Innovative Modeling Research Articles

Innovative thermodynamic modeling for enhanced yeast dough mixing: energy perspectives and applications

A thermodynamic model for the calculation of energy exchange in the chamber of a new mixer with effective use of structural and technological parameters of the mixing process without the necessary introduction of experimental data correlations in the distribution of fluid velocities is proposed, which determines the relevance of this direction of calculation with the perspective of its development. The purpose of the presented work is to determine the specific power by substantiating the effective mode parameters of the preparation of the mixture (dough) as a result of evaluating the thermodynamic energy parameters of the kneading process. The assessment was carried out by developing a methodology for determining specific costs for creating a viscous medium when mixing components, which allows you to establish the required power depending on the design and technological parameters of the new mixer. The considered principle of the proposed open-type thermodynamic system of the description of the working process of mixing made it possible to reveal and determine the ways of converting energy into useful work of interphase heat and mass transfer of a heterogeneous medium. In the conditions of circulation mixing with multiple mechanical effects on the mixture of components in the closed circuit of the cylindrical working chamber, which is an effective way to achieve homogeneity of the environment, it was possible to obtain an analytical determination of the specific work and power of the drive in the absence of a clear description of the model of the interconnection of components. The proposed thermodynamic description of the system's energy balance allows to perform only a few experiments. In general, the practical value of the given calculations is of practical importance for improving productivity and efficiency and minimizing energy consumption for the process while reducing the dynamic loads of the designed mixer.

SDE-based software reliability additive models with masked data using ELS algorithm

The growing complexity of contemporary software systems, with multiple sub-modules, demands innovative modeling approaches, especially in addressing intricate failures and masked data generation. This paper proposed a stochastic differential equation (SDE)-based software reliability additive model tailored for multi-component systems dealing with masked data. However, in the presence of masked data, the challenge of parameter estimation arises due to the inherent complexity of the objective function and numerous parameters. To overcome this, we proposed the Expectation Least Square (ELS) algorithm, providing a strategic solution for the intricate task of parameter estimation with masked data. In order to verify the effectiveness of our proposed model, we conducted a comparative analysis with traditional reliability models using three actual failure data sets. The experimental results emphasize the superior performance of the proposed model and highlight the effectiveness of the ELS algorithm in accurately estimating software reliability.

Exploration of material recovery framework from waste – A revolutionary move towards clean environment

Reduce environmental impacts and guarantee a steady supply of critical chemicals by practising sustainable waste management and chemical production. By advancing circular economy ideas and decreasing dependency on finite resources, this research has the potential to alter the industrial landscape radically. The technological, economic, regulatory, and social barriers to waste material recovery and chemical production are explored in this paper. The key to resolving these issues is the identification of solutions that are both economically viable and environmentally benign. This paper introduces the sustainable Chemical Production and Waste Material Recovery Framework (CP&WMRF), which incorporates innovative recycling and upcycling methods, innovative chemical manufacturing processes, and the incorporation of digital technologies like artificial intelligence (AI) and machine learning (ML) to maximize the efficiency with which resources are employed. It is possible to reduce waste and energy use in the production of Interfaces with the help of CP&WMRF. Chemicals can be manufactured using sustainable feedstocks as an alternative to fossil fuels. The system standardizes how e-waste can be recycled and recovered metals and materials can be used. To prove the viability and efficiency of these methods, they require innovative simulation and modeling tools. The assessments help decision-makers understand the benefits and drawbacks of the proposed technologies in terms of their performance, environmental effect, and economic viability. When pitted against AI-ML, which achieved 94.2 %, CP&WMRF's 96.2 % result reveals a significant edge. AI-ML is less efficient, with a score of 93.8 %. The field of sustainability analysis, with a score of 95.2 %, is higher than AI-ML's decent lower score of 93.2 %. The impressive 97.5 % score of CP&WMRF in terms of resource efficiency substantially surpasses the 92.8 % score ascribed to AI-ML. The remarkable success of CP&WMRF in optimizing waste recovery, with a score of 98.7 %, higher than the 91.5 % associated with AI-ML. The present research establishes the framework for a revolutionary move toward circular and green chemistry by integrating innovative methods, all-encompassing applications, and rigorous simulation analysis.

