N. Alan Heckert

Excitation—Emission Matrix Fluorescence Spectroscopy for Natural Organic Matter Characterization: A Quantitative Evaluation of Calibration and Spectral Correction Procedures:

The influence of different data collection procedures and of wavelength-dependent instrumental biases on fluorescence excitation–emission matrix (EEM) spectral analysis of aqueous organic matter samples was investigated. Particular attention was given to fluorescence contours (spectral shape) and peak fluorescence intensities. Instrumental bias was evaluated by independently applying excitation and emission correction factors to the raw excitation and emission data, respectively. The peak fluorescence intensities of representative natural organic matter and tryptophan were significantly influenced by the application of excitation and emission spectral correction factors and by the manner in which the raw data was collected. Humification and fluorescence indices were also influenced by emission correction factors but were independent of reference (excitation) intensity normalization or correction. EEM surface contours were dependent on normalization of the fluorescence intensity to the reference intensity but were not influenced by either excitation or emission spectral correction factors. Authors should be explicit in how excitation and emission spectral correction procedures are implemented in their investigations, which will help to facilitate intra-laboratory comparisons and data sharing.

Development of urine standard reference materials for metabolites of organic chemicals including polycyclic aromatic hydrocarbons, phthalates, phenols, parabens, and volatile organic compounds

AbstractTwo new Standard Reference Materials (SRMs), SRM 3672 Organic Contaminants in Smokers’ Urine (Frozen) and SRM 3673 Organic Contaminants in Non-Smokers’ Urine (Frozen), have been developed in support of studies for assessment of human exposure to select organic environmental contaminants. Collaborations among three organizations resulted in certified values for 11 hydroxylated polycyclic aromatic hydrocarbons (OH-PAHs) and reference values for 11 phthalate metabolites, 8 environmental phenols and parabens, and 24 volatile organic compound (VOC) metabolites. Reference values are also available for creatinine and the free forms of caffeine, theobromine, ibuprofen, nicotine, cotinine, and 3-hydroxycotinine. These are the first urine Certified Reference Materials characterized for metabolites of organic environmental contaminants. Noteworthy, the mass fractions of the environmental organic contaminants in the two SRMs are within the ranges reported in population survey studies such as the National Health and Nutrition Examination Survey (NHANES) and the Canadian Health Measures Survey (CHMS). These SRMs will be useful as quality control samples for ensuring compatibility of results among population survey studies and will fill a void to assess the accuracy of analytical methods used in studies monitoring human exposure to these organic environmental contaminants. Graphical AbstractMetabolites of PAHs, Phthalates, Phenols, Parabens, and VOCs in Urine SRMs

Testing and analyses of copolymer fibers based on 5-amino-2-(p-aminophenyl)-benzimidazole

Fibers containing 5-amino-2-(p-aminophenyl)benzimidazole are being considered for use as reinforcement in soft body armor applications. Past research in this laboratory has resulted in a suite of tests that have been used to detect degradation in other fibers and are now being applied to the fibers in question. Due to the architecture of the yarns in this study, two methods to extract single filaments for tensile testing were described and analyzed. A dry method resulted in fibers with surface damage, reflected in a high standard deviation in strength. A wet extraction method showed a reduction in surface damage and a lower standard deviation in strength. Fourier Transform Infrared analysis detected signs of hydrolysis in the fibers that were exposed to water. Although no noticeable loss in tensile strength was noted upon exposure, the possibility of hydrolysis in these fibers may indicate a need for further study.

Effect of fiber gripping method on the single fiber tensile test: II. Comparison of fiber gripping materials and loading rates

Single poly(p-phenylene terephthalamide) (PPTA) fiber tensile tests were carried out under quasi-static and high strain rate loading conditions using poly(methyl methacrylate) and rubber grips to investigate effects of grip materials and loading rates on fiber tensile properties. Differences in ultimate tensile strengths, failure strains, and moduli of PPTA fibers obtained by two different grip materials were insignificant. On the other hand, the fiber tensile properties showed significantly rate-dependent behaviors, which were graphically confirmed by kernel density plots as a non-parametric statistical analysis. Strength models considering three aspects (stochastic, fracture mechanics, and polymer chain domain behaviors) were also shown to link the loading rate effect in relation to fracture mechanisms.

