Combinations Of Parameter Values Research Articles

Optimization of hybrid pulse power characterization profile for equivalent circuit model parameter identification of Li-ion battery based on Taguchi method

The modeling of Li-ion batteries is crucial for their stable and efficient operation. The equivalent circuit model (ECM) is the most widely used battery model, for which parameter identification usually involves the hybrid pulse power characteristic (HPPC) test. However, since the HPPC test was designed to determine dynamic power capability of batteries, an investigation of how HPPC parameters affect ECM parameter identification would be reasonable. In this study, the Taguchi method was employed to investigate the effect of four HPPC parameters (positive and negative pulse height, length of pulse and length of relaxation) on ECM's performance under various application conditions (dynamic test, non-dynamic test and quasi-static test). The results show that all four parameters have an effect on ECM performance. For batteries operated only with the predictable charge and discharge patterns, the optimal combination of HPPC parameters values was determined based on Taguchi experiments. If batteries face unpredictable application scenarios, lower positive pulse heights, higher negative pulse heights, shorter pulse lengths and longer relaxation lengths within a certain range should be considered. Compared to established standard parameter values, this makes it possible to build a more accurate general model.

Fast convergence strategy for ambiguous inverse problems based on hierarchical regularization

Mathematical modelling allows to predict the state of a physical system based on a set of parameters. The corresponding inverse problem, i.e., when parameter values are inferred based on constraints imposed on the system state, is commonly ill-posed. Here, we consider consistent underdetermined problems, where infinitely many combinations of possible parameter values achieve a state that accurately matches all constraints. For such problems a unique regularization strategy can be used, where the ambiguity of the solution space is reduced by sequentially incorporating additional constraints. As this regularization approach always yields a consistent problem by construction, we now show that the Gauss-Newton method is the prime choice to achieve fast convergence. Moreover, using the adjoint method allows to efficiently compute the required Jacobian matrix, which makes the overall solution approach ideally suited for large test cases with many constraints and unknown parameters. We present results for several illustrative examples related to network flow, for which we visualize the solution manifolds of the regularized inverse problem. This provides an intuitive explanation of the hierarchical approach to reduce the ambiguity of the solution. Furthermore, we confirm that combining our regularization strategy with a Gauss-Newton method results in an order of magnitude lower computational cost compared to a gradient descent algorithm. This highlights the potential of our regularization strategy in combination with the Gauss-Newton method, which likely is beneficial for many comparable inverse problems, especially with large parameter spaces.

Profiling and Pairing Catchments and Hydrological Models With Latent Factor Model

AbstractHydrological models are simplified representations of catchments. Finding an appropriate model instance (i.e., a combination of structure and parameter values) for a given catchment is a fundamental problem in hydrological modeling. This study develops a latent factor‐based machine learning method to learn to predict the pairwise association between the behavioral characteristics under climate forcing of a catchment and a hydrological model instance. The underlying assumption is that the behaviors of catchments and model instances can be sufficiently well characterized by sets of latent factors that allow accurate prediction of the associations between observed and modeled behaviors. In this study, the proposed method was applied to learn the latent factor vectors (which are numerical values) of 2,649 catchments worldwide and over 1,000,000 model instances from their pairwise associations (i.e., similarities between observed and simulated catchment behaviors). The model instances were created using 36 conceptual model structures or modeling software, and the their parameter values were randomly sampled. The results show that the learned latent factor vectors are able to accurately characterize the associations between catchments and hydrological model instances. The latent factor vector of a “new” catchment (not seen by the training procedures) can be estimated by running a small number of randomly sampled model instances, from which well‐fitting model instances created using different conceptual models can be recommended. The recommended instances can be of comparable quality to those derived using a calibration algorithm, and the method is useful for catchments outside the regions from which the latent factor vectors of the model instances were derived.