Survival models to estimate time to visible mold growth on new paper-based food-contact materials under varying environmental conditions

Effective packaging solutions are pivotal in addressing sustainability challenges associated with food consumption. Sustainable packaging must not only reduce ecological impact but also ensure food quality preservation, minimize the risk of foodborne diseases, and contribute to mitigating the accumulation of plastic waste. While paper-based food packaging is environmentally friendly, it is susceptible to mold growth when exposed to specific humidity and temperatures, potentially leading to spoilage and safety concerns. This study introduces an innovative modeling approach employing survival analysis to estimate time to visible mold growth on paper-based materials used in food-contact packaging under varying environmental conditions. The objective is to establish an alternative statistical framework for assessing the impact of relative humidity and temperature on mold proliferation. This approach is particularly relevant to novel sustainable packaging materials in the food industry, especially when dealing with censored data. Survival heatmaps are developed to simplify the visualization of spoilage likelihood, measured by time to visible growth at a given confidence level. Although the survival model is proposed for paper-based materials, there are no theoretical limitations to its extension for use with other materials within and beyond the food industry.

Enhancing solar still performance through innovative modeling, integration with reflectors, and semi-transparent solar cells: A 3E analysis and multi-objective optimization

An innovative single-slope solar still desalination system is presented, featuring a groundbreaking enhancement in the modelling methodology. The proposed system is evaluated through energy, exergy, and economic points of view. An attempt has been made to produce more distillation water by introducing the concept of critical pressure at the basin of the solar still system. Besides, in the proposed system, the flat reflectors to increase the received energy from the sun and a semi-transparent module to power production are applied. An investigation is conducted to achieve the main results of the system while operating in Boushehr, Lisboa, Riyadh, and Tripoli cities, and a parametric evaluation is carried out to perceive the system's behaviour in various situations. In addition, an integration of an arithmetic optimization algorithm and TOPSIS decision-maker is employed to determine the most optimum condition for each city through four scenarios. The results depicted the values of 14.9, 11.15, 15.58, and 13.48 kg/day for produced distilled water in Boushehr, Lisboa, Riyadh, and Tripoli cities, respectively.

ПРОЦЕСС СОЗДАНИЯ УСТОЙЧИВОГО БИЗНЕСА НА ОСНОВЕ ИННОВАЦИОННОГО МОДЕЛИРОВАНИЯ

The article substantiates the algorithm for creating a sustainable business in the modern economy. The analysis of indicators of the development of sustainable entrepreneurship in Russia is carried out, which demonstrates that it is at the junction of economic, social and environmental problems and is defined as the process of forming and using business opportunities arising from market fluctuations. The scientific novelty and practical significance of the work lies in the development and comparison of two business creation processes: traditional and sustainable. An approach to creating a sustainable business based on innovative modeling has been developed, which helps to build a transition from an idea to the creation of an innovative sustainable business by identifying and using business opportunities.

The ‘Healthcare Access and Quality Index’ revisited: A fuzzy data envelopment analysis approach

Nowadays, health systems comprise a series of resources structured to provide healthcare services to meet our health needs. However, premature deaths still occur. To quantify and understand personal healthcare conditions affecting such amenable mortality, the Healthcare Access and Quality Index (HAQI) was put forward, evaluating 195 countries and territories since 1990. Nevertheless, the literature acknowledges a series of limitations of this framework, such as the drawbacks of using principal component analysis to aggregate individual indicators, the absence of control for financing and environmental conditions, and the presence of a substantial degree of data uncertainty. Accordingly, we propose a methodological alternative to the computation of the HAQI using a novel fuzzy Data Envelopment Analysis model to handle the aforementioned shortcomings. We also propose its extension towards the quantification of efficiency (E-HAQI) – in the sense of value for money – by incorporating financial aspects as modelling inputs. This way, we contribute with innovative modelling approaches that can also deal with the high degree of data uncertainty. Furthermore, in a second-stage analysis, the impact of key exogenous factors on healthcare access and quality is assessed via non-parametric hypothesis testing. Our results show positive and significant correlations of both the revisited HAQI and E-HAQI with the original HAQI 2016 dataset. They also reveal a better use of resources by European and Oceanian countries and territories than by Sub-Saharan African ones. Concerning contextual determinants, socio-demographic development, human development, and the type of health system were found to be statistically significant drivers of healthcare access and quality efficiency.

