Academic literature on the topic 'Depth-average model'

Accurate and reliable prediction of groundwater depth is a critical component in water resources management. In this paper, a new method based on coupling wavelet decomposition method (WA), autoregressive moving average (ARMA) model, and BP neural network (BP) model for groundwater depth forecasting applications was proposed. The relative performance of the proposed coupled model (WA-ARMA-BP) was compared to the regular autoregressive integrated moving average (ARIMA) and BP models for annual average groundwater depth forecasting using leave-one-out cross-validation (LOO-CV). The variables used to develop and validate the models were average groundwater depth data recorded from 1981 to 2010 in Jinghui Canal Irrigation District in the northwest of China. It was found that the WA-ARMA-BP model provided more accurate annual average groundwater depth forecasts compared to the ARIMA and BP models. The results of the study indicate the potential of the WA-ARMA-BP model in forecasting nonstationary time series such as groundwater depth.

Flow measurements in open channels have often utilized velocity-area methods. Thus, estimations of the average velocity in a cross-section of rural canals play an important role in the flow measurement of an irrigation district. This paper derives a model for calculating depth average velocity. This model considers the classical logarithmic formula describing the velocity distribution and flow partitioning theory, which is aimed at finding out a location that represents the depth average velocity (LDAV) along the vertical line from boundary to water surface. Subsequently, the average flow velocity of the whole channel can be further determined by using the velocity-area method in different regions. Moreover, the LDAV has different expressions in different sub-regions according to flow partitioning theory under various aspect ratios. The results are verified by experiments under different experimental conditions, and the formula is highly applicable and has a high theoretical significance and practical value.

The rutting depth is an important index to evaluate the damage degree of the pavement. Therefore, establishing an accurate rutting depth prediction model can guide pavement design and provide the necessary basis for pavement maintenance. However, the sample size of pavement rutting depth data is small, and the sampling is not standardized, which makes it hard to establish a prediction model with high accuracy. Based on the data of RIOHTrack’s asphalt pavement structure, this study builds a reliable data-augmented model. In this paper, different asphalt rutting data augmented models based on Gaussian radial basis neural networks are constructed with the temperature and loading of asphalt pavements as the main features. Experimental results show that the method outperforms classical machine learning methods in data augmentation, with an average root mean square error of 3.95 and an average R-square of 0.957. Finally, the augmented data of rutting depth is constructed for training, and multiple neural network models are used for prediction. Compared with unaugmented data, the prediction accuracy is increased by 50%.

This paper describes the processes of water adsorption and desorption in PE membranes for fuel cells. A simple equation is inferred assuming that the surface of the membrane is uniformly covered by adsorbed molecules to an average depth of some monolayers. The adsorption depth is only controlled by diffusion of adsorbate from the surface towards the bulk through a two-layer or multi-layer mechanisms; so the empty sites formed at the surface can accept further molecules of water. If the diffusion rate is fast enough, cumulative water uptake occurs. The uptake kinetics is described considering the average penetration depth, i.e. neglecting the local concentration spikes below a random number and position of empty sites statistically formed at the surface of the membrane. The model also describes the desorption process, assumed to start at a prefixed time.

Abstract. Bathymetry is a key variable in ocean monitoring and measurement research. It becomes more and more important for development of rapid method to invert shallow sea water depth. In this study, a water depth inversion method based on multi-band model is established to analyze the relationship between different bands of Landsat 8 OLI multi-spectral and measured data. The average absolute error of the model is 1.48m at 10–20m water depth and the average relative error is 13.12%. The water depth inversion accuracy under normal conditions are achieved, indicating that the model will have a promising practical application in the future.

The authors present a simple method to estimate the three-dimensional shape of a face from an input image using a single reference model, based on least squares between the output of the linear-nonlinear (LN) neuronal model applied to blocks from an intensity image and blocks from a depth reference model. The authors present the results obtained by varying the LN model parameters and estimate their best values, which provide an acceptable reconstruction of each subject's depth. The authors show that increasing the light source angle over the horizontal plane in the input image produces slight increases in reconstruction error, but increasing the ambient light proportion produces greater increases in reconstruction error. The authors applied the method to predict each subject's unknown depth using different individual reference models and an average reference model, which provides the best results. As a noise reduction technique, the authors perform a point by point weighted averaging with the average reference model with weights equal to the fractions of the squares of the Laplacian of a Gaussian applied to the prediction and to the reference depth over the sum of both. Finally, the authors present acceptable visual results obtained from external images of faces under arbitrary illumination, having performed an illumination estimation previously.

