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Statistical analysis determines probability of failure for N-1 spares. Analysis can help reduce downtime in industrial processes by determining number of spare parts that must be stockpiled. Analysis can also be inverted to decide if number of spares on hand is sufficient for lifetime needs of system.
Arctic low clouds strongly affect the Arctic surface energy budget, and through this impact influence rest of the Arctic climate system: namely surface and atmospheric temperature, sea ice extent and thickness, and the atmospheric circulation. Arctic clouds are in turn influenced by the Arctic climate system creating the potential for cloud-climate feedbacks. We quantify the influence of atmospheric state on the surface cloud radiative effect (CRE) and the covariability between surface CRE and sea ice concentration (SIC) using instantaneous, active remote sensing satellite footprint data from the NASA A-Train. First, the results indicate significant differences in the surface CRE when stratified by atmospheric state. Second, a statistically insignificant covariability is found between CRE and SIC for most atmospheric regimes. Third, we find a statistically significant increase in the surface longwave CRE at with decreased SIC in fall. Specifically, a +3-5 W m 2 larger longwave CRE is found over footprints with 0% versus 100% SIC. Because systematic changes of 1 W m 2 are sufficient to explain the observed reductions in Arctic sea ice, our results (1) indicate a potentially significant amplifying sea ice-cloud feedback that could delay fall freeze-up influencing sea ice variability under certain atmospheric conditions and (2) suggest that a small change in the frequency of atmosphere states may yield a larger Arctic cloud feedback than any cloud response to sea ice.
Arctic low clouds strongly affect the Arctic surface energy budget, and through this impact influence rest of the Arctic climate system: namely surface and atmospheric temperature, sea ice extent and thickness, and the atmospheric circulation. Arctic clouds are in turn influenced by the Arctic climate system creating the potential for cloud-climate feedbacks. We quantify the influence of atmospheric state on the surface cloud radiative effect (CRE) and the covariability between surface CRE and sea ice concentration (SIC) using instantaneous, active remote sensing satellite footprint data from the NASA A-Train. First, the results indicate significant differences in the surface CRE when stratified by atmospheric state. Second, a statistically insignificant covariability is found between CRE and SIC for most atmospheric regimes. Third, we find a statistically significant increase in the surface longwave CRE at with decreased SIC in fall. Specifically, a +3-5 W m^-2 larger longwave CRE is found over footprints with 0% versus 100% SIC. Because systematic changes of 1 W m^-2 are sufficient to explain the observed reductions in Arctic sea ice, our results (1) indicate a potentially significant amplifying sea ice-cloud feedback that could delay fall freeze-up influencing sea ice variability under certain atmospheric conditions and (2) suggest that a small change in the frequency of atmosphere states may yield a larger Arctic cloud feedback than any cloud response to sea ice.
It is shown that a lack of knowledge concerning interstellar electron density, the true spatial distribution of pulsars, the radio luminosity source distribution of pulsars, the real ages and real aging rates of pulsars, the beaming factor (and other unknown factors causing the known sample of about 350 pulsars to be incomplete to an unknown degree) is sufficient to cause a minimum uncertainty of a factor of 20 in any attempt to determine pulsar birth or death rates in the Galaxy. It is suggested that this uncertainty must impact on suggestions that the pulsar rates can be used to constrain possible scenarios for neutron star formation and stellar evolution in general.
Solid rocket motor (SRM) slag has been identified as a potential source of man-made orbital debris. The possibility that SRMs (in addition to generating dust particles in the sub-millimeter range) may generate particles up to centimeters in size has caused concern regarding their contribution to the debris environment. Returned surfaces from space do not have sufficient area or exposure time to provide a clear picture of the SRM millimeter and centimeter debris population. Currently, radar observation is probably the only way to collect data showing the debris contribution from SRMs. Such observation is used to sample the debris environment, but it is difficult to obtain accurate orbital elements for the detected debris objects. NASA has developed several models to describe the different orbital debris populations, based on assumed debris production mechanisms to create clouds of debris objects that can be propagated in time. The NASA model, LEGEND (LEO-to-GEO Environment Debris), functions as a time-tested debris model for most debris sources. However, the current LEGEND model does not include contributions from the SRM population. An SRM model has recently been developed by NASA, based on purely theoretical details of SRM production and known SRM launches, but verification with hard data is needed. Because the detections of individual SRM objects cannot be deterministically separated from the total debris observed by radar, the validation of the SRM model can only be done by combining it with the LEGEND breakup model and comparing it with data. By applying observational constraints, the degree of SRM slag contribution to the environment may be estimated. This serves as an observationally sound method from which to calibrate a purely theoretical model into something more realistic. For this study, we use the populations observed by the Haystack radar from 1996 to present. For the SRM debris, we use a historical database of SRM launches, propellant masses, and estimated locations and times of tailoff to produce and propagate the SRM debris clouds. Comparisons with radar data from the ensuing years were made, and the SRM model was altered with respect to size and mass production of slag particles to reflect the populations estimated from the data. The result is a model SRM population that fits within the bounds of the observed environment and estimates of the production and contribution of SRM debris to the environment.
