Main Modeling Approaches Research Articles

Multiphysics for nuclear energy applications using a cohesive computational framework

With the recent development of advanced numerical algorithms, software design, and low-cost high-performance computer hardware, reliance on coupled multiphysics to predict the behavior of complex physical systems is beginning to become standard practice. This is especially true in nuclear energy applications where strong nonlinear interdependencies exist between reactor physics, radiation transport, multi-scale nuclear fuels performance, thermal fluids, etc. Resolving these nonlinear dependencies requires choices in multiphysics software approaches. Two main multiphysics modeling and simulation approaches have emerged. The first is based upon “code coupling” where disparate physics codes of different software design, code languages, and spatial and temporal integration schemes are coupled together with relatively complex data passing interfaces. The second multiphysics software approach is to employ a “cohesive” framework where all physics applications are developed with a common software design, i.e., data structures, syntax, input format, integrated spatial and temporal discretization schemes, etc. Here we present the Multiphysics Object-Oriented Simulation Environment (MOOSE) development and runtime framework and describe the framework’s cohesive modeling and simulation multiphysics approach. Then, a “cohesive-like” extension of the MOOSE framework is presented where MOOSE-based physics software applications are efficiently coupled to non-MOOSE (external) physics codes to form multiphysics applications using MOOSE’s unique interface capabilities. Finally, several examples of MOOSE’s cohesive and cohesive-like multiphysics applications will be demonstrated. These multiphysics demonstrations will incorporate both MOOSE-based applications and external codes, including Nek5000, RELAP-7, TRACE, BISON, and Pronghorn.

Understanding Evolutionary Societal Decision‐making for Sustainable Social Systems Engineering Purposes

AbstractTo engineer sustainable social systems for Earth's future, we need to understand how they change over time based on human decision‐making. The problem of the rise and fall (or overshoot and collapse) of cultures has been studied in the social sciences for centuries, so it is a useful pattern for this purpose. To understand this behavior pattern from an operational, social systems engineering perspective, this paper reviews two of the main simulation modeling approaches to this problem and finds them to be limited (from an operational perspective) by a basis in non‐human population dynamics that leads to a problematic dependence on initial conditions. We build upon this previous work, adapting it to include decision‐making and show how these decision‐making processes change the behavior over time. To demonstrate our process, we start with a recent model of Easter Island's collapse and add operational structures that allow human decision‐making to enter the modeling structure. We show how the addition of operational decision‐making structures provides a better fit to the anthropological data and how these structures were used to generate policy on the island of Tikopia. Finally, we argue that these decision‐making structures are, themselves, engineered objects that can be improved through better understanding of their evolutionary nature.

Fluid mechanical response of a pulse tube cryocooler: modelling and experimental validation

Earth observation satellites require cryocoolers to cool down their infrared imagers at very low temperatures. Besides having very good thermodynamic performances, satellite cryocoolers are expected to generate as little vibrations as possible. In order to better understand vibration causes between 50 and 500 Hz, a precise model of the whole cryocooler is necessary. In the literature, two main modelling approaches for pulse tube cryocoolers exist: compressororiented models reduce the thermodynamic system to a linear mass-spring-damper system acting on compressor’s pistons; and thermodynamically oriented models aimed at understanding and predicting thermodynamic performances.In this paper, the mechanical behavior of the thermodynamic system is modelled. Assumptions concerning gas properties and thermodynamic behavior are made based on SAGE software simulations. A simplified Redlich Kwong equation is used. When necessary, polytropic coefficients were identified using simulation data, as were the time-averaged temperatures. The thermodynamic system is split into volumes, pipes and regenerators: conservation laws in volumes are integrated, dynamic mass and momentum conservation equations in pipes are solved using the method of characteristics and equations in regenerators are solved using a finite difference method. Three friction laws are used: one for straight pipes (Moody chart), another for wound pipes (White’s correlation) and a last one for porous media (Modified Ergun equation). Porosity is measured by weighing. The model is built to respect integral causality and propagation phenomena.The developed model is validated using experimental data. The simulations highlight the non-linear mechanical behavior of the thermodynamic system: a sinusoidal motion of the pistons induces a non-sinusoidal pressure in the compression chamber. Among other, first and second harmonics amplitudes are about 3% of fundamental pressure amplitude.This model can now be integrated into a global cryocooler model to predict compressor’s vibrations, power consumption or electrical harmonics. It could also be extended to predict vibrations from the thermodynamic system.

