N. Dianne Bull Ezell

Innovative signal processing for Johnson Noise thermometry

This report summarizes the newly developed algorithm that subtracted the Electromagnetic Interference (EMI). The EMI performance is very important to this measurement because any interference in the form on pickup from external signal sources from such as fluorescent lighting ballasts, motors, etc. can skew the measurement. Two methods of removing EMI were developed and tested at various locations. This report also summarizes the testing performed at different facilities outside Oak Ridge National Laboratory using both EMI removal techniques. The first EMI removal technique reviewed in previous milestone reports and therefore this report will detail the second method.

Johnson Noise Thermometry System Requirements

This document is intended to capture the requirements for the architecture of the developmental electronics for the ORNL-lead drift-free Johnson Noise Thermometry (JNT) project conducted under the Instrumentation, Controls, and Human-Machine Interface (ICHMI) research pathway of the U.S. Department of Energy (DOE) Advanced Small Modular Reactor (SMR) Research and Development (R&D) program. The requirements include not only the performance of the system but also the allowable measurement environment of the probe and the allowable physical environment of the associated electronics. A more extensive project background including the project rationale is available in the initial project report [1].

A Novel Technique Applying Spectral Estimation to Johnson Noise Thermometry

Abstract Johnson noise thermometry is one of many important measurement techniques used to monitor the safety levels and stability in a nuclear reactor. However, this measurement is very dependent on the minimal electromagnetic environment. Properly removing unwanted electromagnetic interference (EMI) is critical for accurate drift-free temperature measurements. The two techniques developed by Oak Ridge National Laboratory (ORNL) to remove transient and periodic EMI are briefly discussed in this paper. Spectral estimation is a key component in the signal processing algorithm used for EMI removal and temperature calculation. The cross-power spectral density is a key component in the Johnson noise temperature computation. Applying either technique requires the simple addition of electronics and signal processing to existing resistive thermometers. With minimal installation changes, the system discussed here can be installed on existing nuclear power plants. The Johnson noise system developed is tested at three locations: ORNL, Sandia National Laboratory, and the Tennessee Valley Authority’s Kingston Fossil Plant. Each of these locations enabled improvement on the EMI removal algorithm. The conclusions made from the results at each of these locations is discussed, as well as possible future work.

Detecting alkali-silica reaction in thick concrete structures using linear array ultrasound

Commercial nuclear power plants (NPPs) depend heavily on concrete structures, making the long-term performance of these structures crucial for safe operation, especially with license period extensions to 60 years and possibly beyond. Alkali-silica reaction (ASR) is a reaction that occurs over time in concrete between alkaline cement paste and reactive, noncrystalline silica (aggregates). In the presence of water, an expansive gel is formed within the aggregates, which results in microcracks in aggregates and adjacent cement paste. ASR can potentially affect concrete properties and performance characteristics such as compressive strength, modulus of elasticity, flexural stiffness, shear strength, and tensile strength. Currently, no nondestructive evaluation methods have proven effective in identifying ASR before surface cracks form. ASR is identified visibly or by petrographic analysis. Although ASR definitely impacts concrete material properties, the performance of concrete structures exhibiting ASR depends on whether or not the concrete is unconfined or confined with reinforcing bars. Confinement by reinforcing bars restrainsthe expansion of ASR-affected concrete, similar to prestressing, thus improving the performance of a structure. Additionally, there is no direct correlation between the mechanical properties of concrete sample cores and the in-situ properties of the concrete. The University of Tennessee–Knoxville, Oak Ridge National Laboratory, and a consortium of universities have developed an accelerated ASR experiment. Three large concrete specimens, representative of NPP infrastructure, were constructed containing both embedded and surface instruments. This paper presents preliminary analysis of these specimens using a frequency-banded synthetic aperture focusing technique.

Automated Detection of Alkali-Silica Reaction in Concrete Using Linear Array Ultrasound Data

This paper documents the development of signal processing and machine learning techniques for the detection of Alkali-silica reaction (ASR). ASR is a chemical reaction in either concrete or mortar between hydroxyl ions of the alkalis from hydraulic cement, and certain siliceous minerals present in some aggregates. The reaction product, an alkali-silica gel, is hygroscopic having a tendency to absorb water and swell, which under certain circumstances, leads to abnormal expansion and cracking of the concrete. This phenomenon affects the durability and performance of concrete cause significant loss of mechanical properties. Developing reliable methods and tools that can evaluate the degree of the ASR damage in existing structures, so that informed decisions can be made toward mitigating ASR progression and damage, is important to the long-term operation of nuclear power plants especially if licenses are extended beyond 60 years. The paper examines the differences in the time-domain and frequency-domain signals of healthy and ASR-damaged specimens. More precisely, we explore the use of the Fast Fourier Transform to observe unique features of ASR damaged specimens and an automated method based on Neural Networks to determine the extent of ASR damage in laboratory concrete specimens.

Johnson Noise Thermometry for Drift-Free Measurements

In order for Johnson Noise Thermometry (JNT) to be beneficial to SMR designers, it must offer advantages beyond the current state-of-the-art technology. Comparisons to traditional RTDs and thermocouples will involve life-cycle costs, installation footprint, reliability, and accuracy. With JNT, there is additional equipment beyond what is required for the traditional RTD measurement. Therefore, the JNT-RTD system will involve additional complexity and this additional complexity must be justified. Operators will want to know that the measurement is reliable and trustworthy. It is also important that the sensor involve little, if any, additional ongoing maintenance work and that it has a low probability of causing any malfunction of the primary measurement channel. If these features can be successfully demonstrated, the JNT-RTD system could potentially save money and increase plant reliability.

Data Acquisition Backend

This document is intended to summarize the development and testing of the data acquisition module portion of the Johnson Noise Thermometry (JNT) system developed at ORNL. The proposed system has been presented in an earlier report [1]. A more extensive project background including the project rationale is available in the initial project report [2].