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Freshwater scarcity has driven the integration of technological advancements and automation systems in agriculture in order to attempt to improve water-use efficiency. For irrigation canals, water-use efficiency is, in great measure, limited by the performance of management systems responsible for controlling the flow and delivering water to the farmers. Recent studies show a significant sensitivity of the results obtained from irrigation canal control algorithms with respect to the Manning’s roughness coefficient value, thus, highlighting the importance of its correct estimation to ensure an accurate and efficient water delivery service. This is the reason why the friction coefficient algorithm was developed, to monitor the real behaviour of any irrigation canal by calculating the Manning’s roughness coefficient constantly. The friction coefficient algorithm was conceived as a powerful offline tool that is integrated in a control diagram of any irrigation canal, concretely in an optimization control algorithm, which can reconfigure canal gates according to the current crop water demand and the real Manning’s roughness coefficient values. The friction coefficient algorithm has been applied in several irrigation canals and different scenarios, with accurate results obtaining an average Manning coefficient deviation among 2 × 10-4 and 4.5 × 10-4., Peer Reviewed, Postprint (published version)

This paper proposes a methodology for incipient fault diagnosis and the corresponding trend prediction in nonlinear closed-loop systems considering stochastic Gaussian and non-Gaussian uncertainties. The proposed approach is based on the use of the particle filtering technique for estimating the system states and outputs. From these estimations, the residual signals would be generated through a mathematical filtering and augmentation technique, allowing the incipient fault estimation that is evaluated using the designed fixed and adaptive thresholds that consider system uncertainties. In this way, the fault detection performance is improved but also the false detection and false alarm problems are comprehensively addressed. Moreover, the augmented Gauss–Newton identification method is used for the incipient fault trend prediction. Finally, to evaluate the effectiveness of the proposed approach, the incipient fault diagnosis in the heat transfer unit built in the nonlinear closed-loop continuous stirred-tank reactor (CSTR) system is used. Besides, the confusion matrix is employed to assess the results from a quantitative point of view.

This paper proposes a methodology for incipient fault diagnosis and the corresponding trend prediction in nonlinear closed-loop systems considering stochastic Gaussian and non-Gaussian uncertainties. The proposed approach is based on the use of the particle filtering technique for estimating the system states and outputs. From these estimations, the residual signals would be generated through a mathematical filtering and augmentation technique, allowing the incipient fault estimation that is evaluated using the designed fixed and adaptive thresholds that consider system uncertainties. In this way, the fault detection performance is improved but also the false detection and false alarm problems are comprehensively addressed. Moreover, the augmented Gauss–Newton identification method is used for the incipient fault trend prediction. Finally, to evaluate the effectiveness of the proposed approach, the incipient fault diagnosis in the heat transfer unit built in the nonlinear closed-loop continuous stirred-tank reactor (CSTR) system is used. Besides, the confusion matrix is employed to assess the results from a quantitative point of view.

[Resumen] Este trabajo aborda la caracterización simultánea del modo principal de vibración y el retardo de un robot teleoperado compuesto por un eslabón flexible. Para ello utiliza las medidas del encoder del motor que mueve el brazo y de unas galgas extensiométricas colocadas en la base del brazo. Estas últimas pueden presentar un offset apreciable y un ruido de alta frecuencia. Se desarrolla un nuevo algoritmo para realizar la identificación en tiempo real de los parámetros característicos de la dinámica del robot teleoperado. Dicho algoritmo es más rápido que otros previamente desarrollados y se obtiene a partir de la metodología de la identificación algebraica, incluyendo su diseño la robustez ante los problemas mencionados en las medidas de las galgas. El estimador obtenido se aplica y evalúa posteriormente en un prototipo. Se presentan resultados simulados y experimentales que demuestran la rapidez, precisión y robustez de la identificación realizada., [Abstract] This paper addresses the simultaneous characterization of the main vibration mode and the time delay of a teleoperated robot with a single flexible link. It uses the measurements of the motor encoder and strain gauges placed at the base of the link. These last ones may have significant offset and high-frequency noise. A new identification algorithm is developed that performs the realtime identification of the characteristic parameters of the dynamics of a teleoperated robot. This algorithm is faster than the ones previously developed and it is obtained using the algebraic estimation framework, its design includes robustness features to the disturbances in the strain gauges already mentioned. The obtained estimator was lately applied and evaluated on an experimental set-up. Simulated and experimental results show the high speed, accuracy and robustness of the identification carried out.

In this work, we investigate some theoretical aspects related to the estimation approach proposed by Liebermeister and Klipp, 2006, in which general rate laws, derived from standardized enzymatic mechanisms, are exploited to kinetically describe the fluxes of a metabolic reaction network, and multiple metabolic steady-state measurements are exploited to estimate the unknown kinetic parameters. Further mathematical details are deeply investigated, and necessary conditions on the amount of information required to solve the identification problem are given. Moreover, theoretical results for the parameter identifiability are provided, and symmetrical and modular properties of the proposed approach are highlighted when the global identification problem is decoupled into smaller and simpler identification problems related to the single reactions of the network. Among the advantages of the proposed innovative approach are (i) non-restrictive conditions to guarantee the solvability of the parameter estimation problem, (ii) the unburden of the usual computational complexity for such identification problems, and (iii) the ease of obtaining the required number of measurements, which are actually steady-state data, experimentally easier to obtain with respect to the time-dependent ones. A simple example concludes the paper, highlighting the mentioned advantages of the method and the implementation of the related theoretical result.

