Physics-based Modeling Approach Research Articles

Compact Modeling of Total Ionizing Dose and Aging Effects in MOS Technologies

This paper presents a physics-based compact modeling approach that incorporates the impact of total ionizing dose (TID) and stress-induced defects into simulations of metal-oxide-semiconductor (MOS) devices and integrated circuits (ICs). This approach utilizes calculations of surface potential (ψ <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">s</sub> ) to capture the charge contribution from oxide trapped charge and interface traps and to describe their impact on MOS electrostatics and device operating characteristics as a function of ionizing radiation exposure and aging effects. The modeling approach is demonstrated for bulk and silicon-on-insulator (SOI) MOS device. The formulation is verified using TCAD simulations and through the comparison of model calculations and experimental I - V characteristics from irradiated devices. The modeling approach is suitable for simulating TID and aging effects in advanced MOS devices and ICs, and is compatible with modern MOSFET compact modeling techniques. A circuit-level demonstration is given for TID and aging effects in SRAM cells.

Modeling of interactions between xenobiotics and cytochrome P450 (CYP) enzymes.

The adverse effects to humans and environment of only few chemicals are well known. Absorption, distribution, metabolism, and excretion (ADME) are the steps of pharmaco/toxicokinetics that determine the internal dose of chemicals to which the organism is exposed. Of all the xenobiotic-metabolizing enzymes, the cytochrome P450 (CYP) enzymes are the most important due to their abundance and versatility. Reactions catalyzed by CYPs usually turn xenobiotics to harmless and excretable metabolites, but sometimes an innocuous xenobiotic is transformed into a toxic metabolite. Data on ADME and toxicity properties of compounds are increasingly generated using in vitro and modeling (in silico) tools. Both physics-based and empirical modeling approaches are used. Numerous ligand-based and target-based as well as combined modeling methods have been employed to evaluate determinants of CYP ligand binding as well as predicting sites of metabolism and inhibition characteristics of test molecules. In silico prediction of CYP–ligand interactions have made crucial contributions in understanding (1) determinants of CYP ligand binding recognition and affinity; (2) prediction of likely metabolites from substrates; (3) prediction of inhibitors and their inhibition potency. Truly predictive models of toxic outcomes cannot be created without incorporating metabolic characteristics; in silico methods help producing such information and filling gaps in experimentally derived data. Currently modeling methods are not mature enough to replace standard in vitro and in vivo approaches, but they are already used as an important component in risk assessment of drugs and other chemicals.

Evaluating Crown Fire Rate of Spread Predictions from Physics-Based Models

Modeling the behavior of crown fires is challenging due to the complex set of coupled processes that drive the characteristics of a spreading wildfire and the large range of spatial and temporal scales over which these processes occur. Detailed physics-based modeling approaches such as FIRETEC and the Wildland Urban Interface Fire Dynamics Simulator (WFDS) simulate fire behavior using computational fluid dynamics based methods to numerically solve the three-dimensional, time dependent, model equations that govern, to some approximation, the component physical processes and their interactions that drive fire behavior. Both of these models have had limited evaluation and have not been assessed for predicting crown fire behavior. In this paper, we utilized a published set of field-scale measured crown fire rate of spread (ROS) data to provide a coarse assessment of crown fire ROS predictions from previously published studies that have utilized WFDS or FIRETEC. Overall, 86% of all simulated ROS values using WFDS or FIRETEC fell within the 95% prediction interval of the empirical data, which was above the goal of 75% for dynamic ecological modeling. However, scarcity of available empirical data is a bottleneck for further assessment of model performance.

