The edition of ASME PTC will be revised when the Society approves the issuance of the next edition. There will be no Addenda issued to ASME PTC. ASME PTC Test Uncertainty [ASME] on *FREE* shipping on qualifying offers. The scope of this Code is to specify procedures for . (Revision of ASME PTC ). Test Uncertainty. Performance Test Codes. AN AMERICAN NATIONAL STANDARD. Two Park Avenue • New York, NY.
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This code or standard was developed under procedures accredited as meeting the criteria for American National Standards. The Standards Committee that approved the code or standard was balanced to assure that individuals from competent and concerned interests have had an opportunity to participate.
The proposed code or standard was made available for public review and comment that provides an opportunity for additional public input from industry, academia, z regulatory agencies, and the public-at-large.
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Uncertainties in Absolute Terms Example Uncertainties in Relative Terms for the Uncalibrated Case. Uncertainties in Relative Terms for the Calibrated Case. Comparison Between Calibrated and Uncalibrated Cases. Uncalibrated, Nonsymmetrical Systematic Uncertainty Case. Uncertainties in Absolute Terms. The following information is based on that document and is included here for emphasis and for the convenience of the user of the Supplement.
ASME Performance Test Codes provide test procedures which yield results of the highest m level of accuracy consistent with the best engineering knowledge and practice currently available. They were developed by balanced committees representing all concerned interests and specify procedures, instrumentation, equipment-operating requirements, calculation methods, and uncertainty analysis. Therefore, it is recommended that the parties to a commercial test agree before starting the test and preferably before signing the contract on the method to be used for comparing the test results to the contractual guarantees.
It is beyond the scope of any Code to determine or interpret how such comparisons shall be www. The effort to update the revision began immediately upon completion of that docu. This revision is notable for the following significant departures from the text: The new nomenclature is expected to render PTC Steele, Vice Chair G. Dieck, Ron Dieck Associates, Inc. Figliola, Clemson University H.
Iyer, Colorado State University J. Rabensteine, Environmental Systems Corp. Soltani, Bechtel National Corp. Friedman, Vice Chair S. For the z practicing engineer, this harmonization means the elimination of such ambiguous terms as bias, precision, bias limit, and precision index.
In addition, careful attention was paid to discriminating be. The former w describes an error source whose effect is systematic or constant for the duration of a test.
ASME PTC 19.1-2005 试验不确定度 Test Uncertainty.pdf
The latter describes the limits to which a systematic error may be expected to go with some confidence. The former describes an error source that causes wscatter in test data. The latter describes the limits harmony with international guidelines and standards. This term, too, was included in this Supplement for improved harmony with international guidelines and standards. While this Supplement is in harmony with the ISO GUM, this Supplement emphasizes the effects of errors rather than the basis of the information utilized in the estimation of their limits.
Type B uncertainties do not have data to calculate a standard deviation and must be estimated by other means.
ASME PTC – Test Uncertainty
This Supplement utilizes two major classifications for errors and uncertainties. In addition, it is applicable for all known measurement and test uncertainty analyses. Analysis of test measurement and result uncertainty is useful because it a facilitates communication regarding measurement and test results; b fosters an understanding of potential error w sources in a measurement system and the effects of those potential error sources on test results; c guides the decision-making process for select- fx ing appropriate and cost-effective measurement sys- tems and methodologies; d reduces the risk of making erroneous deci- www.
Depending on the application, uncertainty sources may be classified either by the presumed effect systematic or random on the measurement or test result, or by the process in which they may be quantified Type A or Type B. The various statistical terms involved are defined in the Nomenclature subsection or Glossary subsection The end result of an uncertainty analysis is a numerical estimate of the test uncertainty with an appropriate confidence level.
The value used is called the assigned value. X and sX are statistics. Other confidence levels may be used, if required. See Nonmanda- z tory Appendix B. Since the true value is unknown, total error w cannot be known and therefore only its expected limits can be estimated.
Total error consists of two components: Accurate measurement requires w minimizing both random and systematic errors see Fig. Elemental random errors may arise from uncontrolled test conditions and nonrepeatabilities in the measurement system, measurement methods, environmental conditions, data reduction techniques, etc.
The total systematic error in a measurement is usually the sum of the contributions of several elemental systematic errors. Elemental systematic errors may arise from imperfect calibration corrections, measurement methods, data reduction techniques, etc. The total uncertainty in a measurement is the combination of uncertainty due to random error and uncertainty due to systematic error.
In successive measurements of the parameter, the values of these elemental random error sources change resulting in the random scatter evident in the successive measurements. The total random population of measurements that is normally dis- error in a measurement is usually the sum of the tributed.
Since only a finite number of measurements are acquired during a test, the true population mean w and population standard deviation are unknown but can be estimated from sample statistics. For a defined frequency distribution, the random standard uncertainty of the sample mean, sX, can be used to define the probable interval about the sample mean that is expected to contain the population mean with a defined level of confidence.