Acuerdo transformativo, In recent years, the sustained increase in the worldwide demand for lithium batteries goes side by side with the need for reliable methods to assess battery performance. Of particular importance is assessing Lithium-Ion cell’s thermal behavior given its role on hazard and aging of batteries. An essential task towards developing battery management systems is the estimation of physical parameters and their uncertainties in terms of both models and observations. Consequently, this paper analyzes the uncertainty in a thermal single particle model for a lithium-ion cell. In the first part of the manuscript, we explore the model adequacy by analyzing the forward and backward uncertainty propagation in the model in terms of diffusion coefficients, reaction rate constants, and observations of cell voltage. In the second part of the manuscript, we infer the cell’s reversible heat given the energy balance equation and the cell’s temperature observations. We argue that the methods proposed here may be extended to analyze other more general lithium-ion models, Spanish Ministry of Science and Innovation, Research group MOMAT, Depto. de Análisis Matemático y Matemática Aplicada, Fac. de Ciencias Matemáticas, TRUE, pub

CRUE-CSIC (Acuerdos Transformativos 2023), In this work, we propose a novel methodology to identify parameters of compartmental epidemiological models. It is based on solving a multi-objective optimization problem that consists in fitting some of the model outputs to real observations. First, according to the available data of the considered epidemic, we define a multi-objective optimization problem where the model parameters are the optimization variables. Then, this problem is solved by considering a particular optimization algorithm called ParWASF-GA (Parallel Weighting Achievement Scalarizing Function Genetic Algorithm). Finally, the decision maker chooses, within the set of possible solutions, the values of parameters that better suit his/her preferences. In order to illustrate the benefit of using our approach, it is applied to estimate the parameters of a deterministic epidemiological model, called Be-CoDiS (Between-Countries Disease Spread), used to forecast the possible spread of human diseases within and between countries. We consider data from different Ebola outbreaks from 2014 up to 2019. In all cases, the proposed methodology helps to obtain reasonable predictions of the epidemic magnitudes with the considered model., Ministerio de Ciencia e Innovación (MICINN), Junta de Andalucía, Universidad Complutense de Madrid, Depto. de Análisis Matemático y Matemática Aplicada, Fac. de Ciencias Matemáticas, Instituto de Matemática Interdisciplinar (IMI), TRUE, pub

An iterative strategy for the reconstruction of objects buried in a medium and the identification of their material parameters is analysed. The algorithm alternates guesses of the domains using topological derivatives with corrections of the parameters obtained by descent techniques. Numerical experiments in geometries with multiple scatterers show that our scheme predicts the number, location and shape of objects, together with their physical parameters, with reasonable accuracy in a few steps., MEC–FEDER, Depto. de Análisis Matemático y Matemática Aplicada, Fac. de Ciencias Matemáticas, TRUE, pub

The recursive Levenberg–Marquardt (L–M) algorithm has been generally implemented for the parameter identification of nonlinear models in online situations. However, owing to the intrinsic structural characteristics (i.e., the global nonlinear local linearity) of the radial basis function autoregressive (RBF-AR) model, the direct application of the recursive L–M (R-LM) algorithm significantly restricts its performance. To improve the comprehensive performance, a hierarchical recursive L–M (H-R-LM) algorithm is proposed in this paper. The concepts of parameter separation and model decomposition are integrated into the algorithm to reduce the model's complexity. Multi-innovation and forgetting factors leverage historical data more reasonably, thus accelerating the convergence. Hierarchical recognition and alternating optimization can improve identification accuracy. Two experiments on analog and actual data sets are used to compare the performance of the RBF-AR model using R-LM and H-R-LM algorithms. Compared with the R-LM algorithm, experiments verify that the new H-R-LM algorithm presents higher identification accuracy, faster convergence rate, improved predictive performance, and less computational effort. © 2023 Elsevier Inc.

Understanding and characterizing the mechanical and structural properties of brain tissue is essential for developing and calibrating reliable material models. Based on the Theory of Porous Media, a novel nonlinear poro-viscoelastic computational model was recently proposed to describe the mechanical response of the tissue under different loading conditions. The model contains parameters related to the time-dependent behavior arising from both the viscoelastic relaxation of the solid matrix and its interaction with the fluid phase. This study focuses on the characterization of these parameters through indentation experiments on a tailor-made polyvinyl alcohol-based hydrogel mimicking brain tissue. The material behavior is adjusted to ex vivo porcine brain tissue. An inverse parameter identification scheme using a trust region reflective algorithm is introduced and applied to match experimental data obtained from the indentation with the proposed computational model. By minimizing the error between experimental values and finite element simulation results, the optimal constitutive model parameters of the brain tissue-mimicking hydrogel are extracted. Finally, the model is validated using the derived material parameters in a finite element simulation., We gratefully acknowledge the financial support by the Deutsche Forschungsgemeinschaft (DFG, German Research Foundation) through grants BU 3728/3-1 and STE 544/70-1 and financial support by the Austrian Science Fund (FWF, Project-Nr. I 4828-N)., Peer Reviewed, Postprint (published version)

As fractional-order models increasingly appear as an option to describe complex systems, they generate a demand for parameter estimation methods in the time and frequency domain. The extended Kalman filter (EKF) is a promising technique in the time domain, but it is sensitive to the initial conditions of the state and error covariance matrices. In the case of integer-order systems, evolutionary algorithms (EAs) can tackle EKF's sensitiveness issues. The algorithm usually uses EAs to optimise the initial conditions for the EK, leading to a better estimate of the system parameters and states. Here, we extend this methodology to fractional-order models to estimate the model's fractional order and parameters. Finally, we demonstrate the effectiveness of this methodology on a simple mechanical model., Part of ISBN 9798350321685QC 20230821

