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An Integrated Framework for Risk Assessment of Safety-related Digital Instrumentation and Control Systems in Nuclear Power Plants: Methodology Refinement and Exploration
This report documents activities performed by Idaho National Laboratory (INL) during Fiscal Year (FY) 2023 for the U.S. Department of Energy (DOE) Light Water Reactor Sustainability (LWRS) Program, Risk Informed Systems Analysis (RISA) Pathway, digital instrumentation and control (DI&C) risk assessment project. In FY 2019, the RISA Pathway initiated a project to develop a risk assessment strategy for delivering a technical basis to support effective, and secure DI&C technologies for digital upgrades/designs. A risk assessment-informed framework was proposed for this strategy, which aims to (1) provide a best-estimate, risk informed capability to quantitatively estimate the safety margin obtained from plant modernization, especially for safety-related DI&C systems, (2) support and supplement existing risk informed DI&C design guides by providing quantitative risk information and evidence, (3) offer a capability of design architecture evaluation of various DI&C systems, (4) assure the long-term safety and reliability of safety-related DI&C systems, and (5) reduce uncertainty in costs and support integration of DI&C systems in the plant. To achieve these technical goals, the LWRS-developed framework provides a means to address relevant technical issues by: (1) defining a risk informed analysis process for DI&C upgrade that integrates hazard analysis, reliability analysis, and consequence analysis, (2) applying risk informed tools to address common cause failures (CCFs) and quantify corresponding failure probabilities for DI&C technologies, particularly software CCFs, (3) evaluating the impact of digital failures at the component level, system level, and plant level, and (4) providing insights and suggestions on designs to manage the risks, thus to support the development and deployment of advanced DI&C technologies in nuclear power plants (NPPs). Adding diversity within a system or components is the primary means to eliminate and mitigate CCFs, but diversity also increases system complexity and may not address all sources of systematic failures. Optimization of diversity and redundancy applications for the safety-critical DI&C systems remains a challenge. To deal with the technical issues in addressing potential software CCFs in safety-related DI&C systems of NPPs and supporting relevant design optimization, the proposed framework provides: (a) A best-estimate, risk informed capability to address new technical digital issues quantitatively, focusing on software CCFs in safety-related DI&C systems of NPPs; (b) A common and a modularized platform for DI&C designers, software developers, cybersecurity analysts, and plant engineers to predict and prevent risk in the early design stage of DI&C systems; (c) Technical bases and risk informed insights to assist users address the risk informed alternatives for evaluation of CCFs in safety-related DI&C systems of NPPs; and (d) A risk informed tool that offers a capability of design architecture evaluation of various DI&C systems to support system design decisions in diversity and redundancy applications. The research and development efforts of this project in FY 2023 are focused on refining current methods on software CCF modeling and estimation and exploring additional innovative approaches to risk assessment of DI&C systems to enable a more comprehensive and complete assessment of various safety-related DI&C design architectures. The primary audience of this report are DI&C designers, engineers, and probabilistic risk assessment (PRA) practitioners. This includes stakeholders, such as the nuclear utilities and regulators who consider the deployment and upgrade of DI&C systems, DI&C software developers and reviewers, and cybersecurity specialists. It should be noted that all the analyses are performed for the demonstration of the methodology, not for the evaluation of an actual digital control system. Results are obtained based on limited design information and testing data.
Development of Miniature, Ultra-Low Loss Superconducting Resonator Arrays
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Efficient exploration of cosmology dependence in the EFT of LSS
The most effective use of data from current and upcoming large scale structure (LSS) and CMB observations requires the ability to predict the clustering of LSS with very high precision. The Effective Field Theory of Large Scale Structure (EFTofLSS) provides an instrument for performing analytical computations of LSS observables with the required precision in the mildly nonlinear regime. In this paper, we develop efficient implementations of these computations that allow for an exploration of their dependence on cosmological parameters. They are based on two ideas. First, once an observable has been computed with high precision for a reference cosmology, for a new cosmology the same can be easily obtained with comparable precision just by adding the difference in that observable, evaluated with much less precision. Second, most cosmologies of interest are sufficiently close to the Planck best-fit cosmology that observables can be obtained from a Taylor expansion around the reference cosmology. These ideas are implemented for the matter power spectrum at two loops and are released as public codes. When applied to cosmologies that are within 3σ of the Planck best-fit model, the first method evaluates the power spectrum in a few minutes on a laptop, with results that have 1% or better precision, while with the Taylor expansion the same quantity is instantly generated with similar precision. Finally, the ideas and codes we present may easily be extended for other applications or higher-precision results.
