Characterizing the cardiovascular functions during atrial fibrillation through lumped-parameter modeling

Atrial fibrillation (AF), causing irregular and rapid heartbeats, is the most common arrhythmia. Due to the widespread impact on the population and the disabling symptoms related to rapid heart rate, AF is a subject of growing interest under several aspects: statistical analyses on the heartbeat distributions, risk factors, impact on quality of life, correlation with other cardiac pathologies. However, several key points on the consequences induced by AF on the cardiovascular system are still not completely understood. The proposed work aims at quantifying the impact of AF on the most relevant cardiovascular parameters by means of a lumped-parameter modeling, paying particular attention to the stochastic nature of the irregular heartbeats and the reduced contractility of the heart. The global response leads to a rather impressive overall agreement with the clinical state-of-the-art measures regarding AF: reduced cardiac output with correlated arterial hypotension, as well as higher left atrial volume and pressure values are some of the most representative outcomes emerging during AF. Moreover, new insights on hemodynamic parameters such as cardiac flow rates, which are difficult to measure and almost never offered in literature, are here provided

Hybrid solar-biomass combined Brayton/organic Rankine-cycle plants integrated with thermal storage: Techno-economic feasibility in select Mediterranean areas

This paper presents a thermodynamic analysis and techno-economic assessment of a novel hybrid solar-biomass power-generation system configuration composed of an externally fired gas-turbine (EFGT) fuelled by biomass (wood chips) and a bottoming organic Rankine cycle (ORC) plant. The main novelty is related to the heat recovery from the exhaust gases of the EFGT via thermal energy storage (TES), and integration of heat from a parabolic-trough collectors (PTCs) field with molten salts as a heat-transfer fluid (HTF). The presence of a TES between the topping and bottoming cycles facilitates the flexible operation of the system, allows the system to compensate for solar energy input fluctuations, and increases capacity factor and dispatchability. A TES with two molten salt tanks (one cold at 200 °C and one hot at 370 °C) is chosen. The selected bottoming ORC is a superheated recuperative cycle suitable for heat conversion in the operating temperature range of the TES. The whole system is modelled by means of a Python-based software code, and three locations in the Mediterranean area are assumed in order to perform energy-yield analyses: Marseille in France, Priolo Gargallo in Italy and Rabat in Morocco. In each case, the thermal storage that minimizes the levelized cost of energy (LCE) is selected on the basis of the estimated solar radiation and CSP size. The results of the thermodynamic simulations, capital and operational costs assessments and subsidies (feed-in tariffs for biomass and solar electricity available in the Italian framework), allow estimating the global energy conversion efficiency and the investment profitability in the three locations. Sensitivity analyses of the biomass costs, size of PTCs, feed-in tariff and share of cogenerated heat delivered to the load are also performed. The results show that the high investment costs of the CSP section in the proposed size range and hybridization configuration allow investment profitability only in the presence of a dedicated subsidy framework such as the one available in the Italian energy market. In particular, the LCE of the proposed system is around 140 Eur/MWh (with the option to discharge the cogenerated heat) and the IRR is around 15%, based on the Italian electricity subsidy tariffs. The recovery of otherwise discharged heat to match thermal energy demand can significantly increase the investment profitability and compensate the high investment costs of the proposed technology

Parametric multi-objective optimization of an Organic Rankine Cycle with thermal energy storage for distributed generation

Abstract This paper focuses on the thermodynamic modelling and parametric optimization of an Organic Rankine Cycle (ORC) which recovers the heat stored in a thermal energy storage (TES). A TES with two molten-salt tanks (one cold and one hot) is selected since it is able to operate in the temperature range useful to recover heat from different sources such as exhaust gas of Externally Fired Gas Turbine (EFGT) or Concentrating Solar Power (CSP) plant, operating in a network for Distributed Generation (DG). The thermal storage facilitates a flexible operation of the power system operating in the network of DG, and in particular allows to compensate the energy fluctuations of heat and power demand, increase the capacity factor of the connected plants, increase the dispatchability of the renewable energy generated and potentially operate in load following mode. The selected ORC is a regenerative cycle with the adoption of a Heat Recovery Vapour Generator (HRVG) that recovers heat from molten salts flowing from the Hot Tank to the Cold Tank of the TES. By considering the properties of molten salt mixtures, a ternary mixture able to operate between 200 and 400 °C is selected. The main ORC parameters, namely the evaporating pressure/temperature and the evaporator/condenser pinch point temperature differences, are selected as variables for the thermodynamic ORC optimization. An automatic optimization procedure is set up by means of a genetic algorithm (GA) coupled with an in-house code for the ORC calculation. Firstly, a mono-objective optimization is carried out for two working fluids of interest (Toluene and R113) by maximization of the cycle thermal efficiency. Afterwards, a multi-objective optimization is carried out for the fluid with the best performance by means of a Non-dominated Sorting Genetic Algorithm (NSGA) in order to evaluate the cycle parameters which maximize the thermal efficiency and minimise the heat exchanger surface areas. Toluene results able to give the best trade-off between efficiency and heat exchanger dimensions for the present application, showing that by with respect to the best efficiency point, the heat exchange area can be reduced by 36% with only a penalty of 1% for the efficiency

Fourier-Hermite decomposition of the collisional Vlasov-Maxwell system: Implications for the velocity-space cascade