Masterarbeit Universität Innsbruck 2023

The lithium-ion battery is the key power source of an electric vehicle. The cornerstone of safe transportation vehicles is reliable real-time state of charge (SOC) information. Since batteries are the primary form of energy storage in electric vehicles (EVs) and the smart grid, estimation of the state of charge is a critical need for batteries. The SOC estimate approach is considered to be precise and simple to apply for such applications. In this paper, After studying a battery model with an appropriate resistor-capacitor (RC) circuit, A lookup table derived from experimental studies describes the nonlinear connection between the Open Circuit Voltage Voc and the the state of charge. However, if temperature or SOC varies, the equivalent circuit model's characteristics will vary, decreasing the accuracy of SOC calculation. The recursive least squares (RLS) and nonlinear Extended Kalman filters are used in this research to offer a charge estimate technique with online parameter identification to handle this problem. RLS dynamically updates the Thevenin model's parameters. In order to improve the precision of SOC prediction under charge and discharge settings, we presented a regression least-squares-extended Kalman filter (RLS-EKF) estimation approach in this study. The objective of this research is to ensure the updating of the battery parameters and to evaluate the influence of this improvement on the convergence of the state of charge towards the real value. The simulation results suggest that the RLS EKF estimation technique, which is based on precise modeling, may greatly increase SOC estimation accuracy.

The dynamics of a moving train are usually described by means of a motion model based on Newton's second law. This model uses as input track geometry data and train characteristics like mass, the parameters that model the running resistance, the maximum tractive effort and power, and the brake rates to be applied. It can reproduce and predict train dynamics accurately if the mentioned train characteristics are carefully calibrated. The model constitutes the core element of a broad variety of railway applications, from timetabling tools to Driver Advisory Systems and Automatic Train Operation. Among the existing train motion model calibration techniques, those that use operational data are of particular interest, as they benefit from on- board recorded data, capturing the train dynamics during operation. In this literature review article we provide an overview of the train motion model calibration techniques that have been published in the scientific literature between January 2000 and December 2021 and either use operational data or can be minimally adapted to use it. To this end, we present a critical overview of the existing train motion model calibration approaches, distinguishing online calibration that analyzes data on- the-go and offline calibration that analyzes historical data batchwise. We propose a research agenda and highlight some potential goals to be tackled in the near future: from devising accurate online calibrators for eco-driving applications to quantitizing the physical sources of parameter variation. Last, we discuss practical recommendations for practitioners and scholars inferred from the current state of the art., Transport and Planning

Micromechanical constitutive parameters are important for many engineering materials, typically in microelectronic applications and material design. Their accurate identification poses a three-fold experimental challenge: (i) deformation of the microstructure is observable only at small scales, requiring SEM or other microscopy techniques; (ii) external loadings are applied at a (larger) engineering or device scale; and (iii) material parameters typically depend on the applied manufacturing process, necessitating measurements on material produced with the same process. In this paper, micromechanical parameter identification in heterogeneous solids is addressed through multiscale experiments combined with Integrated Digital Image Correlation (IDIC) in conjunction with various possible computational homogenization schemes. To this end, some basic concepts underlying multiscale approaches available in the literature are first reviewed, discussing their respective advantages and disadvantages from the computational as well as experimental point of view. A link is made with recently introduced uncoupled methods, which allow for identification of material parameter ratios at the microscale, still lacking a proper normalization. Two multiscale methods are analysed, allowing to bridge the gap between microstructural kinematics and macroscopically measured forces, providing the required normalization. It is shown that an integrated experimental–computational scheme provides relaxed requirements on scale separation. The accuracy and performance of the discussed techniques are analysed by means of virtual experimentation under plane strain and large strain assumptions for unidirectional fibre-reinforced composites. The robustness against image noise is also assessed. The obtained results demonstrate that the expected accuracy is typically within 10% RMS error for all multiscale methods, but decreasing to 1% RMS error for the optimal method.

The ongoing decarbonisation of the electric power system brings new challenges in terms of system dynamics and stability, as the substitution of generation units with rotating masses towards generation units based on power electronics entails a substantial loss of inertia. To meet the new challenges and maintain the reliability of the electrical grid, innovative solutions are required. Therefore, this lecture will present digital twin development challenges and opportunities of a dynamic hardware emulator that can be used for controller hardware in the loop (CHIL) testing and is based on a small-scale laboratory system. To build the simulation model, the parameters of involved synchronous machines, excitation systems, prime movers and transmission lines have been identified and then compared to laboratory measurements to assess the accuracy of the digital twin. Static and dynamic accuracy have been investigated, and an overall good accuracy can be shown with the help of quantification of errors. Furthermore, a case study is presented where the digital twin was used to design a controller to damp inter-area oscillations with the help of wide area measurements. This controller was then implemented and tested within the dynamic hardware emulator in the laboratory.

The recent outbreak of COVID-19 underlined the need for a fast and trustworthy methodology to identify the features of a pandemic, whose early identification is of help for designing non-pharmaceutical interventions (including lockdown and social distancing) to limit the progression of the disease. A common approach in this context is the parameter identification from deterministic epidemic models, which, unfortunately, cannot take into account the inherent randomness of the epidemic phenomenon, especially in the initial stage; on the other hand, the use of raw data within the framework of a stochastic model is not straightforward. This note investigates the stochastic approach applied to a basic SIR (Susceptible, Infected, Recovered) epidemic model to enhance information from raw data generated in silico. The stochastic model consists of a Continuous-Time Markov Model, describing the epidemic outbreak in terms of stochastic discrete infection and recovery events in a given region, and where independent random paths are associated to different provinces of the same region, which are assumed to share the same set of model parameters. The estimation procedure is based on the building of a loss function that symmetrically weighs first-order and second-order moments, differently from the standard approach that considers a highly asymmetrical choice, exploiting only first-order moments. Instead, we opt for an innovative symmetrical identification approach which exploits both moments. The new approach is specifically proposed to enhance the statistical information content of the raw epidemiological data.

