Modeling Framework For Systems Research Articles

Parameterization and Surface Data Improvements and New Capabilities for the Community Land Model Urban (CLMU).

The Community Land Model Urban (CLMU) is an urban parameterization developed to simulate urban climate within a global Earth System Model framework. This paper describes and evaluates parameterization and surface data improvements, and new capabilities that have been implemented since the initial release of CLMU in 2010 as part of version 4 of the Community Land Model (CLM4) and the Community Earth System Model (CESM®). These include: 1) an expansion of model capability to simulate multiple urban density classes within each model grid cell; 2) a more sophisticated and realistic building space heating and air conditioning submodel; 3) a revised global dataset of urban morphological, radiative, and thermal properties utilized by the model, including a tool that allows for generating future urban development scenarios, and 4) the inclusion of a module to simulate various heat stress indices. The model and data are evaluated using observed data from five urban flux tower sites and a global anthropogenic heat flux (AHF) dataset. Generally, the new version of the model simulates urban radiative and turbulent fluxes, surface temperatures, and AHF as well or better than the previous version. Significant improvements in the global and regional simulation of AHF are also demonstrated that are primarily due to the new building energy model. The new model is available as part of the public release of CLM5 and CESM2.0.

How to consider Over-constrained Assemblies with Gaps in Tolerance-Cost Optimization?

Abstract Exact Constraint Design is well established in design theory to ensure a robust and predictable system behavior. In practice, however, it is often inevitable to deviate from this basic concept so that parts are constrained several times for rigidity or resilience. In doing so, gaps between mating parts have to be added to ensure assemblability and functionality. Despite the essential impact of gaps on the total product functionality, occurring uncertainties in part positioning are mostly neglected in tolerance analysis. In the context of tolerance-cost optimization, they have not been studied in detail so far. To overcome this drawback, this article addresses the research question how over-constrained systems with gaps can be considered in tolerance-cost optimization ensuring the identification of both reliable and cost-optimal tolerance values. Based on an initial discussion on the complexity of tolerance analysis of over-constrained systems with gaps, their impact on the results and the efficiency of tolerance-cost optimization are discussed. With the aim to ensure a sufficient model accuracy in time-consuming applications, a framework for tolerance-cost optimization considering assemblies with multiple gap configurations based on surrogate models is presented and applied to a case study of industrial complexity. In doing so, a novel framework for a proper and efficient modelling of systems with gaps in tolerance-cost optimization supporting both researchers and practitioners is presented.

Stochastic Hierarchical Multimodal Hub Location Problem for Cargo Delivery Systems: Formulation and Algorithm

This study aims at developing a stochastic hierarchical multimodal hub location modeling framework for cargo delivery systems to capture uncertainty in hub construction cost and travel time at the strategic level. From a ring-star-star type network design perspective, a stochastic model is established to formulate this problem formally via the expected value and chance-constrained programming techniques. In particular, three types of chance constraints are proposed to ensure that the on-time delivery with pre-specified confidence levels in their respective layer networks. For normal distributions, the original stochastic model can be reformulated as a crisp equivalent mixed-integer linear programming (MILP) model by invoking the central limit theorem. Since the number of constraints and variables increases drastically with the size of cargo delivery distribution network, a memetic algorithm (MA) is designed. This algorithm incorporates genetic search and local intensification to obtain optimal/near-optimal solutions for realistic instance size within a reasonable time limit. For general distributions, it is difficult to convert the stochastic model into its deterministic counterpart. Hence, a hybrid methodology is further designed by combining the MA and Monte Carlo (MC) simulation to solve the proposed stochastic model. To demonstrate the properties of the proposed model and the performance of the designed algorithm, a series of numerical experiments are set up based on the Civil Aeronautics Board (CAB) and Turkish network data sets. Computational results indicate as the confidence level increases, the airport hubs are located further apart in the cargo delivery distribution network for gaining a greater time advantage. In addition, comparative results demonstrate that the MA algorithm proposed herein performs better than the genetic algorithm (GA) in terms of computing speed and quality of the solution.

