Coupled Model Predictions Research Articles

Study on Predicting Liquid Production Profile in Horizontal Wells Considering the Mechanism of Annular Flow Resistance and Water Control

The coated particle ICD water control technology has been widely used in horizontal well completion in offshore oil fields in recent years, but there is a lack of research on the simulation and prediction methods for liquid production profiles. By analyzing the dynamic inflow process of fluid, starting from the laws of momentum conservation and mass conservation, a coupled mathematical model of horizontal well reservoir wellbore pressure drop considering the mechanism of annular flow resistance was established, and solved through programming. Based on the actual well data of a certain block in an offshore oilfield, a horizontal well reservoir production model was established. The calculation results of the example show that the annular flow resistance and water control technology can effectively control the balanced liquid production of horizontal wells, thereby increasing the anhydrous oil recovery period and improving the oil recovery rate of the oilfield; The coupling model simulation and prediction method can fully reflect the mutual influence between the oil reservoir, wellbore annulus, ICD, and horizontal wellbore; The effective diameter of the ICD valve affects the changes in oil production and water content. The placement of the ICD well section has a significant effect on water control, and the compactness of the filled particles has a certain impact on pressure and water content changes.

Experimental Study on the Dynamic Stability of Circular Saw Blades during the Processing of Bamboo-Based Fiber Composite Panels

Bamboo-based fiber composite panel is a new type of composite material with excellent performance. When processing bamboo-based fiber composite panels, the dynamic stability of the circular saw blade affects the surface quality of the product and the life of the machinery and equipment. Sawing heat and vibration characteristics can significantly affect the dynamic stability of circular saw blades. Circular saw blade temperature and vibration characteristics are affected by the processing parameters, and the circular saw blade temperature and vibration characteristics are analyzed by changing the processing parameters. Adopting the thermoset coupling model can be used to analyze the change rule of circular saw blade temperature when sawing bamboo-based fiber composite boards, and at the same time to analyze the change rule of circular saw blade temperature, vibration speed, and vibration acceleration through the use of by CCD experiments. The regression equations for circular saw blade temperature, vibration velocity, and vibration acceleration were derived through the use of ANOVA and significance analysis. The thermoset coupling model predictions agree with the experimental results, and the density of the isotherms is progressively thinner as the temperature is conducted from the serrated region to the body of the saw. Finally, the accuracy of the regression equations for circular saw blade temperature, vibration velocity, and vibration acceleration was checked via error analysis. The temperature change regression equation has the highest fitting accuracy, with an average error of only 1.37%; the vibration velocity and vibration acceleration regression equations have poorer fitting accuracy, with an average error of 9.5% and 11.45%, respectively, but all of them have sufficient accuracy to predict the dynamic stability of circular saw blades. The results of the study can provide some guidance for the innovative design of circular saw blades.

Breakdown of isochronal superpositioning of [formula omitted]- and [formula omitted]-relaxation times in the van der Waals system – Loratadine

In this paper, high-pressure (HP) dielectric studies on the van der Waals active pharmaceutical ingredient (API) - loratadine (LOR) were carried out. It was found that, surprisingly, the secondary (β) relaxation, which, according to the Coupling Model (CM) predictions, was classified as a Johari-Goldstein (JG) process, separates from the structural (α)-mode with compression (although the shape of the α-peak is invariant to the thermodynamic conditions at constant τα). Consequently, there is a breakdown of the isochronal superpositioning of α- and β- relaxation times (τα and τβ) in this system, which is a typical behavior for the secondary process with intermolecular character. Based on these findings, two possibilities concerning the origin of the β-relaxation in LOR have been proposed: 1) it is a non-JG process (probably originating from fluctuations within cyclic rings of the API molecule), which, however, satisfies the CM predictions; or 2) it is a JG-process, which similarly to a few earlier examined H-bonded compounds (e.g., probucol and sorbitol) is characterized by a different sensitivity to compression when compared to the α-process. Moreover, it was found that the activation entropy for the β-process takes negative values, irrespective of the pressure conditions. That may be interpreted as a result of conformational variation or the loss of translational and rotational degrees of freedom, leading to increased order in API due to densification. The results presented herein give a new insight into the relationship between the structural and secondary relaxations in the close vicinity of the glass transition temperature. Furthermore, they show that HP studies are crucial in elucidating the correlation between these processes.

