Physics-based Analytical Model Research Articles

Electrical conductivity model for reactive porous media under partially saturated conditions with hysteresis effects

The electrical conductivity of a porous medium is strongly controlled by the structure of the medium at the microscale as the pore configuration governs the distribution of the conductive fluid. The pore structure thus plays a key role since different geometries translate in variations of the fluid distribution, causing different behaviours measurable at the macroscale. In this study, we present a new physically-based analytical model derived under the assumption that the pore structure can be represented by a bundle of tortuous capillary tubes with periodic variations of their radius and a fractal distribution of pore sizes. By upscaling the microscale properties of the porous medium, we obtain expressions to estimate the total and relative electrical conductivity. The proposed pore geometry allows us to include the hysteresis phenomenon in the electrical conductivity estimates. The variations on these estimates caused by pore structure changes due to reactive processes are accounted by assuming a uniform dissolution of the pores. Under this hypothesis, we describe the evolution of the electrical conductivity during reactive processes. The expressions of the proposed model have been tested with published data from different soil textures, showing a satisfactory agreement with the experimental data. Hysteretic behavior and mineral dissolution are also successfully addressed. By including hysteresis and mineral dissolution/precipitation in the estimates of the electrical conductivity, this new analytical model presents an improvement as it relates those macroscopic physical phenomena to its origins at the microscale. This opens up exciting possibilities for studies involving electrical conductivity measurements to monitor water movement, and hysteretic and reactive processes in porous media.

An Analytical Model for Predicting the Rate Performance of Battery Cells Under Mixed Kinetic Control

The prediction of battery performance conventionally relies on resource-intensive numerical simulations based on the porous electrode theory. For computational speedup, simplified physics-based models have proven attractive as they still capture the underlying reaction mechanisms to a certain degree. In this class of models, physical complexity is usually reduced by assuming a single rate-limiting reaction. For example, the single particle model describes battery cells limited only by the solid diffusion pathway [1]. On the flipside, our previous analytical model exploited inefficiency of electrolyte transport, particularly in thick electrodes [2]. With advancement in both electrode and electrolyte technologies, however, it becomes challenging for these models to handle more realistic batteries.In this work, we propose an improved physics-based analytical model that enables mixed control of both kinetic processes, reflecting both salt depletion and particle size effect. An extension to our uniform reaction (UR) model, the new model uses salt distribution in electrolyte to probe the accessible lithium in the electrode level. The added solid diffusion module calculates the equilibrium potential at particle surface, lithium concentration at particle surface (Cs), and in turn the particle-level depth of discharge. We refer to the model as the URCs model, recognizing its two components. The model features higher tunability as it now accepts function-valued electrode and electrolyte properties. It also allows for prediction of discharge voltage curves and energy output of the cell.The model is compared with numerical simulation over a wide range of cell parameters. It shows good agreement with simulated results in rate performance test, specific energy output test, and discharge voltage curves. Better alignment is observed for the NMC/Li half cell than the NMC/Gr full cell, likely due to mixed reaction type in the graphite anode. Figure 1 shows the comparison of voltage curves across various C rates for a half cell with 70um NMC electrode and particles of 4um radius. Additionally, the cells are optimized for their specific capacity against electrode thickness and porosity. The optimal parameters predicted by the model lie closely to the global optimum from numerical simulation. Figure 2 shows the optimization results for a full cell with particles of 4um radius, overlaid on contour lines generated from the URCs model.The model offers a speed up of over 500 times compared with state-of-art P2D solvers [3]. Furthermore, its compatibility with gradient-based optimization algorithms opens possibilities for more efficient global optimization schemes, in which the number of function calls can be reduced. The high efficiency and the analytical nature of the URCs model render it a powerful tool for both on-board applications and battery cell design.[1] Doyle, M. and Newman, J., 1997. Analysis of capacity–rate data for lithium batteries using simplified models of the discharge process. Journal of Applied Electrochemistry, 27, pp.846-856.[2] Wang, F. and Tang, M., 2020. A quantitative analytical model for predicting and optimizing the rate performance of battery cells. Cell Reports Physical Science, 1(9).[3] Sulzer, V., Marquis, S.G., Timms, R., Robinson, M. and Chapman, S.J., 2021. Python battery mathematical modelling (PyBaMM). Journal of Open Research Software, 9(1). Figure 1