Spatial variability of undrained strength (Cu) has been modelled in several ways in the past. In particular, concepts of time series such as autoregressive moving average models have been used to model the analogous "spatial series" of the values of depth versus undrained strength. It should be noted that the very purpose of such modelling studies is to provide estimates of the values of undrained strength at a given value of depth. In the present paper, the main prerequisite to apply these models, viz. the complete removal of trend present in the spatial series of depth versus Cu, has been focussed. An accurate modelling procedure is recommended which can estimate the values of Cu at a given value of depth better than any other model in this class of models existing in the literature. Sensitivity in the trend patterns of the depth versus Cu data is well taken care of. A computer program has been developed in FORTRAN 77to fit the model in conjunction with a standard nonlinear least-squares routine taken from the literature. One of the advantages of the present model is the speed of convergence of the computer program. Two case studies appearing in the literature have been successfully solved to demonstrate the efficacy of the model developed. Key words : spatial variability, time series analysis, spatial series, nonstationarity, autoregressive moving average models, regression, nonlinear least squares, error sum of squares.

A mathematical model to calculate the grit average cut depth in wire sawing single crystal silicon was founded. So the grit average cut depths were calculated theoretically by choosing different process parameters, and influences of process parameters on grit cut depths of slicing silicon crystal were analyzed. Analysis results indicate that the grit average cut depth relates to the silicon mechanical properties, grit shape and size, wire speed and ingot feed speed, etc. And there is a monotone increasing non-linear correlation between grit average cut depth and the ratio i value of ingot feed speed and wire speed, when the i value is lower, the average grit cut depth is lower.

We analyze the density distribution of marine sediments using density samples taken from 716 drill sites of the Deep Sea Drilling Project (DSDP). The samples taken within the upper stratigraphic layer exhibit a prevailing trend of the decreasing density with the increasing ocean depth (at a rate of −0.05 g/cm3per 1 km). Our results confirm findings of published studies that the density nonlinearly increases with the increasing sediment depth due to compaction. We further establish a 3D density model of marine sediments and propose theoretical models of the ocean-sediment and sediment-bedrock density contrasts. The sediment density-depth equation approximates density samples with an average uncertainty of about 10% and better represents the density distribution especially at deeper sections of basin sediments than a uniform density model. The analysis of DSDP density data also reveals that the average density of marine sediments is 1.70 g/cm3and the average density of the ocean bedrock is 2.9 g/cm3.

