Journal of Research of the National Institute of Standards and Technology最新文献

Uniaxial tensile tests are routinely conducted to obtain stress-strain data for forming applications. It is important to understand the deformation behavior of test specimens at plastic strains, temperatures, and strain rates typically encountered in metal forming processes. In this study, the Johnson-Cook (J-C) flow stress model was used to describe the constitutive behavior of ASTM International (ASTM) A 1008 steel specimens during uniaxial tensile tests at three different average strain rates (10-⁵ s-¹, 10-³ s-¹, and 10-¹ s-¹). The digital image correlation (DIC) technique was used for displacement and strain measurement, and two-dimensional (2D) infrared (IR) imaging was employed for temperature measurement. Separate optimization studies involving relevant finite element (FE) modeling with appropriate measured data yielded optimum values of convective heat transfer coefficients, J-C parameters, and inelastic heat fraction variables. FE modeling employing these optimum parameter values was then used to study the mechanical behavior. While FE predictions matched measured strain localization and thermal field very well in the intermediate- and low-rate experiments, the high-rate test showed narrower strain localization and a sharper temperature peak in the experiment. Possible use of a higher steel thermal conductivity value and/or exclusion of material inhomogeneities may have resulted in discrepancies between computed and measured temperature and strain fields. The study shows that an optimized set of parameters obtained with a controlled test could be reasonably applied for other tests conducted at very different strain rates.

The tragedy of the radium poisoning of young women dial painters in the 1920s has been the subject of best-selling books, plays, and motion pictures. With knowledge about radium and its accurate measurements in the hands of a very few scientists, what responsibilities did they have to sound the alarm and mitigate the hazards to workers and the general public? This two-part analysis looks at the role of the staff of the U.S. Bureau of Standards (the National Bureau of Standards [NBS] after 1934) in developing measurements and standards for accurate determinations of radium-226 and radon-222 that ultimately led to national standards for exposure to radioactive substances. Part I looks at the efforts of Elizabeth Hughes, with guidance from her senior colleague at the NBS, to assist dial painters with obtaining redress for their injuries. Part II examines the role of NBS in establishing the national radiation protection standards that were promulgated by the U.S. Department of Commerce (DOC) and the National Council on Radiation Protection and Measurements (NCRP).

High-quality recordings of radio frequency (RF) emissions from commercial communication hardware in realistic environments are often needed to develop and assess spectrum-sharing technologies and practices, e.g., for training and testing spectrum sensing algorithms and for interference testing. Unfortunately, the time-consuming, expensive nature of such data collections together with data-sharing restrictions pose significant challenges that limit data set availability. Furthermore, developing accurate models of real-world RF emissions from first principles is often very difficult because system parameters and implementation details are at best only partially known, and complex system dynamics are difficult to characterize. Hence, there is a need for flexible, data-driven methods that can leverage existing data sets to synthesize additional similar waveforms. One promising machine-learning approach is unsupervised deep generative modeling with generative adversarial networks (GANs). To date, GANs for RF communication signals have not been studied thoroughly. In this paper, we present the first in-depth investigation of generated signal fidelity for GANs trained with baseband orthogonal frequency-division multiplexing (OFDM) signals, where each subcarrier is digitally modulated with quadrature amplitude modulation (QAM). Building on prior GAN methods, we developed two novel GAN models and evaluated their performance using simulated data sets with known ground truth. Specifically, we investigated model performance with respect to increasing data set complexity over a range of OFDM parameters and conditions, including fading channels. The findings presented here inform the feasibility of use cases and provide a foundation for further investigations into deep generative models for RF communication signals.

We simulated the effects of gimbal-alignment errors and rotational step-size errors on measurements of the sensitivity matrix and intrinsic properties of a triaxial accelerometer. We restricted the study to measurements carried out on a two-axis calibration system using a previously described measurement and analysis protocol. As well as imperfections in the calibration system, we simulated imperfect orthogonality of the accelerometer axes and non-identical sensitivity of the individual accelerometers in an otherwise perfect triaxial accelerometer, but we left characterization of other accelerometer imperfections such as non-linearity for future study. Within this framework, sensitivity-matrix errors are caused by imperfections in the construction and installation of the accelerometer calibration system, but not by the accelerometer imperfections included in the simulations. We use the results of this study to assign type B uncertainties to the components of the sensitivity matrix and related intrinsic properties due to imperfections in the measurement system. For calibrations using a reasonably well manufactured and installed multi-axis rotation stage such as that studied in this paper, we estimated upper bounds to the standard uncertainties of the order of 1×10-5, 2×10-5, 5×10-5, and 2×10-4 for the intrinsic sensitivities, diagonal elements of the sensitivity matrix, off-diagonal elements of the sensitivity matrix, and zero-acceleration offsets, relative to a sensitivity-matrix element of 1, respectively, and 5×10-3 degrees for the intrinsic angles.