In this paper, a traction load model parameter identification method based on the improved sparrow search algorithm (ISSA) is proposed. According to the load characteristics of the AC traction power supply system under transient disturbance, the model structure of the traction load is equated to the composite load model structure of the static load shunt induction motor's dynamic load. The traditional sparrow search algorithm is improved to enhance its accuracy and convergence. The generalization ability of the model was tested, and the accuracy of the proposed model was verified. Using the ISSA to determine the load model from the measured data, the results can verify the effectiveness of the ISSA for comprehensive load model parameter identification. Comparing the ISSA with the traditional SSA and PSO algorithms, it shows that the ISSA has better accuracy and convergence.

In the field of bone regeneration, insertion of scaffolds favours bone formation by triggering the differentiation of mesenchymal cells into osteoblasts. The presence of Calcium ions (Ca2+) in the interstitial fluid across scaffolds is thought to play a relevant role in the process. In particular, the Ca2+ patterns can be used as an indicator of where to expect bone formation. In this work, we analyse the inverse problem for these distribution patterns, using an advection-diffusion nonlinear model for the concentration of Ca2+. That is, given a set of observables which are related to the amount of expected bone formation, we aim at determining the values of the parameters that best fit the data. The problem is solved in a realistic 3D-printed structured scaffold for two uncertain parameters: the amplitude of the velocity of the interstitial fluid and the ionic release rate from the scaffold. The minimization in the inverse problem requires multiple evaluations of the nonlinear model. The computational cost is alleviated by the combination of standard Proper Orthogonal Decomposition (POD), to reduce the number of degrees of freedom, with an adhoc hyper-reduction strategy, which avoids the assembly of a full-order system at every iteration of the Newton’s method. The proposed hyper-reduction method is formulated using the Principal Component Analysis (PCA) decomposition of suitable training sets, devised from the weak form of the problem. In the numerical tests, the hyper-reduced formulation leads to accurate results with a significant reduction of the computational demands with respect to standard POD., Peer Reviewed, Postprint (author's final draft)

In this paper, a traction load model parameter identification method based on the improved sparrow search algorithm (ISSA) is proposed. According to the load characteristics of the AC traction power supply system under transient disturbance, the model structure of the traction load is equated to the composite load model structure of the static load shunt induction motor's dynamic load. The traditional sparrow search algorithm is improved to enhance its accuracy and convergence. The generalization ability of the model was tested, and the accuracy of the proposed model was verified. Using the ISSA to determine the load model from the measured data, the results can verify the effectiveness of the ISSA for comprehensive load model parameter identification. Comparing the ISSA with the traditional SSA and PSO algorithms, it shows that the ISSA has better accuracy and convergence.

In this paper, a traction load model parameter identification method based on the improved sparrow search algorithm (ISSA) is proposed. According to the load characteristics of the AC traction power supply system under transient disturbance, the model structure of the traction load is equated to the composite load model structure of the static load shunt induction motor's dynamic load. The traditional sparrow search algorithm is improved to enhance its accuracy and convergence. The generalization ability of the model was tested, and the accuracy of the proposed model was verified. Using the ISSA to determine the load model from the measured data, the results can verify the effectiveness of the ISSA for comprehensive load model parameter identification. Comparing the ISSA with the traditional SSA and PSO algorithms, it shows that the ISSA has better accuracy and convergence.

The battery is a component with significant impact on both the cost and environmental footprint of a full electric vehicle (EV). Consequently, there is a strong motivation to maximize its degree of utilization. Usage limits are enforced by the battery management system (BMS) to ensure safe operation and limit battery degradation. The limits tend to be conservative to account for uncertainty in battery state estimation as well as changes in the battery's characteristics due to aging. To improve the utilization degree, aging sensitive battery control is necessary. This refers to control that a) adjusts during the battery's life based on its state and b) balances the trade-off between utilization and degradation according to requirements from the specific application. In state-of-the-art battery installations, only three signals are measured; current, voltage and temperature. However, the battery's behaviour is governed by other states that must be estimated such as its state-of-charge (SOC) or local concentrations and potentials. The BMS therefore relies on models to estimate states and to perform control actions. In order to realize points a) and b), the models that are used for state estimation and control must be updated onboard. An updated model can also serve the purpose of diagnosing the battery, since it reflects the changing properties of an aging battery. This thesis investigates identification of physics-based and empirical battery models from operational EV data. The work is divided into three main studies. 1) A global sensitivity analysis was performed on the parameters of a high-order physics-based model. Measured current profiles from real EV:s were used as input and the parameters' impact on both modelled cell voltage and other internal states was assessed. The study revealed that in order to excite all model parameters, an input with high current rates, large SOC span and longer charge or discharge periods was required. This was only present in the da, Elekriska fordon utgör en allt större andel av världens fordonsflotta. Batteriet är en komponent med betydande påverkan både på fordonets kostnadoch dess miljö- och klimatpåverkan. Det är därför viktigt att försöka maximera batteriets utnytjandegrad. Användargränser upprätthålls av batterietsstyrsystem, såkallad BMS, för att garantera säker drift samt för att begränsabatteriets åldrande. Användargränserna tenderar att vara konservativa för attta höjd för osäkerhet i tillståndsestimeringen samt batteriets förändrade egenskaper under dess livstid. För att utöka utnyttjandegraden är ålderskänsligstyrning nödvändig. Med detta avses styrning som a) justeras under batterietslivstid och b) balancerar utnyttjande och prestanda på ett sätt som passar enspecifik applikation. Ombord på fordon mäts typiskt tre signaler; ström, spänning och temperatur. Batteriets beteende bestäms dock av andra tillstånd som måste estimeras, såsom dess laddnivåeller lokala koncentrationer och potentialer. BMS:enförlitar sig därför på modeller för att estimera interna tillstånd och utföra styrning. För att uppfylla punkterna a) och b) måste modellerna som användsuppdateras ombord i takt med att batteriet åldras. En uppdaterad modellkan också fungera som ett diagnostiskt verktyg eftersom det speglar batteriets förändrade egenskaper. Den här avhandlingen undersöker identifieringav fysikbaserade och empiriska modeller från kördata. Arbetet delas in i treseparata studier. 1) En global känslighetsanalys utfördes på parametrarna i en fysikbaseradmodell av hög ordning. Som inputsignal användes uppmätt ström från riktigaelfordon i drift. Parametrarnas effekt på både cellspänning och interna batteritillstånd analyserades. Studien visade att alla modellparametrar exciteradesav strömmen från ett helelektriskt fordon. Anledningen var att batteriernaanvändes inom ett brett SOC spann samt att den dragna strömmen var stor.I fordon med snävare SOC span och lägre strömmar var inte alla parametrarkänsliga. 2) Dynam, QC 20220516

