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Quality control measures for aviation gasoline, and some of the differences between quality control on avgas and mogas are discussed. One thing to keep in mind is that with motor gasoline you can always pull off to the side of the road. It's not so easy to do in an airplane. Consequently, there are reasons for having the tight specifications and the tight quality control measures on avgas as compared to motor gasoline.
El Laboratorio Nacional de Energía Renovable (NREL) ha sido contratado por el Programa de Asistencia para la Climatización (WAP) del Departamento de Energía de EE.UU. (DOE) para desarrollar y mantener los recursos en el marco del proyecto de Directrices para Profesionales de la Energía Doméstica (GHEP). El objetivo del proyecto GHEP es aumentar la calidad del trabajo realizado para la remodelación energética residencial en Estados Unidos a través de WAP y otros programas de remodelación residencial. Para cumplir el objetivo de “Establecer certificaciones nacionales de la mano de obra y normas de formación”, el DOE encargó al NREL el desarrollo de recursos GHEP, incluido un conjunto de certificaciones avanzadas, basadas en aptitud, para el personal de los profesionales de la energía doméstica (HEP). Desde 2010, el NREL ha reclutado voluntarios expertos en la materia de WAP y de la industria del mantenimiento del hogar para que formen parte de comités que desarrollen y actualicen programas de certificación y sus análisis de tareas (JTA) necesarios como base de programas de certificación y formación estandarizados. Las certificaciones HEP apoyan al WAP y al sector del mantenimiento de viviendas residenciales en general mediante el proceso de acreditación y el desarrollo de JTA definidos para auditores energéticos (EA) e inspectores de control de calidad (QCI). Este informe es un resumen de las actualizaciones más recientes (2022) del JTA del QCI. [The National Renewable Energy Laboratory (NREL) is contracted by the U.S. Department of Energy (DOE) Weatherization Assistance Program (WAP) to develop and maintain the resources under the Guidelines for Home Energy Professionals (GHEP) project. The purpose of the GHEP project is to increase the quality of work conducted for residential energy retrofits in the United States through the WAP network and other residential retrofit programs. This is the Spanish translation of NREL/TP-7A40-85789.]
Reliability and quality control requirements for manned space flight - designs, procedures, and schedules
The primary function of the TRMM Ground Validation (GV) Program is to create GV rainfall products that provide basic validation of satellite-derived precipitation measurements for select primary sites. A fundamental and extremely important step in creating high-quality GV products is radar data quality control. Quality control (QC) processing of TRMM GV radar data is based on some automated procedures, but the current QC algorithm is not fully operational and requires significant human interaction to assure satisfactory results. Moreover, the TRMM GV QC algorithm, even with continuous manual tuning, still can not completely remove all types of spurious echoes. In an attempt to improve the current operational radar data QC procedures of the TRMM GV effort, an intercomparison of several QC algorithms has been conducted. This presentation will demonstrate how various radar data QC algorithms affect accumulated radar rainfall products. In all, six different QC algorithms will be applied to two months of WSR-88D radar data from Melbourne, Florida. Daily, five-day, and monthly accumulated radar rainfall maps will be produced for each quality-controlled data set. The QC algorithms will be evaluated and compared based on their ability to remove spurious echoes without removing significant precipitation. Strengths and weaknesses of each algorithm will be assessed based on, their abilit to mitigate both erroneous additions and reductions in rainfall accumulation from spurious echo contamination and true precipitation removal, respectively. Contamination from individual spurious echo categories will be quantified to further diagnose the abilities of each radar QC algorithm. Finally, a cost-benefit analysis will be conducted to determine if a more automated QC algorithm is a viable alternative to the current, labor-intensive QC algorithm employed by TRMM GV.
Quality control techniques and separable connector reliability for use at cryogenic temperatures
This paper describes the Quality Control Program at Daytona Beach Junior College, including course descriptions. The program in quality control required communication between the college and the American Society for Quality Control (ASQC). The college has machinery established for certification of the learning process, and the society has the source of teachers who are competent in the technical field and who are the employers of the educational products. The associate degree for quality control does not have a fixed program, which can serve all needs, any more than all engineering degrees have identical programs. The main ideas which would be common to all quality control programs are the concept of economic control of a repetitive process and the concept of developing individual potentialities into individuals who are needed and productive.
