Physics based Modeling

One difficulty in modelling sediment transport on unpaved road surfaces stems from the inability of physically based models to simulate the flush of loose surface material that is deposited on the road surface prior to the onset of a storm. This work builds upon a prior modelling methodology (referred to as dynamic erodibility) to simulate time-varying sediment transport on unpaved mountain roads by loosely coupling a continuous, exponential decay disturbance model with the erosion algorithm in KINEROS2. The method is tested against sediment transport time-series observed on small-scale rainfall simulation plots for various slope, antecedent soil wetness and pre-storm sediment availability conditions. The new method generally improves prediction errors of total sediment output, peak sediment output and fit of the sediment output time-series. However, for some validation events, the method fails to simulate high initial sediment spikes. This limitation may be a side-effect of using data from small-scale plots, but also may signify the need for additional calibration of the disturbance model subcomponent with data from surfaces having a greater variety of pre-event surface material. Nevertheless, this road erosion modelling approach provides a realistic 'description' of time-varying sediment transport, which is controlled both by the baseline erodibility of the underlying road surface, and importantly, by the removal of a loose, surficial sediment layer by overland flow.

The increase in energy efficiency measures for residential construction in the Building Code of Australia has led to a shift from timber platform to slab on ground housing. Both industry and government have recognised the need to validate AccuRate's thermal simulation modelling of platform floored housing. The University of Tasmania in collaboration with the Wood & Forest Products Research Development Corporation and the Australian Greenhouse Office is undertaking research to validate empirically AccuRate in cool temperate climates. This is an ongoing project involving the construction and monitoring of thermal performance test cells and developer designed and constructed houses. There is an acknowledged shortage of appropriate data on platform floor housing to validate AccuRate. The problem facing platform floors is the perceived thermal limitations in cool temperate climates of Australia. In these regions the need for appropriate solutions of sub floor insulation is becoming urgent. The university has designed and constructed three thermal performance test cells on its Launceston Campus. Launceston has a wide diurnal temperature range in winter, which can assist in the close analysis of building fabric performance. The three thermal performance test cells include an un-enclosed perimeter platform floor, an enclosed perimeter platform floor and a concrete slab on ground floor. The test cells have been designed to allow for future fabric changes to floors, walls, windows and roofing. The platform floored test cells have allowed for the inclusion and modification of sub floor insulation. The designs examined international and national past practise.

Atmospheric visibility is an important input for road and air transportation safety, as well as a good proxy to estimate the air quality. A model-driven approach is presented to monitor the meteorological visibility distance through use of ordinary outdoor cameras. Unlike in previous data-driven approaches, a physics-based model is proposed which describes the mapping function between the contrast in the image and the atmospheric visibility. The model is non-linear, which allows encompassing a large spectrum of applications. The model assumes a continuous distribution of objects with respect to the distance in the scene and is estimated by a novel process. It is more robust to illumination variations by selecting the Lambertian surfaces in the scene. To evaluate the relevance of the approach, a publicly available database is used. When the model is fitted to short range data, the proposed method is shown to be effective and to improve on existing methods. In particular, it allows envisioning an easier deployment of these camera-based techniques on multiple observation sites.

HAL is a multi-disciplinary open access archive for the deposit and dissemination of scientific research documents, whether they are published or not. The documents may come from teaching and research institutions in France or abroad, or from public or private research centers. L'archive ouverte pluridisciplinaire HAL, est destinée au dépôt et à la diffusion de documents scientifiques de niveau recherche, publiés ou non, émanant des établissements d'enseignement et de recherche français ou étrangers, des laboratoires publics ou privés.

Understanding the influence of various variables on surface water quality is extremely important for protecting ecosystem health. The principal aim of this study is to assess the direct (DE), indirect (IE) and total effects (TE) of socioeconomic, terrestrial and hydrological factors on surface water quality via path analysis through the lens of 15 sub-basins located on Indus basin, Pakistan. Four path models were selected based on Comparative Fit Index (CFI) = 0.999 value. First path model showed that rangelands having low population density decline river runoff which decreases instream Electrical Conductivity (EC) because of lower anthropogenic activities. Second path model depicted that croplands having higher population density enhance river runoff due to irrigation tail water discharge which decline instream EC because of dilution. Third path model showed that croplands with higher population density enhance river runoff which increases instream NO3 concentration because of unscientific application of irrigation water. Fourth path model unveiled that croplands enhance Gross Domestic Product (GDP) which enhance river runoff and instream NO3 concentration. To protect ecosystem health, Best Management Practices (BMPs), precision farming and modern irrigation techniques should be adopted to reduce irrigation tail water discharges containing pollutants entry in Indus River.