The coronavirus disease 2019 (COVID-19) pandemic led to the need for tracking of physical contacts and potential exposure to disease. Traditional contact tracing can be augmented by electronic tools called "electronic contact tracing" or "exposure notification.". Some methods were built to work with smartphones; however, smartphones are not prevalent in some high-contact areas (e.g., schools and nursing homes). We present the design and initial testing of low-cost, highly privacy preserving wearable exposure notification devices. Several devices were constructed based on existing hardware and operated independently of a smartphone. The method (devices and analyses) was not able to reliably use the received signal strength indicator (RSSI) as a proxy for distance between pairs of devices; the accuracy of RSSI as a proxy for distance decreased dramatically outside of the idealized conditions. However, even an imperfect device could be useful for research on how people use and move through spaces. With some improvement, these devices could be used to understand disease spread and human or animal interaction in indoor environments.

A documentary standard produced by the American Society of Mechanical Engineers (ASME) for performance evaluation of industrial X-ray computed tomography (XCT) systems for dimensional measurements was released in early 2021. This standard, ASME B89.4.23-2020, specifies test procedures that may be performed to determine whether a system meets the manufacturer's accuracy specifications for acceptance before or after purchase, or for periodic reverification. While there are some core testing requirements in the standard, there is also some flexibility, allowing for a variety of testing configurations that meet the requirements of the standard. It is important that the chosen testing configuration be sensitive to the different systematic sources of error in XCT systems to provide confidence that the system will meet the manufacturer's accuracy specifications for measurements performed by the user subsequent to testing. In this paper, we provide guidance on how to optimally apply the ASME 89.4.23 standard in industry to achieve high sensitivity to geometry errors in cone-beam XCT systems. Through simulation studies, we present some examples of testing configurations that meet the requirements of the ASME B89.4.23 standard and discuss their sensitivity to geometry errors of the detector and the rotation stage. We show that there are some testing configurations that achieve maximal sensitivity to these errors, while other configurations do not capture these error sources with adequate sensitivity.

The multi-source common-view disciplined clock (MSCVDC) is a recent NIST invention designed to support critical infrastructure timing systems that require a verifiably accurate and fail-safe clock. This paper introduces the MSCVDC, provides a technical description of how it works, and discusses its reliability, redundancy, security, and performance. It also discusses the possibility of a commercially available MSCVDC product.

Three types of uncertainties exist in the estimation of the minimum fracture strength of a full-scale component or structure size. The first, to be called the "model selection uncertainty," is in selecting a statistical distribution that best fits the laboratory test data. The second, to be called the "laboratory-scale strength uncertainty," is in estimating model parameters of a specific distribution from which the minimum failure strength of a material at a certain confidence level is estimated using the laboratory test data. To extrapolate the laboratory-scale strength prediction to that of a full-scale component, a third uncertainty exists that can be called the "full-scale strength uncertainty." In this paper, we develop a three-step approach to estimating the minimum strength of a full-scale component using two metrics: One metric is based on six goodness-of-fit and parameter-estimation-method criteria, and the second metric is based on the uncertainty quantification of the so-called A-basis design allowable (99 % coverage at 95 % level of confidence) of the full-scale component. The three steps of our approach are: (1) Find the "best" model for the sample data from a list of five candidates, namely, normal, two-parameter Weibull, three-parameter Weibull, two-parameter lognormal, and three-parameter lognormal. (2) For each model, estimate (2a) the parameters of that model with uncertainty using the sample data, and (2b) the minimum strength at the laboratory scale at 95 % level of confidence. (3) Introduce the concept of "coverage" and estimate the fullscale allowable minimum strength of the component at 95 % level of confidence for two types of coverages commonly used in the aerospace industry, namely, 99 % (A-basis for critical parts) and 90 % (B-basis for less critical parts). This uncertainty-based approach is novel in all three steps: In step-1 we use a composite goodness-of-fit metric to rank and select the "best" distribution, in step-2 we introduce uncertainty quantification in estimating the parameters of each distribution, and in step-3 we introduce the concept of an uncertainty metric based on the estimates of the upper and lower tolerance limits of the so-called A-basis design allowable minimum strength. To illustrate the applicability of this uncertainty-based approach to a diverse group of data, we present results of our analysis for six sets of laboratory failure strength data from four engineering materials. A discussion of the significance and limitations of this approach and some concluding remarks are included.