Turbulence at kinetic scales is an unresolved and ubiquitous phenomenon that characterizes both space and laboratory plasmas. Recently, new theories, {\it in-situ} spacecraft observations and numerical simulations suggest a novel scenario for turbulence, characterized by a so-called phase space cascade -- the formation of fine structures, both in physical and velocity space. This new concept is here extended by directly taking into account the role of inter-particle collisions, modeled through the nonlinear Landau operator or the simplified Dougherty operator. The characteristic times, associated with inter-particle correlations, are derived in the above cases. The implications of introducing collisions on the phase space cascade are finally discussed

Numerical simulation of a complete charging-discharging phase of a shell and tube thermal energy storage with phase change material

Abstract Numerical simulations of a shell and tube energy storage device based on a phase change material (PCM) in vertical position are performed. The heat transfer fluid (HTF) is a diathermic oil and the PCM, made by molten salts, is confined within a closed shell surrounding the tube where the HTF flows. The energy loss through the external wall is included. The test has been carried out within the experimental activity performed by ENEA. A complete cycle is considered: the initial stabilization, the charging phase and the discharging phase. Details of flow behavior within the molten PCM are described highlighting its influence on the device performance

The Challenge of Machine Learning in Space Weather Nowcasting and Forecasting

The numerous recent breakthroughs in machine learning (ML) make imperative to carefully ponder how the scientific community can benefit from a technology that, although not necessarily new, is today living its golden age. This Grand Challenge review paper is focused on the present and future role of machine learning in space weather. The purpose is twofold. On one hand, we will discuss previous works that use ML for space weather forecasting, focusing in particular on the few areas that have seen most activity: the forecasting of geomagnetic indices, of relativistic electrons at geosynchronous orbits, of solar flares occurrence, of coronal mass ejection propagation time, and of solar wind speed. On the other hand, this paper serves as a gentle introduction to the field of machine learning tailored to the space weather community and as a pointer to a number of open challenges that we believe the community should undertake in the next decade. The recurring themes throughout the review are the need to shift our forecasting paradigm to a probabilistic approach focused on the reliable assessment of uncertainties, and the combination of physics-based and machine learning approaches, known as gray-box.Comment: under revie

Space-filter techniques for quasi-neutral hybrid-kinetic models

The space-filter approach has proved a fundamental tool in studying turbulence in neutral fluids, providing the ability to analyze scale-to-scale energy transfer in configuration space. It is well known that turbulence in plasma presents challenges different from neutral fluids, especially when the scale of interests include kinetic effects. The space-filter approach is still largely unexplored for kinetic plasma. Here we derive the space-filtered (or, equivalently "coarse-grained") equations in configuration space for a quasi-neutral hybrid-kinetic plasma model, in which ions are fully kinetic and electrons are a neutralizing fluid. Different models and closures for the electron fluid are considered, including finite electron-inertia effects and full electrons' pressure-tensor dynamics. Implications for the cascade of turbulent fluctuations in real space depending on different approximations are discussed.Comment: 43 pages, 2 figure

Vlasov simulations of multi-ion plasma turbulence in the solar wind

Hybrid Vlasov-Maxwell simulations are employed to investigate the role of kinetic effects in a two-dimensional turbulent multi-ion plasma, composed of protons, alpha particles and fluid electrons. In the typical conditions of the solar-wind environment, and in situations of decaying turbulence, the numerical results show that the velocity distribution functions of both ion species depart from the typical configuration of thermal equilibrium. These non-Maxwellian features are quantified through the statistical analysis of the temperature anisotropy, for both protons and alpha particles, in the reference frame given by the local magnetic field. Anisotropy is found to be higher in regions of high magnetic stress. Both ion species manifest a preferentially perpendicular heating, although the anisotropy is more pronounced for the alpha particles, according with solar wind observations. Anisotropy of the alpha particle, moreover, is correlated to the proton anisotropy, and also depends on the local differential flow between the two species. Evident distortions of the particle distribution functions are present, with the production of bumps along the direction of the local magnetic field. The physical phenomenology recovered in these numerical simulations reproduces very common measurements in the turbulent solar wind, suggesting that the multi-ion Vlasov model constitutes a valid approach to the understanding of the nature of complex kinetic effects in astrophysical plasmas

200 mm Sensor Development Using Bonded Wafers

Sensors fabricated from high resistivity, float zone, silicon material have been the basis of vertex detectors and trackers for the last 30 years. The areas of these devices have increased from a few square cm to $\> 200\ m^2$ for the existing CMS tracker. High Luminosity Large Hadron Collider (HL-LHC), CMS and ATLAS tracker upgrades will each require more than $200\ m^2$ of silicon and the CMS High Granularity Calorimeter (HGCAL) will require more than $600\ m^2$. The cost and complexity of assembly of these devices is related to the area of each module, which in turn is set by the size of the silicon sensors. In addition to large area, the devices must be radiation hard, which requires the use of sensors thinned to 200 microns or less. The combination of wafer thinning and large wafer diameter is a significant technical challenge, and is the subject of this work. We describe work on development of thin sensors on$200 mm$ wafers using wafer bonding technology. Results of development runs with float zone, Silicon-on-Insulator and Silicon-Silicon bonded wafer technologies are reported.Comment: 11 page