Development Of Metamodels Research Articles

Metamodeling for predicting the behavior of airfoils of wind turbine blades: An integration of artificial neural networks

The aim of this paper is to develop a robust metamodel capable of predicting the behavior of wind turbine blade airfoil profiles, despite variations in the input parameters of computational fluid dynamics (CFD) simulations. The metamodel development begins with the specification of essential geometric parameters for the simulations. Subsequently, an empirical analysis of the airfoil profiles is conducted, and the results of the corresponding simulations are presented. These data are used to train and refine the metamodel, which is based on an artificial neural network. The model fitting process is divided into three main stages: training, validation, and testing, during which we strive to minimize the error function using the Levenberg-Marquardt algorithm. In conclusion, we validate our model by a thorough comparison of the results from the metamodel and the CFD simulations, aiming to optimize computation time.

Requirements for Domain-Specific Language in Enterprise Architecture-based SDGs Orchestration

Coherence is a crucial issue in orchestrating Sustainable Development Goals (SDGs) at the society level. The various parties involved with different language standards further increase the complexity of communication. Enterprise Architecture (EA), which emphasizes consistency and coherence as the pillars of its concept, can be used to address coherence issues in the SDGs orchestration. This study aims to build a Requirements Framework for developing an architecture description language as a domain-specific language used for EA-based SDGs orchestration. To clarify the purpose of the research, a motivating scenario was built in the form of the SDGs Participation Platform (SDGs-PP). SDGs-PP becomes the context in which EA-based SDGs orchestration involves society-level actors (orchestrators) and enterprise-level actors (enterprise architects). This Requirement Framework is built using design science methodology as a part of ongoing research. The requirements consisting of context, concept, and collaboration domains and nine action requirements (elicit, separate, connect, classify, manifest, map, arrange, separate, and set up) were successfully formulated. The Context domain was developed from the ISO/IEC/IEEE 42010 standard. The Concept domain focuses on elaborating the SDGs means of implementation and the business ecosystem concept. The Collaboration domain discusses the separation in the model canvas between the enterprise and society domains. Apart from being used as a requirement framework for the meta-model development of an architecture description language, the results of this study can also be used further as a research framework in the domain of EA-based SDGs orchestration.

Building energy performance metamodels for district energy management optimisation platforms

Reactive control strategies lack the flexibility necessary to optimize the operational costs of buildings and district systems. To overcome this limitation and to enable the transition to model predictive control strategies (MPC), the development of dedicated control platforms and models is required. Predictive models for district systems management should provide supply and demand side integrated modelling, high accuracy, generalization capacity and reduced computational times. However, traditionally available MPC solutions do not meet these requirements as simplified models offer short computational times but lack the required accuracy; detailed physics-based models provide satisfactory generalization but at the expense of high computational costs; and the generalization capacity of data models is constrained by the quality and availability of data. In contrast, metamodels developed through the combined use of physics-based models and machine learning techniques offer a powerful alternative at reduced computational cost. This paper describes an upgraded Integrated District Model concept developed through co-simulation coupling metamodels of buildings with a district heating infrastructure Modelica model. Furthermore, the process to produce the metamodels and optimization engine required to generate demand flexibility optimization functionalities for the buildings of the Stepa Stepanovic subnetwork (Belgrade) is depicted. Starting from the development of metamodels of instances of specific buildings (residential and educational use) the process was expanded to provide additional generalization to define, (1) a generic metamodel with the capacity to reproduce the behaviour of any instance of building of the residential typology, and (2) metamodels with generalization capacity in relation to operational settings. As part of this process the potential of several machine learning algorithms (e.g Support Vector Machines, etc) was evaluated including the latest ensemble boosting methods (e.g. Adaboost, Gradient Boosting and Extreme Gradient Boosting) with comparatively low use in the building simulation community. Finally, a virtual test bed consisting in metamodels coupled to an optimization engine based on genetic algorithms, was implemented, and compared to a traditional Physics-based model MPC solution (EnergyPlus-GENOPT), to evaluate the potential of the developed building level optimization functionalities. The metamodels and optimization engine were able to reproduce the optimized settings identified by the EnergyPlus-GENOPT MPC solution with cost savings potentials of 5–10%.