Improving Landslide Prediction: Innovative Modeling and Evaluation of Landslide Scenario with Knowledge Graph Embedding

The increasing frequency and magnitude of landslides underscore the growing importance of landslide prediction in light of factors like climate change. Traditional methods, including physics-based methods and empirical methods, are beset by high costs and a reliance on expert knowledge. With the advancement of remote sensing and machine learning, data-driven methods have emerged as the mainstream in landslide prediction. Despite their strong generalization capabilities and efficiency, data-driven methods suffer from the loss of semantic information during training due to their reliance on a ‘sequence’ modeling method for landslide scenarios, which impacts their predictive accuracy. An innovative method for landslide prediction is proposed in this paper. In this paper, we propose an innovative landslide prediction method. This method designs the NADE ontology as the schema layer and constructs the data layer of the knowledge graph, utilizing tile lists, landslide inventory, and environmental data to enhance the representation of complex landslide scenarios. Furthermore, the transformation of the landslide prediction task into a link prediction task is carried out, and a knowledge graph embedding model is trained to achieve landslide predictions. Experimental results demonstrate that the method improves the F1 score by 5% in scenarios with complete datasets and 17% in scenarios with sparse datasets compared to data-driven methods. Additionally, the application of the knowledge graph embedding model is utilized to generate susceptibility maps, and an analysis of the effectiveness of entity embeddings is conducted, highlighting the potential of knowledge graph embeddings in disaster management.

An agent-based nested model integrating within-host and between-host mechanisms to predict an epidemic

The COVID-19 pandemic has remarkably heightened concerns regarding the prediction of communicable disease spread. This study introduces an innovative agent-based modeling approach. In this model, the quantification of human-to-human transmission aligns with the dynamic variations in the viral load within an individual, termed “within-host” and adheres to the susceptible–infected–recovered (SIR) process, referred to as “between-host.” Variations in the viral load over time affect the infectivity between individual agents. This model diverges from the traditional SIR model, which employs a constant transmission probability, by incorporating a dynamic, time-dependent transmission probability influenced by the viral load in a host agent. The proposed model retains the time-integrated transmission probability characteristic of the conventional SIR model. As observed in this model, the overall epidemic size remains consistent with the predictions of the standard SIR model. Nonetheless, compared to predictions based on the classical SIR process, notable differences existed in the peak number of the infected individuals and the timing of this peak. These nontrivial differences are induced by the direct correlation between the time-evolving transmission probability and the viral load within a host agent. The developed model can inform targeted intervention strategies and public health policies by providing detailed insights into disease spread dynamics, crucial for effectively managing epidemics.

Bayesian Generative Modelling of Student Results in Course Networks

We present an innovative modelling technique that simultaneously constrains student performance, course difficulty, and the sensitivity with which a course can differentiate between students by means of grades. Grade lists are the only necessary ingredient. Networks of courses will be constructed where the edges are populations of students that took both connected course nodes. Using idealized experiments and two real-world data sets, we show that the model, even though simple in its set-up, can constrain the properties of courses very well, as long as some basic requirements in the data set are met: (1) significant overlap in student populations, and thus information exchange through the network; (2) non-zero variance in the grades for a given course; and (3) some correlation between grades for different courses. The model can then be used to evaluate a curriculum, a course, or even subsets of students for a very wide variety of applications, ranging from program accreditation to exam fraud detection. We publicly release the code with examples that fully recreate the results presented here.

