Modular Modeling Framework Research Articles

Cluster-Based BERTopic Modeling on Swedish COVID-19 Vaccine Posts.

This paper explores the prevalent themes across multiple threads on the popular Swedish discussion forum Flashback. Among its diverse array of topics, the forum actively engages users in addressing and debating questions pertaining to COVID-19 vaccines and vaccination. Through distinguishing between positive and negative perspectives within posts across 14 relevant thread discussions, we employ BERTopic, a modular topic modeling framework, which utilizes pre-trained language models and applies clustering techniques to identify prevailing topics. This enables us to conduct a nuanced exploration of overarching themes, offering valuable insights into the multifaceted nature of the discussions regarding COVID-19 vaccines and vaccination in Sweden.

Reduced-order modeling of modular, position-dependent systems with translating interfaces

Many complex mechatronic systems consist of multiple interconnected dynamical subsystems, which are designed, developed, analyzed, and manufactured by multiple independent teams. To support such a design approach, a modular model framework is needed to reduce computational complexity and, at the same time, enable multiple teams to develop and analyze the subsystems in parallel. In such a modular framework, the subsystem models are typically interconnected by means of a static interconnection structure. However, many complex dynamical systems exhibit position-dependent behavior (e.g., induced by translating interfaces) which cannot be captured by such static interconnection models. In this paper, a modular model framework is proposed, which allows to construct an interconnected system model, which captures the position-dependent behavior of systems with translating interfaces, such as linear guide rails, through a position-dependent interconnection structure. Additionally, this framework allows to apply model reduction on subsystem level, enabling a more effective reduction approach, tailored to the specific requirements of each subsystem. Furthermore, we show the effectiveness of this framework on an industrial wire bonder. Here, we show that including a position-dependent model of the interconnection structure (1) enables to accurately model the dynamics of a system over the operating range of the system and, (2) modular model reduction methods can be used to obtain a computationally efficient interconnected system model with guaranteed accuracy specifications.

Outline of an evolutionary morphology generator towards the modular design of a biohybrid catheter.

Biohybrid machines (BHMs) are an amalgam of actuators composed of living cells with synthetic materials. They are engineered in order to improve autonomy, adaptability and energy efficiency beyond what conventional robots can offer. However, designing these machines is no trivial task for humans, provided the field's short history and, thus, the limited experience and expertise on designing and controlling similar entities, such as soft robots. To unveil the advantages of BHMs, we propose to overcome the hindrances of their design process by developing a modular modeling and simulation framework for the digital design of BHMs that incorporates Artificial Intelligence powered algorithms. Here, we present the initial workings of the first module in an exemplar framework, namely, an evolutionary morphology generator. As proof-of-principle for this project, we use the scenario of developing a biohybrid catheter as a medical device capable of arriving to hard-to-reach regions of the human body to release drugs. We study the automatically generated morphology of actuators that will enable the functionality of that catheter. The primary results presented here enforced the update of the methodology used, in order to better depict the problem under study, while also provided insights for the future versions of the software module.

An integrated framework for surface deformation modelling and induced seismicity forecasting due to reservoir operations

Abstract Induced seismicity and surface deformation are common observable manifestations of the geomechanical effect of reservoir operations whether related to geothermal energy production, gas extraction or the storage of carbon dioxide, gas, air or hydrogen. Modelling tools to quantitatively predict surface deformation and seismicity based on operation data could thus help manage such reservoirs. To that effect, we present an integrated and modular modelling framework which combines reservoir modelling, geomechanical modelling and earthquake forecasting. To allow effective computational cost, we assume vertical flow equilibrium, semi-analytical Green's functions to calculate surface deformation and poroelastic stresses and a simple earthquake nucleation model based on Coulomb stress changes. We use the test case of the Groningen gas field in the Netherlands to validate the modelling framework and assess its usefulness for reservoir management. For this application, given the relative simplicity of this sandstone reservoir, we assume homogeneous porosity and permeability and single-phase flow. The model fits the measured pressure well, yielding a root mean square error (RMSE) of 0.95 MPa, and the seismicity observations as well. The pressure residuals show, however, a systematic increase with time that probably reflects groundwater ingression into the depleted reservoir. The interaction with groundwater could be accounted for by implementing a multiphase-flow vertical flow equilibrium (VFE) model. This is probably the major factor that limits the general applicability of the modelling framework. Nevertheless, he modelled subsidence and seismicity fit very well the historical observations in the case of the Groningen gas field.