The aim of the paper was to determine the influence of root systems of chosen tree species found in the Polish Flysch Carpathians on the increase of soil shear strength (root cohesion) in terms of slope stability. The paper's goal was achieved through comprehensive tests on root systems of eight relatively common in the Polish Flysch Carpathians tree species. The tests that were carried out included field work, laboratory work and analytical calculations. As part of the field work, the root area ratio (A IA) of the roots was determined using the method of profiling the walls of the trench at a distance of about 1.0 m from the tree trunk. The width of the. trenches was about 1.0 m, and their depth depended on the ground conditions and ranged from 0.6 to 1.0 m below the ground level. After preparing the walls of the trench, the profile was divided into vertical layers with a height of 0.1 m, within which root diameters were measured. Roots with diameters from 1 to 10 mm were taken into consideration in root area ratio calculations in accordance with the generally accepted methodology for this type of tests. These measurements were made in Biegnik (silver fir), Ropica Polska (silver birch, black locust) and Szymbark (silver birch, European beech, European hornbeam, silver fir, sycamore maple, Scots pine, European spruce) located near Gorlice (The Low Beskids) in areas with unplanned forest management. In case of each tested tree species the samples of roots were taken, transported to the laboratory and then saturated with water for at least one day. Before testing the samples were obtained from the water and stretched in a. tensile testing machine in order to determine their tensile strength and flexibility. In general, over 2200 root samples were tested. The results of tests on root area ratio of root systems and their tensile strength were used to determine the value of increase in shear strength of the soils, called root cohesion. To this purpose a classic Wu-Waldron calculation model was used as well as two types of bundle models, the so called static model (Fiber Bundle Model — FIRM, FBM2, FBM3) and the deformation model (Root Bundle Model— RBM1, RBM2, mRBM1) that differ in terms of the assumptions concerning the way the tensile force is distributed to the roots as well as the range of parameters taken into account during calculations. The stability analysis of 8 landslides in forest areas of Cicikowicleie and Wignickie Foothills was a form of verification of relevance of the obtained calculation results. The results of tests on root area ratio in the profile showed that, as expected, the number of roots in the soil profile and their ApIA values are very variable. It was shown that the values of the root area ratio of the tested tree species with a diameter 1-10 ram are a maximum of 0.8% close to the surface of the ground and they decrease along with the depth reaching the values at least one order of magnitude lower than close to the surface at the depth 0.5-1.0 m below the ground level. Average values of the root area ratio within the soil profile were from 0.05 to 0.13% adequately for Scots pine and European beech. The measured values of the root area ratio are relatively low in relation to the values of this parameter given in literature, which is probably connected with great cohesiveness of the soils and the fact that there were a lot of rock fragments in the soil, where the tests were carried out. Calculation results of the Gale-Grigal function indicate that a distribution of roots in the soil profile is similar for the tested species, apart from the silver fir from Bie§nik and European hornbeam. Considering the number of roots, their distribution in the soil profile and the root area ratio it appears that — considering slope stability — the root systems of European beech and black locust are the most optimal, which coincides with tests results given in literature. The results of tensile strength tests showed that the roots of the tested tree species have different tensile strength. The roots of European beech and European hornbeam had high tensile strength, whereas the roots of conifers and silver birch in deciduous trees — low. The analysis of test results also showed that the roots of the studied tree species are characterized by high variability of mechanical properties. The values Of shear strength increase are mainly related to the number and size (diameter) of the roots in the soil profile as well as their tensile strength and pullout resistance, although they can also result from the used calculation method (calculation model). The tests showed that the distribution of roots in the soil and their tensile strength are characterized by large variability, which allows the conclusion that using typical geotechnical calculations, which take into consideration the role of root systems is exposed to a high risk of overestimating their influence on the soil reinforcement. hence, while determining or assuming the increase in shear strength of soil reinforced with roots (root cohesion) for design calculations, a conservative (careful) approach that includes the most unfavourable values of this parameter should be used. Tests showed that the values of shear strength increase of the soil reinforced with roots calculated using Wu-Waldron model in extreme cases are three times higher than the values calculated using bundle models. In general, the most conservative calculation results of the shear strength increase were obtained using deformation bundle models: RBM2 (RBMw) or mRBM1. RBM2 model considers the variability of strength characteristics of soils described by Weibull survival function and in most cases gives the lowest values of the shear strength increase, which usually constitute 50% of the values of shear strength increase determined using classic Wu-Waldron model. Whereas the second model (mRBM1.) considers averaged values of roots strength parameters as well as the possibility that two main mechanism of destruction of a root bundle - rupture and pulling out - can occur at the same. time. The values of shear strength increase calculated using this model were the lowest in case of beech and hornbeam roots, which had high tensile strength. It indicates that in the surface part of the profile (down to 0.2 m below the ground level), primarily in case of deciduous trees, the main mechanism of failure of the root bundle will be pulling out. However, this model requires the knowledge of a much greater number of geometrical parameters of roots and geotechnical parameters of soil, and additionally it is very sensitive to input data. Therefore, it seems practical to use the RBM2 model to assess the influence of roots on the soil shear strength increase, and in order to obtain safe results of calculations in the surface part of the profile, the Weibull shape coefficient equal to 1.0 can be assumed. On the other hand, the Wu-Waldron model can be used for the initial assessment of the shear strength increase of soil reinforced with roots in the situation, where the deformation properties of the root system and its interaction with the soil are not considered, although the values of the shear strength increase calculated using this model should be corrected and reduced by half. Test results indicate that in terms of slope stability the root systems of beech and hornbeam have the most favourable properties - their maximum effect of soil reinforcement in the profile to the depth of 0.5 m does not usually exceed 30 kPa, and to the depth of 1 m - 20 kPa. The root systems of conifers have the least impact on the slope reinforcement, usually increasing the soil shear strength by less than 5 kPa. These values coincide to a large extent with the range of shear strength increase obtained from the direct shear test as well as results of stability analysis given in literature and carried out as part of this work. The analysis of the literature indicates that the methods of measuring tree's root systems as well as their interpretation are very different, which often limits the possibilities of comparing test results. This indicates the need to systematize this type of tests and for this purpose a root distribution model (RDM) can be used, which can be integrated with any deformation bundle model (RBM). A combination of these two calculation models allows the range of soil reinforcement around trees to be determined and this information might be used in practice, while planning bioengineering procedures in areas exposed to surface mass movements. The functionality of this solution can be increased by considering the dynamics of plant develop¬ment in the calculations. This, however, requires conducting this type of research in order to obtain more data.

AbstractThe behaviors and impacts of flash floods (FF) are different based on the climatic regions. To understand such difference, two case studies were selected for the analysis: Wadi Uday, Oman and Sume Basin, Paraiba, Brazil. The rainfall-runoff inundation model (RRI) was used to simulate the discharge and flood inundation of the recent flood events to understand the severity and frequency of flash floods to better assess the current mitigation measures. The current FF situations in arid and semiarid basins were analyzed, and the hazards associated with flood phenomenon were assessed for various calculated rainfall return periods using RRI model. To this end, a flash flood index (average water depth per total basin area) was calculated as a basis to understand the impact of flash floods. A coupling of this index with the FF histories was included to provide a comprehensive overview of the FF vulnerability of arid and semiarid basins. We concluded that FFs tend to be more severe and extreme in arid regions than in semiarid regions, despite the lower frequency of FFs and the water scarcity in arid regions. Distributed dams also proved to be more effective in preventing FFs in arid regions than in semiarid regions.

