Mechanistic Model For Prediction Research Articles

Investigation on Rod Bundle CHF Mechanistic Model for DNB and DO Prediction Under Wide Parameter Range

For a water cooled reactor, the key thermal-hydraulic parameters span a wide range corresponding to different CHF regimes. Under accident conditions, due to the flow regime transition and interchannel mixing effect, the corresponding CHF can transition from the DO to DNB regime. In order to continuously and accurately predict DNB and DO regime CHF under wide parameter range for rod bundle channel, a comprehensive CHF mechanistic model covering the DNB and DO regime CHF prediction is established based on the rod bundle CHF-regime criterion. The DNB regime CHF mechanistic model of superheated liquid layer depletion under turbulence fluctuation bubbles and the mature DO regime CHF mechanistic model are combined to form the comprehensive CHF model. Furthermore, the comprehensive CHF model is assessed by 5 × 5 rod bundle CHF experimental data independently obtained by the Nuclear Power Institute of China (NPIC). The statistical evaluation and parametric trend analysis show that the maximum mean error of P/M is within ±22%, and the local pressure, mass flux, and quality do not have any effects on the average deviations of the predicted flux P from the measured flux M. This indicates that the comprehensive CHF mechanistic model can accurately and continuously predict the DNB and DO regime CHF in the rod bundle channel.

Machine Learning for Predicting Mycotoxin Occurrence in Maize.

Meteorological conditions are the main driving variables for mycotoxin-producing fungi and the resulting contamination in maize grain, but the cropping system used can mitigate this weather impact considerably. Several researchers have investigated cropping operations’ role in mycotoxin contamination, but these findings were inconclusive, precluding their use in predictive modeling. In this study a machine learning (ML) approach was considered, which included weather-based mechanistic model predictions for AFLA-maize and FER-maize [predicting aflatoxin B1 (AFB1) and fumonisins (FBs), respectively], and cropping system factors as the input variables. The occurrence of AFB1 and FBs in maize fields was recorded, and their corresponding cropping system data collected, over the years 2005–2018 in northern Italy. Two deep neural network (DNN) models were trained to predict, at harvest, which maize fields were contaminated beyond the legal limit with AFB1 and FBs. Both models reached an accuracy >75% demonstrating the ML approach added value with respect to classical statistical approaches (i.e., simple or multiple linear regression models). The improved predictive performance compared with that obtained for AFLA-maize and FER-maize was clearly demonstrated. This coupled to the large data set used, comprising a 13-year time series, and the good results for the statistical scores applied, together confirmed the robustness of the models developed here.

Computational Fluid Dynamics (CFD) Simulations of Taylor Bubbles in Vertical and Inclined Pipes with Upward and Downward Liquid Flow

Summary Two-phase flow is a common occurrence in pipes of oil and gas developments. Current predictive tools are based on the mechanistic two-fluid model, which requires the use of closure relations to predict integral flow parameters such as liquid holdup (or void fraction) and pressure gradient. However, these closure relations carry the highest uncertainties in the model. In particular, significant discrepancies have been found between experimental data and closure relations for the Taylor bubble velocity in slug flow, which has been determined to strongly affect the mechanistic model predictions (Lizarraga-García 2016). In this work, we study the behavior of Taylor bubbles in vertical and inclined pipes with upward and downward flow using a validated 3D computational fluid dynamics (CFD) approach with level set method implemented in a commercial code. A total of 56 cases are simulated, covering a wide range of fluid properties, pipe diameters, and inclination angles: Eo ∈ [10, 700]; Mo ∈ [1×10–6, 5×103]; ReSL ∈ [–40, 10]; θ ∈ [5°, 90°]. For bubbles in vertical upward flows, the simulated distribution parameter, C0, is successfully compared with an existing model. However, the C0 values of downward and inclined slug flows where the bubble becomes asymmetric are shown to be significantly different from their respective vertical upward flow values, and no current model exists for the fluids simulated here. The main contributions of this work are (1) the relatively large 3D numerical database generated for this type of flow, (2) the study of the asymmetric nature of inclined and some vertical downward slug flows, and (3) the analysis of its impact on the distribution parameter, C0.