Modeling System Dynamics with Physics-Informed Neural Networks Based on Lagrangian Mechanics

Identifying accurate dynamic models is required for the simulation and control of various technical systems. In many important real-world applications, however, the two main modeling approaches often fail to meet requirements: first principles methods suffer from high bias, whereas data-driven modeling tends to have high variance. Additionally, purely data-based models often require large amounts of data and are often difficult to interpret. In this paper, we present physics-informed neural ordinary differential equations (PINODE), a hybrid model that combines the two modeling techniques to overcome the aforementioned problems. This new approach directly incorporates the equations of motion originating from the Lagrange Mechanics into a deep neural network structure. Thus, we can integrate prior physics knowledge where it is available and use function approximation--e. g., neural networks--where it is not. The method is tested with a forward model of a real-world physical system with large uncertainties. The resulting model is accurate and data-efficient while ensuring physical plausibility. With this, we demonstrate a method that beneficially merges physical insight with real data. Our findings are of interest for model-based control and system identification of mechanical systems.

Critical Success Factors for the Adoption of Decision Tools in IPM

The rational control of harmful organisms for plants (pests) forms the basis of the integrated pest management (IPM), and is fundamental for ensuring agricultural productivity while maintaining economic and environmental sustainability. The high level of complexity of the decision processes linked to IPM requires careful evaluations, both economic and environmental, considering benefits and costs associated with a management action. Plant protection models and other decision tools (DTs) have assumed a key role in supporting decision-making process in pest management. The advantages of using DTs in IPM are linked to their capacity to process and analyze complex information and to provide outputs supporting the decision-making process. Nowadays, several DTs have been developed, tackling different issues, and have been applied in different climatic conditions and agricultural contexts. However, their use in crop management is restricted to only certain areas and/or to a limited group of users. In this paper, we review the current state-of-the-art related to DTs for IPM, investigate the main modelling approaches used, and the different fields of application. We also identify key drivers influencing their adoption and provide a set of critical success factors to guide the development and facilitate the adoption of DTs in crop protection.

Predicted Northward Expansion of the Geographic Range of the Tick Vector Amblyomma americanum in North America under Future Climate Conditions.

Background:The geographic range of the tick Amblyomma americanum, a vector of diseases of public health significance such as ehrlichiosis, has expanded from the southeast of the United States northward during the 20th century. Recently, populations of this tick have been reported to be present close to the Canadian border in Michigan and New York states, but established populations are not known in Canada. Previous research suggests that changing temperature patterns with climate change may influence tick life cycles and permit northward range expansion of ticks in the northern hemisphere.Objectives:We aimed to estimate minimal temperature conditions for survival of A. americanum populations at the northern edge of the tick’s range and to investigate the possibility of range expansion of A. americanum into northern U.S. states and southern Canada in the coming decades.Methods:A simulation model of the tick A. americanum was used, via simulations using climate data from meteorological stations in the United States and Canada, to estimate minimal temperature conditions for survival of A. americanum populations at the northern edge of the tick’s range.Results:The predicted geographic scope of temperature suitability [ annual cumulative degree days (DD) ] included most of the central and eastern U.S. states east of longitude 110°W, which is consistent with current surveillance data for the presence of the tick in this region, as well as parts of southern Quebec and Ontario in Canada. Regional climate model output raises the possibility of northward range expansion into all provinces of Canada from Alberta to Newfoundland and Labrador during the coming decades, with the greatest northward range expansion (up to by the year 2100) occurring under the greenhouse gas (GHG) emissions of Representative Concentration Pathway (RCP) 8.5. Predicted northward range expansion was reduced by approximately half under the reduced GHG emissions of RCP4.5.Discussion:Our results raise the possibility of range expansion of A. americanum into northern U.S. states and southern Canada in the coming decades, and conclude that surveillance for this tick, and the diseases it transmits, would be prudent. https://doi.org/10.1289/EHP5668

