Modeling Framework For Systems Research Articles

Exploring the Water-Energy Nexus in California via an Integrative Modeling Approach

AbstractA climatically driven, water system modeling framework that captures a full range of interacting components at necessary spatiotemporal scales was developed for the Southwestern United Stat...

A whole system model framework to predict damage in turnouts

This paper presents a whole system model framework that enables a holistic prediction of accumulated track damage in railway turnouts. A modular approach allows for insight into the interaction of different damage mechanisms such as rail profile change due to the plastic deformation and wear as well as track settlement. The methodology focuses on the two most critical areas of the turnout in terms of damage: switch and crossing panels. It is shown that at the beginning of the service life of the turnout the running surface of the rails in these areas change significantly due to plastic deformation, which in turn increases the dynamic impact forces from passing vehicles. These impact forces cause vertical track settlement that is most pronounced in the crossing area. Track settlement introduces additional dynamics into the system leading to self-reinforcing behaviour. It is shown that the proposed methodology can predict accumulated track damage accounting for the coupling between different damage modes such as track settlement, rail profiles plastic deformation and wear. It can therefore be used for holistic assessment of turnouts e.g. in design optimisation studies.

Modular gas‐to‐liquids process with membrane steam‐methane reformer and Fischer–Tropsch reactive distillation

AbstractChallenges related to energy and the environment, alongside risks in capital investment decisions have sparked recent interest in process intensification research. Process intensification has been characterized by novel equipment designs that produce dramatic process improvements including reduction in size, waste production, and energy consumption. Given the current pressing environmental and social concerns on the shale gas industry, it is imperative to design and optimize energy systems to produce solutions that are economically efficient, environmentally sustainable, and socially responsible. Toward this goal, the main research challenge is to develop an integrated energy system modeling framework that can systematically generate optimal designs and operational strategies, and comprehensively improve multiple sustainability criteria. This work highlights opportunities to transform gas‐to‐liquids plants to compact, safe, energy‐efficient, and sustainable processes by using intensified units such as membrane reformers for feed handling and reactive distillation to take advantage of reaction heat to drive separation and enhance yield.

Emission Pathways Towards a Low-Carbon Energy System for Europe: A Model-Based Analysis of Decarbonization Scenarios

The aim of this paper is to showcase different decarbonization pathways for Europe with varying Carbon dioxide (CO2) constraints until 2050. The Global Energy System Model (GENeSYS-MOD) framework, a linear mathematical optimization model, is used to compute low-carbon scenarios for 17 European countries or regions. The sectors power, low-and high-temperature heating, and passenger and freight transportation are included, with the model endogenously constructing capacities in each period. Emission constraints differ between different scenarios and are either optimized endogenously by the model, or distributed on a per-capita basis, GDP-dependent, or based on current emissions. The results show a rapid phase-in of renewable energies, if a carbon budget in line with established international climate targets is chosen. It can be shown that the achievement of the 2° target can be met with low additional costs compared to the business as usual case, while reducing total emissions by more than 30%

Runtime translation of OCL-like statements on Simulink models: Expanding domains and optimising queries

Open-source model management frameworks such as OCL and ATL tend to focus on manipulating models built atop the Eclipse Modelling Framework (EMF), a de facto standard for domain specific modelling. MATLAB Simulink is a widely used proprietary modelling framework for dynamic systems that is built atop an entirely different technical stack to EMF. To leverage the facilities of open-source model management frameworks with Simulink models, these can be transformed into an EMF-compatible representation. Downsides of this approach include the synchronisation of the native Simulink model and its EMF representation as they evolve; the completeness of the EMF representation, and the transformation cost which can be crippling for large Simulink models. We propose an alternative approach to bridge Simulink models with open-source model management frameworks that uses an “on-the-fly” translation of model management constructs into MATLAB statements. Our approach does not require an EMF representation and can mitigate the cost of the upfront transformation on large models. To evaluate both approaches we measure the performance of a model validation process with Epsilon (a model management framework) on a sample of large Simulink models available on GitHub. Our previous results suggest that, with our approach, the total validation time can be reduced by up to 80%. In this paper, we expand our approach to support the management of Simulink requirements and dictionaries, and we improve the approach to perform queries on collections of model elements more efficiently. We demonstrate the use of the Simulink requirements and dictionaries with a case study and we evaluate the optimisations on collection queries with an experiment that compares the performance of a set of queries on models with different sizes. Our results suggest an improvement by up to 99% on some queries.

