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A Novel Physiologically Based Model of Creatinine Renal Disposition to Integrate Current Knowledge of Systems Parameters and Clinical Observations
Creatinine is the most common clinical biomarker of renal function. As a substrate for renal transporters, its secretion is susceptible to inhibition by drugs, resulting in transient increase in serum creatinine and false impression of damage to kidney. Novel physiologically based models for creatinine were developed here and (dis)qualified in a stepwise manner until consistency with clinical data. Data from a matrix of studies were integrated, including systems data (common to all models), proteomics‐informed in vitro – in vivo extrapolation of all relevant transporter clearances, exogenous administration of creatinine (to estimate endogenous synthesis rate), and inhibition of different renal transporters (11 perpetrator drugs considered for qualification during creatinine model development and verification on independent data sets). The proteomics‐informed bottom‐up approach resulted in the underprediction of creatinine renal secretion. Subsequently, creatinine‐trimethoprim clinical data were used to inform key model parameters in a reverse translation manner, highlighting best practices and challenges for middle‐out optimization of mechanistic models.
Coordinated distribution network control of tap changer transformers, capacitors and PV inverters
A power distribution system operates most efficiently with voltage deviations along a feeder kept to a minimum and must ensure all voltages remain within specified limits. Recently with the increased integration of photovoltaics, the variable power output has led to increased voltage fluctuations and violation of operating limits. This study proposes an optimization model based on a recently developed heuristic search method, grey wolf optimization, to coordinate the various distribution controllers. Several different case studies on IEEE 33 and 69 bus test systems modified by including tap changing transformers, capacitors and photovoltaic solar panels are performed. Simulation results are compared to two other heuristic-based optimization methods: harmony search and differential evolution. Finally, the simulation results show the effectiveness of the method and indicate the usage of reactive power outputs of PVs facilitates better voltage magnitude profile.
On the Alternate Stirring Mode of F-EMS for Bloom Continuous Castings
Local solute transportation behaviors under different alternate stirring parameters of final electromagnetic stirring (F-EMS) and their influences on the internal quality of the as-cast bloom are compared and evaluated based on a developed coupled model of electromagnetism, heat, and solute transport. To this end, plant trials were conducted in Shaoguan Steel, China. Under the action of F-EMS, a negative segregation band in an ellipse shape is observed at the central area of strand cross section, where the minimum carbon segregation degree is decreased from 0.98 to 0.84 as the stirring duration increases from 15 to 35 seconds in the alternate stirring mode, while it is reduced to 0.805 in the continuous stirring mode. The white band and shrinkage cavity are simultaneously observed at strand center under the conditions of continuous stirring mode, and alternate stirring mode with a stirring period of 35 seconds because of the local over-sustaining melt rotation. In contrast, the V-shape porosity belt width and strand center segregation fluctuation range increase from 60 to 90 mm and from 0.12 to 0.30, respectively, as the stirring duration is reduced from 25 to 15 seconds in the alternate stirring mode because of the poor mixing of the local melt at the strand center.
Viscosity of Iron Oxide Aluminosilicate Melts
A structure-based viscosity model has been developed for the CaO-MgO-FeO-SiO{sub 2}-Al{sub 2}O{sub 3} system and its subsystems. A critical analysis of the experimental data for iron oxide aluminosilicate systems has been conducted and used in the optimization of a model, which provides a link between the viscosity and the structures of the melts. The structural characteristics of aluminosilicate melt can be described by four types of oxygen: bridging oxygen O{sub i}{sup 0}, non-bridging oxygen O{sub j}{sup −}, free oxygen O{sup 2−}, and excess bridging oxygen O{sup *}. The present model is capable of predicting the viscosities in the CaO-MgO-FeO-SiO{sub 2}-Al{sub 2}O{sub 3} system and its subsystems over wide ranges of composition and temperature above the liquidus within experimental uncertainties, and the average of relative errors for this model was found to be 19.94 pct. This model includes FeO-SiO{sub 2}, CaO-SiO{sub 2}-FeO, Al{sub 2}O{sub 3}-SiO{sub 2}-FeO, MgO-SiO{sub 2}-FeO, CaO-SiO{sub 2}-MgO-FeO, CaO-SiO{sub 2}-Al{sub 2}O{sub 3}-FeO, and CaO-MgO-SiO{sub 2}-Al{sub 2}O{sub 3}-FeO. It was found that the effects of different metal cations on the viscosity can be determined, and the ability to reduce viscosity in the silicate melts follows the order: FeO > CaO > MgO. In the present model, the predicted viscosity decreased when CaO was initially initial added to the Al{sub 2}O{sub 3}-MgO-FeO-SiO{sub 2} system at a fixed SiO{sub 2} content because Ca{sup 2+} has the highest priority for charge compensation and FeO is the strongest modifier for this slag.