A data-driven phase and isostable reduced modeling framework for oscillatory dynamical systems.

Phase-amplitude reduction is of growing interest as a strategy for the reduction and analysis of oscillatory dynamical systems. Augmentation of the widely studied phase reduction with amplitude coordinates can be used to characterize transient behavior in directions transverse to a limit cycle to give a richer description of the dynamical behavior. Various definitions for amplitude coordinates have been suggested, but none are particularly well suited for implementation in experimental systems where output recordings are readily available but the underlying equations are typically unknown. In this work, a reduction framework is developed for inferring a phase-amplitude reduced model using only the observed model output from an arbitrarily high-dimensional system. This framework employs a proper orthogonal reduction strategy to identify important features of the transient decay of solutions to the limit cycle. These features are explicitly related to previously developed phase and isostable coordinates and used to define so-called data-driven phase and isostable coordinates that are valid in the entire basin of attraction of a limit cycle. The utility of this reduction strategy is illustrated in examples related to neural physiology and is used to implement an optimal control strategy that would otherwise be computationally intractable. The proposed data-driven phase and isostable coordinate system and associated reduced modeling framework represent a useful tool for the study of nonlinear dynamical systems in situations where the underlying dynamical equations are unknown and in particularly high-dimensional or complicated numerical systems for which standard phase-amplitude reduction techniques are not computationally feasible.

Flood Inundation Generation Mechanisms and Their Changes in 1953–2004 in Global Major River Basins

AbstractDespite the serious threats posed by floods, the driving mechanisms of floods are still not well understood. Here we apply a physically based inundation model coupled with a river routing model (Model for Scale Adaptive River Transport (MOSART)) within the Energy Exascale Earth System Model (E3SM) framework to investigate flood inundation dynamics. After calibration using observed streamflow and satellite‐derived flood extent, the model is used to simulate global flood inundation from 1953 to 2004. The mean date and seasonality of annual maximum flood, defined based on flood extent, exhibit significant regional differences across 16 major basins. Generally, soil moisture and monthly maximum daily rainfall (MMR) are the dominant drivers of flood in tropical basins while monthly maximum daily snowmelt (MMS) is the dominant driver in high‐latitude basins. From 1953–1982 to 1975–2004, significant changes in flood generation mechanisms are found in some basins such as Amazon, Lena, Yenisey, and Kolyma. Analysis of the rainfall seasonality and water balance at grid scale reveals a stronger rainfall seasonality in the Amazon during the later period that increases the synchrony between extreme rainfall and wet soil. With high antecedent soil moisture coinciding with rainfall, MMR contributes more to flood in the later period. Fewer extreme rainfall events and increasing soil moisture reduced the contribution of MMR and increased the role of MMS in floods in the Lena and Yenisey basins, respectively. Lastly, increased soil moisture and frequency of large MMS reduced the contribution of the latter to floods in the Kolyma basin.

A Framework for Offshore Load Environment Modeling1

Abstract This paper presents a novel decision analytical framework for systems modeling in the context of the risk-informed integrity management of offshore facilities. Our focus concerns the development of system models representing environmental loads associated with storm events. Appreciating that system models in general serve to facilitate the optimal ranking of decision alternatives, we formulate the problem of systems modeling as an optimization problem to be solved jointly with the ranking of integrity management decision alternatives. Taking offset in recent developments in structure learning and Bayesian regression techniques, a generic approach for the modeling of environmental loads is established, which accommodates for a joint utilization of phenomenological understanding and knowledge contained in databases of observations. In this manner, we provide a framework and corresponding techniques supporting the combination of bottom-up and top-down modeling. Moreover, since phenomenological understanding and analysis of databases may lead to the identification of several competing system models, we include these in the formulation of the optimization problem. The proposed framework and utilized techniques are illustrated in an example. The example considers systems modeling and decision optimization in the context of a possible evacuation of an offshore facility in the face of an emerging storm event.