An agent-based modelling framework for the simulation of large-scale consumer participation in electricity market ecosystems

The role of consumers as price-sensitive participants in electricity markets is considered essential to ensure efficient and secure operations of electricity systems. Yet the uncertain or unknown consequences of active market participation remain a large barrier for active consumer-side market participation. Simulations are a powerful tool to reduce this uncertainty by giving consumers an insight on the potential benefits and costs of market participation. However, the simulation setup must be adapted to each market context and each consumer market participation strategy. To simplify the simulation development process and improve the comparability of simulation results, this paper proposes a modular yet systematic electricity market modelling framework. The framework applies object-oriented programming concepts for business ecosystem modelling presented in previous works to develop an agent-based model of a consumer-centric electricity market ecosystem. The market ecosystem is represented by a multitude of interacting submarkets with their own logic. Within submarkets, context-independent and context-dependent elements are distinguished to provide model abstraction which can be adapted to different contexts. This framework is illustrated by applying it to three different submarkets in the Western Danish electricity market context: the Nordpool day-ahead market, the Nordpool intraday market, and the Frequency Containment Reserve market. The submarket role abstractions allow to benefit from the commonalities between the analysed submarkets during model implementation, while the role parametrisations allow to quickly adapt the roles to each market context. The implementation of the modelling framework in the Nordic context highlights the benefits of a modular approach in a liberalised and unbundled market context.

Expert-Based Modular Simulator for Municipal Waste Processing Technology Design

One of the significant problems in our society is the handling and processing of the vast amount of waste produced by households and industrial processes. Nowadays, packaging material regulations are constantly changing, which can significantly impact the quality of municipal waste, requiring the continuous development and redesign of waste processing plants. Since only a few uncertain measurements (composition, mass, etc.) are available for this task, analysing and redesigning waste processing technologies is challenging. This research aims to develop a modelling and simulation concept that can integrate all the available information and can also handle the uncertainty of the measurements. The proposed modular modelling framework can serve as a basis for designing and redesigning the technologies needed to process ever-changing municipal waste. The most important steps of the framework are as follows: identifying the typical equipment, these are the elements; building models of the elements; determining the characteristic parameters of the equipment; exploring the possible relationships between the elements. For example, the information needed to define the model parameters can be gathered from measurements, industrial experience, and expert knowledge. In many cases, the data obtained represent ranges. The stationary model framework applies efficiency factors and divides the solids into substreams based on expert knowledge. Furthermore, a modular simulator framework was developed to simulate the technological schemes with various connections. The specifications for all widely used waste industrial equipment (shredders, air separators, sieves, magnetic-, eddy current-, optical-, and ballistic separators) were used to construct the developed simulator. This simulator can open new opportunities for the design of waste sorting technological networks. The model was calibrated based on expertise gained from operating the studied technology. The changes in the material parameters can be considered, and the modular simulator can lead to flexible waste sorting technologies capable of adapting to governmental and environmental regulations changes. The main result of the work is that a methodology for designing a modular simulator, model development, and a validation method has been proposed, which provides the possibility to deal with uncertainty. All this is successfully presented through the analysis of an operating waste separation system.

Variational multiscale reinforcement learning for discovering reduced order closure models of nonlinear spatiotemporal transport systems

