Open-source Modeling Framework Research Articles

TorqTwin—An open-source reference multibody modeling framework for wind turbine structural dynamics

This paper presents an open-source multibody modeling framework for wind turbine structural dynamics, namely TorqTwin (Torque Towards wind). The multibody formulation includes both rigid and flexible bodies. For flexible bodies, such as towers and blades, the floating frame of reference formulation and modal reduction approaches are applied. The TorqTwin tool relies on Kane’s method and the symbolic Python library SymPy. The multibody formulation in TorqTwin is validated using the OpenFAST-ElastoDyn module and the commercial software Bladed. For both the fixed-bottom and floating IEA 15MW reference wind turbines, the rigid motions and flexible deflections calculated by TorqTwin are compared to those by ElastoDyn. Also, blade deflections predicted by TorqTwin are compared to those of the commercial software Bladed. The overall very good agreement between TorqTwin and other software validates the multibody formulation in TorqTwin. The strength of TorqTwin is that it contains the full functionality of the state-of-the-art open-source ElastoDyn module and includes other modeling capabilities of commercial software, such as the bend-twist coupling effect of blades and more flexible modes. TorqTwin is designed to benefit both the wind energy research community and industry, as it helps understand and verify the theory behind the ElastoDyn module, provides a reference for formulating more complicated configurations, such as multi-rotor turbines, and helps to develop aeroelastic simulation tools.

An advanced farm flow estimator for the real-time evaluation of the potential wind power of a down-regulated wind farm

This work aims at verifying the predictions of OnWaRDS, an open-source wake modelling framework that captures the main features of the wake dynamics, including its meandering, in ancillary services scenarios. OnWaRDS brings together Lagrangian flow modeling and flow sensing and runs in parallel with a wind farm environment (here Large-Eddy simulations coupled to an Actuator Disk model (LES-AD)) in order to use the available rotor states to predict the flow field. The performances of OnWaRDS are first assessed when it runs synchronously with the LES-AD of a down-regulated wind farm and tends to mimic the LES-AD behavior. This synchronous mode implies a continuous feeding of the wake model with LES-AD rotor data. Then, OnWaRDS is used in a predictive mode, in order to predict an alternate reality for the wind farm. In this study, OnWaRDS aims at evaluating, in real-time, what the potential power production would be when the LES-AD is down-regulated to provide operating reserve capacity to the electricity network. Switching to a predictive mode implies that certain measurements at the wind turbine level can no longer be used, because the flow and the rotor behavior change between LES-AD and OnWaRDS. The second part of this study thus aims at verifying the predictions of OnWaRDS, and highlighting the impact of switching from a synchronous to a predictive mode in OnWaRDS.

A comprehensive patient-specific prediction model for temporomandibular joint osteoarthritis progression

Temporomandibular joint osteoarthritis (TMJ OA) is a prevalent degenerative disease characterized by chronic pain and impaired jaw function. The complexity of TMJ OA has hindered the development of prognostic tools, posing a significant challenge in timely, patient-specific management. Addressing this gap, our research employs a comprehensive, multidimensional approach to advance TMJ OA prognostication. We conducted a prospective study with 106 subjects, 74 of whom were followed up after 2 to 3 y of conservative treatment. Central to our methodology is the development of an innovative, open-source predictive modeling framework, the Ensemble via Hierarchical Predictions through Nested cross-validation tool (EHPN). This framework synergistically integrates 18 feature selection, statistical, and machine learning methods to yield an accuracy of 0.87, with an area under the ROC curve of 0.72 and an F1 score of 0.82. Our study, beyond technical advancements, emphasizes the global impact of TMJ OA, recognizing its unique demographic occurrence. We highlight key factors influencing TMJ OA progression. Using SHAP analysis, we identified personalized prognostic predictors: lower values of headache, lower back pain, restless sleep, condyle high gray level-GL-run emphasis, articular fossa GL nonuniformity, and long-run low GL emphasis; and higher values of superior joint space, mouth opening, saliva Vascular-endothelium-growth-factor, Matrix-metalloproteinase-7, serum Epithelial-neutrophil-activating-peptide, and age indicate recovery likelihood. Our multidimensional and multimodal EHPN tool enhances clinicians' decision-making, offering a transformative translational infrastructure. The EHPN model stands as a significant contribution to precision medicine, offering a paradigm shift in the management of temporomandibular disorders and potentially influencing broader applications in personalized healthcare.