Predicting the occurrence of short-chain PFAS in groundwater using machine-learned Bayesian networks

In the past two decades, global manufacturing of per- and polyfluoroalkyl substances (PFAS) has shifted from long-chain compounds to short-chain alternatives in response to evidence of the health hazards of long-chain formulations. However, accumulating data indicate that short-chain PFAS also pose health risks and are highly mobile and persistent in the environment. Because short-chain PFAS are relatively new chemicals, comprehensive knowledge needed to predict their environmental fate is lacking. This study evaluated the capacity of machine-learned Bayesian networks (BNs) to predict risks of exposure to short-chain PFAS in a Minnesota region affected by PFAS releases from the 3M Cottage Grove facility. Models were trained using long-term monitoring data provided by the Minnesota Department of Health (n = 12,406), which we coupled to a comprehensive dataset created by curating 88 other variables that describe potential PFAS sources, soil and hydrogeologic characteristics, and land use. Model performance was assessed using the area under the receiver-operating characteristic curve (AUC), a common measure of the accuracy of machine-learned classification algorithms. In addition, exposure risks were visualized spatially by coupling model predictions to a geographic information system. We found that machine-learned BN models had robust predictive performance, with AUCs above 0.96 in cross-validation. Significant risk factors identified by the BNs include distance to the 3M factory, distance to a former landfill, and areal extent of wetlands and developed land. We also found that risks of exposure to and the areal extent of perfluorosulfonic acids were greater than for perfluorocarboxylic acids with the same carbon number. The results suggest that machine-learned BNs could provide a promising screening tool for assessing short-chain PFAS exposure risks in groundwater.

GFDL's SPEAR Seasonal Prediction System: Initialization and Ocean Tendency Adjustment (OTA) for Coupled Model Predictions

AbstractThe next‐generation seasonal prediction system is built as part of the Seamless System for Prediction and EArth System Research (SPEAR) at the Geophysical Fluid Dynamics Laboratory (GFDL) of the National Oceanic and Atmospheric Administration (NOAA). SPEAR is an effort to develop a seamless system for prediction and research across time scales. The ensemble‐based ocean data assimilation (ODA) system is updated for Modular Ocean Model Version 6 (MOM6), the ocean component of SPEAR. Ocean initial conditions for seasonal predictions, as well as an ocean state estimation, are produced by the MOM6 ODA system in coupled SPEAR models. Initial conditions of the atmosphere, land, and sea ice components for seasonal predictions are constructed through additional nudging experiments in the same coupled SPEAR models. A bias correction scheme called ocean tendency adjustment (OTA) is applied to coupled model seasonal predictions to reduce model drift. OTA applies the climatological temperature and salinity increments obtained from ODA as three‐dimensional tendency terms to the MOM6 ocean component of the coupled SPEAR models. Based on preliminary retrospective seasonal forecasts, we demonstrate that OTA reduces model drift—especially sea surface temperature (SST) forecast drift—in coupled model predictions and improves seasonal prediction skill for applications such as El Niño–Southern Oscillation (ENSO).

Applications of phase field fracture in modelling hydrogen assisted failures

The phase field fracture method has emerged as a promising computational tool for modelling a variety of problems including, since recently, hydrogen embrittlement and stress corrosion cracking. In this work, we demonstrate the potential of phase field-based multi-physics models in transforming the engineering assessment and design of structural components in hydrogen-containing environments. First, we present a theoretical and numerical framework coupling deformation, diffusion and fracture, which accounts for inertia effects. Several constitutive choices are considered for the crack density function, including choices with and without an elastic phase in the damage response. The material toughness is defined as a function of the hydrogen content using an atomistically-informed hydrogen degradation law. The model is numerically implemented in 2D and 3D using the finite element method. The resulting computational framework is used to address a number of case studies of particular engineering interest. These are intended to showcase the model capabilities in: (i) capturing complex fracture phenomena, such as dynamic crack branching or void-crack interactions, (ii) simulating standardised tests for critical components, such as bolts, and (iii) enabling simulation-based paradigms such as Virtual Testing or Digital Twins by coupling model predictions with inspection data of large-scale engineering components. The evolution of defects under in-service conditions can be predicted, up to the ultimate failure. By reproducing the precise geometry of the defects, as opposed to re-characterising them as sharp cracks, phase field modelling enables more realistic and effective structural integrity assessments.