AbstractValuation studies have shown that drought occurrences have more severe economic impact compared to other natural disasters such as floods. In Kenya, drought has presented complex negative effects on farming communities. The main objective of this chapter is to analyze the economic impacts of drought and identify appropriate climate change adaptation measures in Kenya. To achieve this objective, an empirical approach, combined with secondary data mined from World Bank Climate Knowledge Portal and FAOSTAT databases, has been used in three main steps. First, historical links between population size and land degradation, temperature and rainfall changes with drought events were established. Second, economic impacts of drought on selected economic indicators such as quantities of staple food crop, average food value production, number of undernourished people, gross domestic product, agriculture value added growth, and renewable water resources per annum in Kenya were evaluated. Third, different climate change adaptation measures among farmers in Makueni county were identified using focused group discussions and in-depth interviews, for which the use of bottom-up approach was used to elicit responses. Findings from the binary logistic regression model show a statistical relationship between drought events and a selected set of economic indicators. More specifically, drought events have led to increased use of pesticides, reduced access to credit for agriculture and the annual growth of gross domestic product. One of the main recommendations of this chapter is to involve farmers in designing and implementing community-based climate change adaptation measures, with support from other relevant stakeholders.

With the help of the space-to-depth and depth-to-space modules, we provide a convolutional neural network design for depth estimation. We show designs that down sample the spatial information of the picture utilizing space-to-depth (SD) as opposed to the widely used pooling methods (Max-pooling and Average-pooling). The space-to-depth module may shrink the image while maintaining the spatial information of the image in the form of additional depth information. This technique is far superior to Max-pooling, which diminishes the image’s information and features. We also suggest a lightweight decoder step that builds a high-resolution depth map out of many low-resolution feature maps using the depth-to-space (DS) module. The suggested architecture effectively learns depth estimation with high processing speed and accuracy. We trained and evaluated our suggested model on NYU-depthV2 dataset and attained low error values (RMSE=0.342) and high delta accuracies (δ3=0.996) at a fast-processing speed (25Fps).

The performance of IP mobility protocols is highly dependent on the change of mobile nodes’ (MNs’) mobility and traffic-related characteristics. Therefore, it is essential to investigate the effects of these characteristics and to conduct an in-depth performance study of these protocols. In this paper, we introduce a novel analytical approach using a continuous-time Markov chain model and hierarchical network model for the performance analysis of IPv6 mobility protocols: Mobile IPv6 (MIPv6) and Hierarchical Mobile IPv6 (HMIPv6). According to these analytical models, we derive the location update costs (i.e., binding update costs plus binding renewal costs), packet tunneling costs, and total signaling costs, which are generated by an MN during its average domain residence time, when MIPv6 or HMIPv6 is deployed under the same network architecture, respectively. In addition, based on these derived costs, we investigate the effects of various parameters, such as the average speed of an MN, binding lifetime period, the ratio of the network scale, and packet arrival rate, on the signaling costs generated by an MN under MIPv6 and HMIPv6. Moreover, we conduct the performance comparison between these two protocols by showing the relative total signaling costs under the various conditions. The analytical results show that as the average speed of an MN gets higher and the binding lifetime period is set to the larger value or as its packet arrival rate gets lower, the total signaling cost generated by an MN during its average domain residence time under HMIPv6 will get relatively lower than that under MIPv6, and that under the reverse conditions, the total signaling cost under MIPv6 will get relatively lower than that under HMIPv6.

This chapter deals with the mathematics of ocean acoustics. A number of environmental factors affect the transmission of sound in the ocean, including the depth and configuration of the bottom, the sound velocity structure within the ocean, and the shape of the ocean surface. The depths in the ocean are distributed in a peculiar manner, and the solution of underwater-sound problems may be grouped into two categories that differ mainly in terms of dimension: the average depths of water for deep-water transmission are 10,000 to 20,000 feet, whereas those for shallow-water transmission are less than 300 feet. The chapter first provides an overview of ocean acoustic waveguides before discussing one-dimensional waves in an inhomogeneous medium. It also considers a mathematical model of acoustic wave propagation in a stratified fluid and concludes with an analysis of the one-dimensional time-independent Schrödinger equation for solving the potential well problem.

<em>Abstract.</em>—The 1996 Sustainable Fisheries Act states that all federal fisheries management plans should contain a description of essential fish habitat (EFH). While much emphasis has been placed on estimating EFH for marine stocks, very little attention has been paid to doing so for Pacific salmon <em>Oncorhynchus </em>spp., in part due to their complex life histories. An earlier assessment of EFH for Pacific salmon across the west coast of the United States focused on the freshwater component of EFH due to limited knowledge about marine distributions. That analysis concluded that a more in-depth and smaller-scale examination was needed to assess how freshwater habitat affects the various life stages. Here we use a detailed life history model for Pacific salmon to estimate the freshwater component of EFH for two threatened populations of Chinook salmon within a large watershed draining into Puget Sound, Washington, USA. By accounting for proposed harvest rates, hatchery practices, and habitat structure, we identified 23 of 50 subbasins as EFH for ensuring no significant decrease in the total number of spawners relative to current average escapement. Our analytical framework could be easily applied to other populations or species of salmon to aid in developing recovery and management plans.

