The Experts below are selected from a list of 57546 Experts worldwide ranked by ideXlab platform
Brian J. Steffenson - One of the best experts on this subject based on the ideXlab platform.
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Hyperspectral imaging and improved Feature Variable selection for automated determination of deoxynivalenol in various genetic lines of barley kernels for resistance screening
Food chemistry, 2020Co-Authors: Ce Yang, Yanhong Dong, Ryan Johnson, Rae Page, Tamas Szinyei, Cory D. Hirsch, Brian J. SteffensonAbstract:Abstract Fusarium head blight (FHB), a fungus disease of small grain cereal crops, results in reduced yields and diminished value of harvested grain due to the presence of deoxynivalenol (DON), a mycotoxin produced by the causal pathogen Fusarium graminearum. DON and other tricothecene mycotoxins pose serious health risks to both humans and livestock, especially swine. Due to these health concerns, barley used for malting, food or feed is routinely assayed for DON levels. Various methods are available for assaying DON levels in grain samples including enzyme-linked immunosorbent assay (ELISA) and gas chromatography-mass spectrometry (GC-MS). ELISA and GC-MS are very accurate; however, assaying grain samples by these techniques are laborious, expensive and destructive. In this study, we explored the feasibility of using hyperspectral imaging (382–1030 nm) to develop a rapid and non-destructive protocol for assaying DON in barley kernels. Samples of 888 and 116 from various genetic lines were selected for calibration and prediction. Full-wavelength locally weighted partial least squares regression (LWPLSR) achieved high accuracy with the coefficient of determination in prediction (R2P) of 0.728 and root mean square error of prediction (RMSEP) of 3.802. Competitive adaptive reweighted sampling (CARS) was used to choose potential Feature wavelengths, and these selected Variables were further optimized using the iterative selection of successive projections algorithm (ISSPA). The CARS-ISSPA-LWPLSR model developed using 7 Feature Variables yielded R2P of 0.680 and RMSEP of 4.213 in DON content prediction. Based on the 7 wavelengths selected by CARS-ISSPA, partial least square discriminant analysis (PLSDA) discriminated barley kernels having lower DON (
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Hyperspectral imaging and improved Feature Variable selection for automated determination of deoxynivalenol in various genetic lines of barley kernels for resistance screening.
Food chemistry, 2020Co-Authors: Ce Yang, Yanhong Dong, Ryan Johnson, Rae Page, Tamas Szinyei, Cory D. Hirsch, Brian J. SteffensonAbstract:Fusarium head blight (FHB), a fungus disease of small grain cereal crops, results in reduced yields and diminished value of harvested grain due to the presence of deoxynivalenol (DON), a mycotoxin produced by the causal pathogen Fusarium graminearum. DON and other tricothecene mycotoxins pose serious health risks to both humans and livestock, especially swine. Due to these health concerns, barley used for malting, food or feed is routinely assayed for DON levels. Various methods are available for assaying DON levels in grain samples including enzyme-linked immunosorbent assay (ELISA) and gas chromatography-mass spectrometry (GC-MS). ELISA and GC-MS are very accurate; however, assaying grain samples by these techniques are laborious, expensive and destructive. In this study, we explored the feasibility of using hyperspectral imaging (382-1030 nm) to develop a rapid and non-destructive protocol for assaying DON in barley kernels. Samples of 888 and 116 from various genetic lines were selected for calibration and prediction. Full-wavelength locally weighted partial least squares regression (LWPLSR) achieved high accuracy with the coefficient of determination in prediction (R2P) of 0.728 and root mean square error of prediction (RMSEP) of 3.802. Competitive adaptive reweighted sampling (CARS) was used to choose potential Feature wavelengths, and these selected Variables were further optimized using the iterative selection of successive projections algorithm (ISSPA). The CARS-ISSPA-LWPLSR model developed using 7 Feature Variables yielded R2P of 0.680 and RMSEP of 4.213 in DON content prediction. Based on the 7 wavelengths selected by CARS-ISSPA, partial least square discriminant analysis (PLSDA) discriminated barley kernels having lower DON (less than1.25 mg/kg) levels from those with higher levels (including 1.25-3 mg/kg, 3-5 mg/kg, and 5-10 mg/kg), with Matthews correlation coefficient in cross-validation (M-RCV) of as high as 0.931. The results demonstrate that hyperspectral imaging have potential for accelerating non-destructive DON assays of barley samples.
