Physics-based Modeling Approach Research Articles

Predictability of extreme southwest monsoon rainfall over Myanmar by using a physics-based empirical modeling approach

The study examines the ability to predict extreme rainfall days (ERDs) in Myanmar, a country frequently affected by severe flooding due to intense rainfall. We used a physics-based model combined with real-world data to forecast ERDs based on the atmospheric ERA5 reanalysis data and records rainfall from 79 different stations in Myanmar. The findings reveal that ERDs are more common in specific regions of Myanmar during certain seasons and are associated with particular atmospheric conditions. In June and July, El Niño's rapid growth phase—which was linked to a large anticyclone anomaly over the Indian Ocean—caused an increase in ERDs in central Myanmar. The region experiences higher rainfall as a result of this anomaly's enhanced moisture transfer. Similar to this, a zonal SLP dipole in eastern Myanmar contributes to higher ERDs from April to September. Additionally, the study finds indicators of possible causation, such as SST anomalies and the SIO dipole index, that are precursors to higher ERDs. Using these physical indicators, prediction models with strong TCC values for monsoon core region and northern Himalayas foothill region of Myanmar at different times demonstrated notable accuracy. The study finds a strong correlation between summer ERDs and monsoon rainfall, indicating that predicting mean rainfall accurately is essential for forecasting ERDs. To comprehend and forecast future changes in these relationships, more research is necessary due to the non-stationarity of predictor-predictand interactions. The discovery clarifies important atmospheric variables that impact extreme rainfall episodes in Myanmar and advances our understanding of these occurrences.

Coupled Electrical – Thermal – Electrochemical Model of a Battery Module

A battery module comprises of several lithium-ion cells connected in series and parallel configuration to obtain the desired voltage, current and power specifications. The overall response of a module can be affected by (i) the electrical topology, (ii) the thermal topology and (iii) the cell-to-cell variations. The distribution of current depends on the series/parallel assembly and variation in interconnect resistance in the module. Operation of a cell is accompanied by an increase in temperature. In a module, the thermal environment seen by a cell is different depending on whether it is an interior cell or a cell at the boundary of module exposed to environment. There is additional thermal cross talk between cells through heat conduction in metallic interconnects. Finally, there can be several reasons for cell-to-cell variation in a module. The cells used for building a module can have variable state of charge and internal resistance at the manufacturing level. As the module is cycled, the ageing of cells in a module can differ due to variation in current, evolution of range of lithiation of electrodes and the temperature of the cell.In this research work, we develop a coupled electrical, thermal, and electrochemical model to simulate the charge and discharge of a battery module with the overall aim of identifying the cell with largest performance degradation and most likelihood of failure. This physics-based modeling approach comprises of a simplified electrochemical and lumped thermal model of an individual cell. At the module level, the modeling framework is extended to include the electrical and thermal connectivity along with the electrochemical response.

(Invited) Generic Compact Model for Variability and Low-Frequency Noise in Organic Thin-Film Transistors