Nonintrusive reduced order modeling framework for quasigeostrophic turbulence.

In this study, we present a nonintrusive reduced order modeling (ROM) framework for large-scale quasistationary systems. The framework proposed herein exploits the time series prediction capability of long short-term memory (LSTM) recurrent neural network architecture such that (1) in the training phase, the LSTM model is trained on the modal coefficients extracted from the high-resolution data snapshots using proper orthogonal decomposition (POD) transform, and (2) in the testing phase, the trained model predicts the modal coefficients for the total time recursively based on the initial time history. Hence, no prior information about the underlying governing equations is required to generate the ROM. To illustrate the predictive performance of the proposed framework, the mean flow fields and time series response of the field values are reconstructed from the predicted modal coefficients by using an inverse POD transform. As a representative benchmark test case, we consider a two-dimensional quasigeostrophic ocean circulation model which, in general, displays an enormous range of fluctuating spatial and temporal scales. We first demonstrate that the conventional Galerkin projection-based reduced order modeling of such systems requires a high number of POD modes to obtain a stable flow physics. In addition, ROM-Galerkin projection (ROM-GP) does not seem to capture the intermittent bursts appearing in the dynamics of the first few most energetic modes. However, the proposed nonintrusive ROM framework based on LSTM (ROM-LSTM) yields a stable solution even for a small number of POD modes. We also observe that the ROM-LSTM model is able to capture quasiperiodic intermittent bursts accurately, and yields a stable and accurate mean flow dynamics using the time history of a few previous time states, denoted as the lookback time window in this paper. We show several features of ROM-LSTM framework such as significantly higher accuracy than ROM-GP, and faster performance using larger time step size. Throughout the paper, we demonstrate our findings in terms of time series evolution of the field values and mean flow patterns, which suggest that the proposed fully nonintrusive ROM framework is robust and capable of predicting chaotic nonlinear fluid flows in an extremely efficient way compared to the conventional projection-based ROM framework.

Off-design performance of the supercritical carbon dioxide recompression Brayton cycle with NDDCT cooling for concentrating solar power

The aim of this study is to investigate the off-design performance of an air-cooled supercritical carbon dioxide recompression Brayton cycle for concentrating solar thermal power generation. Off-design component models were developed in a system modelling framework. The components were designed for 25 MWe net power generation at ambient temperature 30 °C, with design point cycle thermal efficiency of 46.2%. The off-design performance was investigated for a range of heat source temperatures, ambient temperatures and cycle mass flow rates. Key elements of the off-design control scheme used are independent compressor shaft speeds, fixed low side pressure (assuming inventory control), and fixed turbine speed (for synchronous operation). The cycle can maintain nominal net power generation at 50 °C ambient temperature with increased cycle mass flow rate and turbine inlet temperature. At design point turbine inlet temperature and mass flow rate, net power generation decreases by approximately 10% for each 10 °C increase above the design point ambient temperature. The high design point ambient temperature limits the beneficial effect low ambient temperature. The effect of decreasing the design point ambient temperature was investigated. While this allows higher peak cycle efficiency, it also leads to much greater deterioration of cycle efficiency with increasing ambient temperature.