One class of important problems involves diffusion in a single spatial dimension, for example, height profiles of reactive constituents in a turbulently mixing atmosphere, profiles of concentration as a function of depth in the ocean or other body of water, diffusion and diagenesis within sediments, and calculation of temperatures as a function of depth or position in a variety of media. The one-dimensional diffusion problem typically yields a chain of interacting reservoirs that exchange the species of interest only with the immediately adjacent reservoirs. In the mathematical formulation of the problem, each differential equation is coupled only to adjacent differential equations and not to more distant ones. Substantial economies of computation can therefore be achieved, making it possible to deal with a larger number of reservoirs and corresponding differential equations. In this chapter I shall explain how to solve a one-dimensional diffusion problem efficiently, performing only the necessary calculations. The example I shall use is the calculation of the zonally averaged temperature of the surface of the Earth (that is, the temperature averaged over all longitudes as a function of latitude). I first present an energy balance climate model that calculates zonally averaged temperatures as a function of latitude in terms of the absorption of solar energy, which is a function of latitude, the emission of long-wave planetary radiation to space, which is a function of temperature, and the transport of heat from one latitude to another. This heat transport is represented as a diffusive process, dependent on the temperature gradient or the difference between temperatures in adjacent latitude bands. I use the energy balance climate model first to calculate annual average temperature as a function of latitude, comparing the calculated results with observed values and tuning the simulation by adjusting the diffusion parameter that describes the transport of energy between latitudes. I then show that most of the elements of the sleq array for this problem are zero. Nonzero elements are present only on the diagonal and immediately adjacent to the diagonal. The array has this property because each differential equation for temperature in a latitude band is coupled only to temperatures in the adjacent latitude bands.

Eye movements provide information on subconscious reactions in response to stimuli and are a reflection of attention and focus. With regard to visual activity, four types of eye movements—fixations, saccades, smooth pursuits and blinks—can be distinguished. Fixations—the number and distribution, total fixation time or average fixation duration are among the most common measures. The capabilities of this research method also allow the determination of scanpaths that track gaze on the image as well as heat- and focus maps, which visually represent points of gaze focus. A key concept in eye-tracking that allows for more in-depth analysis is areas of interest (AOI)—measures can then be taken for selected parts of the visual stimulus. On the other hand, the area of gaze outside the scope of analysis is called white space. The software allows for comparisons of static and non-static stimuli and provides a choice of template, dataset, metrics or data format. In conducting eye-tracking research, proper calibration is crucial, which means that the participant’s gaze should be adjusted to the internal model of the eye-tracking software. In addition, attention should be paid to such aspects as time and spatial control. The exposure time for each participant should be identical. The testing space should be well-lit and at a comfortable temperature.

The determination of pull out capacity (Q) of small ground anchor is an imperative task in civil engineering. This chapter employs three data mining techniques (Genetic Programming [GP], Gaussian Process Regression [GPR], and Minimax Probability Machine Regression [MPMR]) for determination of Q of small ground anchor. Equivalent anchor diameter (Deq), embedment depth (L), average cone resistance (qc) along the embedment depth, average sleeve friction (fs) along the embedment depth, and Installation Technique (IT) are used as inputs of the models. The output of models is Q. GP is an evolutionary computing method. The basic idea of GP has been taken from the concept of Genetic Algorithm. GPR is a probabilistic non-parametric modelling approach. It determines the parameter from the given datasets. The output of GPR is a normal distribution. MPMR has been developed based on the principal mimimax probability machine classification. The developed GP, GPR, and MPMR are compared with the Artificial Neural Network (ANN). This chapter also gives a comparative study between GP, GPR, and MPMR models.

The determination of pull out capacity (Q) of small ground anchor is an imperative task in civil engineering. This chapter employs three data mining techniques (Genetic Programming [GP], Gaussian Process Regression [GPR], and Minimax Probability Machine Regression [MPMR]) for determination of Q of small ground anchor. Equivalent anchor diameter (Deq), embedment depth (L), average cone resistance (qc) along the embedment depth, average sleeve friction (fs) along the embedment depth, and Installation Technique (IT) are used as inputs of the models. The output of models is Q. GP is an evolutionary computing method. The basic idea of GP has been taken from the concept of Genetic Algorithm. GPR is a probabilistic non-parametric modelling approach. It determines the parameter from the given datasets. The output of GPR is a normal distribution. MPMR has been developed based on the principal mimimax probability machine classification. The developed GP, GPR, and MPMR are compared with the Artificial Neural Network (ANN). This chapter also gives a comparative study between GP, GPR, and MPMR models.

