Modeling Of Physical Processes Research Articles

Comparative Investigation of the Anammox Process Using Free-Floating Carriers of Activated Sludge-Attached Biocenosis

For ammonium removal from wastewater, anammox technologies are among the most efficient and rapidly developing ones. Due to the low growth rate of anammox bacteria and their sensitivity to various inhibitors, technologies using attached biocenosis carriers (ABCs) provide for reliable operation. The goal of the present work was to investigate a new ABC type, ETEK biochips based on a nonwoven fibrous material. The work involved the techniques of materials science (design of a new ABC type) and physical modeling of the anammox process (in a laboratory bioreactor), as well as electron microscopy and molecular profiling of activated sludge communities. Comparison of the ETEK biochips with the ABCs of foamed polyethylene BF33 and Mutag revealed more rapid accumulation (5-fold) of the activated sludge biomass on ETEK biochips upon reactor launching, as well as comparable buoyancy and reactor productivity regarding N removal. The specific rate of nitrogen removal obtained with ETEK biochips considerably exceeded that for foamed polyethylene with a filler: 1.5–3 times higher per chip and 1.5 times higher per activated sludge biomass unit. The studied ABC shared the same issue of floating to the surface due to the active formation of gas (N2). The algorithm for calculating the downward flows in bioreactors with rapidly surfacing ABC is proposed, and a new hydrodynamic type of a bioreactor (with hybrid hydrodynamics) is described, a moving bed–sequencing batch reactor (MB-SBR).

The Brewer–Dobson circulation in the JRA‐3Q reanalysis and the impact of changes in model physical processes

AbstractThis study evaluates the climatology and trends of the Brewer–Dobson circulation (BDC) in the Japanese Reanalysis for Three Quarters of a Century (JRA‐3Q) reanalysis compared with those in the Japanese 55‐year reanalysis (JRA‐55) and ERA5 reanalyses. The spatial structure of the BDC shows good agreement among the reanalyses. However, zonal mean tropical upwelling near the lower stratosphere in JRA‐3Q is approximately 80% of that in JRA‐55 throughout the reanalysis period. The long‐term trend of BDC intensity for the period 1948–2022 showed a slight increase in JRA‐3Q and in JRA‐55, whereas that in ERA5 decreased. To investigate the cause of the difference in tropical upwelling between JRA‐3Q and JRA‐55, data assimilation sensitivity experiments were conducted by modifying the forecast model used in the data assimilation system. These results indicate that the difference in BDC intensity between JRA‐3Q and JRA‐55 can be primarily attributed to diabatic heating in the tropical troposphere produced in the forecast model. However, the difference between JRA‐3Q and ERA5 cannot be accounted for by diabatic heating in the forecast model. It is also found that the implementation of the non‐orographic gravity wave drag parameterisation scheme in the JRA forecast model is not the reason for the weaker BDC in JRA‐3Q.

Advancing Proton Therapy: A Review of Geant4 Simulation for Enhanced Planning and Optimization in Hadron Therapy.

Proton therapy is a cancer treatment method that uses high-energy proton beams to target and destroy cancer cells. In recent years, the use of proton therapy in cancer treatment has increased due to its advantages over traditional radiation methods, such as higher accuracy and reduced damage to healthy tissues. For accurate planning and delivery of proton therapy, advanced software tools are needed to model and simulate the interaction between the proton beam and the patient's body. One of these tools is the Monte Carlo simulation software called Geant4, which provides accurate modeling of physical processes during radiation therapy. The purpose of this study is to investigate the effectiveness of the Geant4 toolbox in proton therapy in the conducted research. This review article searched for published articles between 2002 and 2023 in reputable international databases including Scopus, PubMed, Scholar, Google Web of Science, and ScienceDirect. Geant4 simulations are reliable and accurate and can be used to optimize and evaluate the performance of proton therapy systems. Obtaining some data from experiments carried out in the real world is very effective. This makes it easy to know how close the values obtained from simulations are to the behavior of ions in reality.

