Detailed Physical Model Research Articles

Combination of Data-driven Feature Selection Methods with Domain Knowledge for Diagnosis of Railway Vehicles

Railway vehicles are generally maintained preventively within certain time periods. Condition based predictive maintenance strategies have a great economic potential so that modern trains are equipped with many sensors in order to perform diagnostics and prognostics of components.Methods for fault detection need appropriate feature subsets in order to achieve small in-sample and out-sample errors. In our case the typical feature selection approach using pure data-driven methods is difficult, as the number of possible feature sets is very large. On the other hand there exists rich domain knowledge and detailed physical models of the mechanical system. The aim is to combine this knowledge with the often used mathematical methods for feature selection for improving classification of cases when a faulty damper is present. Based on the dynamic equations of motion, this paper presents heuristic feature selection via the analysis of transfer functions. We describe several wellknown methods of automated feature selection and a workflow which combines domain knowledge with automated methods. Results show that it is difficult to definefeatures based only on domain-knowledge, but in combination with data-driven techniques good classification performance can be achieved.

Modeling and Offset-Free Model Predictive Control of a Hydraulic Mini Excavator

During the virtual development and experimental testing of advanced construction machinery, automation approaches for automated task execution can prove very valuable. In this paper, modeling and automation approaches for a hydraulic mini excavator are developed. In particular, a physical model for detailed system analysis and a simplified Hammerstein model for controller tuning are developed and validated with measurement data from the mini excavator. For attitude estimation of the excavator, inertial measurement units and extended Kalman filters are used in a sensor fusion framework. The control concept for automation is based on a virtual driver consisting of a state machine for task coordination as well as offset-free model predictive controllers (MPCs) for decentralized and robust tracking control of all motion axes. The constrained MPC optimization problems are solved in real time by means of the accelerated proximal gradient method. Experimental results from the mini excavator prove the developed control approach to be valuable for virtual development and automated testing during the commissioning of hydraulic machinery. Note to Practitioners —In this paper, a hydraulic mini excavator is considered for demonstrating the benefits of automation with regard to the development of advanced mobile machinery. A detailed physical model and a simplified model are introduced for virtual analysis and comissioning of the excavator. This allows for detailed system analysis even at an early development stage. Then, a framework for automated testing of the real prototype is introduced. This concept is based on attitude estimation filters, a state machine, and model predictive controllers and closely resembles the human driver in its behavior, but allows for reproducible testing results and therefore reduces commissioning efforts and development costs. Particular attention is paid to the robustness of the control concept, since the coupling of the hydraulic axes and digging forces lead to disturbances that need to be compensated. A simple, yet efficient and real-time capable algorithm is provided for numerical optimization. Experimental results show that the developed methods can contribute to the automation of hydraulic machinery, and that the introduced framework can easily be extended in order to automate other types of machinery with simple hydraulics.

Experimental analysis of electro-pneumatic optimization of hot stamping machine control systems with on-delay timer

The sustainability criterion in the manufacturing industries is imperative, especially in the automobile industries. Currently, efforts are being made by the industries to mitigate CO2 emission by the total vehicle weight optimization, machine utilization and resource efficiency. In lieu of this, it is important to understudy the manufacturing machines adopted in the automobile industries. One of such machine is the hot stamping machine that is used for about 35% of the manufacturing operations within the automobile industries. Therefore, the standardization and optimization of the hot stamping process could reduce the carbon footprint within the automobile industries. This work understudied the on-delay timer functional valve of the hot stamping machine in order to determine various process parameters affecting it. The detailed physical model of the pneumatic and electro-pneumatic cylinder systems for the control is simulated and optimized for both the pneumatic and electro-pneumatic cylinder systems. Experimental and simulation model were established at the FESTO work station and FESTO FluidSIM® 5.1 respectively to evaluate the effective velocity, accelerations, displacement, and flow rate for the pneumatic and electro-pneumatic actuator on both systems. Comparisons were made between pneumatic and electro-pneumatic cylinder systems on their characteristic curve in order to optimize the process variables. The result favours the electro-pneumatic cylinder systems in stability and in designing hot stamping machines. The result obtained could elucidate the understanding of the pressing arm of a hot stamping machine.