TH-CD-303-01: On Characterizing 4D Lung Motion Using Coupled Multi-Physics Framework of Computational Fluid Dynamics, Linear Elasticity Estimation and 4D Lung Deformable Image Registration

Purpose: Modeling human lung dynamics is critical in quantitatively representing the subject specific 4D tumor motion. However, the process is numerically complicated by fluid-structure interactions (FSI) between air and the visco-elastic lung tissue and the lack of information on the anisotropic lung elasticity. Methods: We present a mathematical method to couple image registration and physics-based modeling approaches in order to represent a consistent volumetric lung dynamics. The Computational Fluid Dynamics (CFD) simulation assumes the lung to be a poro-elastic medium with spatially dependent elastic property. The simulation is performed on a 3D lung geometry reconstructed from four-dimensional computed tomography (4DCT) scan dataset of a human subject. The heterogeneous linear elastic Young’s Modulus (YM) values are estimated from a linear elastic deformation model with the same lung geometry and 4D lung Deformable Image Registration (DIR). The deformation obtained from the CFD simulation is then coupled with the displacement obtained from the DIR by means of the Tikhonov regularization (TR) algorithm. Numerical results present an analytic inter-model consistent estimation of the volumetric lung dynamics. The fusion method was validated by comparing the optimal displacement with 5D model-generated landmarks. Results: A set of five 4DCT datasets was employed for this study. For the landmarks inside the lung, the optimization results in a significant reduction in error. Specifically, in the left lung, the average error for the landmarks on the surface and edges is reduced by 1.11 mm while this value is 2.96 mm for the nodes, while, in the right lung, the average displacement errors after optimization are reduced by 1.34 mm and 0.86 mm for the interior and surface nodes respectively. Conclusion: These results shows that although optimization reduces the displacement error both at the surface and the interior nodes, it is more effective for the error reduction of interior nodes.

A high-resolution simulation of groundwater and surface water over most of the continental US with the integrated hydrologic model ParFlow v3

Abstract. Interactions between surface and groundwater systems are well-established theoretically and observationally. While numerical models that solve both surface and subsurface flow equations in a single framework (matrix) are increasingly being applied, computational limitations have restricted their use to local and regional studies. Regional or watershed-scale simulations have been effective tools for understanding hydrologic processes; however, there are still many questions, such as the adaptation of water resources to anthropogenic stressors and climate variability, that can only be answered across large spatial extents at high resolution. In response to this grand challenge in hydrology, we present the results of a parallel, integrated hydrologic model simulating surface and subsurface flow at high spatial resolution (1 km) over much of continental North America (~ 6.3 M km2). These simulations provide integrated predictions of hydrologic states and fluxes, namely, water table depth and streamflow, at very large scale and high resolution. The physics-based modeling approach used here requires limited parameterizations and relies only on more fundamental inputs such as topography, hydrogeologic properties and climate forcing. Results are compared to observations and provide mechanistic insight into hydrologic process interaction. This study demonstrates both the feasibility of continental-scale integrated models and their utility for improving our understanding of large-scale hydrologic systems; the combination of high resolution and large spatial extent facilitates analysis of scaling relationships using model outputs.

Building-level power demand forecasting framework using building specific inputs: Development and applications

Abstract In this paper, the development of a general framework for building level power demand forecasting and its applications to supervisory control and demand management are presented. Models of thermal loads, while rigorous and insightful, do not directly extrapolate to measures of power consumption and cannot be easily applied to a variety of buildings. Ultimately, building operators are interested in managing power consumption as energy costs and opportunities are directly related to the power variable. Our work develops Auto-Regressive models with eXogeneous inputs (ARX) to forecast power demand in conjunction with existing physics based modeling approaches and enhances the current control framework for building energy management. The main contributions of this work are identifying and incorporating building level measurements as inputs, and evaluating the use of power forecast models for supervisory control and demand response (DR). The move towards a smarter grid is expected to provide extensive data on building conditions and power consumption, which we can include in the model development. Options for model inputs and outputs are investigated depending on possible measurements, and their effect (or sensitivity) on the modeling and decision making processes are evaluated. It is shown that an appropriate selection of exogenous inputs related to the control action is necessary to capture the effect of common demand management practices such as precooling. The forecasting capabilities are also demonstrated on a simplified building model and on data collected from a real building.

