SEARCH · Engineering Papers
Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.
Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.
Confidence Limits Program (CLP) calculates upper and lower confidence limits associated with observed outcome of N independent trials with M occurrences of event of interest. Calculates probability of event of interest for confidence levels of 50, 55, 60, 65, 70, 75, 80, 85, 90, 95, 96, 97, 98, and 99 percent. Provides graphical presentation of all limits and how they relate to maximum-likelihood value. Written in IBM PC BASIC.
Confidence limit is a routine requirement for Fourier spectral analysis. But this confidence limit is established based on ergodic theory: For stationary process, temporal average equals the ensemble average. Therefore, one can divide the data into n-sections and treat each section as independent realization. Most natural processes in general, and climate data in particular, are not stationary; therefore, there is a need for the Hilbert Spectral analysis for such processes. Here ergodic theory is no longer applicable. We propose to use various adjustable parameters in the shifting processes of the Empirical Mode Decomposition (EMD) method to obtain an ensemble of Intrinsic Mode Function 0 sets. Based on such an ensemble, we introduce a statistical measure in. a form of confidence limits for the Intrinsic Mode Functions, and consequently, the Hilbert spectra. The criterion of selecting the various adjustable parameters is based on the orthogonality test of the resulting M F sets. Length-of-day data from 1962 to 2001 will be used to illustrate this new approach. Its implication in climate data analysis will also be discussed.
Extension of exponential tolerance and confidence limits to class of distributions with monotone failure rate
Tolerance and confidence limits for classes of distributions based on failure rates
The utility of pivotal functions is established for assessing software reliability. Based on the Moranda geometric de-eutrophication model of reliability growth, confidence limits for attained reliability and prediction limits for the time to the next failure are derived using a pivotal function approach. Asymptotic approximations to the confidence and prediction limits are considered and are shown to be inadequate in cases where only a few bugs are found in the software. Departures from the assumed exponentially distributed interfailure times in the model are also investigated. The effect of these departures is discussed relative to restricting the use of the Moranda model.
Two different methods, classical and Bayesian, for determining confidence intervals involving Poisson-distributed data are compared. Particular consideration is given to cases where the number of counts observed is small and is comparable to the mean number of background counts. Reasons for preferring the Bayesian over the classical method are given. Tables of confidence limits calculated by the Bayesian method are provided for quick reference.
The calculation of limits for small numbers of astronomical counts is based on standard equations derived from Poisson and binomial statistics; although the equations are straightforward, their direct use is cumbersome and involves both table-interpolations and several mathematical operations. Convenient tables and approximate formulae are here presented for confidence limits which are based on such Poisson and binomial statistics. The limits in the tables are given for all confidence levels commonly used in astrophysics.
Technique developed for estimating reliability of software by use of Moranda geometric de-eutrophication model. Pivotal method enables straightforward construction of exact bounds with associated degree of statistical confidence about reliability of software. Confidence limits thus derived provide precise means of assessing quality of software. Limits take into account number of bugs found while testing and effects of sampling variation associated with random order of discovering bugs.
We consider the problem of inferring constraints on a high-dimensional parameter space with a computationally expensive likelihood function. We propose a machine learning algorithm that maps out the Frequentist confidence limit on parameter space by intelligently targeting likelihood evaluations so as to quickly and accurately characterize the likelihood surface in both low- and high-likelihood regions. We compare our algorithm to Bayesian credible limits derived by the well-tested Markov Chain Monte Carlo (MCMC) algorithm using both multi-modal toy likelihood functions and the seven yr Wilkinson Microwave Anisotropy Probe cosmic microwave background likelihood function. We find that our algorithm correctly identifies the location, general size, and general shape of high-likelihood regions in parameter space while being more robust against multi-modality than MCMC.
Many astrophysical observations are characterized by a single, non-repeatable measurement of a source brightness or intensity, from which we are to construct estimates for the true intensity and its uncertainty. For example, the hard X-ray count spectrum from transient events such as solar flares can be observed only once, and from this single spectrum one must determine the best estimate of the underlying source spectrum I({epsilon}), and hence the form of the responsible electron spectrum F(E). Including statistical uncertainties on the measured count spectrum yields a 'confidence strip' that delineates the boundaries of electron spectra that are consistent with the observed photon spectrum. In this short article, we point out that the expectation values of the source brightness and its variance in a given photon energy bin are in general not (as has been assumed in prior works) equal to n, the number of counts observed in that energy bin. Rather, they depend both on n and on prior knowledge of the overall photon spectrum. Using Bayesian statistics, we provide an explicit procedure and formulas for determining the 'confidence strip' (Bayesian credible region) for F(E), thus providing rigorous bounds on the intensity and shape of the accelerated electron spectrum.