A Systematic Assessment of Various Stability/Instability Criteria in Predicting the Hot Deformation-Related Instabilities in Super-304H Stainless Steels
In this study, different existing stability/instability criteria (viz Prasad, Murty, Gegel, Alexander, Jonas, and Semiatin) are used to characterize the hot deformation instabilities of super-304H stainless steel. The instability maps are developed based on flow stress data acquired from uniaxial hot compression tests performed at various temperatures (1173-1423 K) and strain rates (0.001-10 s{sup −1}). The deformation instabilities are further validated through detailed microstructural observations. Flow localization and cracking have been identified as the signature of deformation instabilities in this alloy. The microcracks have been found to be associated with the grain boundary precipitation of coarse (~ 1- to 5-µm-sized) Nb-rich particles (mainly NbC). The instability criteria developed based on dynamic material modeling (i.e., Prasad’s and Murty’s criteria) could accurately predict the formation of microcracks in the entire hot deformation range employed in this study. Albeit the Gegel’s and Alexander’s stability criteria could correctly predict the flow localization, it has overestimated the deformation instabilities at high temperatures (> 1250 K) and low strain rates (≤ 0.1 s{sup −1}). In contrast, Jonas’ and Semiatin’s instability criteria have under-predicted the unstable domains in the studied alloy.
Constitutive Behavior and Hot Workability of a Hot Isostatic Pressed Ti-22Al-25Nb Alloy during Hot Compression
A Ti-22Al-25Nb alloy was fabricated from prealloyed powders by hot isostatic pressing for 2 h at a temperature of 1050 °C and pressure of 100 MPa. The hot deformation behavior of the Ti-22Al-25Nb alloy was characterized by isothermal compression testing at deformation temperatures between 900 and 1060 °C and strain rates between 0.001 and 1 s{sup −1}. Based on the true stress–strain curves, a constitutive equation was constructed to describe the flow stress as a function of the strain rate and deformation temperature. Three-dimensional (3D) processing maps were developed based on dynamic material model theory using the stress flow data to identify the instability and optimization regions of the hot processing parameters. The results show that the flow stress decreases with increasing deformation temperature and decreasing strain rate, and the softening mechanisms are different under different deformation conditions. The apparent activation energies in the (α{sub 2} + β/B2 + O) and (α{sub 2} + B2) phase regions are calculated to be 865.177 and 590.661 kJ/mol, respectively, which suggests that the hot isostatic pressed (HIPed) Ti-22Al-25Nb alloy requires a high hot deformation activation energy. Combined with microstructural observations, the optimal processing domains are determined to be a temperature range of 1030-1060 °C and strain rate range of 0.01-0.1 s{sup −1} in the α{sub 2} + B2 phase region. Moreover, the results indicate that adiabatic shear bands and severe inhomogeneous deformation cause flow instability at lower temperatures (900-1005 °C) and higher strain rates (> 0.1 s{sup −1}).
The value of in-reservoir energy storage for flexible dispatch of geothermal power
Geothermal systems making use of advanced drilling and well stimulation techniques have the potential to provide tens to hundreds of gigawatts of clean electricity generation in the United States by 2050. With near-zero variable costs, geothermal plants have traditionally been envisioned as providing “baseload” power, generating at their maximum rated output at all times. However, as variable renewable energy sources (VREs) see greater deployment in energy markets, baseload power is becoming increasingly less competitive relative to flexible, dispatchable generation and energy storage. Herein we conduct an analysis of the potential for future geothermal plants to provide both of these services, taking advantage of the natural properties of confined, engineered geothermal reservoirs to store energy in the form of accumulated, pressurized geofluid and provide flexible load-following generation. We develop a linear optimization model based on multi-physics reservoir simulations that captures the transient pressure and flow behaviors within a confined, engineered geothermal reservoir. We then optimize the investment decisions and hourly operations of a power plant exploiting such a reservoir against a set of historical and modeled future electricity price series. Further, we find that operational flexibility and in-reservoir energy storage can significantly enhance the value of geothermal plants in markets with high VRE penetration, with energy value improvements of up to 60% relative to conventional baseload plants operating under identical conditions. Across a range of realistic subsurface and operational conditions, our modeling demonstrates that confined, engineered geothermal reservoirs can provide large and effectively free energy storage capacity, with round-trip storage efficiencies comparable to those of leading grid-scale energy storage technologies. Optimized operational strategies indicate that flexible geothermal plants can provide both short- and long-duration energy storage, prioritizing output during periods of high electricity prices. Sensitivity analysis assesses the variation in outcomes across a range of subsurface conditions and cost scenarios.