Prognostic Metamodel Development for Waste-Derived Biogas-Powered Dual-Fuel Engines Using Modern Machine Learning with K-Cross Fold Validation

Attention over greenhouse gas emissions has driven interest in cleaner energy sources including alternative fuels. Waste-derived biogas, which is produced by the anaerobic digestion of organic waste such as municipal solid waste, agricultural residues, and wastewater sludge, is an intriguing biofuel source due to its abundant availability and promise of lowering emissions. We investigate the potential of waste-derived biogas as an alternative fuel for a dual-fuel engine that also uses diesel as a secondary fuel in this study. We suggest using a modern machine learning XGBoost model to forecast engine performance. Data acquired with thorough lab-based text will be used to create prognostic models for each output in this effort. Control factors impacting engine performance, including pilot fuel injection pressure, engine load, and pilot fuel injection time, will be employed. The effects of these control elements on engine reaction variables such as brake thermal efficiency (BTE), peak pressure (Pmax), nitrogen oxides (NOx), carbon monoxide (CO), and unburned hydrocarbons (UHC) were simulated. The created models were tested using a variety of statistical approaches, including the coefficient of determination (0.9628–0.9892), Pearson’s coefficient (0.9812–0.9945), mean absolute error (0.4412–5.89), and mean squared error (0.2845–101.7), all of which indicated a robust prognostic model. The use of the increased compression ratio helped in the improvement of BTE with a peak BTE of 26.12%, which could be achieved at an 18.5 compression ratio 220 bar fuel injection pressure peak engine load. Furthermore, our findings give light regarding how to improve the performance of dual-fuel engines that run on waste-derived biogas, with potential implications for cutting emissions in the transportation sector.

Metamodelling of the load-displacement response of offshore piles in sand

The paper illustrates the development of metamodels of the response of steel piles driven in sand and subjected to pull-out. The metamodels are created for the prediction of the pile tensile capacity and secant stiffness. They were developed using the results of finite element analyses, which made use of finite element models of robustness assessed employing a selection of available data from large-scale model pile tests. Four hundred finite element analyses allowed for the calibration of very accurate metamodels, which were also demonstrated to closely track the outputs of the experimental results. Once calibrated, the metamodels can be used independently from the finite element models they stemmed from. The outcomes of the study show that metamodels of piles response can yield very accurate results within a wide and realistic range of soil-pile configuration, avoiding the laborious implementation and computational cost which underpins the use of finite element models. As the use of metamodels in this context is new, the paper relies on particularly simplified problem, but the procedure could be extended to accommodate modelling features of higher complexity.

Development of Traffic Delay and Queue Metamodel on Landslide Zone Based on Simulation Using VISSIM

Landslides occur due to instability in the soil triggered by vibrations, high rainfall and topographical conditions of an area. Landslide is a natural disaster that often occurs in West Sumatra. This landslide certainly has an impact on traffic around the area. Congestion is the impact caused by landslides due to falling chunks of material onto the road. Lack of traffic control during landslides makes travel ineffective and inefficient. Therefore, the traffic control and its impact on possible landslides were tested using a VISSIM microscopic simulation software by setting the queue and delay parameters as the outputs. Based on this output of simulations, the metamodel was developed using regression analysis. This model could be used to predict the queue and delay of traffic because of landslide at road network. Two traffic control conditions that were simulated, open and close lane is used if the width of the landslides covering the road is more than one lane. Two-way traffic control in landslide zone is used when the width of the landslides covering the road is less than one lane.

Hwang’s Philosophy for Developing an Indigenous Cultural Psychology

Kwang-Kuo Hwang provides numerous recommendations for how to develop an indigenous cultural psychology. These recommendations may be understood to suggest proceeding according to three stages: (1) beginning with philosophical reflection on the meaning of modernization and exploring the topics of reality and structure (where such reflections ground one’s subsequent development of meta-models concerning self and social interaction), (2) adopting such meta-models as a hermeneutic for interpreting texts associated with one’s cultural tradition, and (3) conducting experimental studies on the basis of hypotheses derived from such cultural interpretations. Hwang’s philosophy is valuable in that it exemplifies a postmodern approach to cultural psychology that integrates traditional pre-modern forms of wisdom with modern methods of scientific investigation.

A new perspective in construction management; the metaverse

A conceptual construction management model can be applied to the metaverse 3D technology in this study, which demonstrates how accelerated back-and-forth replays of the construction process can be used to evaluate the impact of new decisions on the project. Participants in the proposed model will be able to see exactly how their decisions during construction affect the progress of the project. In order to develop this model, three phases are required: 1) Analyzing the problem and planning the algorithm, 2) collecting data and designing the hybrid model utilizing artificial intelligence and construction network scheduling techniques, and 3) coding the algorithm. The design-based research will contribute to the development of new meta-models for construction management, present innovative ideas for students, engineers, and managers working in the field of construction management.