The lower mass limit for carbon detonation (about 4 solar masses) is due to an argument by Paczynski (1970) that stars in the range between 1.4 and 3.5 solar masses will lose their envelopes prior to carbon detonation. This argument is based on two criteria for mass loss: (1) the envelope is unstable to adiabatic oscillations, and (2) the total energy of the envelope is positive. Recent hydrodynamic calculations of red-giant envelopes have shown that the envelopes are highly nonadiabatic and that the second criterion is not a sufficient condition for mass loss. Thus, a calculation which takes into account the nonadiabatic nature of the envelope is required in order to determine the lower mass limit for carbon detonation. This lower mass limit is directly related to the formation mechanisms and statistics of supernovae and pulsars.
Weight and dimensional relationships that are used in aircraft preliminary design studies are analyzed. These relationships are relatively simple to prove useful to the preliminary designer, but they are sufficiently detailed to provide meaningful design tradeoffs. All weight and dimensional relationships are developed from data bases of existing and conceptual turbofan engines. The total propulsion system is considered including both engine and nacelle, and all estimating relations stem from physical principles, not statistical correlations.
Nongray gas radiation in a plane parallel slab bounded by gray, diffusely reflecting walls is studied using the discrete ordinates method. The spectral equation of transfer is averaged over a narrow wavenumber interval preserving the spectral correlation effect. The governing equations are derived by considering the history of multiple reflections between two reflecting wails. A closure approximation is applied so that only a finite number of reflections have to be explicitly included. The closure solutions express the physics of the problem to a very high degree and show relatively little error. Numerical solutions are obtained by applying a statistical narrow-band model for gas properties and a discrete ordinates code. The net radiative wail heat fluxes and the radiative source distributions are obtained for different temperature profiles. A zeroth-degree formulation, where no wall reflection is handled explicitly, is sufficient to predict the radiative transfer accurately for most cases considered, when compared with increasingly accurate solutions based on explicitly tracing a larger number of wail reflections without any closure approximation applied.
We have phase connected a sequence of Rossi X-ray Timing Explorer Proportional Counter Array observations of SGR 1806-20 covering 178 days. We find a simple secular spin-down model does not adequately fit the data. The period derivative varies gradually during the observations between 8.1 and 11.7 x 10(exp -11) s/s (approx. 40% larger than the long term trend), while the average burst rate as seen with the Burst and Transient Source Experiment drops throughout the time interval. The phase residuals show no evidence for periodicity, but more closely resemble timing noise as seen ill radio pulsars. The magnitude of the timing noise., however, is large relative to the noise level typically found in radio pulsars (Delta(sub 8) = 4.8; frequency derivative average power approx. = 8 X 10(exp -20) Hz/sq s). Combined with the noise levels measured for some AXPs. we find all magnetar candidates have A8 values larger than those expected from a simple extrapolation of the correlation found in radio pulsars. We find the timing noise ill SGR 1806-20 is greater than or equal to the levels found in some accreting systems (e.g., Vela X-1, 4U 1538-52 and 4U 1626-67). Alternatively, an orbital model with a period P(sub orb) = 733 days provides a statistically acceptable fit to the data. If the phase residuals are created by Doppler shifts from a gravitationally bound companion. then the allowed parameter space for the mass function (small) and orbital separation (large) rule out the possibility of accretion from the companion sufficient to power the persistent emission from the SGR.
We have phase-connected a sequence of Rossi X-Ray Timing Explorer Proportional Counter Array observations of SGR 1806-20 covering 178 days. We find that a simple secular spin-down model does not adequately fit the data. The period derivative varies gradually during the observations between 8.1 x 10(exp -11) and 11.7 x 10(exp -11) s/s (at its highest, approximately 40% larger than the long-term trend), while the average burst rate as seen with the Burst and Transient Source Experiment drops throughout the time interval. The phase residuals give no compelling evidence for periodicity, but more closely resemble timing noise as seen in radio pulsars. The magnitude of the timing noise, however, is large relative to the noise level typically found in radio pulsars (Delta(sub 8) = 4.8; frequency derivative average power approximately 7 x 10(exp -20) sq cycles/cubic s). Combining these results with the noise levels measured for some anomalous X-ray pulsars, we find that all magnetar candidates have Delta(sub 8) values larger than those expected from a simple extrapolation of the correlation found in radio pulsars. We find that the timing noise in SGR 1806-20 is greater than or equal to the levels found in some accreting systems (e.g., Vela X-1, 4U 1538-52, and 4U 1626-67), but the spin-down of SGR 1806-20 has thus far maintained coherence over 6 yr. Alternatively, an orbital model with a period P(sub orb) = 733 days provides a statistically acceptable fit to the data. If the phase residuals are created by Doppler shifts from a gravitationally bound companion, then the allowed parameter space for the mass function (small) and orbital separation (large) rule out the possibility of accretion from the companion sufficient to power the persistent emission from the SGR.