Enhancing Short-Term Electrical Load Forecasting for Sustainable Energy Management in Low-Carbon Buildings

Accurate short-term forecasting of electrical energy loads is essential for optimizing energy management in low-carbon buildings. This research presents an innovative two-stage model designed to address the unique challenges of Electricity Load Forecasting (ELF). In the first phase, robust data preprocessing techniques are employed to handle issues such as outliers, missing values, and data normalization, which are common in electricity consumption datasets in the context of low-carbon buildings. This data preprocessing enhances data quality and reliability, laying the foundation for accurate modeling. Subsequently, an advanced data-driven modeling approach is introduced. The model combines a novel residual Convolutional Neural Network (CNN) with a layered Echo State Network (ESN) to capture both spatial and temporal dependencies in the data. This innovative modeling approach improves forecasting accuracy and is tailored to the specific complexities of electrical power systems within low-carbon buildings. The model performance is rigorously evaluated using datasets from low-carbon buildings, including the Individual-Household-Electric-Power-Consumption (IHEPC) dataset from residential houses in Sceaux, Paris, and the Pennsylvania–New Jersey–Maryland (PJM) dataset. Beyond traditional benchmarks, our model undergoes comprehensive testing on data originating from ten diverse regions within the PJM dataset. The results demonstrate a significant reduction in forecasting error compared to existing state-of-the-art models. This research’s primary achievement lies in its ability to offer an efficient and adaptable solution tailored to real-world electrical power systems in low-carbon buildings, thus significantly contributing to the broader framework of modeling, simulation, and analysis within the field.

Battery electric vehicle energy demand in urban energy system modeling: A stochastic analysis of added flexibility for home charging and battery swapping stations

The successive upsurge in the penetration rate of battery electric vehicles (BEV) in the transport sector stresses electricity grids. Concurrently, batteries in BEVs add flexibility, while the reintroduction of battery swapping stations challenges home charging with regards to added flexibility. To address research gaps regarding the role of BEV charging systems in urban decarbonization pathways, this paper introduces an innovative modeling approach that reflects the implications of home charging and battery swapping stations in urban environments and accounts for the stochasticity of vehicle use.The developed method is demonstrated using a case study that comprises a sample residential area equipped with rooftop PV. The comparison of home charging and battery swapping stations shows that both approaches allow significant increases of self-consumption of locally provided electricity over conventional opportunistic home charging, with battery swapping stations evincing the highest increase in flexibility. This increase in added flexibility might, however, not justify a system-wide introduction of battery swapping stations for BEVs, as required surplus batteries and industry-wide standardization entail added efforts over home charging.Policy recommendations derived by this work support building regulations regarding the installation of charging stations and the utilization of PV rooftop potentials considering the anticipated market penetration of BEVs and the possibility to share electricity within local energy hubs.

Effect of varying hydrologic regime on seasonal total maximum daily loads (TDML) in an agricultural watershed

Rising hypoxia due to the eutrophication of riverine ecosystems is primarily caused by the transport of nutrients. The majority of existing TMDL models cannot be efficienty applied to represent nutrient concentrations in riverine ecosystems having varying flow regimes due to seasonal differences. Accurate TMDL assessment requires nutrient loads and suspended matter estimation under varying flow regimes with minimal uncertainty. Though a large database can enhance accuracy, it can be resource intensive. This study presents the design of an innovative modeling strategy to optimize the use of existing datasets to effectively represent streamflow-load dynamics while minimizing uncertainty. The study developed an approach to assess TMDLs using six different flux models and kriging techniques (i) to enhance the accuracy of nutrient load estimation under different hydrologic regimes (flow stratifications) and (ii) to derive an optimal modeling strategy and sampling scheme for minimizing uncertainty. The flux models account for uncertainty in load prediction across varying flow strata, and the deployment of multiple load calculation procedures. Further, the proposed flux approach allows the determination of load exceedance under different TMDL scenarios aimed at minimizing uncertainty to achieve reliable load predictions. The study employed a 10-year dataset (2009–2018) consisting of daily flow data (m3/sec) and weekly data (mg/L) for nitrogen (N), phosphorus (P) and total suspended solids (TSS) concentrations in three distinct agricultural sites in+ the Minnesota River Watershed. The outcomes were analyzed geospatially in a Geographic Information System (GIS) environment using the kriging interpolation technique. The study recommends (i) triple stratification of flows to obtain accurate load estimates, and (ii) an optimal sampling scheme for nitrogen and phosphorous with 30.6 % and 49.8 % datapoints from high flow strata. The study outcomes are expected to contribute to the planning of economically and technically sound combinations of best management practices (BMPs) required for achieving total maximum daily loads (TMDL) in a watershed.