With more and more application of digital I and C systems in nuclear power plants (NPP), more and more sensors are also required. Large amount of sensors really can make contribution to the condition monitoring of systems and equipment in NPP, whereas the heavy use also increases the malfunction risk of sensor self. Therefore the reliability and effectiveness of sensors must be guaranteed first. Thus the condition of sensors should be monitored first in a NPP. Since the precise mathematical models of sensors in complex systems are relative difficult to establish, however quantity of operating data can be easily obtained due to the wide application of digital I and C systems, thus this article adopts principal component analysis (PCA) to carry out condition monitoring for sensors. The PCA model developed for sensors is proved to be effective with operating data acquired from a real NPP. It can detect and isolate the fault when an artificial fault is imposed to the normal data. Then the monitoring results are further processed by another control limit (second confidence limit) besides T 2 and Q statistics (first confidence limit) in PCA. The value of second confidence limit is calculated based on the probability statistics of T 2 and Q statistics. And simulation tests show that the application of the second confidence limit can greatly reduce false alarms in the PCA model and the model performance can be greatly improved with the application of false alarms reducing method. (authors)
The purpose of this activity was to provide a bounding estimate of the volume of hydrogen gas generated during Enhanced Chemical Cleaning (ECC) of residual sludge remaining in a Type I or Type II treatment tank as well as to provide results independent of the sludge volume in the waste tank to be cleaned. Previous testing to support Chemical Cleaning was based on a 20:1 oxalic acid to sludge ratio. Hydrogen gas evolution is the primary safety concern. Sealed vessel coupon tests were performed to estimate the hydrogen generation rate due to corrosion of carbon steel by 2.5 wt.% oxalic acid. These tests determined the maximum instantaneous hydrogen generation rate, the rate at which the generation rate decays, and the total hydrogen generated. These values were quantified based on a small scale methodology similar to the one described in WSRC-STI-2007-00209, Rev. 0. The measured rates support identified Safety Class functions. The tests were performed with ASTM A285 Grade C carbon steel coupons. Bounding conditions were determined for the solution environment. The oxalic acid concentration was 2.5 wt.% and the test temperature was 75 C. The test solution was agitated and contained no sludge simulant. Duplicate tests were performed and showed excellent reproducibility for the hydrogen generation rate and total hydrogen generated. The results showed that the hydrogen generation rate was initially high, but decayed rapidly within a couple of days. A statistical model was developed to predict the instantaneous hydrogen generation rate as a function of exposure time by combining both sets of data. An upper bound on the maximum hydrogen generation rate was determined from the upper 95% confidence limit. The upper bound confidence limit for the hydrogen generation rate is represented by the following equation. ln (G{sub v}) = -8.22-0.0584 t + 0.0002 t{sup 2}. This equation should be utilized to estimate the instantaneous hydrogen generation rate per unit surface area, G{sub v}, at a given time, t. The units for G{sub v} and t are ft{sup 3}/ft{sup 2}/min and hours, respectively. The total volume of hydrogen gas generated during the test was calculated from the model equation. An upper bound on the total gas generated was determined from the upper 95% confidence limit. The upper bound limit on the total hydrogen generated during the 163 hour test was 0.332 ft{sup 3}/ft{sup 2}. The maximum instantaneous hydrogen generation rate for this scenario is greater than that previously measured in the 8 wt.% oxalic acid tests due to both the absence of sludge in the test (i.e., greater than 20:1 ratio of acid to sludge) and the use of polished coupons (vs. mill scale coupons). However, due to passivation of the carbon steel surface, the corrosion rate decays by an order of magnitude within the first three days of exposure such that the instantaneous hydrogen generation rates are less than that previously measure in the 8 wt.% oxalic acid tests. While the results of these tests are bounding, the conditions used in this study may not be representative of the ECC flowsheet, and the applicability of these results to the flowsheet should be evaluated for the following reasons: (1) The absence of sludge results in higher instantaneous hydrogen generation rates than when the sludge is present; and (2) Polished coupons do not represent the condition of the carbon steel interior of the tank, which are covered with mill scale. Based on lower instantaneous corrosion rates measured on mill scale coupons exposed to oxalic acid, lower instantaneous hydrogen generation rates are expected for the tank interior than measured on the polished coupons. Corrosion rates were determined from the coupon tests and also calculated from the measured hydrogen generation rates. Excellent agreement was achieved between the time averaged corrosion rate calculated from the hydrogen generation rates and the corrosion rates determined from the coupon tests. The corrosion rates were on the order of 18 to 28 mpy. Good agreement was also observed between the maximum instantaneous corrosion rate as calculated from the hydrogen generation rate and the corrosion rate determined by previous electrochemical tests.