Leveraging AI for advanced analytics to forecast altered tourism industry parameters: A COVID-19 motivated study

COVID-19 pandemic has given a sudden shock to economy indices worldwide and especially to the tourism sector, which is already very sensitive to such crises as natural calamities, terrorist activities, virus outbreaks and unwanted conditions. The economic implications for a reduction in tourism demand, and the need to analyse post-COVID-19 tourism motivates our research.This study aims to forecast the future trends for foreign tourist arrivals and foreign exchange earnings for India and to formulate a model to predict the future trends based on the COVID-19 parameters, vaccinations and stringency index (Government travelling guidelines). In the study, we have developed artificial intelligence models (random forest, linear regression) using the stacked based ensemble learning method for the development of base models and meta models for the study of COVID-19 and its effect on the tourism industry. The architecture of a stacking model consists of two or more base models, often referred to as level-0 models, and a meta-model that combines the predictions of the base models, and is referred to as a level-1 model (Smyth & Wolpert, 1999). The results show that the projected losses require quick action on developing new practices to sustain and complement the resilience of tourism per se.

Addressing the different sources of excitation variability in seismic response distribution estimation using kriging metamodeling

AbstractThis paper investigates the impact of the different sources of excitation variability within the stochastic kriging framework recently developed by the authors for the estimation of the distribution of engineering demand parameters (EDPs) in applications that the seismic hazard is described through stochastic ground motion models. For a given seismic event, described by seismicity characteristics such as the moment magnitude and the distance to source, one can distinguish two type of uncertainties in the excitation description: (g.i) the stochastic sequence utilized within the excitation model; (g.ii) the predictive models relating the ground motion parameters, such as significant duration, arias intensity, or frequency properties of seismic waves, to the seismicity characteristics. The original formulation of the authors treats (g.i) as aleatoric uncertainty and (g.ii) as parametric uncertainty, including the latter in the model parameters for the risk characterization, representing the metamodel input. Implementation establishes the EDP distribution approximation by utilizing a database of EDP estimates for a set of different input parameters, considering replications of these estimates for different stochastic sequences for some small part of this database. The database with replications is first leveraged to approximate the heteroscedastic behavior with respect to the aleatoric uncertainty, and the entire database is subsequently used, coupled with the previous heteroscedasticity approximation, to establish the stochastic kriging predictions for the EDP distribution. For excitation models with a large number of ground motion parameters, the original formulation leads to a significant increase of the input dimensionality for the metamodel development, requiring a larger database for facilitating accurate EDP approximations. Here, an alternative formulation is investigated, considering some (or perhaps even all) of the ground motion parameters to be part of the aleatoric uncertainty of the excitation description. It is shown that careful selection is needed for the exact ground motion parameters that can be treated as part of this aleatoric uncertainty, to accommodate an accurate overall EDP approximation. Similarities are discussed for applications that consider the description of the seismic hazard through the selection of ground motions based on intensity measures. An extensive validation of this new approach is presented considering two different stochastic ground motion models.

Metamodel Development to Predict Thermal Loads for Single-family Residential Buildings

Metamodel Development to Predict Thermal Loads for Single-family Residential Buildings

Integration of Node Classification in Storm Surge Surrogate Modeling

Surrogate models, also referenced as metamodels, have emerged as attractive data-driven, predictive models for storm surge estimation. They are calibrated based on an existing database of synthetic storm simulations and can provide fast-to-compute approximations of the expected storm surge, replacing the numerical model that was used to establish this database. This paper discusses specifically the development of a kriging metamodel for the prediction of peak storm surges. For nearshore nodes that have remained dry in some of the synthetic storm simulations, a necessary first step, before the metamodel calibration, is the imputation of the database to address the missing data corresponding to such dry instances to estimate the so-called pseudo-surge. This imputation is typically performed using a geospatial interpolation technique, with the k nearest-neighbor (kNN) interpolation being the one chosen for this purpose in this paper. The pseudo-surge estimates obtained from such an imputation may lead to an erroneous classification for some instances, with nodes classified as inundated (pseudo-surge greater than the node elevation), even though they were actually dry. The integration of a secondary node classification surrogate model was recently proposed to address the challenges associated with such erroneous information. This contribution further examines the above integration and offers several advances. The benefits of implementing the secondary surrogate model are carefully examined across nodes with different characteristics, revealing important trends for the necessity of integrating the classifier in the surge predictions. Additionally, the combination of the two surrogate models using a probabilistic characterization of the node classification, instead of a deterministic one, is considered. The synthetic storm database used to illustrate the surrogate model advances corresponds to 645 synthetic tropical cyclones (TCs) developed for a flood study in the Louisiana region. The fact that various flood protective measures are present in the region creates interesting scenarios with respect to the groups of nodes that remain dry for some storms behind these protected zones. Advances in the kNN interpolation methodology, used for the geospatial imputation, are also presented to address these unique features, considering the connectivity of nodes within the hydrodynamic simulation model.