Cauchy problem for modeling of unsteady mass transfer processes in an unbounded plate by the integral laplace transform method

The article outlines the formulation and solution of the problem of physical and mathematical modeling of non-stationary processes of mass transfer of chemical components of the structure of reinforced concrete enclosing structures under the influence of factors (chemical, biological) of the operating environment. The theory of operational calculus (integral transformations) is used as a mathematical apparatus for jointly solving Cauchy and Laplace problems. To solve the problem and study the processes considered in the article, a dimensionless plate with a dimensionless concentration of aggressive components on its surface was chosen as an idealized model of the enclosing structure. Carbon dioxide, dissolved in the liquid and penetrating with it into the material of the structure through pores and microcracks, was chosen as an aggressive component acting on the enclosing structure. The final solutions of the considered boundary value problems are presented for the case of constant values of the kinetic coefficients of external and internal mass transfer. The results presented in this work can be used in the development of software for predicting the strength characteristics of enclosing structures operating in aggressive environments. Thanks to the obtained solutions to the problems of non-stationary mass transfer processes, using the example of the consequences of carbon dioxide corrosion, it is possible to consider the time period of the life cycle of buildings and structures, the timing of repair work with greater accuracy.

Discovering an interpretable mathematical expression for a full wind-turbine wake with artificial intelligence enhanced symbolic regression

The rapid expansion of wind power worldwide underscores the critical significance of engineering-focused analytical wake models in both the design and operation of wind farms. These theoretically derived analytical wake models have limited predictive capabilities, particularly in the near-wake region close to the turbine rotor, due to assumptions that do not hold. Knowledge discovery methods can bridge these gaps by extracting insights, adjusting for theoretical assumptions, and developing accurate models for physical processes. In this study, we introduce a genetic symbolic regression (SR) algorithm to discover an interpretable mathematical expression for the mean velocity deficit throughout the wake, a previously unavailable insight. By incorporating a double Gaussian distribution into the SR algorithm as domain knowledge and designing a hierarchical equation structure, the search space is reduced, thus efficiently finding a concise, physically informed, and robust wake model. The proposed mathematical expression (equation) can predict the wake velocity deficit at any location in the full-wake region with high precision and stability. The model's effectiveness and practicality are validated through experimental data and high-fidelity numerical simulations.

Service spacecraft for space debris removal

The problem of space debris removal from the near-Earth space is being studied in almost every country taking part in space exploration. This is due to the global threat of critical increase in the number of space debris objects predicted for the near future, in particular as a result of significant growth in demand for multi-satellite constellations for various purposes. To date, researchers have proposed a large number of different methods for space debris removal. The work presented is the development of one of the promising contactless methods of space debris removal by an ion beam. The results of modeling and optimization of physical processes in the ion source to be mounted on board a service spacecraft that is necessary for implementing the considered method of space debris removal are presented. Besides, the results of thermal modeling and thermal mapping of the ion source during its operation are presented also, and the calculation results of its output parameters are compared to experimental data, which verified the small divergence angles of the generated ion beam. To assess the prospects of using a system for contactless space debris transportation by an ion beam using the obtained data on the operating parameters of the ion source, the trajectory design and mission analysis were carried out, which revealed the feasibility of removing seven space debris objects out of the protected region in geostationary orbit by a single service spacecraft. Besides, the preliminary service spacecraft design is presented.

Analysis of The Influence of Models of Individual Physical Processes and Phenomena on the Calculation Time of the Source Term in Severe Accidents

Dependence of the CPU time needs for calculation of the source term during a severe accident at nuclear power plants with WWER reactors from the individual physical processes and phenomena used as part of the calculation tools similar to the SOСRAT/V3 severe accident code is investigated. This analysis allows revealing the most expensive models in terms of runtime. The simplification of these models can ensure the greatest acceleration of the calculation. The relevance of the task draws from the need to develop new or adapt existing calculation tools for assessing the intensity of radioactive emission sources for the tasks of emergency preparedness and response considering the specific requirements for the accuracy of numerical estimates and time to obtain them. The paper demonstrates the possibility of reducing CPU time without significant loss of accuracy of the numerical estimates by simplifying the spectrum of aerosols sizes. The efficiency of the proposed approach is demonstrated by the example of modeling the Phebus FPT1 experiment.

