A comparative study fourth order runge kutta-tvd Scheme and fluent software case of inlet flow problems

Inlet as part of aircraft engine plays important role in controlling the rate of airflow entering to the engine. The shape of inlet has to be designed in such away to make the rate of airflow does not change too much with angle of attack and also not much pressure losses at the time, the airflow entering to the compressor section. It is therefore understanding on the flow pattern inside the inlet is important. The present work presents on the use of the Fourth Order Runge Kutta – Harten Yee TVD scheme for the flow analysis inside inlet. The flow is assumed as an inviscid quasi two dimensional compressible flow. As an initial stage of computer code development, here uses three generic inlet models. The first inlet model to allow the problem in hand solved as the case of inlet with expansion wave case. The second inlet model will relate to the case of expansion compression wave. The last inlet model concerns with the inlet which produce series of weak shock wave and end up with a normal shock wave. The comparison result for the same test case with Fluent Software [1, 2] indicates that the developed computer code based on the Fourth Order Runge Kutta – Harten – Yee TVD scheme are very close to each other. However for complex inlet geometry, the problem is in the way how to provide an appropriate mesh model

Code-level modeling of the Hodgkin -Huxley neuron model using an open source version of SPICE

There have been numerous studies presented in the literature demonstrating proof of principle neural-electronic circuitry. Some of these studies involve simulations of neural detection using synthetic electronic circuitry, while others involve simulations of neural excitation using external electronics. A common feature of these studies is the simplicity of the overall circuit topology. Some of these studies implement the circuit equations in conventional numerical ordinary differential equation solvers. This process involves the algebraic manipulation of the circuit equations which is a tedious process for all but the simplest circuit topologies. As the overall complexity of the network topology increases, the numerical solver approach quickly becomes intractable necessitating an alternate implementation strategy. SPICE implementations of the Hodgkin-Huxley neuron model have sought to remedy this problem. There have been multiple studies associated with implementing the Hodgkin-Huxley model in the open source circuit simulator, SPICE. In this dissertation, a novel implementation of a portable SPICE device model developed using the Hodgkin-Huxley active membrane model is implemented using the code-level modeling functionality of an open source version of SPICE. The model is validated by comparison with standard Hodgkin-Huxley model simulations including gating variable dynamics simulations, accommodation, anodebreak excitation, and others. A further validation study is carried out demonstrating two blocking phenomenon described in the literature. The device model fully parameterizes the Hodgkin-Huxley membrane model to include temperature, internal and external concentrations used in the Nernst equations, and other user specified parameter values. This parameterization allows for making changes to the underlying neuron model rapidly and with minimal implementation complexity. The novelty and robustness of the modeling approach described herein is based on the ease of implementation. A wide variety of active membranes can be simulated using this code model approach. These biologically realistic components can be integrated with artificial electronic components allowing for the simulation of hybrid neuralelectronic circuitry under the SPICE simulation platform. These types of hybrid circuit simulations are not currently achievable using other neural simulators such as NEURON or GENESIS. While this implementation uses the Hodgkin-Huxley neuron model with its known limitations, the process of developing the device model can be used to implement any neuron model which can be described mathematically

A NOVEL DUAL MODELING METHOD FOR CHARACTERIZING HUMAN NERVE FIBER ACTIVATION

Presented in this work is the investigation and successful illustration of a coupled model of the human nerve fiber. SPICE netlist code was utilized to describe the electrical properties of the human nervous membrane in tandem with COMSOL Multiphysics, a finite element analysis software tool. The initial research concentrated on the utilization of the Hodgkin-Huxley electrical circuit representation of the nerve fiber membrane. Further development of the project identified the need for a linear circuit model that more closely resembled the McNeal linearization model augmented by the work of Szlavik which better facilitated the coupling of both SPICE and COMSOL programs. Related literature was investigated and applied to validate the model. This combination of analysis tools allowed for the presentation of a consistent model and revealed that a coupled model produced not only a qualitatively comparable, but also a quantitatively comparable result to studies presented in the literature. All potential profiles produced during the simulation were compared against the literature in order to meet the purpose of presenting an advanced computational model of human neural recruitment and excitation. It was demonstrated through this process that the correct usage of neuron models within a two dimensional conductive space did allow for the approximate modeling of human neural electrical characteristics

Communications Biophysics

Contains reports on five research projects.National Institutes of Health (Grant 5 P01 GM14940-03)National Institutes of Health (Grant 5 TOl GM01555-03)National Aeronautics and Space Administration (Grant NGL 22-009-304

The Effect of the Nonlinearity of the Response of Lipid Membranes to Voltage Perturbations on the Interpretation of Their Electrical Properties. A New Theoretical Description

Our understanding of the electrical properties of cell membranes is derived from experiments where the membrane is exposed to a perturbation (in the form of a time-dependent voltage or current change) and information is extracted from the measured output. The interpretation of such electrical recordings consists in finding an electronic equivalent that would show the same or similar response as the biological system. In general, however, there is no unique circuit configuration, which can explain a single electrical recording and the choice of an electric model for a biological system is based on complementary information (most commonly structural information) of the system investigated. Most of the electrophysiological data on cell membranes address the functional role of protein channels while assuming that the lipid matrix is an insulator with constant capacitance. However, close to their melting transition the lipid bilayers are no inert insulators. Their conductivity and their capacitance are nonlinear functions of both voltage, area and volume density. This has to be considered when interpreting electrical data. Here we show how electric data commonly interpreted as gating currents of proteins and inductance can be explained by the nonlinear dynamics of the lipid matrix itself

Detecting and Estimating Signals in Noisy Cable Structures, I: Neuronal Noise Sources

In recent theoretical approaches addressing the problem of neural coding, tools from statistical estimation and information theory have been applied to quantify the ability of neurons to transmit information through their spike outputs. These techniques, though fairly general, ignore the specific nature of neuronal processing in terms of its known biophysical properties. However, a systematic study of processing at various stages in a biophysically faithful model of a single neuron can identify the role of each stage in information transfer. Toward this end, we carry out a theoretical analysis of the information loss of a synaptic signal propagating along a linear, one-dimensional, weakly active cable due to neuronal noise sources along the way, using both a signal reconstruction and a signal detection paradigm. Here we begin such an analysis by quantitatively characterizing three sources of membrane noise: (1) thermal noise due to the passive membrane resistance, (2) noise due to stochastic openings and closings of voltage-gated membrane channels (Na^+ and K^+), and (3) noise due to random, background synaptic activity. Using analytical expressions for the power spectral densities of these noise sources, we compare their magnitudes in the case of a patch of membrane from a cortical pyramidal cell and explore their dependence on different biophysical parameters