In organic thin-film transistors (OTFT), low-frequency noise (LFN) is dominated mainly by grain-boundary traps and mobility fluctuation [1]. Accurate LFN modeling of OTFTs has been successfully demonstrated in [2] by adopting the theory of carrier-number and correlated mobility fluctuations (ΔN noise) [3,4,5] and the empirical Hooge approach accounting for mobility fluctuation due to percolative effects (Δµ noise) [6,7]. Furthermore, OTFTs are sensitive to process variability. Charges being trapped in the channel region cause a local variation of the accumulated charge density, having impact on the threshold voltage of the device [8,9] and reducing the effective carrier mobility in the channel by Coulomb scattering. As a result, the drain-current variability is also correlated to carrier-number and correlated mobility fluctuations and therefore allows for a similar modeling approach as for ΔN noise [9].Based on the results published in [2,9], we present a generic physics-based modeling approach for drain-current fluctuations by carrier-number and correlated mobility fluctuations, which leads to similar expressions for drain-current variability and ΔN noise in OTFTs. The model is based on the charge-based compact model for OTFTs presented in [10]. Starting from the local fluctuation of the current in the channel, closed-form model equations have been derived for the fluctuation of the total device current. The final expressions (1) for drain-current variability and (2) for ΔN noise show the same bias dependent part B*(qch) given by equation (5). An individual bias-independent prefactor C* is obtained. In case of drain-current variability this factor in equation (3) depends on the density Nt of trapped charges. For ΔN noise, in (4), NT is the density of traps per energy available for capture and release of charge carriers.Measurements performed on fabricated OTFTs in staggered (C10-DNTT) or coplanar (Dph-DNTT) architectures (Fig. 1) show that the drain-current variability in the subthreshold regime is dominated by carrier-number fluctuations, whereas for above threshold operation the mobility-fluctuation effect by correlated Coulomb scattering comes to the fore (Fig. 2) [9]. In case of LFN, measurements on staggered DNTT OTFTs have shown that ΔN noise alone is not sufficient to describe the noise spectra in the deep subthreshold regime of operation (Fig. 3) [2]. Here, LFN due to mobility fluctuation (Δµ noise) must be included with equations (6-8), which results in an additional and different bias dependent expression with a prefactor including the empirical Hooge parameter. The total LFN is given by (9).In conclusion, following a generic modeling approach, the combined equations for carrier-number and correlated mobility fluctuations and for percolative mobility fluctuation allow consideration drain-current variability and LFN noise in a charge-based compact model of OTFTs in all regions of operation and is in good agreement with results from measurements. Acknowledgements: This project was funded by the German Federal Ministry of Education and Research ("SOMOFLEX", No. 13FH015IX6), German Research Foundation (DFG) under Grant KL 1042/9-2 (SPP FFlexCom), the Spanish Ministry of Science (PRX21/00726), and the EU EIC-PATHFINDER (BAYFLEX, no 101099555). We acknowledge AdMOS GmbH, Germany, for support, and Max Planck Institute for Solid State Research Stuttgart, Germany, for fabrication of devices.

A Thermal Tanks-in-Series Model for Simulating over-Discharge Cycling in Lithium-Ion Batteries

Energy storage will play a crucial role in the transition to clean and renewable sources of energy production to meet future consumption demands.1 Lithium-ion (Li-ion) batteries are widely adopted due to their high energy density, low self-discharge rate and fast charging capabilities for applications like electric vehicles and grid storage.2 Li-ion batteries for high voltage operations, where several cells are connected in series, can experience inadvertent over-discharge due to differences between cells3,4. These differences can arise due to many reasons including changes in internal resistance and deviation in electrochemical degradation that can cause divergence of cell open circuit potentials. Over-discharge is a condition when the manufacturer recommended lower voltage limit is crossed while discharging4, which results in various side effects such as electrolyte decomposition, capacity degradation, cathode morphology change and possible internal short circuit3,5. Increase in anode potential during over-discharge leads to copper dissolution from the anode current collector and subsequent plating within cell components. The Solid Electrolyte Interface (SEI) can decompose during excessive deintercalation and reform during charging, reducing the lithium inventory. Excessive heat generation during cycle life testing after occurrences of over-discharges has been found in some cases to lead to temperatures >150 °C6,7.Temperature plays a key role in the electrochemical performance of individual cells, affecting the diffusivities of ions, kinetics of reactions and rate of degradation of the battery. A physics-based modeling approach coupled with a thermal model can be employed to understand the effect of over-discharge on the electrochemical behavior of Li-ion batteries and provides insights into the interplay of various degradation mechanisms that cause capacity fade.The Thermal Tank-In-Series9,10(TTiS) model is a systematically volume-averaged form of the standard pseudo-2-dimensional (p2D)11 model with energy balance equations for the current collectors, cathode, separator and anode. This reduces the number of equations in the model and improves computational speed while maintaining accuracy, as it is sensitive to changes in the battery’s state of charge (SOC) and cell temperature, which helps predict the overpotential changes to the cell electrodes and thermodynamics.We use the TTiS model to identify the dominant fade mechanisms in 3 sets of experimental cycling and temperature data for the: i) normal operating window (2.5V – 4.2V), ii) 5 initial cycles of over-discharge (1.5V – 4.2V) followed by normal operation until 20% capacity fade and iii) 5 cycles of periodic over-discharge every 20 cycles, until 20% capacity fade. Analysis on the effect of C-rate, ambient temperature and voltage window of operation will facilitate understanding the temperature behavior and overpotentials in a cell sandwich. Key considerations required in extending a model to simulate over-discharge will also be examined. References S.P. S. Badwal, S. S. Giddey, C. Munnings, A. I. Bhatt, and A. F. Hollenkamp, Frontiers in Chemistry, 2, 28 (2014).G. Pistoia. Lithium-Ion Batteries: Advances and Applications. First ed. Elsevier, Amsterdam (2014).R. Guo, L. Lu., M. Ouyang, X. Feng. Sci Rep 6, 30248 (2016).G.Zhang, X. Wei, S. Chen, J. Zhu, G. Han, and H. Dai. J. Power Sources, 521, 230990. (2022).C. Fear, D. Juarez-Robles, J. A. Jeevarajan, and P. P. Mukherjee, J. Electrochem. Soc., 165, A1639–A1647 (2018).D. Juarez-Robles, Purdue Univ. Ph.D. Thesis (2019).H. Maleki and J. N. Howard, J. Power Sources, 160, 1395–1402 (2006).H. Liu, Z. Wei, W. He, and J. Zhao, Energy Convers. Manag., 150, 304–330 (2017).A. Subramaniam, S. Kolluri, S. Santhanagopalan, and V. R. Subramanian, J. Electrochem. Soc., 167, 113506 (2020).A. Subramaniam, S. Kolluri, C. D. Parke, M. Pathak, S. Santhanagopalan, and V. R. Subramanian, J. Electrochem. Soc., 167, 013534 (2020).M. Doyle, T. F. Fuller, and J. Newman, J. Electrochem. Soc., 140, 1526 (1993)