Enhancement of Skin Permeability Prediction through PBPK Modeling, Bayesian Inference, and Experiment Design.

Physiologically based pharmacokinetic (PBPK) models of skin absorption are a powerful resource for estimating drug delivery and chemical risk of dermatological products. This paper presents a PBPK workflow for the quantification of the mechanistic determinants of skin permeability and the use of these quantities in the prediction of skin absorption in novel contexts. A state-of-the-art mechanistic model of dermal absorption was programmed into an open-source modeling framework. A sensitivity analysis was performed to identify the uncertain compound-specific, individual-specific, and site-specific model parameters that impact permeability. A Bayesian Markov Chain Monte Carlo algorithm was employed to derive distributions of these parameters given in vitro experimental permeability measurements. Extrapolations to novel contexts were generated by simulating the model following its update with samples drawn from the learned distributions as well as parameters that represent the intended scenario. This algorithm was applied multiple times, each using a unique set of permeability measurements sourced under experimental contexts that differ in terms of the compound, vehicle pH, skin sample anatomical site, and the number of compounds under which each subject's skin samples were tested. Among the data sets used in this study, the highest accuracy and precision in the extrapolated permeability was achieved in those that include measurements conducted under multiple vehicle pH levels and in which individual subjects' skin samples are tested under multiple compounds. This work thus identifies factors for consideration in the design of experiments for the purpose of training dermal models to robustly estimate drug delivery and chemical risk.

Improving volume-averaged simulations of matrix-stabilized combustion through direct X-ray µCT characterization: Application to NH3/H2-air combustion

Porous media combustion (PMC) relies on internal heat recirculation in an open-cell ceramic foam matrix to enhance the flame speed of fuels with poor combustion properties. Volume-averaged simulations are often used to study the combustion performance and pollutant emissions of such systems. However, due to the varying complexity of matrix geometries found in practical burners, as well as the wide range of closure models for the constitutive relations of the solid phase, contradicting statements about the predictive accuracy of these volume-averaged models can be found in the literature. In this work, we propose an open-source modeling framework for accurate volume-averaged PMC simulations by using first-principles methods to determine effective properties used in closure models. This framework relies on adequately characterizing the topology of the solid matrix, using commonly available X-ray computed microtomography. With this approach, significant improvements in accuracy are reported compared to empirical models from the literature. The framework based on first-principle evaluations of constitutive relations is compared against experimental measurements conducted on an interface-stabilized burner operated with premixed NH3/H2-air. The model shows good agreement for exhaust gas composition and stability limits. The proposed simulation framework performs significantly better than state-of-the-art techniques that employ commonly used empirical correlations for effective matrix properties. Statement of SignificanceWe present a new open-source simulation framework for improved characterization of porous media combustion. By utilizing µCT techniques, accurate effective matrix properties can be determined from first-principle simulations. These effective properties are used in closure models for 1D volume-averaged reacting flow simulations using appropriate sub-models for heat recirculation. This modeling framework is able to reliably predict stability limits while conventional closure models yield erroneous trends. Assessment of the resulting modeling framework is performed using experiments with exhaust gas characterization performed on a NH3/H2-air porous media burner.

Integration of renewable energy resources into the water-energy-food nexus–Modeling a demand side management approach and application to a microgrid farm in Morocco