Meteorological image sequences acquired with remote sensing devices contain huge amounts of available data featuring the temporal evolution of highly deformable and complex structures. In this study is presented a robust and effective model-based segmentation procedure for approximating highly deformable cloud structures. The whole image sequence may be viewed as a 3D data set, but the classical techniques for performing 3D segmentation are not meaningful here, because cloud structures are not shapes in the classical sense. The model-based segmentation presented here uses level-sets controlled by particle systems. A specific energy formulation is introduced, permitting the use of robust conjugate gradient techniques. The model is validated on a real meteorological image sequence. We outline the generalizations of the this particle system to perform image analysis tasks on truly 3D images.

The concepts of system load and capacity are pivotal in risk analysis. The complexity in risk analysis increases when the input parameters are either stochastic (aleatory uncertainty) and/or missing (epistemic uncertainty). The aleatory and epistemic uncertainties related to input parameters are handled through simulation-based parametric and non-parametric probabilistic techniques. The complexities increase further when the empirical relationships are not strong enough to derive physical-based models. In this paper, ordered weighted averaging operators (OWA) are proposed to estimate the system load. The risk of failure is estimated by assuming normally distributed reliability index. The proposed methodology for risk analysis is illustrated using an example of nine-input parameters. Sensitivity analyses identified that the risk of failure is dominated by the attitude of a decision-maker to generate OWA weights, missing input parameters and system capacity.

The concepts of system load and capacity are pivotal in risk analysis. The complexity in risk analysis increases when the input parameters are either stochastic (aleatory uncertainty) and/or missing (epistemic uncertainty). The aleatory and epistemic uncertainties related to input parameters are handled through simulation-based parametric and non-parametric probabilistic techniques. The complexities increase further when the empirical relationships are not strong enough to derive physical-based models. In this paper, ordered weighted averaging operators (OWA) are proposed to estimate the system load. The risk of failure is estimated by assuming normally distributed reliability index. The proposed methodology for risk analysis is illustrated using an example of nine-input parameters. Sensitivity analyses identified that the risk of failure is dominated by the attitude of a decision-maker to generate OWA weights, missing input parameters and system capacity.

Urban Air Mobility (UAM) introduces significant new challenges due to atmospheric boundary layer and wake interactions in low-altitude environments. The wind field inhomogeneity due to these phenomena can result in hazardous flight upsets due to the asymmetric thrust and aerodynamic loads induced on the UAM vehicle. To support the study of these risks and the required advances in Flight Control System (FCS) design for Vertical TakeOff and Landing (VTOL) platforms, this paper presents an innovative technique to estimate the local wind field characteristics based on measurements of power drain from the distributed electric VTOL motors equipped with Electronic Speed Controllers (ESC). By fusing these measurements with rotor speed (RPM) sensors experienced by the UAM vehicle propellers, the technique allows to estimate the wind velocity components affecting each rotor and thus the asymmetric load affecting the vehicle. A simulation case study in a representative UAM flow field verifies the validity of the proposed approach and allows us to estimate its performance in these realistic conditions.

The conceptual premise of the current study is that the chronological effect of geologic parameters and hydrologic variation has been enhancing the toe slope since then, and hence the natural slope has remained stable. To capture the phenomenon, in addition to an intensive literature review and the subsequent findings and rationales, comprehensive laboratory testing, physical flume experiments, and numerical modeling were performed. The flume experiments were extensively instrumented (pore pressure transducers, suction sensors, strain transducers, and tracer chemicals) to observe the retrogressive failure mechanism. The results show that the abrupt change in slope toe stress caused by an increase in slope angle, rise in pore water pressure, and dissipation of soil suction were responsible for radical change in strain and sudden failure. Findings show that the slope toe section was the spot of failure initiation and was subjected to sudden failure associated with accelerated retrogressions. Physical flume assessment and numerical simulations both show that modifying the slope toe could increase overall slope stability. Hence, slope toe contributes far more to slope stability than previously thought. Even though the soil type range and slope angle at which the retrogressive failure occurs remain a source of considerable ambiguity, proper slope toe treatment can be regarded as a critical remedy measure.