Coupled Multiphysics Modeling of Lithium-Ion Batteries for Automotive Crashworthiness Applications

Abstract Considerable advances have been made in battery safety models, but achieving predictive accuracy across a wide range of conditions continues to be challenging. Interactions between dynamically evolving mechanical, electrical, and thermal state variables make model prediction difficult during mechanical abuse scenarios. In this study, we develop a physics-based modeling approach that allows for choosing between different mechanical and electrochemical models depending on the required level of analysis. We demonstrate the use of this approach to connect cell-level abuse response to electrode-level and particle-level transport phenomena. A pseudo-two-dimensional model and simplified single-particle models are calibrated to electrical–thermal cycling data and applied to mechanically induced short-circuit scenarios to understand how the choice of electrochemical model affects the model prediction under abuse scenarios. These models are implemented using user-defined subroutines on ls-dyna finite element software and can be coupled with existing automotive crash safety models.

A novel physics-based modeling approach for tangential fretting contact behavior of jointed interface considering multi-scale effects

A novel physics-based constitutive model has been developed to map the tangential fretting behavior of joint surfaces. The model integrates the fractal normal contact model, which considers multi-scale effects, and the Iwan model through the Coulomb friction law. In this model, a new distribution of yield force is proposed for Jenkins elements, which is determined by fractal topography and material parameters and related to the scale of asperity. The effects of fractal topography, material parameters, and the normal load applied to the joint surface on the tangential responses such as tangential force, tangential stiffness, energy dissipation, and the distribution of yield force have been discussed. It has been found that the fractal parameters D and G have opposite effects on the tangential responses and yield force distribution.

A systematic analysis and optimization of bicarbonate electrolysis based on a bipolar membrane through multiscale simulation