Long-term variability and source signature of gases emitted from oil & natural gas and cattle feedlot operations in the Colorado front range

Multiple tropospheric gases are analyzed in ten years of observations (2010–2019) using a high-resolution ground-based solar absorption Fourier Transform InfraRed (FTIR) instrument in the Colorado Northern Front Range (CNFR). The first year of measurements in 2010 coincides with the start of the remarkable increase of oil and natural (O&NG) extraction in the region. We show seasonal variations and trends of atmospheric gases related to O&NG (C2H6), cattle feedlot activities (NH3), urban emissions (CO, C2H2), biomass burning (HCN), and volatile organic compounds related to photochemistry and ozone production (H2CO, HCOOH). The long-term time series of C2H6 shows clear episodic peak-enhancements related to local O&NG emissions that contribute to a positive trend of 0.9 ± 0.3% ⋅yr−1. NH3 also shows episodic enhancements and has the greatest rate of change for the gases studied here (2.7 ± 0.7% ⋅yr−1). Simulations of all gases are presented using the Community Atmosphere Model with chemistry (CAM-chem) within the Community Earth System Model (CESM) framework. Modeled gases are compared to the observations using different combinations of global emissions in order to determine the best combination for the CNFR. For most gases, using best emissions, the annual rate of change obtained with CAM-chem agrees with the FTIR observations, except for NH3, which is underestimated by a factor of 6. Nevertheless, simulations of NH3 show that the positive trend in NH3 is due to a decrease in its removal via reaction with H2SO4 from a reduction in coal power plant emissions. The seasonal variations of all gases are generally well represented in the model, although magnitudes are often underestimated. The anthropogenic emissions of CO, C2H2, and C2H6 are underestimated by about 20%, 40%, and 65%, respectively, independent of emission inventories. While NH3 summer values are underestimated significantly, other months show low relative mean difference between FTIR observations and CAM-chem simulations. Excellent agreement is found for H2CO, but for HCOOH a factor of 2–3 is needed in the simulations to match observations, pointing to a significant missing source. HCN, a tracer for biomass burning emissions, is well reproduced by the NCAR Fire Inventory (FINN). Furthermore, we show here a simple approach to identify local enhancements of gases related to nearby O&NG and concentrated animal feeding operations. These enhancements above ambient levels are used to estimate emission ratios (ER) of C2H6, H2CO, and HCOOH relative to CO from O&NG air masses. A median ER (and standard deviation) of 0.48 ± 0.41, 0.26 ± 0.15, and 0.13 ± 0.09 [ppb/ppb] are estimated for C2H6, H2CO, and HCOOH, respectively and are representative of the ten years of observations. ER can be used to estimate emission factors (EF), although CO emissions from O&NG need to be well-known. To complement the FTIR observations, we show results derived with airborne observations during The Front Range Air Pollution and Photochemical Experiment (FRAPPE) in 2014. Even though FTIR and FRAPPE observations can not be compared easily they both yield similar median ER. Finally, plumes of fires originated in the western United States in August 2015 were identified and EF are estimated.

Image Information Collection System Based on Python Web Crawler Technology

Collecting data from the Internet is the key to solve the problem of data sources. This paper studies the image information collection system based on Python web crawler technology.This paper studies and develops a data acquisition system based on Python web crawler technology, which realizes the automatic collection of subject data. In this paper, we use urllib, beautiful soup, threading library to design and develop a system model framework including data crawling, exception handling, robots protocol management and multithreading management modules. Through the application of specific cases, this paper introduces the data acquisition process. Experimental data show that compared with the traditional manual data acquisition, the proposed method greatly improves the work efficiency.

Introduction to Freva – A Free Evaluation System Framework for Earth System Modeling

Freva – Free Evaluation System Framework for Earth system modeling is an efficient solution to handle evaluation systems of research projects, institutes or universities in the climate community. It is a scientific software framework for high performance computing that provides all its available features both in a shell and web environment. The main system design is equipped with the programming interface, history of evaluations, and a standardized model database. Plugin – a generic application programming interface allows scientific developers to connect their analysis tools with the evaluation system independently of the programming language. History – the configuration sub-system stores every analysis performed with the evaluation system in a database. Databrowser – an implemented meta data system with its advanced but easy-to-handle search tool supports scientists and their plugins to retrieve the required information of the database. The combination of these three core components, increases the scientific outcome and enables transparency and reproducibility for research groups using Freva as their framework for evaluation of Earth system models.