In this article, the bias-dependence of intrinsic channel thermal noise of single-layer graphene field-effect transistors (GFETs) is thoroughly investigated by experimental observations and compact modeling. The findings indicate an increase of the specific noise as drain current increases whereas a saturation trend is observed at very high carrier density regime. Besides, short-channel effects like velocity saturation also result in an increment of noise at higher electric fields. The main goal of this work is to propose a physics-based compact model that accounts for and accurately predicts the above experimental observations in short-channel GFETs. In contrast to long-channel MOSFETbased models adopted previously to describe thermal noise in graphene devices without considering the degenerate nature of graphene, in this work a model for short-channel GFETs embracing the 2D material's underlying physics and including a bias dependency is presented. The implemented model is validated with de-embedded high frequency data from two short-channel devices at Quasi-Static region of operation. The model precisely describes the experimental data for a wide range of low to high drain current values without the need of any fitting parameter. Moreover, the consideration of the degenerate nature of graphene reveals a significant decrease of noise in comparison with the nondegenerate case and the model accurately captures this behavior. This work can also be of outmost significance from circuit designers' aspect, since noise excess factor, a very important figure of merit for RF circuits implementation, is defined and characterized for the first time in graphene transistors.

The performance of managed artificial recharge (MAR) facilities by means of surface ponds (SP) is controlled by the temporal evolution of the global infiltration capacity I c of topsoils. Cost-effective maintenance operations that aim to maintain controlled infiltration values during the activity of the SP require the full knowledge of the spatio-temporal variability of I c . This task is deemed uncertain. The natural reduction in time of I c depends on complex physical, biological and chemical reactions that clog the soil pores and has been observed to decay exponentially to an asymptotic non-zero value. Moreover, the relative influence of single clogging processes depend on some initial parameters of the soil, such as the initial infiltration capacity (I c,0 ). This property is also uncertain, as aquifers are typically heterogeneous and scarcely characterized in practical situations. We suggest a method to obtain maps of I c using a geostatistical approach, which is suitable to be extended to engineering risk assessment concerning management of SP facilities. We propose to combine geostatistical inference and a temporally-lumped physical model to reproduce non-uniform clogging in topsoils of a SP, using field campaigns of local and large scale tests and additionally by means of satellite images as secondary information. We then postulate a power-law relationship between the parameter of the exponential law, k, and I c,0 . It is found that calibrating the two parameters of the power law model it is possible to fit the temporal evolution of total infiltration rate at the pond scale in a MAR test facility. The results can be used to design appropriate measures to selectively limit clogging during operation, extending the life of the infiltration pond.

Conversion was selected for research. This board is a half-bridge converter built on the EPC2022 eGaN® transistors and contains a driver for controlling these transistors. To simplify the assessment of the conversion efficiency, it is suggested to use a computer model of the development board and LISN, which simulates the active load with the LC filter. References 10, tables 3, figures 5.

Neural networks were used to estimate the offset, depth, and conductivity‐area product of a conductive target given an electromagnetic ellipticity image of the target. Five different neural network paradigms and five different representations of the ellipticity image were compared. The networks were trained with synthetic images of the target and tested on field data and more synthetic data. The extrapolation capabilities of the networks were also tested with synthetic data lying outside the spatial limits of the training set. The data representations consisted of the whole image, the subsampled image, the peak and adjacent troughs, the peak, and components from a two‐dimensional (2-D) fast Fourier transform. The paradigms tested were standard back propagation, directed random search, functional link, extended delta bar delta, and the hybrid combination of self‐organizing map and back propagation. For input patterns with less than 100 elements, the directed random search and functio...

In chemoembolization, chemotherapy drugs and thrombotic agents are directly injected into the liver tumor through a catheter navigated to the artery that supplies the tumor. In order to help surgeons to train their hand-eye coordination skills to reduce the risk of injecting the thrombotic agents incorrectly and deprive normal tissue of its blood supply, this paper proposes a method for rendering the flow particles in simulation of chemotherapy drugs injection. Firstly, a three dimensional (3-D) region growing technique is used to extract vessels from clinical CT images. These vessels are skeletonized using a 3-D thinning algorithm, and their geometries are reconstructed using cubic b-splines. Quadrilaterals which are aligned along the viewing direction are rendered to visualize the movement of particles through the flow modeled using Hagen-Poiseuille Flow. Our visualization method achieves a computational efficient and good visual approximation of the flow of particles inside the vessels under fluoroscopic imaging.

Deep levels in n-InP caused by implantation of Ar and P ions were investigated by deep level transient spectroscopy. Among several electron traps observed in as-implanted InP, E2 (0.52 eV) in Ar ion implanted InP and E,, (0.47 eV) in P ion implanted InP were the major peaks which were considered to be of the same origin and associated with P-vacancies. After annealing at several hundreds "C, the E,, level in P-ion implanted InP was decreased, whereas the E2 level in Ar-ion implanted InP remained almost unchanged. This result indicates that P-ion implantation into InP can compensate P-vacancy related defects inevitably created in various phases of device processing.