HEAD Access Control Metamodel: Distinct Design, Advanced Features, and New Opportunities

Access control (AC) policies are a set of rules administering decisions in systems and they are increasingly used for implementing flexible and adaptive systems to control access in today’s internet services, networks, security systems, and others. The emergence of the current generation of networking environments, with digital transformation, such as the internet of things (IoT), fog computing, cloud computing, etc., with their different applications, bring out new trends, concepts, and challenges to integrate more advanced and intelligent systems in critical and heterogeneous structures. This fact, in addition to the COVID-19 pandemic, has prompted a greater need than ever for AC due to widespread telework and the need to access resources and data related to critical domains such as government, healthcare, industry, and others, and any successful cyber or physical attack can disrupt operations or even decline critical services to society. Moreover, various declarations have announced that the world of AC is changing fast, and the pandemic made AC feel more essential than in the past. To minimize security risks of any unauthorized access to physical and logical systems, before and during the pandemic, several AC approaches are proposed to find a common specification for security policy where AC is implemented in various dynamic and heterogeneous computing environments. Unfortunately, the proposed AC models and metamodels have limited features and are insufficient to meet the current access control requirements. In this context, we have developed a Hierarchical, Extensible, Advanced, and Dynamic (HEAD) AC metamodel with substantial features that is able to encompass the heterogeneity of AC models, overcome the existing limitations of the proposed AC metamodels, and follow the various technology progressions. In this paper, we explain the distinct design of the HEAD metamodel, starting from the metamodel development phase and reaching to the policy enforcement phase. We describe the remaining steps and how they can be employed to develop more advanced features in order to open new opportunities and answer the various challenges of technology progressions and the impact of the pandemic in the domain. As a result, we present a novel approach in five main phases: metamodel development, deriving models, generating policies, policy analysis and assessment, and policy enforcement. This approach can be employed to assist security experts and system administrators to design secure systems that comply with the organizational security policies that are related to access control.

Developing machine-learning meta-models for high-rise residential district cooling in hot and humid climate

Cooling accounts for a significant amount of energy consumption in hot and humid climates, and district cooling is an energy-efficient solution. During its planning stage, an accurate and fast prediction of district cooling loads is required to assist decision-making. This study investigated the development of machine-learning-based meta-models to predict the cooling load of high-rise residential buildings at a district scale. Four machine-learning models have been evaluated, including Multiple Linear Regression, Support Vector Regression, Artificial Neural Networks (ANN), and eXtreme Gradient Boosting. The meta-model development starts with the sensitivity analysis to determine the critical parameters as independent variables. The results show that the ANN model with 30 neurons of the hidden layer, trained by eight epochs with 20% sample data, manifested superior performance when predicting monthly energy use intensity for the testing cases. A minimum of eight independent variables for the meta-model was also shown to be acceptable.

Metamodels’ Development for High Pressure Die Casting of Aluminum Alloy

Simulation is a very useful tool in the design of the part and process conditions of high-pressure die casting (HPDC), due to the intrinsic complexity of this manufacturing process. Usually, physics-based models solved by finite element or finite volume methods are used, but their main drawback is the long calculation time. In order to apply optimization strategies in the design process or to implement online predictive systems, faster models are required. One solution is the use of surrogate models, also called metamodels or grey-box models. The novelty of the work presented here lies in the development of several metamodels for the HPDC process. These metamodels are based on a gradient boosting regressor technique and derived from a physics-based finite element model. The results show that the developed metamodels are able to predict with high accuracy the secondary dendrite arm spacing (SDAS) of the cast parts and, with good accuracy, the misrun risk and the shrinkage level. Results obtained in the predictions of microporosity and macroporosity, eutectic percentage, and grain density were less accurate. The metamodels were very fast (less than 1 s); therefore, they can be used for optimization activities or be integrated into online prediction systems for the HPDC industry. The case study corresponds to several parts of aluminum cast alloys, used in the automotive industry, manufactured by high-pressure die casting in a multicavity mold.