Innovative Legal Modeling for Interdisciplinary Studies on Law and Economic Behavior

This study introduces a new paradigm for interdisciplinary empirical legal research by presenting a groundbreaking perspective on the integration and interconnectedness of law and economics. Our research argues for the necessity of this shift. The conventional pragmatic paradigm, which risks fragmenting concepts and methods by reducing meaning to mere data and facts, is eschewed in favor of an integrationinterconnection theory. This paper investigates the theoretical foundations of the proposed paradigm through library research and philosophical inquiry. Our research uncovers two distinct approaches within the new empirical legal studies paradigm. Firstly, the historical approach requires a restructuring of the theory, leading to multidimensional and holistic models. Secondly, the philosophyasasystem approach necessitates method restructuring by utilizing cognitive, holistic, open, and purposeful models. The integration aspect of the paradigm necessitates a comprehensive theoretical restructuring, while the interconnection aspect requires a broadened viewpoint. The historical approach is demonstrated by historical reformation, which encompasses multidimensional and holistic models. In contrast, the philosophyasasystem approach entails methodical reforms through the incorporation of models that focus on cognition, wholeness, openness, and purposefulness. The historical and methodical restructuring prototypes can be seen in the Integrative Legal Theory Model and the Critical Legal Functionalism Model. Our study highlights the significance of incorporating economic behavior into the interdisciplinary framework of empirical legal studies. This will enable a more comprehensive understanding of the complex connections between law and economic dynamics.

Genetic predisposition to Alzheimer’s disease is differentially associated with joint volumetric variations in the disease continuum

AbstractBackgroundImaging genetics studies commonly assess the individual association between genetic information and brain features without considering joint brain modulations. The aim of this study was to explore the structural variation of the brain at different stages of Alzheimer’s disease (AD), depending on the genetic predisposition to AD.MethodA total of 351 cognitively unimpaired (CU) middle‐age participants from the ALFA (Alzheimer’s and FAmilies) study and 330 cognitively impaired participants from ADNI (Alzheimer’s Disease Neuroimaging Initiative) with magnetic resonance imaging scans, genetic information and CSF‐amyloid status were included. They were classified as CU Aβ‐, CU Aβ+, (mild cognitive impairment) MCI Aβ+ and AD Aβ+[Table 1]. Freesurfer was used to obtain cortical and subcortical parcellations using the Desikan‐Killiany atlas. A polygenic risk score estimating each participant’s genetic predisposition to AD (PRS‐AD) was calculated including genetic variants at the genome‐wide suggestive level (5×10‐6). Individuals were classified into high/low genetic predisposition groups. Statistical analysis was based on compositional data analysis. First, we computed the optimal multivariate brain structural variation signature (elastic net selection). Then, a logistic regression was performed to determine the association between the optimal signature and the PRS‐AD. Models were stratified by disease stage and adjusted for age and sex.ResultIndividuals at different stages on the AD continuum displayed different brain volumetric modulations associated with higher genetic predisposition to AD [Figure 1]. For instance, the optimal signature associated with an increased risk of AD was mainly characterized by increased hippocampal volumes in Aβ‐ individuals, but decreased volumes in MCI individuals along with the modulation of other temporal regions. Moreover, in AD patients at higher genetic predisposition of AD, the optimal signature was characterized by increased and decreased volumes in frontal regions (e.g. pars triangularis, lateral orbitofrontal vs. medial orbitofrontal, respectively) [Figure 2].ConclusionResults showed AD stage‐specific multivariate volumetric variation associated with an increased genetic risk of AD. Main results suggested that regions that modulated together, although differing according to AD stage, were implicated in the structural brain topology of specific memory networks. The analysis of the joint volumetric variation of brain subregions brings an innovative modeling perspective for neurogenetic studies of AD.