In chemoembolization, chemotherapy drugs and thrombotic agents are directly injected into the liver tumor through a catheter navigated to the artery that supplies the tumor. In order to help surgeons to train their hand-eye coordination skills to reduce the risk of injecting the thrombotic agents incorrectly and deprive normal tissue of its blood supply, this paper proposes a method for rendering the flow particles in simulation of chemotherapy drugs injection. Firstly, a three dimensional (3-D) region growing technique is used to extract vessels from clinical CT images. These vessels are skeletonized using a 3-D thinning algorithm, and their geometries are reconstructed using cubic b-splines. Quadrilaterals which are aligned along the viewing direction are rendered to visualize the movement of particles through the flow modeled using Hagen-Poiseuille Flow. Our visualization method achieves a computational efficient and good visual approximation of the flow of particles inside the vessels under fluoroscopic imaging.

In this paper, the nonlinear transmission lines (NLTLs) for picosecond shockwaves generation, manufactured on GaAs membrane are analysed for the first time. A comparison between the performances obtained by using NETLs on GaAs bulk as well as on GaAs membrane is performed by computer simulation. It is shown that the NLTL realised on GaAs membrane is able to generate shockwaves

Your irre Votre n(Umnc8 Our file Notre refd?ence L'auteur a accordé une licence non exclusive permettant à la Bibliothèque nationale du Canada de reproduire, prêter, distribuer ou vendre des copies de cette thèse sous la forme de microfiche/film, de reproduction sur papier ou sur format électronique. L'auteur conserve la propriété du droit d'auteur qui protège cette thèse. Ni la thèse ni des extraits substantiels de celle-ci ne doivent être imprimés ou autrement reproduits sans son autorisation. Visually B e l i e v a b le E x p l o s i o n s I n Real T h e by Claude M a r t i n s a thesis submitted to the Faculty of Graduate Studies of York University in partial fulfillment of the requirements for the degree of M a s t e r Of Science O Permission has been granted to the LIBRARY OF YORK UNIVERSITY to lend or seIl copies of this thesis. to the NATIONAL LIBRARY OF CANADA to microfilm this thesis and to lend o r seII copies of the film, and to UNIVERSITY MICROFILMS to publish an abstract of this thesis. The author reserves other publication rights, and neither the thesis nor extensive extracts from it may be printed or othewise reproduced without the author's written permission.

The paper explores the physically-based modeling of a blast wave impact on surrounding objects. We propose a connected voxel representation of objects to model explosions that result in realistic solid debris, rather than flat polygons. The paper also presents improved fracture algorithms capable of accounting for the damage of multiple explosions. The important implementation issues and the results of the simulation are discussed.

Any sufficiently powerful explosion in air creates an expanding blast wave that propagates outwards from the source. The paper explores the physically based simulation of a blast wave impact on surrounding objects. The emphasis is on simplifying the underlying physical and chemical governing equations in order to achieve a visually believable result that performs in real time. A connected voxel model is used to represent objects, so that realistic solid debris is generated instead of flat polygons. The model permits arbitrary voxel shapes, which allow the creation of more complex objects with a lower number of voxels when compared to models using uniform voxel shapes. This model also overcomes certain limitations of the spring–mass particle model when it comes to representing rigid bodies. The paper also explores auxiliary visual effects caused by the blast wave, such as flame, smoke and dust. Each of these cues increases the visual plausibility of the explosion being simulated with...

Increasing emissions standards and fuel economy have forced engine designers to incorporate increasing numbers of degrees of control freedom into the engine. Consequently, calibration is becoming progressively more complex where the issue is most acute with diesel engines. This paper presents a project in which a closed loop controller is used in diesel engine fuel path, where no new calibration is required if any variation occurs. This is an initial investigation into diesel engine fuel path control. In order to prove that such a control technology could be developed over the whole speed and torque operating range, a high speed (2050 rpm) and the high torque (357Nm) point are chosen for investigations into control. This represents a challenging engine conditions for the application of control systems because of the limited time available for air supply and the need to complete combustion quickly. At these conditions the control authority is limited and the ability to detect for example small variations in torque and speed are quite limited owing to the high torque conditions. This paper demonstrates a closed-loop control at a high load condition and presents an architecture of controllable injection for a Caterpillar C6.6 engine using predictive control that includes exhaust temperature, of nitrogen oxides (NO x ) and particulate matters (PM) without changing the fuel quantity.

