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This article [Opt. Eng. 51, 011002 (2012)] was originally published on 8 February 2012 with an error in Eq. (13). The corrected equation appears below. The manuscript was corrected online on 15 February 2012. The article appears correctly in print.
Residual stress (RS) significantly impacts the mechanical performance of components. Measurement of RS often provides incomplete data in terms of components of stress and spatial density. Employing such fields in finite element simulations results in significant modification of the field to achieve equilibrium and compatibility among strains. To overcome this, an iterative stress reconstruction algorithm (ISRA) is developed to estimate 3D RS fields that satisfy equilibrium, are stress component-wise complete, and represent the characterized data sampled. An Al 7075-T651 plate and an additively manufactured (AM) A36 steel wall are considered for RS reconstruction using measurement data from the literature. A maximum variation of ~2.5 MPa in the Al plate, and ~10 MPa in the steel wall are observed between the reconstructed and measured stresses. Furthermore, unknown stress components emerge and reach significant magnitudes (upto ~2.3 MPa in the Al plate and ~45 MPa in the AM wall) during ISRA. Indeed, it is found that minor errors in measurement or data processing are eliminated through the physical requirements during ISRA. Employing a reconstructed RS field is hence not just more accurate given its compatibility, but it additionally corrects for minor errors in measurement. Furthermore, it is found that spatially dense measurement data result in convergence with fewer iterations. Finally, although ISRA yields a nonunique solution dependent on boundary conditions, measurement errors, fitting errors, and mesh density, it accommodates for uncertainties and inaccuracies in measurement, as opposed to failing to reach a physically realistic converged solution.
A Data Base Management System and an Archival Mass Memory System are being developed that will have a 10 to the 12th bit on-line and a 10 to the 13th off-line storage capacity. The integrated system will accept packetized data from the data staging area at 50 Mbps, create a comprehensive directory, provide for file management, record the data, perform error detection and correction, accept user requests, retrieve the requested data files and provide the data to multiple users at a combined rate of 50 Mbps. Stored and replicated data files will have a bit error rate of less than 10 to the -9th even after ten years of storage. The integrated system will be demonstrated to prove the technology late in 1981.
State estimation techniques serve effectively to provide mean state estimates. However, the state error covariance matrices provided as part of these techniques suffer from some degree of lack of confidence in their ability to adequately describe the uncertainty in the estimated states. A specific problem with the traditional form of state error covariance matrices is that they represent only a mapping of the assumed observation error characteristics into the state space. Any errors that arise from other sources (environment modeling, precision, etc.) are not directly represented in a traditional, theoretical state error covariance matrix. Consider that an actual observation contains only measurement error and that an estimated observation contains all other errors, known and unknown. It then follows that a measurement residual (the difference between expected and observed measurements) contains all errors for that measurement. Therefore, a direct and appropriate inclusion of the actual measurement residuals in the state error covariance matrix will result in an empirical state error covariance matrix. This empirical state error covariance matrix will fully account for the error in the state estimate. By way of a literal reinterpretation of the equations involved in the weighted least squares estimation algorithm, it is possible to arrive at an appropriate, and formally correct, empirical state error covariance matrix. The first specific step of the method is to use the average form of the weighted measurement residual variance performance index rather than its usual total weighted residual form. Next it is helpful to interpret the solution to the normal equations as the average of a collection of sample vectors drawn from a hypothetical parent population. From here, using a standard statistical analysis approach, it directly follows as to how to determine the standard empirical state error covariance matrix. This matrix will contain the total uncertainty in the state estimate, regardless as to the source of the uncertainty. Also, in its most straight forward form, the technique only requires supplemental calculations to be added to existing batch algorithms. The generation of this direct, empirical form of the state error covariance matrix is independent of the dimensionality of the observations. Mixed degrees of freedom for an observation set are allowed. As is the case with any simple, empirical sample variance problems, the presented approach offers an opportunity (at least in the case of weighted least squares) to investigate confidence interval estimates for the error covariance matrix elements. The diagonal or variance terms of the error covariance matrix have a particularly simple form to associate with either a multiple degree of freedom chi-square distribution (more approximate) or with a gamma distribution (less approximate). The off diagonal or covariance terms of the matrix are less clear in their statistical behavior. However, the off diagonal covariance matrix elements still lend themselves to standard confidence interval error analysis. The distributional forms associated with the off diagonal terms are more varied and, perhaps, more approximate than those associated with the diagonal terms. Using a simple weighted least squares sample problem, results obtained through use of the proposed technique are presented. The example consists of a simple, two observer, triangulation problem with range only measurements. Variations of this problem reflect an ideal case (perfect knowledge of the range errors) and a mismodeled case (incorrect knowledge of the range errors).