Charge transport and glassy dynamics in a room temperature ionic liquid- [BMPyr][TFSI

In this paper, thermal and spectroscopic investigations on a room temperature ionic liquid, 1-Butyl-1-methyl pyrrolidinium bis(trifluoromethylsulfonyl)imide ([BMPyr][TFSI]) were carried out using differential scanning calorimetry and broadband dielectric spectroscopy over wide temperature to study the molecular mobility, charge transport properties, and conductivity relaxation. The quantum mechanical calculations were used to study its polar nature. The isochronal cooling and heating were measured to get an estimation of the glass transition temperature. The study revealed that [BMPyr][TFSI] is an intermediate fragile system with a fragility index of 96.3 and Tg around 182 K. A strong translational–rotation coupling of the [BMPyr][TFSI] molecules were observed in the glassy state due to intermolecular Johari Goldstein relaxation. It showed an excellent agreement with the coupling model predictions. The conductivity behavior of [BMPyr][TFSI] follows Johncher's power law, and its temperature dependence well fits with the Arrhenius equation having an activation energy of 686.3 kJ/mol. The glass transition temperature obtained from DSC, isochronal cooling data and heating data were in good agreement.

Influence of Annealing in the Close Vicinity of Tg on the Reorganization within Dimers and Its Impact on the Crystallization Kinetics of Gemfibrozil.

In this paper, broadband dielectric spectroscopy (BDS) has been applied to study the molecular dynamics and crystallization kinetics of the antihyperlipidemic active pharmaceutical ingredient (API), gemfibrozil (GEM), as well as its deuterated (dGEM) and methylated (metGEM) derivatives, characterized by different types and strengths of intermolecular interactions. Moreover, calorimetric and infrared measurements have been carried out to characterize the thermal properties of examined samples and to probe a change in the H-bonding pattern in GEM, respectively. We found that the dielectric spectra of all examined compounds, collected below the glass transition temperature (Tg), reveal the presence of two secondary relaxations (β, γ). According to the coupling model (CM) predictions, it was assumed that the slower process (β) is of JG type, whereas the faster one (γ) has an intramolecular origin. Interestingly, the extensive crystallization kinetics measurements performed after applying two paths, i.e., the standard procedure (cooling and subsequently heating up to the appropriate temperature, Tc), as well as annealing at two temperatures in the vicinity of Tg and further heating up to Tc, showed that the annealing increases the crystallization rate in the case of native API, while the thermal history of the sample has no significant impact on the pace of this process in the two derivatives of GEM. Analysis of the dielectric strength (Δε) of the α-process during annealing, together with the results of Fourier transform infrared spectroscopy (FTIR) measurements, suggested that the reorganization within dimeric structures formed between the GEM molecules is responsible for the observed behavior. Importantly, our results differ from those obtained by Tominaka et al. (TominakaS.; KawakamiK.; FukushimaM.; MiyazakiA.Physical Stabilization of Pharmaceutical Glasses Based on Hydrogen Bond Reorganization under Sub-Tg TemperatureMol. Pharm.20171426427310.1021/acs.molpharmaceut.6b00866.28043129), who demonstrated that the sub-Tg annealing of ritonavir (RTV), which is able to form extensive supramolecular hydrogen bonds, protects this active substance against crystallization. Therefore, based on these contradictory reports, one can hypothesize that materials forming H-bonded structures, characterized by varying architecture, may behave differently after annealing in the vicinity of the glass transition temperature.

Multiscale Coupling of an Agent-Based Model of Tissue Fibrosis and a Logic-Based Model of Intracellular Signaling.