This work was aimed at exploring means for utilizing computer technology in quality inspection and evaluation. Inspection of metallic welds was selected as the main application for this development and primary emphasis was placed on visual inspection, as opposed to other inspection methods, such as radiographic techniques. Emphasis was placed on methodologies with the potential for use in real-time quality control systems. Because quality evaluation is somewhat subjective, despite various efforts to classify discontinuities and standardize inspection methods, the task of using a computer for both inspection and evaluation was not trivial. The work started out with a review of the various inspection techniques that are used for quality control in welding. Among other observations from this review was the finding that most weld defects result in abnormalities that may be seen by visual inspection. This supports the approach of emphasizing visual inspection for this work. Quality control consists of two phases: (1) identification of weld discontinuities (some of which may be severe enough to be classified as defects), and (2) assessment or evaluation of the weld based on the observed discontinuities. Usually the latter phase results in a pass/fail judgement for the inspected piece. It is the conclusion of this work that the first of the above tasks, identification of discontinuities, is the most challenging one. It calls for sophisticated image processing and image analysis techniques, and frequently ad hoc methods have to be developed to identify specific features in the weld image. The difficulty of this task is generally not due to limited computing power. In most cases it was found that a modest personal computer or workstation could carry out most computations in a reasonably short time period. Rather, the algorithms and methods necessary for identifying weld discontinuities were in some cases limited. The fact that specific techniques were finally developed and successfully demosntrated to work illustrates that the general approach taken here appears to be promising for commercial development of computerized quality inspection systems. Inspection based on these techniques may be used to supplement or substitute more elaborate inspection methods, such as x-ray inspections.
The BSRN data have been used in validation of the satellite-based GEWEX SRB data, and the recent Release 4.0-IP data cover the 34-year period from July 1983 to June 2017. Here we focus on the validation of the shortwave monthly means from 1992 to 2017 against the BSRN Global 1. We first perform the BSRN-recommended quality-control on the original data. We then calculate the monthly-hourly means from which we calculate the monthly means. Twenty-four (24) monthly-hourly means are required to calculate a monthly mean. To guarantee the quality of the monthly mean, we also require that 95% or more hourly means be available in order for a monthly-hourly mean to be calculated. Since the above quality-control significantly reduces the number of available monthly means, we are curious how many monthly means we would get if we relax the quality-control, and how the resulting monthly means would compare with the GEWEX SRB data. In a version we experimented, only constant upper and lower bounds are imposed on various fluxes, and the original data are largely retained. We found that many more monthly means would be available, and the overall bias and RMS would not be significantly different from that of the strict quality-control. With the strict quality-control, if we let Avail (minimum percentage of available hourly means required for a monthly-hourly mean to be computed) change from 3.2% to 95%, the Bias/RMS/N change from -2.77/20.56/9756 to -0.35/13.41/4581, where N is the number of available monthly means, and Bias and RMS are in the unit of W/sq. m. On the other hand, with the lax quality-control, when Avail changes from 3.2% to 95%, the Bias/RMS/N change from -0.82/19.53/9819 to -0.21/14.41/5875. South Pole (SPO) is one of the sites we are particularly interested in. With the strict quality-control, and when Avail changes from 3.2% to 95%, the Bias/RMS/N change from 5.89/15.85/283 to 1.23/5.52/133; with the lax quality-control, the Bias/RMS/N change from 1.17/11.21/284 to 1.88/7.9/177.
Reliability and quality control in manned spacecraft
The AIRS Science Team Version 5 retrieval algorithm has been finalized and is now operational at the Goddard DAAC in the processing (and reprocessing) of all AlRS data. Version 5 contains accurate case-by-case error estimates for most derived products, which are also used for quality control. We have conducted forecast impact experiments assimilating AlRS quality controlled temperature profiles using the NASA GEOS-5 data assimilation system, consisting of the NCEP GSI analysis coupled with the NASA FVGCM. Assimilation of quality controlled temperature profiles resulted in significantly improved forecast skill in both the Northern Hemisphere and Southern Hemisphere Extra-Tropics, compared to that obtained from analyses obtained when all data used operationally by NCEP except for AlRS data is assimilated. Experiments using different Quality Control thresholds for assimilation of AlRS temperature retrievals showed that a medium quality control threshold performed better than a tighter threshold, which provided better overall sounding accuracy; or a looser threshold, which provided better spatial coverage of accepted soundings. We are conducting more experiments to further optimize this balance of spatial coverage and sounding accuracy from the data assimilation perspective. In all cases, temperature soundings were assimilated well below cloud level in partially cloudy cases. The positive impact of assimilating AlRS derived atmospheric temperatures all but vanished when only AIRS stratospheric temperatures were assimilated. Forecast skill resulting from assimilation of AlRS radiances uncontaminated by clouds, instead of AlRS temperature soundings, was only slightly better than that resulting from assimilation of only stratospheric AlRS temperatures. This reduction in forecast skill is most likely the result of significant loss of tropospheric information when only AIRS radiances unaffected by clouds are used in the data assimilation process.
