Spatial Density Of Measurements Research Articles

Reliability of smart noise pollution map

AbstractThe problem of noise pollution in Baghdad, the capital city of Iraq, is getting worse every day as a result of the increased volume of traffic. This presents a considerable risk, particularly on the main roads that connect densely populated neighborhoods such as the Al-Sadr City district with the central neighborhoods of the capital. In order to inform decision-makers in urban development and environmental policy about the high values of noise pollution that require remediation and regulation, noise maps are produced. However, two fundamental problems are generally faced in creating a more reliable noise map in the shortest possible time: the excessive time requirements for measuring noise and determining the method of map creation. Therefore, the role of geographic information system (GIS) software in producing noise maps is evident due to the difficulty of increasing the spatial density of measurements and integrating them with spatial information. Hence, an appropriate interpolation method is required. In this article, Moran’sIindex was calculated to assess the spatial autocorrelation of measured traffic noise points. A comparison was made between the Smart Map Plugin ordinary kriging (OK) and the inverse distance weighting (IDW) deterministic interpolation method to determine the best method for producing noise maps for the main entrance and exit roads of Al-Sadr City. The noise values were modeled using the best-performing method. Furthermore, the predictive raster data are displayed in the spatial context as a starting point and reference for identifying and understanding the levels of traffic noise in the selected study area. The locations of selected points for measuring traffic noise values were determined in an organized and homogeneous manner, where noise points for the main entrance and exit roads were opposite each other, and the distance between consecutive noise points on each road was 100 m. Traffic noise measurements were carried out at each selected point using the SVAN977 sound and vibration analyzer. At each measurement point, three noise values (LAeq, Max, Min) were obtained during the three peak times, 7–9 AM, 12–2 PM, and 4–6 PM. QGIS software was used to compare the two interpolation methods, with its strength lying in the use of plugins that facilitate spatial analysis, processing tools, and algorithms. The Smart Map Plugin provided facilities to choose the appropriate semi-variogram in the OK interpolation method. The root mean square error was used to compare the two interpolation methods in order to determine the most suitable method for producing traffic noise maps in the study area. The results indicated that the Smart Map Plugin using OK outperformed the IDW method, as spatial distribution pattern and homogeneity affect the accuracy of interpolation. Moreover, based on the analysis of the three noise attributes (LAeq, Max, Min), the performance of the Smart Map Plugin (OK) was found to be better than IDW when the Moran’sIvalue was high.

Analysis and Prediction of Pothole Formation Rate Using Spatial Density Measurements and Pavement Condition Indicators

Potholes represent one of the dangerous distress types on roads. They form as a result of various factors, including water ingress, freeze–thaw, and pavement condition deterioration. In the UK, 1.7 million potholes were repaired in 2021; this critical number causes significant economic, social, and environmental impacts; there is no tool able to predict the number of potholes that might appear in a road network. This study aims to analyze pothole formation and its relationship with other distress types and their severity, and to develop a simple tool able to predict the number of potholes that might appear in a road network based on the network condition. Significant pothole data from the road network of Greater London between 2017 and 2020, in addition to surface distress data, were used in this study. ‘Spatial density’ and ‘join’ tools embedded in ArcGIS were used to correlate pothole spatial density (PSD) with the road condition surrounding the potholes. This analysis was then used to calculate PSD as a function of different condition indicators, such as road condition index and crack intensity, allowing prediction of the number of potholes based on the length and condition of the sections being analyzed. The results demonstrate that potholes are significantly concentrated in sections with deteriorated conditions. They also show that it is possible to predict the number of potholes using PSD with reasonable accuracy. Lastly, it was found that sections with low crossfall are more susceptible to pothole formation, presumably because of water ponding and consequent damage.

Monitoring deformations of infrastructure networks: A fully automated GIS integration and analysis of InSAR time-series