This paper introduces a framework aimed at analyzing the water-energy-food nexus (WEF) within the context of sustainable farming practices utilizing renewable energy sources, specifically Solar PV, to optimize water management efficiency. The focus lies on investigating the interplay between the water-energy-food nexus and both technical and institutional factors. The study particularly delves into the utilization of distributed energy systems and microgrids for electricity distribution. To achieve the objectives outlined, the framework is applied to a case study involving an off-grid farm in Morocco, aligned with the country's “Green Morocco Plan” of 2008. The study employs the AnyMOD open-source modeling framework in combination with the publicly available decision support tool CropWat (Version 8.0). Through this coupling, a linear optimization model is created to assess various irrigation practices, thus evaluating the energy and water supply variations across different crop growth stages. By employing scenario analysis, the study reveals that the integration of a smart microgrid alongside storage technologies proves beneficial in terms of reducing overall system costs. This integration presents cost-effective solutions and enables the establishment of a sustainable energy supply driven by renewable energy resources. Furthermore, the investigation highlights that constraining irrigation to specific hours of the day results in increased storage requirements and higher associated costs. In conclusion, the study underscores that enhancing the water-energy-food nexus through the integration of a renewable-based microgrid is a complex task. However, it significantly contributes to the development of sustainable farming solutions. This research sheds light on the challenges and opportunities associated with aligning renewable energy, water management, and agricultural practices, ultimately facilitating the pursuit of environmentally conscious and efficient farming methodologies.

Predicer: abstract stochastic optimisation model framework for multi-market operation

An open-source modelling framework Predicer, standing for Predictive Decider, for multi-market day-ahead market operation purposes is described in this paper. The Predicer model uses scenario-based stochastic optimisation to obtain decision variables and bid matrixes for energy and reserve markets by maximising the risk-adjusted expected value of the profit during the model time frame. The modelled energy system structure is abstract, that is, based on basic elements such as nodes representing different energy types and processes representing flows between nodes. The abstract model structure enables user to construct arbitrary energy systems and define links between assets, commodities, energy markets and reserve markets. Predicer model can include properties such as unit ramp rates, online units, dynamic energy storages, market realisation and market bidding requirements. The aggregation of unit-based energy and reserve opportunities into a virtual power plant allows the asset owner to make optimized portfolio-level bids for different market products. The model scenarios consist of user defined forecasts for market prices, renewable energy supply, energy demand and other system related time series. Predicer is implemented in Julia programming language and uses the JuMP optimisation package.

A generalized natural hazard risk modelling framework for infrastructure failure cascades

Critical infrastructures are more exposed than ever to natural hazards in a changing climate. To understand and manage risk, failure cascades across large, real-world infrastructure networks, and their impact on people, must be captured. Bridging established methods in both infrastructure and risk modelling communities, we develop an open-source modelling framework which integrates a network-based interdependent infrastructure system model into the globally consistent and spatially explicit natural hazard risk assessment platform CLIMADA. The model captures infrastructure damages, triggers failure cascades and estimates resulting basic service disruptions for the dependent population. It flexibly operates on large areas with publicly available hazard, exposure and vulnerability information, for any set of infrastructure networks, hazards and geographies of interest. In a validated case study for 2018’s Hurricane Michael across three US states, the model reproduced important failure dynamics among six infrastructure networks, and provided a novel spatial map where people were likely to experience disruptions in access to healthcare, loss of power and other vital services. Our generalized approach allows for a view on infrastructure risks and their social impacts also in areas where detailed information and risk assessments are traditionally scarce, informing humanitarian activities through hotspot analyses and policy frameworks alike.

A global assessment of national road network vulnerability

Every country relies on a well-functioning road system. However, we do not have a clear understanding yet of the vulnerability of each of these road networks to different forms of disruption. In this study, we aim to better understand how road networks are affected by different disruptive events, to identify hotspots of road network vulnerabilities, and to better target where and what type of future investments can be made to develop more resilient networks. To do so, we developed a fully open-source modelling framework to expose over 200 country road systems across the world to random, local, and targeted disruption schemes. For each country, we assessed the impact of such disruptions on intra-country travel activities and regional accessibility. The results highlight the vulnerability of road systems in mountainous and small-island countries owing to the limited availability of alternative routes. Additionally, we find that, on average, low-income countries experience a collapse of road-system services with much fewer disruptions, relative to high-income countries, due to the lack of redundancy in their systems. While the value of goods and services disrupted may be higher in wealthier countries, the results highlight that from an equity perspective, transport infrastructure investments are more desired in low-income country networks.