Genetic predisposition to Alzheimer’s disease is differentially associated with joint volumetric variations in the disease continuum

AbstractBackgroundImaging genetics studies commonly assess the individual association between genetic information and brain features without considering joint brain modulations. The aim of this study was to explore the structural variation of the brain at different stages of Alzheimer’s disease (AD), depending on the genetic predisposition to AD.MethodA total of 351 cognitively unimpaired (CU) middle‐age participants from the ALFA (Alzheimer’s and FAmilies) study and 330 cognitively impaired participants from ADNI (Alzheimer’s Disease Neuroimaging Initiative) with magnetic resonance imaging scans, genetic information and CSF‐amyloid status were included. They were classified as CU Aβ‐, CU Aβ+, (mild cognitive impairment) MCI Aβ+ and AD Aβ+[Table 1]. Freesurfer was used to obtain cortical and subcortical parcellations using the Desikan‐Killiany atlas. A polygenic risk score estimating each participant’s genetic predisposition to AD (PRS‐AD) was calculated including genetic variants at the genome‐wide suggestive level (5×10‐6). Individuals were classified into high/low genetic predisposition groups. Statistical analysis was based on compositional data analysis. First, we computed the optimal multivariate brain structural variation signature (elastic net selection). Then, a logistic regression was performed to determine the association between the optimal signature and the PRS‐AD. Models were stratified by disease stage and adjusted for age and sex.ResultIndividuals at different stages on the AD continuum displayed different brain volumetric modulations associated with higher genetic predisposition to AD [Figure 1]. For instance, the optimal signature associated with an increased risk of AD was mainly characterized by increased hippocampal volumes in Aβ‐ individuals, but decreased volumes in MCI individuals along with the modulation of other temporal regions. Moreover, in AD patients at higher genetic predisposition of AD, the optimal signature was characterized by increased and decreased volumes in frontal regions (e.g. pars triangularis, lateral orbitofrontal vs. medial orbitofrontal, respectively) [Figure 2].ConclusionResults showed AD stage‐specific multivariate volumetric variation associated with an increased genetic risk of AD. Main results suggested that regions that modulated together, although differing according to AD stage, were implicated in the structural brain topology of specific memory networks. The analysis of the joint volumetric variation of brain subregions brings an innovative modeling perspective for neurogenetic studies of AD.

Tool Condition Monitoring Based on Nonlinear Output Frequency Response Functions and Multivariate Control Chart

Tool condition monitoring (TCM) is a key technology for intelligent manufacturing. The objective is to monitor the tool operation status and detect tool breakage so that the tool can be changed in time to avoid significant damage to workpieces and reduce manufacturing costs. Recently, an innovative TCM approach based on sensor data modelling and model frequency analysis has been proposed. Different from traditional signal feature-based monitoring, the data from sensors are utilized to build a dynamic process model. Then the nonlinear output frequency response functions, a concept which extends the linear system frequency response function to the nonlinear case, over the frequency range of the tooth passing frequency of the machining process are extracted to reveal tool health conditions. In order to extend the novel sensor data modelling and model frequency analysis to unsupervised condition monitoring of cutting tools, in the present study, a multivariate control chart is proposed for TCM based on the frequency-domain properties of machining processes derived from the innovative sensor data modelling and model frequency analysis. The feature dimension is reduced by principal component analysis first. Then the moving average strategy is exploited to generate monitoring variables and overcome the effects of noises. The milling experiments of titanium alloys are conducted to verify the effectiveness of the proposed approach in detecting excessive flank wears of solid carbide end mills. The results demonstrate the advantages of the new approach over conventional TCM techniques and its potential in industrial applications.