Systematic evaluation of generative machine learning capability to simulate distributions of observables at the large hadron collider

Monte Carlo simulations are a crucial component when analysing the Standard Model and New physics processes at the Large Hadron Collider. This paper aims to explore the performance of generative models for complementing the statistics of classical Monte Carlo simulations in the final stage of data analysis by generating additional synthetic data that follows the same kinematic distributions for a limited set of analysis-specific observables to a high precision. Several deep generative models are adapted for this task and their performance is systematically evaluated using a well-known benchmark sample containing the Higgs boson production beyond the Standard Model and the corresponding irreducible background. The paper evaluates the autoregressive models and normalizing flows and the applicability of these models using different model configurations is investigated. The best performing model is chosen for a further evaluation using a set of statistical procedures and a simplified physics analysis. By implementing and performing a series of statistical tests and evaluations we show that a machine-learning-based generative procedure can be used to generate synthetic data that matches the original samples closely enough and that it can therefore be incorporated in the final stage of a physics analysis with some given systematic uncertainty.

ФИЗИКАЛЫК ПРОЦЕССТЕРДИ КОМПЬЮТЕРДИК МОДЕЛДӨӨ

The article studies the processes of using modern pedagogical technology of computer modeling of physical processes in teaching the subject of physics and presents its main results/ In the experiment, the skills of activating students' knowledge in the physics profile were implemented in computer science lessons with the help of modern pedagogical technologies. As research methods, it was proposed to use computer programs capable of accurately and clearly modeling physical processes and phenomena. The results obtained are the conditions for obtaining visual dynamic illustrations of physical phenomena in nature using computer programs of its simplified model. The scientific novelty of the results obtained allows us to explain to students and improve their fundamental knowledge through modeling physical processes. The practical significance of the results obtained is that with the help of computer simulation it is possible to explain physical phenomena and processes, i.e. ranging from the magnetic field in space to the structure of a single atom. Practical recommendations - development of didactic conditions for interdisciplinary communication of special disciplines in the formation of students' professional competence in the direction of physics and mathematics education.

TPANet: A novel triple parallel attention network approach for remaining useful life prediction of lithium-ion batteries

Accurate remaining useful life (RUL) prediction for lithium-ion batteries (LIBs) is important for proper equipment operation. Among the numerous existing studies on battery research, data-driven approaches have gained significant attention because they obviate the need for complex chemical and physical modeling of battery processes. However, in most current data-driven methods, the sliding window size values are determined empirically, which affects both the time required for model prediction and the prediction accuracy. To address this issue, this paper proposes a two-stage prediction method for battery capacity aging trajectories. In the first stage, a false nearest neighbor (FNN) technique is utilized to infer the sliding window size of the battery, reducing the error associated with manually selecting the sliding window size. In the second stage, a novel triple parallel attention network (TPANet) based on convolutional neural networks (CNNs) and attention units is developed, which extracts the input battery capacity degradation features through a parallel mechanism. The introduction of attention units in each parallel branch allows the model to enhance the capture of capacity regeneration phenomena as well as long-term dependence on input data. The proposed approach is validated on two publicly available datasets as well as a battery developed by ourselves. Diverse simulation results demonstrate the ability of the proposed approach to accurately predict the RUL of LIBs in a short time with broad generalization capabilities.

Integrative spatiotemporal modeling of biomolecular processes: application to the assembly of the Nuclear Pore Complex.

Dynamic processes involving biomolecules are essential for the function of the cell. Here, we introduce an integrative method for computing models of these processes based on multiple heterogeneous sources of information, including time-resolved experimental data and physical models of dynamic processes. We first compute integrative structure models at fixed time points and then optimally select and connect these snapshots into a series of trajectories that optimize the likelihood of both the snapshots and transitions between them. The method is demonstrated by application to the assembly process of the human Nuclear Pore Complex in the context of the reforming nuclear envelope during mitotic cell division, based on live-cell correlated electron tomography, bulk fluorescence correlation spectroscopy-calibrated quantitative live imaging, and a structural model of the fully-assembled Nuclear Pore Complex. Modeling of the assembly process improves the model precision over static integrative structure modeling alone. The method is applicable to a wide range of time-dependent systems in cell biology, and is available to the broader scientific community through an implementation in the open source Integrative Modeling Platform software.