MUSE: An open-source agent-based integrated assessment modelling framework

Integrated assessment models (IAMs) are a cornerstone of an effective approach to climate change mitigation. Despite the variety of methodologies for characterising the energy system, land use change, economics, and climate response, the modelling community has an open and urgent request for tools capable of more realistic interpretation of the energy transition, capturing human behaviour, and embodying the principles of transparency, reproducibility, and flexibility of use.This paper presents an open-source modelling framework designed to fill that gap. Named MUSE (ModUlar energy systems Simulation Environment), this new agent-based model supports flexible characterisation of agent decision-making, including individual goals, bounded-rationality, imperfect foresight, and limited knowledge during the decision process. MUSE integrates this agent-based approach in a partial-equilibrium framework and enables a technology-rich description of the energy systems with an unprecedented degree of flexibility for including technological, temporal, and geographical granularity. The structure of MUSE creates the ability to produce climate change mitigation assessments that are more grounded, and more tangible model outputs for conceiving effective approaches to mitigation. MUSE is available open source under a GNU General Public License v3.0 on GitHub at this link https://github.com/SGIModel/MUSE_OS.

Large-eddy simulations with ClimateMachine v0.2.0: a new open-source code for atmospheric simulations on GPUs and CPUs

Abstract. We introduce ClimateMachine, a new open-source atmosphere modeling framework which uses the Julia language and is designed to be scalable on central processing units (CPUs) and graphics processing units (GPUs). ClimateMachine uses a common framework both for coarser-resolution global simulations and for high-resolution, limited-area large-eddy simulations (LESs). Here, we demonstrate the LES configuration of the atmosphere model in canonical benchmark cases and atmospheric flows using a total energy-conserving nodal discontinuous Galerkin (DG) discretization of the governing equations. Resolution dependence, conservation characteristics, and scaling metrics are examined in comparison with existing LES codes. They demonstrate the utility of ClimateMachine as a modeling tool for limited-area LES flow configurations.

Between path dependencies and renewable energy potentials: A case study of the Egyptian power system

Egypt’s power system is facing major changes. Rising demand, scarcity of fossil resources and the falling capital costs of solar and wind energy technologies pose the potential to fundamentally remake the national power fleet. Against this backdrop, the paper at hand investigates potential pathways of the domestic power system with a particular focus on regional deployment and associated impacts on the transmission network. To this end, the open-source modelling framework OSeMOSYS was adapted and deployed. The analysis evaluates renewable energy potentials for 320 sites across Egypt in eight different sub-regions. The study concludes that wind and PV installations prove to be cost-competitive and capable of shouldering a large share of projected demand growth, while their regional deployment should take required grid investments into consideration. The results also indicate that a restrained expansion of renewable energy technologies targeted by the government proves to be more costly than a more aggressive deployment pathway.

Modeling weather-driven long-distance dispersal of spruce budworm moths (Choristoneura fumiferana). Part 1: Model description

Long-term studies of insect populations in the North American boreal forest have shown the vital importance of long-distance dispersal to the maintenance and expansion of insect outbreaks. In this work, we extend several concepts established previously in an empirically-based dispersal flight model with recent work on the physiology and behavior of the adult eastern spruce budworm (SBW) moth, Choristoneura fumiferana (Clemens). An outbreak of defoliating SBW in Quebec, ongoing since the mid-2000s, already covers millions of hectares of forests in eastern Canada and threatens to spread into neighboring areas through annual summertime episodes of long-distance dispersal. Such flight events in favorable conditions frequently include billions of SBW moths dispersing in the warm atmospheric boundary layer, typically starting around sunset and often lasting through several hours of wind-driven transport over hundreds of kilometers. Successful SBW dispersal to possibly distant host forest areas depends acutely on the weather. Here we describe the components and results of SBW–pyATM, an open-source individual-based modeling framework developed in Python for the simulation of these weather-driven SBW dispersal events. Using seasonal SBW phenology results from BioSIM at known outbreak locations and high-resolution Weather Research and Forecasting (WRF) model output, we focus on modeling dispersal flights over two successive nights in July 2013 in southern Quebec. Our flight model closely reproduces the SBW spatial patterns and motions observed by weather surveillance radar over the St. Lawrence estuary. With SBW–pyATM we can estimate landing locations for both male and female SBW and the resulting spatial patterns of egg distribution, allowing us eventually to forecast future larval defoliation activity in new locations where immigration could help overcome local limitations on SBW populations. This information could then support forest management decisions where SBW outbreaks threaten valuable resources.