An Advanced Extreme Environment Wireless Telemetry System for Turbine Blade Instrumentation
As advanced natural gas power generation systems evolve, the thrust for increased efficiencies and reduced emissions results in increasingly harsh conditions inside the turbine environment. These high temperatures, pressures, and corrosive atmospheres result in accelerated rates of degradation, leading to failure of turbine materials and components. The University of Arkansas (UA) and Siemens, in collaboration with the DoE's National Energy Technology Laboratory (NETL), are developing a reliable and long-term monitoring capability in the turbine hot gas path in the form of novel ceramic-based thermocouples and integrated wide band gap instrumentation electronics that will contribute to the overall reliability of gas turbines. When equipped with better monitoring and controls, power plants can operate with increased fuel-burning efficiency, improved process dynamics and gas concentrations, and increased overall longevity of the power plant components. This will result in increased turbine availability and a reduction in outages and maintenance costs. One of the key aspects to driving forward turbine monitoring capability is the development of high temperature capable integrated circuit (IC) electronics. Previous papers have described 500 °C + electronics that were developed primarily from a combination of discrete single transistors combined with supporting high temperature passive components. While these circuits have been tested successfully in high temperature spin test environments, the move to an IC approach will greatly increase the performance and reliability of turbine monitoring systems. This program is developing such capability through the implementation of silicon carbide (SiC) based ICs, and this paper details the initial approach and early testing of the developed devices. This research represents an important step towards the realization of a field deployable high reliability turbine condition monitoring system.
Methane, Ethane, and Propane Sensor for Real-time Leak Detection and Diagnostics
The Phase I effort demonstrated the technical viability of a fast, sensitive, mobile hydrocarbon monitor. The instrument will enable the oil and gas industry, researchers, and regulators to rapidly identify and chemically profile leaks from facilities. This capability will allow operators to quickly narrow down and mitigate probable leaking equipment, minimizing product loss and penalties due to regulatory non-compliance. During the initial development phase, we demonstrated operation of a prototype monitor that is capable of measuring methane, ethane, and propane at sub-part-per-billion sensitivities in 1 second, using direct absorption infrared spectroscopy. To our knowledge, this is the first instrument capable of fast propane measurements at atmospheric concentrations. In addition, the electrical requirements of the monitor have been reduced from the 1,200 W typical of a spectrometer, to <500 W, making it capable of being powered by a passenger vehicle, and easily deployed by the industry. The prototype monitor leverages recent advances in laser technology, using high-efficiency interband cascade lasers to access the 3 μm region of the mid-infrared, where the methane, ethane, and propane absorptions are strongest. Combined with established spectrometer technology, we have achieved precisions below 200 ppt for each compound. This allows the monitor to measure fast plumes from oil and gas facilities, as well as ambient background concentrations (typical ambient levels are 2 ppm, 1.5 ppb, and 0.7 ppb for methane, ethane and propane, respectively). Increases in instrument operating pressure were studied in order to allow for a smaller 125 W pump to be used, and passive cooling was explored to reduce the cooling load by almost 90% relative to active (refrigerated) cooling. In addition, the simulated infrared absorption profiles of ethane and propane were modified to minimize crosstalk between species, achieving <1% crosstalk between ethane and propane. Finally, a monitor was designed based upon the commercial compact mini-spectrometer capable of dual-laser operation. We intend to build and test this during phase II. Multiple opportunities for improvement were also identified. First, the reported ethane and propane concentrations are susceptible to external acceleration acting upon the instrument. During phase II we will address this “motion-sickness”. Second, significant software development will be needed operate the monitor at 1 second resolution in real time, and provide rapid, actionable data to a driver or passenger.