Abstract A challenging step in reservoir modeling is capturing fluid composition variation. This is a complex task as fluid samples taken from wells in different areas of the reservoir usually have large areal and vertical compositional variation. Modeling representative composition variation with depth in the presence of multiple samples is critical for reservoir simulation and hydrocarbon initially in place assessment, and, on the other hand, a technically challenging task. In this paper, we present an automated workflow integrated in a commercial exploration and production (E&P) software that addresses compositional variation for reservoir simulation model initialization for multiple fluid samples. Composition variation with depth requires a depth window, number of depth points, composition, temperature, pressure and reference depth for all fluid samples. Using a specific equation of state (EoS), the workflow is executed for every fluid sample by performing compositional variation with depth based on Gibbs conditions for thermodynamic equilibrium. The output of this step is a composition variation with depth distribution for every fluid sample. Finally, the best-matching model is chosen by comparing each model results with the data for all existing fluid samples. The proposed workflow was tested using a specific EoS in a reservoir with several fluid samples. One composition variation with depth model was generated for every fluid sample. In the next step, all models were evaluated by calculating the average errors between the model and each fluid sample. Finally, the best-matching models were selected, and the results were evaluated. It was observed that the best-matching models were able to accurately predict the pressure and saturation pressure for large number of fluid samples. The proposed workflow was also integrated into an industry-leading E&P modeling software platform to serve as an automated functionality that outputs the required files to perform initialization with equilibration of the dynamic reservoir model. Capturing fluid composition variation in the reservoir is an important step in reservoir modeling. The proposed work presents an automated workflow that generates the best-matching composition variation with depth model for multiple samples. Using traditional approaches, this is a challenging and time-consuming step as fluid samples taken from wells in different areas of a reservoir can have significant areal compositional variation.

An interaction probability model is used to model the Energy Dispersive X-Ray Fluorescent (EDXRF) response from a given gold sample to determine the purity (or karatage) of gold. The application of this model is considered for estimating experimental results using a spectrometer with a X-Ray source of 18.6 keV which counts L-shell fluorescence in gold. In the elementary model, Compton scattering is largely ignored as it forms an insignificant contribution compared with photoelectric absorption in the energies of interest. The model is used to infer the “average depth” to which this analysis can be expected to provide reliable results, since ‘depth profiling’ is a major limitation of XRF. This model is further strengthened by carrying out a ‘deep-penetration’ analysis for the energy-dependent photon transport equation with the moments method. The first four moments are computed and used to obtain the spatial profile for the energy-dependent photon flux in the vicinity of L-shell phenomena. This analysis is useful to understand the contribution from interaction phenomena deeper within the material. For validation of depth profile, 2 gold-coated discs of thickness 10 microns and 40 microns were analyzed on the same XRF spectrometer. Transport theory depth-profiling results were validated from detection of the nickel-silver substrate of the discs.

Wave transmission and pneumatic efficiency of an oscillating water column (OWC) type wave energy device resting on group of piles is investigated using physical model study. The caisson blocks 45% of the water depth. The co-efficient of transmission of the device varies from 0.1 to 0.4 for B/L range of 0.1 to 0.7, where ‘B’ is the width of the caisson in the direction of wave propagation and ‘L’ is the wavelength. The pneumatic efficiency varies from 20% to 50% with an average value of 0.35. The results of the present study can be used in the design of OWC caisson used for both wave energy conversion and breakwater in deeper water.

A numerical study was performed to simulate the tip leakage flow and heat transfer on the first stage rotor blade in GE-E3 engines. Calculations were performed for both flat and squealer blade tips by means of solving Reynolds-averaged N-S equations in conjunction with the k-ω two-equation turbulence model. For squealer tip blades, several cases were considered with five different tip gap-blade span ratios of 0.4%, 0.7%, 1.0%, 1.3%, and 1.6%, and four different groove depth-blade span ratios of 1%, 2%, 3%, and 4%. A linear experimental cascade was calculated first to validate this turbulence model, and the results show good agreement with that of the experiment. With the results of several cases discussed and compared, flow structure and heat transfer characteristics in the tip gap are illustrated. The effects of the tip gap and groove depth on flow and heat transfer are significant. The leakage flow is weakened by deepening the tip groove. However, when the groove depth is larger than 3% of the blade span, no further leakage flow reduction will occur. The average heat transfer rate on the tip surface reduces as groove depth increases. An increase in tip gap height will increase the leakage flow, and thus lead to an increase in average heat transfer rate on the tip surface.

A model testing campaign of the Tupperwave device was carried out to prove the working principal and validate a numerical modelling that had previously been developed. An appropriate and challenging scaling method was applied to the floating device to correctly model the air compressibility in the Tupperwave PTO. In parallel, a model scale conventional OWC was also built using the same axisymmetric structure geometry and both devices were tested and compared. The testing showed that the Tupperwave device produced less average useful pneumatic power than the conventional OWC. The primary losses were attributed to pneumatic power dissipation through the valves. The pneumatic power delivered by the Tupperwave device was however significantly smoother. The paper describes the experimental set-up and the methods used to assess the devices performance. The results provide a direct comparison between the two physical models pneumatic power performances and an in-depth analysis of the valves behaviour is shown.