The Nimbus 7 ERB MATRIX Tape is a computer program in which radiances and irradiances are converted into fluxes which are used to compute the basic scientific output parameters, emitted flux, albedo, and net radiation. They are spatially averaged and presented as time averages over one-day, six-day, and monthly periods. MATRIX data for the period November 16, 1978 through October 31, 1979 are presented. Described are the Earth Radiation Budget experiment, the Science Quality Control Report, Items checked by the MATRIX Science Quality Control Program, and Science Quality Control Data Analysis Report. Additional material from the detailed scientific quality control of the tapes which may be very useful to a user of the MATRIX tapes is included. Known errors and data problems and some suggestions on how to use the data for further climatologic and atmospheric physics studies are also discussed.
This document provides five brief reports that address several quality control procedures under the auspices of the Calibration and Validation Element (CVE) within the Sea-viewing Wide Field-of-view Sensor (SeaWiFS) Project. Chapter 1 describes analyses of the 32 sensor engineering telemetry streams. Anomalies in any of the values may impact sensor performance in direct or indirect ways. The analyses are primarily examinations of parameter time series combined with statistical methods such as auto- and cross-correlation functions. Chapter 2 describes how the various onboard (solar and lunar) and vicarious (in situ) calibration data will be analyzed to quantify sensor degradation, if present. The analyses also include methods for detecting the influence of charged particles on sensor performance such as might be expected in the South Atlantic Anomaly (SAA). Chapter 3 discusses the quality control of the ancillary environmental data that are routinely received from other agencies or projects which are used in the atmospheric correction algorithm (total ozone, surface wind velocity, and surface pressure; surface relative humidity is also obtained, but is not used in the initial operational algorithm). Chapter 4 explains the procedures for screening level-, level-2, and level-3 products. These quality control operations incorporate both automated and interactive procedures which check for file format errors (all levels), navigation offsets (level-1), mask and flag performance (level-2), and product anomalies (all levels). Finally, Chapter 5 discusses the match-up data set development for comparing SeaWiFS level-2 derived products with in situ observations, as well as the subsequent outlier analyses that will be used for evaluating error sources.
For the first time, a problem of the meteorological observation quality control (QC) was formulated by L.S. Gandin at the Main Geophysical Observatory in the 70's. Later in 1988 L.S. Gandin began adapting his ideas in complex quality control (CQC) to the operational environment at the National Centers for Environmental Prediction. The CQC was first applied by L.S.Gandin and his colleagues to detection and correction of errors in rawinsonde heights and temperatures using a complex of hydrostatic residuals.Later, a full complex of residuals, vertical and horizontal optimal interpolations and baseline checks were added for the checking and correction of a wide range of meteorological variables. some other of Gandin's ideas were applied and substantially developed at other meteorological centers. A new statistical QC was recently implemented in the Goddard Data Assimilation System. The central component of any quality control is a buddy check which is a test of individual suspect observations against available nearby non-suspect observations. A novel feature of this test is that the error variances which are used for QC decision are re-estimated on-line. As a result, the allowed tolerances for suspect observations can depend on local atmospheric conditions. The system is then better able to accept extreme values observed in deep cyclones, jet streams and so on. The basic statements of this adaptive buddy check are described. Some results of the on-line QC including moisture QC are presented.
We have previously conducted a number of data assimilation experiments using AIRS Version-5 quality controlled temperature profiles as a step toward finding an optimum balance of spatial coverage and sounding accuracy with regard to improving forecast skill. The data assimilation and forecast system we used was the Goddard Earth Observing System Model , Version-5 (GEOS-5) Data Assimilation System (DAS), which represents a combination of the NASA GEOS-5 forecast model with the National Centers for Environmental Prediction (NCEP) operational Grid Point Statistical Interpolation (GSI) global analysis scheme. All analyses and forecasts were run at a 0.5deg x 0.625deg spatial resolution. Data assimilation experiments were conducted in four different seasons, each in a different year. Three different sets of data assimilation experiments were run during each time period: Control; AIRS T(p); and AIRS Radiance. In the "Control" analysis, all the data used operationally by NCEP was assimilated, but no AIRS data was assimilated. Radiances from the Aqua AMSU-A instrument were also assimilated operationally by NCEP and are included in the "Control". The AIRS Radiance assimilation adds AIRS observed radiance observations for a select set of channels to the data set being assimilated, as done operationally by NCEP. In the AIRS T(p) assimilation, all information used in the Control was assimilated as well as Quality Controlled AIRS Version-5 temperature profiles, i.e., AIRS T(p) information was substituted for AIRS radiance information. The AIRS Version-5 temperature profiles were presented to the GSI analysis as rawinsonde profiles, assimilated down to a case-by-case appropriate pressure level p(sub best) determined using the Quality Control procedure. Version-5 also determines case-by-case, level-by-level error estimates of the temperature profiles, which were used as the uncertainty of each temperature measurement. These experiments using GEOS-5 have shown that forecasts resulting from analyses using the AIRS T(p) assimilation system were superior to those from the Radiance assimilation system, both with regard to global 7 day forecast skill and also the ability to predict storm tracks and intensity.