Atmospheric rivers in high-resolution simulations of the Paleocene Eocene Thermal Maximum (PETM)

Atmospheric rivers (ARs) and related precipitation are analyzed in high resolution climate simulations during the Eocene using a sophisticated Earth System Model framework. Simulations are conducted across different forcing scenarios spanning the transition from the cooler Late Paleocene period to the Paleocene Eocene Thermal Maximum (PETM), testing both greenhouse gas and orbital sensitivities. AR metrics are compiled and compared across the various simulations and shown to reflect underlying storm tracks where changes in the low-level jet are the primary forcing mechanism for landfall location. Precipitation attributable to ARs is characterized by intensity and amount and compared to total precipitation. Generally, AR precipitation intensity is predominantly moderate, however, this varies by forcing scenario, region, and latitudinal location depending on location of the storm tracks. Extreme rates exist particularly for elevated greenhouse gases and an orbital configuration that maximizes Northern Hemisphere summer insolation. Three regions are diagnosed for landfalling ARs: Western North America, Europe, and Australia. Proxy evidence from the Pyrenees is directly compared to modelled European streamflow and suggests that precipitation produced by ARs can potentially explain the proxy hydrological signal.

Topological network and GIS approach to modeling earthquake risk of infrastructure systems: A case study in Japan

In this study, we propose a network-geographic information system (GIS) modeling framework to construct a risk map for revealing infrastructure vulnerability to seismic hazards. The framework considers the cascading network of interdependencies among the effects and intermediate effects of earthquakes. Network modeling is applied to analyze infrastructure vulnerabilities and identify the infrastructure types that are vulnerable to earthquakes, while geographical modeling is applied to identify seismic hazards in terms of ground motion intensity and earthquake source faults. Reported earthquake cases in Japan are selected for analysis, and an illustrative example of the modeling of a risk map is conducted on a section of a conventional train line in Japan. In the network modeling, statistics and network diagrams are developed to analyze the relational earthquake incident data. The results demonstrate that earthquakes can cause direct and indirect effects on infrastructure, and interdependencies can be found among the effects and intermediate effects. In the geographical modeling, probabilistic distribution is adopted to predict the seismic characteristics; the results reveal the intensity occurrence probability and cascading consequences of different earthquake intensities, and predict specific degrees of destruction. Infrastructure vulnerability and seismic hazard maps are developed according to the analyzed results. A risk map is then constructed by integrating the infrastructure vulnerability map with the seismic hazard map. This study characterizes the risk of earthquakes to infrastructures, capturing the complex interdependencies to reveal the cascading effects from earthquakes, and quantifying and demonstrating infrastructure areas vulnerable to seismic hazards.

Model Predictive Control of Smart Districts With Fifth Generation Heating and Cooling Networks

Fifth Generation District Heating and Cooling (5GDHC) networks, in which low temperature water is distributed to water-source heat pumps (WSHPs) in order to meet thermal demands, are expected to have a significant impact on the decarbonisation of energy supply. Thermal storage installed in these networks offers operational flexibility that can be leveraged to integrate renewable electrical and thermal energy sources. Thus, when considered as part of a smart multi-energy district, 5GDHC substation devices (e.g., WSHPs, storage) may be optimally operated using Model Predictive Control (MPC) in order to match demand with low-cost supply of electricity. However, the application of MPC requires the ability to model 5GDHC networks within the context of a multi-energy system. Hence, this paper extends an existing, generalised control-oriented modelling framework for multi-energy systems to accommodate 5GDHC networks. Additions include the ability to represent hydraulic pumps, thermodynamic cycle devices (such as WSHPs) and multi-energy networks within the framework. Furthermore, an economic MPC (eMPC) scheme is proposed for energy management of 5GDHC-based smart districts. Finally, a case study is presented in which the proposed eMPC controller is compared with rule-based control for economic operation of a smart district.

A unified framework for the mathematical modelling, predictive analysis, and optimization of reaction systems using computational fluid dynamics, deep neural network and genetic algorithm: A case of butadiene synthesis