Estimating uncertainty in lifetime cancer risk for human exposure to space radiation is a unique challenge. Conventional risk assessment with low-linear-energy-transfer (LET)-based risk from Japanese atomic bomb survivor studies may be inappropriate for relativistic protons and nuclei in space due to track structure effects. This paper develops a Monte Carlo mixture model (MCMM) for transferring additive, National Institutes of Health multiplicative, and multiplicative excess cancer incidence risks based on Japanese atomic bomb survivor data to determine excess incidence risk for various US astronaut exposure profiles. The MCMM serves as an anchor point for future risk projection methods involving biophysical models of DNA damage from space radiation. Lifetime incidence risks of radiation-induced cancer for the MCMM based on low-LET Japanese data for nonleukemia (all cancers except leukemia) were 2.77 (90% confidence limit, 0.75-11.34) for males exposed to 1 Sv at age 45 and 2.20 (90% confidence limit, 0.59-10.12) for males exposed at age 55. For females, mixture model risks for nonleukemia exposed separately to 1 Sv at ages of 45 and 55 were 2.98 (90% confidence limit, 0.90-11.70) and 2.44 (90% confidence limit, 0.70-10.30), respectively. Risks for high-LET 200 MeV protons (LET=0.45 keV/micrometer), 1 MeV alpha-particles (LET=100 keV/micrometer), and 600 MeV iron particles (LET=180 keV/micrometer) were scored on a per particle basis by determining the particle fluence required for an average of one particle per cell nucleus of area 100 micrometer(2). Lifetime risk per proton was 2.68x10(-2)% (90% confidence limit, 0.79x10(-3)%-0. 514x10(-2)%). For alpha-particles, lifetime risk was 14.2% (90% confidence limit, 2.5%-31.2%). Conversely, lifetime risk per iron particle was 23.7% (90% confidence limit, 4.5%-53.0%). Uncertainty in the DDREF for high-LET particles may be less than that for low-LET radiation because typically there is very little dose-rate dependence. Probability density functions for high-LET radiation quality and dose-rate may be preferable to conventional risk assessment approaches. Nuclear reactions and track structure effects in tissue may not be properly estimated by existing data using in vitro models for estimating RBEs. The method used here is being extended to estimate uncertainty in spacecraft shielding effectiveness in various space radiation environments.
We investigate the allowed range of reheating temperature values in light of the Planck 2015 results and the recent joint analysis of Cosmic Microwave Background (CMB) data from the BICEP2/Keck Array and Planck experiments, using monomial and binomial inflationary potentials. While the well studied ϕ{sup 2} inflationary potential is no longer favored by current CMB data, as well as ϕ{sup p} with p>2, a ϕ{sup 1} potential and canonical reheating (w{sub re}=0) provide a good fit to the CMB measurements. In this last case, we find that the Planck 2015 68% confidence limit upper bound on the spectral index, n{sub s}, implies an upper bound on the reheating temperature of T{sub re}≲6×10{sup 10} GeV, and excludes instantaneous reheating. The low reheating temperatures allowed by this model open the possibility that dark matter could be produced during the reheating period instead of when the Universe is radiation dominated, which could lead to very different predictions for the relic density and momentum distribution of WIMPs, sterile neutrinos, and axions. We also study binomial inflationary potentials and show the effects of a small departure from a ϕ{sup 1} potential. We find that as a subdominant ϕ{sup 2} term in the potential increases, first instantaneous reheating becomes allowed, and then the lowest possible reheating temperature of T{sub re}=4 MeV is excluded by the Planck 2015 68% confidence limit.