Bicarbonate electrolysis facilitated by bipolar membrane has emerged as a compelling alternative to gaseous CO2 electrolysis, primarily because of its enhanced product concentration and streamlined processes. However, despite increasing attention, a comprehensive understanding of bicarbonate electrolysis has been eclipsed by the complexity of the intertwined electrode reactions and water dissociation promotion mechanisms of bipolar membranes. A physics-based modeling approach has substantial potential to untangle the impact of intertwined physicochemical processes and expedite the optimization of bicarbonate electrolysis. In this study, a multiscale model coupled with a 1D bipolar membrane and a 2D cathode model is presented to explore the effects of bipolar membranes on cathode reactions and performance optimization using H+ flux as a bridge. For a bipolar membrane, the attenuation mechanism of water dissociation involving ohmic and concentration losses is presented and described using mathematical eqs. A quantification metric, the water dissociation efficiency of a bipolar membrane, is proposed to denote the degree of promoted water dissociation. Two noteworthy parameters related to the fixed functional group concentration are determined and detailed: the onset current density required to stimulate water dissociation and the limited water dissociation efficiency caused by attenuation. For the cathode, a synergistic enhancement mechanism among the operating parameters, membrane properties, and cathode properties is identified. The combined effect of these parameters on the electrolytic performance surpasses the sum of their individual enhancements. Following parameter optimization, there is a notable increase in the current density and Faraday efficiency of bicarbonate electrolysis by 68.6 mA/cm2 and 18.3%, respectively. This study provides a holistic understanding of bicarbonate electrolysis and substantial guidance for its future design.

On the temperature sensitivity of near-surface seismic wave speeds: application to the Groningen region, the Netherlands

SUMMARY Subsurface temperature measurements play a crucial role, for instance, in optimizing geothermal power plants and monitoring heat-storage systems. Previous studies have demonstrated that time-lapse variations in temperature can be correlated with variations in seismic wave speeds, offering the potential for temperature monitoring via seismic surveys. However, an apparent discrepancy has emerged between field and laboratory experiments. Field studies predominantly report positive correlations between temperature and seismic wave speeds, while laboratory experiments often show anticorrelations. This inconsistency underscores the need for a more comprehensive, physics-based understanding of temperature-induced wave speed changes. In this study, we strive to bridge the gap between field and laboratory findings by examining several mechanisms governing temperature-induced seismic wave speed changes, namely the intrinsic temperature dependency of elastic parameters and thermally induced elasticity. We present a physics-based modelling approach to identify the primary mechanisms responsible for temperature-induced seismic wave speed changes. By considering several end-member models, we find that intrinsic temperature dependency of elastic parameters (negative correlation) compete with thermal pressure effects (positive correlation). The precise initial and boundary conditions and physical parameters of the system under consideration will determine the weight of both effects. Temperature-related dilatation does not seem to play an important role. We apply our approach to loosely consolidated sediments in the shallow subsurface of the Groningen region, where subsurface temperature fluctuations are driven by seasonal atmospheric temperature fluctuations roughly between −5 and $30\, ^\circ$C. For these models, we predict seasonal temperature-induced changes in body-wave speeds of up to 8 per cent in the first few metres of the subsurface, high-frequency (above 2 Hz) surface wave phase velocity variations in the range of 1–2 per cent, and relative changes in site amplification on the order of 4 per cent. These findings contribute to a more comprehensive understanding of the intricate relationship between temperature and near-surface seismic properties, offering insights for applications as subsurface temperature monitoring systems.

Physics-Based Modeling and Parameter Tracing for Industrial Demand-Side Management Applications: A Novel Approach

The transition to sustainable energy sources presents significant challenges for energy distribution and consumption systems. Specifically, the intermittent availability of renewable energy sources and the decreasing usage of fossil fuels pose challenges to energy flexibility and efficiency. An approach to tackle these challenges is demand-side management, aiming to adapt energy consumption and demand. A key requirement for demand-side management is the traceability of the energy flow among individual energy consumers. In recent years, advancements in industrial information and communication technology have provided additional potential for data acquisition. Complementary to acquired data, a physics-based modeling and analysis approach is proposed, which describes the energy consumption with physical parameters. This results in comprehensive options for monitoring actual energy consumption and planning future energy demand supporting energy efficiency and demand-response goals. To validate the proposed approach, a case study with a 3D printer covering approximately 110 h of active printing time is conducted. The medium-term study results indicate a consistent parameter trend over time, suggesting its conceptual suitability for industrial application. The approach helps to monitor energy efficiency among manufacturing assets by identifying peak loads and consumption hotspots, and provides parameters to estimate energy consumption of manufacturing processes. Results indicate up to 50% energy savings when switching the printing material and indicate further potentials.

Review of RF Device Behavior Model: Measurement Techniques, Applications, and Challenges.

