Automated Noise Calibration System (VT-1000)

This paper details an automated Noise Source calibration system in development at Jet Propulsion Laboratory, California Institute of Technology (JPL). The paper begins with a discussion on noise figure and excess-noise-ratio (ENR) theory, fundamentals and governing equations. As part of the fundamentals there is a discussion of the system’s use of the Y-factor method to obtain accurate measurements of the unit under test (UUT), and how these measurements are compared against a known ENR standard to obtain the UUT’s ENR values. There is also an in-depth discussion on uncertainty quantification for noise source system calibrations. The architecture of the automated calibration system is provided, which includes both the system’s hardware and software configuration. The software is written in Python 3, and provides the user detailed instruction on how to proceed, including step-by-step connection requirements. This system automates much of the measurement process, including real-time uncertainty quantification and report generation, as well as real-time feedback to the user to allow intervention. The system takes advantage of a database of results from previous measurements to compare calibration history of the ENR measurements. The automated system presented here operates over a frequency range from 10 MHz to 50 GHz, and has shown substantial time savings over traditional manual methods of performing this calibration

North American High Voltage Interlaboratory Comparison

This is the report for an interlaboratory comparison (ILC) of high voltage measurements performed by ten laboratories in the USA and Canada from 2018-2019. The measurement ranges were 20 kV to 100 kV DC and 15-70 kV RMS at 60 Hz AC. The ILC was designed to verify strengths and reveal weaknesses in high voltage measurements in commercial, military and energy sector calibration laboratories. The ILC was performed among members of NCSL International, with the generous support of National Research Council Canada (NRC).

Verification of Gas Flow Traceability from 0.1 sccm to 1 sccm Using a Piston Gauge

Fluke Calibration is accredited for gas flow measurements in the range of 0.1 sccm to 6000 slm in nitrogen and air. Traceability is maintained directly through a gravimetric f low standard but only recently from 1 sccm to 10 sccm. The traceability of flow in the range of 0.1 sccm to 1 sccm is based on extrapolation of the use of laminar flow elements (LFE) below 1 sccm. This part of the range has never been completely verified through interlaboratory comparisons, proficiency testing or other means of measurement assurance. In an internal document from DH Instruments in the early 1990s it was suggested that a piston gauge might improve traceability for very low gas flows. In order to prove out traceability in this range an attempt was made to use a piston gauge using a piston-cylinder size of 35 mm diameter as a reference. One reason for choosing a piston gauge as a reference is its pressure control. This is crucial when measuring gas flow through a LFE in this design and range. In addition, the effective area is known to within 0.001 %, leaving the vertical displacement of the piston to dominate the uncertainty of the dimensional part of the flow test. This was a challenge because the measurements required absolute mode and the internal piston position sensor supplied with the piston gauge did not have sufficient precision. This paper describes the theory and design of the gas flow measurement system, the current results, and improvements desired or suggested. Two different designs are discussed, one with a single piston gauge as a reference and one with two piston gauges measuring flow on either side of the laminar flow element. Note: sccm (standard cubic centimeters per minute) is an industry accepted alternative to kg/s [1]. It is used out of convenience to normalize flow rates of gases with significant differences in density.

A Comprehensive Open-Source R Software For Statistical Metrology Calculations: From Uncertainty Evaluation To Risk Analysis

Whether calibrating equipment or inspecting products on the factory floor, metrology requires many complicated statistical calculations to achieve a full understanding and evaluation of measurement uncertainty and quality. In order to assist its workforce in performing these calculations in a consistent and rigorous way, the Primary Standards Lab at Sandia National Laboratories (SNL) has developed a free and open-source software package for computing various metrology calculations from uncertainty propagation to risk analysis. In addition to propagating uncertainty through a measurement model using the well-known Guide to Expression of Uncertainty in Measurement or Monte Carlo approaches, evaluating the individual Type A and Type B uncertainty components that go into the measurement model often requires other statistical methods such as analysis of variance or determining uncertainty in a fitted curve. Once the uncertainty in a measurement has been calculated, it is usually evaluated from a risk perspective to ensure the measurement is suitable for making a particular conformance decision. SNL’s software can perform all these calculations in a single application via an easy-to-use graphical interface, where the different functions are integrated so the results of one calculation can be used as inputs to another calculation.