Purpose: Tomotherapy treatments are characterized by rotational deliveries of flattening-filter free fields resulting in high-gradient dose distributions. Small volume, rotationally independent detectors are needed for accurate dosimetry. PTWs microDiamond detector, with its small sensitive volume (0.004mm{sup 3}), could potentially be an ideal detector for Tomotherapy. The microDiamond detector was tested against a small volume Exradin A1SL ion chamber for Tomotherapy open-field and IMRT commissioning measurements. Methods: Custom detector holders were fabricated to allow A1SL and microDiamond measurements in the Tomotherapy Cheese phantom and a square solid water phantom. The microDiamond rotational dependence within the Tomotherapy phantom was tested by incrementally rotating the detector in between static-gantry angle Tomotherapy irradiations. Longitudinal Tomotherapy profiles, for all field sizes, were measured with the microDiamond and A1SL detectors at 1.5cm depth in the square phantom, and compared to film. Detector axes were aligned parallel to table motion. Per TG-119 recommendations, both detectors were calibrated to known doses in phantoms and used to measure high-dose points in TG-119 H and N and Prostate plans. The measurements were compared to the treatment planning system and subsequently compared to published TG-119 confidence limits. Results: The microDiamond angular dependence was less than 0.5%. The average difference between the detectors and film-measured longitudinal profile 80–20% penumbras were 0.03+/-0.04mm and 1.36+/-0.22mm for the microDiamond and A1SL, respectively. The average difference between the detector and filmmeasured field sizes were 0.07+/-0.01mm and 0.09+/-0.02mm for the microDiamond and A1SL, respectively. The measured confidence limits were 0.023 and 0.015 for microDiamond and A1SL, respectively. TG-119 reported a confidence limit of 0.034. Conclusion: The microDiamond measured open-field longitudinal Tomotherapy profiles more closely resembled film measurements, compared with the A1Sl chamber. Furthermore, the microDiamond was shown to have low angular dependence and both detectors were able to accurately measure the highdose points in TG-119 H and N and prostate plans. Our institution has research grants with PTW.
We determine the best-fit values and confidence limits for dynamical dark energy parameters together with other cosmological parameters on the basis of different datasets which include WMAP9 or Planck-2013 results on CMB anisotropy, BAO distance ratios from recent galaxy surveys, magnitude-redshift relations for distant SNe Ia from SNLS3 and Union2.1 samples and the HST determination of the Hubble constant. We use a Markov Chain Monte Carlo routine to map out the likelihood in the multi-dimensional parameter space. We show that the most precise determination of cosmological parameters with the narrowest confidence limits is obtained for the Planck+HST+BAO+SNLS3 dataset. The best-fit values and 2σ confidence limits for cosmological parameters in this case are Ω{sub de} = 0.718±0.022, w{sub 0} = −1.15{sup +0.14}{sub −0.16}, c{sub a}{sup 2} = −1.15{sup +0.02}{sub −0.46}, Ω{sub b}h{sup 2} = 0.0220±0.0005, Ω{sub cdm}h{sup 2} = 0.121±0.004, h = 0.713±0.027, n{sub s} = 0.958{sup +0.014}{sub −0.010}, A{sub s} = (2.215{sup +0.093}{sub −0.101})⋅10{sup −9}, τ{sub rei} = 0.093{sup +0.022}{sub −0.028}. For this dataset, the ΛCDM model is just outside the 2σ confidence region, while for the dataset WMAP9+HST+BAO+SNLS3 the ΛCDM model is only 1σ away from the best fit. The tension in the determination of some cosmological parameters on the basis of two CMB datasets WMAP9 and Planck-2013 is highlighted.
We conducted a historical cohort study of mortality among 195 astronauts who were exposed to space and medical sources of radiation between 1959 and 1991. Cumulative occupational and medical radiation exposures were obtained from the astronaut radiation exposure history data base. Causes of death were obtained from obligatory death certificates and autopsy reports that were on file in the medical records. A total of 18 deaths occurred during the 32-year follow-up period for which the all-cause standardized mortality ratio (SMR) was 142 (95 percent confidence interval 84 225). There was one cancer death in the buccal cavity and pharynegeal ICD-9 rubric whose occurrence was significantly beyond expectation. Mortality for coronary disease was 59 percent lower than expected (2 deaths; SMR = 41; 95 percent confidence limit 5 147). The crude death rate for 10 occupationally related accidents was 400 deaths per 100,000 person-years, which is an order of magnitude greater than accidental death rates in mining industries. The SMR of 1027 for fatal accidents was significantly beyond expectation (14 deaths; 95 percent confidence limit 561 1723) and was similar to SMRs for accidents among aerial pesticide applications. The 10-year cumulative risk of occupational fatalities based on the exponential, Weibull, Gompertz, and linear-exponential distributions was 10 percent. Mortality from motor vehicle accidents was slightly higher than expected but was not significant (1 death; SMR = 145; 95 percent confidence limit 2 808). Radiation exposures from medical procedures accounted for a majority of cumulative dose when compared with space radiation exposures. The results of the study do not confirm the impression that astronauts are at increased risk of cancer, but this does not obviate the need for further study. Overall, it was found that astronauts are at a health disadvantage as a result of catastrophic accidents.