The random standard uncertainty of the sample mean is related to the sample standard deviation as follows: In general, increasing the number of measurements collected during a test and used in the preceding formulas is beneficial as w a it improves the sample mean as an estimator of the true population mean; b it improves the sample standard deviation as an estimator of the true population standard devia- w tion; and c it typically reduces the value of the random wstandard uncertainty of the sample mean.
Knowledge of the frequency distribution and standard deviation of this population permits de- The elemental systematic sources. As with elemental systematic error, total systematic error is constant, unknown, and may be postulated to z come from a population of possible error values from which a single sample error value is drawn and imparted to the average measurement for the. Typically, total systematic standard uncertainty is quantified by a identifying all elemental sources of systematic w error for the measurement; b evaluating elemental systematic standard uncertainties as the standard deviations of the possible systematic error distributions; and w c combining the elemental systematic standard uncertainties into an estimate of the total systematic standard uncertainty for the average measurement.
Attempting to identify all elemental sources of systematic error requires a thorough understanding of the test objectives and test process. For further discussion refer to subsection Once all elemental sources of systematic error are identified, elemental system- atic standard uncertainties for each source are evaluated. By definition, an elemental systematic standard uncertainty is a value that quantifies the dispersion of the at the standard population of possible deviation level.
Therefore, the evaluation of an elemental systematic standard uncertainty requires that a standard deviation be evaluated from engineering judgment, published information, or special data.
When neither published information or special data is avail- atic Error. Attempting to identify all of the elemen- able, it is often necessary to rely upon engineering tal sources of systematic error for a measurement judgment to quantify the dispersion of errors asso- is an important step of an uncertainty analysis, ciated with an elemental error source.
In uncertainty statement, or a multiple of a standard other words, an interval is estimated which is deviation. Based upon these assumptions, the elemen- m tal systematic standard uncertainty is estimated as follows: In certain situations, knowledge of the physics of the measurement pyc will lead the analyst to believe that the limits of w error are nonsymmetric likely to be larger in either the positive or negative direction.
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For treatment of fx nonsymmetric systematic uncertainty see subsec- tion The value of 2 in the equation is based on the assumption that the population of possible systematic errors is normally distributed. Also, there is some level of uncertainty associated with the estimate of BXk.
This uncertainty in the estimate can be converted into a degrees of freedom for the systematic standard uncertainty as shown in w Nonmandatory Appendix B. For some elemental systematic error sources, published infor- wmation from calibration reports, instrument specifi- interval divided by a statistic that is appropriate for the frequency distribution of the error population.
The specific value of this statistic must be selected on the basis of the defined confidence level and degrees of freedom associated with the confidence interval. For situations in which the frequency distribution and degrees of freedom are unspecified, a normal distribution and large degrees of freedom are often assumed. For situations involving other frequency distributions, refer to an appropriate statistics textbook. If the published information is presented as a multiple of a standard deviation, then the elemental systematic standard uncertainty is estimated as the multiple of the standard deviation divided by the multiplier.
Possible sources of this special data include a interlaboratory uncertainyt interfacility tests; and b comparisons of independent measurements that depend on different principles or that have been made by independently calibrated instruments; for example, in a gas turbine test, airflow can be measured with an orifice or a bell mouth nozzle, or computed from compressor speed-flow rig data, turbine flow parameters, or jet nozzle calibrations.
For these cases, the elemental systematic standard uncertainty may be evaluated as follows: The results from each of the measurement methods each determined as fx an average value over the duration of the test are used as input to eq.
The results from each of the independent laboratories each de- termined as an offset to be applied to the instrument w when measuring a specific input level are used as input to eq. Otherwise, these elemental systematic twst uncertainties are combined per subsection In some cases, elemental systematic standard uncertainties may arise from the same elemental error source and are therefore correlated. See subsection for a detailed discussion.
The combined standard uncertainty of the measurement mean, which is the total uncertainty at the standard deviation level, is calculated as follows: UX p 2uX Expanded uncertainty is used to establish a confidence inter- Once evaluated, all of the ele- to unertainty the true value.
A pretest uncertainty analysis is based on data and information that exist before the test, such as calibration histories, previous tests with similar instrumentation, prior w measurement uncertainty analyses, expert opinions, and, if necessary, special tests. A pretest uncertainty analysis allows corrective action to be taken, prior to expending resources w to conduct a test, either to decrease the expected uncertainty to a level consistent with the overall uncdrtainty of the test or tesh reduce the cost of the test while still attaining the objectives.
Additionally, a pretest uncertainty analysis facilitates communication between all parties to the test about the pt quality of the test. This can be essential to establishing agreement on any deviations from applicable test code requirements and can help reduce the risk that disagreements regarding the testing method will surface after conducting the test.
A posttest uncertainty analysis serves to 1 validate the quality of the test result by demonstrating compliance with test requirements; 2 facilitate communication of the quality of the test result to all parties to the test; and 3 facilitate interpretation of the quality of the test by those using the test result.