Abstract In the marine environment, the corrosion on a compressor blade caused by salt spray severely affects the compressor performance and poses a threat to its operation reliability. This study simulated the corrosion on the blade made of X20Cr13 martensitic stainless steel caused by salt spray by using the static acceleration test. Average depth and density of surface corrosion pit were calculated by the actual corrosion with peak valley and different shapes and sizes. The value of average deviation Ra was calculated by average depth and density of corrosion pit. According to the empirical formula proposed by Koch and Smith (ks = 6.2 Ra), setting the equivalent sand grain roughness ks in CFX. NASA Stage67 as calculation model. Then analyzed the effects of various surface roughness on compressor performance and internal flow field through the numerical simulation method. The results revealed that with the increase in corrosion time, the flow rate, pressure ratio, torque, and power of the compressor decreased. The internal flow indicated the increasing internal loss and decreasing efficiency. On the basis of loss analysis, the binomial relationships between the service time and relative values of total pressure loss and efficiency loss, respectively, were established.

Thermal methods consist of measuring thermal gradient and satellite data, which can be used to determine the Earth's surface temperature and thermal inertia of surficial materials, of thermal infrared radiation emitted at the Earth's surface. Thermal gradient measuring, with a knowledge of the thermal conductivity provides a measure of heat flow. Conditions that may increase or decrease and heat flow are influenced by hydrologic, topographic factors and anomalous thermal conductivity. Also, oxidation of sulphide bodies in-place or on waste deposits, if sufficiently rapid, can generate thermal anomalies, which can provide a measure of the amount of metal being released to the environment. The geothermal gradient on the territory of Romania, the increase of the temperature with the depth, has an average value of 2.5°-3°C/100m, which corresponds to a temperature of 100° C at 3000 m deep. There are many areas where the value of the geothermal gradient differs considerably from this average. For example, in areas where the rock plate suffered rapid dips and the basin was filled with sediment "very young "from a geological point of view, the geothermal gradient may be less than 1° C/100m. On the other hand, in other geothermal areas the gradient exceeds much this average. These areas are true underground thermal reservoirs of potentially high geothermal energy which under certain favourable conditions can be exploited to serve heating installations and domestic hot water systems. The geothermal prospecting for the entire territory of Romania, carried out by temperature measurements allowed the development of geothermal maps, highlighting the temperature distribution at different depths. Geophysical data obtained through various methods and geophysical modelling provide generalized and non-unique solutions to the geometry of underground geological relations as well as to the physical characteristics of different formations. The non-uniqueness of these models (solutions to the direct problem) arises from the impossibility of knowing the boundary conditions between different strata, which together with the propagation equations of the different fields (depending on the geophysical method used for the investigation of the basement) form the systems that offer the solutions of the model

Monopile support structures for offshore wind turbines may experience ringing-type responses in steep wave conditions. In order to experimentally capture the statistical distribution of the hydrodynamic loads and structural responses, traditional practice is to generate many 3-hour (full scale) realizations of the relevant sea states. An experimental campaign at 1:48 scale was carried out in the Lilletanken wave tank at NTNU/MARINTEK in order to examine the possibility of using shorter time windows to recreate irregular wave ringing events. Wave elevations and hydrodynamic loads on a rigid vertical circular cylinder in 27 m water depth were measured for a variety of 3-hour, 450 s (7.5-minute), 800 s (13.3-minute), 1150 s (19.2-minute), and 1500 s (25-minute) wave realizations, where all durations are listed in full scale. Wavelet transformations and a single degree-of-freedom oscillator were used to investigate the magnitude and repeatability of the high-frequency content of the wave loads. Large variations in the repeatability were seen among events. On average, the repeatability in the ringing response was 4.2 % for 3-hour tests, while 13.3-minute tests reproduced the same events within 9.1 %. The maximum deviation was, nonetheless, much higher when only 13.3 minutes were used.

This paper describes the use of the second-order polynomial dynamic linear model (DLM) to characterize the growth of the depth of corrosion defects on energy pipelines using imperfect data obtained from multiple high-resolution in-line inspections (ILI). The growth model is formulated by incorporating the general form of the measurement error (including the biases and random scattering error) of the ILI tools as well as the correlations between the random scattering errors of different tools. The temporal variability of the corrosion growth is captured by allowing the average growth rate between two successive inspections to vary with time. The Markov Chain Monte Carlo simulation is employed to carry out the Bayesian updating of the growth model and evaluate the posterior distributions of the model parameters. An example involving real ILI data collected from an in-service natural gas pipeline is employed to illustrate and validate the growth model. The analysis results show that the defect depths predicted by the proposed model agree well with the actual depths and are more accurate than those predicted by the Gamma process-based growth models reported in the literature.