Current Development of Ontology-Based Context Modeling

Any information used to characterize the situation of an entity: a person, a place, or an object, can be considered as context. Indeed, context is crucial to avoid semantic ambiguity in data interpretation. However, linking data to its context is a recognized research issue. Adopting an ontology-based approach to model formally the context enables automatic interpretation and reasoning capabilities. This article discusses the main context modeling approaches based ontology by highlighting their principles, scenarios, use cases, benefits, and challenges to explore the use of ontologies to represent contexts.

Dynamic Simulation Studies of Control Design for Engine Bleed Air Test Facility

This paper describes a model-based method for the control design of engine bleed air test facility for the bleed air system. In order to perform the control design, a dynamic simulation model for the facility is developed to simulate the dynamic behaviour. The main equipment dynamic modelling approaches are described. This simplified dynamic model may be used to perform trade studies and to optimize component characteristics for performance and cost comparison purposes. With the performance shown, this engine bleed air test facility should be quite capable to emulate the engine for bleed air system testing and evaluation.

Port competition with accessibility and congestion: a theoretical framework and literature review on empirical studies

ABSTRACTThis review interprets recent studies of the interactions between the competition of gateway seaports and the accessibility of landside transportation links to inland regions. Port competition is treated as part of a rivalry between two transportation chains. This article identifies the main modeling approaches and defines the different types of hinterland access systems described in the literature. A general theoretical framework is then proposed to incorporate those key components. The importance of the proposed framework is revealed by comparing and extending results from the literature. Major assumptions that need further empirical verification are identified and discussed, and related empirical studies are reviewed. Finally, avenues for further research are discussed.

Literature review of modelling approaches for ASR in concrete: a new perspective

The assessment of concrete structures affected by alkali–silica reaction (ASR) is a complex problem due to the multiscale nature of this long-term phenomenon. The reaction starts within the concrete constituents and developed at aggregate level by inducing swelling and deterioration of concrete material, which eventually affect the capacity of the structure. Due to this multiscale nature of the phenomenon, the problem is studied by various researchers in numerous manners. In this paper, a literature review of the main modelling approaches for ASR in concrete is presented within a new perspective. The models are categorised on the basis of their input and output parameters, instead of adopting the classical classification on the base of the scale (e.g. micro, meso and macro). The main aim of the review is to understand to which extent the available models are able to describe the deteriorating impact induced by ASR in concrete material and if the approaches can ultimately be extended to structural analyses.

Overview of Computational Fluid Dynamics Simulation of Reactor-Scale Biomass Pyrolysis

Computational fluid dynamics (CFD) has been widely used in both scientific studies and industrial applications of reactor-scale biomass pyrolysis. In this Perspective, the state-of-the-art progress in CFD modeling of reactor-scale biomass pyrolysis was summarized and discussed. First, because of the importance of biomass pyrolysis reaction kinetics to the predictability of CFD, the commonly used pyrolysis reaction kinetics in CFD modeling of reactor-scale biomass pyrolysis were reviewed. The characteristics of each reaction kinetics were described. Then, the theoretical basis and practical applications of three main CFD modeling approaches, i.e., porous media model, multifluid model, and discrete particle model for simulating reactor-scale biomass pyrolysis were presented. The activities and progresses with respect to each CFD modeling approach for reactor-scale biomass pyrolysis were reviewed. Aspects such as experimental validation, modeling speed, and capability were discussed. Finally, the paper was c...