Tempered fractional Sobolev spaces

The fractional Laplacian operator Δs is the infinitesimal generator of the isotropic (2s)-stable Lévy process on Rn, which is the scaling limit of the Lévy flight with isotropic power law measure |x|−n−2s. However, their second and higher moments are divergent, leading to the difficulty in the modeling of practical physical processes. Replacing |x|−n−2s by the measure of an isotropic tempered power law with the tempering exponent λ (i.e., e−λ|x||x|−n−2s), the tempered fractional Laplacian operator (Δ+λ)s was introduced in [17] as the infinitesimal generator of the tempered Lévy process. In this paper, guided by the fractional Sobolev spaces Ws,p corresponding to the fractional Laplacian operator Δs, we deal with the tempered fractional Sobolev spaces Ws,λ,p associated with the tempered fractional Laplacian (Δ+λ)s. First, the definition of the tempered fractional Sobolev spaces Ws,λ,p is given via the Gagliardo approach, and some of their basic properties were studied. Subsequently, we focus on the Hilbert case Hs,λ based on the Fourier transform. In particular, we deal with its relation with the tempered fractional Sobolev space Ws,λ,2 and analyze their role in the trace theory, overcoming the challenges posed by the tempering exponent λ. Then we investigate the asymptotic behavior of λ→0+,s→1− and s→0+ that appear in the definition of the tempered fractional Laplacian operator (Δ+λ)s. Moreover, we show continuous and compact embeddings investigating the problem of the extension domains and the generalized Hölder regularity results. As an application of the tempered fractional Sobolev spaces, we prove that the process defined by the tempered Lévy process is a solution of some PDE with tempered fractional Lapalcian operator.

TorchClim v1.0: a deep-learning plugin for climate model physics

Abstract. Climate models are hindered by the need to conceptualize and then parameterize complex physical processes that are not explicitly numerically resolved and for which no rigorous theory exists. Machine learning and artificial intelligence methods (ML and AI) offer a promising paradigm that can augment or replace the traditional parameterized approach with models trained on empirical process data. We offer a flexible and efficient plugin, TorchClim, that facilitates the insertion of ML and AI physics surrogates into the climate model to create hybrid models. A reference implementation is presented for the Community Earth System Model (CESM), where moist physics and radiation parameterizations of the Community Atmospheric Model (CAM) are replaced with such a surrogate. We present a set of best-practice principles for doing this with minimal changes to the general circulation model (GCM), exposing the surrogate model as any other parameterization module, and discuss how to accommodate the requirements of physics surrogates such as the need to avoid unphysical values and supply information needed by other GCM components. We show that a deep-neural-network surrogate trained on data from CAM itself can produce a model that reproduces the climate and variability in the original model, although with some biases. The efficiency and flexibility of this approach open up new possibilities for using physics surrogates trained on offline data to improve climate model performance, better understand model physical processes, and flexibly incorporate new processes into climate models.

Influence of Rolling Resistance and Particle Size Distribution in the Simulation of Sand Infiltration into the Static Gravel Bed

Fine sediment infiltration and subsequent clogging in a gravel bed affect several fluvial, ecological, and biological processes, resulting in the degradation of the river ecosystem. Despite many experimental and a few numerical studies, the process is yet to be entirely understood. We employed a pure Lagrangian framework, called the Discrete Element Method (DEM), to numerically investigate the infiltration process. Special attention is given to tackling the issue of non-spherical and irregular particle shapes and particle size distributions (PSDs) in numerical simulations. Due to computational limitations, these aspects were either not considered or simplified in previous numerical studies. We implicitly included non-spherical and irregular shape effects through rolling resistance models, which do not cause excessive computational overhead. Our study shows that rolling resistance models greatly influence packing and fine sediment infiltration. However, they may also lead to unphysical particle behavior; thus, they should be carefully used in numerical simulations. Oversimplified PSDs do not mirror natural systems, and full PSDs pose computational challenges. Sufficient grain classes are needed to mimic the non-homogeneity and poly-dispersity found in natural fluvial sediments. The infiltrating characteristics of sand concerning PSD and shape effects are linked to size ratio D15,Gravel/D85,Sand, assuring physical and realistic modeling of the infiltration process.