Analytical Wake Modeling in Atmospheric Boundary Layers: Accounting for Wind Veer and Thermal Stratification

Reliable characterization of wind turbine wakes in the presence of Atmospheric Boundary Layer (ABL) flows is crucial to accurately predict wind farm performance. Wind veering in the ABL shears the wake in the lateral direction, and wind veer strength depends on the thermal stability of the ABL. Analytical wake modeling approaches must capture these ABL effects to ensure correct prediction of the wake structure under varied atmospheric conditions. To this end, a new physics-based analytical wake model is developed in this study that is capable of predicting the shape of wakes influenced by wind veer and thermal stratification effects. This model combines a novel ABL wind field model with the Gaussian wake model. The new ABL wind model is capable of predicting both the streamwise and spanwise velocity components in conventionally neutral (CNBL) and stable (SBL) ABL flows. The analytical expressions for both of these horizontal velocity components adhere to Monin-Obukhov Similarity Theory (MOST) in the surface layer, while capturing wind veering in the outer layer of the ABL. Incorporating this ABL model with the Gaussian wake model predicts laterally deflected wake shapes in a fully predictive and self-consistent fashion for a wide range of atmospheric conditions. The results also demonstrate that the enhanced wake model gives improved predictions relative to Large Eddy Simulations of power losses due to wake interactions under strongly stably stratified atmospheric conditions, where wind veer effects are dominant.

Analytical Model of Quantitative Texture Prediction Considering Heat Transfer Based on Single-Phase Material in Laser Powder Bed Fusion

Laser powder bed fusion (LPBF) is widely used in metal additive manufacturing to create geometrically complex parts, where heat transfer and its affected temperature distribution significantly influence the parts’ materials’ microstructure and the resulting materials’ properties. Among all the microstructure representations, crystallographic orientations play a paramount role in determining the mechanical properties of materials. This paper first developed a physics-based analytical model to predict the 3D temperature distribution in PBF considering heat transfer boundary conditions; heat input using point-moving heat source solutions; and heat loss due to heat conduction, convection, and radiation. The superposition principle obtained temperature distributions based on linear heat sources and linear heat loss solutions. Then, the temperature distribution was used to analytically obtain the texture grown on a substrate with random grain orientations considering columnar-to-equiaxed transition (CET). Thus, the link between process parameters and texture was established through CET models and physical rules. Ti-6Al-4V was selected to demonstrate the capability of the analytical model in a single-phase situation. By applying advanced thermal models, the accuracy of the texture prediction was evaluated based on a comparison of experimental data from the literature and past analytical model results. Hence, this work not only provides a method of the fast analytical simulation of texture prediction in the single-phase mode for metallic materials but also paves the road for subsequent studies on microstructure-affected or texture-affected materials’ properties for both academic research and industrial applications. The prediction of single-phase material texture has never been achieved before, and the scalability has been expanded.

Analytical modelling of the transport in analog filamentary conductive-metal-oxide/HfOx ReRAM devices.