Modeling geomagnetically induced currents

AbstractUnderstanding the geomagnetic hazard to power systems requires the ability to model the geomagnetically induced currents (GIC) produced in a power network. This paper presents the developments in GIC modeling starting with an examination of fundamental questions about where the driving force for GIC is located. Then we outline the two main network modeling approaches that are mathematically equivalent and show an example for a simple circuit. Accurate modeling of the GIC produced during real space weather events requires including the appropriate system characteristics, magnetic source fields, and Earth conductivity structure. It is shown how multiple voltage levels can be included in GIC modeling and how the network configuration affects the GIC values. Magnetic source fields can be included by using “plane wave” or line current models or by using geomagnetic observatory data with an appropriate interpolation scheme. Earth conductivity structure can be represented by 1‐D, 2‐D, or 3‐D models that are used to calculate the transfer function between electric and magnetic fields at the Earth's surface. For 2‐D and 3‐D structures this will involve a tensor impedance function and electric fields that are not necessarily orthogonal to the magnetic field variations. It is now technically possible to include all these features in the modeling of GIC, and various software implementations are being developed to make these features more accessible for use in risk studies.

A review of cathode-arc coupling modeling in GTAW

Material properties of welds are strongly influenced by the thermal history, including the thermo-fluid and electromagnetic phenomena in the weld pool and the arc heat source. A necessary condition for arc heat source models to be predictive is to include the plasma column, the cathode, and the cathode layer providing their thermal and electric coupling. Different cathode layer models based on significantly different physical assumptions are being used. This paper summarizes today’s state of the art of cathode layer modeling of refractory cathodes used in GTAW at atmospheric pressure. The fundamentals of the cathode layer and its physics are addressed. The main modeling approaches, namely (i) the diffusion approach, (ii) the partial LTE approach, and (iii) the hydrodynamic approach are discussed and compared. The most relevant publications are systematically categorized with regard to the respective physical phenomena addressed. Results and process understanding gained with these models are summarized. Finally, some open questions are underlined.

Commentary: Lessons from the Analysis of Non-human Primates for Understanding Human Aging and Neurodegenerative Diseases.

Commentary: Lessons from the Analysis of Non-human Primates for Understanding Human Aging and Neurodegenerative Diseases.

ILP-2 modeling and virtual screening of an FDA-approved library:a possible anticancer therapy.

The members of the inhibitors of apoptosis protein (IAP) family inhibit diverse components of the caspase signaling pathway, notably caspase 3, 7, and 9. ILP-2 (BIRC-8) is the most recently identified member of the IAPs, mainly interacting with caspase 9. This interaction would eventually lead to death resistance in the case of cancerous cells. Therefore, structural modeling of ILP-2 and finding applicable inhibitors of its interaction with caspase 9 are a compelling challenge. Three main protein modeling approaches along with various model refinement measures were harnessed to achieve a reliable 3D model, using state-of-the-art software. Thereafter, the selected model was employed to perform virtual screening of an FDA approved library. A model built by a combinatorial approach (homology and ab initio approaches) was chosen as the best model. Model refinement processes successfully bolstered the model quality. Virtual screening of the compound library introduced several high affinity inhibitor candidates that interact with functional residues of ILP2. Given the 3D structure of the ILP2 molecule, we found promising inhibitory molecules. In addition to high affinity towards the ILP2 molecule, these molecules interact with residues that play pivotal rules in ILP2-caspase interaction. These molecules would inhibit ILP2-caspase interaction and consequently would lead to reactivated cell apoptosis through the caspases pathway.

Design Principles as a Guide for Constraint Based and Dynamic Modeling: Towards an Integrative Workflow.

During the last 10 years, systems biology has matured from a fuzzy concept combining omics, mathematical modeling and computers into a scientific field on its own right. In spite of its incredible potential, the multilevel complexity of its objects of study makes it very difficult to establish a reliable connection between data and models. The great number of degrees of freedom often results in situations, where many different models can explain/fit all available datasets. This has resulted in a shift of paradigm from the initially dominant, maybe naive, idea of inferring the system out of a number of datasets to the application of different techniques that reduce the degrees of freedom before any data set is analyzed. There is a wide variety of techniques available, each of them can contribute a piece of the puzzle and include different kinds of experimental information. But the challenge that remains is their meaningful integration. Here we show some theoretical results that enable some of the main modeling approaches to be applied sequentially in a complementary manner, and how this workflow can benefit from evolutionary reasoning to keep the complexity of the problem in check. As a proof of concept, we show how the synergies between these modeling techniques can provide insight into some well studied problems: Ammonia assimilation in bacteria and an unbranched linear pathway with end-product inhibition.