This work presents a systematic investigation of the influence of weather conditions on the calibration errors by using Gaussian fitness, least chi-square linear fitness, and wavelet transform to analyze the calibration coefficients from observations of the Chinese Solar Broadband Radio Spectrometers (at frequency bands of 1.0–2.0 GHz, 2.6–3.8 GHz, and 5.2–7.6 GHz) during 1997–2007. We found that calibration coefficients are influenced by the local air temperature. Considering the temperature correction, the calibration error will reduce by about 10%–20% at 2800 MHz. Based on the above investigation and the calibration corrections, we further study the radio emission of the quiet Sun by using an appropriate hybrid model of the quiet-Sun atmosphere. The results indicate that the numerical flux of the hybrid model is much closer to the observation flux than that of other ones.
The NASA Langley group led by William Smith produced GOES-16 satellite cloud retrievals over an approximate 10 by 10 degree region over the CACTI field campaign location. These retrievals are described here: https://www.arm.gov/capabilities/vaps/visst and are available for download here . They use algorithms historically called VISST that are now referred to as SatCORPS. More information can be found in Trepte et al. (2019), Minnis et al. (2021), and Yost et al. (2021). If using this dataset, please cite these references, the CACTI VISST dataset DOI found at the download link above, and this dataset’s DOI. The CACTI VISST pixel-level retrievals are on a 2 km spatial grid and available every 15 minutes (every 10 minutes late in the campaign), producing 21,765 files for the entire field campaign between October 2018 and April 2019. They are not corrected for parallax error, which is an offset in the actual geographical location of a cloud above the surface due to the satellite viewing the cloud partly from the side off nadir. This dataset applies a correction for parallax using the location relative to the satellite and the retrieved cloud top height above the surface, which allows the dataset to be geo-located with surface-based observations. The parallax correction for each location depends on the longitude, latitude and cloud top height above ground level (AGL) for that longitude and latitude in the original VISST files. The cloud top height AGL requires first computing the surface elevation at each VISST grid point. Data from the Advanced Spaceborne Thermal Emission and Reflection (ASTER) Global Digital Elevation Map Version 3 at 30-m resolution is projected onto the VISST grid using conservative coarsening (conserving surface elevation) in the xESMF Python package. The surface elevation is then subtracted from the VISST-retrieved cloud top height above mean sea level. These cloud top heights AGL are then combined with longitude and latitude to estimate the latitude and longitude corrections. Due to variability in cloud top height, the parallax shifts produce an irregular grid of values since higher cloud tops are shifted further than lower cloud tops. A ball tree-based neighbor search with Haversine distance is performed using the Python-based scikit-learn library to find the nearest VISST grid point to each parallax correction-shifted point. The data value of the shifted point is then assigned to that VISST grid point. In this manner, the irregular geographical shifts to correct for parallax are projected back to the rectilinear VISST grid. Because relatively higher clouds should obscure lower clouds, the variable values for the highest cloud top are preferentially chosen if two or more values are assigned to a grid point. The parallax correction should be viewed as an improved but still imperfect estimation of the cloud top locations, largely because the cloud top height is an imperfect retrieval. Please see the attached README document for further information. Users are encouraged to contact the authors with any additional questions.
Landsat MSS data were input to a discriminant analysis of 21 crops on each of eight dates in 1979 using a total of 4,142 fields in southern Fresno County, California. The 21 crops, which together account for over 70 percent of the agricultural acreage in the southern San Joaquin Valley, were analyzed to quantify the spectral separability, defined as omission error, between all pairs of crops. On each date the fields were segregated into six groups based on the mean value of the MSS7/MSS5 ratio, which is correlated with green biomass. Discriminant analysis was run on each group on each date. The resulting contingency tables offer information that can be profitably used in conjunction with crop calendars to pick the best dates for a classification. The tables show expected percent correct classification and error rates for all the crops. The patterns in the contingency tables show that the percent correct classification for crops generally increases with the amount of greenness in the fields being classified. However, there are exceptions to this general rule, notably grain.