The fight against climate change and environmental pollution have set the agenda for efficient reaction systems design. It has necessitated now more than ever, the careful design and optimization of chemical processes that result in carbon production. In this study, a unified framework for the mathematical modelling and simulation, prediction, and optimization of reaction systems is proposed. A computational fluid dynamics (CFD) simulation of butadiene synthesis over a ferrite catalyst in a 3D shell and multi-tubular reactor was executed. A rigorous mathematical formulation of the process kinetics and multiscale heat transfer was incorporated into an OpenFOAM CFD model using a porous media. The CFD model was validated against experimental data using gas concentrations, temperature, and CO2 partial pressure with a maximum error of 3.2% (97% accuracy). The developed model was then used to generate data sets for both prediction and optimization of the reaction conditions in terms of temperature, flow rate, and feed compositions using Deep Neural Network (DNN) and Genetic Algorithm (GA). Another surrogate model was developed for temperature control (cooling side) optimization by utilizing the coolant flow rate, flow directions (co-current and countercurrent), coolant types (water and solar salt), and velocity. Dynamic evolution and steady-state contours of the species concentration distributions were predicted with our DNN model with only an error of 1.26% (98.74% accuracy). Our results clearly indicated that higher reactor performance indices that exceed those of the conventional optimization approach in terms of conversion (99.98%), yield (93.28%), and selectivity (92.3%) are obtainable with the proposed framework. The mathematical models, DNN surrogate models, and the genetic algorithms that were integrated as a unified framework can be adopted for designing other reactor systems.

An Efficient Formal Modeling Framework for Hybrid Cloud-Fog Systems

Advanced communication technologies (e.g., 5G) probably elicit a complete change of network and its applications. For example, a growing number of services begin shifting from central clouds to vast mobile devices, as the hybrid use of cloud and fog computing technologies can provide enhanced quality of service and efficient utilization of resources. However, to design such complex hybrid cloud-fog (HCF) systems, it remains a challenge to implement time-consuming modeling and inefficient evaluation in its early design stage based on the conventional simulation or practical experimentation. Therefore, how to reduce design cost and improve development efficiency becomes a crucial issue in the process of designing large-scale HCF systems. To address the issue, this paper proposes a novel modeling framework for large-scale HCF systems based on a high-level formal language, i.e. performance evaluation process algebra (PEPA). Toward the key components of an HCF system, the proposed framework includes three crucial model prototypes: compositional architecture model, abstract communication model and scheduling model. Moreover, the scheduling model is designed with a novel smart scheduling scheme that integrates two atomic scheduling algorithms and a decision module to make an efficient algorithm selection. The smart scheduling algorithm can well adapt the HCF systems by yielding stable and fast response to end-users, particularly under dynamical system conditions. Finally, the framework is the first research achieving the full potential of formal methods to implement industry-level modeling and evaluation.

Research on Building Fire Risk Management Based on BIM

Based on the current building development needs and the existing problems of fire risk management, this paper analyzes the specific application of BIM technology in building fire safety analysis, and also puts forward the direction of thinking, and then builds a BIM-based fire risk management system model framework, which can provide references and ideas for the informatization and intelligent development of building fire risk management.

Sustainable Design of Urban Rooftop Food-Energy-Land Nexus

SummaryUrban rooftop functional design offers a promising option to enable multi-function urban land-use to deliver multiple ecosystem services, e.g., food production by rooftop agriculture and energy supply by installing photovoltaic (PV) panels. To identify the best rooftop utilization strategy considering multiple decision criteria and understand the impact of rooftop solution on the design of urban energy systems, we propose a whole system modeling framework that integrates biogeochemical simulation and multi-objective energy system optimization. We apply the framework to evaluate three rooftop agriculture options, namely, basic rooftop farming, unconditioned greenhouse, and conditioned greenhouse, and one rooftop energy supply option, i.e., PV panels, for an urban energy eco-design case in Shanghai, China. Enabling rooftop agriculture options brings more flexibility to the design and operation of energy systems. PV panels provide cost-optimal solutions, whereas conditioned greenhouse potentially delivers environmentally sustainable land-use by contributing to climate regulation ecosystem services.

Modelling and model assessment of grid based Multi-Energy Systems

Two main strategies should be implemented to decarbonise the energy sector: substituting fossil fuels with renewable energies, and increasing system efficiency. Both strategies pose challenges for today's energy systems and their operators, because renewable energy is mainly decentralized, not always predictable, and introduces a degree of volatility into grids. Multi-energy systems, which incorporate multiple energy sectors, allow flexibility options to be used across energy carriers and thus further increase system flexibility. In addition, these multi-energy systems can also improve the overall energy efficiency. They enable cascaded energy use and allow for seasonal storage between different energy carriers. A comprehensive system modelling framework should consider all profound interactions between relevant system control variables. The aim of this proposed paper is to show the correlation between major aspects of grid based MES and how they can be combined in a system modelling framework.