This study presents examples and considerations for differentiating linear radiographic indications produced by gas tungsten arc welds in a 0.05-in-thick sheet of Inconel 718. A series of welds with different structural features, including the enigma indications and other defect indications such as lack of fusion and penetration, were produced, radiographed, and examined metallographically. The enigma indications were produced by a large columnar grain running along the center of the weld nugget occurring when the weld speed was reduced sufficiently below nominal. Examples of respective indications, including the effect of changing the x-ray source location, are presented as an aid to differentiation. Enigma, nominal, and hot-weld specimens were tensile tested to demonstrate the harmlessness of the enigma indication. Statistical analysis showed that there is no difference between the strengths of these three weld conditions.
The intense surface heating over arid land surfaces produces dry well-mixed layers (WML) via dry convection. These layers are characterized by nearly constant potential temperature and low, nearly constant water vapor mixing ratio. To further the study of dry WMLs, we created a detection methodology and supporting software to automate the identification and characterization of dry WMLs from multiple data sources including rawinsondes, remote sensing platforms, and model products. The software is a modular code written in Python, an open source language. Radiosondes from a network of synoptic stations in North Africa were used to develop and test the WML detection process. The detection involves an iterative decision tree that ingests a vertical profile from an input data file, performs a quality check for sufficient data density, and then searches upward through the column for successive points where the simultaneous changes in water vapor mixing ratio and potential temperature are less than the specified maxima. If points in the vertical profile meet the dry WML identification criteria, statistics are generated detailing the characteristics of each layer in the profile. At the end of the vertical profile analysis, there is an option to plot analyzed profiles in a variety of file formats. Initial results show that the detection methodology can be successfully applied across a wide variety of input data and North African environments and for all seasons. It is sensitive enough to identify dry WMLs from other types of isentropic phenomena such as subsidence layers and distinguish the current day’s dry WML from previous days.
As part of a larger analysis of country systems described elsewhere, named a Crop Country Inventory, CCI, large variations in annual crop yield for selected climate sensitive agricultural regions or sub-regions within a country have been studied over extended periods in decades. These climate sensitive regions, principally responsible for large annual variations in an entire country s crop production, generally are characterized by distinctive patterns of atmospheric circulation and synoptic processes that result in large seasonal fluctuations in temperature, precipitation and soil moisture as well as other climate properties. The immediate region of interest is drought prone Kazakhstan in Central Asia, part of the Former Soviet Union, FSU. As a partial validation test in a dry southern region of Kazakhstan, the Almati Oblast was chosen. The Almati Oblast, a sub-region of Kazakhstan located in its southeast corner, is one of 14 oblasts within the Republic of Kazahstan. The climate data set used to characterize this region was taken from the results of the current maturely developed Global Climate Model, GCM. In this paper, the GCM results have been compared to the meteorological station data at the station locations, over various periods. If the empirical correlation of the data sets from both the GCM and station data is sufficiently significant, this would validate the use of the superior GCM profile mapping and integration for the climatic characterization of a sub-region. Precipitation values interpolated from NCEP Reanalysis II data, a global climate database spanning over 5 decades since 1949, have been statistically correlated with monthly-averaged station data from 1949 through 1993, and with daily station data from April through August, 1990 for the Almati Oblast in Kazakhstan. The resultant correlation is significant, which implies that the methodology may be extended to different regions globally for Crop Country Inventory studies.
The MODIS Level-3 optical thickness and effective radius cloud product is a gridded l deg. x 1 deg. dataset that is derived from aggregation and subsampling at 5 km of 1 km, resolution Level-2 orbital swath data (Level-2 granules). This study examines the impact of the 5 km subsampling on the mean, standard deviation and inhomogeneity parameter statistics of optical thickness and effective radius. The methodology is simple and consists of estimating mean errors for a large collection of Terra and Aqua Level-2 granules by taking the difference of the statistics at the original and subsampled resolutions. It is shown that the Level-3 sampling does not affect the various quantities investigated to the same degree, with second order moments suffering greater subsampling errors, as expected. Mean errors drop dramatically when averages over a sufficient number of regions (e.g., monthly and/or latitudinal averages) are taken, pointing to a dominance of errors that are of random nature. When histograms built from subsampled data with the same binning rules as in the Level-3 dataset are used to reconstruct the quantities of interest, the mean errors do not deteriorate significantly. The results in this paper provide guidance to users of MODIS Level-3 optical thickness and effective radius cloud products on the range of errors due to subsampling they should expect and perhaps account for, in scientific work with this dataset. In general, subsampling errors should not be a serious concern when moderate temporal and/or spatial averaging is performed.