Collaboration of simulations and experiments for development and uncertainty quantification of a reduced char combustion model

Computational fluid dynamics (CFD) plays a decisive role in the development of cost-effective oxy-coal combustion technologies to improve process efficiency and to decrease pollutant emissions. The implementation of detailed physical models describing coal devolatilization, char oxidation, gas-phase reactions and pollutant formation ensures accurate CFD simulations of coal combustion but is still challenging for large-scale combustors due to the significant computational efforts required, especially in the framework of Large Eddy Simulation (LES). The development of reduced physics model with quantified model-form uncertainty is needed to overcome the challenges of performing LES of industrial coal-fired boilers. Reduced models must reproduce the main features of the detailed models and the capability of bridging scales and being predictive. A tight coupling of simulation and experiments is necessary to ensure predictivity with uncertainty quantification for a reduced model. This work proposes a combined experimental/numerical methodology that uses global sensitivity analysis to rank fundamental input parameters of a reduced char oxidation and gasification model describing reactions between char carbon and O2, CO2 and H2O reagents in both air and oxy-coal conditions. A careful evaluation of uncertainty in the data, in the model form and in the model parameters is performed. The reference dataset, consisting of the experiments carried out in a laminar entrained flow reactor operated by Sandia National Laboratories, has been exploited. The methodology is based on the use of so-called instrument models to include all the physical sub-models and the sources of uncertainty considered in the experiments and in the numerical simulations and affecting the main quantities of interest, e.g. reaction rates. The quantified uncertainty in the instrument models provides the range of uncertainty for the reduced char combustion model. Then, the reduced model with quantified uncertainty will be validated against the experimental data. The reduced model capability to address heterogeneous reaction at the particle surface, mass transport of species in particle boundary layer, pore diffusion and internal surface area changes will be assessed.

Faster catalog matching on Graphics Processing Units

Abstract One of the most fundamental problems in observational astronomy is the cross-identification of sources. Observations are made at different times in different wavelengths with separate instruments, resulting in a large set of independent observations. The scientific outcome is often limited by our ability to quickly perform associations across catalogs. The matching, however, is difficult scientifically, statistically as well as computationally. The former two require detailed physical modeling and advanced probabilistic concepts; the latter is due to the large volumes of data and the problem’s combinatorial nature. In order to tackle the computational challenge and to prepare for future surveys we developed a new implementation on Graphics Processing Units. Our solution scales across multiple devices and can process hundreds of trillions of crossmatch candidates per second in a single machine.

Improving Operation of a 2.5kW SOFC Power System with Supervisory Control

Operation of the SOFC power system can be improved by upgrading the low-level controllers with a supervisory controller (see Figure 1). In this work a simple, model-free supervisory optimizer that adjusts references for the low level controllers is proposed. The optimization problem for the SOFC power system is formulated with the cost function depending on the electrical efficiency of the system and deviations of the system temperatures from their limits. Electrical efficiency is calculated as the ratio of the net electrical power to the total power obtained by burning the fuel. The dominant parasitic losses of the system are assumed to be mainly related to the air blower consumption. Based on efficiency analysis, which demands a number of simulations on SOFC power system model, the references for the burner lambda and stack outlet and inlet air temperatures are selected as optimization variables. The optimization problem is solved by using the extremum-seeking approach where optimum is sought directly on the process. An advantage of this approach is that it does not need a model of the system and exceeds the weakness of model-based optimizers, which assume nominal process condition all the time and do not take into account the degradation processes in fuel cells. Its disadvantage is it needs to probe the system at all time by imposing small variations on manipulated variables and delivers a convergence speed which is somewhat slower compared to the model based approach. A simple algorithm is designed to perform the model-free optimization. The algorithm is additionally augmented to incorporate changes in the current load in order to increase the convergence speed of the optimization at large load changes. The efficiency of the optimizer is governed by properly tuned parameters of the optimization algorithm. Proposed supervisory optimizer is assessed on a simulated detailed physical model of a 2.5 kW SOFC power system by applying large step current load changes. Simulation results indicate that the optimizer improves efficiency of the system by approximately 10 % compared to the low-level controllers and keeps the system temperatures within the safe ranges (see Figure 2). Due to its simplicity the optimizer appears appropriate for practical applications. Figure 1

LEM Characterization of Synthetic Jet Actuators Driven by Piezoelectric Element: A Review.