Применение генетических алгоритмов при решении задач планирования перевозочного процесса городской рельсовой транспортной системы

The article investigates the possibility to apply genetic algorithms at automation of planned schedules compilation for subway passenger train traffic. Research main goal – to improve automated system of planned schedule compilation for passenger trains for to provide for processes evenness at the use of various resources and consideration of existing limitations. Necessary definitions of resources and limitations on conditions of genetic algorithm usage are narrowed down to interconnected and unified tables. On probability approach basis, the influence of various combinations of genetic algorithm parameter values on population compositions in the process of search for effective results of transportation process planning for urban rail transport system has been studied. For investigation needs, computer software has been performed on high-level languages C# and Python. Genetic algorithm adaptation to the solution of the task for compilation automation of planned schedule of subway passenger train traffic has been made and there has been shown the algorithm applicability to automation of the complex of interconnected tasks for the transportation process planning: electric rolling stock turnout schedule compilation and locomotive team work schedule. There have been calculated probability values to get favorable outcome – the presence of all possible allele values at various combinations of the values of primary population size and allele needed quantity in the results of train traffic planned schedules.

Numerical Simulation of Flow Over Airfoil and Its Optimization

Airfoils are one of the most important factors in determining an airplane’s ability to fly. As such, their optimization holds important ramifications for the continual need to improve airplane performance. In this paper, the optimization of airfoil design is explored through numerical computational fluid dynamics. This paper first investigates the effects of Angle of Attack on airfoil lift/drag ratios, which is shown to have a significant influence on the performance of airfoils. Then, the effects from thickness are explored, and it is found that the influence of thickness is highly dependent on the specific shape of airfoil. In addition, in order to further improve airfoil aerodynamic performance, a type of modified airfoil with an additional tip is developed and simulated. The results indicate that airfoils with additional tips can perform better, but only with the proper combination of tip parameter values. For example, the modified airfoil with a tip of 0.03 L length at a 25° tip angle bested that same airfoil without an additional tip. Finally, it is found that the Backpropagation machine-learning algorithm, when trained with prior simulation data, can quickly predict lift and drag coefficients of airfoils. The results in this paper could facilitate the further optimization of airfoil design.

Dycdtools: an R Package for Assisting Calibration and Visualising Outputs of an Aquatic Ecosystem Model

The high complexity of aquatic ecosystem models (AEMs) necessitates a large number of parameters that need calibration, and visualisation of their multifaceted and multi-layered simulation results is necessary for effective communication. Here we present an R package "dycdtools" that contains calibration and post-processing tools for a widely applied aquatic ecosystem model (DYRESM-CAEDYM). The calibration assistant function within the package automatically tests a large number of combinations of parameter values and returns corresponding values for goodness-of-fit, allowing users to narrow parameter ranges or optimise parameter values. The post-processing functions enable users to visualise modelling outputs in four ways: as contours, profiles, time series, and scatterplots. The "dycdtools" package is the first open-source calibration and post-processing tool for DYRESM-CAEDYM, and can also be adjusted for other AEMs with a similar structure. This package is useful to reduce the calibration burden for users and to effectively communicate model results with a broader community.

A fuzzy bayesian network based method for CO2 leakage risk evaluation during geological sequestration process

CO2 leakage risk is a critical threat to a safe and long-term storage of CO2 in the underground space. However, there is a lack of effective risk assessment methods for CO2 leakage in the storage system. In this paper, a novel method is constructed in which Fuzzy Set Theory (FST) and Bayesian Network (BN) are incorporated to evaluate CO2 leakage risk. Firstly, a Bayesian Directed Acyclic Graph, or BDAG, is constructed after analysis of the storage system failure mechanism including wellbore integrity, caprock integrity and the entire storage system, induced by different factors. Secondly, the priori probability and Conditional Probability Table, or CPT, of nodes in BN model are obtained by defuzzification of the fuzzy number given by experienced experts. Finally, the probability of CO2 leakage and the potential sources of damage is analyzed based on the posteriori probability of the Fuzzy Bayesian Network model, or FBN. A case study shows that under the condition of different combinations of parameter values of root nodes, the probability of leakage of the entire storage system (leaf node) ranges from 13% to 74%, and the leakage probability through wellbore is higher than that through caprock, which are both intermediate nodes. A sensitivity analysis is also conducted to study the influence of these variables on CO2 leakage probability. The ranking of CO2 leakage risk factors from strong to weak is cementing quality, cement corrosion, Net-to-Gross ratio, and caprock thickness, respectively. Moreover, the FBN model can also help to look for the risk sources of the CO2 storage system by posteriori probability calculation. In our case, the CO2 leakage risk source is mainly the wellbore with poor cementing quality, which should be paid more attention during operation.