Multiplex Markov chains: Convection cycles and optimality

Multiplex networks are a common modeling framework for interconnected systems and multimodal data, yet we still lack fundamental insights for how multiplexity affects stochastic processes. We introduce a novel ``Markov chains of Markov chains'' model called multiplex Markov chains (MMCs) such that with probably $(1-\omega)\in [0,1]$ random walkers remain in the same layer and follow (layer-specific) intralayer Markov chains, whereas with probability $\omega$ they move to different layers following (node-specific) interlayer Markov chains. One main finding is the identification of multiplex convection, whereby a stationary distribution exhibits circulating flows that involve multiple layers. Convection cycles are well understood in fluids, but are insufficiently explored on networks. Our experiments reveal that one mechanism for convection is the existence of imbalances for the (intralayer) degrees of nodes in different layers. To gain further insight, we employ spectral perturbation theory to characterize the stationary distribution for the limits of small and large $\omega$, and we show that MMCs inherently exhibit optimality for intermediate $\omega$ in terms of their convergence rate and the extent of convection. As an application, we conduct an MMC-based analysis of brain-activity data, finding MMCs to differ between healthy persons and those with Alzheimer's disease. Overall, our work suggests MMCs and convection as two important new directions for network-related research.

Analytic pavement modelling with a fragmented layer

ABSTRACT Layered elastic theory is a familiar and commonly accepted framework for modelling and analysis of pavement systems. In actuality however, the suitability of the theory is challenged whenever the system includes a fragmented layer – which violates the inherent layer continuity assumption. This paper addressed the modelling challenge in these situations by introducing a fragmented layer into the accepted framework. A new formulation was proposed, based on the hypothesis that the thickness of the fragmented layer can be incorporated as part of its governing parameters. Doing so allowed for treating the fragmented layer as a new kind of interface. Subsequently, the work focused on formulation development and clear step-by-step presentation of new derived expressions applicable to the case of a stratified half-space containing a fragmented layer. Several synthetic cases were generated and interrogated to verify the correctness of underlying equations and to demonstrate capabilities. Based on this interrogation the proposed formulation is deemed rich of prospective utility, especially for modelling pavements surfaced with concrete paving blocks, pavement systems that contain cracked-and-seated (or rubblised) Portland cement concrete, and aged or rehabilitated pavement systems that contain fatigued (multi-fractured) asphalt concrete layers.

Neuro-fuzzy control of sit-to-stand motion using head position tracking

Based on the clinical evidence that head position measured by the multisensory system contributes to motion control, this study suggests a biomechanical human-central nervous system modeling and control framework for sit-to-stand motion synthesis. Motivated by the evidence for a task-oriented encoding of motion by the central nervous system, we propose a framework to synthesize and control sit-to-stand motion using only head position trajectory in the high-level-task-control environment. First, we design a generalized analytical framework comprising a human biomechanical model and an adaptive neuro-fuzzy inference system to emulate central nervous system. We introduce task-space training algorithm for adaptive neuro-fuzzy inference system training. The adaptive neuro-fuzzy inference system controller is optimized in the number of membership functions and training cycles to avoid over-fitting. Next, we develop custom human models based on anthropometric data of real subjects. Using the weighting coefficient method, we estimate body segment parameter. The subject-specific body segment parameter values are used (1) to scale human model for real subjects and (2) in task-space training to train custom adaptive neuro-fuzzy inference system controllers. To validate our modeling and control scheme, we perform extensive motion capture experiments of sit-to-stand transfer by real subjects. We compare the synthesized and experimental motions using kinematic analyses. Our analytical modeling-control scheme proves to be scalable to real subjects’ body segment parameter and the task-space training algorithm provides a means to customize adaptive neuro-fuzzy inference system efficiently. The customized adaptive neuro-fuzzy inference system gives 68%–98% improvement over general adaptive neuro-fuzzy inference system. This study has a broader scope in the fields of rehabilitation, humanoid robotics, and virtual characters’ motion planning based on high-level-task-control scheme.

Heterogeneous co-simulation for embedded and cyber-physical systems design

The growing complexity of embedded and cyber-physical systems makes the design of all system components from scratch increasingly impractical. Consequently, already from early stages of a design flow, designers rely on prior experience, which comes in the form of legacy code or third-party intellectual property (IP) blocks. Current approaches partly address the co-simulation problem for specific scenarios in an ad hoc style. This work suggests a general method for co-simulation of heterogeneous IPs with a system modeling and simulation framework. The external IPs can be integrated as high-level models running in an external simulator or as software- and hardware-in-the-loop simulation with minimal effort. Examples of co-simulation scenarios for wrapping models with different semantics are presented together with their practical usage in two case studies. The presented method is also used to formulate a refinement-by-replacement workflow for IP-based system design.