Robust Engineering Design for Failure Prevention

To advance the state of the art of engineering design, we introduce a new concept on the “robustness” of a structure by measuring its ability to sustain a sudden loss of a part without causing an immediate collapse. The concept is based on the premise that most structures have built-in redundancy such that when the loss of a single part leads to a load redistribution, the “crippled” structure tends to seek a new stability configuration without immediate collapse. This property of a “robust” structure, when coupled with a continuous or periodic inspection program using nondestructive evaluation (NDE) techniques, is useful in failure prevention, because such structure is expected to display “measurable” signs of “weakening” long before the onset of catastrophic failure. To quantify this “robustness” concept, we use a large number of simulations to develop a metric to be named the “Robustness Index (RBI).” To illustrate its application, we present two examples: (1) the design of a simple square grillage in support of a water tank, and (2) a classroom model of a 3-span double-Pratt-truss bridge. The first example is a “toy” problem, which turned out to be a good vehicle to test the feasibility of the RBI concept. The second example is taken from a textbook in bridge design (Tall, L., Structural Steel Bridge , 2nd ed., page 99, Fig. 4.3(b), Ronald Press, New York NY, 1974). It is not a case study for failure analysis, but a useful classroom exercise in an engineering design course. Significance and limitations of this new approach to catastrophic failure avoidance through “robust” design, are discussed.Copyright

A New Approach to Finding a Risk-Informed Safety Factor for “Fail-Safe” Pressure Vessel and Piping Design

The purpose of this paper is to present a new approach to finding a risk-informed safety factor for the “fail-safe” design of a high-consequence engineering system. The new approach is based on the assumption of a 99.99 % confidence level and a 99.99 % coverage, and the application of the classical theory of tolerance limits, error propagation, and a method of statistical model parameter estimation known as the bootstrap method. To illustrate this new approach, we first apply the methodology to the UTS data of six materials ranging from glass, ceramics, to a high-strength steel at both 20 C and 600 C, and then to the fatigue life estimation of a BK-7 glass using two available additional sets of laboratory test data. Significance and limitations of our new approach to the “fail-safe” UTS design and fatigue life prediction of an aging PVP or aircraft are presented and discussed.

An Economics-Based Intelligence (EI) Tool for Pressure Vessel and Piping (PVP) Failure Consequence Estimation

This paper is built around ASTM E 2506, Standard Guide for Developing a Cost-Effective Risk Mitigation Plan for New and Existing Constructed Facilities. E 2506 establishes a three-step protocol—perform risk assessment, specify combinations of risk mitigation strategies for evaluation, and perform economic evaluation—to insure that the decision maker is provided the requisite information to choose the most cost effective combination of risk mitigation strategies. Because decisions associated with low-probability, high-consequence events involve uncertainty both in terms of appropriate evaluation procedures and event-related measures of likelihood and consequence, NIST developed a Risk Mitigation Toolkit. This paper uses (a) a data center undergoing renovation for improved security, and (b) a PVP-related failure event to illustrate how to perform the E 2506 three-step protocol with particular emphasis on the third step—perform economic evaluation. The third step is built around the Cost-Effectiveness Tool for Capital Asset Protection (CET), which was developed by NIST. Version 4.0 of CET is used to analyze the security- or failure-related event with a focus on consequence estimation and consequence assessment via Monte Carlo techniques. CET 4.0 includes detailed analysis and reporting features designed to identify key cost drivers, measure their impacts, and deliver estimated consequence parameters with uncertainty bounds. Significance of this economics-based intelligence (EI) tool is presented and discussed for security- or failure-consequence estimation to risk assessment of failure of critical structures or components.