In this paper, a traction load model parameter identification method based on the improved sparrow search algorithm (ISSA) is proposed. According to the load characteristics of the AC traction power supply system under transient disturbance, the model structure of the traction load is equated to the composite load model structure of the static load shunt induction motor's dynamic load. The traditional sparrow search algorithm is improved to enhance its accuracy and convergence. The generalization ability of the model was tested, and the accuracy of the proposed model was verified. Using the ISSA to determine the load model from the measured data, the results can verify the effectiveness of the ISSA for comprehensive load model parameter identification. Comparing the ISSA with the traditional SSA and PSO algorithms, it shows that the ISSA has better accuracy and convergence.

Despite the advantages of mathematical bioprocess modeling, successful model implementation already starts with experimental planning and accordingly can fail at this early stage. For this study, two different modeling approaches (mechanistic and hybrid) based on a four-dimensional antibody-producing CHO fed-batch process are compared. Overall, 33 experiments are performed in the fractional factorial four-dimensional design space and separated into four different complex data partitions subsequently used for model comparison and evaluation. The mechanistic model demonstrates the advantage of prior knowledge (i.e., known equations) to get informative value relatively independently of the utilized data partition. The hybrid approach displayes a higher data dependency but simultaneously yielded a higher accuracy on all data partitions. Furthermore, our results demonstrate that independent of the chosen modeling framework, a smart selection of only four initial experiments can already yield a very good representation of a full design space independent of the chosen modeling structure. Academic and industry researchers are recommended to pay more attention to experimental planning to maximize the process understanding obtained from mathematical modeling., Bundesministerium für Bildung und Forschung (BMBF)

The battery is a component with significant impact on both the cost and environmental footprint of a full electric vehicle (EV). Consequently, there is a strong motivation to maximize its degree of utilization. Usage limits are enforced by the battery management system (BMS) to ensure safe operation and limit battery degradation. The limits tend to be conservative to account for uncertainty in battery state estimation as well as changes in the battery's characteristics due to aging. To improve the utilization degree, aging sensitive battery control is necessary. This refers to control that a) adjusts during the battery's life based on its state and b) balances the trade-off between utilization and degradation according to requirements from the specific application. In state-of-the-art battery installations, only three signals are measured; current, voltage and temperature. However, the battery's behaviour is governed by other states that must be estimated such as its state-of-charge (SOC) or local concentrations and potentials. The BMS therefore relies on models to estimate states and to perform control actions. In order to realize points a) and b), the models that are used for state estimation and control must be updated onboard. An updated model can also serve the purpose of diagnosing the battery, since it reflects the changing properties of an aging battery. This thesis investigates identification of physics-based and empirical battery models from operational EV data. The work is divided into three main studies. 1) A global sensitivity analysis was performed on the parameters of a high-order physics-based model. Measured current profiles from real EV:s were used as input and the parameters' impact on both modelled cell voltage and other internal states was assessed. The study revealed that in order to excite all model parameters, an input with high current rates, large SOC span and longer charge or discharge periods was required. This was only present in the da, Elekriska fordon utgör en allt större andel av världens fordonsflotta. Batteriet är en komponent med betydande påverkan både på fordonets kostnadoch dess miljö- och klimatpåverkan. Det är därför viktigt att försöka maximera batteriets utnytjandegrad. Användargränser upprätthålls av batterietsstyrsystem, såkallad BMS, för att garantera säker drift samt för att begränsabatteriets åldrande. Användargränserna tenderar att vara konservativa för attta höjd för osäkerhet i tillståndsestimeringen samt batteriets förändrade egenskaper under dess livstid. För att utöka utnyttjandegraden är ålderskänsligstyrning nödvändig. Med detta avses styrning som a) justeras under batterietslivstid och b) balancerar utnyttjande och prestanda på ett sätt som passar enspecifik applikation. Ombord på fordon mäts typiskt tre signaler; ström, spänning och temperatur. Batteriets beteende bestäms dock av andra tillstånd som måste estimeras, såsom dess laddnivåeller lokala koncentrationer och potentialer. BMS:enförlitar sig därför på modeller för att estimera interna tillstånd och utföra styrning. För att uppfylla punkterna a) och b) måste modellerna som användsuppdateras ombord i takt med att batteriet åldras. En uppdaterad modellkan också fungera som ett diagnostiskt verktyg eftersom det speglar batteriets förändrade egenskaper. Den här avhandlingen undersöker identifieringav fysikbaserade och empiriska modeller från kördata. Arbetet delas in i treseparata studier. 1) En global känslighetsanalys utfördes på parametrarna i en fysikbaseradmodell av hög ordning. Som inputsignal användes uppmätt ström från riktigaelfordon i drift. Parametrarnas effekt på både cellspänning och interna batteritillstånd analyserades. Studien visade att alla modellparametrar exciteradesav strömmen från ett helelektriskt fordon. Anledningen var att batteriernaanvändes inom ett brett SOC spann samt att den dragna strömmen var stor.I fordon med snävare SOC span och lägre strömmar var inte alla parametrarkänsliga. 2) Dynam, QC 20220516