Arctic low clouds strongly affect the Arctic surface energy budget. Through this impact Arctic low clouds influence important aspects of the Arctic climate system, namely surface and atmospheric temperature, sea ice extent and thickness, and atmospheric circulation. Arctic clouds are in turn influenced by these elements of the Arctic climate system, and these interactions create the potential for Arctic cloud-climate feedbacks. To further our understanding of potential Arctic cloudclimate feedbacks, the goal of this paper is to quantify the influence of atmospheric state on the surface cloud radiative effect (CRE) and its covariation with sea ice concentration (SIC). We build on previous research using instantaneous, active remote sensing satellite footprint data from the NASA A-Train. First, the results indicate significant differences in the surface CRE when stratified by atmospheric state. Second, there is a weak covariation between CRE and SIC for most atmospheric conditions. Third, the results show statistically significant differences in the average surface CRE under different SIC values in fall indicating a 3-5 W m(exp -2) larger LW CRE in 0% versus 100% SIC footprints. Because systematic changes on the order of 1 W m(exp -2) are sufficient to explain the observed long-term reductions in sea ice extent, our results indicate a potentially significant amplifying sea ice-cloud feedback, under certain meteorological conditions, that could delay the fall freeze-up and influence the variability in sea ice extent and volume. Lastly, a small change in the frequency of occurrence of atmosphere states may yield a larger Arctic cloud feedback than any cloud response to sea ice.
Examination of NASA-STD-6001 Test 1 data suggests burn length outcome for a given environment has a large statistical variation from run to run. Large data sets show that burn length data form cumulative probability distribution curves, which describe a material's characteristic to burn in a specific environment, suggesting that the current practice of testing three samples at specific conditions is inadequate. Sufficient testing can establish material characteristics probability curves to provide the probability that a material will sustain a burn length of at least 15.24 cm (6.0 in.) or will sustain burning until all material is consumed. A simple pasdfail criterion may not be possible or practical. Future application of flammability data for some material classes may require the engineer to assess risk based on the probability of an occurrence and the probable outcome with different materials as characterized with cumulative burn length distributions for specific use conditions.
Spur-gear surface fatigue tests were conducted with five lubricants using a single lot of consumable-electrode vacuum melted (CVM) AISI 9310 spur gears. The lot of gears was divided into five groups, each of which was tested with a different lubricant. The test lubricants are classified as either a synthetic hydrocarbon, mineral oil, or ester-based lubricant. All five lubricants have imilar viscosity and pressure-viscosity coefficients. A pentaerythritol base stock without sufficient antiwear additives produced a surface fatigue life pproximately 22 percent that of the same base stock with chlorine and phosphorus type additives. The presence of sulfur type antiwear additives in the lubricant did not appear to affect the surface fatigue life of the gears tested. No statistical difference in the 10-percent surface fatigue life was produced with four of the five lubricants.
Human behavior often consists of a series of distinct activities, each characterized by a unique pattern of interaction with the visual environment. This is true even in a restricted domain, such as a pilot flying an airplane; in this case, activities with distinct visual signatures might be things like communicating, navigating, monitoring, etc. We propose a novel analysis method for gaze-tracking data, to perform blind discovery of these hypothetical activities. We compare, not individual fixations, but groups of fixations aggregated over a fixed time interval (Tau). We assume that the environment has been divided into a finite set of discrete areas-of-interest (AOIs). For a given time interval, we compute the proportion of time spent fixating each AOI, resulting in an N-dimensional vector, where N is the number of AOIs. These proportions can be converted to integer counts by multiplying by Tau divided by the average fixation duration, a parameter that we fix at 283 milliseconds. We compare different intervals by computing the chi-squared statistic. The p-value associated with the statistic is the likelihood of observing the data under the hypothesis that the data in the two intervals were generated by a single process with a single set of probabilities governing the fixation of each AOI. We cluster the intervals, first by merging adjacent intervals that are sufficiently similar, optionally shifting the boundary between non-merged intervals to maximize the difference. Then we compare and cluster non-adjacent intervals. The method is evaluated using synthetic data generated by a hand-crafted set of activities. While the method generally finds more activities than put into the simulation, we have obtained agreement as high as 80 percent between the inferred activity labels and ground truth.