A central challenge in the computational modeling and simulation of a multitude of science applications is to achieve robust and accurate closures for their coarse-grained representations due to underlying highly nonlinear multiscale interactions. These closure models are common in many nonlinear spatiotemporal systems to account for losses due to reduced order representations, including many transport phenomena in fluids. Previous data-driven closure modeling efforts have mostly focused on supervised learning approaches using high fidelity simulation data. On the other hand, reinforcement learning (RL) is a powerful yet relatively uncharted method in spatiotemporally extended systems. In this study, we put forth a modular dynamic closure modeling and discovery framework to stabilize the Galerkin projection based reduced order models that may arise in many nonlinear spatiotemporal dynamical systems with quadratic nonlinearity. However, a key element in creating a robust RL agent is to introduce a feasible reward function, which can be constituted of any difference metrics between the RL model and high fidelity simulation data. First, we introduce a multi-modal RL to discover mode-dependant closure policies that utilize the high fidelity data in rewarding our RL agent. We then formulate a variational multiscale RL (VMRL) approach to discover closure models without requiring access to the high fidelity data in designing the reward function. Specifically, our chief innovation is to leverage variational multiscale formalism to quantify the difference between modal interactions in Galerkin systems. Our results in simulating the viscous Burgers equation indicate that the proposed VMRL method leads to robust and accurate closure parameterizations, and it may potentially be used to discover scale-aware closure models for complex dynamical systems.

Evaluating Multiple Canopy‐Snow Unloading Parameterizations in SUMMA With Time‐Lapse Photography Characterized by Citizen Scientists

AbstractCanopy‐snow unloading is the complex physical process of snow unloading from the canopy through meltwater drip, sublimation to the atmosphere, or solid snow unloading to the snowpack below. This process is difficult to parameterize due to limited observations. Time‐lapse photographs of snow in the canopy were characterized by citizen scientists to create a data set of snow interception observations at multiple locations across the western United States. This novel interception data set was used to evaluate three snow unloading parameterizations in the Structure for Unifying Multiple Modeling Alternatives (SUMMA) modular hydrologic modeling framework. SUMMA was modified to include a third snow unloading parameterization, termed Wind‐Temperature (Roesch et al., 2001, https://doi.org/10.1007/s003820100153), which includes wind‐dependent and temperature‐dependent unloading functions. It was compared to a meltwater drip unloading parameterization, termed Melt (Andreadis et al., 2009, https://doi.org/10.1029/2008wr007042), and a time‐dependent unloading parameterization, termed Exponential‐Decay (Hedstrom & Pomeroy, 1998, https://doi.org/10.1002/(SICI)1099-1085(199808/09)12:10/11<1611::AID-HYP684>3.0.CO;2-4). Wind‐Temperature performed well without calibration across sites, specifically in cold climates, where wind dominates unloading and rime accretion is low. At rime prone sites, Wind‐Temperature should be calibrated to account for longer interception events with less sensitivity to wind, otherwise Melt can be used without calibration. The absence of model physics in Exponential‐Decay requires local calibration that can only be transferred to sites with similar unloading patterns. The choice of unloading parameterization can result in 20% difference in SWE on the ground below the canopy and 10% difference in estimated average winter canopy albedo. These novel observations shed light on processes that are often overlooked in hydrology.

Applying a Coupled Hydrologic-Economic Modeling Framework: Evaluating Alternative Options for Reducing Impacts for Downstream Locations in Response to Upstream Development

Economic input-output and watershed models provide useful results, but these kinds of models do not use the same spatial units, which typically limits their integration. A modular hydrologic-economic modeling framework is designed to couple the Rectangular Choice-of-Technology (RCOT) model, a physically constrained, input-output (I-O) model, with the Hydrological Simulation Program-Fortran (HSPF). Integrating these two models can address questions relevant to both economists and hydrologists, beyond addressing only administrative or watershed concerns. This framework is utilized to evaluate alternative future development prospects within Fauquier County, northern Virginia, specifically residential build-up, and agricultural intensification in the upstream location of the local watershed. Scenarios are designed to evaluate the downstream impacts on watershed health caused by upstream development and changes made within the economic sectors in response to these impacts. In the first case, an alternative residential water technology is more efficient than the standard for ensuring adequate water supply downstream. For scenarios involving upstream agricultural intensification, a crop shift from grains to fruits and vegetables is the most efficient of the alternatives considered. This framework captures two-way feedback between watershed and economic systems that expands the types of questions one can address beyond those that can be analyzed using these models individually.