A Physics-Based Modeling Approach for Performance Monitoring in Gas Turbine Engines

Performance deterioration monitoring is an essential part of the prognostics and health management (PHM) of gas turbine engines (GTEs). This paper proposes a physics-based modeling approach for performance deterioration monitoring with two model-based performance indicators, heat loss index and power deficit index, for GTE PHM applications. A comprehensive nonlinear thermodynamic model for a single shaft GTE is developed to establish the relation between the operating conditions and the cycle parameters. The model, once properly calibrated, is able to predict the GTE cycle parameters in a healthy condition as the baseline, while in reality, the measured parameters gradually deviate from the baseline, which reflects the performance deterioration of the GTE. To represent the degradation level, the heat loss index is defined as the normalized measure of the thermal power that is being wasted in the GTE compared to the healthy condition. Similarly, the power deficit index is defined as the deficiency ratio of the GTE output power due to the performance deterioration. The effectiveness of the performance indicators in monitoring performance deterioration and their robustness to the variations of the operating conditions are examined by using three years of typical operating data of an industrial GTE. The results clearly reveal the trends of both the short term recoverable deterioration due to fouling effects in the compressor, and the long term non-recoverable deterioration caused by structural degradation. The technique is especially advantageous for prognostic applications where there is no access to internal cycle parameters of a GTE, and only the operating data are available, hence no additional sensors are required.

Physics-based process model approach for detecting discontinuity during friction stir welding

The goal of this work is to develop a method for detecting the creation of discontinuities during friction stir welding. This in situ weld monitoring method could significantly reduce the need for post-process inspection. A process force model and a discontinuity force model were created based on the state-of-the-art understanding of flow around an friction stir welding (FSW) tool. These models are used to predict the FSW forces and size of discontinuities formed in the weld. Friction stir welds with discontinuities and welds without discontinuities were created, and the differences in force dynamics were observed. In this paper, discontinuities were generated by reducing the tool rotation frequency and increasing the tool traverse speed in order to create “cold” welds. Experimental force data for welds with discontinuities and welds without discontinuities compared favorably with the predicted forces. The model currently overpredicts the discontinuity size.

Building energy consumption on-line forecasting using physics based system identification

Model based control has become a promising solution for building operation optimization and energy saving. Accuracy and computationally efficiency are two of the most important requirements for building energy models. Existing studies in this area have mostly been focusing on reducing computation burden using simplified physics based modeling approach. However, creating even the simplified physics based model is often challenging and time consuming. Pure date-driven statistical models have also been adopted in a lot of studies. Such models, unfortunately, often require long training period and are bounded to building operating conditions that they are trained for. Therefore, this study proposes a novel methodology to develop building energy estimation models for on-line building control and optimization using a system identification approach. Frequency domain spectral density analysis is implemented in this on-line modeling approach to capture the dynamics of building energy system and forecast the energy consumption with more than 90% accuracy and less than 2min computational speed. A systematic analysis of system structure, system order and system excitation selection are also demonstrated. The forecasting results from this proposed model are validated against detailed physics based simulation results using a mid-size commercial building EnergyPlus model.

Atoms to Assemblies: A Physics-Based Hierarchical Modelling Approach for Polymer Composite Components

The development of new composite materials requires analysis and experimentation spanning scales from nanometres to metres, from “atoms to assemblies”. In this paper, concerned primarily with fibre reinforced epoxy composites, a methodology is presented which allows continuum level structural simulation to account for nanoand micro-scale size effects in composites. The novelty of this approach is the modular hierarchical nature of the simulation which ensures computational tractability, regardless of the length scales considered. Linking the nanoscale to the macroscopic scale in a single simulation allows for holistic materials development, including the addition of nanoadditives to polymer resin systems.

Assessing grain size as a predictor of mid-frequency bottom backscattering strengths

Scattering from the seabed can be a complex mix of surface roughness and volume heterogeneity contributions. A series of mid-frequency (MF; 1.5–4.5 kHz) bottom backscattering strength data collected by the Naval Research Laboratory at a number of shallow-water locations (Stanton Banks, Malta Plateau, Heceta Bank) is first used to demonstrate the inadequacies of using Lambert’s Law to model bottom backscattering strengths, and that more general empirical power laws, where not only the strength but the angular exponent can vary, are needed to match the data at a given frequency. The Stanton Banks data, where sediment types range from mud to gravel, are then used to explore the extent to which easy-to-access geophysical data (such as surficial grain size distributions from bottom grab samples) may be capable of providing suitable estimates of key model inputs (such as sediment sound speeds/attenuations, density and roughness/volume spectral strengths/exponents). These results show that both grain size and “bottom type” are in general unreliable predictors of the measured MF bottom backscattering strengths, and that a physics-based modeling approach coupled with in-situ environmental characterization is required. [Work supported by ONR.]

