No Cover Image

Journal article 161 views 23 downloads

Assessing the predictive performance of creep models using absolute rather than squared prediction errors: an application to 2.25Cr-1Mo steel and 316H stainless steel

Mark Evans Orcid Logo

Materials at High Temperatures, Pages: 1 - 12

Swansea University Author: Mark Evans Orcid Logo

  • 64687 (2).pdf

    PDF | Version of Record

    © 2023 The Author(s). Published by Informa UK Limited, trading as Taylor & Francis Group. Distributed under the terms of a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License (CC BY-NC-ND 4.0).

    Download (1.43MB)

Abstract

A reliable means of assessing the accuracy of a creep model’s predictions is fundamental to safe power plant operation. This paper introduces a method of decomposing the mean absolute prediction error for such a purpose to overcome the limitations that are inherent in the traditional approach of squ...

Full description

Published in: Materials at High Temperatures
ISSN: 0960-3409 1878-6413
Published: Informa UK Limited 2023
Online Access: Check full text

URI: https://cronfa.swan.ac.uk/Record/cronfa64687
Tags: Add Tag
No Tags, Be the first to tag this record!
Abstract: A reliable means of assessing the accuracy of a creep model’s predictions is fundamental to safe power plant operation. This paper introduces a method of decomposing the mean absolute prediction error for such a purpose to overcome the limitations that are inherent in the traditional approach of squaring prediction errors to prevent over and underestimates of life offsetting each other. When this method is applied to 2.25Cr-1Mo steel and 316 H stainless steel, it was found that squared errors leads to overestimates of the average prediction error associated with a particular creep model, and it also dramatically underestimates the proportion of this error that is systematic in nature. These differences were more noticeable for 316 H stainless steel.
Keywords: Mean percentage squared error, mean percentage absolute error, error decomposition, parametric creep models, life assessment
College: Faculty of Science and Engineering
Funders: Swansea University
Start Page: 1
End Page: 12