Development of In Silico Methods for Toxicity Prediction in Collaboration Between Academia and the Pharmaceutical Industry.

The pharmaceutical industry would benefit from the collaboration with academic groups in the development of predictive safety models using the newest computational technologies. However, this collaboration is sometimes hampered by the handling of confidential proprietary information and different working practices in both environments. In this manuscript, we propose a strategy for facilitating this collaboration, based on the use of modeling frameworks developed for facilitating the use of sensitive data, as well as the development, interchange, hosting, and use of predictive models in production. The strategy is illustrated with a real example in which we used Flame, an open-source modeling framework developed in our group, for the development of an in silico eye irritation model. The model was based on bibliographic data, refined during the company-academic group collaboration, and enriched with the incorporation of confidential data, yielding a useful model that was validated experimentally.

The Full Multi: An open-source framework for modelling the transport and fate of nano- and microplastics in aquatic systems

We present a freely available open-source modelling framework to explore environmental transport and fate of nano-and microplastics in aquatic systems. The “Full Multi” models 1) fragmentation of plastic into a predefined set of size classes, 2) speciation of plastic particles between pristine, heteroaggregated, biofouled, and biofouled & heteroaggregated species, 3) dynamic vertical exchange between water layers and sediment of a freshwater system, and 4) horizontal particle transport by eddy diffusion and advective flow. The Full Multi framework relates emission rates to environmental exposure concentrations while considering the intrinsic properties of plastic particles and variable environmental system properties. The model can be applied to analyse scenarios with different process descriptions, plastic types, emission routes and environmental parameters for hypothesis generation, to identify dominant fate processes, and in hazard and risk assessment. Here, we introduce and illustrate the framework by modelling plastic particles with a range of densities in a generic flowing river system.

Advancing the representation of reservoir hydropower in energy systems modelling: The case of Zambesi River Basin.

In state-of-the-art energy systems modelling, reservoir hydropower is represented as any other thermal power plant: energy production is constrained by the plant's installed capacity and a capacity factor calibrated on the energy produced in previous years. Natural water resource variability across different temporal scales and the subsequent filtering effect of water storage mass balances are not accounted for, leading to biased optimal power dispatch strategies. In this work, we aim at introducing a novelty in the field by advancing the representation of reservoir hydropower generation in energy systems modelling by explicitly including the most relevant hydrological constraints, such as time-dependent water availability, hydraulic head, evaporation losses, and cascade releases. This advanced characterization is implemented in an open-source energy modelling framework. The improved model is then demonstrated on the Zambezi River Basin in the South Africa Power Pool. The basin has an estimated hydropower potential of 20,000 megawatts (MW) of which about 5,000 MW has been already developed. Results show a better alignment of electricity production with observed data, with a reduction of estimated hydropower production up to 35% with respect to the baseline Calliope implementation. These improvements are useful to support hydropower management and planning capacity expansion in countries richly endowed with water resource or that are already strongly relying on hydropower for electricity production.

A modeling framework for the integration of electrical and thermal energy systems in greenhouses

Greenhouse horticulture is associated to a significant energy consumption in temperate countries, mainly for lighting and for heating. Interestingly, the potential for energy optimization and energy savings is high but requires detailed models capable of considering various system configurations and control systems. This paper provides an open-source modeling framework capable of simulating and optimizing the design and the control of both the greenhouse and the generation systems covering all energy needs. The proposed model is composed of sub-models from different scientific fields: a greenhouse climate model, a crop yield model, a large number of energy generation and storage units models and different rule-based control strategies. The association of such state-of-the-art models in a single framework provides a powerful tool for optimization purposes and allows the definition of completely customized systems by means of an object-oriented interface. In this work, various control strategies are defined and simulated, thus demonstrating the capabilities of the proposed model. Results indicate that, by performing minor changes to the control of the thermal screen, heating consumption can be reduced by 3% without any loss in crop yield. The control of heat-generation units also has a significant impact on the operational costs, which vary by up to 17% when self-consumption levels are accounted for in the control strategy.