Wound healing and fibrosis following myocardial infarction (MI) is a dynamic process involving many cell types, extracellular matrix (ECM), and inflammatory cues. As both incidence and survival rates for MI increase, management of post-MI recovery and associated complications are an increasingly important focus. Complexity of the wound healing process and the need for improved therapeutics necessitate a better understanding of the biochemical cues that drive fibrosis. To study the progression of cardiac fibrosis across spatial and temporal scales, we developed a novel hybrid multiscale model that couples a logic-based differential equation (LDE) model of the fibroblast intracellular signaling network with an agent-based model (ABM) of multi-cellular tissue remodeling. The ABM computes information about cytokine and growth factor levels in the environment including TGFβ, TNFα, IL-1β, and IL-6, which are passed as inputs to the LDE model. The LDE model then computes the network signaling state of individual cardiac fibroblasts within the ABM. Based on the current network state, fibroblasts make decisions regarding cytokine secretion and deposition and degradation of collagen. Simulated fibroblasts respond dynamically to rapidly changing extracellular environments and contribute to spatial heterogeneity in model predicted fibrosis, which is governed by many parameters including cell density, cell migration speeds, and cytokine levels. Verification tests confirmed that predictions of the coupled model and network model alone were consistent in response to constant cytokine inputs and furthermore, a subset of coupled model predictions were validated with in vitro experiments with human cardiac fibroblasts. This multiscale framework for cardiac fibrosis will allow for systematic screening of the effects of molecular perturbations in fibroblast signaling on tissue-scale extracellular matrix composition and organization.

The Application of the SVD Method to Reduce Coupled Model Biases in Seasonal Predictions of Rainfall

AbstractThe large systematic biases in coupled models impact seasonal prediction results. With a motivation to reduce the influence of coupled‐model biases on seasonal predictions, the singular value decomposition method was applied in our study to improve the ability to predict flood season precipitation. Based on the coupled climate model, CAS‐ESM‐C, we conducted ensemble seasonal prediction experiments from 1982 to 2018, with initial conditions provided by the assimilation system. The prediction system was integrated from March to August of each year with a focus on the June to August precipitation in China. The results showed that the prediction skills for anomalous summer precipitation were very low without bias corrections. However, the system effectively predicted the interannual variabilities in large‐scale atmospheric circulation systems that were associated with anomalous summer precipitation. We used the singular value decomposition method to reduce pattern‐dependent precipitation errors by replacing prediction patterns with observation patterns, and the predictive skill for precipitation dramatically improved. The results demonstrated that this correction method is a viable tool to reduce systematic biases in coupled model predictions.

Dielectric spectroscopic studies in supercooled liquid and glassy states of Acemetacin, Brucine and Colchicine

The physical and chemical instability of amorphous pharmaceuticals during storage has become a major problem for the pharmaceutical industry, as most of the pharmaceuticals including some life saving drugs are also found to be poorly water soluble. Molecular mobility is a key factor to understand the various crucial parameters that determine the stability of amorphous phase. The molecular dynamics in the supercooled liquid and glassy states of three Active Pharmaceutical Ingredients (API) Acemetacin, Brucine and Colchicine were characterized by molecular relaxation (mobility) using broadband dielectric spectroscopy (BDS) in a wide frequency range. Primary structural relaxation and intra-molecular (non Johari-Goldstein) secondary relaxation were observed above and below Tg respectively in all three title compounds. The dielectric spectra were fitted to the Havriliak-Negami (HN) function. The temperature dependence of the structural relaxation time followed Vogel-Fulcher-Tamman (VFT) equation and the secondary relaxation followed Arrhenius equation. By Coupling model (CM) prediction, the secondary relaxations in all title compounds were found to be non JG. The width of the structural relaxation peak decreases with temperature for brucine and colchicine while it increases for acemetacin. By Differential Scanning Calorimetry (DSC) experiment, we confirmed that the selelcted compounds are good glass formers and their structural properties and hydrogen bonding were studied by Fourier Transform Infrared spectroscopy (FTIR) and verified by Density Functional theory (DFT). By BDS, the parameters which describe the molecular dynamics in the supercooled and glassy states of the title compounds, like glass transition temperature (Tg) the width of the α relaxation (βKWW), the temperature dependence of α-relaxation times (τα), the fragility (m) and the activation energy of the secondary relaxation (Ea-γ), were determined. The higher values of Tg determined (307.9 K for acemetacin, 366.2 K for brucine and 374.5 K for colchicine) indicates stability of their amorphous phase at normal storage conditions. The fragility index values of the title compounds indicate intermediately fragile behaviour. The FTIR analysis of the crystalline and amorphous states of brucine, acemetacin and colchicine shows the presence of hydrogen bonding in amorphous phase of the title compounds and verified that their molecular structures were retained in the amorphous state.