Unraveling Debris-Enhanced Local Scour Patterns around Non-Cylindrical Bridge Piers: Experimental Insights and Innovative Modeling

Bridge structures face a critical threat from localized scour-induced damage, prompting urgent attention to civil infrastructure resilience. Prior research has primarily focused on the influence of pier shapes on scour patterns. However, the exploration of the combined effects of various debris shapes, each possessing distinct properties, on predictive scour depth models around the non-cylindrical pier has hitherto remained less researched. This study explored the complex dynamics governing local scour around bridge piers, focusing on the influence of surface and near-surface debris. This research shed light on changes in scour depth by investigating factors like pier geometries, debris arrangements, and submersion depths. The experiments and analysis revealed the effects of various pier shapes—cylindrical, square, rectangular, oblong, oval, and lenticular—on scour patterns. Different geometries influenced primary scour zones and affected areas, with square piers causing the deepest scour and lenticular ones showing shallower instances. Scour depths typically peaked upstream across geometries, but ogival and lenticular shapes exhibited unique patterns. The research also introduced a formula that integrated debris attributes into predictive scour depth modeling, validated with favorable accuracy. Ultimately, this predictive model advances scour prediction, particularly in debris-laden flows, offering valuable insights for engineering and management practices in understanding real-world scour mechanisms and hydraulic dynamics.

Construction of digital twin for clamped near-net-shape blade in adaptive manufacturing

The near-net-shape engine blade body has already achieved design accuracy after blanking process, while the leading and trailing edges (LE/TE) require further CNC machining. When a blade blank with a thin-walled structure body is formed, the forming deformation caused by stress, temperature and pressure cause the design LE/TE profile to mismatch with the blank body. To solve the mismatch, the LE/TE design model is modified to an adaptive model that matches the deformed body and meets the design requirements for aerodynamic performance. However, the clamping deformation occurs when clamping a formed blank with a design fixture, making the adaptive model unsuitable for guiding the machining of the blank. This paper provides an innovative modelling method to construct a clamped adaptive digital twin (CADT). With the spring-back after clamp release, the profile of the blade machined by the CADT will be consistent with that of the adaptive model. A CADT is constructed for a large-sized near-net-shape fan blade blank. The maximum clamping deformation of the blank is over 0.44 mm, while the average relative error of the machined profile to adaptive model is only 0.49 %. The results show that the proposed method can accurately and efficiently construct a CADT to guide the finishing of a near-net-shape blade.

Hybrid Methodology for Parametrisation of Proton Exchange Membrane Fuel Cell Model for Diagnostics and Control Applications

Abstract Electrochemical impedance spectroscopy (EIS) is a very powerful tool for the diagnosis and characterisation of fuell cells (FC). However, there is still a lack of physico-chemically consistent models that include parameters with a clear physical meaning and can be related to intrinsic parameters of FC. To fill this knowledge gap, this paper presents a novel, mechanistically-based and computationally-efficient FC modeling framework for time and frequency domain simulations. Furthermore, the model consistently handles forward and backward reactions, ensuring its validity at all current densities. These features enable the development of a hybrid methodology for parameterising the FC model in both domains, resulting in unprecedented accuracy in determining the internal states around which the EIS perturbation is applied. Furthermore, innovative modeling framework incorporates a 1D analytical solution of FC impedance that for the first time accounts for both electrodes, the membrane and individual effects of the electrodes coupled to the respective GDL and channel, all significantly impacting the accuracy of the model. This was confirmed by state-of-the-art reproduction of experimental data with R$^2$ values exceeding 0.965 for data not used in the parameterisation. The presented modeling framework thus provides a modelling basis for observer functionalities beyond the state-of-the-art.