Integrating Life Cycle Assessment and Agent-Based Modeling: A Dynamic Modeling Framework for Sustainable Agricultural Systems

Abstract As food demand increases, it is critical to develop effective strategies and evaluate their potential in reducing Greenhouse Gas (GHG) emissions and other environmental footprints of large-scale agricultural systems. This study addresses the challenge by developing a dynamic system modeling framework integrating Life Cycle Assessment (LCA), Agent-Based Modeling (ABM), and Techno-Economic Analysis (TEA). LCA and TEA were coupled with dynamic simulation models of crop yields, costs, and prices, allowing for the estimation of life-cycle environmental impacts and profitability of crop planting activities under changing climate and economic conditions. The framework was demonstrated by a case study for an agricultural system, including 1,000 farms in the United States over a 30-year time frame. The results indicated that information exchange among farmers, farmers' environmental awareness, access to environmental information, and farm size are key factors driving the system's environmental impacts. The results can provide a broad range of stakeholders (e.g., policymakers, nonprofits, agriculture companies) with insightful information to tailor their strategies for effectively managing the environmental footprints of large-scale agricultural systems. The integrated modeling framework has the potential to address sustainability challenges in other systems that are dynamic, involve human behaviors, and have complex interactions among human and nature systems.

PEAT-CLSM: A Specific Treatment of Peatland Hydrology in the NASA Catchment Land Surface Model.

Peatlands are poorly represented in global Earth system modeling frameworks. Here we add a peatland-specific land surface hydrology module (PEAT-CLSM) to the Catchment Land Surface Model (CLSM) of the NASA Goddard Earth Observing System (GEOS) framework. The amended TOPMODEL approach of the original CLSM that uses topography characteristics to model catchment processes is discarded, and a peatland-specific model concept is realized in its place. To facilitate its utilization in operational GEOS efforts, PEAT-CLSM uses the basic structure of CLSM and the same global input data. Parameters used in PEAT-CLSM are based on literature data. A suite of CLSM and PEAT-CLSM simulations for peatland areas between 40°N and 75°N is presented and evaluated against a newly compiled data set of groundwater table depth and eddy covariance observations of latent and sensible heat fluxes in natural and seminatural peatlands. CLSM’s simulated groundwater tables are too deep and variable, whereas PEAT-CLSM simulates a mean groundwater table depth of −0.20 m (snow-free unfrozen period) with moderate temporal fluctuations (standard deviation of 0.10 m), in significantly better agreement with in situ observations. Relative to an operational CLSM version that simply includes peat as a soil class, the temporal correlation coefficient is increased on average by 0.16 and reaches 0.64 for bogs and 0.66 for fens when driven with global atmospheric forcing data. In PEAT-CLSM, runoff is increased on average by 38% and evapotranspiration is reduced by 19%. The evapotranspiration reduction constitutes a significant improvement relative to eddy covariance measurements.

A context‐aware framework for modelling and verification of smart parking systems in urban cities

AbstractParking spaces have been considered as vital resources in urban areas. Finding parking spaces in jam‐packed areas is often challenging, stressful, and uncertain for the drivers that causes traffic congestion with a consequent of wastage of time, fuel, and increase of pollution. In recent years, context‐aware computing paradigm has been considered to be the most effective approach to address these kinds of issues. Context‐aware systems acquire and understand contextual information according to the current situation, perform reasoning, and then act intelligently on behalf of the user. These applications often run on tiny resource‐bounded smart devices with the incorporation of embedded or attached sensors on these devices and they often exhibit complex and adaptive behaviour. In this paper, we propose a context‐aware parking application framework to assist drivers in finding parking slots dynamically while moving and/or arriving at the destination. We optimize the context‐aware parking framework with bounds on computational resources for the decision support dynamically in a highly decentralized environment. To illustrate the use of the proposed system, we model the context‐aware parking system using Uppaal model checker for formal analysis and verify the correctness properties of the system.