A Risk-Uncertainty Formula Accounting for Uncertainties of Failure Probability and Consequence in a Nuclear Powerplant

This paper is a continuation of a recent ASME Conference paper entitled “Design of a Python-Based Plug-in for Benchmarking Probabilistic Fracture Mechanics Computer Codes with Failure Event Data” (PVP2009-77974). In that paper, which was co-authored by Fong, deWit, Marcal, Filliben, Heckert, and Gosselin, we designed a probability-uncertainty plug-in to automate the estimation of leakage probability with uncertainty bounds due to variability in a large number of factors. The estimation algorithm was based on a two-level full or fractional factorial design of experiments such that the total number of simulations will be small as compared to a Monte-Carlo method. This feature is attractive if the simulations were based on finite element analysis with a large number of nodes and elements. In this paper, we go one step further to derive a risk-uncertainty formula by computing separately the probability-uncertainty and the consequence-uncertainty of a given failure event, and then using the classical theory of error propagation to compute the risk-uncertainty within the domain of validity of that theory. The estimation of the consequence-uncertainty is accomplished by using a public-domain software package entitled “Cost-Effectiveness Tool for Capital Asset Protection, version 4.0, 2008” (http://www.bfrl.nist.gov/oae/ or NIST Report NISTIR-7524 ), and is more fully described in a companion paper entitled “An Economics-based Intelligence (EI) Tool for Pressure Vessels & Piping (PVP) Failure Consequence Estimation,” (PVP2010-25226, Session MF-23.4 of this conference). A numerical example of an application of the risk-uncertainty formula using a 16-year historical database of probability and consequence of main steam and hot reheat piping systems is presented. Implication of this risk-uncertainty estimation tool to the design of a risk-informed in-service inspection program is discussed.Copyright

Design of a Python-based Plug-in for Benchmarking Probabilistic Fracture Mechanics Computer Codes with Failure Event Data

In a 2007 paper entitled “Application of Failure Event Data to Benchmark Probabilistic Fracture Mechanics (PFM) Computer Codes” (Simonen, F. A., Gosselin, S. R., Lydell, B. O. Y., Rudland, D. L., & Wikowski, G. M. Proc. ASME PVP Conf., San Antonio, TX, Paper PVP2007-26373), it was reported that the two benchmarked PFM models, PRO-LOCA and PRAISE, predicted significantly higher failure probabilities of cracking than those derived from field data in three PWR and one BWR cases by a factor ranging from 30 to 10,000. To explain the reasons for having such a large discrepancy, the authors listed ten sources of uncertainties: (1) Welding Residual Stresses. (2) Crack Initiation Predictions. (3) Crack Growth Rates. (4) Circumferential Stress Variation. (5) Operating temperatures different from design temperatures. (6) Temperature factor in actual activation energy vs. assumed. (7) Under reporting of field data due to NDE limitations. (8) Uncertainty in modeling initiation, growth, and linking of multiple cracks around the circumference of a weld. (9) Correlation of crack initiation times and growth rates. (10) Insights from NUREG/CR-6674 (2000) fatigue crack growth models using conservative inputs for cyclic strain rates and environmental parameters such as oxygen content. In this paper we design a Python-based plug-in that allows a user to address those ten sources of uncertainties. This approach is based on the statistical theory of design of experiments with a 2-level factorial design, where a small number of runs is enough to estimate the uncertainties in the predictions of PFM models due to some combination of the source uncertainties listed by Simonen et al (PVP2007-26373).

Uncertainty Estimate of Charpy Data Using a 5-Factor 8-Run Design of Experiments

Scatter in laboratory data with duplicates on Charpy impact tests is analyzed by identifying several sources of variability such as temperature, manganese sulfide, initial strain, mis-orientation, and notch radius in order to estimate the predictive 95% confidence intervals of the mean energy of absorption for each specific test temperature. Using a combination of real and virtual data on a high-strength pressure vessel grade steel (ASTM A517) over a range of temperatures from −40 °C (−40 °F) to 182 °C (360 °F), and the concept of a statistical design of experiments, we present an uncertainty estimation methodology using a public-domain statistical analysis software named DATAPLOT. A numerical example for estimating the mean, standard deviation, and predictive intervals of the Charpy energy at 48.9 °C (120 °F) is included. To illustrate the application potential of this methodology, we enhance it with formulas of error propagation to estimate the mean, standard deviation, and predictive intervals of the associated static crack initiation toughness, KIc . A discussion of the significance and limitations of the proposed methodology, and a concluding remark are given at the end of this paper.Copyright