Improving building energy efficiency is essential for promoting sustainable construction practices and minimizing operational costs. This study introduces a physics-based framework that integrates domain-specific constraints into a regularized Bayesian Neural Network (BNN) to enhance occupancy detection accuracy, an essential factor for optimizing building energy management. Unlike conventional approaches, our method leverages a unique combination of environmental sensor data (temperature, humidity, light, CO2) and engineered features such as heating degree days (HDD) to improve predictive performance. Additionally, a physics-based regularizer is incorporated within the BNN model to ensure predictions adhere to the fundamental physical principles of the building, enhancing both reliability and uncertainty estimation. Tested on office building data from the University of Mons in Belgium, the proposed framework achieves 96–99 % accuracy across three test cases, outperforming traditional methods like Gradient Boosting Machine, Support Vector Machine, and Naïve Bayes. A user-friendly graphical interface was developed to facilitate real-world adoption, enabling facility managers, energy analysts, and building operators to seamlessly implement the approach without extensive technical expertise. By improving the precision of occupancy detection, this research supports more efficient HVAC control, enhanced occupant comfort, and substantial energy savings, an impact well-documented in previous studies that report potential reductions in energy consumption ranging from 20 to 30 %. The findings contribute to the advancement of intelligent building automation, offering a scalable solution for reducing carbon footprints and operational costs while promoting sustainable construction practices.

Expanded deployment of Electro-Mechanical Actuators (EMAs) in critical applications has created much interest in EMA Prognostic Health Management (PHM), a key enabling technology of Condition Based Maintenance (CBM). As such, Impact Technologies, LLC is collaborating with the NASA Ames Research Center to perform a number of research efforts in support of NASA's Integrated Vehicle Health Management (IVHM) initiatives. These efforts have combined experimental test stand development, laboratory seeded fault testing, and physical model-based health monitoring in a comprehensive PHM system development strategy. This paper discusses two closely related EMA research programs being conducted by Impact and NASA Ames. The first of these efforts resulted in the creation of an electro-mechanical actuator test stand for the Prognostics Center of Excellence at the NASA Ames Research Center. The second effort is ongoing and is utilizing physics-based modeling techniques to develop an algorithm and software package toolset for PHM of aircraft EMA systems using a hybrid (virtual sensor) approach. 1,2

Being relatively new to the field, electromechanical actuators in aerospace applications lack the knowledge base compared to ones accumulated for the other actuator types, especially when it comes to fault detection and characterization. Lack of health monitoring data from fielded systems and prohibitive costs of carrying out real flight tests push for the need of building system models and designing affordable but realistic experimental setups. This paper presents our approach to accomplish a comprehensive test environment equipped with fault injection and data collection capabilities. Efforts also include development of multiple models for EMA operations, both in nominal and fault conditions that can be used along with measurement data to generate effective diagnostic and prognostic estimates. A detailed description has been provided about how various failure modes are inserted in the test environment and corresponding data is collected to verify the physics based models under these failure modes that have been developed in parallel. A design of experiment study has been included to outline the details of experimental data collection. Furthermore, some ideas about how experimental results can be extended to real flight environments through actual flight tests and using real flight data have been presented. Finally, the roadmap leading from this effort towards developing successful prognostic algorithms for electromechanical actuators is discussed. *

The paper deals with the problem of minimizing the cost of pumping groundwater from a number of wells. A genetic algorithm approach is presented with the following characteristics: (a) Well locations are treated as optimization parameters and the well configurations are represented by a single integer combinatorial variable. (b) The objective function chosen is flat and appropriate scaling is performed. (c) No penalty functions are involved in constraint handling. Instead, the feasible region is transformed into an n-dimensional cube and at the same time a suitable boundary extension is introduced. Finally, the optimal well configuration is rapidly approached.

We developed and evaluated a three-layer snow model for application in general circulation models. This onedimensional snow model has many features of the detailed physically based model SNTHERM, yet is computationally much simpler. We have also extended the point model to vegetated areas using the parameterization concepts of the Biosphere-Atmosphere Transfer Scheme (BATS). Results of model applications for two types of vegetated ®elds Ð a short grassland in the French Alps and an old aspen forest in the southern study area of BOREAS Ð were presented. The results, on one hand, indicate the suitability of the model structure and parameter setting; on the other hand, the results explore the limitation of using `point' ®eld observations to evaluate an area model.