In the last decades, Synthetic jet actuators have gained much interest among the flow control techniques due to their short response time, high jet velocity and absence of traditional piping, which matches the requirements of reduced size and low weight. A synthetic jet is generated by the diaphragm oscillation (generally driven by a piezoelectric element) in a relatively small cavity, producing periodic cavity pressure variations associated with cavity volume changes. The pressured air exhausts through an orifice, converting diaphragm electrodynamic energy into jet kinetic energy. This review paper considers the development of various Lumped-Element Models (LEMs) as practical tools to design and manufacture the actuators. LEMs can quickly predict device performances such as the frequency response in terms of diaphragm displacement, cavity pressure and jet velocity, as well as the efficiency of energy conversion of input Joule power into useful kinetic power of air jet. The actuator performance is also analyzed by varying typical geometric parameters such as cavity height and orifice diameter and length, through a suited dimensionless form of the governing equations. A comprehensive and detailed physical modeling aimed to evaluate the device efficiency is introduced, shedding light on the different stages involved in the process. Overall, the influence of the coupling degree of the two oscillators, the diaphragm and the Helmholtz frequency, on the device performance is discussed throughout the paper.

Structural analysis based sensor measurement fault diagnosis in cement industries

This investigation presents a fault diagnosis methodology for detecting sensor faults in cement industries pyro processing section. It works in three steps: (a) modelling, (b) analysis, and (c) validation. In the modelling, the actual data from the cement pyro processing is used to do a correlation analysis between output and input variables. The structural model is obtained from the correlation tests. During the analysis phase the Structural analysis Tool (SaTool) is used to detect the detectability and isolability of the faults. The results of the structural analysis are validated in a cement industry using residual analysis performed using structural sensor model and real-time measurements. The main advantages of this fault diagnosis technique are: (a) it requires only correlation analysis to obtain the structural model without a detailed physical model as in other methods, (b) conclusions regarding detectability and isolability can be easily drawn during the analysis stage itself, and (c) the method is simple compared to the model-based, and data-history based methods. The effectiveness of the proposed method is illustrated using data from cement pyro processing plant and its performance is compared with model based approaches for four different types of sensor faults: (1) bias, (2) drift, (3) stuck, and (4) measurement failures. Our results demonstrate that the structural method is able to detect the sensor faults even in the presence of noisy information, and its performance is comparable with that of model based approaches without employing a physical model.

Dehumidification and Thermal Behavior of Desiccant Wheels: Correlations Based on Experimental and Manufacturer Data

ABSTRACTDesiccant cooling systems (DCS) represent a suitable alternative to conventional systems for air-conditioning purposes. Their benefits should be correctly assessed by means of dynamic simulations, taking into account both the operating context and the available control variables. Several models are available in literature to model solid DCS based on desiccant wheels (DW). Nevertheless, physical models are rather complex to be implemented in dynamic simulation tools of building-integrated energy systems, while constant effectiveness models have low performance. Regression models can represent a suitable alternative, as they can provide high accuracy but with a low modeling effort. In this paper, experimental data are used to develop correlations to predict the dehumidification and thermal performance of a DW, as a function of inlet air temperature and humidity ratio, regeneration temperature, air flow rates, and rotational speed. Statistical tools are used to investigate the effect of those independent operating variables. Furthermore, the selection software provided by another DW manufacturer is used to generate operational data of a further desiccant rotor and to derive the related correlations. At last, the proposed model is compared with the correlations found in the relevant literature. The results show that a very good agreement is found in the comparison between measured and predicted values, with maximum relative errors not higher than 5%. Furthermore, an excellent behavior of the proposed model is also found when it is used to simulate a generic desiccant wheel, without the need of a detailed physical model. Finally, a better agreement is found with respect to other models based on correlations developed in literature, even using a higher number of coefficients to be calibrated.