Multiscale analysis of large-strain deformation behaviour of random cross-linked elastomers

ABSTRACTThe hyperelastic behaviour of elastomers is characterised by a complex and strongly non-linear response at large deformations, which leads to several modelling challenges. While the existing macroscopic-strain-invariants based models can qualitatively capture the trend of elastomer’s deformation behaviour, incorporating network scale dynamics of polymeric chains becomes important in order to account for lower length-scale phenomena like chemical cross-linking and Langevin chain dynamics. In this work, the mechanical behaviour of SBR-1502 (unfilled) cross-linked elastomer has been simulated using a multiscale modelling approach. All atomistic MD simulations were performed to compute the equilibrium density, glass transition temperature and two lower length scale parameters – volumetric segment density of polymeric chains () and the average number of Kuhn segments between two cross-linking points (). These parameters are fed into a constitutive modelling framework based on the 8-chain network concept of Arruda and Boyce to obtain the bulk equilibrium stress-strain response up to large values of strain. Hyperelastic stress-strain plots are obtained for uniaxial, biaxial and shear modes of deformation (up to ∼750% strain) and limiting chain extensibility is investigated for the random network structure. Coupled model predictions are compared with available experimental data for SBR-1502 and the results are encouraging.

Dielectric spectroscopic studies of three important active pharmaceutical ingredients - clofoctol, droperidol and probucol

Nowadays the study of physiochemical stability of amorphous pharmaceuticals is of great interest in the field of medicinal application due to its enhanced water solubility and bioavailability than its crystalline counterpart. Molecular relaxations play an important role in understanding the physical stability of amorphous drugs. Hence herein, we investigated the molecular dynamics of three pharmaceutically important drugs, namely clofoctol, droperidol and probucol by means of broadband dielectric spectroscopy (BDS) and differential scanning calorimetry (DSC). The dielectric spectra in the supercooled state were fitted by Havriliak-Negami (HN) function, while in the glassy state with Cole-Cole equation. The structural relaxation followed non-Arrhenius temperature dependence and followed time-honored Vogel-Fulcher-Tamman (VFT) equation and the secondary relaxation followed Arrhenius equation. The Coupling model (CM) prediction was used to find the origin of the secondary relaxations. Although the three drugs were found to be fragile, clofoctol and droperidol showed recrystallization tendency. It was amazing that the three samples showed a good correlation between the stretch exponent βKWW and the dielectric strength Δε(Tg). In addition, the molecular simulation was used to verify the presence of non-JG (Johari-Goldstein) secondary relaxation due to side chain rotation.

Application of grey model (1, 1) -linear regression coupling model in the prediction of regional nursing human resource

Objective Taking the prediction of the number of nurses per thousand cases as an example, to discuss the application of grey model GM (1, 1) -linear regression Coupling Model in the prediction of regional nursing human resource, so as to provide methodology reference for health human resource prediction. Methods The grey model GM (1, 1) was built up by EXCEL formula to predict the influencing factors of the number of nurses per thousand people. The predictive value of the influencing factors were substituted into the regression equation for the annual prediction of regional nursing human resource. Results The fitting error between predicted value and actual value was small when using the grey model GM (1, 1) -linear regression coupling model in the prediction of regional nursing human resource. The accuracy level of this coupling model prediction is excellent, and the prediction result can be used as a reference for target year. Conclusions The coupling model not only makes up for the lack of 1inear factors in the grey system model, but also improves the defect that exponential growth cannot be expressed in the linear regression prediction model, so the construction of the coupling model is reasonable and feasible. Key words: Nursing administration research; Grey model (1, 1); Linear regression models; Nursing human resource; Forecasting

Effects of ocean initial perturbation on developing phase of ENSO in a coupled seasonal prediction model