Project 57 Air Monitoring Report: January 1 through December 31, 2015
On April 24, 1957, the Atomic Energy Commission (AEC, now the Department of Energy [DOE]) conducted the Project 57 safety experiment in western Emigrant Valley north east of the Nevada National Security Site (NNSS, formerly the Nevada Test Site) on lands withdrawn by the Department of Defense (DOD) for the Nevada Test and Training Range (NTTR). The test was undertaken to develop (1) a means of estimating plutonium distribution resulting from a non-nuclear detonation; (2) biomedical evaluation techniques for use in plutonium-laden environments; (3) methods of surface decontamination; and (4) instruments and field procedures for prompt estimation of alpha contamination (Shreve, 1958). Although the test did not result in the fission of nuclear materials, it did disseminate plutonium across the land surface. Following the experiment, the AEC fenced the contaminated area and returned control of the surrounding land to the DOD. Various radiological surveys were performed in the area and in 2007, the DOE expanded the demarked Contamination Area by posting signs 200 to 400 feet (60 to 120 meters) outside of the original fence.
Developing a Human Factors Engineering Program Plan and End State Vision to Support Full Nuclear Power Plant Modernization
The Light Water Reactor Sustainability (LWRS) Program, which is sponsored by the United States (U.S.) Department of Energy (DOE), conducts research and development (R&D) to establish the technical bases to sustain U.S. nuclear assets. One area in the LWRS program is the Plant Modernization pathway, which includes human factors R&D, human factors engineering (HFE), and ergonomics. LWRS program researchers in this pathway conduct targeted R&D to address aging and reliability concerns with the legacy instrumentation and control (I&C) and related information systems in commercial nuclear power plants (NPPs). One key aspect of this pathway is to develop a strategy for full nuclear plant modernization, which previous LWRS plant modernization milestone reports have begun to describe. To further support this comprehensive plan and vision for full plant modernization, additional details need to be further developed. In particular, a core aspect of a strategy for full nuclear plant modernization is an HFE program plan and an end state vision for plant modernization that is well-integrated with both 1) a technically defensible approach to migrating the existing, mostly analog I&C infrastructure to a digital I&C infrastructure, and 2) a valid business case methodology to cost-justify the modernization activity. Thus, the purpose of this LWRS R&D activity and milestone report is threefold. First, the report provides guidance for operating commercial NPPs to support their development and evaluation of an HFE program management plan (HFE PMP) and an end state vision for plant modernization. The report presents best practices and lessons learned related to HFE PMPs based on MCR modifications in which analog systems were replaced by digital I&C systems. Second, the report summarizes the rationale for full digital I&C integration, highlights a number of factors that are critical to consider, and describes how HFE should merge with full digital I&C integration. Finally, the report summarizes and integrates the economic factors that must be considered to cost-justify HFE involvement in plant modernization.
Embedded acoustic sensing and monitoring techniques for small modular reactors
The goal of this project was to study the applicability of specific innovative instrumentation techniques for assessing parameters needed for safe small modular reactor operation and safeguarding of nuclear material. Small modular reactor core designs are currently being developed to provide energy more effectively and efficiently than in the past because they can be built as modules at fabrication sites and then transported to a power-producing facility. However, these modules and/or the final core will often be sealed and not accessible again until disposal. Instead of instruments that access the core directly, during and after operation, to monitor flux/dose and structural integrity, as current power plants use, new sensors need to be designed that can be built into the reactor initially to determine operating history, structural integrity through operation of the system, and nuclear material accountancy after shutdown and before disposition of the core. Embedded sensors already exist that can provide neutron flux, gamma dose, and temperatures, but techniques to expand upon these for assessing structural health and material inventory in the system over time need to be developed. Structural health assessments include the detection and imaging of cracks in the components that could eventually cause radioactive fission products to be released.