The recent co-optimization of memristive technologies and programming algorithms enabled neural networks training with in-memory computing systems. In this context, novel analog filamentary conductive-metal-oxide (CMO)/HfOx redox-based resistive switching memory (ReRAM) represents a key technology. Despite device performance enhancements reported in literature, the underlying mechanism behind resistive switching is not fully understood. This work presents the first physics-based analytical model of the current transport and of the resistive switching in these devices. As a case study, analog TaOx/HfOx ReRAM devices are considered. The current transport is explained by a trap-to-trap tunneling process, and the resistive switching by a modulation of the defect density within the sub-band of the TaOx that behaves as electric field and temperature confinement layer. The local temperature and electric field distributions are derived from the solution of the electric and heat transport equations in a 3D finite element ReRAM model. The intermediate resistive states are described as a gradual modulation of the TaOx defect density, which results in a variation of its electrical conductivity. The drift-dynamics of ions during the resistive switching is analytically described, allowing the estimation of defect migration energies in the TaOx layer. Moreover, the role of the electro-thermal properties of the CMO layer is unveiled. The proposed analytical model accurately describes the experimental switching characteristic of analog TaOx/HfOx ReRAM devices, increasing the physical understanding and providing the equations necessary for circuit simulations incorporating this technology.

Modeling and Simulation of Current Voltage Characteristics for Cylindrical CNTFET Transistor

Abstract: In this study, we present a physics-based analytical model for the current-voltage characteristics of a near-ballistic CNTFET with a single-wall zig-zag carbon nanotube (16, 0). This model is based on the calculation of the potential and threshold voltage in the channel of a cylindrical gate CNTFET. Consequently, a new expression for the mobility law as a function of the CNT geometric parameters is proposed. Subsequently, a new model of I-V characteristics is presented, accounting for the impact of edge resistances. The paper also discusses the DIBL, subthreshold slope (SS), and the impact of some geometric and physical parameters on the drain current. A comparison of the I-V characteristics shows a good agreement between our model and the FETToy model. Keywords: Cylindrical gate CNTFET, I-V characteristics, Threshold voltage, Analytical model, Mobility law.

Modeling Flame Transfer Functions of an Industrial Premixed Burner

Abstract This paper presents an analysis of the unsteady heat release rate response of industrially relevant axisymmetric premixed flames to harmonic velocity perturbations. The heat release rate response, quantified using the flame transfer function (FTF) definition, is measured from an acoustically forced swirl burner under perfectly premixed conditions. To understand the features of the measured FTF, a physics-based analytical model is developed in this study. To describe the heat release rate dynamics, a model for the flame spatiotemporal response is derived in the linear limit using the G-equation formulation. Inputs to the flame response model are selected to be consistent with values observed in the corresponding industrial burner, based on experimental and numerical studies. The relative contributions of acoustic and convecting vortical disturbances on specific features of the FTF are explored in this study. The results highlight the importance of capturing the appropriate disturbance velocity field as an input to the flame response model for accurate FTF predictions.

Physics-based analytical model for trap assisted biosensing in dual cavity negative capacitance junctionless accumulation mode FET

The design of a ferroelectric-based biosensor for detecting various biomolecules like proteins and DNA has captivated the interest of researchers in early disease diagnostics and treatment monitoring. Doomed by this interest, an analytical model for trap-assisted biosensing in Dual Cavity Negative Capacitance Junctionless Accumulation Mode FET is proposed for detecting various biomolecules. The biomolecules are treated as traps that tunnel through the biosensor's ferroelectric layer. The analytical model is validated through extensive numerical device simulations. The device incorporates the detection of charged and uncharged biomolecules in terms of dielectric constants and biomolecule concentration. The nano-cavities are created inside the gate dielectric stack to collect biomolecules like proteins and DNA. As the biomolecules are infused as traps in cavity areas, electrical properties of the biosensor, like input characteristics, transconductance, threshold voltage, on-state current, and subthreshold swing change. The sensitivity obtained for the proposed biosensor is compared with the existing biosensor and found that it is best suited for susceptible biosensing applications.