This review presents a concise overview of RF (radio frequency) power transistor behavior models, which is crucial for optimizing RF performance in high-frequency applications like wireless communication, radar, and satellites. The paper highlights the significance of accurate modeling in understanding transistor behavior and traces the evolution of behavior modeling techniques. Different behavior modeling strategies, such as LUT (look-up table) based models, polynomial equation-based models, and machine learning based models, are discussed along with their unique characteristics and modeling challenges. The review explores the difference between behavior models and the conventional empirical or physics-based modeling approaches, addressing the challenges of the accurate characterization of transistors at high frequencies and power levels. This paper concludes with an outlook of emerging trends, such as physical models combined with behavior models, shaping the future of RF power transistor modeling for more efficient communication systems.

Microstructure Based Flow Stress Modelling of Superalloy 718

In order to get the insights about microstructural changes that occurs under the thermo-mechanical processing conditions, the physics based modelling approach is very useful. Therefore, the flow curves of alloy 718 are theoretical simulated using a dislocation density dependent constitutive model for different conditions. Presented model considers the microstructural ingredients that are immobile dislocation density, effective grain size and dislocation cell size as the variables to address the creep. The simulated flow curves show a good agreement with the experimental flow curves. The magnitude of immobile dislocation density and dislocation cell size in between 3.87× 1014 - 3.87× 1014­ m-2 and 8.29-8.45 μm, respectively, at the completion of the simulation. Furthermore, this approach also provides the possibility to quantify and depict the variation in each strengthening contributions.

Physics-based model and data dual-driven approaches for predictive evacuation

Physics-based models or data-driven methodologies can help acquire the evacuation process for optimizing evacuation and rescue plans. However, neither of these methodologies can predict the process rapidly and precisely. Physics-based models rely on physical rules of human behavior but are computationally expensive. Data-driven approaches need a significant amount of diverse data but lack an understanding of underlying human behavior in spinning emergencies. This short communication aims to initiate systematic discussions about a physics-based model and data dual-driven approach, combining both approaches’ strengths. This combined approach puts forward iterative updating loops, using physics-based models to identify evacuation stages, capture operational mechanisms, and act as rational boundaries while using data-driven methods to process each stage’s results rapidly. The authors synthesize a roadmap highlighting bottlenecks and potential research directions for achieving such a combined approach, calling for attention and collaboration in predictive evacuation for disaster response.

Simulation of tilt-rotor UAV flight dynamics in horizontal flight

Tilt-rotor unmanned aerial vehicles (UAV) have gained significant importance in the aeronautical industry due to their ability to transition between vertical and horizontal flight. One of the important steps in the development of such UAVs is to assess their performance and stability characteristics. Simulation of flight dynamics is an essential tool that enables the designers to test and optimize the flight characteristics of these UAVs. This paper presents a complete procedure for developing a 6-degree-of-freedom flight dynamics model of a tilt-rotor UAV in fixed-wing mode using a physics-based modeling approach. All necessary sub-models are developed and integrated into the Simulink simulation environment to allow for predicting the UAV’s dynamic response. The developed model is verified for correct operation by simulating horizontal steady-level flight and exciting its natural longitudinal modes.

Model selection for dynamic interfacial tension of dead crude oil/brine to estimate pressure and temperature effects on the equilibrium tension