Model design for networks of heterogeneous Hodgkin–Huxley neurons

We present a novel modular, scalable and adaptable modelling framework to accurately model neuronal networks composed of neurons with different dynamic properties and distinct firing patterns based on a control-inspired feedback structure. We consider three important classes of neurons: inhibitory Fast spiking neurons, excitatory regular spiking with adaptations neurons, and excitatory intrinsic bursting neurons. We also take into consideration two basic means of neuronal interconnection: electrical and chemical synapses. By separating the neuronal dynamics from the network dynamics, we have developed a fully flexible feedback structure that can be further augmented to incorporate additional types of neurons and/or synapses. We use an augmented version of the Hodgkin–Huxley model to describe the individual neuron dynamics and graph theory to define the network structure. We provide simulation results for small fundamental neuron motifs as well as bigger neuronal networks and we verify the accuracy, flexibility and scalability of the proposed method. Therefore, we provide the basis for a comprehensive modelling framework that is able to imitate the dynamics of individual neurons and neuronal networks and is able to replicate basic normal brain function. The structure of the proposed framework is ideal for applications of control and optimization methods both for modelling the effect of pharmacological substances as well as for modelling diseased neuron and network conditions.

A modular mechanistic framework for estimating exposure to SVOCs: Next steps for modeling emission and partitioning of plasticizers and PFAS.

Estimates of human exposure to semi-volatile organic compounds (SVOCs) such as phthalates, phthalate alternatives, and some per- and polyfluoroalkyl substances (PFAS) are required for the risk-based evaluation of chemicals. Recently, a modular mechanistic modeling framework to rapidly predict SVOC emission and partitioning in indoor environments has been presented, in which several mechanistically consistent source emission categories (SECs) were identified. However, not all SECs have well-developed emission models. In addition, data on model parameters are missing even for frequently studied SVOCs. These knowledge gaps impede the comprehensive prediction of the fate of SVOCs indoors. In this paper, sets of high-priority phthalates, phthalate alternatives, and PFAS were identified based on chemical occurrence indoors and additional selection criteria. These high-priority chemicals served as the basis for exploring model parameter availability for existing indoor SVOC emission and partitioning models. The results reveal that additional experimental and modeling work is needed to fully understand the behavior of SVOCs indoors and to predict exposures with greater confidence and lower uncertainty. Modeling approaches to fill some of the identified gaps are proposed. The prioritized sets of chemicals and proposed new modeling approaches will help guide future research. The inclusion of polar phases in the framework will further expand its applicability and scope. IMPACT STATEMENT: This paper compiles data on high-priority chemicals commonly found indoors and information on the availability of applicable models and model parameters to predict emission, partitioning, and subsequent exposure to these chemicals. Modeling approaches for a selection of the missing SECs (source emission categories) are proposed, to illustrate the path forward. The comprehensive data set helps inform researchers, exposure assessors, and policy makers to better understand the state of the science regarding modeling of indoor exposure to semi-volatile organic compounds (SVOCs) and per- and polyfluoroalkyl substances (PFAS).

A multi‐objective approach to select hydrological models and constrain structural uncertainties for climate impact assessments

AbstractThe assessment of climate change impacts on water resources and flood risk is typically underpinned by hydrological models calibrated and selected based on observed streamflow records. Yet, changes in climate are rarely accounted for when selecting hydrological models, which compromises their ability to robustly represent future changes in catchment hydrology. In this paper, we test a simple framework for selecting an ensemble of calibrated hydrological model structures in catchments where contrasting climatic conditions have been observed. We start by considering 78 model structures produced with the FUSE modular modelling framework and rely on a Pareto scheme to select model structures maximizing model efficiency in both wet and dry periods. The application of this approach in three case study basins in Peru enables the identification of structures with good robustness, but also good performance according to hydrological signatures not used for model selection. We also highlight that some model structures that perform well according to traditional efficiency metrics have low performance in contrasting climates or suspicious internal states and fluxes. Importantly, the model selection approach followed here helps to reduce the spread in precipitation elasticities and temperature sensitivities, providing a clearer picture of future hydrological changes. Overall, this work demonstrates the potential of using contrasting climatic conditions in a multi‐objective framework to produce robust and credible simulations, and to constrain structural uncertainties in hydrological projections.