Ageing stock and extreme weather events pose a threat to the safety of infrastructure networks. In most countries, funding allocated to infrastructure management is insufficient to perform systematic inspections over large transport networks. As a result, early signs of distress can develop unnoticed, potentially leading to catastrophic structural failures. Over the past 20 years, a wealth of literature has demonstrated the capability of satellite-based Synthetic Aperture Radar Interferometry (InSAR) to accurately detect surface deformations of different types of assets. Thanks to the high accuracy and spatial density of measurements, and a short revisit time, space-borne remote-sensing techniques have the potential to provide a cost-effective and near real-time monitoring tool. Whilst InSAR techniques offer an effective approach for structural health monitoring, they also provide a large amount of data. For civil engineering procedures, these need to be analysed in combination with large infrastructure inventories. Over a regional scale, the manual extraction of InSAR-derived displacements from individual assets is extremely time-consuming and an automated integration of the two datasets is essential to effectively assess infrastructure systems. This paper presents a new methodology based on the fully automated integration of InSAR-based measurements and Geographic Information System-infrastructure inventories to detect potential warnings over extensive transport networks. A Sentinel dataset from 2016 to 2019 is used to analyse the Los Angeles highway and freeway network, while the Italian motorway network is evaluated by using open access ERS/Envisat datasets between 1992 and 2010, COSMO-SkyMed datasets between 2008 and 2014 and Sentinel datasets between 2014 and 2020. To demonstrate the flexibility of the proposed methodology to different SAR sensors and infrastructure classes, the analysis of bridges and viaducts in the two test areas is also performed. The outcomes highlight the potential of the proposed methodology to be integrated into structural health monitoring systems and improve current procedures for transport network management.

Spatial Rainfall Variability in Urban Environments—High-Density Precipitation Measurements on a City-Scale

Rainfall runoff models are frequently used for design processes for urban infrastructure. The most sensitive input for these models is precipitation data. Therefore, it is crucial to account for temporal and spatial variability of rainfall events as accurately as possible to avoid misleading simulation results. This paper aims to show the significant errors that can occur by using rainfall measurement resolutions in urban environments that are too coarse. We analyzed the spatial variability of rainfall events from two years with the validated data of 22 rain gauges spread out over an urban catchment of 125 km2. By looking at the interstation correlation of the rain gauges for different classes of rainfall intensities, we found that rainfall events with low and intermediate intensities show a good interstation correlation. However, the correlation drops significantly for heavy rainfall events suggesting higher spatial variability for more intense rainstorms. Further, we analyzed the possible deviation from the spatial rainfall interpolation that uses all available rain gauges when reducing the number of rain gauges to interpolate the spatial rainfall for 24 chosen events. With these analyses we found that reducing the available information by half results in deviations of up to 25% for events with return periods shorter than one year and 45% for events with longer return periods. Assuming uniformly distributed rainfall over the entire catchment resulted in deviations of up to 75% and 125%, respectively. These findings are supported by the work of past research projects and underline the necessity of a high spatial measurement density in order to account for spatial variability of intense rainstorms.

Satellite Remote Sensing of the Greenland Ice Sheet Ablation Zone: A Review

The Greenland Ice Sheet is now the largest land ice contributor to global sea level rise, largely driven by increased surface meltwater runoff from the ablation zone, i.e., areas of the ice sheet where annual mass losses exceed gains. This small but critically important area of the ice sheet has expanded in size by ~50% since the early 1960s, and satellite remote sensing is a powerful tool for monitoring the physical processes that influence its surface mass balance. This review synthesizes key remote sensing methods and scientific findings from satellite remote sensing of the Greenland Ice Sheet ablation zone, covering progress in (1) radar altimetry, (2) laser (lidar) altimetry, (3) gravimetry, (4) multispectral optical imagery, and (5) microwave and thermal imagery. Physical characteristics and quantities examined include surface elevation change, gravimetric mass balance, reflectance, albedo, and mapping of surface melt extent and glaciological facies and zones. The review concludes that future progress will benefit most from methods that combine multi-sensor, multi-wavelength, and cross-platform datasets designed to discriminate the widely varying surface processes in the ablation zone. Specific examples include fusing laser altimetry, radar altimetry, and optical stereophotogrammetry to enhance spatial measurement density, cross-validate surface elevation change, and diagnose radar elevation bias; employing dual-frequency radar, microwave scatterometry, or combining radar and laser altimetry to map seasonal snow depth; fusing optical imagery, radar imagery, and microwave scatterometry to discriminate between snow, liquid water, refrozen meltwater, and bare ice near the equilibrium line altitude; combining optical reflectance with laser altimetry to map supraglacial lake, stream, and crevasse bathymetry; and monitoring the inland migration of snowlines, surface melt extent, and supraglacial hydrologic features.