Magnetic field formation in the Milky Way like disc galaxies of the Auriga project

AbstractThe magnetic fields observed in the Milky Way and nearby galaxies appear to be in equipartition with the turbulent, thermal and cosmic ray energy densities, and hence are expected to be dynamically important. However, the origin of these strong magnetic fields is still unclear, and most previous attempts to simulate galaxy formation from cosmological initial conditions have ignored them altogether. Here, we analyse the magnetic fields predicted by the simulations of the Auriga Project, a set of 30 high-resolution cosmological zoom simulations of Milky Way like galaxies, carried out with a moving-mesh magnetohydrodynamics code and a detailed galaxy formation physics model. We find that the magnetic fields grow exponentially at early times owing to a small-scale dynamo with an e-folding time of roughly 100 Myr in the centre of haloes until saturation occurs around z = 2–3, when the magnetic energy density reaches about 10 per cent of the turbulent energy density with a typical strength of $10\text{--}50\,\rm {\mu G}$. In the galactic centres, the ratio between magnetic and turbulent energies remains nearly constant until z = 0. At larger radii, differential rotation in the discs leads to linear amplification that typically saturates around z = 0.5–0. The final radial and vertical variations of the magnetic field strength can be well described by two joint exponential profiles, and are in good agreement with observational constraints. Overall, the magnetic fields have only little effect on the global evolution of the galaxies as it takes too long to reach equipartition. We also demonstrate that our results are well converged with numerical resolution.

The Effect of Kinetic Nonlinearity on Battery Electrode Design: A Closer Look at Electrode Processes

The idea of using modeling in battery design was first introduced by W. Tiedemann and J. Newman in 19751. They used an ohmically limited porous electrode model to maximized the cell effective capacity by controlling electrode thickness and porosity. Newman later applied the reaction-zone model to maximize the specific energy of the system by considering mass as well2. V. Ramadesigan et al.3went one step further with the ohmic resistance model in the electrode by including electrode kinetics. They assumed the linear kinetics, and minimized the ohmic resistance in the positive electrode by varying porosity using control vector parameterization (CVP) method. They also introduced the idea of using layered graded electrode to get a porosity distribution to further reduce the ohmic resistance in the system. Since the introduction of the pseudo-2D (P2D) model3, many efforts have been made in applying the P2D model in battery design. W. Du et al. applied an optimization framework based on surrogate method4. S. Golmon et al. performed the multi-objective and multi-design-parameter optimization problem with adjoint sensitivity analysis5 with expanded P2D model incorporating mechanical stress-strain relationship. S. De and his coworkers6 used a reformulated model developed by the Subramanian group7, to perform simultaneous optimization of multiple design parameters. N. Xue et al. 8 applied the gradient-based algorithm framework to optimize the cell design. More recently, Y. Dai and V. Srinivasan9used a gradient-free direct search method to maximize the specific energy. Generally, there are two purposes of using models to simulate the battery system; one is to develop a better understanding of the system, and the other is to use the models as guidance to achieve better performance. For the latter purpose, a model with fewer assumptions and more equations capturing as many processes as possible is desired to simulate the system better. However, for a better understanding of the system, it may be worth taking a step back, and examining a simpler model where the effects of different processes can be observed clearly, even though more detailed physical models with much less assumptions would be used to guide the actual design. This is the purpose and idea behind this study. The model used in this study is an ohmic resistance model, with the assumption that the electrolyte concentration is uniform and double-layer charging can be ignored. Ohm’s law for both solid and electrolyte phases was included in the model, as well as a polarization equation representing the electrode kinetics. The polarization equation describes the charge transfer between the two phases, of which the linear form together with Ohm’s law for the two phases can be found in Ref10. In such a model, the trade-off between ohmic potential drop and reaction kinetics can be captured by current distribution between the two phases. In this work, we want to quantify the impact of nonlinearity on the architecture design by carefully examining the effect of nonlinearity on the ohmic resistance model. For linear kinetics, the optimal design to minimize overall resistance is independent of operation conditions (Fig. 1). However, for nonlinear kinetics, the optimal porosity is bigger for higher charge/discharge rates. This suggests that for batteries built for different applications, the optimal design varies depending on the operation conditions, especially at high rates, where the kinetics fall outside the linear regime. Figure 1 . Optimal Porosity under Different Charge/Discharge Rates for Linear and Nonlinear Polarization Acknowledgements The authors are thankful for the financial support of this work by the Clean Energy Institute (CEI) at the University of Washington.