Preventing diabetic foot ulcers (DFU) is critical for diabetes mellitus (DM) patients. Increased stiffness of plantar foot may cause higher plantar pressure leading to a higher risk of DFU. Soft tissue stiffness can be determined by measuring the soft tissue thickness with indentation depth and stress. Therefore, we hypothesized that the deep learning model could detect the ultrasound image pixel change under soft tissue compression. This study aimed to apply the deep learning model to analyze the ultrasound image pixel thickness of plantar foot, then predict the soft tissue indentation depth and loading force for estimating the stiffness. This study has developed a motor-driven ultrasound indentation system to apply programmable compression and simultaneously assess soft tissue mechanical properties and responses in indentation depth and loading force. In addition, the effective Young's modulus was calculated to characterize mechanical properties of soft tissues in the first metatarsal head. The deep learning method employed the YOLOv5x model to train and detect the small object in the indentation depth, such as ultrasound image pixel changes. Finally, the dataset images were processed with labeling annotation from the soft tissue indentation depth and loading force. The deep learning results showed 0.995 in mean Average Precision (mAP), 0.999 in precision, 1.000 in Recall, and 0.013 in Loss. A significant correlation was found between the ultrasound image pixel changes and soft tissue indentation depth (r = 0.98, p < 0.05). Furthermore, a significant correlation was observed between the ultrasound image pixel changes and the loading force in the first metatarsal head (r = 0.85, p < 0.05). The validation and prediction models were lower than the training models in the effective Young's modulus results. However, the results of the initial modulus were similar between the three models. Our findings recommend that applying deep learning in the ultrasound image can predict soft tissue indentation depth and loading force to calculate the stiffness of the plantar foot.

Small (5.56 mm, 7.62 mm and 9 mm) and medium (12.7 mm) arms rounds were fired at snow-filled 1.5m cubic gabions in a mid-winter condition in Fairbanks, Alaska. The rounds were excavated and penetration by each ammunition type was measured. A distribution and average of penetration depth was determined. All 320 rounds fired were captured within 1.5m after entering the snow barrier. Comparison with published models of ballistics penetration of snow showed mixed results with several matching our data within 10% and all but one within 32%. However, most of these models are simplistic in that they accommodate limited variables and therefore may not be expected to perform well in all settings. We conclude that snow-based ballistics protection structures can be quickly and efficiently erected in suitable environments and with minimal size, can provide reliable protection against small and medium arms fire.

The new EU strategy on adaptation to climate change highlights the urgency of adaptation measures while bringing forth adaptation as vitally important as a response to climate change as mitigation. In order to provide information on how adaptation to climate change has been promoted in Finland and what calls for attention next, we have compiled a comprehensive information package focusing on the following themes: adaptation policy, impacts of climate change including economic impacts, regional adaptation strategies, climate and flood risks in regions and sea areas, and the availability of scientific data. This report consists of two parts. Part 1 of the report examines the work carried out on adaptation in Finland and internationally since 2005, emphasising the directions and priorities of recent research results. The possibilities of adaptation governance are examined through examples, such as how adaptations steering is organised in of the United Kingdom. We also examine other examples and describe the Canadian Climate Change Adaptation Platform (CCAP) model. We apply current information to describe the economic impacts of climate change and highlight the related needs for further information. With regard to regional climate strategy work, we examine the status of adaptation plans by region and the status of the Sámi in national adaptation work. In part 2 of the report, we have collected information on the temporal and local impacts of climate change and compiled extensive tables on changes in weather, climate and marine factors for each of Finland's current regions, the autonomous Åland Islands and five sea areas, the eastern Gulf of Finland, the western Gulf of Finland, the Archipelago Sea, the Bothnian Sea and the Bay of Bothnia. As regards changes in weather and climate factors, the changes already observed in 1991-2020 are examined compared to 1981-2010 and future changes until 2050 are described. For weather and climate factors, we examine average temperature, precipitation, thermal season duration, highest and lowest temperatures per day, the number of frost days, the depth and prevalence of snow, the intensity of heavy rainfall, relative humidity, wind speed, and the amount of frost per season (winter, spring, summer, autumn). Flood risks, i.e. water system floods, run-off water floods and sea water floods, are discussed from the perspective of catchment areas by region. The impacts of floods on the sea in terms of pollution are also assessed by sea area, especially for coastal areas. With regard to marine change factors, we examine surface temperature, salinity, medium water level, sea flood risk, waves, and sea ice. We also describe combined risks towards sea areas. With this report, we demonstrate what is known about climate change adaptation, what is not, and what calls for particular attention. The results can be utilised to strengthen Finland's climate policy so that the implementation of climate change adaptation is strengthened alongside climate change mitigation efforts. In practice, the report serves the reform of the National Climate Change Adaptation Plan and the development of steering measures for adaptation to climate change both nationally and regionally. Due to its scale, the report also serves e.g. the United Nations’ aim of protecting marine life in the Baltic Sea and the national implementation of the EU strategy for adaptation to climate change. As a whole, the implementation of adaptation policy in Finland must be speeded up swiftly in order to achieve the objectives set and ensure sufficient progress in adaptation in different sectors. The development of binding regulation and the systematic evaluation, monitoring and support of voluntary measures play a key role.