Prediction of compressor efficiency by means of Bayesian Hierarchical Models

The prediction of time evolution of gas turbine performance is an emerging requirement of modern prognostics and health management systems, aimed at improving system reliability and availability, while reducing life cycle costs. In this work, a data-driven Bayesian Hierarchical Model (BHM) is employed to perform a probabilistic prediction of gas turbine future behavior. The BHM approach is applied to field data, taken from the literature and representative of gas turbine degradation over time for a time frame of 7-9 years. The predicted variable is compressor efficiency collected from three power plants characterized by high degradation rate. The capabilities of the BHM prognostic method are assessed by considering two different forecasting approaches, i.e. single-step and multi-step forecast. For the considered field data, the prediction accuracy is very high for both approaches. In fact, the average values of the prediction errors are lower than 0.3% for single-step prediction and lower than 0.6% for multi- step prediction

Application of a physics-based model to predict the performance curves of pumps as turbines

This paper presents the application of a physics-based simulation model, aimed at predicting the performance curves of pumps as turbines (PATs) based on the performance curves of the respective pump. The simulation model implements the equations for estimating head, power and efficiency for both direct and reverse operation. Model tuning on a given machine is performed by using loss coefficients and specific parameters identified by means of an optimization procedure, which simultaneously optimizes both the pump and PAT operation. The simulation model is calibrated in this paper on data taken from the literature, reporting both pump and PAT performance curves for head and efficiency over the entire range of operation. The performance data refer to twelve different centrifugal pumps, running in both pump and PAT mode. The accuracy of the predictions of the physics-based simulation model is quantitatively assessed against both pump and PAT performance curves and best efficiency point. Prediction consistency from a physical point of view is also evaluated

Comparison of different approaches to predict the performance of pumps as turbines (PATs)

This paper deals with the comparison of different methods which can be used for the prediction of the performance curves of pumps as turbines (PATs). The considered approaches are four, i.e., one physics-based simulation model ("white box" model), two "gray box" models, which integrate theory on turbomachines with specific data correlations, and one "black box" model. More in detail, the modeling approaches are: (1) a physics-based simulation model developed by the same authors, which includes the equations for estimating head, power, and efficiency and uses loss coefficients and specific parameters; (2) a model developed by Derakhshan and Nourbakhsh, which first predicts the best efficiency point of a PAT and then reconstructs their complete characteristic curves by means of two ad hoc equations; (3) the prediction model developed by Singh and Nestmann, which predicts the complete turbine characteristics based on pump shape and size; (4) an Evolutionary Polynomial Regression model, which represents a data-driven hybrid scheme which can be used for identifying the explicit mathematical relationship between PAT and pump curves. All approaches are applied to literature data, relying on both pump and PAT performance curves of head, power, and efficiency over the entire range of operation. The experimental data were provided by Derakhshan and Nourbakhsh for four different turbomachines, working in both pump and PAT mode with specific speed values in the range 1.53-5.82. This paper provides a quantitative assessment of the predictions made by means of the considered approaches and also analyzes consistency from a physical point of view. Advantages and drawbacks of each method are also analyzed and discussed.This paper deals with the comparison of different methods which can be used for the prediction of the performance curves of pumps as turbines (PATs). The considered approaches are four, i.e., one physics-based simulation model ("white box" model), two "gray box" models, which integrate theory on turbomachines with specific data correlations, and one "black box" model. More in detail, the modeling approaches are: (1) a physics-based simulation model developed by the same authors, which includes the equations for estimating head, power, and efficiency and uses loss coefficients and specific parameters; (2) a model developed by Derakhshan and Nourbakhsh, which first predicts the best efficiency point of a PAT and then reconstructs their complete characteristic curves by means of two ad hoc equations; (3) the prediction model developed by Singh and Nestmann, which predicts the complete turbine characteristics based on pump shape and size; (4) an Evolutionary Polynomial Regression model, which represents a data-driven hybrid scheme which can be used for identifying the explicit mathematical relationship between PAT and pump curves. All approaches are applied to literature data, relying on both pump and PAT performance curves of head, power, and efficiency over the entire range of operation. The experimental data were provided by Derakhshan and Nourbakhsh for four different turbomachines, working in both pump and PAT mode with specific speed values in the range 1.53-5.82. This paper provides a quantitative assessment of the predictions made by means of the considered approaches and also analyzes consistency from a physical point of view. Advantages and drawbacks of each method are also analyzed and discussed