Mathematical understanding (hermeneutic aspects of educational activities)

The article discusses the issues of mathematical understanding as an educational practice of identifying the algorithmic meanings of mathematical (mathematized) educational texts. The hermeneutic orientation towards following the “guiding thread of language” (G.-G. Gadamer), as it is shown, cannot be narrowed to the attitude towards natural languages inherent in humanitarian educational practices. The “common place” of mathematical understanding in educational practices is: some of created languages (of scientific communication) being consolidated around the mathematical installation are not reducible to its sign universality or educational generalizations. But it often turns out to be outside the field of view of practicing teachers, in a didactically abstract position. The article substantiates the possibility of rehabilitating the hermeneutic attitude in mathematical education in connection with its development and strengthening in understanding the prerequisites for the algorithmic solution of mathematical problems (the need for additional information, non-standard solution techniques, transfer of educational goal setting to the area of interdisciplinary relationships or life experience, etc.). The author shows and comments on the effectiveness of the hermeneutic approach in mathematics education using examples of solving problems in algebra, geometry and in constructing mathematical models of various physical processes.

Evaluation of influence of thermoplastic slurry flow conditions on heat transfer coefficient during beryllium ceramic formation

The article presents the results of calculation of the influence of thermoplastic slurry flow conditions on the total heat transfer coefficient when forming ceramic products. Methods of detailed description of casting processes and thermal calculations have been developed to ensure reliable correspondence of calculated data and experimental results implemented on the basis of calculational experiment. The modeling of physical processes occurring during the formation of products allows you to discover new opportunities for improving the quality of castings by allowing you to more closely track the change in the temperature-phase fields of the process and clearly present the solidification kinetics depending on the casting modes. The speed of heat removal from the casting during the solidification period determines the rate of movement of the slurry, along with the temperature field on which the width of the transition region depends. These factors have a direct influence on the formation of the structure of beryllium ceramics. The study of the heat exchange process in the formation of ceramic products depending on temperature, heat at phase transition is the main task, since they largely determine the technological and operational characteristics of beryllium ceramics. The design data allows to determine the optimal conditions of the ceramic casting process and to obtain a solidified product with a uniform structure at the outlet.

Process‐Based Atmosphere‐Hydrology‐Malaria Modeling: Performance for Spatio‐Temporal Malaria Transmission Dynamics in Sub‐Saharan Africa

AbstractWith the goal of eradication by 2030, Malaria poses a significant health threat, profoundly influenced by meteorological and hydrological conditions. In support of malaria vector control efforts, we present a high‐resolution, coupled physically‐based modeling approach integrating WRF‐Hydro and VECTRI. This model approach accurately captures topographic details at the scale of larvae habitats in the Nouna Health and Demographic Surveillance Systems in Sub‐Saharan Africa. Our study demonstrates the proficiency of the high‐resolution hydrometeorological model, WRF‐Hydro, in replicating observed climate characteristics. Comparisons with in‐situ local weather data reveal root mean square errors between 0.6 and 0.87 mm/day for rainfall and correlations ranging from 0.79 to 0.87 for temperatures. Additionally, WRF‐Hydro's surface hydrology reproduces the seasonal and intraseasonal variability of the ponded water fraction with 96% accuracy, validated against Sentinel‐1 data at a 100‐m resolution. The VECTRI model demonstrates sensitivity to surface hydrology representation, particularly when comparing conceptual and detailed physical process models, for variables such as larvae density, mosquito abundance, and EIR. The model's ability to replicate the seasonality of malaria transmission aligns well with available cohort malaria data suggesting its potential for predicting the impacts of climate change on mosquito abundance and transmission intensity in endemic tropical and subtropical zones. This integrated approach opens avenues for enhanced understanding and proactive management of malaria.