Triple Plate Mold Final Report: Optimization of the Mold Design and Casting Parameters for a Thin U-10mo Fuel Casting
This work describes the experiments and modeling that have been performed to improve and try to optimize the simultaneous casting of three plates of U-10wt%Mo in a single coil vacuum induction melting (VIM) furnace. The plates of interest are 280 mm wide by 203 mm tall by 5 mm thick (11" x 8" x 0.2"). The initial mold design and processing parameters were supplied by Y-12. The mold and casting cavity were instrumented with a number of thermocouples, and the casting performed to determine the thermal history of the mold and casting. The resulting cast plates were radiographed and numerous defects identified. Metallography was performed to help identify the nature of the radiographically observed defects. This information was then used to validate a mold filling and solidification model of that casting. Based on the initial casting, good casting design practice, and process simulation of several design alternatives, a revised design was developed with the goal of minimizing casting defects such as porosity. The redesigned mold had a larger hot-top and had its long axis along the horizontal direction. These changes were to try to develop a strong thermal gradient conducive to good feeding and minimization of micro- and macroporosity in the cast plates. An instrumented casting was then performed with the revised mold design and a linear distributor. This design yielded cast plates with significantly less radiographically identified defects. Unfortunately, there was significant variation in plate weight and metal content in their hot-tops. Fluid flow simulations were then performed on this mold/distributor design. This helped identify the issue with this linear distributor design. Additional simulations were then performed on candidate distributor redesigns and a preferred distributor annular design was identified. This improved annular design was used to produce a third instrumented casting with favorable results. These refined designs and their radiographic characterization are compared to the initial design.
A phase model approach for thermostatically controlled load demand response
A significant portion of electricity consumed worldwide is used to power thermostatically controlled loads (TCLs) such as air conditioners, refrigerators, and water heaters. Because the short-term timing of operation of such systems is inconsequential as long as their long-run average power consumption is maintained, they are increasingly used in demand response (DR) programs to balance supply and demand on the power grid. Here, we present an ab initio phase model for general TCLs, and use the concept to develop a continuous oscillator model of a TCL and compute its phase response to changes in temperature and applied power. This yields a simple control system model that can be used to evaluate control policies for modulating the power consumption of aggregated loads with parameter heterogeneity and stochastic drift. We demonstrate this concept by comparing simulations of ensembles of heterogeneous loads using the continuous state model and an established hybrid state model. The developed phase model approach is a novel means of evaluating DR provision using TCLs, and is instrumental in estimating the capacity of ancillary services or DR on different time scales. We further propose a novel phase response based open-loop control policy that effectively modulates the aggregate power of a heterogeneous TCL population while maintaining load diversity and minimizing power overshoots. This is demonstrated by low-error tracking of a regulation signal by filtering it into frequency bands and using TCL sub-ensembles with duty cycles in corresponding ranges. As a result, control policies that can maintain a uniform distribution of power consumption by aggregated heterogeneous loads will enable distribution system management (DSM) approaches that maintain stability as well as power quality, and further allow more integration of renewable energy sources.
Data processing pipeline for Tianlai experiment
The Tianlai project is a 21cm intensity mapping experiment for detecting dark energy by measuring the baryon acoustic oscillation (BAO) features in the large scale structure power spectrum. This experiment provides an opportunity to test the data processing methods for cosmological 21cm signal extraction, which is still a great challenge in current radio astronomy research. The 21cm signal is much weaker than the foregrounds and easily aected by the imperfections in the instrumental responses. Furthermore, processing the large volumes of interferometer data poses a practical challenge. We have developed a data processing pipeline called tlpipe to process the drift scan survey data from the Tianlai experiment. It performs oine data processing tasks such as radio frequency interference (RFI) agging, array calibration, binning, and map-making, etc. It also includes utility functions needed for the data analysis, such as data selection, transformation, visualization and others. A number of new algorithms are implemented, for example the eigenvector decomposition method for array calibration and the Tikhnov regularization for m-mode analysis. In this paper we describe the design and implementation of the pipeline and illustrate its functions with some analysis of real data. Finally, we outline directions for future development of this publicly code.
Particulate and Trace Gas Emissions from Prescribed Burns in Southeastern U.S. fuel types: Summary of a 5-year Project
Prescribed burning is an accepted practice to manage biomass in the United States and throughout the world. It is a particularly important practice in pine forests throughout the world, many of which have evolved in the presence of fire [1]. A recent survey [2] of prescribed fire use reported that 2.62 x 106 ha of forest land in the southeastern U.S. (defined by the National Association of State Foresters) burned in 2011 for silvicultural purposes such as hazardous fuel reduction, wildlife habitat improvement, and forest regeneration. Earlier surveys reported 0.9 to 1.7 x 106 ha of prescribed burning in the southern U.S. [3–5] using different survey techniques. While it is not possible to determine confidence intervals on these estimates, it is clear that the use of prescribed burning has increased since the first published estimate of 1 x 106 ha we were able to locate or the recent survey captured more information. Smoke is an important consequence of prescribed burning that must be managed, [6] and a great deal of research has been performed since the 1970s trying to characterize the composition, production, and transport of smoke from such fires. A recent review of the state of science behind estimation of the contribution of wildland fire to greenhouse gases and black carbon in the U.S. identified several areas of research that must be performed [7]. In particular, two areas of knowledge that still need improvement are fuel characterization and smoke emissions, and the correlation(s) between the two. While many fuel types in the southeastern U.S. have been described for fire behavior and fire danger prediction, characterization of fuel bed components important for smoke production is more limited. Emissions characterization, both chemical and particulate, is needed to determine potential impacts of prescribed burning on nutrient cycling, planetary albedo, human health, and highway visibility [e.g. 8–10].