An evaluation of the Biosphere±Atmosphere Transfer Scheme (BATS) snow submodel was conducted, both in a stand-alone mode and within the National Center for Atmospheric Research (NCAR) Community Climate Model version 3 (CCM3). We evaluated, in the stand-alone mode, the performance of BATS parameterizations at local scales using ground-based observations from the former Soviet Union and from Mammoth Mountain, California. The BATS snow scheme reproduces well the seasonal evolution of snow water equivalent in both sites, and the results for the Mammoth Mountain site compare well with those from a more complex, physically based model (SNTHERM). In the coupled mode, we evaluated the modelled snow cover extent, snow mass, precipitation and temperature from BATS as linked to the NCAR CCM3 using available observations. The coupled models capture the broad pattern of seasonal and geographical distribution of snow cover, with better overall performance than the passive microwave snow data derived from the Nimbus-7 Scanning Multi-channel Microwave Radiometer (SMMR) which generally underestimates snow depth. In terms of continents, the snow mass is better simulated during the accumulation period than during the melt period, which is the case for both North America and Eurasia. The simulation of snow mass, precipitation and air temperature for North America is slightly better than that for Eurasia. A rigorous evaluation of snow simulations in coupled land±atmosphere models requires high quality global datasets of snow cover extent, snow depth and snow water equivalent. The available datasets and model outputs are not yet ready to ful®l this objective.

We report the development of physics based models for resistive random-access memory (RRAM) devices. The models are based on a generalized memristive system framework and can explain the dynamic resistive switching phenomena observed in a broad range of devices. Furthermore, by constructing a simple subcircuit, we can incorporate the device models into standard circuit simulators such as SPICE. The SPICE models can accurately capture the dynamic effects of the RRAM devices such as the apparent threshold effect, the voltage dependence of the switching time, and multi-level effects under complex circuit conditions. The device and SPICE models can also be readily expanded to include additional effects related to internal state changes, and will be valuable to help in the design and simulation of memory and logic circuits based on resistive switching devices.

STRACT: The paper presents results of analysis of experimental data on activity of snow AS tallization under temperature gradient conditions. In addition to temperature and temperature recds nt effects, recently accepted as the main factors influencing the snow recrystallization process, the g~ I~ of the water vapor flux and the volumetric mass production, related to the formed in snow heate ~cmass-fluxes, were estimated. The results suggest that the reported discrepancies between the an ults of observation and the modeled by presently used empirical construction recrystallization rates :~ be related to neglected before parameters of the water vapor in the pore space of snow.

The current status of applications of thin film transistors (TFTs) in ULSI is reviewed. Problems and solutions in areas of structures, materials, processes, and devices are discussed. Possible future applications of TFTs in ULSI and related fields are examined. Nano size TFTs are potential solutions for many current problems, but there are critical technical challenges for scientists and engineers.

Unmanned Underwater Vehicles (UUVs) are typically used for monitoring and inspection tasks where it's critical to locate objects and create 3D models of the surroundings. One of the most dependable techniques for accomplishing 3D localization and modeling jobs is stereo vision. This isn't the case in underwater conditions, though, as cameras will record distorted views, which results in inaccurate 3D estimation. In real-world applications, it is more practicable and a more difficult task to place the two cameras of the stereo vision system inside a single dome glass. The goal of this study is to recover the axial image views of the two cameras within a single dome window from the refracted views that were recorded. The cameras' position (along the Z and X axes) and orientation (pan and tilt) within a single dome windowof which there are 448 possible combinations-are taken into account by the kinematics model. The approach has been improved and validated using around 18 million experiments. Lastly, when compared to the original axial images, our approach may report axial images with an average overlapped similarity of 96%. Keywords Stereo vision • Dome window • Axial image • Refracted image • UUV

Over the last few decades, several work has been done on behavioral models and simulation tools. However, there is always some space for improvements and enhancements for previous work. The VHDL-AMS language makes possible the description of semiconductor devices based on mathematical equations. Moreover, VHDL-AMS is a useful tool for the analog design community. Also, the efficient semiconductor device models must be investigated in order to finely predict and extract its technological and electrical parameters. In this sense, the authors in this paper demonstrate the potential merits of the VHDL-AMS in the field of modeling of a SiC power Schottky diode. The diode's analytical model and its working principles are presented in this paper. This is carried out by taking into account the technological and electrical effects with VHDL-AMS, and with relevant parameters hitting a deep match between the simulated characteristics of the VHDL-AMS model and the measured characteristics of the SiC Schottky diode.

