Development of a rat capnoperitoneum phantom to study drug aerosol deposition in the context of anticancer research on peritoneal carcinomatosis

Pressurized Intraperitoneal Aerosol Chemotherapy (PIPAC) is a promising approach with a high optimization potential for the treatment of peritoneal carcinomatosis. To study the efficacy of PIPAC and drugs, first rodent cancer models were developed. But inefficient drug aerosol supply and knowledge gaps concerning spatial drug distribution can limit the results based on such models. To study drug aerosol supply/deposition, computed tomography scans of a rat capnoperitoneum were used to deduce a virtual and a physical phantom of the rat capnoperitoneum (RCP). RCP qualification was performed for a specific PIPAC method, where the capnoperitoneum is continuously purged by the drug aerosol. In this context, also in-silico analyses by computational fluid dynamic modelling were conducted on the virtual RCP. The physical RCP was used for ex-vivo granulometric analyses concerning drug deposition. Results of RCP qualification show that aerosol deposition in a continuous purged rat capnoperitoneum depends strongly on the position of the inlet and outlet port. Moreover, it could be shown that the droplet size and charge condition of the drug aerosol define the deposition efficiency. In summary, the developed virtual and physical RCP enables detailed in-silico and ex-vivo analyses on drug supply/deposition in rodents.

NUMERICAL ANALYSIS OF FUEL EFFECTS ON ADVANCED COMPRESSION IGNITION USING A COOPERATIVE FUEL RESEARCH ENGINE COMPUTATIONAL FLUID DYNAMICS MODEL

Growing environmental concerns and demand for better fuel economy are driving forces that motivate the research for more advanced engines. Multi-mode combustion strategies have gained attention for their potential to provide high thermal efficiency and low emissions for light-duty applications. These strategies target optimizing the engine performance by correlating different combustion modes to load operating conditions. The extension from boosted SI mode at high loads to advanced compression ignition (ACI) mode at low loads can be achieved by increasing compression ratio and utilizing intake air heating. Further, in order to enable an accurate control of intake charge condition for ACI mode and rapid mode-switches, it is essential to gain fundamental insights into the autoignition process. Within the scope of ACI, homogeneous charge compression ignition (HCCI) mode is of significant interest. It is known for its potential benefits, operation at low fuel consumption, low NOx and PM emissions. In the present work, a virtual Cooperative Fuel Research (CFR) engine model is used to analyze fuel effects on ACI combustion. In particular, the effect of fuel Octane Sensitivity (S) (at constant RON) on autoignition propensity is assessed under beyond-RON (BRON) and beyond-MON (BMON) ACI conditions. The 3D CFR engine computational fluid dynamics (CFD) model employs finite-rate chemistry approach with multi-zone binning strategy to capture autoignition. Two binary blends with Research Octane Number (RON) of 90 are chosen for this study: Primary reference fuel (PRF) with S = 0, and toluene-heptane (TH) blend with S = 10.8, representing paraffinic and aromatic gasoline surrogates. Reduced mechanisms for these blends are generated from a detailed gasoline surrogate kinetic mechanism. Simulation results with the reduced mechanisms are validated against experimental data from an in-house CFR engine, with respect to in-cylinder pressure, heat release rate and combustion phasing. Thereafter, the sensitivity of combustion behavior to ACI operating condition (BRON vs BMON), air-fuel ratio (λ = 2 and 3), and engine speed (600 and 900rpm) is analyzed for both fuels. It is shown that the sensitivity of a fuel's autoignition characteristics to λ and engine speed significantly differs at BRON and BMON conditions. Moreover, this sensitivity is found to vary among fuels, despite the same RON. It is also observed that the presence of low temperature heat release (LTHR) under BRON condition leads to more sequential autoignition and longer combustion duration than BMON condition. Finally, the study indicates that the octane index (OI) fails to capture the trend in the variation of autoignition propensity with S under BMON condition.

Prediction of State of Charge (SOC) of Battery Electric Vehicle

Batteries have an important thing in development of energy needs. A good performance battery, will support the device it supports. The energy that can save a battery is limited, so the battery will increase its charge and discharge cycles. Incorrect charging and discharging processes can cause battery performance to decrease. Therefore battery management is needed so that the battery can reach the maximum. One aspect of battery management is setting the state which is the ratio of available energy capacitance to maximum energy capacity. One method for estimating load states is the fuzzy logic method, namely by assessing the input and output systems of prediction. Predictor of State of Charge use Mamdani Fuzzy Logic that have temperature and voltage as input variables and State of Charge as output variable. A result of prediction State of Charge battery is represented by the number of Root Mean Square Error. Battery in charge condition has 2.7 for RMSE and level of accuracy 81.5%. Whereas Battery in discharge condition has RMSE 1.5 and level of accuracy 84.7%.