A Mechanistic Evolutionary Model Explains the Time-Dependent Pattern of Substitution Rates in Viruses

Estimating viral timescales is fundamental to understanding the evolutionary biology of viruses. Molecular clocks are widely used for revealing their recent evolutionary histories but may severely underestimate their longer-term origins. This is because inferred rates of evolution are inversely correlated to the timescale of their measurement1,2. This rate decay is well described by a power law with a slope of -0.65 and can be observed across all known viral genome types3, implying a common process. We provide the first predictive mechanistic model that can readily explain the pattern of rate decay over a wide range of timescales and recapitulates the ubiquitous power-law rate decay with a slope of -0.65 (95% HPD: -0.72, -0.52). We show that standard substitution models fail to correctly estimate divergence times once the most rapidly evolving sites saturate, typically after hundreds of years in RNA viruses and thousands of years in DNA viruses, whereas we can successfully re-create the observed pattern of decay and explain the evolutionary processes involved in creating the time-dependent rate phenomenon. We apply our model to re-date the diversification of genotypes of hepatitis C virus (HCV) to 396,000 (95% HPD: 326,000 - 425,000) years before present, a time preceding the dispersal of modern humans out of Africa, and also showed that the most recent common ancestor of sarbecoviruses dates back to 23,500 (95% HPD: 21,100 - 25,300) years ago, nearly thirty times older than previous estimates4. This not only creates a radical new perspective for our understanding the origins of these viruses, but also suggests a substantial revision of evolutionary timescales of other viruses can be similarly achieved.

The ecophysiological plasticity of Aedes aegypti and Aedes albopictus concerning overwintering in cooler ecoregions is driven by local climate and acclimation capacity.

Aedes aegypti and Aedes albopictus transmit diseases such as dengue, and are of major public health concern. Driven by climate change and global trade/travel both species have recently spread to new tropic/subtropic regions and Ae. albopictus also to temperate ecoregions. The capacity of both species to adapt to new environments depends on their ecophysiological plasticity, which is the width of functional niches where a species can survive. Mechanistic distribution models often neglect to incorporate ecophysiological plasticity especially in regards to overwintering capacity in cooler habitats. To portray the ecophysiological plasticity concerning overwintering capability, we conducted temperature experiments with multiple populations of both species originating from an altitudinal gradient in South Asia and tested as follows: the cold tolerance of eggs (-2°C- 8days and-6°C- 2days) without and with an experimental winter onset (acclimation: 10°C- 60days), differences between a South Asian and a European Ae. albopictus population and the temperature response in life cycles (13°C, 18°C, 23°C, 28°C). Ecophysiological plasticity in overwintering capacity in Ae. aegypti is high in populations originating from low altitude and in Ae. albopictus populations from high altitude. Overall, ecophysiological plasticity is higher in Ae. albopictus compared to Ae. aegypti. In both species acclimation and in Ae. albopictus temperate continental origin had a huge positive effect on survival. Our results indicate that future mechanistic prediction models can include data on winter survivorship of both, tropic and subtropic Ae. aegypti, whereas for Ae. albopictus this depends on the respective temperate, tropical region the model is focusing on. Future research should address cold tolerance in multiple populations worldwide to evaluate the full potential of the ecophysiological plasticity in the two species. Furthermore, we found that Ae. aegypti can survive winter cold especially when acclimated and will probably further spread to colder ecoregions driven by climate change.

A comparison of numerical simulations with experimental and theoretical investigations of highly-viscous oil-aqueous foam horizontal flow

A possible innovative technology of foam injection for facilitating the transportation of heavy oils is explored. To this aim, a numerical study on flow characteristics of highly viscous oil flowing through a 25 mm i. d. horizontal pipe under the action of aqueous foam was conducted. The Volume of Fluid (VOF) multiphase flow modeling method embodied in ANSYS Fluent was applied to simulate the Newtonian oil/non-Newtonian foam two-phase flow. Simulations were conducted covering oil and foam superficial velocities of 0.1–0.9 m/s and 0.05–0.84 m/s, respectively, corresponding to the experimental range. Cross-sectional axial velocity profiles, oil volume fractions, phase-distribution and pressure gradients were extracted from the CFD simulations. The flow patterns were compared to actual images taken from a high-speed, high resolution camera. Pressure gradients, drag reduction ratios and oil-transport efficiencies were compared with the experimental data and mechanistic model predictions. Good agreement was achieved among the simulated, experimental and predicted data over a wide range of operational conditions. With the complete encapsulation of the oil core by the foam annulus, a critical foam fraction can be established to reach maximum drag reduction ratio. An optimum foam/oil flow rate ratio for the highest oil-transport operational coefficient was identified.