Nassim Razaaly - One of the best experts on this subject based on the ideXlab platform.
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Optimal Design of ORC Turbine Blades Under Geometric and Operational Uncertainties
2019Co-Authors: Nassim Razaaly, Giacomo Persico, Pietro Marco CongedoAbstract:Typical energy sources for Organic Rankine Cycle (ORC) power systems Feature Variable heat load, hence turbine inlet/outlet thermodynamic conditions. The use of organic compounds with heavy molecular weight introduces uncertainties in the fluid thermodynamic modeling and complexity in the turbomachinery aerodynamics, with supersonic flows and strong shocks, which grow in relevance in the aforementioned off-design conditions. These Features also depend strongly on the local blade shape, which can be influenced by the geometric tolerances of the blade manufacturing. This study presents a Robust Optimization (RO) analysis on a typical supersonic nozzle cascade for ORC applications under the combined effect of uncertainties associated to operating conditions and geometric tolerances: a classical formulation consisting in minimizing the mean of a well-suited performance function, constraining the average mass flow rate to be within a prescribed range is addressed, by means of a bi-level Gaussian Process (GP) surrogate-based approach. Influence of the operating conditions range and geometric variability are investigated considering several scenarios, in which the different effects act in combination or separated; results indicate that the combination of different classes of uncertainites has an impact on the robust-optimal blade shape and, in turn, in their response in the frame of uncertain scenarios.
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Rare Event Estimation and Robust Optimization Methods with Application to ORC Turbine Cascade
2019Co-Authors: Nassim RazaalyAbstract:This thesis aims to formulate innovative Uncertainty Quantification (UQ) methods in both Robust Optimization (RO) and Reliability-Based Design Optimization (RBDO) problems. The targeted application is the optimization of supersonic turbines used in Organic Rankine Cycle (ORC) power systems.Typical energy sources for ORC power systems Feature Variable heat load and turbine inlet/outlet thermodynamic conditions. The use of organic compounds with a heavy molecular weight typically leads to supersonic turbine configurations featuring supersonic flows and shocks, which grow in relevance in the aforementioned off-design conditions; these Features also depend strongly on the local blade shape, which can be influenced by the geometric tolerances of the blade manufacturing. A consensus exists about the necessity to include these uncertainties in the design process, so requiring fast UQ methods and a comprehensive tool for performing shape optimization efficiently.This work is decomposed in two main parts. The first one addresses the problem of rare events estimation, proposing two original methods for failure probability (metaAL-OIS and eAK-MCS) and one for quantile computation (QeAK-MCS). The three methods rely on surrogate-based (Kriging) adaptive strategies, aiming at refining the so-called Limit-State Surface (LSS) directly, unlike Subset Simulation (SS) derived methods. Indeed, the latter consider intermediate threshold associated with intermediate LSSs to be refined. This direct refinement property is of crucial importance since it enables the adaptability of the developed methods for RBDO algorithms. Note that the proposed algorithms are not subject to restrictive assumptions on the LSS (unlike the well-known FORM/SORM), such as the number of failure modes, however need to be formulated in the Standard Space. The eAK-MCS and QeAK-MCS methods are derived from the AK-MCS method and inherit a parallel adaptive sampling based on weighed K-Means. MetaAL-OIS Features a more elaborate sequential refinement strategy based on MCMC samples drawn from a quasi-optimal ISD. It additionally proposes the construction of a Gaussian mixture ISD, permitting the accurate estimation of small failure probabilities when a large number of evaluations (several millions) is tractable, as an alternative to SS. The three methods are shown to perform very well for 2D to 8D analytical examples popular in structural reliability literature, some featuring several failure modes, all subject to very small failure probability/quantile level. Accurate estimations are performed in the cases considered using a reasonable number of calls to the performance function.The second part of this work tackles original Robust Optimization (RO) methods applied to the Shape Design of a supersonic ORC Turbine cascade. A comprehensive Uncertainty Quantification (UQ) analysis accounting for operational, fluid parameters and geometric (aleatoric) uncertainties is illustrated, permitting to provide a general overview over the impact of multiple effects and constitutes a preliminary study necessary for RO. Then, several mono-objective RO formulations under a probabilistic constraint are considered in this work, including the minimization of the mean or a high quantile of the Objective Function. A critical assessment of the (Robust) Optimal designs is finally investigated.