Evaluating performance of different generative adversarial networks for large-scale building power demand prediction
We report as an unsupervised-learning data-driven model, Generative Adversarial Networks (GANs) have recently attracted a lot of attention for various applications. There is potential to apply GANs for large-scale building power demand prediction, which is needed for power grid operation. However, there are many GAN variations and it is unclear which GAN is suitable for this application. To answer this question, this paper identifies five promising GANs (Original GAN, cGAN, SGAN, InfoGAN, and ACGAN) and evaluates their performance for predicting building power demand at a large scale. Physics-based building energy models are developed to generate training and reference data. A new evaluation indicator that combines accuracy and reproducibility is proposed to evaluate the performance of different GANs in predicting building power demand. The results show that SGAN and InfoGAN are not suitable because they cannot control the number of generated building samples for different building types. The prediction performance among the Original GAN, cGAN, and ACGAN can vary depending on training sample sizes and number of building types. If the training sample size is sufficiently large, Original GAN and cGAN can predict building power demand more accurately than ACGAN with the same number of samples. If training samples are limited, Original GAN provides better accuracy than cGAN and ACGAN. When the number of building types increase, the prediction accuracy increases for cGAN, decreases for ACGAN, and remains the same for Original GAN. As a result, cGAN and Original GAN are recommended for large-scale building power demand prediction.
A hybrid inventory policy with split delivery under regular and surge demand
This paper proposes a hybrid inventory policy with split delivery under regular demand and surge demand. The combination of regular demand and surge demand can be observed in many areas, such as healthcare inventory and humanitarian supply chain management. The arrival rate for regular demand is typically higher than the arrival rate of surge demand; whereas, the volume of a regular demand is typically lower than the volume of a surge demand. This paper proposes an inventory management model that considers both emergency and regular replenishment corresponding to both demand patterns. The equilibrium equations developed for this model are based on the level crossing theory. This paper finally presents a search-based heuristics that can be used to identify near optimal inventory management policies. Numerical results show that proposed hybrid inventory policy with split delivery outperforms similar hybrid inventory policy without split delivery.
Corrosion of Al-Fe self-pierce riveting joints with multiphysics-based modeling and experiments
Self-piercing riveting (SPR) is an extensively used joining technique to assemble dissimilar materials. However, this joining of the dissimilar materials can generate galvanic/crevice corrosion, which can drastically impact the mechanical properties and the service life of the joint. In this study, a multiphysics-based hybrid modeling approach is developed for the galvanic corrosion of the Al-Fe SPR joints, which can consider the corrosion initiation and corrosion evaluation jointly. Experimental studies are first performed to extract information regarding the corrosion initiation sites, corrosion evolution and overall corrosion induced material loss in SPR joints. The initiation information is then passed onto the multiphysics FE model thus making it a hybrid model. This model can help understand the influences of metal microstructure on the corrosion propagation while the morphology changes can also be analyzed. Using the developed hybrid modeling approach, thorough parametric studies can be performed to explore the coupled impacts of multiple corrosion factors on the corrosion behavior of the joints. The developed hybrid model is validated on the prediction of galvanic corrosion for the SPR joints by comparing with experimental observations.
In-air and pressurized water reactor environment fatigue experiments of 316 stainless steel to study the effect of environment on cyclic hardening
Argonne National Laboratory (ANL), under the sponsorship of Department of Energy’s Light Water Reactor Sustainability (LWRS) program, is trying to develop a mechanistic approach for more accurate life estimation of LWR components. In this context, ANL has conducted many fatigue experiments under different test and environment conditions on type 316 stainless steel (316SS) material which is widely used in the US reactors. Contrary to the conventional S~N curve based empirical fatigue life estimation approach, the aim of the present DOE sponsored work is to develop an understanding of the material ageing issues more mechanistically (e.g. time dependent hardening and softening) under different test and environmental conditions. Better mechanistic understanding will help develop computer-based advanced modeling tools to better extrapolate stress-strain evolution of reactor components under multi-axial stress states and hence help predict their fatigue life more accurately. In this paper (part-I) the fatigue experiments under different test and environment conditions and related stress-strain results for 316 SS are discussed. In a second paper (part-II) the related evolutionary cyclic plasticity material modeling techniques and results are discussed.