Inverse problem to estimate lips parameters values of outward-striking trumpet model for successive playing registers.

The objective of this work is to estimate by inverse problem lip parameters values of trumpet model so that the oscillation thresholds for successive playing registers occur for the same blowing pressure as the one measured on several trumpet players. The lips vibration is modeled through an oscillator including unknown parameters such as resonance frequency, quality factor, surface mass, stiffness, and opening at rest of the lips. The oscillation threshold is calculated through linear stability analysis of the outward-striking model including the nonlinear coupling with the bore of the trumpet. It appears that many combinations of parameter values are suitable to obtain the same blowing pressure at threshold as in the experiments. According to the analysis of the possible parameter values, some hypotheses are formulated about the playing strategies used by the trumpeter to select the different registers of the instrument. In addition to the resonance frequency of the lips, controlling the lips opening at rest appears to be a viable strategy to match experimental oscillation thresholds in terms of blowing pressure. Numerical values for the lips parameters are given and through sound synthesis, allow the successive registers of the trumpet to be played.

DNA Strand Breaks and Denaturation as Probes of Chemical Reactivity versus Thermal Effects of Atmospheric Pressure Plasma Jets.

An atmospheric pressure plasma jet (APPJ) is being advanced as an alternative radiation type that offers excellent efficacy in an array of medical applications against specific biological targets such as DNA. This work explores the possibility of implementing DNA and its damage as a probe for specific plasma diagnostics such as reactive plasma species formation and transient local heating. We analyzed both APPJ characteristics based on the detection of plasma-induced strand breaks and DNA denaturation. Further, we implemented a machine learning model based on artificial neural networks to predict the type and extent of DNA damage for a given combination of APPJ parameter values. This methodology is an important step toward deciphering and explaining the potential adverse effects of APPJ on biological samples of any prospective interest in medicine.

Optimization of Hydrolysis in Ethanol Production from Bamboo

This research involved optimizing acid hydrolysis in the development of ethanol, a promising alternative energy source for restricted crude oil, from lignocellulosic materials (bamboo). The conversion of bamboo to ethanol can mainly be accomplished through three process steps: pretreatment of bamboo wood for the removal of lignin and hemicellulose, acid hydrolysis of pretreated bamboo for the conversion of cellulose into sugar reduction (glucose) and fermentation of sugars into ethanol using anaerobic Saccharomyces cerevisiae. The effects of parameters (factors) in the hydrolysis step were investigated and the optimum combination of parameters values (temperature, time and acid concentration) was set by experimentation. Factorial design of three-factors-at-two-level with a replica of two (23 = 8, 8•2 = 16) was applied to the hydrolysis step to investigate the effect of hydrolysis parameters on the response variable (ethanol yield) using Design-Expert® 7 software.

Size, shape and orientation matter: fast and semi-automatic measurement of grain geometries from 3D point clouds

Abstract. The grain-scale morphology and size distribution of sediments are important factors controlling the erosion efficiency, sediment transport and the aquatic ecosystem quality. In turn, characterizing the spatial evolution of grain size and shape can help understand the dynamics of erosion and sediment transport in coastal, hillslope and fluvial environments. However, the size distribution of sediments is generally assessed using insufficiently representative field measurements, and determining the grain-scale shape of sediments remains a real challenge in geomorphology. Here we determine the size distribution and grain-scale shape of sediments located in coastal and river environments with a new methodology based on the segmentation and geometric fitting of 3D point clouds. Point cloud segmentation of individual grains is performed using a watershed algorithm applied here to 3D point clouds. Once the grains are segmented into several sub-clouds, each grain-scale morphology is determined by fitting a 3D geometrical model applied to each sub-cloud. If different geometrical models can be tested, this study focuses mostly on ellipsoids to describe the geometry of grains. G3Point is a semi-automatic approach that requires a trial-and-error approach to determine the best combination of parameter values. Validation of the results is performed either by comparing the obtained size distribution to independent measurements (e.g., hand measurements) or by visually inspecting the quality of the segmented grains. The main benefits of this semi-automatic and non-destructive method are that it provides access to (1) an un-biased estimate of surface grain-size distribution on a large range of scales, from centimeters to meters; (2) a very large number of data, mostly limited by the number of grains in the point cloud data set; (3) the 3D morphology of grains, in turn allowing the development of new metrics that characterize the size and shape of grains; and (4) the in situ orientation and organization of grains. The main limit of this method is that it is only able to detect grains with a characteristic size significantly greater than the resolution of the point cloud.