Pre-stack full waveform inversion of ultra-high-frequency marine seismic reflection data

The full waveform inversion (FWI) of seismic reflection data aims to reconstruct a detailed physical properties model of the subsurface, fitting both the amplitude and the traveltime of the reflections generated at physical discontinuities in the propagation medium. Unlike reservoir-scale seismic exploration, where seismic inversion is a widely adopted remote characterization tool, ultrahigh-frequency (UHF, 0.2–4.0 kHz) multichannel marine reflection seismology is still most often limited to a qualitative interpretation of the reflections’ architecture. Here we propose an elastic FWI methodology, custom-tailored for pre-stack UHF marine data in vertically heterogeneous media to obtain a decimetric-scale distribution of P-impedance, density and Poisson’s ratio within the shallow subseabed sediments. We address the deterministic multiparameter inversion in a sequential fashion. The complex trace instantaneous phase is first inverted for the P-wave velocity to make up for the lack of low frequency in the data and reduce the nonlinearity of the problem. This is followed by a short-offset P-impedance optimization and a further step of full offset range Poisson’s ratio inversion. Provided that the seismogram contains wide reflection angles (>40°), we show that it is possible to invert for density and decompose a posteriori the relative contribution of P-wave velocity and density to the P-impedance. A broad range of synthetic tests is used to prove the potential of the methodology and highlights sensitivity issues specific to UHF seismic. An example application to real data is also presented. In the real case, trace normalization is applied to minimize the systematic error deriving from an inaccurate source wavelet estimation. The inverted model for the top 15 m of the subseabed agrees with the local lithological information and core-log data. Thus, we can obtain a detailed remote characterization of the shallow sediments using a multichannel sub-bottom profiler within a reasonable computing cost and with minimal pre-processing. This has the potential to reduce the need of extensive geotechnical coring campaigns.

Benchmarking test of empirical root water uptake models

Abstract. Detailed physical models describing root water uptake (RWU) are an important tool for the prediction of RWU and crop transpiration, but the hydraulic parameters involved are hardly ever available, making them less attractive for many studies. Empirical models are more readily used because of their simplicity and the associated lower data requirements. The purpose of this study is to evaluate the capability of some empirical models to mimic the RWU distribution under varying environmental conditions predicted from numerical simulations with a detailed physical model. A review of some empirical models used as sub-models in ecohydrological models is presented, and alternative empirical RWU models are proposed. All these empirical models are analogous to the standard Feddes model, but differ in how RWU is partitioned over depth or how the transpiration reduction function is defined. The parameters of the empirical models are determined by inverse modelling of simulated depth-dependent RWU. The performance of the empirical models and their optimized empirical parameters depends on the scenario. The standard empirical Feddes model only performs well in scenarios with low root length density R, i.e. for scenarios with low RWU compensation. For medium and high R, the Feddes RWU model cannot mimic properly the root uptake dynamics as predicted by the physical model. The Jarvis RWU model in combination with the Feddes reduction function (JMf) only provides good predictions for low and medium R scenarios. For high R, it cannot mimic the uptake patterns predicted by the physical model. Incorporating a newly proposed reduction function into the Jarvis model improved RWU predictions. Regarding the ability of the models to predict plant transpiration, all models accounting for compensation show good performance. The Akaike information criterion (AIC) indicates that the Jarvis (2010) model (JMII), with no empirical parameters to be estimated, is the best model. The proposed models are better in predicting RWU patterns similar to the physical model. The statistical indices point to them as the best alternatives for mimicking RWU predictions of the physical model.

Automatic CT-based finite element model generation for temperature-based death time estimation: feasibility study and sensitivity analysis.