Local stress within a granular molecular solvent matrix, a mechanism for individual ion hydration
The activity of water is important in many natural systems and directed processes. To better understand water activity, the hydration of individual ions is approximated for a recently developed speciation-based solution model. Here a matrix of values for hydration of complete salts (anion and cation) was used as source data to calculate individual ion hydration values. Some electrolytes were excluded from calculations of individual ion hydration due to the prevalence of ion pairing and resulting liberation of water upon ion pairing. Evaluation of the periodic trends emerging from individual ion hydration values suggests that ionic volume, relative to the volume of molecular water, dictates both modeled hydration requirements (related to vapor-liquid-equilibrium-derived activity) as well as properties historically described by hydrodynamic radius (ion diffusion and solution viscosity). When water is conceptualized as a granular three-dimensional matrix, solutes may function as substitutional defects (dilation or constriction) inducing local compressive or tensile stress. These results have implications on the mechanism of the Hofmeister series and series reversals.
Prediction and evaluation of route dependent dosimetry of BPA in rats at different life stages using a physiologically based pharmacokinetic model
Bisphenol A (BPA) has received considerable attention throughout the last decade due to its widespread use in consumer products. For the first time a physiologically based pharmacokinetic (PBPK) model was developed in neonatal and adult rats to quantitatively evaluate age-dependent pharmacokinetics of BPA and its phase II metabolites. The PBPK model was calibrated in adult rats using studies on BPA metabolism and excretion in the liver and gastrointestinal tract, and pharmacokinetic data with BPA in adult rats. For immature rats the hepatic and gastrointestinal metabolism of BPA was inferred from studies on the maturation of phase II enzymes coupled with serum time course data in pups. The calibrated model predicted the measured serum concentrations of BPA and BPA conjugates after administration of 100 μg/kg of d6-BPA in adult rats (oral gavage and intravenous administration) and postnatal days 3, 10, and 21 pups (oral gavage). The observed age-dependent BPA serum concentrations were partially attributed to the immature metabolic capacity of pups. A comparison of the dosimetry of BPA across immature rats and monkeys suggests that dose adjustments would be necessary to extrapolate toxicity studies from neonatal rats to infant humans. - Highlights: • A PBPK model predicts the kinetics of bisphenol A (BPA) in young and adult rats. • BPA metabolism within enterocytes is required for fitting of oral BPA kinetic data. • BPA dosimetry in young rats is different than adult rats and young monkeys.
Developing a Predictive Solubility Model for Monomeric and Oligomeric Cyclopropenium-Based Flow Battery Catholytes
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Cathode porosity is a missing key parameter to optimize lithium-sulfur battery energy density
While high sulfur loading has been pursued as a key parameter to build realistic high-energy lithium-sulfur batteries, less attention has been paid to the cathode porosity, which is much higher in sulfur/carbon composite cathodes than in traditional lithium-ion battery electrodes. For high-energy lithium-sulfur batteries, a dense electrode with low porosity is desired to minimize electrolyte intake, parasitic weight, and cost. Here we report the profound impact on the discharge polarization, reversible capacity, and cell cycling life of lithium-sulfur batteries by decreasing cathode porosities from 70 to 40%. According to the developed mechanism-based analytical model, we demonstrate that sulfur utilization is limited by the solubility of lithium-polysulfides and further conversion from lithium-polysulfides to Li2S is limited by the electronically accessible surface area of the carbon matrix. Finally, we predict an optimized cathode porosity to maximize the cell level volumetric energy density without sacrificing the sulfur utilization.
Regulation of RNA-binding proteins affinity to export receptors enables the nuclear basket proteins to distinguish and retain aberrant mRNAs
Export of messenger ribonucleic acids (mRNAs) into the cytoplasm is a fundamental step in gene regulation processes, which is meticulously quality controlled by highly efficient mechanisms in eukaryotic cells. Yet, it remains unclear how the aberrant mRNAs are recognized and retained inside the nucleus. Using a new modelling approach for complex systems, namely the agent-based modelling (ABM) approach, we develop a minimal model of the mRNA quality control (QC) mechanism. Our results demonstrate that regulation of the affinity of RNA-binding proteins (RBPs) to export receptors along with the weak interaction between the nuclear basket protein (Mlp1 or Tpr) and RBPs are the minimum requirements to distinguish and retain aberrant mRNAs. Our results show that the affinity between Tpr and RBPs is optimized to maximize the retention of aberrant mRNAs. In addition, we demonstrate how the length of mRNA affects the QC process. Since longer mRNAs spend more time in the nuclear basket to form a compact conformation and initiate their export, nuclear basket proteins could more easily capture and retain them inside the nucleus.