A Convex Modular Modelling (CMM) framework for developing thermodynamically consistent constitutive models

This paper presents a theoretical framework termed the convex modular modelling (CMM) framework, which provides a convenient and expedient approach for constructing thermodynamically consistent constitutive models. This paper demonstrates how the CMM framework can be used to build increasingly complex constitutive models by mixing and matching re-usable components from a library of convex base functions in a systematic manner. It also describes the use of the modified LogSumExp (MLSE) function as a general and smooth approximation to the pointwise maximum function for any yield function (e.g. the Mohr-Coulomb/Tresca yield function). The MLSE function is then used to develop several new yield functions such as a convex and smooth approximation of the Matsuoka-Nakai yield function, a generalised polygonal yield function and a ‘Reuleaux triangle’-shaped yield function. As CMM is simple to use, it potentially offers a more accessible path for constitutive modellers to take advantage of the hyperplasticity framework to develop robust constitutive models.

Numerical daemons of hydrological models are summoned by extreme precipitation

Abstract. Hydrological models are usually systems of nonlinear differential equations for which no analytical solutions exist and thus rely on numerical solutions. While some studies have investigated the relationship between numerical method choice and model error, the extent to which extreme precipitation such as that observed during hurricanes Harvey and Katrina impacts numerical error of hydrological models is still unknown. This knowledge is relevant in light of climate change, where many regions will likely experience more intense precipitation. In this experiment, a large number of hydrographs are generated with the modular modeling framework FUSE (Framework for Understanding Structural Errors), using eight numerical techniques across a variety of forcing data sets. All constructed models are conceptual and lumped. Multiple model structures, parameter sets, and initial conditions are incorporated for generality. The computational cost and numerical error associated with each hydrograph were recorded. Numerical error is assessed via root mean square error and normalized root mean square error. It was found that the root mean square error usually increases with precipitation intensity and decreases with event duration. Some numerical methods constrain errors much more effectively than others, sometimes by many orders of magnitude. Of the tested numerical methods, a second-order adaptive explicit method is found to be the most efficient because it has both a small numerical error and a low computational cost. A small literature review indicates that many popular modeling codes use numerical techniques that were suggested by this experiment to be suboptimal. We conclude that relatively large numerical errors may be common in current models, highlighting the need for robust numerical techniques, in particular in the face of increasing precipitation extremes.

Deep Learned Process Parameterizations Provide Better Representations of Turbulent Heat Fluxes in Hydrologic Models

AbstractDeep learning (DL) methods have shown great promise for accurately predicting hydrologic processes but have not yet reached the complexity of traditional process‐based hydrologic models (PBHM) in terms of representing the entire hydrologic cycle. The ability of PBHMs to simulate the hydrologic cycle makes them useful for a wide range of modeling and simulation tasks, for which DL methods have not yet been adapted. We argue that we can take advantage of each of these approaches by embedding DL methods into PBHMs to represent individual processes. We demonstrate that this is viable by developing DL‐based representations of turbulent heat fluxes and coupling them into the Structure for Unifying Multiple Modeling Alternatives (SUMMA), a modular PBHM modeling framework. We developed two DL parameterizations and integrated them into SUMMA, resulting in a one‐way coupled implementation which relies only on model inputs and a two‐way coupled implementation, which also incorporates SUMMA‐derived model states. Our results demonstrate that the DL parameterizations are able to outperform calibrated standalone SUMMA benchmark simulations. Further we demonstrate that the two‐way coupling can simulate the long‐term latent heat flux better than the standalone benchmark and one‐way coupled configuration. This shows that DL methods can benefit from PBHM information, and the synergy between these modeling approaches is superior to either approach individually.