Synergies and Trade-Offs in Hybrid Propulsion Systems Through Physics-Based Electrical Component Modeling

Abstract Hybrid-electric propulsion is recognized as an enabling technology for reducing aviation's environmental impact. In this work, a serial/parallel hybrid configuration of a 19-passenger commuter aircraft is investigated. Two underwing-mounted turboprop engines are connected to electrical branches via generators. One rear fuselage-mounted electrically driven ducted fan is coupled with an electric motor and respective electrical branch. A battery system completes the selected architecture. Consistency in modeling accuracy of propulsion systems is aimed for by development of an integrated framework. A multipoint synthesis scheme for the gas turbine and electric fan is combined with physics-based analytical modeling for electrical components. Influence of turbomachinery and electrical power system design points on the integrated power system is examined. An opposing trend between electrical and conventional powertrain mass is driven by electric fan design power. Power system efficiency improvements in the order of 2% favor high-power electric fan designs. A trade-off in electrical power system mass and performance arises from oversizing of electrical components for load manipulation. Branch efficiency improvements of up to 3% imply potential to achieve battery mass reduction due to fewer transmission losses. A threshold system voltage of 1 kV, yielding 32% mass reduction of electrical branches and performance improvements of 1–2%, is identified. This work sets the foundation for interpreting mission-level electrification outcomes that are driven by interactions on the integrated power system. Areas of conflicting interests and synergistic opportunities are highlighted for optimal conceptual design of hybrid powertrains.

A physics-based analytical model for ZnO based HEMT

In this brief, the electrical performance of MgZnO/ZnO HEMT is analysed using a physics-based analytical model. The proposed model is successfully validated with the existing experimental results. The output conductance (gd) of MgZnO/ZnO HEMT is calculated as 1479.7 μA/V for 2 nm thickness of MgZnO barrier layer having gate length of 1 μm. The magnitude of gd is reduced to 791.4 μA/V when the thickness of the barrier layer is reduced to 1.5 nm for MgZnO/ZnO HEMT having gate length of 2 μm. The peak magnitude of figure-of-merit metrics: VIP2, VIP3, and IIP3 is calculated as 15.73, 19.67, and 23.32 dBm respectively at the gate voltage (Vg) of −1.5 V at 2 nm thickness of barrier layer having gate length of 1 μm. The lowest magnitude of IMD3 is obtained as −41.9 dBm at Vg = −2.5 V for 2 nm thickness of barrier layer having gate length of 1 μm.

A Risk-Informed Decision-Support Framework for Optimal Operation of Hurricane-Impacted Transportation Networks

To reduce life threats and financial losses under hurricane weather and traffic conditions, stakeholders need to make a sequence of decisions (e.g., enforcing traffic control and/or broadcasting travel advisory) with the presence of various uncertainties, which could be formulated as a Markov decision process (MDP). To effectively solve the MDP for optimal operation of hurricane-impacted transportation networks, a risk-informed decision-support framework is proposed consisting of both a decision-making environment and tool. The decision-making environment involves essentially three coupled modules of hurricane hazard, transportation infrastructure, and traffic flow, where the uncertainties from natural environment, built environment, and human behavior are examined. Specifically, multiple correlated hazards for each specific storm are capsulated in the hurricane hazard module, representative hurricane-vulnerable infrastructure components and associated moving vehicles are evaluated in the transportation infrastructure module, and network-level traffic relations are addressed in the traffic flow module. In view of the complexity and intractability of a hurricane-transportation infrastructure-traffic system through full model representation, the efficient low-dimensional modeling approaches including both physics-based analytical and data-driven surrogate models are utilized in all these three simulation modules. Considering an explicit probabilistic model for the decision-making environment (and hence state transition function) is not available, the operation optimization (involving competing objectives of traffic safety and mobility) is accomplished through a deep reinforcement learning-based decision-making tool that learns to act optimally (involving both traffic control and travel advisory actions) by directly interacting with the environment. A hypothetical case study is conducted to verify the applicability and effectiveness of the proposed framework as a testbed for optimal operation of hurricane-impacted transportation networks.