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Impact of Geometric, Operational, and Model Uncertainties on the Non-ideal Flow Through a Supersonic ORC Turbine Cascade
Energy, 2018Co-Authors: Nassim Razaaly, Giacomo Persico, Pietro Marco CongedoAbstract:Typical energy sources for Organic Rankine Cycle (ORC) power systems Feature Variable heat load and turbine in-let/outlet thermodynamic conditions. The use of organic compounds with heavy molecular weight introduces uncertainties in the fluid thermodynamic modeling. In addition, the peculiarities of organic fluids typically leads to supersonic turbine configurations featuring supersonic flows and shocks, which grow in relevance in the aforemen-tioned off-design conditions; these Features also depends strongly on the local blade shape, which can be influenced by the geometric tolerances of the blade manufacturing. This study presents an Uncertainty Quantification (UQ) analysis on a typical supersonic nozzle cascade for ORC applications, by considering a two-dimensional high-fidelity turbulent Computational Fluid Dynamic (CFD) model. Kriging-based techniques are used in order to take into account at a low computational cost, the combined effect of uncertainties associated to operating conditions, fluid parameters, and geometric tolerances. The geometric variability is described by a finite Karhunen-Loeve expansion representing a non-stationary Gaussian random field, entirely defined by a null mean and its autocorrelation function. Several results are illustrated about the ANOVA decomposition of several quantities of interest for different operating conditions, showing the importance of geometric uncertainties on the turbine performances.
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Robust Optimization of ORC blades turbines under a low quantile constraint
2017Co-Authors: Nassim Razaaly, Giacomo Persico, Pietro Marco CongedoAbstract:Heat sources for ORC turbines typically Feature Variable energy sources such as WHR (Waste Heat Recovery) and solar energy. Advanced uncertainty quantification and robust optimization methodologies could be used during the ORC turbines design process in order to account for multiple uncertainties. This study presents an original robust shape optimization approach for ORC blade turbines, to overcome the limitation of a deterministic optimization that neglects the effect of uncertainties of operating conditions or design Variables. Starting from a baseline blade, we search for an optimal shape that maximizes the 5% quantile of the expander isentropic efficiency, which is evaluated by means of an Euler 2D simulation. Real-gas effects are modeled through the use of a Peng-Robinson-Stryjek-Vera equation of state. The 5% quantile of the expander isentropic efficiency is estimated using a tail probability strategy: points are iteratively added on the failure branches in order to build a reliable metamodel from which a Monte-Carlo sampling method is used. In order to speed-up the optimization process, an additional Gaussian Process model is built to approximate the isentropic efficiency. The robustly optimized ORC turbine shape is finally compared to the initial configuration and the deterministic optimal shape.
Ce Yang - One of the best experts on this subject based on the ideXlab platform.
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Hyperspectral imaging and improved Feature Variable selection for automated determination of deoxynivalenol in various genetic lines of barley kernels for resistance screening
Food chemistry, 2020Co-Authors: Ce Yang, Yanhong Dong, Ryan Johnson, Rae Page, Tamas Szinyei, Cory D. Hirsch, Brian J. SteffensonAbstract:Abstract Fusarium head blight (FHB), a fungus disease of small grain cereal crops, results in reduced yields and diminished value of harvested grain due to the presence of deoxynivalenol (DON), a mycotoxin produced by the causal pathogen Fusarium graminearum. DON and other tricothecene mycotoxins pose serious health risks to both humans and livestock, especially swine. Due to these health concerns, barley used for malting, food or feed is routinely assayed for DON levels. Various methods are available for assaying DON levels in grain samples including enzyme-linked immunosorbent assay (ELISA) and gas chromatography-mass spectrometry (GC-MS). ELISA and GC-MS are very accurate; however, assaying grain samples by these techniques are laborious, expensive and destructive. In this study, we explored the feasibility of using hyperspectral imaging (382–1030 nm) to develop a rapid and non-destructive protocol for assaying DON in barley kernels. Samples of 888 and 116 from various genetic lines were selected for calibration and prediction. Full-wavelength locally weighted partial least squares regression (LWPLSR) achieved high accuracy with the coefficient of determination in prediction (R2P) of 0.728 and root mean square error of prediction (RMSEP) of 3.802. Competitive adaptive reweighted sampling (CARS) was used to choose potential Feature wavelengths, and these selected Variables were further optimized using the iterative selection of successive projections algorithm (ISSPA). The CARS-ISSPA-LWPLSR model developed using 7 Feature Variables yielded R2P of 0.680 and RMSEP of 4.213 in DON content prediction. Based on the 7 wavelengths selected by CARS-ISSPA, partial least square discriminant analysis (PLSDA) discriminated barley kernels having lower DON (
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Hyperspectral imaging and improved Feature Variable selection for automated determination of deoxynivalenol in various genetic lines of barley kernels for resistance screening.