Interfacial tension (IFT) measurements using pendant drop techniques are typically time-dependent and embed the underlying physics that can be exploited. A physics-based modeling approach of the time-varying IFT is chosen to estimate the equilibrium IFT and to identify which phenomena are leading the dynamic. This approach is both consistent and reproducible, hence advantageous for routine research and industrial applications because typical approaches are especially challenging when the techniques rely on long-time measurements or detailed characterization of phase composition and transport properties. The approach is applied to the Brazilian pre-salt dead crude oil/brine system when the equilibrium between the phases and between bulk and interface concentrations are not assured initially. Selected models based on a different physical phenomenon, mainly focused on diffusion and sorption, and with a particular time scale behavior are tested on measured data. The model which better fits the data in its full-time range should point out which phenomenon is dominant. The results indicate that the model with t time scale seems preferable for this oil/brine system, which is associated with a diffusion-controlled process in multiple regimes, for instance, bulk-to-interface diffusion of naturally present interface chemicals and their rearrangement at the interface. The effect of pressure (14.7 psi–4000 psi) and temperature (22 °C–60 °C) variations on the equilibrium IFT of the Brazilian pre-salt reservoir dead crude oil/brine at various conditions is characterized and discussed using the consistent physics-based estimate for the equilibrium IFT. The results show that the equilibrium IFT does not change significantly with pressure and increases with temperature, further supporting the identified presence of naturally present interface chemicals in the transient IFT measurements and the brine ionic valence influence. Furthermore, the wettability of carbonate rock samples with different mineralogical compositions to the dead crude oil/formation water is assessed using adhesion tension which embeds both IFT and contact angle contributions. Pressure changes do not affect wettability considerably, while the effects of temperature increase show that every sample tended to be wetter to its respective fluid affinity, following the IFT temperature behavior.

Predicting Protein Corona Formation on Carbon Nanotubes

Engineered nanoparticles are poised to transform sensing, imaging, and delivery in biological systems. In particular, single-walled carbon nanotubes (SWCNTs) are uniquely suited for biological sensing and imaging due to their tissue-transparent and photostable near-infrared fluorescence. SWCNTs have been functionalized with various biomolecules, including synthetic peptides and proteins, to construct fluorescence-based nanosensors. Optimizing these -nanoparticle interactions is key in enhancing nanotechnology function. Yet, functionalized SWCNTs and other nanotechnologies more broadly suffer from as-of-yet unpredictable interactions with the biological environments in which they are applied: when nanoparticles are introduced into biological systems, endogenous proteins rapidly bind and often lead to decreased ability of the nanoparticles to perform their intended functions. As such, testing compatibility and function of nanotechnologies in biological systems requires a heuristic approach, where this unpredictable protein corona formation often prevents effective implementation.In this work, we develop a classifier that identifies proteins binding to nanotubes based solely on the protein’s amino acid sequence. We build and validate our classifier using mass spectrometry-based quantitative proteomic data that characterizes proteins bound to SWCNT-based nanosensors. Distribution changes among protein properties allow us to determine protein features associated with increased likelihood of SWCNT binding, including high content of solvent-exposed glycines and non-secondary structure-associated amino acids. Understanding these key protein features that drive protein-nanotube binding informs the design of physics-based modeling approaches of such interfacial nano-bio interactions. Lastly, we apply the classifier to rapidly identify candidate proteins with high binding affinity to SWCNTs and experimentally validate adsorption of these proteins with a kinetic exchange assay to evaluate the predictive power of our model. In sum, this classifier provides a step toward undertaking the otherwise intractable problem of predicting protein-nanoparticle interactions.

Deep learning assisted physics-based modeling of aluminum extraction process

Modeling complex physical processes such as the extraction of aluminum is mainly done using pure physics-based models derived from first principles. However, the accuracy of these models can often suffer due to a partial understanding of the process, uncertainty in the input parameters, and numerous modeling assumptions. More recently, with the ever-increasing availability of data, there has been an explosion of interest in applying modern machine learning methods because of their ability to learn complex mappings directly from data. Unfortunately, these models tend to be black boxes, require an enormous amount of data, and do not utilize existing domain knowledge. In this work, we develop a novel approach combining physics-based and data-driven modeling approaches while eliminating some weaknesses. We use a data-driven model to correct a misspecified physics-based model of the Hall–Héroult process in an aluminum electrolysis cell using a corrective source term added to the set of governing ordinary differential equations. Our approach ensures that the existing knowledge is utilized to the maximum extent possible while relying on the data-driven models only to model those aspects which the physics-based model does not represent well. We compare this approach with an end-to-end learning approach and an ablated physics-based model, showing that the proposed hybrid method is more accurate, consistent, and stable for long-term predictions.