Towards a better understanding of grass bed dynamics using remote sensing at high spatial and temporal resolutions

Wetlands conservation and resilience capacities are key issues in many places over the globe. Understanding these issues will benefit from a precise knowledge of seagrass species occupancy and coverage over time and over space. Such information can be obtained from remote sensing images and their classification thanks to a vegetation index, to be used in a complementary manner to field work inventories. Sentinel-2 data, which are available with a frequent revisit time (<5 days) and a high spatial resolution (10m pixel size) can be used to map grassbeds at the surface or slightly below the surface of permanent lagoons, hence enabling the characterization of its seasonal dynamics, which was not possible with previous remote-sensing tools. We have proved the feasibility of such a method in the natural reserve of the Bagnas (Herault, France) where Stuckenia pectinata coverage can be tracked over a full year thanks to Sentinel-2 images and field work. Inter-annual dynamics (seasonal growth and senescence) can be mapped over time with 10m resolution and will be extended to pluriannual studies thanks to the long-term objective of the Sentinel-2 mission. This opens the way to a concerted management of natural reserves based on data analysis and field knowledge, a better understanding of seagrass coverage with fluctuating environmental conditions, and predictive mechanistic and/or stochastic models of future qualitative trends.

Energy prediction for community water supply: An integrative application of scaling analysis and life cycle assessment

Community water supply is the local, consumer-level segment of water supply. For its ubiquity, an ability to predict the energy demand for community water supply would help better quantify the water-energy nexus at the community level. However, communities differ in size and distance from the water source, among other variations; no formalism is currently in use for making this energy prediction. Here, we describe a conceptual model and mechanistic formulation for predicting the energy demand of a community water supply in the context of a regional, multi-community supply system. We make the novel methodological integration of scaling analysis and life cycle assessment to account for both network effects and life cycle effects on energy intensity. We also make the novel mechanistic integration of hydraulic energy loss and allometric energy scaling to account for both linear transmission energy and nonlinear distribution energy. With these integrative steps, we create a novel “leaf on a stem” network model and mechanistically parameterized formula for predicting the energy demand of a community water supply, given in life cycle quantity and as the sum of community distribution energy in sublinear scaling with community size and regional transmission energy in linear scaling with the community’s distance from the regional water source. We validate the predictive model by empirically verifying the model parameters through a detailed case study of consumption communities within a regional supply system in the US Great Lakes region. This simple, mechanistic predictive model may help substantiate the concept of water-energy nexus and facilitate energy assessment for community water supply.

Thermal tolerance differs between co-occurring congeneric beetle species in marine supratidal rockpools

In the context of global change, it is important to know the physiological limits and behavioural responses of species to environmental changes, especially for those living in extreme environments such as supratidal rockpools. Our main study objective was to determine the thermal tolerance of 3 beetle species (Ochthebius quadricollis, O. subintegerandO. lejolisii) co-occurring on the western Mediterranean coast using physiological and behavioural thermoregulatory responses to predict their sensitivity to climate change. To this end, we: (1) compared the heat coma (HC) among species and populations; (2) explored the effect of salinity on HC in adults and larvae ofO. quadricollisandO. lejolisii; and (3) determined the temperature thresholds for avoidance responses (water emersion and flight) of adult stages against heat and salinity stressors. We found significant interspecific and interpopulation differences in HC.O. quadricollislarvae and adults were the most heat tolerant, showing similar HC values under different salinity conditions. InO. lejolisii, high salinity (90 g l-1) conferred greater thermal tolerance in larvae but lower tolerance in adults. The temperature thresholds for avoidance responses were generally lower than 40°C, but interspecific variation followed their obtained HC patterns. In both of these species, salinity affected the sublethal temperature thresholds for water emersion. An additive effect of temperature and salinity was observed for the frequency of emergence and flight in both species. Our results provide relevant information for estimating thermal safety margins and developing mechanistic predictive models for the survival of these species when faced with current and future climate change scenarios.