Time-space identification of mechanical impacts and distributed random excitations on plates and membranes

The identification of dynamic loads acting on structures is a key aspect of several engineering domains involving structure-borne sound and vibration problems, stress analysis, or even the study of fatigue-induced structural damages. This work is concerned with the reconstruction of localized transient and distributed random excitations on plates and membranes from their measured vibration response. In previous investigations by the authors, the virtual fields method, an identification approach based on the principle of virtual work, was employed to identify mechanical and acoustic loadings applied to a bending panel. However, vibration data were obtained using scanning laser Doppler vibrometry, which limits the application of the virtual fields method to stationary excitations in both space and frequency. In contrast, the deflectometry technique used here is an optical method that directly provides a full-field measurement of local slopes. With the addition of a high-speed camera, the measurements are resolved in both space and time, enabling the study of nonstationary excitations. Moreover, since the acquisition time is independent of the number of measurement points, high spatial density measurements can be performed in seconds. This paper reviews the principles of the virtual fields method for nonstationary excitations on plates and membranes. The deflectometry technique is then demonstrated and experimental reconstruction results on an aluminum panel are presented for two different load cases: impacting metal marbles (multiple unknown transient excitations) and a diffuse acoustic field excitation. Finally, the identification of acoustic and aerodynamic (turbulent boundary layer) excitations is considered using a membrane as the receiving structure.

A Novel Approach for 3D-Structural Identification through Video Recording: Magnified Tracking.

Advancements in optical imaging devices and computer vision algorithms allow the exploration of novel diagnostic techniques for use within engineering systems. A recent field of application lies in the adoption of such devices for non-contact vibrational response recordings of structures, allowing high spatial density measurements without the burden of heavy cabling associated with conventional technologies. This, however, is not a straightforward task due to the typically low-amplitude displacement response of structures under ambient operational conditions. A novel framework, namely Magnified Tracking (MT), is proposed herein to overcome this limitation through the synergistic use of two computer vision techniques. The recently proposed phase-based motion magnification (PBMM) framework, for amplifying motion in a video within a defined frequency band, is coupled with motion tracking by means of particle tracking velocimetry (PTV). An experimental campaign was conducted to validate a proof-of-concept, where the dynamic response of a shear frame was measured both by conventional sensors as well as a video camera setup, and cross-compared to prove the feasibility of the proposed non-contact approach. The methodology was explored both in 2D and 3D configurations, with PTV revealing a powerful tool for the measurement of perceptible motion. When MT is utilized for tracking “imperceptible” structural responses (i.e., below PTV sensitivity), via the use of PBMM around the resonant frequencies of the structure, the amplified motion reveals the operational deflection shapes, which are otherwise intractable. The modal results extracted from the magnified videos, using PTV, demonstrate MT to be a viable non-contact alternative for 3D modal identification with the benefit of a spatially dense measurement grid.

Investigation of the thermo-mechanical behaviour of clutches using fibre optic sensing technology with high spatial measurement density

Non-lubricated clutches are still subject of numerous investigations. Measuring the temperature distribution in the friction contact would deliver a more detailed view of the live friction process. Main restriction remaining to establish this information is the low spatial measurement density of conventional thermal measurement facilities. On this account, fibre optic sensing technology has been used for the first time to measure the temperature distribution with high spatial resolution in a pressure plate of a clutch system. One sensing fibre offers not less than 700 measurement points in the pressure plate. An academic example was carried out to validate the new measuring method. On one hand, the focus of this investigation was the comparison of the measured temperature between fibre and thermocouple. On the other hand, the manner of inserting the fibre, especially the influences on the part by inserting holes, was investigated. The target is minimally changing the friction contact by the measurement instrumentation. By means of this measurement method, it is possible to analyse the thermo-mechanical behaviour of a friction system with a high level of details. This method enables the control of temperature distribution in the counter plate. Currently, an optimization method is in development, which should make it possible to rate design changes, for example of the pressure plate, linked to the temperature distribution.

Efficient model updating of a multi-story frame and its foundation stiffness from earthquake records using a timoshenko beam model

We present a new approach to estimate the dynamic stiffnesses of soil-foundation systems using earthquake-induced response signals recorded on multi-story buildings with sparsely instrumentation. Identification of soil-foundation dynamic stiffness parameters from real-life data, especially earthquake-induced response signals, is arguably one of the most challenging problems in structural/geotechnical earthquake engineering. This is because the said parameters are frequency-dependent, and the non-stationary input excitation is not measurable due to soil-structure interaction effects. It is possible to identify these parameters using recently developed blind source separation techniques, provided that a finite element model of the superstructure is available. However, developing and updating a finite element model is usually a laborious undertaking, and its success strongly depends on the spatial density of measurements. In the present study, we offer a new method that is based on the use of a Timoshenko beam model to represent the superstructure. In this method, key parameters of the Timoshenko beam model – and those of its soil-foundation system – are adjusted through a systematic procedure, until the systems’ overall (flexible-based) modal properties match those identified from real-life data. The proposed method is robust against sensor sparseness, and yields accurate results even if the foundation rocking is not measured. The proposed procedure is first verified using a synthetic problem, and subsequently applied to real-life data recorded at the Millikan Library building, which is located at the Caltech campus in Pasadena, California.