Mechanical loading of the ventricular wall as a spatial indicator for periventricular white matter degeneration

Progressive white matter degeneration in periventricular and deep white matter regions appears as white matter hyperintensities (WMH) on MRI scans. To date, periventricular WMHs are often associated with vascular dysfunction. Here, we demonstrate that ventricular inflation resulting from cerebral atrophy and hemodynamic pulsation with every heartbeat leads to a mechanical loading state of periventricular tissues that significantly affects the ventricular wall. Specifically, we present a physics-based modeling approach that provides a rationale for ependymal cell involvement in periventricular WMH formation. Building on eight previously created 2D finite element brain models, we introduce novel mechanomarkers for ependymal cell loading and geometric measures that characterize lateral ventricular shape. We show that our novel mechanomarkers, such as maximum ependymal cell deformations and maximum curvature of the ventricular wall, spatially overlap with periventricular WMH locations and are sensitive predictors for WMH formation. We also explore the role of the septum pellucidum in mitigating mechanical loading of the ventricular wall by constraining the radial expansion of the lateral ventricles during loading. Our models consistently show that ependymal cells are stretched thin only in the horns of the ventricles irrespective of ventricular shape. We therefore pose that periventricular WMH etiology is strongly linked to the deterioration of the over-stretched ventricular wall resulting in CSF leakage into periventricular white matter. Subsequent secondary damage mechanisms, including vascular degeneration, exacerbate lesion formation and lead to progressive growth into deep white matter regions.

Trends in the 40 + -year development of advanced structural acoustic and vibration computational modeling

Over the course of the past 40-45 years of computational modeling in the fields of structures, structural acoustics, and vibrations, there have been extreme advancements made in numerous areas that have greatly increased and improved the size, accuracy, efficiency, and complexity of numerical solutions possible. Most of this progress has been made through a combination of improvements in the distinct areas of computer processing approaches/speed and available memory, primarily evolutionary enhancements in detailed numerical material and continuum mechanics models, development of entirely new physics-based numerical vibroacoustic modeling approaches, etc. This paper presents a historical review of the detailed progress made in computational structural acoustics and vibration from the “early days” in the 1980s through current state-of-the-art advanced numerical approaches, much of which would not have been dreamed possible back four decades ago! While most of the examples utilized in this talk are pulled from the speaker’s specific technical area of undersea vehicle/ system structural acoustics, the overall trends presented are certainly represented across the broad spectrum of vibro-acoustics modeling applications.

Reduced modeling of liquid desiccant falling film absorbers

Liquid desiccant falling film absorbers are an energy-efficient solution for humidity control and dehumidification in buildings and cold stores. This work presents a reduced one-dimensional physics-based modeling approach for this technology and compares it to a high-fidelity three-dimensional computational fluid dynamics (CFD) model. Both models capture the complex heat and mass transfer mechanisms of vertical falling films on two opposing walls and a horizontal crossflow of air. Additionally, both models were verified against experimental data from previous work, and the results showed good agreement within the uncertainties of the measurement equipment. A parameter variation study was also performed, comparing the results of both models under conditions relevant to cold store applications. The reduced model was found to be over 400 times faster than the high-fidelity model, while still achieving an average difference of less than ±0.14K, ±1.3%, and ±3.9% for the calculated air outlet temperature, absorbed water vapor mass flow rate, and air-side pressure drop, respectively. The reduced model is suitable for optimization studies and easy to implement in system simulations, making it a valuable tool for the design and optimization of liquid desiccant systems.

Unified low-order modeling approach to can-annular combustors

We present a unified, physics-based low-order modeling approach to can-annular combustors, combining a linear stability analysis and a nonlinear Langevin approach to unravel the highly complex, multi-physical dynamics in this type of system. In the linear part, Howe's Rayleigh conductivity model is combined with the projected Helmholtz equation and a Bloch wave ansatz to arrive at a single equation for the frequency spectrum of an N-can combustor. Starting from first principles, we illustrate and give a physical explanation for the coupling-induced amplification and suppression of thermoacoustic instabilities. Adding another layer of complexity, we then take into account nonlinearities in the flame response and the aeroacoustic coupling. The nonlinear dynamics of a symmetric model combustor are explored by exploiting the gradient structure of the averaged slow-flow dynamics. We obtain exact analytical expressions for the steady-state statistics, highlighting the connection between different emergent patterns and the resistive coupling between the cans. We leverage our analysis to explain the intermittent energy transfer between Bloch modes observed in real-world gas turbines.