Mixed analytical model to estimate the anisotropic effective thermal conductivity and in-reactor performance of metallic microcell UO2

A mixed analytical model is utilized to estimate the effective thermal conductivity of microcell UO2 pellets. The proposed model consists of a mixture of parallel and series structural models that collectively take into account the unique cell geometry of the anisotropic metal network and the corresponding fractional contribution to the heat flow. This relatively simple structure model can explain the anisotropy of the thermal conductivity of UO2-Mo microcells, as observed in both experimental tests and numerical calculations. The physics-based mixed analytical model allows us to accommodate the effects of the UO2 burnup and wall structure changes on the effective thermal conductivity of microcell UO2 pellets. To validate the model's ability to predict the burnup dependence, the reduced fuel centerline temperature in a UO2 -5vol% Cr microcell under irradiation was estimated at selected burnup levels and compared to the temperature evolution observed in a Halden reactor test, in which the sample pellets were irradiated up to 16MWd/kgU. The comparison demonstrated that the mixed analytical model suitably predicts the fuel temperature changes in irradiated UO2 -5vol% Cr microcells. The model proposed here is a preliminary type based on limited experimental data, and the parameters of the model could be modified further with the accumulation of more measurement data.

Compact I-V model for back-gated and double-gated TMD FETs

A physics-based analytical DC compact model for double and single gate TMD FETs is presented. The model is developed by calculating the charge density inside the 2D layer which is expressed in terms of the Lambert W function that recently has become the standard in SPICE simulators. The current is then calculated in terms of the charge densities at the drain and source ends of the channel. We validate our model against measurement data for different device structures. A superlinear current increase above certain gate voltage has been observed in some MoS2 FET devices, where we present a new mobility model to account for the observed phenomena. Despite the simplicity of the model, it shows very good agreement with the experimental data.

Analytical modeling and doping optimization for enhanced analog performance in a Ge/Si interfaced nanowire MOSFET

This paper critically investigates the effect of doping on different device characteristics of a Ge/Si interfaced nanowire MOSFET (GSI-NWM) for analog performance enhancement. The doping of source, channel, and drain has a prominent effect on important device characteristics, which has been investigated through DC and AC analysis performed on the SILVACO TCAD simulator. A numerical computational-based simulation study has been used to investigate the modulation of various device characteristics, such as threshold voltage, cut-off frequency, subthreshold swing, MTPG, current ratio, channel resistance, and transconductance. The investigation revealed a strong dependence of most of these characteristics on the source, channel, and drain doping levels, providing valuable insights into device performance. Proper optimization in doping can significantly improve the performance of the device. A compact physics-based analytical model has been mathematically evaluated and proposed in this work, showing an excellent in-line agreement with the simulated results. This is a novel approach for improving the analog performance parameters of a nanowire MOSFET through doping optimization, which incorporates gate oxide stacking and germanium as a source material. In this work, the biosensing capability of the GSI-NWM has also been discussed and evaluated.

Modeling Methodology Based on Fast and Refined Neural Networks for Non-Isolated DC–DC Converters With Configurable Parameter Settings

Compared with conventional physics-based methods, e.g., analytical modeling and numerical modeling, data-driven methods can extract input-to-output relationships from the data without much prior knowledge of the physical system, thus showing great potential in modeling power electronics (PE) converters with complex switching behaviors and configurable parameter settings. Previous data-driven PE circuit modeling approaches are mostly based on sequential neural networks, and their execution speed suffers from large sequential lengths due to a high sampling rate for high modeling accuracy. Moreover, modeling of refined singular ripples is missing and configurable parameter settings are not available in these data-driven modeling approaches. To address the above-mentioned issues, this paper proposes a hybrid physics-informed machine learning (ML) method to model the non-isolated DC-DC converters. The approach empirically decomposes the output signals into transient large signals and periodic small signals. For transient large signals, a fully-connected neural network (NN) is used to map circuit parameters with system characteristics, such that configurable circuit parameter settings are allowed. For periodic signals, a long short-time memory (LSTM) network together with convolutional neural network (CNN) is used to accelerate the simulation by predicting signal features in the compressed latent space. A buck converter with configurable parameter settings is modeled by the proposed hybrid physics-informed ML method. Periodic ripples are successfully generated, while execution speed is about 10 times faster than that of conventional numerical methods.