We present an efficient method for visual simulations of shock phenomena in compressible, inviscid fluids. Our algorithm is derived from one class of the finite volume method especially designed for capturing shock propagation, but offers improved efficiency through physically-based simplification and adaptation for graphical rendering. Our technique is well suited for parallel implementation on multicore architectures and is also capable of handling complex, bidirectional object-shock interactions stably and robustly. We describe its applications to various visual effects, including explosion, sonic booms and turbulent flows. Categories and Subject Descriptors (according to ACM CCS): I.3.7 [Computer Graphics]: Three-Dimensional Graphics and Realism G.1.8 [Mathematics of Computing]: Partial Differential Equations Physically correct methods for shockwave modeling focus less on conventional metrics of accuracy (such as order of convergence) and emphasize the ability to propagate discontinuities stably and with minimal diffusion. Specifically, techniques based on the finite volume method (FVM) have been developed that handle discontinuities well and al-

A common practice in pre-processing step of hydrological modeling is to ignore observations with any missing variable values at any given time step even if it is only one of the independent variable that is missing. These rows of data are labeled incomplete and would not be used in either model building or subsequent testing and verification steps. This is not necessarily the best way of doing it as information is lost when incomplete rows of data are thrown out. Learning algorithms are affected by such problems more than physically-based models as they rely heavily on the data to learn the underlying input/output relationships. In this study, the extent of damage to the performance of the learning algorithm due to missing data is explored in a field-scale application. We have tested and compared the performance of two well- known learning algorithms, namely Artificial Neural Networks (ANNs) and Support Vector Machines (SVMs) for short-term prediction of groundwater levels in a well...

A robotic manipulation system that is supposed to replace a human operator needs to deal with a variety of possible manipulation actions. These actions may be more or less constrained in their motion profile and in the accuracy of the transport goals. Some of this variation can be used to simplify the control and to optimize the base placement to improve the efficiency of the generated motion. We present an analysis tool that uses the abstraction of the human actions to generate path with efficient motion profiles. We compare the dynamics of a robot for different paths. We show in experiments that certain path properties are preferred to support efficient control and that the intuitive solution does not necessarily agree with the results optimizing for efficiency.

In this work, a simplified fire-spread model is presented. The mean features of a spreading surface fire-front such as rate of spread, tilt angle, flame height, are expounded. The model improves a previous work. The flame sub model was based on the McCaffrey law. In order to eliminate this weakness, it evolves thanks to simplified hypotheses and energy balance into an analytical relationship. The model is tested on a set of experiments carried out at the Instituto Superior Técnico in 2003 at laboratory scale. The comparison between the model simulations and the experiments are in good agreement. The simplified physical model represents the behavior of the rate of spread and the flame height as well. Indeed, the model represents the increase of rate of spread when slope and/or wind speed increase. And, the flame height increase to a maximum and its decrease are explained by a relation between flame height and flame tilt angle, which is not provided by the literature. Introduction Fir...

A fire spread experiment was conducted in the field under wind-blown conditions. The study area was located in south Corsica (France). The fuel consists in tall and dense Mediterranean shrub vegetation. The plot area was about 30 m wide and 80 m long. The fire spread experiment was made using a point ignition. This experiment was conducted not only in order to increase the knowledge and understanding of the fire behaviour in the field but to provide data for the validation of physics based models of fire spread too. In particular, the effects of wind on the geometric and thermal properties of the flame front in the field are investigated. The flame temperature along the vertical direction and the radiation emitted ahead of the flame front, were measured. The methodology employed in this experiment and some quantitative measurements of wind velocity and direction, flame geometric properties, are also presented and discussed.

We developed and evaluated a three-layer snow model for application in general circulation models. This onedimensional snow model has many features of the detailed physically based model SNTHERM, yet is computationally much simpler. We have also extended the point model to vegetated areas using the parameterization concepts of the Biosphere-Atmosphere Transfer Scheme (BATS). Results of model applications for two types of vegetated ®elds Ð a short grassland in the French Alps and an old aspen forest in the southern study area of BOREAS Ð were presented. The results, on one hand, indicate the suitability of the model structure and parameter setting; on the other hand, the results explore the limitation of using `point' ®eld observations to evaluate an area model.