Robots manipulating deformable linear objects (DLOs) – such as surgical sutures in medical robotics, or cables and hoses in industrial assembly – can benefit substantially from accurate and fast differentiable predictive models. However, the off-the-shelf analytic physics models fall short of differentiability. Recently, neural-network-based data-driven models have shown promising results in learning DLO dynamics. These models have additional advantages compared to analytic physics models, as they are differentiable and can be used in gradient-based trajectory planning. Still, the data-driven approaches demand a large amount of training data, which can be challenging for real-world applications. In this paper, we propose a framework for learning a differentiable data-driven model for DLO dynamics with a minimal set of real-world data. To learn DLO twisting and bending dynamics in a 3D environment, we first introduce a new suitable DLO representation. Next, we use a recurrent network module to propagate effects between different segments along a DLO, thereby addressing a critical limitation of current state-of-the-art methods. Then, we train a data-driven model on synthetic data generated in simulation, instead of foregoing the time-consuming and laborious data collection process for real-world applications. To achieve a good correspondence between real and simulated models, we choose a set of simulation model parameters through parameter identification with only a few trajectories of a real DLO required. We evaluate several optimization methods for parameter identification and demonstrate that the differential evolution algorithm is efficient and effective for parameter identification. In DLO shape control tasks with a model-based controller, the data-driven model trained on synthetic data generated by the resulting models performs on par with the ones trained with a comparable amount of real-world data which, however, would be intractable to collect.

The capability of high-resolution modeling of crystals subjected to large plastic strain is essential in predicting many important phenomena occurring in polycrystalline materials, such as microstructure, deformation localization, and in-grain texture evolution. However, due to the heterogeneity of the plastic deformation in polycrystals, the simulation mesh gets distorted during the deformation. In this work, an adaptive remeshing approach is introduced for simulating large deformation of 3D polycrystals with high resolution under periodic boundary conditions. In the next step, high-resolution three-dimensional crystal plasticity simulations are used to investigate deformation heterogeneity and microstructure evolution during the cold rolling of interstitial free (IF-) steel. A Fast Fourier Transform (FFT)-based spectral solver is used to conduct crystal plasticity simulations using a dislocation-density-based crystal plasticity model. The in-grain texture evolution and misorientation spread are consistent with experimental results obtained using electron backscatter diffraction (EBSD) experiments. Crystal plasticity simulation shows that two types of strain localization develop during the large deformation of IF-steel. The first type forms band-like areas with large strain accumulation that appear as river patterns extending across the specimen. In addition to these river-like patterns, a second type of strain localization with rather sharp and highly localized in-grain shear bands is identified. These localized features are dependent on the crystallographic orientation of the grain and extend within a single grain. In addition to the strain localization, the evolution of in-grain orientation gradients, dislocation density, kernel average misorientation, and stress in major texture components are discussed., Team Jilt Sietsma

Physics-based crystal plasticity models rely on certain statistical assumptions about the collective behavior of dislocation populations on one slip system and their interactions with the dislocations on the other slip systems. One main advantage of using such physics-based constitutive dislocation models in crystal plasticity kinematic frameworks is their suitability for predicting the mechanical behavior of polycrystals over a wide range of deformation temperatures and strain rates with the same physics-based parameter set. In this study, the ability of a widely used temperature-dependent dislocation-density-based crystal plasticity formulation to reproduce experimental results, with a main focus on the yield stress behavior, is investigated. First, the material parameters are identified from experimental macroscopic stress–strain curves using a computationally efficient optimization methodology that uses a genetic algorithm along with the response surface methodology. For this purpose, a systematic set of compression tests on interstitial free (IF) steel samples is performed at various temperatures and strain rates. Next, the influence of the individual parameters on the observed behavior is analyzed. Based on mutual interactions between various parameters, the ability to find a unique parameter set is discussed. This allows identifying shortcomings of the constitutive law and sketch ideas for possible improvements. Particular attention is directed toward identifying possibly redundant material parameters, narrowing the acceptable range of material parameters based on physical criteria, and modifying the crystal plasticity formulation numerically for high-temperature use., Team Jilt Sietsma, Team Maria Santofimia Navarro

The increasing deployment of new technologies to contribute to the decarbonization of power systems is imposing new challenges in terms of system dynamics and stability. To deal with different operating and control issues in this sense, and support actual needs, advanced tools and solutions are required. Therefore, this paper presents a digital twin of a dynamic hardware emulator that can be used for controller hardware in the loop (CHIL) testing and is based on a small-scale laboratory system. To build the simulation model, the parameters of involved synchronous machines, excitation systems, prime movers and transmission lines have been identified and then compared to laboratory measurements to assess the accuracy of the digital twin. Static and dynamic accuracy have been investigated and an overall good accuracy can be shown with the help of quantification of errors. Furthermore, a case study is presented where the digital twin was used to design a controller to damp inter-area oscillations with the help of wide area measurements. This controller was then implemented and tested within the dynamic hardware emulator in the laboratory.

In this paper, we propose a parametric system identification approach for a class of continuous-time Lur'e-type systems. Using the Mixed-Time-Frequency (MTF) algorithm, we show that the steady-state model response and the gradient of the model response with respect to its parameters can be computed in a numerically fast and efficient way, allowing efficient use of global and local optimization methods to solve the identification problem. Furthermore, by enforcing the identified model to be inside the set of convergent models, we certify a stability property of the identified model, which allows for reliable generalized usage of the model also for other excitation signals than those used to identify the model. The effectiveness and benefits of the proposed approach are demonstrated in a simulation case study. Furthermore, we have experimentally shown that the proposed approach provides fast identification of both medical equipment and patient parameters in mechanical ventilation and, thereby, enables improved patient treatment.