An independent verification of the CARE 3 mathematical model and computer code was conducted and reported in NASA Contractor Report 166096, Review and Verification of CARE 3 Mathematical Model and Code: Interim Report. The study uncovered some implementation errors that were corrected and are reported in this document. The corrected CARE 3 program is called version 4. Thus the document, correction. improvement, and model verification of CARE 3, version 3 was written in April 1984. It is being published now as it has been determined to contain a more accurate representation of CARE 3 than the preceding document of April 1983. This edition supercedes NASA-CR-166122 entitled, 'Correction and Improvement of CARE 3,' version 3, April 1983.
The SEAHT program is designed to process multiple passes of altimeter data with intersecting ground tracks, with the estimation of corrections for orbital errors to each pass such that the data has the best overall agreement at the crossover points. Orbit error for each pass is modeled as a polynomial in time, with optional orders of 0, 1, or 2. One or more passes may be constrained in the adjustment process, thus allowing passes with the best orbits to provide the overall level and orientation of the estimated sea surface heights. Intersections which disagree by more than an input edit level are not used in the error parameter estimation. In the program implementation, passes are grouped into South-North passes and North-South passes, with the North-South passes partitioned out for the estimation of orbit error parameters. Computer core utilization is thus dependent on the number of parameters estimated for the set of South-North arcs, but is independent on the number of North-South passes. Estimated corrections for each pass are applied to the data at its input data rate and an output tape is written which contains the corrected data.
The Q-weak collaboration has made the first direct measurement of the proton's weak charge at Jefferson Lab to tightly constrain new, parity-violating electron-quark interactions at the multi-TeV scale. Our experiment measured the ?200 ppb parity-violating asymmetry in the scattering of longitudinally polarized electrons from an unpolarized liquid hydrogen target at a luminosity of 1.8x10^39. Careful setup of the injector helped to suppress helicity correlated differences in beam position, angle, and energy which create false asymmetries due to changes in detector acceptance and the kinematic dependences of the e+p cross section. Measured differences in beam position were consistent among all 6 beam position monitors in front of the target, with a precision of ?1 nanometer. The size of corrections depends on the helicity correlated beam parameter differences, and averaged about 35 ppb per shift of data. The data were regressed using 10 different sets of Independent Variables. These were expected to give similar corrections yet showed differences at the +-10 ppb level. In this presentation I'll summarize how these different schemes have not only been helpful in understanding the sensitivities of the main detector but in assigning errors to the corrections.
Integrated and spectral error energetics of the GLAS General circulation model are compared with observations for periods in January 1975, 1976, and 1977. For two cases the model shows significant skill in predicting integrated energetics quantities out to two weeks, and for all three cases, the integrated monthly mean energetics show qualitative improvements over previous versions of the model in eddy kinetic energy and barotropic conversions. Fundamental difficulties remain with leakage of energy to the stratospheric level, particularly above strong initial jet streams associated in part with regions of steep terrain. The spectral error growth study represents the first comparison of general circulation model spectral energetics predictions with the corresponding observational spectra on a day by day basis. The major conclusion is that eddy kinetics energy can be correct while significant errors occur in the kinetic energy of wavenumber 3. Both the model and observations show evidence of single wavenumber dominance in eddy kinetic energy and the correlation of spectral kinetics and potential energy.
Error analysis of archived total 03 from total ozone mapping spectrometer (TOMS) (version 6) presented. Daily total 03 maps for the tropics, from the period October 6-21, 1992, are derived from TOMS radiances following correction for these errors. These daily maps, averaged together, show a wavelike feature, which is observed in all latitude bands, underlying sharp peaks which occur at different longitudes depending on the latitude. The wave pattern is used to derive both time-averaged stratospheric and tropospheric 03 fields. The nature of the wave pattern (stratospheric or tropospheric) cannot be determined with certainty due to missing data (no Pacific sondes, no lower stratospheric Stratospheric Aerosol and Gas Experiment (SAGE) ozone for 18 months after the Mt. Pinatubo eruption) and significant uncertainties in the corroborative satellite record in the lower stratosphere (solar backscattered ultraviolet (SBUV), microwave limb sounder (MLS)). However, the time- averaged tropospheric ozone field, based on the assumption that the wave feature is stratospheric, agrees within 10% with ultraviolet differential absorption laser Transport and Atmospheric Chemistry near the Equator-Atlantic) (TRACE A) 03 measurements from the DC-8 and with ozonesonde measurements over Brazzaville, Congo, Ascension Island, and Natal, Brazil, for the period October 6-21, 1992. The derived background (nonpolluted) Indian Ocean tropospheric ozone amount, 26 Dobson units (DU), agrees with the cleanest African ozonesonde profiles for September-October 1992. The assumption of a totally tropospheric wave (flat stratosphere) gives 38 DU above the western Indian Ocean and 15-40% disagreements with the sondes. Tropospheric column 03 is high from South America to Africa, owing to interaction of dynamics with biomass burning emissions. Comparison with fire distributions from advanced very high resolution radiometer (AVHHR) during October 1992 suggests that tropospheric 03 produced from biomass burning in South America and Africa dominates the 03 budget in the tropical southern hemisphere during the study period.