Detailed simulation of morphodynamics: 3. Ripples and dunes

We present a 3-D physics-based high-resolution modeling approach to the dynamics of underwater ripples and dunes. The flow is modeled by large eddy simulation on a Cartesian grid with local refinements. The sediment transport is modeled by computing pickup, transport over the bed, transport in the water column, and deposition of rigid spherical particles in a Lagrangian framework. The morphological development of the bed is modeled by a sediment balance equation in which the pickup and deposition from the sediment motion submodels appear as source and sink terms. The model realistically replicated the formation and migration of dunes. Model results showed a good agreement with data from five flume experiments. We subsequently applied the model to investigate the effect of sediment grain size on ripples. Finer sediments were found to yield more superimposed ripples than coarser sediments. Moreover, under the same hydrodynamic conditions, the finer sediments yielded two-dimensional bed forms, whereas for coarser sediment irregularities increased. We extended the tests to pronounced 3-D morphologies by simulating the development of local scour at a pier. The results agreed well with experimental data. The model contributes to unraveling the complex problem of small-scale morphodynamics and may be used in a wide range of applications, for instance, to develop more reliable parameterizations of small-scale processes for application in large-scale morphodynamic models.

Application of Operational Effectiveness Models in Naval Ship concept exploration and design

Traditionally, the Concept and Requirements Exploration process is the first stage of ship design. Concept and Requirements Exploration responds to a stated mission need with early high-level assessment of a broad range of ship design options and technologies. Our design group uses a Multi-Objective Optimization approach to explore the design space and identify non-dominated designs ranked by cost, risk, and effectiveness. Our method of calculating effectiveness in this approach has, in the past, been based on expert opinion. In this study, the use of a more direct physics-based Operational Effectiveness Model approach is considered to provide greater confidence in the validity of effectiveness results and a perception that results are more unbiased and rational. This approach requires significant early investment. How much analysis is enough and is there significant payoff for this significant effort? This paper presents this approach and explores these questions.

Analytical Modeling and Parameter Extraction of Organic Thin Film Transistor: Effect of Contact Resistance, Doping Concentration and Field Dependent Mobility

For what is believed to be for the first time, the device physics based modeling approach to derive the generic model current equations of organic thin film transistor (OTFT) is described. Firstly, the current model equation is derived by considering the dependence of mobility on gate voltage and doping density, which is more realistic and relevant to organic materials containing TFTs. To model small molecule or polymer TFT, the potential drop across contacts is taken into account as contacts are not ohmic due to some morphological disorders. Further the effect of contact resistance is included and accordingly the generic model current equation is modified. It shows a good agreement of proposed current equation with simulated results which validates the proposed OTFT model from ohmic to saturation regime. Moreover, the analytical model is used to extract the contact and channel resistances in linear and saturation region and these resistances are verified through potential cut line (PCL) and transmission line methods (TLM). The extracted parameters are not only used to verify the electrical characteristics but also exhibit insight on contact potential, charge injection and transport phenomenon for organic TFT operation.

Sensorless Control of PM Synchronous Machines by Physics-Based EMF Observer

A sensorless rotor position estimation and speed control of three-phase radial flux surface-mounted permanent magnet synchronous machine (SMPMSM) is presented. A novel thermoelectric physics-based modeling approach was developed to evaluate more realistic information about the instantaneous behavior of the machine during operation. The extracted information from the physics-based models is employed in the back electromotive force observer. The mathematical modeling, stability, and transient analysis of the observer are developed and described in detail. The sensorless operation of a 0.2-kW SMPMSM was verified numerically and experimentally for medium low, low, and very low speeds at different operational contingencies. The results show that, in spite of significant machine model variations and increased noise level especially at very low speeds, the accuracy of estimated speed and rotor position using the developed observer is acceptable.