Quality control in processing of telemetry data
AIRS was launched on EOS Aqua on May 4, 2002, together with AMSU-A and HSB, to form a next generation polar orbiting infrared and microwave atmospheric sounding system. The primary products of AIRS/AMSU-A are twice daily global fields of atmospheric temperature-humidity profiles, ozone profiles, sea/land surface skin temperature, and cloud related parameters including OLR. Also included are the clear column radiances used to derive these products which are representative of the radiances AIRS would have seen if there were no clouds in the field of view. All products also have error estimates. The sounding goals of AIRS are to produce 1 km tropospheric layer mean temperatures with an rms error of 1K, and layer precipitable water with an rms error of 20 percent, in cases with up to 90 percent effective cloud cover. The products are designed for data assimilation purposes for the improvement of numerical weather prediction, as well as for the study of climate and meteorological processes. With regard to data assimilation, one can use either the products themselves or the clear column radiances from which the products were derived. The AIRS Version 5 retrieval algorithm is now being used operationally at the Goddard DISC in the routine generation of geophysical parameters derived from AIRS/AMSU data. A major innovation in Version 5 is the ability to generate case-by-case level-by-level error estimates for retrieved quantities and clear column radiances, and the use of these error estimates for Quality Control. The temperature profile error estimates are used to determine a case-by-case characteristic pressure pbest, down to which the profile is considered acceptable for data assimilation purposes. The characteristic pressure p(sub best) is determined by comparing the case dependent error estimate (delta)T(p) to the threshold values (Delta)T(p). The AIRS Version 5 data set provides error estimates of T(p) at all levels, and also profile dependent values of pbest based on use of a Standard profile dependent threshold (Delta)T(p). These Standard thresholds were designed as a compromise between optimal use for data assimilation purposes, which requires highest accuracy (tighter Quality Control), and climate purposes, which requires more spatial coverage (looser Quality Control). Subsequent research using Version 5 sounding and error estimates showed that tighter Quality Control performs better for data assimilation proposes, while looser Quality Control better spatial coverage) performs better for climate purposes. We conducted a number of data assimilation experiments using the NASA GEOS-5 Data Assimilation System as a step toward finding an optimum balance of spatial coverage and sounding accuracy with regard to improving forecast skill. The model was run at a horizontal resolution of 0.5 degree latitude x 0.67 degree longitude with 72 vertical levels. These experiments were run during four different seasons, each using a different year. The AIRS temperature profiles were presented to the GEOS-5 analysis as rawinsonde profiles, and the profile error estimates (delta)T(p) were used as the uncertainty for each measurement in the data assimilation process.
High Pressure Liquid Chromatography (HPLC) as a quality control tool for polyimide resins and prepregs are presented. A data base to help establish accept/reject criteria for these materials was developed. This work is intended to supplement, not replace, standard quality control tests normally conducted on incoming resins and prepregs. To help achieve these objectives, the HPLC separation of LARC-160 polyimide precursor resin was characterized. Room temperature resin aging effects were studied. Graphite reinforced composites made from fresh and aged resin were fabricated and tested to determine if changes observed by HPLC were significant.
A new statistical quality control algorithm was recently implemented in the Goddard Earth Observing System Data Assimilation System (GEOS DAS). The final step in the algorithm consists of an adaptive buddy check that either accepts or rejects outlier observations based on a local statistical analysis of nearby data. A basic assumption in any such test is that the observed field is spatially coherent, in the sense that nearby data can be expected to confirm each other. However, the buddy check resulted in excessive rejection of moisture data, especially during the Northern Hemisphere summer. The analysis moisture variable in GEOS DAS is water vapor mixing ratio. Observational evidence shows that the distribution of mixing ratio errors is far from normal. Furthermore, spatial correlations among mixing ratio errors are highly anisotropic and difficult to identify. Both factors contribute to the poor performance of the statistical quality control algorithm. To alleviate the problem, we applied the buddy check to relative humidity data instead. This variable explicitly depends on temperature and therefore exhibits a much greater spatial coherence. As a result, reject rates of moisture data are much more reasonable and homogeneous in time and space.