Numerical Analysis of Fuel Effects on Advanced Compression Ignition Using a Virtual Cooperative Fuel Research Engine Model

Growing environmental concerns and demand for better fuel economy are driving forces that motivate the research for more advanced engines. Multi-mode combustion strategies have gained attention for their potential to provide high thermal efficiency and low emissions for light-duty applications. These strategies target optimizing the engine performance by correlating different combustion modes to load operating conditions. The extension from boosted SI mode at high loads to advanced compression ignition (ACI) mode at low loads can be achieved by increasing compression ratio and utilizing intake air heating. Further, in order to enable an accurate control of intake charge condition for ACI mode and rapid mode-switches, it is essential to gain fundamental insights into the autoignition process. Within the scope of ACI, homogeneous charge compression ignition (HCCI) mode is of significant interest. It is known for its potential benefits, operation at low fuel consumption, low NOx and PM emissions. In the present work, a virtual Cooperative Fuel Research (CFR) engine model is used to analyze fuel effects on ACI combustion. In particular, the effect of fuel Octane Sensitivity (S) (at constant RON) on autoignition propensity is assessed under beyond-RON (BRON) and beyond-MON (BMON) ACI conditions. The 3D CFR engine computational fluid dynamics (CFD) model employs finite-rate chemistry approach with multi-zone binning strategy to capture autoignition. Two binary blends with Research Octane Number (RON) of 90 are chosen for this study: Primary reference fuel (PRF) with S = 0, and toluene-heptane (TH) blend with S = 10.8, representing paraffinic and aromatic gasoline surrogates. Reduced mechanisms for these blends are generated from a detailed gasoline surrogate kinetic mechanism. Simulation results with the reduced mechanisms are validated against experimental data from an in-house CFR engine, with respect to in-cylinder pressure, heat release rate and combustion phasing. Thereafter, the sensitivity of combustion behavior to ACI operating condition (BRON vs BMON), air-fuel ratio (λ = 2 and 3), and engine speed (600 and 900rpm) is analyzed for both fuels. It is shown that the sensitivity of a fuel’s autoignition characteristics to λ and engine speed significantly differs at BRON and BMON conditions. Moreover, this sensitivity is found to vary among fuels, despite the same RON. This study also indicates that the octane index (OI) fails to capture the trend in the variation of autoignition propensity with S under BMON conditions.

Active Charge Balancing Strategy Using the State of Charge Estimation Technique for a PV-Battery Hybrid System

Charging a group of series-connected batteries of a PV-battery hybrid system exhibits an imbalance issue. Such imbalance has severe consequences on the battery activation function and the maintenance cost of the entire system. Therefore, this paper proposes an active battery balancing technique for a PV-battery integrated system to improve its performance and lifespan. Battery state of charge (SOC) estimation based on the backpropagation neural network (BPNN) technique is utilized to check the charge condition of the storage system. The developed battery management system (BMS) receives the SOC estimation of the individual batteries and issues control signal to the DC/DC Buck-boost converter to balance the charge status of the connected group of batteries. Simulation and experimental results using MATLAB-ATMega2560 interfacing system reveal the effectiveness of the proposed approach.

Fuzzy Logic Controlled PV Based Quasi Impedance Source Inverter for Charge Balancing

A photovoltaic power generation system based on battery-energy quasi impedance source cascaded multi-level inverter incorporates the advantages of a quasi-impedance source inverter, a CMI, and a battery-energy storage unit. Unbalanced battery charging between cascaded H-bridge inverter modules will degrade the efficiency of an entire system and reduce the lifetime of the battery. This paper proposed a control method for tracking the maximum power point and balancing the SOC of the battery under the large variation of solar input. A fuzzy based optimized linearizer for the battery charge and maximum power point tracking are proposed. The fuzzy controller is proposed to enhance the battery state of charge regulation takes as a individual modules as a loop to reduce over charge condition. The Proposed method validation is done by comparing it with base work control by PI and P&O with the performance parameters. The base work and proposed work are designed and simulated in the Matlab Simulink software.