Modeling and simulation of short channel length effect in open drain MOSFET THz detectors

THz radiation detection using FET devices has attracted increasing attention lately. In this paper, we further study a simulated model of FET rectification detection in short channel length. To achieve this, both physics-based analytic model and a detailed TCAD simulation were contacted and compared. The analytical model provided detailed dependence of the response on the channel length below the extension length of the radiation. However, the simulation results were validated by comparison with the experimental data to confirm the validity of the theoretical model. These results present a new model of rectification for short channel lengths and its dependence on the extinction of AC signal through the channel.

Physics-Based Modeling Strategies of Phase-Change Random Access Memory

Phase-change random access memory (PCRAM) has been regarded as one of the most promising candidates for future “universal” memory due to its mature fabrication techniques and profitable commercialized prospect. To further improve its physical performance and exploit more application areas, it is imperative to develop a physically realistic model that can closely mimic the electrical, thermal, and phase-change mechanisms of PCRAM. Although plenty of phase-change memory models have been proposed during the last two decades, they were constructed from various physical principles and, thus, targeted for different practical applications. As a result, a comprehensive review to elucidate the state-of-the-art modeling strategies of PCRAMs based on their phase-change mechanisms and application targets remains mysterious. To address this issue, we here reviewed the reported modeling approaches of PCRAMs mainly in terms of the finite-element method, the physics-based analytical model, and compact modeling techniques. For each technique, its adopted strategies to simulate the electrothermal and phase-change processes of phase-change materials at the device level were elaborated, respectively, and their respective merits and drawbacks were also given. The application fields, for which these models suit, were eventually discussed.

Compound Knowledge Graph-Enabled AI Assistant for Accelerated Materials Discovery

Materials scientists are facing increasingly challenging multi-objective performance requirements to meet the needs of modern systems such as lighter-weight and more fuel-efficient aircraft engines, and higher heat and oxidation-resistant steam turbines. While so-called second wave statistical machine learning techniques are beginning to accelerate the materials development cycle, most materials science applications are data-deprived when compared to the vastness and complexity of the search space of possible solutions. In line with DARPA’s vision of third wave AI approaches, we believe a combination of data-driven statistical machine learning and domain knowledge will be required to achieve a true revolution in materials discovery. To that end, we envision and have begun reducing to practice a system that fuses three forms of knowledge—factual scientific knowledge, physics-based and/or data-driven analytical models, and domain expert knowledge—into a single ‘Compound Knowledge Graph’ in which contextual reasoning and adaptation can be performed to answer increasingly complex questions. We believe this Compound Knowledge Graph-based system can be the nucleus of a collaborative AI assistant that supports stateful natural language back-and-forth dialogs between materials scientists and the AI to accelerate the development and discovery of new materials. This paper details our vision, summarizes our progress to date on a steam turbine blade coating use case, and outlines our thoughts on the key challenges in making this vision a reality.

Physics-based analytical modeling of keyhole mode in laser powder bed fusion

Physics-based analytical modeling of keyhole mode in laser powder bed fusion

Hierarchical Mixture-of-Experts approach for neural compact modeling of MOSFETs

With scaling, physics-based analytical MOSFET compact models are becoming more complex. Parameter extraction based on measured or simulated data consumes a significant time in the compact model generation process. To tackle this problem, ANN-based approaches have shown promising performance improvements in terms of accuracy and speed. However, most previous studies used a multilayer perceptron (MLP) architecture which commonly requires a large number of parameters and train data to guarantee accuracy. In this article, we present a Mixture-of-Experts approach to neural compact modeling. It is 78.43% more parameter-efficient and achieves higher accuracy using fewer data when compared to a conventional neural compact modeling approach. It also uses 43.8% less time to train, thus, demonstrating its computational efficiency.