IEEE 1597.1-2022 PDF

IEEE 1597.1-2022 PDF

This standard specifies methods to validate computational electromagnetics (CEM) computer modeling and simulation (M&S) techniques, codes, and models. It is applicable to a wide variety of electromagnetic (EM) applications including but not limited to the fields of electromagnetic compatibility (EMC), radar cross section (RCS), signal integrity (SI), and antennas. Validation of a particular solution data set can be achieved by comparison to the data set obtained by measurements, alternative codes, canonical methods, or analytic methods.
This standard provides a defined framework for comparing the results of various CEM techniques, codes, and models in a repeatable way against a set of references, which may include “golden” benchmarks, standard validation, and canonical problem sets as well as self-validation. These data are based on theoretical formulations or obtained as a result of performing high-quality measurements and, in certain cases, based on accurate analyses that have undergone and withstood peer validation. The importance of this standard is that it provides an agreed and repeatable framework for undertaking the validation process. The outcome of such validation is the quantification of any resultant comparisons and the logical explanation by which that quantification has taken place. In all cases, the outcome from this standard is the support for logical and informed engineering decision making. The standard itself is not a replacement for good engineering practice but an approach to provide supporting evidence for logical decision making
Revision Standard – Active. A method to validate computational electromagnetics computer modeling and simulation techniques, codes, and models is defined in this standard. It is applicable to a wide variety of electromagnetic applications including, but not limited to, the fields of electromagnetic compatibility, radar cross section, signal integrity, and antennas. Validation of a particular solution data set can be achieved by comparison to the data set obtained by measurements, alternative codes, canonical methods, or analytic methods.