A Lentiviral Vector-Based, Herpes Simplex Virus 1 (HSV-1) Glycoprotein B Vaccine Affords Cross-Protection against HSV-1 and HSV-2 Genital Infections

Genital herpes is caused by herpes simplex virus 1 (HSV-1) and HSV-2, and its incidence is constantly increasing in the human population. Regardless of the clinical manifestation, HSV-1 and HSV-2 infections are highly transmissible to sexual partners and enhance susceptibility to other sexually transmitted infections. An effective vaccine is not yet available. Here, HSV-1 glycoprotein B (gB1) was delivered by a feline immunodeficiency virus (FIV) vector and tested against HSV-1 and HSV-2 vaginal challenges in C57BL/6 mice. The gB1 vaccine elicited cross-neutralizing antibodies and cell-mediated responses that protected 100 and 75% animals from HSV-1- and HSV-2-associated severe disease, respectively. Two of the eight fully protected vaccinees underwent subclinical HSV-2 infection, as demonstrated by deep immunosuppression and other analyses. Finally, vaccination prevented death in 83% of the animals challenged with a HSV-2 dose that killed 78 and 100% naive and mock-vaccinated controls, respectively. Since this FLY vector can accommodate two or more HSV immunogens, this vaccine has ample potential for improvement and may become a candidate for the development of a truly effective vaccine against genital herpes

APP Processing Induced by Herpes Simplex Virus Type 1 (HSV-1) Yields Several APP Fragments in Human and Rat Neuronal Cells

Lifelong latent infections of the trigeminal ganglion by the neurotropic herpes simplex virus type 1 (HSV-1) are characterized by periodic reactivation. During these episodes, newly produced virions may also reach the central nervous system (CNS), causing productive but generally asymptomatic infections. Epidemiological and experimental findings suggest that HSV-1 might contribute to the pathogenesis of Alzheimer's disease (AD). This multifactorial neurodegenerative disorder is related to an overproduction of amyloid beta (Aβ) and other neurotoxic peptides, which occurs during amyloidogenic endoproteolytic processing of the transmembrane amyloid precursor protein (APP). The aim of our study was to identify the effects of productive HSV-1 infection on APP processing in neuronal cells. We found that infection of SH-SY5Y human neuroblastoma cells and rat cortical neurons is followed by multiple cleavages of APP, which result in the intra- and/or extra-cellular accumulation of various neurotoxic species. These include: i) APP fragments (APP-Fs) of 35 and 45 kDa (APP-F35 and APP-F45) that comprise portions of Aβ; ii) N-terminal APP-Fs that are secreted; iii) intracellular C-terminal APP-Fs; and iv) Aβ1-40 and Aβ1-42. Western blot analysis of infected-cell lysates treated with formic acid suggests that APP-F35 may be an Aβ oligomer. The multiple cleavages of APP that occur in infected cells are produced in part by known components of the amyloidogenic APP processing pathway, i.e., host-cell β-secretase, γ-secretase, and caspase-3-like enzymes. These findings demonstrate that HSV-1 infection of neuronal cells can generate multiple APP fragments with well-documented neurotoxic potentials. It is tempting to speculate that intra- and extracellular accumulation of these species in the CNS resulting from repeated HSV-1 reactivation could, in the presence of other risk factors, play a co-factorial role in the development of AD

Development of a physics-based model to predict the performance of pumps as turbines