An evaluation of the Biosphere±Atmosphere Transfer Scheme (BATS) snow submodel was conducted, both in a stand-alone mode and within the National Center for Atmospheric Research (NCAR) Community Climate Model version 3 (CCM3). We evaluated, in the stand-alone mode, the performance of BATS parameterizations at local scales using ground-based observations from the former Soviet Union and from Mammoth Mountain, California. The BATS snow scheme reproduces well the seasonal evolution of snow water equivalent in both sites, and the results for the Mammoth Mountain site compare well with those from a more complex, physically based model (SNTHERM). In the coupled mode, we evaluated the modelled snow cover extent, snow mass, precipitation and temperature from BATS as linked to the NCAR CCM3 using available observations. The coupled models capture the broad pattern of seasonal and geographical distribution of snow cover, with better overall performance than the passive microwave snow data derived from the Nimbus-7 Scanning Multi-channel Microwave Radiometer (SMMR) which generally underestimates snow depth. In terms of continents, the snow mass is better simulated during the accumulation period than during the melt period, which is the case for both North America and Eurasia. The simulation of snow mass, precipitation and air temperature for North America is slightly better than that for Eurasia. A rigorous evaluation of snow simulations in coupled land±atmosphere models requires high quality global datasets of snow cover extent, snow depth and snow water equivalent. The available datasets and model outputs are not yet ready to ful®l this objective.

Automatic mapping of urban materials from remotely sensed radiance images remains difficult because of the complex phenomena induced by relief. Indeed, shadows and environment effects disrupt the radiance reaching the sensor. The measured radiance also depends on the illumination conditions of the observed area. This paper describes a new physical model, ICARE, able to solve the radiative transfer inversion problem in urban areas, in the reflective domain (0.4-2.5 mm), from high spatial and spectral resolution images. This new approach takes into account the relief, the spatial heterogeneity of the scene and atmospheric effects, in order to extract rigorously the ground surface reflectance. The resolution method consists of modelling separately the irradiance and radiance components at ground and sensor levels. Three input data are required to solve this inverse problem: atmospheric parameters (aerosol type and visibility), 3D digital vector models of the scene, and spectral at-sensor calibrated images of the scene. The validation of ICARE is checked through the CAPITOUL field campaign, carried out over Toulouse (France). Measurements were performed using two airborne Pelican image systems consisting of 8 high spatial (20 cm) and spectral (30 nm) resolution cameras. Results are presented over typical urban structures in Toulouse center. ICARE performance is expected to be better than 0.04 in the reflectance retrieval, even in shadowed areas. To quantify the gain brought by ICARE, a comparison between classification based on radiance and retrieved reflectance images, obtained by inversion, is introduced. The results show that the classification is improved from 54% for a flat ground assumption to 74% using ICARE.

High strength components exposed to cyclic loading such as gas turbine disks fail in an insidious manner, giving no prior indication that damage has occurred. Cracking takes place on a very small scale and the critical damage state is reached when the crack is very small. Unfortunately, cracks of these sizes are difficult to detect. Often, long crack damage is considered when performing fatigue diagnostics. However, an accurate onboard prognostic capability should consider total life as initiation and long crack growth. Prognostication of small cracks requires simulating fatigue damage accumulation from the evolution of micro scale damage initiation. This paper discusses methods for predicting the probability of fatigue failure from cracks starting on the micro scale. These methods predict the variation in fatigue life based on the statistical variation in the microstructure of the material. Material parameters at the metallic grain level are integrated with fundamental physics-based models to predict the damage as it accumulates. Public reporting burden for the collection of information is estimated to average 1 hour per response, including the time for reviewing instructions, searching existing data sources, gathering and maintaining the data needed, and completing and reviewing the collection of information. Send comments regarding this burden estimate or any other aspect of this collection of information, including suggestions for reducing this burden, to Washington Headquarters Services, Directorate for Information Operations and Reports, 1215 Jefferson Davis Highway, Suite 1204, Arlington VA 22202-4302. Respondents should be aware that notwithstanding any other provision of law, no person shall be subject to a penalty for failing to comply with a collection of information if it does not display a currently valid OMB control number.

The established paradigm tells us that the ozone layer is one of the most important and delicate components of the Earth's atmosphere, being essential for the protection of life on the planet. Despite being present in extremely small quantities (just 0.000004% by volume of the global atmosphere), this gas plays a crucial role in absorbing a significant portion of the ultraviolet (UV) radiation emitted by the Sun, which could be harmful to living beings. This document explores how much ultraviolet energy is absorbed by ozone, analyzing fundamental aspects such as its concentration in the atmosphere, its vertical distribution, and the physical properties that allow it to interact with solar radiation. Additionally, a quantitative analysis is carried out to determine the exact percentage of UV energy that ozone can stop, considering factors such as the molecular density of ozone, its absorption cross-section, and the average flux of UV radiation at the Earth's surface. This study not only highlights the true importance of ozone in regulating the climate and protecting against harmful radiation but also reveals how its low concentration limits its ability to completely absorb UV radiation, making it clear that other atmospheric gases, such as oxygen (21% of the total atmosphere), play a fundamental complementary role.