Coupled prediction systems for seasonal and inter-annual variability in the tropical Pacific are initialized from ocean analyses. In ocean initial states, small scale perturbations are inevitably smoothed or distorted by the observational limits and data assimilation procedures, which tends to induce potential ocean initial errors for the El Nino-Southern Oscillation (ENSO) prediction. Here, the evolution and effects of ocean initial errors from the small scale perturbation on the developing phase of ENSO are investigated by an ensemble of coupled model predictions. Results show that the ocean initial errors at the thermocline in the western tropical Pacific grow rapidly to project on the first mode of equatorial Kelvin wave and propagate to the east along the thermocline. In boreal spring when the surface buoyancy flux weakens in the eastern tropical Pacific, the subsurface errors influence sea surface temperature variability and would account for the seasonal dependence of prediction skill in the NINO3 region. It is concluded that the ENSO prediction in the eastern tropical Pacific after boreal spring can be improved by increasing the observational accuracy of subsurface ocean initial states in the western tropical Pacific.

A coupled model of TiN inclusion growth in GCr15SiMn during solidification in the electroslag remelting process

TiN inclusions observed in an ingot produced by electroslag remelting (ESR) are extremely harmful to GCr15SiMn steel. Therefore, accurate predictions of the growth size of these inclusions during steel solidification are significant for clean ESR ingot production. On the basis of our previous work, a coupled model of solute microsegregation and TiN inclusion growth during solidification has been established. The results demonstrate that compared to a non-coupled model, the coupled model predictions of the size of TiN inclusions are in good agreement with experimental results using scanning electron microscopy with energy disperse spectroscopy (SEM-EDS). Because of high cooling rate, the sizes of TiN inclusions in the edge area of the ingots are relatively small compared to the sizes in the center area. During the ESR process, controlling the content of Ti in the steel is a feasible and effective method of decreasing the sizes of TiN inclusions.

Resource allocation for code development in partitioned models

Purpose– Physical phenomena interact with each other in ways that one cannot be analyzed without considering the other. To account for such interactions between multiple phenomena, partitioning has become a widely implemented computational approach. Partitioned analysis involves the exchange of inputs and outputs from constituent models (partitions) via iterative coupling operations, through which the individually developed constituent models are allowed to affect each other’s inputs and outputs. Partitioning, whether multi-scale or multi-physics in nature, is a powerful technique that can yield coupled models that can predict the behavior of a system more complex than the individual constituents themselves. The paper aims to discuss these issues.Design/methodology/approach– Although partitioned analysis has been a key mechanism in developing more realistic predictive models over the last decade, its iterative coupling operations may lead to the propagation and accumulation of uncertainties and errors that, if unaccounted for, can severely degrade the coupled model predictions. This problem can be alleviated by reducing uncertainties and errors in individual constituent models through further code development. However, finite resources may limit code development efforts to just a portion of possible constituents, making it necessary to prioritize constituent model development for efficient use of resources. Thus, the authors propose here an approach along with its associated metric to rank constituents by tracing uncertainties and errors in coupled model predictions back to uncertainties and errors in constituent model predictions.Findings– The proposed approach evaluates the deficiency (relative degree of imprecision and inaccuracy), importance (relative sensitivity) and cost of further code development for each constituent model, and combines these three factors in a quantitative prioritization metric. The benefits of the proposed metric are demonstrated on a structural portal frame using an optimization-based uncertainty inference and coupling approach.Originality/value– This study proposes an approach and its corresponding metric to prioritize the improvement of constituents by quantifying the uncertainties, bias contributions, sensitivity analysis, and cost of the constituent models.