Unprecedented Retention Capabilities of Extensive Green Roofs—New Design Approaches and an Open-Source Model

Green roofs are a proven measure to increase evapotranspiration at the expense of runoff, thus complementing contemporary stormwater management efforts to minimize pluvial flooding in cities. This effect has been quantified by numerous studies, ranging from experimental field campaigns to modeling experiments and even combinations of both. However, up until now, most green roof studies consider standard types of green roof dimensions, thus neglecting varying flow length in the substrate. For the first time, we present a comprehensive investigation of green roofs that involves artificial rainfall experiments under laboratory conditions (42 experiments in total). We consider varying flow length and slope. The novelty lies especially in the consideration of flow lengths beyond 5 m and non-declined roofs. This experimental part is complemented by numerical modeling, employing the open-source Catchment Modeling Framework (CMF). This is set-up for Darcy and Richards flow in the green roof and calibrated utilizing a multi-objective approach, considering both runoff and hydraulic head. The results demonstrate that through maximizing flow length and minimizing slope, the runoff coefficient (i.e., percentage of rainfall that becomes runoff) for a 100 years design rainfall is significantly decreased: from ~30% to values below 10%. These findings are confirmed through numerical modeling, which proves its value in terms of achieved model skill (Kling-Gupta Efficiency ranging from 0.5 to 0.95 with a median of 0.78). Both the experimental data and the numerical model are published as open data and open-source software, respectively. Thus, this study provides new insights into green roof design with high practical relevance, whilst being reproducible.

In-building heat recovery mitigates adverse temperature effects on biological wastewater treatment: A network-scale analysis of thermal-hydraulics in sewers

Heat recovery from wastewater is a robust and straightforward strategy to reduce water-related energy consumption. Its implementation, though, requires a careful assessment of its impacts across the entire wastewater system as adverse effects on the water and resource recovery facility and competition among heat recovery strategies may arise.A model-based assessment of heat recovery from wastewater therefore implies extending the modeling spatial scope, with the aim of enabling thermal-hydraulic simulations from the household tap along its entire flow path down to the wastewater resource recovery facility. With this aim in mind, we propose a new modeling framework interfacing thermal-hydraulic simulations of (i) households, (ii) private lateral connections, and (iii) the main public sewer network.Applying this framework to analyze the fate of wastewater heat budgets in a Swiss catchment, we find that heat losses in lateral connections are large and cannot be overlooked in any thermal-hydraulic analysis, due to the high-temperature, low-flow wastewater characteristics maximizing heat losses to the environment. Further, we find that implementing shower drain heat recovery devices in 50% of the catchment's households lower the wastewater temperature at the recovery facility significantly less – only 0.3 K – than centralized in-sewer heat recovery, due to a significant thermal damping effect induced by lateral connections and secondary sewer lines. In-building technologies are thus less likely to adversely affect biological wastewater treatment processes.The proposed open-source modeling framework can be applied to any other catchment. We thereby hope to enable more efficient heat recovery strategies, maximizing energy harvesting while minimising impacts on biological wastewater treatment.

Crop2ML: An open-source multi-language modeling framework for the exchange and reuse of crop model components

Process-based crop models are popular tools to analyze and simulate the response of agricultural systems to weather, agronomic, or genetic factors. They are often developed in modeling platforms to ensure their future extension and to couple different crop models with a soil model and a crop management event scheduler. The intercomparison and improvement of crop simulation models is difficult due to the lack of efficient methods for exchanging biophysical processes between modeling platforms. We developed Crop2ML, a modeling framework that enables the description and the assembly of crop model components independently of the formalism of modeling platforms and the exchange of components between platforms. Crop2ML is based on a declarative architecture of modular model representation to describe the biophysical processes and their transformation to model components that conform to crop modeling platforms. Here, we present Crop2ML framework and describe the mechanisms of import and export between Crop2ML and modeling platforms.