounts for Calculation of Durability Loads

2014 ◽  
Author(s):  
Christian Scheiblegger ◽  
Nantu Roy ◽  
Orlando Silva Parez ◽  
Andrew Hillis ◽  
Peter Pfeffer ◽  
...  
Keyword(s):  
Linear Modeling ◽  
Non Linear

Assessment of nominal data requirements for robust estimation of fractional snow cover in alpine-forested terrain

2021 ◽  
Author(s):  
Elzbieta Wisniewski ◽  
Wit Wisniewski
Keyword(s):  
Snow Cover ◽  
Input Data ◽  
Model Performance ◽  
Slope Aspect ◽  
Ann Model ◽  
Linear Modeling ◽  
Nominal Data ◽  
Fractional Snow Cover ◽  
Non Linear ◽  
Input Variables

<p>The presented research examines what minimum combination of input variables are required to obtain state-of-the-art fractional snow cover (FSC) estimates for heterogeneous alpine-forested terrains. Currently, one of the most accurate FSC estimators for alpine regions is based on training an Artificial Neural Network (ANN) that can deconvolve the relationships among numerous compounded and possibly non-linear bio-geophysical relations encountered in alpine terrain. Under the assumption that the ANN optimally extracts available information from its input data, we can exploit the ANN as a tool to assess the contributions toward FSC estimation of each of the data sources, and combinations thereof. By assessing the quality of the modeled FSC estimates versus ground equivalent data, suitable combinations of input variables can be identified. High spatial resolution IKONOS images are used to estimate snow cover for ANN training and validation, and also for error assessment of the ANN FSC results. Input variables are initially chosen representing information already incorporated into leading snow cover estimators (ex. two multispectral bands for NDSI, etc.). Additional variables such as topographic slope, aspect, and shadow distribution are evaluated to observe the ANN as it accounts for illumination incidence and directional reflectance of surfaces affecting the viewed radiance in complex terrain. Snow usually covers vegetation and underlying geology partially, therefore the ANN also has to resolve spectral mixtures of unobscured surfaces surrounded by snow. Multispectral imagery if therefore acquired in the fall prior to the first snow of the season and are included in the ANN analyses for assessing the baseline reflectance values of the environment that later become modified by the snow. In this study, nine representative scenarios of input data are selected to analyze the FSC performance. Numerous selections of input data combinations produced good results attesting to the powerful ability of ANNs to extract information and utilize redundancy. The best ANN FSC model performance was achieved when all 15 pre-selected inputs were used. The need for non-linear modeling to estimate FSC was verified by forcing the ANN to behave linearly. The linear ANN model exhibited profoundly decreased FSC performance, indicating that non-linear processing more optimally estimates FSC in alpine-forested environments.</p>

Exhaust System Evaluation and Design by Non-Linear Modeling

1992 ◽  
Author(s):  
Steve M. Sapsford ◽  
Vanessa C.M. Richards ◽  
Duane R. Amlee ◽  
Tom Morel ◽  
Mary T. Chappell
Keyword(s):  
Exhaust System ◽  
System Evaluation ◽  
Linear Modeling ◽  
Non Linear

Piping System Structural Integrity Simulation for Post LOCA Water Hammer Loads

2002 ◽  
Author(s):  
B. W. Manning ◽  
T. Stevens ◽  
G. Morandin ◽  
R. G. Sauve´ ◽  
R. Richards ◽  
...  
Keyword(s):  
Structural Damage ◽  
Structural Integrity ◽  
Water Hammer ◽  
Piping System ◽  
Linear Modeling ◽  
Linear Elastic ◽  
Beam Elements ◽  
Loss Of Coolant Accident ◽  
Loss Of Coolant ◽  
Non Linear

The Canadian Nuclear Safety Commission (CNSC) required as part of the operating license for Ontario Power Generation’s Darlington Nuclear Generating Station, that the structural integrity of the piping following a loss of coolant accident (LOCA) be demonstrated. This is necessary to ensure that no subsequent pressure boundary failures will impede the ability to maintain fuel cooling. The injection of cold emergency coolant following a LOCA creates the potential for the occurrence of condensation-induced water hammers (CIWH) in the primary heat transport (PHT) system piping. Classical linear elastic piping analysis using the class 1 NB-3656 rules of the ASME Boiler & Pressure Vessel Code failed to demonstrate the adequacy of the piping and/or its supports that were designed using the linear elastic rules of subsection NF for nine of the twelve piping models that comprise the PHT system. A decision was made to undertake a state-of-the-art non-linear explicit analysis in order to qualify the piping. Strain rather than stress limits would be applied similar to those being developed by ASME for nuclear packaging undergoing accidental impact during transportation. In order to address the feasibility of this approach, a non-linear analysis was performed on a portion of one of the piping systems. The piping was modeled as shells and again as beam elements with and without detailed modeling of the supports. After these initial simulations, it was determined that the piping could be modeled with simplified beam elements, however, the supports would require a more detailed modeling in order to determine the extent of support damage and the effect the supports have on the integrity of the piping system itself. This paper addresses the non-linear modeling of the piping models and discusses the modeling details, assumptions and analysis results. This approach is shown to be a useful alternative for predicting the extent of structural damage that can be expected by a Level D event such as a condensation induced water hammer following a loss of coolant accident.

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