Coordinated Speed Oscillations in Schooling Killifish Enrich Social Communication

We examine the spatial dynamics of individuals in small schools of banded killifish (Fundulus diaphanus) that exhibit rhythmic, oscillating speed, typically with sustained, coordinated, out-of-phase speed oscillations as they move around a shallow water tank. We show that the relative motion among the fish yields a periodically time-varying network of social interactions that enriches visually driven social communication. The oscillations lead to the regular making and breaking of occlusions, which we term “switching.” We show that the rate of convergence to consensus (biologically, the capacity for individuals in groups to achieve effective coordinated motion) governed by the switching outperforms static alternatives, and performs as well as the less practical case of every fish sensing every other fish. We show further that the oscillations in speed yield oscillations in relative bearing between fish over a range that includes the angles previously predicted to be optimal for a fish to detect changes in heading and speed of its neighbors. To investigate systematically, we derive and analyze a dynamic model of interacting agents that move with oscillatory speed. We show that coordinated circular motion of the school leads to systematic cycling of spatial ordering of agents and possibilities for enriched spatial density of measurements of the external environment. Our results highlight the potential benefits of dynamic communication topologies in collective animal behavior, and suggest new, useful control laws for the distributed coordination of mobile robotic networks.

A new approach for the optimization of the thermo-mechanical behaviour of dry-running clutches using fibre-optic sensing technology with high spatial measurement density

Measuring temperature in tribological contacts remains being a huge challenge. The present paper proposes a new approach to analyze and optimize the thermo-mechanical behaviour of a dry-running clutch without affecting the real interacting surfaces. Using fibre-optic sensing technology makes it possible to gain a very detailed view of temperature distribution in the system. The interpretation of the measurements for different load situations has shown that the behaviour in the first 0.8 s is very similar. But after 0.8 s slip-time, the temperature distribution varies considerably according to the load situations. This behaviour is linked to the clutch engagement parameters and the resulting slip time. Non-lubricated clutches are still subject to numerous investigations in order to determine their dynamic behaviour, especially the dynamic behaviour. Measuring the temperature distribution in the friction contact would deliver a more detailed view of the live friction process. Main restriction remaining to establish this information is the low spatial measurement density of conventional thermal measurement facilities. On this account, fibre-optic sensing technology has been used at the IPEK to measure the temperature distribution with high spatial resolution in a pressure plate of a clutch system. One sensing fibre offers not less than 700 measurement points, divided across 28 measurement sections belonging to the pressure plate. In addition to the very high spatial measurement density, there are two more advantages. Through the low thermal mass of the optical fibre, fast variations of the temperature can be measured. By using only one sensing fibre, with a low capacity to transfer heat in itself, the heat dissipation through the sensor is very low. By means of this measurement method, it is possible to analyze the thermo-mechanical behaviour of a friction system with a high level of details. This method enables the control of temperature distribution in the counter plate. This parameter is an indicator of the load capacity of the system as an optimal homogenization leads to the elimination of local temperature peaks and so to wear reduction. Another topic is the specific adaptation of a system to its working area. The investigation of the influence on the thermo-mechanical behaviour of parameters such as friction energy or power allows the system to be optimally designed regarding to its working area. Another field of application is the validation of the thermal CAE-models.

Shallow Penetrometer Penetration Resistance

AbstractShallow penetrometers—such as the hemiball and toroid—were conceived as potential in situ testing devices with the ability to measure: (1) soil strength parameters during vertical penetration, (2) soil consolidation characteristics during dissipation tests postpenetration, and (3) interface friction during torsional loading. Knowledge of the response of soil to such tests is critical to the design of subsea pipelines and the ability to measure the response of soil to all three types of test using a single device in situ from a mobile testing platform, such as a remotely operated vehicle (ROV), would be highly advantageous. Potential benefits of the employment of such devices could include significant time and cost savings and improved spatial measurement density, since more tests could be conducted along the route of a pipeline if an ROV is used as a mobile in situ testing platform. This paper presents an assessment of the ability of the hemiball and toroid to measure soil strength parameters dire...

