Number Of Measurement Stations Research Articles

Greenhouse gas network design using backward Lagrangian particle dispersion modelling − Part 1: Methodology and Australian test case

Abstract. This paper describes the generation of optimal atmospheric measurement networks for determining carbon dioxide fluxes over Australia using inverse methods. A Lagrangian particle dispersion model is used in reverse mode together with a Bayesian inverse modelling framework to calculate the relationship between weekly surface fluxes, comprising contributions from the biosphere and fossil fuel combustion, and hourly concentration observations for the Australian continent. Meteorological driving fields are provided by the regional version of the Australian Community Climate and Earth System Simulator (ACCESS) at 12 km resolution at an hourly timescale. Prior uncertainties are derived on a weekly timescale for biosphere fluxes and fossil fuel emissions from high-resolution model runs using the Community Atmosphere Biosphere Land Exchange (CABLE) model and the Fossil Fuel Data Assimilation System (FFDAS) respectively. The influence from outside the modelled domain is investigated, but proves to be negligible for the network design. Existing ground-based measurement stations in Australia are assessed in terms of their ability to constrain local flux estimates from the land. We find that the six stations that are currently operational are already able to reduce the uncertainties on surface flux estimates by about 30%. A candidate list of 59 stations is generated based on logistic constraints and an incremental optimisation scheme is used to extend the network of existing stations. In order to achieve an uncertainty reduction of about 50%, we need to double the number of measurement stations in Australia. Assuming equal data uncertainties for all sites, new stations would be mainly located in the northern and eastern part of the continent.

Modelling and optimization of novel laser multilateration schemes for high-precision applications

A computer simulation technique has been developed to investigate the performance of established and novel laser multilateration schemes. The models focus on analysing the impact of variations in system configuration, optimizing the system self-calibration process and evaluating the volumetric measurement error propagation. Careful optimization of the system configuration and self-calibration planning significantly reduces the uncertainty of system self-calibration and measurement. Comparing the simulated performances of multilateration systems with different number of measurement stations, e.g. four, five and six stations, quantifies the performance improvement obtained by increasing the number of stations. Specific recommendations for optimization of multilateration set-ups and measurement plans and for minimizing measurement uncertainty are set out in the paper. A novel, sequential multilateration set-up for high-precision calibration of small artefacts has been defined, and measurements have been made to support the analysis presented here.

Theory for the estimation of structural vibration parameters from incomplete data

N system identification and modal analysis, the investigator is often faced with the problem of having to construct a model of finite order to represent a continuous vibrating structure. In other words, he is charged with obtaining a reduced-order model which, by definition, must be limited in its representation of the continuous system to a finite range of frequencies. The quality of the reduced-order model will be contingent upon the completeness of the measured data: a common cause of incompletenes s occurs when the number of modes excited in the test is fewer than the number of measurement stations (i.e., less than the order of the identified model). When it is required to construct a structural model (in terms of mass, stiffness, and damping parameters) from measured data, it is vital that such a model should in some sense be physically meaningful. The problems associated with the construction of reduced-order structural models from incomplete data have been highlighted by Berman and Flannelly1 and Herman.2 It is obvious that a reduced-order model cannot possibly reproduce the behavior of a continuous structure across an infinite range of frequencies. In the real structure the mass is distributed, whereas in the model the mass is discretized. In the finite-element method, consistent mass and stiffness matrices may be formulated by the minimization of an energy functional and, for this reason, such matrices may be considered to possess physical meaning. In reality, finite-element models do not exactly represent the dynamics of vibrating structures because they are incapable of dealing with boundary conditions such as imperfect hinges or flexibility in welded joints. It is then necessary to modify the finite-element model slightly in order that it should replicate the observed dynamics of the vibrating structure in a required range of frequencies. Methods have been developed for the estimation of structural parameters based on measured modal data. Chen and Garba3 presented a least-squares technique that may be applied iteratively together with matrix perturbation (for computation of the Jacobian matrix and reanalysis of the eigenvalues and eigenvectors) until the measured and computed eigendata are sufficiently in agreement. However, modal methods are known to have difficulty in treating closely spaced modes and can only deal with out-of-range modes by the addition of residual flexibilities. In this Note, a method is presented that does not depend on a modal decomposition but uses measured frequency responses to construct a structural model that varies minimally from an initial finite-element representation. A model constructed by this method is thus physically meaningful in the sense that a finite-element model is meaningful. A simple problem is contrived to illustrate an extreme case of incomplete data, which is solved using a continuous-fre quency- domain, least-squares filter.4 Using this approach, the Jacobian matrix is a constant matrix. Other problems associated with measure

Borehole Position Uncertainty - Analysis of Measuring Methods and Derivation of Systematic Error Model

Summary This article presents a new model for describing well-position uncertainties. An analysis for surveying position uncertainties. An analysis for surveying errors is given that demonstrates that they are mainlysystematic rather than random. The error model, based on systematic errors, compares well withpractical experience. A graph is presented that shows practical experience. A graph is presented that shows typical lateral position uncertainties of deviated wellsfor various kinds of surveys. Introduction During the past 10 years, the uncertainties involvedin determining the true course of a borehole havebecome a cause for concern. The more deviated anddeeper the holes were drilled, the more often were theoperators faced with inexplicable differences betweenvarious surveys made in the same well. As early as 1971, Truex mentioned that possible lateral positionerrors of highly inclined wells could be up to 30 m ata depth of only 2000 m. Two years before that, Walstrom et al. introduced the ellipse-of-uncertainty concept to describe the positionuncertainty, which can be expected with various surveymethods. Experience, however, has shown that theellipse calculated by this random error model isunrealistically small, which is thought to be duemainly to the nature of the statistical error modelused.The essential differences between the existingrandom error model and the model proposed in thisarticle are illustrated by the following simplifiedexample. Consider the straight and inclined part of awell with these directional characteristics: total depthalong hole (AHD or DAH) 2500 m, surveyed at100 stations at 25-m intervals, and all having aninclination of I Delta I = 30 0.5 and an azimuthof A Delta A 90 1.The bottomhole position of this well in north, east, and vertical coordinates easily is found as N = D AH sin I cos A = 0, E = D AH sin I sin A = 1250 m, andV = D AH cos I = 2165 m. The position uncertainty of the bottom of thiswell, according to the error model presented in thisarticle, follows straightforwardly from theassumption that the measuring errors at all 100 stations havethe same magnitude (they are correlated fully).Hence, by simple trigonometry, as sketched in Fig. 1, and In the random error model, however, it is assumedthat the measuring errors vary randomly from onestation to another, which gives them a tendency tocompensate one another. This randomness of themeasuring errors causes the position uncertainty tobe smaller than the former values - in our example, by a factor equal to the square root of the number ofmeasuring stations, which is 100 = 10. JPT P. 2339