Data-driven risk analysis of unmanned aircraft system operations considering spatiotemporal characteristics of population distribution

One of the challenges of Unmanned Aircraft System (UAS) operations is to operate an unmanned aircraft with minimal risk to people on the ground. The purpose of this study is to define and measure such risks as population risk, by incorporating spatiotemporal changes in population density. Unlike previous studies, we use high-resolution de facto population data instead of residential population data to reflect the spatiotemporal characteristics of population distribution. Furthermore, we analyze the impact of mitigation measures based on population risk in the context of airspace management. We set a restricted airspace by using population risk and an acceptable level of safety. Scenario analysis of the study area in Seoul, South Korea provides a richer set of findings regarding spatiotemporal differences in restricted airspace. During the daytime, there are many restricted airspaces around commercial areas, but few around residential areas. Additionally, we observe the difference between restricting airspace based on population risk derived from the residential population and from the de facto population. These findings confirm the importance of accurately considering population density when assessing and mitigating the population risk associated with UAS operations. Sensitivity analysis also reveals the need to precisely estimate population density when estimating population risk with combinations of multiple parameter values. The proposed approach captures spatiotemporal characteristics of population distribution when assessing the population risk associated with UAS.

Uni-variate and bi-variate Inverted Exponential Teissier distribution in Bayesian and non-Bayesian framework to model stochastic dynamic variation of climate data.

This article provides a new Inverted Exponential Teissier (IET) distribution to model an extreme value data set and explain temporal dependence in environmental statistics employing bi-variate probability distribution. We deduce its various statistical properties, including descriptive statistics, characterization, and different measurements of reliability. The model parameters are estimated using Bayesian and non-Bayesian frameworks. For exploring the dependency structures between two geographical Random Variables (RV), we extend the IET to bi-variate IET (BIET) distribution. We introduce a novel time series forecasting algorithm based upon copula assuming stationarity of the data set. We validate the proposed method using extensive simulation studies with different possible combinations of parameter values. This method is applied to the seasonal rainfall data of Kerala from 1901 to 2017. We estimate the monsoon rainfall using median regression derived from BIET, where summer rainfall data is used as an important covariate. We found the Mean Absolute Percentage Error (MAPE) is on the test data set.

Can the Kuznetsov Model Replicate and Predict Cancer Growth in Humans?

Several mathematical models to predict tumor growth over time have been developed in the last decades. A central aspect of such models is the interaction of tumor cells with immune effector cells. The Kuznetsov model (Kuznetsov et al. in Bull Math Biol 56(2):295–321, 1994) is the most prominent of these models and has been used as a basis for many other related models and theoretical studies. However, none of these models have been validated with large-scale real-world data of human patients treated with cancer immunotherapy. In addition, parameter estimation of these models remains a major bottleneck on the way to model-based and data-driven medical treatment. In this study, we quantitatively fit Kuznetsov’s model to a large dataset of 1472 patients, of which 210 patients have more than six data points, by estimating the model parameters of each patient individually. We also conduct a global practical identifiability analysis for the estimated parameters. We thus demonstrate that several combinations of parameter values could lead to accurate data fitting. This opens the potential for global parameter estimation of the model, in which the values of all or some parameters are fixed for all patients. Furthermore, by omitting the last two or three data points, we show that the model can be extrapolated and predict future tumor dynamics. This paves the way for a more clinically relevant application of mathematical tumor modeling, in which the treatment strategy could be adjusted in advance according to the model’s future predictions.