Effect of Measurement Density on Characterizing Air Velocity Distribution in Commercial Broiler Houses

Abstract. Increasing air velocity of tunnel ventilation systems in commercial broiler facilities improves production efficiency. As a consequence, many housing design specifications require a minimum air velocity in the house. Air velocities are typically assessed with a hand-held anemometer at random locations, rather than systematic traverses. Simultaneous measurement of air velocity at multiple locations in the facility would provide a more accurate estimation of air velocity distribution. The objective of this study was to assess the effect of measurement density on accuracy of estimating air velocity distribution in a tunnel-ventilated broiler production facility. An array of 40 anemometers was placed on a series of transverse cross-sections in a commercial broiler production facility with curtain sidewalls (no birds present) measuring 12.8 A— 121.9 m. The house was equipped with ten 121.9 cm exhaust fans. Cross-sectional air velocity measurements were taken along the length of the house in increments of 3.05 m axially. Data were sampled at 1 Hz for 2 min; three 2 min subsamples were obtained at each cross-section. Horizontal plane air velocity distribution maps were generated using 12.19, 6.10, and 3.05 m axial measurement distances between cross-sections at 0.46 m above the litter. Vertical plane air velocity distribution maps were created using 10, 20, and 40 symmetrical sampling points from the original data set. Cross-validation analysis revealed that higher spatial measurement density in the axial direction yielded a higher correlation between observed and predicted values (79%) and lower mean squared prediction error (MSPE; 0.10 m s -1 ) when compared to decreased sampling densities. Vertical cross-section measurement density comparisons showed a reduction in MSPE and an increase in correlation between observed and predicted values at higher sampling densities in all cases tested excluding one. In the case of improved interpolation results with fewer measurement points, the cross-section demonstrated high variation in air velocity and velocity values being very low. Axial cross-sectional measurement distances of ≤3.05 m and vertical plane measurement densities of ≥40 measurement points should be used to accurately characterize air velocity distribution in a 12.8 A— 121.9 m broiler production facility. Although more sensors and time are required to collect 40-point cross-sections at 3.05 m, the improved visualization allows better identification of distribution effects caused by equipment placement in the facility.

Multiobjective design of aquifer monitoring networks for optimal spatial prediction and geostatistical parameter estimation

[1] Effective sampling of hydrogeological systems is essential in guiding groundwater management practices. Optimal sampling of groundwater systems has previously been formulated based on the assumption that heterogeneous subsurface properties can be modeled using a geostatistical approach. Therefore, the monitoring schemes have been developed to concurrently minimize the uncertainty in the spatial distribution of systems' states and parameters, such as the hydraulic conductivity K and the hydraulic head H, and the uncertainty in the geostatistical model of system parameters using a single objective function that aggregates all objectives. However, it has been shown that the aggregation of possibly conflicting objective functions is sensitive to the adopted aggregation scheme and may lead to distorted results. In addition, the uncertainties in geostatistical parameters affect the uncertainty in the spatial prediction of K and H according to a complex nonlinear relationship, which has often been ineffectively evaluated using a first-order approximation. In this study, we propose a multiobjective optimization framework to assist the design of monitoring networks of K and H with the goal of optimizing their spatial predictions and estimating the geostatistical parameters of the K field. The framework stems from the combination of a data assimilation (DA) algorithm and a multiobjective evolutionary algorithm (MOEA). The DA algorithm is based on the ensemble Kalman filter, a Monte-Carlo-based Bayesian update scheme for nonlinear systems, which is employed to approximate the posterior uncertainty in K, H, and the geostatistical parameters of K obtained by collecting new measurements. Multiple MOEA experiments are used to investigate the trade-off among design objectives and identify the corresponding monitoring schemes. The methodology is applied to design a sampling network for a shallow unconfined groundwater system located in Rocky Ford, Colorado. Results indicate that the effect of uncertainties associated with the geostatistical parameters on the spatial prediction might be significantly alleviated (by up to 80% of the prior uncertainty in K and by 90% of the prior uncertainty in H) by sampling evenly distributed measurements with a spatial measurement density of more than 1 observation per 60 m × 60 m grid block. In addition, exploration of the interaction of objective functions indicates that the ability of head measurements to reduce the uncertainty associated with the correlation scale is comparable to the effect of hydraulic conductivity measurements.