Mechanistic models for prediction of cutting forces and power consumption considering chip geometry

A novel approach for modeling and simulation of cutting force and power consumption in relation to chip geometry has been proposed in end milling‌ of AISI D2 steel. It is carried out in two stages: experimental work and finite element method based numerical simulation. In the first stage, experiments are conducted on the AISI D2 steel at two levels of spindle rotational speed, axial depth of cuts and four levels of feed per tooth using 10 mm and 8 mm diameter mill cutters. Cutting forces and amplitude of cutter vibration are measured in X and Y directions. Mechanistic models in terms of chip geometry and cutting force coefficients are developed to predict cutting forces and power consumption relative to chip geometry at 30°, 60° and 90° of cutter rotation. In the second stage, numerical simulation is carried out to predict cutting forces and power consumption relative to chip geometry at 30°, 60° and 90° of cutter rotation and compared with estimated values of cutting forces and power consumption. The maximum error between the two approaches for the cutting forces in X and Y directions and power consumption is estimated as 10.80%, 8.33% and 7.70% respectively. At spindle rotational speed of 2000 rpm, 0.3 mm of axial depth of cut and 50 µm of feed per tooth, the cutting forces and cutting power consumption are found minimum.

Computational biomedicine. Part 1: molecular medicine

Computational biomedicine. Part 1: molecular medicine

Forecasting industrial aging processes with machine learning methods

Accurately predicting industrial aging processes makes it possible to schedule maintenance events further in advance, ensuring a cost-efficient and reliable operation of the plant. So far, these degradation processes were usually described by mechanistic or simple empirical prediction models. In this paper, we evaluate a wider range of data-driven models, comparing some traditional stateless models (linear and kernel ridge regression, feed-forward neural networks) to more complex recurrent neural networks (echo state networks and LSTMs). We first examine how much historical data is needed to train each of the models on a synthetic dataset with known dynamics. Next, the models are tested on real-world data from a large scale chemical plant. Our results show that recurrent models produce near perfect predictions when trained on larger datasets, and maintain a good performance even when trained on smaller datasets with domain shifts, while the simpler models only performed comparably on the smaller datasets.

Association of Mobile Phone Location Data Indications of Travel and Stay-at-Home Mandates With COVID-19 Infection Rates in the US

A stay-at-home social distancing mandate is a key nonpharmacological measure to reduce the transmission rate of severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2), but a high rate of adherence is needed. To examine the association between the rate of human mobility changes and the rate of confirmed cases of SARS-CoV-2 infection. This cross-sectional study used daily travel distance and home dwell time derived from millions of anonymous mobile phone location data from March 11 to April 10, 2020, provided by the Descartes Labs and SafeGraph to quantify the degree to which social distancing mandates were followed in the 50 US states and District of Columbia and the association of mobility changes with rates of coronavirus disease 2019 (COVID-19) cases. State-level stay-at-home orders during the COVID-19 pandemic. The main outcome was the association of state-specific rates of COVID-19 confirmed cases with the change rates of median travel distance and median home dwell time of anonymous mobile phone users. The increase rates are measured by the exponent in curve fitting of the COVID-19 cumulative confirmed cases, while the mobility change (increase or decrease) rates were measured by the slope coefficient in curve fitting of median travel distance and median home dwell time for each state. Data from more than 45 million anonymous mobile phone devices were analyzed. The correlation between the COVID-19 increase rate and travel distance decrease rate was -0.586 (95% CI, -0.742 to -0.370) and the correlation between COVID-19 increase rate and home dwell time increase rate was 0.526 (95% CI, 0.293 to 0.700). Increases in state-specific doubling time of total cases ranged from 1.0 to 6.9 days (median [interquartile range], 2.7 [2.3-3.3] days) before stay-at-home orders were enacted to 3.7 to 30.3 days (median [interquartile range], 6.0 [4.8-7.1] days) after stay-at-home social distancing orders were put in place, consistent with pandemic modeling results. These findings suggest that stay-at-home social distancing mandates, when they were followed by measurable mobility changes, were associated with reduction in COVID-19 spread. These results come at a particularly critical period when US states are beginning to relax social distancing policies and reopen their economies. These findings support the efficacy of social distancing and could help inform future implementation of social distancing policies should they need to be reinstated during later periods of COVID-19 reemergence.