Approaches for modelling the energy flow in food chains

The heavy reliance of the global food chain on the use of fossil fuels and anticipated rise in global population threatens future global food security. Due to the complexity of the food and energy systems, the impact of adequate food, climate or energy policies should be carefully examined in a modelling framework which considers the interaction of the food and energy systems. However, due to the different modelling approaches available, it can be very difficult to identify which method best suits the required purpose. This paper presents the three main modelling approaches as ‘top-down’, ‘bottom-up’ and hybrids. It reviews different models under each category in terms of the practicality, benefits and limitations with reference to different past studies. Bottom-up approaches generally tend to provide high levels of details, but their specificity to particular products/processes detracts their application to holistic models. On the other hand, top-down approaches consider the holistic aspects of the food chain, but the limited level of disaggregation prevents the identification of energy and environmental hot-spots. As a result, hybrid models seek to reduce the limitations of the individual approaches. This paper shows that the choice of one modelling approach over another depends on a variety of criteria including data requirements, uncertainty, available tools, time and labour intensity. Furthermore, future models and studies have to increasingly consider the inter-dependence of implementing social, demographic, economic and climate considerations in a holistic context to predict both short- and long-term impacts of the food chain.

Descriptive vs. mechanistic network models in plant development in the post-genomic era.

Network modeling is now a widespread practice in systems biology, as well as in integrative genomics, and it constitutes a rich and diverse scientific research field. A conceptually clear understanding of the reasoning behind the main existing modeling approaches, and their associated technical terminologies, is required to avoid confusions and accelerate the transition towards an undeniable necessary more quantitative, multidisciplinary approach to biology. Herein, we focus on two main network-based modeling approaches that are commonly used depending on the information available and the intended goals: inference-based methods and system dynamics approaches. As far as data-based network inference methods are concerned, they enable the discovery of potential functional influences among molecular components. On the other hand, experimentally grounded network dynamical models have been shown to be perfectly suited for the mechanistic study of developmental processes. How do these two perspectives relate to each other? In this chapter, we describe and compare both approaches and then apply them to a given specific developmental module. Along with the step-by-step practical implementation of each approach, we also focus on discussing their respective goals, utility, assumptions, and associated limitations. We use the gene regulatory network (GRN) involved in Arabidopsis thaliana Root Stem Cell Niche patterning as our illustrative example. We show that descriptive models based on functional genomics data can provide important background information consistent with experimentally supported functional relationships integrated in mechanistic GRN models. The rationale of analysis and modeling can be applied to any other well-characterized functional developmental module in multicellular organisms, like plants and animals.

Modeling lotsizing and scheduling problems with sequence dependent setups

Several production environments require simultaneous planing of sizing and scheduling of sequences of production lots. Integration of sequencing decisions in lotsizing and scheduling problems has received an increased attention from the research community due to its inherent applicability to real world problems. A two-dimensional classification framework is proposed to survey and classify the main modeling approaches to integrate sequencing decisions in discrete time lotsizing and scheduling models. The Asymmetric Traveling Salesman Problem can be an important source of ideas to develop more efficient models and methods to this problem. Following this research line, we also present a new formulation for the problem using commodity flow based subtour elimination constraints. Computational experiments are conducted to assess the performance of the various models, in terms of running times and upper bounds, when solving real-word size instances.

A Survey of Radio Propagation Modeling for Tunnels

Radio signal propagation modeling plays an important role in designing wireless communication systems. The propagation models are used to calculate the number and position of base stations and predict the radio coverage. Different models have been developed to predict radio propagation behavior for wireless communication systems in different operating environments. In this paper we shall limit our discussion to the latest achievements in radio propagation modeling related to tunnels. The main modeling approaches used for propagation in tunnels are reviewed, namely, numerical methods for solving Maxwell equations, waveguide or modal approach, ray tracing based methods and two-slope path loss modeling. They are discussed in terms of modeling complexity and required information on the environment including tunnel geometry and electric as well as magnetic properties of walls.