An Approach to the Health Monitoring of a Pumping Unit in an Aircraft Engine Fuel System

This paper provides an approach for health monitoring through an early detection of failure modes premises. It is a physics-based model approach that captures the knowledge of the system and its degradations. The component under study is the pumping unit such as those found in aircraft engines fuel systems. First, a complete component analysis is performed to determine its potential degradation and a physics-based relevant component health indicator (CHI) is defined. Then, degradations are modelled and their impacts on the CHI are quantified using an AMESim® physics-based model. Assuming that in-flight measures are available, a model updating is performed and a healthy distribution of the CHI is computed. Eventually, a fault detection algorithm is developed and statistical validation is performed through the computation of key performance indicators (KPI). In parallel, a degradation severity indicator (DSI) is defined and prognostic is performed based on the monitoring of this DSI.

Integrated Modeling of Groundwater and Surface Water Interactions in a Manmade Wetland

A manmade pilot wetland in south Florida, the Everglades Nutrient Removal (ENR) project, was modeled with a physics-based integrated approach using WASH123D (Yeh et al. 2006). Storm water is routed into the treatment wetland for phosphorus removal by plant and sediment uptake. It overlies a highly permeable surficial groundwater aquifer. Strong surface water and groundwater interactions are a key component of the hydrologic processes. The site has extensive field measurement and monitoring tools that provide point scale and distributed data on surface water levels, groundwater levels, and the physical range of hydraulic parameters and hydrologic fluxes. Previous hydrologic and hydrodynamic modeling studies have treated seepage losses empirically by some simple regression equations and, only surface water flows are modeled in detail. Several years of operational data are available and were used in model historical matching and validation. The validity of a diffusion wave approximation for two-dimensional overland flow (in the region with very flat topography) was also tested. The uniqueness of this modeling study is notable for (1) the point scale and distributed comparison of model results with observed data; (2) model parameters based on available field test data; and (3) water flows in the study area include two-dimensional overland flow, hydraulic structures/levees, three-dimensional subsurface flow and one-dimensional canal flow and their interactions. This study demonstrates the need and the utility of a physics-based modeling approach for strong surface water and groundwater interactions.

Microwave puffing: Determination of optimal conditions using a coupled multiphase porous media – Large deformation model

Increased interest in microwave puffing is due to its ability to obtain low-fat and ready-to-eat healthy products. Determination of optimal conditions for this complex process has been difficult and although several patents exist on the concept, we are yet to see any large scale commercial use. A fundamental physics based modeling approach integrated with relevant experimentation, developed in this work, is an ideal framework to understand and optimize microwave puffing. The results showed that puffing may not be successful unless carried out using an intensive heating source such as microwaves. Addition of infrared and hot air leads to better quality product whereas using forced air convection is not desirable. There is an optimum initial moisture content depending on the puffing conditions. The study provides critical guidelines to food product/process developers for successful development, control and automation of microwave puffing, thereby leading to value-added nutritious products.

Remaining useful life, technical health, and life extension

Life extension has for a long time been an important and highly discussed issue in nuclear and aviation industries, and has recently attracted considerable attention in the subsea oil and gas industry. Decision-making related to life extension is a multidisciplinary problem, but it primarily depends on the remaining useful life. This paper clarifies the concepts of remaining useful life and technical health, and discusses various influencing factors. An overall model with the capability to handle a heterogeneous combination of requirements, such as degradation modelling, uncertain environmental and operational conditions, uncertain sensor data and expert opinion is suggested for life extension decision-making. It is concluded that a physics-based modelling approach is appropriate for equipment in the subsea oil and gas industry.

Multiobjective Design Optimization of Coupled PM Synchronous Motor-Drive Using Physics-Based Modeling Approach

This paper deals with an optimal design of surface-mounted permanent magnet motor geometry for achieving minimum torque ripple, minimum RMS value of phase current, and minimum total harmonic distortion of phase currents, simultaneously. A classic multiobjective function is formed as a combination of these single objectives. A dynamic physics-based phase variable modeling approach is used to indirectly couple the motor geometry in the finite element domain to the drive circuit in a simulink environment. The physical behavior of motor is calculated by nonlinear transient FE analysis with motion. A fast hybrid genetic-particle swarm optimization process is developed for shape optimization of the motor. The results before and after optimization show the expected performance improvements while reducing magnet material and copper size.