Automatic diagnosis of newly emerged heart failure from serial electrocardiography by repeated structuring & learning procedure

Heart failure (HF) diagnosis, typically visually performed by serial electrocardiography, may be supported by machine-learning approaches. Repeated structuring & learning procedure (RS&LP) is a constructive algorithm able to automatically create artificial neural networks (ANN); it relies on three parameters, namely maximal number of hidden layers (MNL), initializations (MNI) and confirmations (MNC), arbitrarily set by the user. The aim of this study is to evaluate RS&LP robustness to varying values of parameters and to identify an optimized combination of parameter values for HF diagnosis. To this aim, the Leiden University Medical Center HF database was used. The database is constituted by 129 serial ECG pairs acquired in patients who experienced myocardial infarction; 48 patients developed HF at follow-up (cases), while 81 remained clinically stable (controls). Overall, 15 ANNs were created by considering 13 serial ECG features as inputs (extracted from each serial ECG pair), 2 classes as outputs (cases/controls), and varying values of MNL (1, 2, 3, 4 and 10), MNI (50, 250, 500, 1000 and 1500) and MNC (2, 5, 10, 20 and 50). The area under the curve (AUC) of the receiver operating characteristic did not significantly vary with varying parameter values (P ≥ 0.09). The optimized combination of parameter values, identified as the one showing the highest AUC, was obtained for MNL = 3, MNI = 500 and MNC = 50 (AUC = 86 %; ANN structure: 3 hidden layers of 14, 14 and 13 neurons, respectively). Thus, RS&LP is robust, and the optimized ANN represents a potentially useful clinical tool for a reliable automatic HF diagnosis.

Utility of constraints reflecting system stability on analyses for biological models.

Simulating complex biological models consisting of multiple ordinary differential equations can aid in the prediction of the pharmacological/biological responses; however, they are often hampered by the availability of reliable kinetic parameters. In the present study, we aimed to discover the properties of behaviors without determining an optimal combination of kinetic parameter values (parameter set). The key idea was to collect as many parameter sets as possible. Given that many systems are biologically stable and resilient (BSR), we focused on the dynamics around the steady state and formulated objective functions for BSR by partial linear approximation of the focused region. Using the objective functions and modified global cluster Newton method, we developed an algorithm for a thorough exploration of the allowable parameter space for biological systems (TEAPS). We first applied TEAPS to the NF-κB signaling model. This system shows a damped oscillation after stimulation and seems to fit the BSR constraint. By applying TEAPS, we found several directions in parameter space which stringently determines the BSR property. In such directions, the experimentally fitted parameter values were included in the range of the obtained parameter sets. The arachidonic acid metabolic pathway model was used as a model related to pharmacological responses. The pharmacological effects of nonsteroidal anti-inflammatory drugs were simulated using the parameter sets obtained by TEAPS. The structural properties of the system were partly extracted by analyzing the distribution of the obtained parameter sets. In addition, the simulations showed inter-drug differences in prostacyclin to thromboxane A2 ratio such that aspirin treatment tends to increase the ratio, while rofecoxib treatment tends to decrease it. These trends are comparable to the clinical observations. These results on real biological models suggest that the parameter sets satisfying the BSR condition can help in finding biologically plausible parameter sets and understanding the properties of biological systems.

A discrete element method investigation within vertical stirred milling: Changing the grinding media restitution and sliding friction coefficients

The paper investigates the effects of changing the particle–particle restitution and sliding friction coefficients of grinding media within a vertical stirred mill using the discrete element method (DEM). A 190 mm diameter attritor mill containing 3491 particles was simulated to explore combinations of parameter values, across five rotational velocities. The results show variation over the parameter space, becoming significant if the sliding friction falls below 0.1, as single-body motion forms from the reduced resistance. Above this, forces are sensitive to changes in both restitution and friction, particularly the latter. Higher rotational velocities also lead to increased effectiveness. However, it also comes at a cost of increased power draw. The results suggest that a compromise would need to be made between effectiveness and efficiency, but could also form a basis for making simplifying assumptions in more complex scenarios. A template version of the simulation can be found at https://github.com/darhyme147/ligggghts_stirred_mill_template . • We simulate the effect of changing stirred grinding media friction and restitution • Friction has a greater influence on grinding than the restitution coefficient • Frictionless systems form single-body media motion, reducing grinding effectiveness • Results show a potential efficacy reduction if media become smoother through wear.