Physics-informed neural network simulation of two-phase flow in heterogeneous and fractured porous media

Physics-informed neural networks (PINNs) have received great attention as a promising paradigm for forward, inverse, and surrogate modeling of various physical processes with limited or no labeled data. However, PINNs are rarely used to predict two-phase flow in heterogeneous and fractured porous media, which is critical to lots of subsurface applications, due to the significant challenges in their training. In this work, we present an Enriched Physics-Informed Neural Network (E-PINN) to overcome these barriers and realize the simulation of such flow. Specifically, the Embedded Discrete Fracture Model (EDFM) is adopted to explicitly represent fractures, and then the finite volume method (FVM) instead of the Automatic Differentiation (AD) is used to evaluate spatial derivatives and construct the physics-informed loss function, so that the flux continuity between neighboring elements with different properties (e.g. matrix and fracture) can be defined rigorously. Besides, we develop a novel physics-informed neural network (NN) architecture adopting the adjacency-location anchoring, adaptive activation function, skip connection and gated updating to enrich the pressure information and enhance the learning ability of NN. Additionally, the initial and boundary conditions are constrained through a hard approach, which encodes them into network design, to improve the accuracy and efficiency of network training. In order to further reduce the difficulty of training, the Implicit-Pressure Explicit-Saturation (IMPES) scheme is used to calculate pressure and saturation, in which only the pressure needs to be solved by training NN. Finally, the superiority and applicability of E-PINN to complex practical problems is demonstrated through the simulations of immiscible displacement in 2D/3D heterogeneous and fractured reservoirs.

BASES OF THE METHOD OF PHYSICAL MODELING OF THE PROCESS OF CRUSHING OF MUNICIPAL SOLID WASTE

A large number of studies are aimed at increasing the energy efficiency of grinding processes of various solid materials, while maintaining the values ​​of other important indicators, such as material consumption, productivity, etc. Based on this trend, a new method for physically modeling the process of grinding municipal solid waste (MSW) was proposed for the first time. Existing physical modeling techniques are designed for homogeneous and isotropic materials (for example, soil, crushed stone, snow, coal, etc.). The strength properties of solid waste vary widely due to the significant heterogeneity of their components. Consequently, when crushing solid waste, traditional crusher designs have low efficiency in terms of energy intensity, material intensity and product quality. The purpose of this work is to develop a new technique for physical modeling of the grinding process, based on the main principles of similarity theory and modeling, considering the properties of waste heterogeneity. As a result of the research, a block diagram of the physical modeling methodology for the interaction of the working bodies of impact crushing machines with solid waste was developed. A list of tasks for the modeling process and similarity criteria have been determined based on the development of rheological models of the “working body - municipal solid waste” system and the laws of mechanics that characterize the waste grinding process. Based on the developed similarity criteria, scale equations for the grinding process are substantiated and formulas are derived for determining the expected parameters of the original based on the parameters measured on the model. The developed methodology makes it possible to create a crusher design with improved energy efficiency indicators with the least material and labor costs.

Challenges and directions for digital twin implementation in otorhinolaryngology.

Digital twin technology heralds a transformative era in Otorhinolaryngology (ORL), merging the physical and digital worlds to offer dynamic, virtual models of physical entities or processes. These models, capable of simulating, predicting, and optimizing real-world counterparts, are evolving from static replicas to intelligent, adaptive systems. Fueled by advancements in communication, sensor technology, big data analytics, Internet of Things (IoT), and simulation technologies, artificial intelligence (AI), digital twins in ORL promise personalized treatment planning, virtual experimentation, and therapeutic intervention optimization. Despite their potential, the integration of digital twins in ORL faces challenges including data privacy and security, data integration and interoperability, computational demands, model validation and accuracy, ethical and regulatory considerations, patient engagement, and cost and accessibility issues. Overcoming these challenges requires robust data protection measures, seamless data integration, substantial computational resources, rigorous validation studies, ethical transparency, patient education, and making the technology accessible and affordable. Looking ahead, the future of digital twins in ORL is bright, with advancements in AI and machine learning, omics data integration, real-time monitoring, virtual clinical trials, patient empowerment, seamless healthcare integration, longitudinal data analysis, and collaborative research. These developments promise to refine diagnostic and treatment strategies, enhance patient care, and facilitate more efficient and tailored ORL research, ultimately leading to more effective and personalized ORL management.