Food chemistry, 2020Co-Authors: Ce Yang, Yanhong Dong, Ryan Johnson, Rae Page, Tamas Szinyei, Cory D. Hirsch, Brian J. SteffensonAbstract:Fusarium head blight (FHB), a fungus disease of small grain cereal crops, results in reduced yields and diminished value of harvested grain due to the presence of deoxynivalenol (DON), a mycotoxin produced by the causal pathogen Fusarium graminearum. DON and other tricothecene mycotoxins pose serious health risks to both humans and livestock, especially swine. Due to these health concerns, barley used for malting, food or feed is routinely assayed for DON levels. Various methods are available for assaying DON levels in grain samples including enzyme-linked immunosorbent assay (ELISA) and gas chromatography-mass spectrometry (GC-MS). ELISA and GC-MS are very accurate; however, assaying grain samples by these techniques are laborious, expensive and destructive. In this study, we explored the feasibility of using hyperspectral imaging (382-1030 nm) to develop a rapid and non-destructive protocol for assaying DON in barley kernels. Samples of 888 and 116 from various genetic lines were selected for calibration and prediction. Full-wavelength locally weighted partial least squares regression (LWPLSR) achieved high accuracy with the coefficient of determination in prediction (R2P) of 0.728 and root mean square error of prediction (RMSEP) of 3.802. Competitive adaptive reweighted sampling (CARS) was used to choose potential Feature wavelengths, and these selected Variables were further optimized using the iterative selection of successive projections algorithm (ISSPA). The CARS-ISSPA-LWPLSR model developed using 7 Feature Variables yielded R2P of 0.680 and RMSEP of 4.213 in DON content prediction. Based on the 7 wavelengths selected by CARS-ISSPA, partial least square discriminant analysis (PLSDA) discriminated barley kernels having lower DON (less than1.25 mg/kg) levels from those with higher levels (including 1.25-3 mg/kg, 3-5 mg/kg, and 5-10 mg/kg), with Matthews correlation coefficient in cross-validation (M-RCV) of as high as 0.931. The results demonstrate that hyperspectral imaging have potential for accelerating non-destructive DON assays of barley samples.
R Krishnan - One of the best experts on this subject based on the ideXlab platform.
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single controllable switch based switched reluctance motor drive for low cost Variable speed applications
IEEE Transactions on Power Electronics, 2012Co-Authors: Jaehyuck Kim, Keunsoo Ha, R KrishnanAbstract:A new low-cost, brushless Variable-speed drive requiring only a single controllable switch is presented. The proposed converter (referred to as new single-switch converter) overcomes the drawback of the original single-switch-based four-quadrant motor drive in terms of recovery energy circulation. The drive system is realized using an asymmetric two-phase switched reluctance motor (SRM), the proposed converter, and DSP controller. The new drive system retains the unique Features of self-starting for all rotor position and four quadrant operation of the original single-switch-based SRM drive system. This paper presents operation principle, modeling, simulation, and design considerations of the converter in conjunction with the motor. Simulation results are based on a nonlinear model of the motor drive system. A prototype drive has been built and tested to verify its practical viability. The experimental results correlate well with the simulation, and demonstrate a performance comparable to conventional asymmetric bridge converter-based drive with two switches per phase. The market relevance of this new drive system is primarily due to its lowest cost structure, packaging compactness, self-starting Feature, Variable-speed operation and four-quadrant capability. Because of these Features, the new drive system offers a viable alternative to conventional fixed-speed brush-commutator motors and Variable-speed permanent magnet brushless dc motor drives in many high volume applications in the low-cost, energy efficient, high-volume categories such as fans, blowers, hand tools, and small appliances.