Ocean surface waves in Hurricane Ike (2008) and Superstorm Sandy (2012): Coupled model predictions and observations

Forecasting hurricane impacts of extreme winds and flooding requires accurate prediction of hurricane structure and storm-induced ocean surface waves days in advance. The waves are complex, especially near landfall when the hurricane winds and water depth varies significantly and the surface waves refract, shoal and dissipate. In this study, we examine the spatial structure, magnitude, and directional spectrum of hurricane-induced ocean waves using a high resolution, fully coupled atmosphere–wave–ocean model and observations. The coupled model predictions of ocean surface waves in Hurricane Ike (2008) over the Gulf of Mexico and Superstorm Sandy (2012) in the northeastern Atlantic and coastal region are evaluated with the NDBC buoy and satellite altimeter observations. Although there are characteristics that are general to ocean waves in both hurricanes as documented in previous studies, wave fields in Ike and Sandy possess unique properties due mostly to the distinct wind fields and coastal bathymetry in the two storms. Several processes are found to significantly modulate hurricane surface waves near landfall. First, the phase speed and group velocities decrease as the waves become shorter and steeper in shallow water, effectively increasing surface roughness and wind stress. Second, the bottom-induced refraction acts to turn the waves toward the coast, increasing the misalignment between the wind and waves. Third, as the hurricane translates over land, the left side of the storm center is characterized by offshore winds over very short fetch, which opposes incoming swell. Landfalling hurricanes produce broader wave spectra overall than that of the open ocean. The front-left quadrant is most complex, where the combination of windsea, swell propagating against the wind, increasing wind–wave stress, and interaction with the coastal topography requires a fully coupled model to meet these challenges in hurricane wave and surge prediction.

Error Covariance Estimation for Coupled Data Assimilation Using a Lorenz Atmosphere and a Simple Pycnocline Ocean Model

Abstract Coupled data assimilation uses a coupled model consisting of multiple time-scale media to extract information from observations that are available in one or more media. Because of the instantaneous exchanges of information among the coupled media, coupled data assimilation is expected to produce self-consistent and physically balanced coupled state estimates and optimal initialization for coupled model predictions. It is also expected that applying coupling error covariance between two media into observational adjustments in these media can provide direct observational impacts crossing the media and thereby improve the assimilation quality. However, because of the different time scales of variability in different media, accurately evaluating the error covariance between two variables residing in different media is usually very difficult. Using an ensemble filter together with a simple coupled model consisting of a Lorenz atmosphere and a pycnocline ocean model, which characterizes the interaction of multiple time-scale media in the climate system, the impact of the accuracy of coupling error covariance on the quality of coupled data assimilation is studied. Results show that it requires a large ensemble size to improve the assimilation quality by applying coupling error covariance in an ensemble coupled data assimilation system, and the poorly estimated coupling error covariance may otherwise degrade the assimilation quality. It is also found that a fast-varying medium has more difficulty being improved using observations in slow-varying media by applying coupling error covariance because the linear regression from the observational increment in slow-varying media has difficulty representing the high-frequency information of the fast-varying medium.

A mechanically coupled reaction–diffusion model for predicting the response of breast tumors to neoadjuvant chemotherapy

There is currently a paucity of reliable techniques for predicting the response of breast tumors to neoadjuvant chemotherapy. The standard approach is to monitor gross changes in tumor size as measured by physical exam and/or conventional imaging, but these methods generally do not show whether a tumor is responding until the patient has received many treatment cycles. One promising approach to address this clinical need is to integrate quantitative in vivo imaging data into biomathematical models of tumor growth in order to predict eventual response based on early measurements during therapy. In this work, we illustrate a novel biomechanical mathematical modeling approach in which contrast enhanced and diffusion weighted magnetic resonance imaging data acquired before and after the first cycle of neoadjuvant therapy are used to calibrate a patient-specific response model which subsequently is used to predict patient outcome at the conclusion of therapy. We present a modification of the reaction–diffusion tumor growth model whereby mechanical coupling to the surrounding tissue stiffness is incorporated via restricted cell diffusion. We use simulations and experimental data to illustrate how incorporating tissue mechanical properties leads to qualitatively and quantitatively different tumor growth patterns than when such properties are ignored. We apply the approach to patient data in a preliminary dataset of eight patients exhibiting a varying degree of responsiveness to neoadjuvant therapy, and we show that the mechanically coupled reaction–diffusion tumor growth model, when projected forward, more accurately predicts residual tumor burden at the conclusion of therapy than the non-mechanically coupled model. The mechanically coupled model predictions exhibit a significant correlation with data observations (PCC = 0.84, p < 0.01), and show a statistically significant >4 fold reduction in model/data error (p = 0.02) as compared to the non-mechanically coupled model.