How Do We Optimally Sample Model Grids of Exoplanet Spectra?

The construction and implementation of atmospheric model grids is a popular tool in exoplanet characterization. These typically vary a number of parameters linearly, containing one model for every combination of parameter values. Here we investigate alternative methods of sampling parameters, including random sampling and Latin hypercube (LH) sampling, and how these compare to linearly sampled grids. We use a random forest to analyze the performance of these grids for two different models, as well as investigate the information content of the particular model grid from Goyal et al. (2019). We also use nested sampling to implement mock atmospheric retrievals on simulated James Webb Space Telescope transmission spectra by interpolating on linearly sampled model grids. Our results show that random or LH sampling outperforms linear sampling in parameter predictability for our higher-dimensional models, requiring fewer models in the grid, and thus allowing for more computationally intensive forward models to be used. We also found that using a traditional retrieval with interpolation on a linear grid can produce biased posterior distributions, especially for parameters with nonlinear effects on the spectrum. In particular, we advise caution when performing linear interpolation on the C/O ratio, cloud properties, and metallicity. Finally, we found that the information content analysis of the grid from Goyal et al. (2019) was able to highlight key areas of the spectra where the presence or absence of certain molecules can be detected, providing good indicators for parameters such as temperature and C/O ratio.

An in-silico bone drilling protocol to control thrust forces using finite element analysis coupled with the constitutive models

In orthopaedic surgeries, drilling through bone is most widely used to fix the plates and implants. The uncontrolled large thrust forces generated during bone drilling cause micro-crack, and fragmentation around the local host bone which further loosen the implant and fixation. Owing to the experimental limitation and the risk of infection in handling the bone specimens, conducting experiments with all possible combinations of parameter values and selecting the suitable combination is largely limited. So in this paper, finite element analysis coupled with the constitutive models was used to early predict the thrust forces generated in the surgical bone drilling process. The constitutive models developed in the earlier study, namely, Johnson–Cook, Cowper–Symonds, and Johnson–Cook combined with Cowper–Symonds that consider the strain rate dependence of plastic curve were used for the simulations. Results revealed that the trends of the thrust force values predicted from the constitutive models matched well with the experiments. However, the study showed that the Johnson–Cook model combined with the Cowper–Symonds model predicted the thrust forces with a maximum error of only 9.51% followed by the Johnson–Cook model and Cowper–Symonds model with 15.83% and 21.89%, respectively. The outcomes of the study can be used to predict the thrust forces generated in the bone drilling process, and thus, suitable parameters values can be selected to avoid the mechanical damages around the bone drilling site. The outcomes can also be used to plan and rehearse the in-silico bone drilling trials with any combinations of parameter values before performing the actual surgery.

Nonchaotic Behavior and Transition to Chaos in Lorenz-like Systems Having Invariant Algebraic Surfaces

The famous and well-studied Lorenz system is considered a paradigm for chaotic behavior in three-dimensional continuous differential systems. After the appearance of such a system in 1963, several Lorenz-like chaotic systems have been proposed and studied in the related literature, as Rössler system, Chen-Ueta system, Rabinovich system, Rikitake system, among others. However, these systems are parameter dependent and are chaotic only for suitable combinations of parameter values. This raises the question of when such systems are not chaotic, which can be seen as a dual problem regarding chaotic systems. In this paper, we give sufficient algebraic conditions for a generalized class of Lorenz-like systems to be nonchaotic. Using the general results obtained, we give some examples of nonchaotic behavior of some classical ``chaotic'' Lorenz-like systems, including the Lorenz system itself. The nonchaotic differential systems presented here have invariant algebraic surfaces, which contain the stable (or unstable) invariant manifolds of their equilibrium points. We show that, in some cases, the deformation of these invariant manifolds through the destruction of the invariant algebraic surfaces, by perturbing the parameter values, can reorganize the global structure of the phase space, leading to a transition from nonchaotic to chaotic behavior of such differential systems.