Uncertainty analysis of system identification results obtained for a seven-story building slice tested on the UCSD-NEES shake table

A full-scale seven-story reinforced concrete building section/slice was tested on the Network for Earthquake Engineering Simulation (NEES) shake table at the University of California San Diego during the period of October 2005 to January 2006. Three output-only system identification methods were used to extract the modal parameters (natural frequencies, damping ratios, and mode shapes) of the test structure at different damage states. In this study, the performance of these system identification methods is investigated in two cases: (Case I) when these methods are applied to the measured dynamic response of the structure and (Case II) when these methods are applied to the dynamic response of the structure simulated using a three-dimensional nonlinear finite element model thereof. In both cases, the uncertainty/variability of the identified modal parameters due to the variability of several input factors is quantified through analysis of variance (ANOVA). In addition to ANOVA, meta-models are used for effect screening in Case II (based on the simulated data), which also capture the effects of linear interactions of the input factors. The four input factors considered in Case I are amplitude of input excitation, spatial density of measurements, length of response data used for system identification, and model order used in the parametric system identification methods. In the second case of uncertainty analysis, in addition to these four input factors, measurement noise is also considered. The results show that for all three methods considered, the amplitude of excitation is the most significant factor explaining the variability of the identified modal parameters, especially the natural frequencies. Copyright © 2013 John Wiley & Sons, Ltd.

An updated and quality controlled surface mass balance dataset for Antarctica

Abstract. We present an updated and quality controlled surface mass balance (SMB) database for the Antarctic ice sheet. Importantly, the database includes formatted metadata, such as measurement technique, elevation, time covered, etc, which allows any user to filter out the data. Here, we discard data with limited spatial and temporal representativeness, too small measurement accuracy, or lack of quality control. Applied to the database, this filtering process gives four times more reliable data than when applied to previously available databases. New data with high spatial resolution are now available over long traverses, and at low elevation in some areas. However, the quality control led to a considerable reduction in the spatial density of data in several regions, particularly over West Antarctica. Over interior plateaus, where the SMB is low, the spatial density of measurements remains high. This quality controlled dataset was compared to results from ERA-Interim reanalysis to assess whether field data allow us to reconstruct an accurate description of the main SMB distribution features in Antarctica. We identified large areas where data gaps impede model validation: except for very few areas (e.g., Adelie Land), measurements in the elevation range between 200 m and 1000 m above sea level are not regularly distributed and do not allow a thorough validation of models in such regions with complex topography, where the highest scattering of SMB values is reported. Clearly, increasing the spatial density of field measurements at low elevations, in the Antarctic Peninsula and in West Antarctica is a scientific priority.

FORMALDEHYDE DENSITOMETRY OF STARBURST GALAXIES: DENSITY-INDEPENDENT GLOBAL STAR FORMATION

Accurate techniques which allow for the derivation of the spatial density in star formation regions are rare. A technique which has found application for the derivation of spatial densities in Galactic star formation regions utilizes the density-sensitive properties of the K-doublet transitions of formaldehyde (H2CO). In this paper, we present an extension of our survey of the formaldehyde 1(10)-1(11) (lambda = 6.2 cm) and 2(11)-2(12) (lambda = 2.1 cm) K-doublet transitions of H2CO in a sample of 56 starburst systems (Mangum etal. 2008). We have extended the number of galaxies in which both transitions have been detected from 5 to 13. We have improved our spatial density measurements by incorporating kinetic temperatures based upon NH3 measurements of 11 of the galaxies with a total of 14 velocity components in our sample (Mangum etal. 2013). Our spatial density measurements lie in a relatively narrow range of from 10^(4.5) to 10^(5.5) cm^(-3). This implies that the Schmidt-Kennicutt relation between L_(IR) and M_(dense): (1) Is an indication of the dense gas mass reservoir available to form stars, and (2) Is not directly dependent upon a higher average density driving the star formation process in the most luminous starburst galaxies. We have also used our H2CO measurements to derive two separate measures of the dense gas mass which are generally smaller, in many cases by a factor of 10^2-10^3, than those derived using HCN. This disparity suggests that H2CO traces a denser, more compact, component of the giant molecular clouds in our starburst galaxy sample. We also report measurements of the rotationally-excited lambda = 6.3 cm 2P_(1/2) J=1/2 state of OH and the H111alpha radio recombination line taken concurrently with our H2CO 1(10)-1(11) measurements.