A modeling and optimization framework for power systems design with operational flexibility and resilience against extreme heat waves and drought events

Operational flexibility is an important attribute for the design of sustainable power systems with a high share of intermittent renewable energy sources (IRES). Resilience against extreme weather is also becoming an important concern. In this study, a modeling and optimization framework for power systems planning, which considers both operational flexibility and resilience against extreme weather events, is proposed. In particular, a set of piece-wise linear models are developed to capture the impact of extreme heat waves and drought events on the performance of the power generation units and on the system load. A method is, also, proposed to incorporate the impact models within a modified optimal power system planning problem that can adequately accommodate high shares of IRES. The framework is applied to a case study based on real future climate projections from the Coupled Model Intercomparison Project phase 5 (CMIP5) under different levels of IRES penetration (up to 50%) and severity of the extreme weather events. A sensitivity analysis is conducted for planning under different Representative Concentration Pathways (RCPs) that cover the impact of different trajectories of greenhouse gas concentration on future climate. In particular, RCPs with increase in radiative forcing of +8.5 Wm-2, +4.5 Wm-2 and +2.6 Wm-2 of the pre-industrial levels are considered. The results demonstrate that significant improvements in terms of load supply under an extreme heat wave and drought events can be achieved following the resilient planning framework proposed, compared to conventional planning methods. It is also shown how renewable generation units can improve the system performance against those extreme climate events. Moreover, the quantitative assessment indicates an important interaction between the resilience of the system and its flexibility, and the compound impact of failing to consider either aspect in the power system design phase.

An enthalpy based discrete thermal modelling framework for particulate systems with phase change materials

The latent thermal energy storage of phase change materials (PCM) is an attractive technique to use renewable energy. Systems with PCM capsules can be found in a wide variety of applications, but PCMs are usually approximated as a continuous phase in previous studies. The current work investigates this problem from the discontinuous point of view. The main objective is to develop an enthalpy based discrete thermal formulation that can take both heat conduction and phase change transition into consideration. The computational aspect of the formulation is fully discussed. The resulting algorithm is simple and effective. Its validity is demonstrated by solving a discrete/particle version of the one-phase Stenfan problem. In addition, the equivalent thermal properties of bulk particle materials with phase change are also derived based on a simple multi-scale modelling scheme. Numerical simulations are conducted to illustrate the effectiveness of the proposed enthalpy based discrete thermal modelling (DTEM) framework.

Study of Mountainous Long Span Prestressed Concrete Box-Girder Bridge Cantilever Construction Safety Monitoring System Based on Multi-Agent System

Mountainous long span prestressed concrete box-girder bridge cantilever construction has too many risk factors, and it is difficult to control the construction safety. To ensure the construction safety of Xi-jun bridge, a mountainous long span prestressed concrete box-girder bridge cantilever construction safety monitoring system based on Multi-Agent System is built in this paper. To make full use of cooperativity and interaction of Agent, there are six kinds of Agent in MAS construction safety monitoring system model framework. The monitoring system is built based on. NET Framework. The communication mechanisms of Agent are built based on WinSocket. Then the automatic updating of finite element analysis model is achieved, and the analysis and forecast of box-girder bridge cantilever construction safety state can be done in the system. The safe sate of Xi-jun bridge construction is always be in controlled by use of the system.

HyFlow—A Hybrid Load Flow-Modelling Framework to Evaluate the Effects of Energy Storage and Sector Coupling on the Electrical Load Flows

HyFlow is a grid-based multi-energy system (MES) modelling framework. It aims tomodel the status quo of current energy systems, future scenarios with a high share of fluctuatingenergy sources or additional consumers like electric vehicles, and to compare solution strategies ifcertain parts of the infrastructure are congested. In order to evaluate the congestion limits and thefeasibility and suitability of solution strategies (e.g., energy storage, sector coupling technologies,demand response (DR)), load flow calculations of all three main grid-bound energy carriers areimplemented in one single modelling framework. In addition to the implemented load flow models,it allows the interaction of these grids with the use of hybrid elements. This measure enables aproper assessment of future scenarios, not only for the infrastructure of one energy carrier, but forthe overall energy system. The calculation workflow of HyFlow, including the implemented loadflow calculations, as well as the implementation of the flexibility options, is described in detail inthe methodology section. To demonstrate the wide range of applicability of HyFlow with differentspatial ranges, two case studies referring to current research problems are presented: a city and aregion surrounding the mentioned city. The calculations for the mentioned case studies areperformed for three levels. A “status quo” level, a “high-stress” level with added fluctuatingenergy sources and consumers, and an “improvement” level, where flexibility options areintroduced to the system. The effect of the flexibility options on future energy grids is, therefore,analyzed and evaluated. A wide variety of evaluation criteria can be selected. For example, themaximum load of certain power lines, the self-sufficiency of the overall system, the total transportlosses or the total energy consumption.