Crack nucleation from non-metallic inclusions in aluminum alloys described by peridynamics simulations

Conventional engineering methods oftentimes have challenges in the quantification of crack nucleation processes from manufacturing defects that are relevant for engineering component lifing. We present peridynamic simulation framework for the description of the crack nucleation process from cluster of non-metallic inclusions in aluminum alloys. Our non-local simulation framework characterizes crack nucleation process as multiple micro-crack nucleation events from individual inclusions, and eventually one micro-crack dominates. We define individual stages of the crack nucleation process, i.e., nucleation, micro-crack, technical, and crack initiation, that allow a quantification and meta model development of both the individual stages and the entire crack nucleation process.

Acceleration of PDE-Based Biological Simulation Through the Development of Neural Network Metamodels.

Partial differential equation (PDE) mathematical models of biological systems and the simulation approaches used to solve them are widely used to test hypotheses and infer regulatory interactions based on optimization of the PDE model against the observed data. In this review, we discuss the ability of powerful machine learning methods to accelerate the parametric screening of biophysical informed- PDE systems. A major shortcoming in more broad adaptation of PDE-based models is the high computational complexity required to solve and optimize the models and it requires many simulations to traverse the very high-dimensional parameter spaces during model calibration and inference tasks. For instance, when scaling up to tens of millions of simulations for optimization and sensitivity analysis of the PDE models, compute times quickly extend from months to years for sufficient coverage to solve the problems. For many systems, this brute-force approach is simply not feasible. Recently, neural network metamodels have been shown to be an efficient way to accelerate PDE model calibration and here we look at the benefits and limitations in extending the PDE acceleration methods to improve optimization and sensitivity analysis. We use an example simulation to quantitatively and qualitatively show how neural network metamodels can be accurate and fast and demonstrate their potential for optimization of complex spatiotemporal problems in biology. We expect these approaches will be broadly applied to speed up scientific research and discovery in biology and other systems that can be described by complex PDE systems.

Development of a CFD-Based Artificial Neural Network Metamodel in a Wastewater Disinfection Process with Peracetic Acid

AbstractComputational fluid dynamics (CFD) have been applied to predict the performance of chemical water treatment disinfection systems in recent decades. However, computation times remain suffici...

Common Ontology of Sustainable Development

Sustainable development in its various aspects is a multifaceted and contradictory concept, interpreted by multiple sources. This study exposes the need to explore and develop a productive coevolutionary approach to build modular ontological metamodel of sustainable development including a set of interconnected ontologies and instance databases. Based on a review and on an analysis of existing ontologies in this area, a methodology for building common ontology of sustainable development is proposed as a top level of the metamodel. Using terminology from well-established and authoritative sources in the area, as well as the methods, the resources, and the standards of Semantic Web, the methodology contains stages: exploring the SD concepts; controlled vocabulary and thesaurus creating; ontology formalization, verification, querying, and publication. Relationships with the respective domain and other related to sustainable development ontologies are established. In conclusion, the results and the future development of the ontological metamodel are discussed.

Consistent management and evaluation of building models in the early design stages

The early stages of building design involve the consideration of different design variants and their assessment regarding various performance criteria including energy consumption and costs. During the design process, the involved experts from different disciplines frequently exchange building information to develop a design that satisfies the project’s requirements and objectives. In the course of this iterative process, the building design evolves throughout multiple refinement stages. At the same time, different variants are developed. In BIM-based projects, the maturity of the design information provided by the model is expressed by the notion of Level of development (LOD). So far, however, there is no method to formally define the information requirements of a LOD. In particular, there are no means for expressing the uncertainty involved with the provided information. By contrast, despite the insufficient information available in early design stages, a BIM model appears precise and certain. This situation leads to false assumptions and model evaluations, for example, in the case of energy efficiency calculations or structural analysis. Hence, this paper presents an overview of a set of approaches that were developed to alleviate and preserve the consistency of the designed solutions. The approach includes the development of a multi-LOD meta-model, which allows one to explicitly describe the LOD requirements of each building component type incorporating the possible uncertainties, e.g. concerning the building dimensions. On the basis of this multi-LOD model, methods for evaluating a building design’s performance regarding the building’s structure and life cycle energy performance are proposed that take the defined uncertainties into account. To support the management of design variants in one consistent model, a graph-based approach is introduced. Finally, a minimized communication protocol is described to facilitate the workflow and communicate the evaluation results for supporting the decision-making process.