Estimation of a semi-physical GLBE model using dual EnKF learning algorithm coupled with a sensor network design strategy: Application to air field monitoring

In this paper, we present the fusion of two complementary approaches for modeling and monitoring the spatio-temporal behavior of a fluid flow system. We also propose a mobile sensor deployment strategy to produce the most accurate estimate of the true system state. For this purpose, deterministic and statistical information was used. We adopted a filtering method based on a semi-physical model which derives from a fluid flow numerical model known as lattice Boltzmann model (LBM). The a priori physical knowledge was introduced by the Navier–Stokes equations which were discretized by the lattice Boltzmann approach. Moreover, its multiple-relaxation-time (MRT) variant not only improved the stability, but also enabled the introduction of additional degrees of freedom to be estimated like the synaptic weights of a neural network. The statistical knowledge was then introduced into the model by performing a sequential learning of these parameters and an estimation of the speed field of the fluid flow starting from measurements. The low spatial density of measurements, the large amount of data inherent to environmental issues and the nonlinearity of the generalized lattice Boltzmann equations (GLBEs) enjoined us to use the ensemble Kalman filter (EnKF) for the recursive estimation procedure. A dual state-parameter estimation which results in a significantly reduced computation time was used by combining two filters consecutively activated in the same iteration. Finally, we proposed to complete the lack of spatial information of the sparse-observation network by adding a mobile sensor, which was routed to the location where the cell-by-cell output estimation error was the highest. Experimental results in the context of the standard lid-driven cavity problem revealed the presence of few zones of interest, where fixed sensors can be deployed to increase performances in terms of convergence speed and estimation quality. Finally, the study showed the feasibility of introducing some additional parameters which act as degrees of freedom, to perform large-eddy simulation of turbulent flows without numerical instabilities.

The effects of floodplain soil heterogeneity on meander planform shape

Past analytical studies of meander planform development have mostly focused on the complexity of the governing equations, i.e., hydrodynamics, and less so on the stream bank resistance to erosion, whose spatial heterogeneity is difficult to describe deterministically. This motivated the use of a Monte Carlo approach to examine the effects of floodplain soils and their distribution on planform development, with the goal of including bank erosion properties in the analysis. Simulated bank erosion rates are controlled by the resistance to hydraulic erosion of the bank soils using an excess shear stress approach. The spatial distribution of critical shear stress across the floodplain is delineated on a rectangular, equidistant grid with varying degrees of variability. The corresponding erodibility coefficient is computed using a field‐derived empirical relation. For a randomly disturbed distribution, in which the mean resistance to erosion exponentially increases away from the valley centerline, two relevant parameters are identified: the standard deviation of the critical shear stress distribution, which controls skewness and variability of the channel centerline, and the cross‐valley increase in soil resistance, which constrains lateral migration and also affects bend skewness. For a purely random distribution, migrated centerlines exhibit larger variability for increasing spatial scales of floodplain soil heterogeneity. For equal stochastic variability of the corresponding governing parameters, relating meander migration to hydraulic erosion of the bank soils produces more variability and shape complexity than the “classic” bank migration approach of Ikeda et al. (1981), which relates migration rate to excess velocity at the outer bank. Finally, the proposed stochastic approach provides a foundation for estimating a suitable spatial density of measurements to characterize the physical properties of floodplain soils and vegetation.

Joint analysis of displacement time series retrieved from SAR phase and amplitude: Impact on the estimation of volcanic source parameters

We study the impact of jointly analyzing the phase and amplitude information of a SAR dataset on the interpretation of the surface displacement over time in areas characterized by large dynamics. In particular, we compute the ground displacement time series over Fernandina and Sierra Negra calderas (Galápagos islands), by applying the Small BAseline Subset (SBAS) and the Pixel Offset (PO)‐SBAS techniques to 25 ENVISAT SAR images acquired between 2003 and 2007, when both volcanoes experienced catastrophic eruptions. By merging the SBAS and PO‐SBAS time series results the spatial density of measurements increased substantially. In addition, the joint analysis of the time series allows us to better constrain the temporal evolution of the magmatic source volume changes. Our results show that the joint use of the SBAS and PO‐SBAS approaches may help for a more correct evaluation of large deformation affecting the Earth's surface caused by eruptions, earthquakes, and landslide phenomena.