Temperature-based death time estimation is based either on simple phenomenological models of corpse cooling or on detailed physical heat transfer models. The latter are much more complex but allow a higher accuracy of death time estimation, as in principle, all relevant cooling mechanisms can be taken into account.Here, a complete workflow for finite element-based cooling simulation is presented. The following steps are demonstrated on a CT phantom: Computer tomography (CT) scan Segmentation of the CT images for thermodynamically relevant features of individual geometries and compilation in a geometric computer-aided design (CAD) model Conversion of the segmentation result into a finite element (FE) simulation model Computation of the model cooling curve(MOD) Calculation of the cooling time(CTE) For the first time in FE-based cooling time estimation, the steps from the CT image over segmentation to FE model generation are performed semi-automatically. The cooling time calculation results are compared to cooling measurements performed on the phantoms under controlled conditions. In this context, the method is validated using a CT phantom. Some of the phantoms' thermodynamic material parameters had to be determined via independent experiments.Moreover, the impact of geometry and material parameter uncertainties on the estimated cooling time is investigated by a sensitivity analysis.

Bridge damage detection using spatiotemporal patterns extracted from dense sensor network

The alarmingly degrading state of transportation infrastructures combined with their key societal and economic importance calls for automatic condition assessment methods to facilitate smart management of maintenance and repairs. With the advent of ubiquitous sensing and communication capabilities, scalable data-driven approaches is of great interest, as it can utilize large volume of streaming data without requiring detailed physical models that can be inaccurate and computationally expensive to run. Properly designed, a data-driven methodology could enable fast and automatic evaluation of infrastructures, discovery of causal dependencies among various sub-system dynamic responses, and decision making with uncertainties and lack of labeled data. In this work, a spatiotemporal pattern network (STPN) strategy built on symbolic dynamic filtering (SDF) is proposed to explore spatiotemporal behaviors in a bridge network. Data from strain gauges installed on two bridges are generated using finite element simulation for three types of sensor networks from a density perspective (dense, nominal, sparse). Causal relationships among spatially distributed strain data streams are extracted and analyzed for vehicle identification and detection, and for localization of structural degradation in bridges. Multiple case studies show significant capabilities of the proposed approach in: (i) capturing spatiotemporal features to discover causality between bridges (geographically close), (ii) robustness to noise in data for feature extraction, (iii) detecting and localizing damage via comparison of bridge responses to similar vehicle loads, and (iv) implementing real-time health monitoring and decision making work flow for bridge networks. Also, the results demonstrate increased sensitivity in detecting damages and higher reliability in quantifying the damage level with increase in sensor network density.

Long-Term Stability and Reliability of Black Phosphorus Field-Effect Transistors

Black phosphorus has been recently suggested as a very promising material for use in 2D field-effect transistors. However, due to its poor stability under ambient conditions, this material has not yet received as much attention as for instance MoS2. We show that the recently demonstrated Al2O3 encapsulation leads to highly stable devices. In particular, we report our long-term study on highly stable black phosphorus field-effect transistors, which show stable device characteristics for at least eight months. This high stability allows us to perform a detailed analysis of their reliability with respect to hysteresis as well as the arguably most important reliability issue in silicon technologies, the bias-temperature instability. We find that the hysteresis in these transistors depends strongly on the sweep rate and temperature. Moreover, the hysteresis dynamics in our devices are reproducible over a long time, which underlines their high reliability. Also, by using detailed physical models for oxide traps developed for Si technologies, we are able to capture the channel electrostatics of the black phosphorus FETs and determine the position of the defect energy band. Finally, we demonstrate that both hysteresis and bias-temperature instabilities are due to thermally activated charge trapping/detrapping by oxide traps and can be reduced if the device is covered by Teflon-AF.

New insights into the urban washoff process with detailed physical modelling

Current urban washoff models still rely on empirical catchment-scale functions, that have not been substantially updated during the last 40years. This paper introduce a new approach using the physical model FullSWOF to evaluate urban washoff process. The modelling approach is performed for a Parisian road catchment. Water flow simulation is validated by outlet discharge measurements and local observations of water depth. Water quality modelling of three classes of particles (d50=7μm, 70μm, and 250μm) is applied using the Hairsine-Rose model. Analysis of the washoff process at the catchment scale indicates that most (>90%) of the finest particles are removed at the beginning of a rainfall event, about 10%–20% of medium-sized particles are moved over the latest part of the event, and almost no coarse particles can be transferred into the sewer inlet. Spatial analysis of washoff process reveals that the concentration of suspended solids on road and sidewalk surface is more sensitive to rainfall intensities than that on gutter surface, while coarser particles tend to accumulate in the gutter over the later part of a rainfall event. Investigation of the driving force behind the detachment process indicates that rainfall-driven effects are two orders of magnitude higher than flow-driven effects. Moreover, it is observed that rainfall-driven detachment is considerably decreased with the rising water depth, while flow-driven detachment occurs only in gutter areas. Finally, several controversial arguments on the use of physical models for assessing the washoff process, and perspectives on development of physical urban washoff models are discussed.