An Introduction to the Benchmarking and Publications for Non-Targeted Analysis Working Group
Non-targeted analysis (NTA) encompasses a rapidly evolving set of mass spectrometry techniques aimed at characterizing the chemical composition of complex samples, identifying unknown compounds, and/or classifying samples, without prior knowledge regarding the chemical content of the samples. Recent advances in NTA are the result of improved and more accessible instrumentation for data generation, and analysis tools for data evaluation and interpretation. As researchers continue to develop NTA approaches in various scientific fields, there is a growing need to identify, disseminate, and adopt community-wide method reporting guidelines. In 2018, NTA researchers formed the Benchmarking and Publications for Non-Targeted Analysis Working Group (BP4NTA) to address this need. Consisting of participants from around the world and representing fields ranging from environmental science and food chemistry to ‘omics and toxicology, BP4NTA provides resources addressing a variety of challenges associated with NTA. Furthermore, thus far, BP4NTA group members have aimed to establish consensus on NTA-related terms and concepts, and to create consistency in reporting practices by providing resources on a public website, including consensus definitions, reference content, and lists of available tools. Moving forward, BP4NTA will provide a setting for NTA researchers to continue discussing emerging challenges and contribute to additional harmonization efforts.
An integrated high-throughput robotic platform and active learning approach for accelerated discovery of optimal electrolyte formulations
Solubility of redox-active molecules is an important determining factor of the energy density in redox flow batteries. However, the advancement of electrolyte materials discovery has been constrained by the absence of extensive experimental solubility datasets, which are crucial for leveraging data-driven methodologies. In this study, we design and investigate a highly automated workflow that synergizes a high-throughput experimentation platform with a state-of-the-art active learning algorithm to significantly enhance the solubility of redox-active molecules in organic solvents. Our platform identifies multiple solvents that achieve a remarkable solubility threshold exceeding 6.20 M for the archetype redox-active molecule, 2,1,3-benzothiadiazole, from a comprehensive library of more than 2000 potential solvents. Significantly, our integrated strategy necessitates solubility assessments for fewer than 10% of these candidates, underscoring the efficiency of our approach. Our results also show that binary solvent mixtures, particularly those incorporating 1,4-dioxane, are instrumental in boosting the solubility of 2,1,3-benzothiadiazole. Beyond designing an efficient workflow for developing high-performance redox flow batteries, our machine learning-guided high-throughput robotic platform presents a robust and general approach for expedited discovery of functional materials.