This paper presents the development of a physics-based simulation model, aimed at predicting the performance curves of pumps as turbines (PATs) based on the performance curves of the respective pump. The simulation model implements the equations to be used for the estimation of head, power and efficiency for both direct and reverse operation. Model tuning on a given machine is performed by using loss coefficients and specific parameters identified by means of an optimization procedure, which is first applied to the considered pumps and subsequently to the same machine running in PAT mode.The simulation model is calibrated on data taken from literature, reporting both pump and PAT performance curves for head, power and efficiency over the entire range of operation. The performance data were acquired experimentally from four different centrifugal pumps, running in both pump and PAT mode and characterized by specific speed values in the range of 1.53-5.82. The accuracy of the predictions of the physics-based simulation model is quantitatively assessed against both pump and PAT experimental performance curves. Prediction consistency from a physical point of view is also evaluated.The results presented in this paper highlight that all the performance curves predicted by the simulation model are physically consistent over the entire range of operation. In general, the prediction error on the head of PATs is acceptable, while the accuracy of the prediction of PAT power, and thus of PAT efficiency, is more case sensitive and usually higher. The relative deviation of model prediction with respect to the field data regarding head and power at the PAT best efficiency point always seems acceptable compared to the uncertainty of the original experimental data and to typical deviations of other methods available in literature.As a conclusion, the physics-based simulation model developed in this paper represents a powerful and reliable tool for estimating PAT performance curves over the entire range of operation based on pump characteristics.This paper presents the development of a physics-based simulation model, aimed at predicting the performance curves of pumps as turbines (PATs) based on the performance curves of the respective pump. The simulation model implements the equations to be used for the estimation of head, power and efficiency for both direct and reverse operation. Model tuning on a given machine is performed by using loss coefficients and specific parameters identified by means of an optimization procedure, which is first applied to the considered pumps and subsequently to the same machine running in PAT mode. The simulation model is calibrated on data taken from literature, reporting both pump and PAT performance curves for head, power and efficiency over the entire range of operation. The performance data were acquired experimentally from four different centrifugal pumps, running in both pump and PAT mode and characterized by specific speed values in the range of 1.53–5.82. The accuracy of the predictions of the physics-based simulation model is quantitatively assessed against both pump and PAT experimental performance curves. Prediction consistency from a physical point of view is also evaluated. The results presented in this paper highlight that all the performance curves predicted by the simulation model are physically consistent over the entire range of operation. In general, the prediction error on the head of PATs is acceptable, while the accuracy of the prediction of PAT power, and thus of PAT efficiency, is more case-sensitive and usually higher. The relative deviation of model prediction with respect to the field data regarding head and power at the PAT best efficiency point always seems acceptable compared to the uncertainty of the original experimental data and to typical deviations of other methods available in literature. As a conclusion, the physics-based simulation model developed in this paper represents a powerful and reliable tool for estimating PAT performance curves over the entire range of operation based on pump characteristics

Vettori erpetici per la terapia genica di patologie del sistema nervoso centrale.

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Gas Turbine Health State Prognostics by means of Bayesian Hierarchical Models

The prediction of time evolution of gas turbine performance is an emerging requirement of modern prognostics and health management (PHM), aimed at improving system reliability and availability, while reducing life cycle costs. In this work, a data-driven Bayesian Hierarchical Model (BHM) is employed to perform a probabilistic prediction of gas turbine future behavior, thanks to its capability to deal with fleet data from multiple units. First, the theoretical background of the predictive methodology is outlined to highlight the inference mechanism and data processing for estimating BHM predicted outputs. Then, the BHM approach is applied to both simulated and field data representative of gas turbine degradation to assess its prediction reliability and grasp some rules of thumb for minimizing BHM prediction error. For the considered field data, the average values of the prediction errors are found to be lower than 1.0 % or 1.7 % for single- or multi- step prediction, respectively

Gas Turbine Health State Prognostics by Means of Bayesian Hierarchical Models

The prediction of time evolution of gas turbine (GT) performance is an emerging requirement of modern prognostics and health management (PHM), aimed at improving system reliability and availability, while reducing life cycle costs. In this work, a data-driven Bayesian hierarchical model (BHM) is employed to perform a probabilistic prediction of GT future behavior, thanks to its capability to deal with fleet data from multiple units. First, the theoretical background of the predictive methodology is outlined to highlight the inference mechanism and data processing for estimating BHM-predicted outputs. Then, the BHM approach is applied to both simulated and field data representative of GT degradation to assess its prediction reliability and grasp some rules of thumb for minimizing BHM prediction error. For the considered field data, the average values of the prediction errors are found to be lower than 1.0% or 1.7% for single- or multi-step prediction, respectively

Optimal design and energy management of a renewable energy plant with seasonal energy storage

The exploitation of fossil fuels is undoubtedly responsible of environmental problems such as global warming and sea level rise. Unlike energy plants based on fossil fuels, energy plants based on renewable energy sources may be sustainable and reduce greenhouse gas emissions. However, they are unpredictable because of the intermittent nature of environmental conditions. For this reason, energy storage technologies are needed to meet peak energy demands thanks to the stored energy. Moreover, the renewable energy systems composing the plant must be optimally designed and operated. Therefore, this paper investigates the challenge of the optimal design and energy management of a grid connected renewable energy plant composed of a solar thermal collector, photovoltaic system, ground source heat pump, battery, one short-term thermal energy storage and one seasonal thermal energy storage. To this aim, this paper develops a methodology based on a genetic algorithm that optimally designs a 100% renewable energy plant with the aim of minimizing the electrical energy taken from the grid. The load profiles of thermal, cooling and electrical energy during a whole year are taken into account for the case study of the Campus of the University of Parma (Italy)