A two parameter model to simulate thermal infrared directional effects for remote sensing applications

Abstract Measurements of land surface temperature (LST) performed in the thermal infrared (TIR) domain are prone to strong directional anisotropy. Instead of detailed analytical physical TIR models requiring too much input information and computational capacities, simplified parametric approaches capable to mimic and correct with precision the angular effects on LST will be deemed suitable for practical satellite applications. In this study, we present a simple two parameters model, so-called RL (Roujean-Lagouarde), which shows capabilities to properly depict the directional signatures of both urban and vegetation targets within an accuracy better than 1 °C. This latter value is the RMSE (root mean square error) obtained as the best adjustment of the RL model against in situ datasets. Then the RL approach was compared to a synthetic dataset generated by the model Soil Canopy Observation, Photochemistry and Energy fluxes (SCOPE) in which large variability in meteorological scenarios, canopy structure and water status conditions was accounted for. Results indicate RMSE ≤ 0.6 °C which is a very hopeful result. Besides, the RL model performs even better than the popular parametric model of Vinnikov that encompasses two unknowns. The ability of RL model to better reproduce the hotspot phenomenon explains this feature. The RL model appears as a potential candidate for future operational processing chains of TIR satellite data because it fulfills the requirements of both simple analytical formulation and limited number of input parameters. Efforts nevertheless remain to be done on inversion methodologies.

Echo State Network for the Remaining Useful Life Prediction of a Turbofan Engine

Among the various data-driven approaches used for RUL prediction, Recurrent Neural Networks (RNNs) have certain prima facie advantages over other approaches because the connections between internal nodes form directed cycles, thus creating internal states which enables the network to encapsulate dynamic temporal behavior and also to properly handle the noise affecting the collected signals. However, the application of traditional RNNs is limited by the difficulty of optimizing their numerous internal parameters and the significant computational effort associated with the training process. In this work, we explore the use of the Echo State Network (ESN), a relatively new type of Recurrent Neural Network (RNN). One of the main advantages of ESN is the training procedure, which is based on a simple linear regression. Unlike traditional RNNs, ESNs can be trained with fairly little computational effort, while still providing the generalization capability characteristic of RNNs. In this paper, we use Differential Evolution (DE) for the optimization of the ESN architecture for RUL prediction of a turbofan engine working under variable operating conditions. A procedure for pre-processing of the monitored signals and for identification of the onset of acceleration of degradation (i.e., the so-called elbow point in the degradation trend) will be shown. The datasets used to validate the approach have been taken from the NASA Ames Prognostics CoE Data Repository. These datasets were generated using a turbofan engine simulator, based on a detailed physical model that allows input variations of health-related parameters under variable operating conditions and records values from some specific sensor measurements. The results obtained on these data confirm the ESN’s capability to provide accurate RUL predictions.

Numerical analysis of quantitative measurement of hydroxyl radical concentration using laser-induced fluorescence in flame**Project supported by the National Natural Science Foundation of China (Grant No. 11272338) and the Fund from the Science and Technology on Scramjet Key Laboratory, China (Grant No. STSKFKT2013004).

The aim of the present work is to quantitatively measure the hydroxyl radical concentration by using LIF (laser-induced fluorescence) in flame. The detailed physical models of spectral absorption lineshape broadening, collisional transition and quenching at elevated pressure are built. The fine energy level structure of the OH molecule is illustrated to understand the process with laser-induced fluorescence emission and others in the case without radiation, which include collisional quenching, rotational energy transfer (RET), and vibrational energy transfer (VET). Based on these, some numerical results are achieved by simulations in order to evaluate the fluorescence yield at elevated pressure. These results are useful for understanding the real physical processes in OH-LIF technique and finding a way to calibrate the signal for quantitative measurement of OH concentration in a practical combustor.