Gamma-ray blazar spectra with H.E.S.S. II mono analysis: The case of PKS 2155$-$304 and PG 1553+113
In this paper, the addition of a 28 m Cherenkov telescope (CT5) to the H.E.S.S. array extended the experiment’s sensitivityto lower energies. The lowest energy threshold is obtained using monoscopic analysis of data taken with CT5, providing access to gamma-ray energies below 100 GeV for small zenith angle observations. Such an extension of the instrument’s energy range is particularly beneficial for studies of active galactic nuclei with soft spectra, as expected for those at a redshift ≥0.5. The high-frequency peaked BL Lac objects PKS 2155–304 (z = 0.116) and PG 1553+113 (0.43 < z < 0.58) are among the brightest objects in the gamma-ray sky, both showing clear signatures of gamma-ray absorption at E > 100 GeV interpreted as being due to interactions with the extragalactic background light (EBL). Furthermore, the aims of this work are twofold: to demonstrate the monoscopic analysis of CT5 data with a low energy threshold, and to obtain accurate measurements of the spectral energy distributions (SED) of PKS 2155–304 and PG 1553+113 near their SED peaks at energies ≈100 GeV. Multiple observational campaigns of PKS 2155–304 and PG 1553+113 were conducted during 2013 and 2014 using the full H.E.S.S. II instrument (CT1–5). A monoscopic analysis of the data taken with the new CT5 telescope was developed along with an investigation into the systematic uncertainties on the spectral parameters which are derived from this analysis. As a result, using the data from CT5, the energy spectra of PKS 2155–304 and PG 1553+113 were reconstructed down to conservative threshold energies of 80 GeV for PKS 2155–304, which transits near zenith, and 110 GeV for the more northern PG 1553+113. The measured spectra, well fitted in both cases by a log-parabola spectral model (with a 5.0σ statistical preference for non-zero curvature for PKS 2155–304 and 4.5σ for PG 1553+113), were found consistent with spectra derived from contemporaneous Fermi-LAT data, indicating a sharp break in the observed spectra of both sources at E ≈ 100 GeV. When corrected for EBL absorption, the intrinsic H.E.S.S. II mono and Fermi-LAT spectrum of PKS 2155–304 was found to show significant curvature. For PG 1553+113, however, no significant detection of curvature in the intrinsic spectrum could be found within statistical and systematic uncertainties.
Millimeter-wave system-on-chip advancement for fusion plasma diagnostics
Recent advances in radio-frequency system-on-chip technology have provided mm-wave fusion plasma diagnostics with the capability to overcome major challenges such as space inefficiency, inflexible installation, sensitivity, susceptibility to EMI, and prohibitively high cost of conventional discrete component assemblies as higher imaging resolution and data accuracy are achieved by increasing the number of channels. Nowadays, shrinking transistor gate lengths on fabrication techniques have enabled hundreds of GHz operation, which is suitable for millimeter-wave diagnostics on current and future tokamaks. The Davis Millimeter Wave Research Center (DMRC) has successfully developed V-band (55-75 GHz) transmitter and receiver chips for Microwave Imaging Reflectometer (MIR) instruments. The transmitter can illuminate 8 different frequencies simultaneously within 55-75 GHz. Moreover, the receiver has the capability to amplify the reflected signal (>30 dB) while offering 10-30× reduction in noise temperature compared to current MIR instruments. Plasma diagnostics requires ultra-wideband (more than 20 GHz) operation which is approximately nine times wider bandwidth than the recent commercial impetus for communication systems. Current efforts are underway for gallium-arsenide monolithic microwave integrated circuit receiver chips at W-band (75-110 GHz) and F-band (90–140 GHz) permitting measurements at higher toroidal magnetic fields.
Rapid, antibiotic incubation-free determination of tuberculosis drug resistance using machine learning and Raman spectroscopy
Tuberculosis (TB) is the world’s deadliest infectious disease, with over 1.5 million deaths and 10 million new cases reported anually. The causative organism Mycobacterium tuberculosis (Mtb) can take nearly 40 d to culture, a required step to determine the pathogen’s antibiotic susceptibility. Both rapid identification and rapid antibiotic susceptibility testing of Mtb are essential for effective patient treatment and combating antimicrobial resistance. Here, we demonstrate a rapid, culture-free, and antibiotic incubation-free drug susceptibility test for TB using Raman spectroscopy and machine learning. We collect few-to-single-cell Raman spectra from over 25,000 cells of the Mtb complex strain Bacillus Calmette-Guérin (BCG) resistant to one of the four mainstay anti-TB drugs, isoniazid, rifampicin, moxifloxacin, and amikacin, as well as a pan-susceptible wildtype strain. By training a neural network on this data, we classify the antibiotic resistance profile of each strain, both on dried samples and on patient sputum samples. On dried samples, we achieve >98% resistant versus susceptible classification accuracy across all five BCG strains. In patient sputum samples, we achieve ~79% average classification accuracy. We develop a feature recognition algorithm in order to verify that our machine learning model is using biologically relevant spectral features to assess the resistance profiles of our mycobacterial strains. Finally, we demonstrate how this approach can be deployed in resource-limited settings by developing a low-cost, portable Raman microscope that costs <$5,000. We show how this instrument and our machine learning model enable combined microscopy and spectroscopy for accurate few-to-single-cell drug susceptibility testing of BCG.