Predictive mechanistic model for single-layered pressure-sensitive adhesive (PSA) joints : Part I: Uniaxial tensile stress-strain response.

Modeling the deformation of structures containing pressure-sensitive adhesive (PSA) joints can be a challenging task because of the dependence of the deformation mechanism on a) PSA adhesive properties and b) the bonding substrate's surface properties, such as surface energy and surface roughness. These parameters have significant and unique effects on the mechanical response of the joint. This paper is part of a two-part series, where a mechanism-based predictive modeling approach, supported by empirical observations, is presented for modeling the uniaxial tensile mechanical behavior of single-layered PSA joints based on acrylic PSA materials. This paper (Part I) addresses the stress-strain response, while Part II of this series will address the creep behavior. The underlying model is based on multiple mechanisms: i) cavity nucleation and growth in the bulk adhesive material of the PSA system, as well as at the interfaces between the PSA and the substrate; ii) fibrillation of the cavitated adhesive layer and iii) interfacial slippage between the adhesive and the bonding substrate; iv) PSA delamination from the substrate. This predictive model can be used as a virtual testing tool to generate stress-strain curves for constitutive models of PSA joints under different tensile loading conditions.

Abstract 5483: An image-based mechanistic computational model for early prediction of organ-confined untreated prostate cancer growth

Abstract According to the American Cancer Society, prostate cancer (PCa) is the most common newly-diagnosed cancer and the second leading cancer-related cause of death among men in the US in 2019. The current clinical protocols of PCa are based on two key strategies: regular screening of men over fifty and patient triaging in risk groups. Prostatic tumors are usually detected at an early organ-confined stage, which poses a low to intermediate risk to the patient and may not produce any symptoms or require treatment for long time. However, most patients are prescribed a radical treatment immediately after diagnosis (e.g., surgery or radiation therapy), which may adversely impact their lives without necessarily prolonging their longevity or quality of life. Moreover, some patients who initially delay their treatment ultimately succumb to PCa due to an inaccurate initial diagnosis. Thus, while regular screening enables the detection of the majority of tumors at an early and mild stage, the limited individualization of patient monitoring and treatment has led to significant rates of both overtreatment and undertreatment. To overcome these fundamental limitations in the clinical management of PCa, we propose the use of a mechanistic mathematical model for which we can perform computer simulations to forecast patient-specific tumor growth. This computational model is based on a set of partial differential equations that describe the main mechanisms involved in organ-confined PCa growth. The model is parameterized using longitudinal multiparametric magnetic resonance (MR) images and the available clinical data for each patient. Tumor growth is simulated over the patient's prostate extracted from T2-weighted MR images. We use isogeometric analysis to accurately and efficiently address the computational challenges arising in this application. Our preliminary simulation results show that our computational technology can predict tumor growth and associated serum Prostate Specific Antigen (PSA, a key biomarker in clinical management of PCa) with reasonable accuracy. We also explore the potential of model parameters and variables to characterize tumor aggressivity. Thus, we believe that our imaging-based modeling approach could be a promising tool capable of being implemented in current PCa protocols to assist physicians in the clinical management of PCa. Citation Format: Guillermo Lorenzo, Thomas J. Hughes, Alessandro Reali, Hector Gomez, Thomas E. Yankeelov. An image-based mechanistic computational model for early prediction of organ-confined untreated prostate cancer growth [abstract]. In: Proceedings of the Annual Meeting of the American Association for Cancer Research 2020; 2020 Apr 27-28 and Jun 22-24. Philadelphia (PA): AACR; Cancer Res 2020;80(16 Suppl):Abstract nr 5483.