The battery is a component with significant impact on both the cost and environmental footprint of a full electric vehicle (EV). Consequently, there is a strong motivation to maximize its degree of utilization. Usage limits are enforced by the battery management system (BMS) to ensure safe operation and limit battery degradation. The limits tend to be conservative to account for uncertainty in battery state estimation as well as changes in the battery's characteristics due to aging. To improve the utilization degree, aging sensitive battery control is necessary. This refers to control that a) adjusts during the battery's life based on its state and b) balances the trade-off between utilization and degradation according to requirements from the specific application. In state-of-the-art battery installations, only three signals are measured; current, voltage and temperature. However, the battery's behaviour is governed by other states that must be estimated such as its state-of-charge (SOC) or local concentrations and potentials. The BMS therefore relies on models to estimate states and to perform control actions. In order to realize points a) and b), the models that are used for state estimation and control must be updated onboard. An updated model can also serve the purpose of diagnosing the battery, since it reflects the changing properties of an aging battery. This thesis investigates identification of physics-based and empirical battery models from operational EV data. The work is divided into three main studies. 1) A global sensitivity analysis was performed on the parameters of a high-order physics-based model. Measured current profiles from real EV:s were used as input and the parameters' impact on both modelled cell voltage and other internal states was assessed. The study revealed that in order to excite all model parameters, an input with high current rates, large SOC span and longer charge or discharge periods was required. This was only present in the da, Elekriska fordon utgör en allt större andel av världens fordonsflotta. Batteriet är en komponent med betydande påverkan både på fordonets kostnadoch dess miljö- och klimatpåverkan. Det är därför viktigt att försöka maximera batteriets utnytjandegrad. Användargränser upprätthålls av batterietsstyrsystem, såkallad BMS, för att garantera säker drift samt för att begränsabatteriets åldrande. Användargränserna tenderar att vara konservativa för attta höjd för osäkerhet i tillståndsestimeringen samt batteriets förändrade egenskaper under dess livstid. För att utöka utnyttjandegraden är ålderskänsligstyrning nödvändig. Med detta avses styrning som a) justeras under batterietslivstid och b) balancerar utnyttjande och prestanda på ett sätt som passar enspecifik applikation. Ombord på fordon mäts typiskt tre signaler; ström, spänning och temperatur. Batteriets beteende bestäms dock av andra tillstånd som måste estimeras, såsom dess laddnivåeller lokala koncentrationer och potentialer. BMS:enförlitar sig därför på modeller för att estimera interna tillstånd och utföra styrning. För att uppfylla punkterna a) och b) måste modellerna som användsuppdateras ombord i takt med att batteriet åldras. En uppdaterad modellkan också fungera som ett diagnostiskt verktyg eftersom det speglar batteriets förändrade egenskaper. Den här avhandlingen undersöker identifieringav fysikbaserade och empiriska modeller från kördata. Arbetet delas in i treseparata studier. 1) En global känslighetsanalys utfördes på parametrarna i en fysikbaseradmodell av hög ordning. Som inputsignal användes uppmätt ström från riktigaelfordon i drift. Parametrarnas effekt på både cellspänning och interna batteritillstånd analyserades. Studien visade att alla modellparametrar exciteradesav strömmen från ett helelektriskt fordon. Anledningen var att batteriernaanvändes inom ett brett SOC spann samt att den dragna strömmen var stor.I fordon med snävare SOC span och lägre strömmar var inte alla parametrarkänsliga. 2) Dynam, QC 20220516

The battery is a component with significant impact on both the cost and environmental footprint of a full electric vehicle (EV). Consequently, there is a strong motivation to maximize its degree of utilization. Usage limits are enforced by the battery management system (BMS) to ensure safe operation and limit battery degradation. The limits tend to be conservative to account for uncertainty in battery state estimation as well as changes in the battery's characteristics due to aging. To improve the utilization degree, aging sensitive battery control is necessary. This refers to control that a) adjusts during the battery's life based on its state and b) balances the trade-off between utilization and degradation according to requirements from the specific application. In state-of-the-art battery installations, only three signals are measured; current, voltage and temperature. However, the battery's behaviour is governed by other states that must be estimated such as its state-of-charge (SOC) or local concentrations and potentials. The BMS therefore relies on models to estimate states and to perform control actions. In order to realize points a) and b), the models that are used for state estimation and control must be updated onboard. An updated model can also serve the purpose of diagnosing the battery, since it reflects the changing properties of an aging battery. This thesis investigates identification of physics-based and empirical battery models from operational EV data. The work is divided into three main studies. 1) A global sensitivity analysis was performed on the parameters of a high-order physics-based model. Measured current profiles from real EV:s were used as input and the parameters' impact on both modelled cell voltage and other internal states was assessed. The study revealed that in order to excite all model parameters, an input with high current rates, large SOC span and longer charge or discharge periods was required. This was only present in the da, Elekriska fordon utgör en allt större andel av världens fordonsflotta. Batteriet är en komponent med betydande påverkan både på fordonets kostnadoch dess miljö- och klimatpåverkan. Det är därför viktigt att försöka maximera batteriets utnytjandegrad. Användargränser upprätthålls av batterietsstyrsystem, såkallad BMS, för att garantera säker drift samt för att begränsabatteriets åldrande. Användargränserna tenderar att vara konservativa för attta höjd för osäkerhet i tillståndsestimeringen samt batteriets förändrade egenskaper under dess livstid. För att utöka utnyttjandegraden är ålderskänsligstyrning nödvändig. Med detta avses styrning som a) justeras under batterietslivstid och b) balancerar utnyttjande och prestanda på ett sätt som passar enspecifik applikation. Ombord på fordon mäts typiskt tre signaler; ström, spänning och temperatur. Batteriets beteende bestäms dock av andra tillstånd som måste estimeras, såsom dess laddnivåeller lokala koncentrationer och potentialer. BMS:enförlitar sig därför på modeller för att estimera interna tillstånd och utföra styrning. För att uppfylla punkterna a) och b) måste modellerna som användsuppdateras ombord i takt med att batteriet åldras. En uppdaterad modellkan också fungera som ett diagnostiskt verktyg eftersom det speglar batteriets förändrade egenskaper. Den här avhandlingen undersöker identifieringav fysikbaserade och empiriska modeller från kördata. Arbetet delas in i treseparata studier. 1) En global känslighetsanalys utfördes på parametrarna i en fysikbaseradmodell av hög ordning. Som inputsignal användes uppmätt ström från riktigaelfordon i drift. Parametrarnas effekt på både cellspänning och interna batteritillstånd analyserades. Studien visade att alla modellparametrar exciteradesav strömmen från ett helelektriskt fordon. Anledningen var att batteriernaanvändes inom ett brett SOC spann samt att den dragna strömmen var stor.I fordon med snävare SOC span och lägre strömmar var inte alla parametrarkänsliga. 2) Dynam, QC 20220516