Basis Function Matrix-Based Flexible Coefficient Autoregressive Models: A Framework for Time Series and Nonlinear System Modeling.

We propose, in this paper, a framework for time series and nonlinear system modeling, called the basis function matrix-based flexible coefficient autoregressive (BFM-FCAR) model. It has very flexible nonlinear structure. We show that many famous nonlinear time series models can be derived under this framework by choosing the proper basis function matrices. Some probabilistic properties (the conditions of geometrical ergodicity) of the BFM-FCAR model are investigated. Taking advantage of the model structure, we present an efficient parameter estimation algorithm for the proposed framework by using the variable projection method. Finally, we show how new models are generated from the proposed framework.

A unified modeling of muti-sources cyber-attacks with uncertainties for CPS security control

In this paper, the issue of CPS security is investigated. By analyzing the process of multi-sources cyber-attacks of denial-of-service (DoS), information disclosure, stealthy attack and replay attack, a unified system model with uncertainties is formulated. Under this system model framework, robust control theory is applied to design the control scenarios for cyber-attack prevention. Furthermore, a double closed-loop NCS framework combined with information integration technology is proposed, necessary conditions for security guarantee are derived. Finally, a DC motor speed moderating example is given to demonstrate the problem.

Ocean mesoscale structure–induced air–sea interaction in a high-resolution coupled model

Using the Community Earth System Model framework, the authors build a very-high-resolution quasi-global coupled model by coupling an eddy-resolving quasi-global ocean model with a high-resolution a...

Effects of Population Dynamics on Establishment of a Restriction-Modification System in a Bacterial Host.

In vivo dynamics of protein levels in bacterial cells depend on both intracellular regulation and relevant population dynamics. Such population dynamics effects, e.g., interplay between cell and plasmid division rates, are, however, often neglected in modeling gene expression regulation. Including them in a model introduces additional parameters shared by the dynamical equations, which can significantly increase dimensionality of the parameter inference. We here analyse the importance of these effects, on a case of bacterial restriction-modification (R-M) system. We redevelop our earlier minimal model of this system gene expression regulation, based on a thermodynamic and dynamic system modeling framework, to include the population dynamics effects. To resolve the problem of effective coupling of the dynamical equations, we propose a “mean-field-like” procedure, which allows determining only part of the parameters at a time, by separately fitting them to expression dynamics data of individual molecular species. We show that including the interplay between kinetics of cell division and plasmid replication is necessary to explain the experimental measurements. Moreover, neglecting population dynamics effects can lead to falsely identifying non-existent regulatory mechanisms. Our results call for advanced methods to reverse-engineer intracellular regulation from dynamical data, which would also take into account the population dynamics effects.

On the geometric formulation of non-isothermal mass action chemical reaction networks

Abstract In this paper we link the classical theory of chemical reaction networks, as originating in the work of Horn, Jackson and Feinberg, and exposed in compact graph-theoretic formulation in van der Schaft, Rao, Jayawardhana (2012a), van der Schaft, Rao, Jayawardhana (2012b), to the geometric modeling framework for thermodynamic systems as recently proposed in van der Schaft, Maschke (2018b), extending earlier work by Balian, Valentin (2001). While the theory of chemical reaction networks is mostly confined to theisothermal case, this has the potential to extend the powerful tools of chemical reaction network theory to the non-isothermal case, and to analyze chemical reaction networks with material in/outflows, as well as heat flows.