This report refers to or contains K g values for glasses LAWA44, LAWB45 and LAWC22 affected by calculations errors as identified by Papathanassiu et al. (2011). The corrected K g values are reported in an erratum included in the revised version of the original report. The revised report can be referenced as follows: Pierce E. M. et al. (2004) Waste Form Release Data Package for the 2005 Integrated Disposal Facility Performance Assessment. PNNL-14805 Rev. 0 Erratum. Pacific Northwest National Laboratory, Richland, WA, USA.
This report refers to or contains K g values for glasses LAWA44, LAWB45 and LAWC22 affected by calculations errors as identified by Papathanassiu et al. (2011). The corrected K g values are reported in an erratum included in the revised version of the original report. The revised report can be referenced as follows: Pierce E. M. et al. (2004) Waste Form Release Data Package for the 2005 Integrated Disposal Facility Performance Assessment. PNNL-14805 Rev. 0 Erratum. Pacific Northwest National Laboratory, Richland, WA, USA.
This report refers to or contains K g values for glasses LAWA44, LAWB45 and LAWC22 affected by calculations errors as identified by Papathanassiu et al. (2011) The corrected K g values are reported in an erratum included in the revised version of the original report. The revised report can be referenced as follows: Pierce E. M. et al. (2004) Waste Form Release Data Package for the 2005 Integrated Disposal Facility Performance Assessment. PNNL-14805 Rev. 0 Erratum. Pacific Northwest National Laboratory, Richland, WA, USA.
The altimetry bias in GLAS (Geoscience Laser Altimeter System) or other laser altimeters resulting from atmospheric multiple scattering is studied in relationship to current knowledge of cloud properties over the Antarctic Plateau. Estimates of seasonal and interannual changes in the bias are presented. Results show the bias in altitude from multiple scattering in clouds would be a significant error source without correction. The selective use of low optical depth clouds or cloudfree observations, as well as improved analysis of the return pulse such as by the Gaussian method used here, are necessary to minimize the surface altitude errors. The magnitude of the bias is affected by variations in cloud height, cloud effective particle size and optical depth. Interannual variations in these properties as well as in cloud cover fraction could lead to significant year-to-year variations in the altitude bias. Although cloud-free observations reduce biases in surface elevation measurements from space, over Antarctica these may often include near-surface blowing snow, also a source of scattering-induced delay. With careful selection and analysis of data, laser altimetry specifications can be met.
This report presents the magnetic tuning results and the final magnetic performance of the In-Vacuum Undulator 2.8 m S/N A13006. In order to improve the magnetic field quality in term of phase error and to reduce the field integral errors as well as the multipole field errors, different inhouse corrections have been carried out with Neomax's engineers. Although the vendor’s data were found satisfactory, the discrepancy between their magnetic field measurement results and BNL’s was not acceptable both for field integrals and phase error [1].
A control system architecture for an actively controlled segmented reflector is described along with a design realization for achieving precision alignment of reflector panels. Performance requirements are derived in part from the Large Deployable Reflector, which is a representative mission, and error allocations are made which consider mirror panel surface errors, position measurement and figure estimation, and position control of both quasi-static and dynamic disturbances. The design uses multiple wavelength interferometric edge sensors and voice coil actuators in conjunction with a hybrid control strategy to correct panel position errors. A unit cell shown to be central to the concept is analyzed. The cell integrates the sensing, actuation, and mechanical functions of a control module together with a reflector panel to form a unitized assembly.