A Novel Method for Predicting Power Transient CHF via the Heterogeneous Spontaneous Nucleation Trigger Mechanism

Using the Laplace transform for solving a two-region (cladding/liquid) conduction problem with an exponentially increasing heat flux boundary condition, an analytic temperature profile has been found. The rate of the temperature increase in the second region (liquid) is used to determine energy deposition in the thermal boundary layer of the liquid. Energy deposition rates are then compared to the latent heat capacity of the growing thermal boundary layer to create a condition for predicting transient critical heat flux (CHF) via the heterogeneous spontaneous nucleation (HSN) trigger mechanism. These analytic predictions are then compared to existing data for exponential power ramp transients with periods ranging from 5 ms up to 10 s. Comparison with experimental data show that the trends of the expected HSN-triggered CHF are in good agreement with the magnitude being controlled by the determination of the maximum boundary layer energy. This work presents the first known attempts to derive a mechanistic CHF prediction model for HSN. Though further work is necessary to develop the HSN model (and is being pursued in parallel to this research), this work will allow for a quantitative prediction of HSN-triggered CHF. Further developments of the HSN model will inform the boundary layer energy threshold that triggers CHF.

Extraction and Refolding Determinants of Chaperone-Driven Aggregated Protein Reactivation

Reactivation of protein aggregates plays a fundamental role in numerous situations, including essential cellular processes, hematological and neurological disorders, and biotechnological applications. The molecular details of the chaperone systems involved are known to a great extent but how the overall reactivation process is achieved has remained unclear. Here, we quantified reactivation over time through a predictive mechanistic model and identified the key parameters that control the overall dynamics. We performed new targeted experiments and analyzed classical data, covering multiple types of non-ordered aggregates, chaperone combinations, and experimental conditions. We found that, irrespective of the behavior observed, the balance of surface disaggregation and refolding in solution universally determines the reactivation dynamics, which is broadly described by two characteristic times. This characterization makes it possible to use activity measurements to accurately infer the underlying loss of aggregated protein and to quantify, for the first time, the refolding rates of the soluble intermediates.

Crop science: A foundation for advancing predictive agriculture

Crop science: A foundation for advancing predictive agriculture

A mechanistic prediction model for thrust force and torque during drilling of CFRP/Ti stacks

Drilling holes on carbon fiber reinforced plastic/polymer (CFRP) and Ti stack structures is significantly important assembling process in aerospace manufacturing. However, it is quite a challenge that the excessive and completely different drilling loads of two materials with distinguish physical and mechanical properties will lead to frequent CFRP drilling damages and Ti burrs, as well as rapid tool failures. Therefore, understanding and controlling the drilling loads are crucial to ensure low damage and high efficiency drilling of CFRP and Ti stacks. In this paper, a novel mechanistic model was established to predict the thrust force and the torque in the drilling of CFRP and Ti stacks. The cutting loads of a twist drill were discretized along chisel edge, primary cutting edge, and minor cutting edge. An orthogonal cutting model and an oblique cutting model were proposed to represent the discretized cutting load for CFRP and Ti with the consideration of fiber cutting angles, flank face friction, and drilling parameters. Integrating the discretized cutting load, a mechanistic model for predicting thrust force and torque was developed for drilling of CFRP/Ti stack. After the coefficient calibrations, verification experiments showed that the developed model has accurately predicted the thrust force and torque under various spindle speeds/feed rates as well as the changing fiber cutting angles.

Mechanistic prediction for cutting force in rotary ultrasonic machining of BK7 glass based on probability statistics

Material removal in rotary ultrasonic machining (RUM) of hard-brittle material is an abnormal complicated process, which involves the combination effects of the numerous abrasive grains with the random distributions in the dimensions and penetration depths. These stochastic characteristics result in the evident differences in the extrusion loading between the material and each individual grain, and their aggregate effects serve to significantly affect the cutting force of the diamond tool. However, few mechanistic prediction models of the cutting force have incorporated in the random distribution features of the abrasive grains, restricting the current optimization methods for the reduction of the cutting force during the formal RUM process. Giving consideration to the abrasive processing kinematics and their distribution features on the tool end-face, the number of the effective grains together with their penetration depths was calculated utilizing the probability statistics. Subsequently, the novel theoretical model of the cutting force was established by incorporating the gaussian distribution characteristics of the grain size and their penetration depths. Afterward, the confirmatory experiments were performed for the validation of the proposed cutting force model, revealing that the predicted results were accordant well with the experimental measurements. Furthermore, it was found that the number of the active abrasive grains accounted for 2.972% of the total number on the tool end-face at the specific processing parameters. Additionally, the mechanistic predictions of the developed model represented that the cutting force depicting an irregular decreasing trend with the grain dimension increasing, which was attributed to the coupling effects between the grain size and their number.