Pietro Marco Congedo - One of the best experts on this subject based on the ideXlab platform.
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Optimal Design of ORC Turbine Blades Under Geometric and Operational Uncertainties
2019Co-Authors: Nassim Razaaly, Giacomo Persico, Pietro Marco CongedoAbstract:Typical energy sources for Organic Rankine Cycle (ORC) power systems Feature Variable heat load, hence turbine inlet/outlet thermodynamic conditions. The use of organic compounds with heavy molecular weight introduces uncertainties in the fluid thermodynamic modeling and complexity in the turbomachinery aerodynamics, with supersonic flows and strong shocks, which grow in relevance in the aforementioned off-design conditions. These Features also depend strongly on the local blade shape, which can be influenced by the geometric tolerances of the blade manufacturing. This study presents a Robust Optimization (RO) analysis on a typical supersonic nozzle cascade for ORC applications under the combined effect of uncertainties associated to operating conditions and geometric tolerances: a classical formulation consisting in minimizing the mean of a well-suited performance function, constraining the average mass flow rate to be within a prescribed range is addressed, by means of a bi-level Gaussian Process (GP) surrogate-based approach. Influence of the operating conditions range and geometric variability are investigated considering several scenarios, in which the different effects act in combination or separated; results indicate that the combination of different classes of uncertainites has an impact on the robust-optimal blade shape and, in turn, in their response in the frame of uncertain scenarios.
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Impact of Geometric, Operational, and Model Uncertainties on the Non-ideal Flow Through a Supersonic ORC Turbine Cascade
Energy, 2018Co-Authors: Nassim Razaaly, Giacomo Persico, Pietro Marco CongedoAbstract:Typical energy sources for Organic Rankine Cycle (ORC) power systems Feature Variable heat load and turbine in-let/outlet thermodynamic conditions. The use of organic compounds with heavy molecular weight introduces uncertainties in the fluid thermodynamic modeling. In addition, the peculiarities of organic fluids typically leads to supersonic turbine configurations featuring supersonic flows and shocks, which grow in relevance in the aforemen-tioned off-design conditions; these Features also depends strongly on the local blade shape, which can be influenced by the geometric tolerances of the blade manufacturing. This study presents an Uncertainty Quantification (UQ) analysis on a typical supersonic nozzle cascade for ORC applications, by considering a two-dimensional high-fidelity turbulent Computational Fluid Dynamic (CFD) model. Kriging-based techniques are used in order to take into account at a low computational cost, the combined effect of uncertainties associated to operating conditions, fluid parameters, and geometric tolerances. The geometric variability is described by a finite Karhunen-Loeve expansion representing a non-stationary Gaussian random field, entirely defined by a null mean and its autocorrelation function. Several results are illustrated about the ANOVA decomposition of several quantities of interest for different operating conditions, showing the importance of geometric uncertainties on the turbine performances.
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Robust Optimization of ORC blades turbines under a low quantile constraint
2017Co-Authors: Nassim Razaaly, Giacomo Persico, Pietro Marco CongedoAbstract:Heat sources for ORC turbines typically Feature Variable energy sources such as WHR (Waste Heat Recovery) and solar energy. Advanced uncertainty quantification and robust optimization methodologies could be used during the ORC turbines design process in order to account for multiple uncertainties. This study presents an original robust shape optimization approach for ORC blade turbines, to overcome the limitation of a deterministic optimization that neglects the effect of uncertainties of operating conditions or design Variables. Starting from a baseline blade, we search for an optimal shape that maximizes the 5% quantile of the expander isentropic efficiency, which is evaluated by means of an Euler 2D simulation. Real-gas effects are modeled through the use of a Peng-Robinson-Stryjek-Vera equation of state. The 5% quantile of the expander isentropic efficiency is estimated using a tail probability strategy: points are iteratively added on the failure branches in order to build a reliable metamodel from which a Monte-Carlo sampling method is used. In order to speed-up the optimization process, an additional Gaussian Process model is built to approximate the isentropic efficiency. The robustly optimized ORC turbine shape is finally compared to the initial configuration and the deterministic optimal shape.