The impact of hydrological model structure on the simulation of extreme runoff events

Abstract. Hydrological extremes affect societies and ecosystems around the world in many ways, stressing the need to make reliable predictions using hydrological models. However, several different hydrological models can be selected to simulate extreme events. A difference in hydrological model structure results in a spread in the simulation of extreme runoff events. We investigated the impact of different model structures on the magnitude and timing of simulated extreme high- and low-flow events by combining two state-of-the-art approaches: a modular modelling framework (FUSE) and large ensemble meteorological simulations. This combination of methods created the opportunity to isolate the impact of specific hydrological process formulations at long return periods without relying on statistical models. We showed that the impact of hydrological model structure was larger for the simulation of low-flow compared to high-flow events and varied between the four evaluated climate zones. In cold and temperate climate zones, the magnitude and timing of extreme runoff events were significantly affected by different parameter sets and hydrological process formulations, such as evaporation. In the arid and tropical climate zones, the impact of hydrological model structures on extreme runoff events was smaller. This novel combination of approaches provided insights into the importance of specific hydrological process formulations in different climate zones, which can support adequate model selection for the simulation of extreme runoff events.

The Community Firn Model (CFM) v1.0

Abstract. Models that simulate the evolution of polar firn are important for several applications in glaciology, including converting ice-sheet elevation change measurements to mass change and interpreting climate records in ice cores. We have developed the Community Firn Model (CFM), an open-source, modular model framework designed to simulate numerous physical processes in firn. The modules include firn densification, heat transport, meltwater percolation and refreezing, water isotope diffusion, and firn-air diffusion. The CFM is designed so that new modules can be added with ease. In this paper, we first describe the CFM and its modules. We then demonstrate the CFM's usefulness in two model applications that utilize two of its novel aspects. The CFM currently has the ability to run any of 13 previously published firn densification models, and in the first application we compare those models' results when they are forced with regional climate model outputs for Summit, Greenland. The results show that the models do not agree well (spread greater than 10 %) when predicting depth-integrated porosity, firn age, or the trend in surface elevation change. In the second application, we show that the CFM's coupled firn-air and firn densification models can simulate noble gas records from an ice core better than a firn-air model alone.

Can model structure families be inferred from model output?

In model studies, a careful consideration of uncertainty is needed. Parameter and data uncertainty can be explored by sampling, but model structure uncertainty is more challenging to capture since the underlying hypotheses of many models are not directly clear. This study explores whether model structure can be inferred from model output. We created a dendrogram (family tree) based on model structure using a modular modelling framework. Subsequently, we created dendrograms based on model output. We determined the correlation between both dendrograms to analysed if model structure families could be inferred from model output. Results from this experiment over 671 climate instances showed that the performance of the inference depends on the type of output evaluated, and the climate. However, the performance of the inference is overall low, implying that model structure cannot be inferred from model output. These results demonstrate the need to further investigate opportunities to sample model space.

Unified Modular State-Space Modeling of Grid-Connected Voltage-Source Converters

This article proposes a modular state-space modeling framework for grid-connected voltage-source converters, where the different control loops, including the ac current control, the phase-locked loop, the dc-link voltage control, and the ac voltage magnitude control, can be modeled separately as building blocks. Moreover, the mathematical relationship between state-space models in the rotating ( dq -) frame and the stationary ( αβ -) frame is explicitly established, and, thus, the modal analysis can be performed directly in the αβ -frame, which allows intuitive interpretation of voltage and current oscillation modes in the αβ -frame. Experimental tests of a 3-kW back-to-back converter system validate the effectiveness of the unified modular state-space modeling and analysis.

Bayesian Network-Based Modeling and Operational Adjustment of Plantwide Flotation Industrial Process

The operational variables of the flotation industrial process (FIP) are controlled by the operators and are usually not adjusted in time. This makes it difficult to control the technical indexes such as concentrate grade within acceptable ranges. To resolve this problem, a Bayesian network (BN)-based modeling and operational adjustment method is investigated. Considering the complexity of modeling, a modular BN modeling framework for plantwide FIP is proposed. First, the plantwide FIP is decomposed into several related submodules, and corresponding local BNs are established through structure learning and parameter learning. Then, on the basis of the process knowledge and associated variables, each local BN is fused into the global BN. In the application, a novel operational adjustment framework for plantwide FIP is proposed. First, a global operational adjustment is inferred by setting the index of concentrate grade. Then, according to the obtained operational adjustment of each submodule from local to global, the concentrate grade is further predicted. Once the predicted result meets a certain condition, the current operational adjustment will be implemented right away. Data experiments evaluate the performance of the proposed method in the decision-making of operational adjustment.