The increasing deployment of new technologies to contribute to the decarbonization of power systems is imposing new challenges in terms of system dynamics and stability. To deal with different operating and control issues in this sense, and support actual needs, advanced tools and solutions are required. Therefore, this paper presents a digital twin of a dynamic hardware emulator that can be used for controller hardware in the loop (CHIL) testing and is based on a small-scale laboratory system. To build the simulation model, the parameters of involved synchronous machines, excitation systems, prime movers and transmission lines have been identified and then compared to laboratory measurements to assess the accuracy of the digital twin. Static and dynamic accuracy have been investigated and an overall good accuracy can be shown with the help of quantification of errors. Furthermore, a case study is presented where the digital twin was used to design a controller to damp inter-area oscillations with the help of wide area measurements. This controller was then implemented and tested within the dynamic hardware emulator in the laboratory.

In this paper, a traction load model parameter identification method based on the improved sparrow search algorithm (ISSA) is proposed. According to the load characteristics of the AC traction power supply system under transient disturbance, the model structure of the traction load is equated to the composite load model structure of the static load shunt induction motor's dynamic load. The traditional sparrow search algorithm is improved to enhance its accuracy and convergence. The generalization ability of the model was tested, and the accuracy of the proposed model was verified. Using the ISSA to determine the load model from the measured data, the results can verify the effectiveness of the ISSA for comprehensive load model parameter identification. Comparing the ISSA with the traditional SSA and PSO algorithms, it shows that the ISSA has better accuracy and convergence.

We investigate numerically a quasi-static elasticity system of Kachanov-type. To do so we propose an Euler time discretization combined with a suitable finite elements scheme (FEM) to handle the discretization is space. We use ODE-type arguments to prove the consistency of the scheme as well as its convergence rate. We rely on the computational platform FEniCS© to perform the FEM discretizations in space needed to compute the model output. The simulation results show a good agreement with both the physics of the problem and with our previous qualitative mathematical analysis results obtained for precisely the same problem setting. Furthermore, our implementation recovers nicely the theoretically expected convergence rate. This is a preliminary study preparing the framework for the rigorous numerical identification of the damage process in Kachanovtype models.

The battery is a component with significant impact on both the cost and environmental footprint of a full electric vehicle (EV). Consequently, there is a strong motivation to maximize its degree of utilization. Usage limits are enforced by the battery management system (BMS) to ensure safe operation and limit battery degradation. The limits tend to be conservative to account for uncertainty in battery state estimation as well as changes in the battery's characteristics due to aging. To improve the utilization degree, aging sensitive battery control is necessary. This refers to control that a) adjusts during the battery's life based on its state and b) balances the trade-off between utilization and degradation according to requirements from the specific application. In state-of-the-art battery installations, only three signals are measured; current, voltage and temperature. However, the battery's behaviour is governed by other states that must be estimated such as its state-of-charge (SOC) or local concentrations and potentials. The BMS therefore relies on models to estimate states and to perform control actions. In order to realize points a) and b), the models that are used for state estimation and control must be updated onboard. An updated model can also serve the purpose of diagnosing the battery, since it reflects the changing properties of an aging battery. This thesis investigates identification of physics-based and empirical battery models from operational EV data. The work is divided into three main studies. 1) A global sensitivity analysis was performed on the parameters of a high-order physics-based model. Measured current profiles from real EV:s were used as input and the parameters' impact on both modelled cell voltage and other internal states was assessed. The study revealed that in order to excite all model parameters, an input with high current rates, large SOC span and longer charge or discharge periods was required. This was only present in the da, Elekriska fordon utgör en allt större andel av världens fordonsflotta. Batteriet är en komponent med betydande påverkan både på fordonets kostnadoch dess miljö- och klimatpåverkan. Det är därför viktigt att försöka maximera batteriets utnytjandegrad. Användargränser upprätthålls av batterietsstyrsystem, såkallad BMS, för att garantera säker drift samt för att begränsabatteriets åldrande. Användargränserna tenderar att vara konservativa för attta höjd för osäkerhet i tillståndsestimeringen samt batteriets förändrade egenskaper under dess livstid. För att utöka utnyttjandegraden är ålderskänsligstyrning nödvändig. Med detta avses styrning som a) justeras under batterietslivstid och b) balancerar utnyttjande och prestanda på ett sätt som passar enspecifik applikation. Ombord på fordon mäts typiskt tre signaler; ström, spänning och temperatur. Batteriets beteende bestäms dock av andra tillstånd som måste estimeras, såsom dess laddnivåeller lokala koncentrationer och potentialer. BMS:enförlitar sig därför på modeller för att estimera interna tillstånd och utföra styrning. För att uppfylla punkterna a) och b) måste modellerna som användsuppdateras ombord i takt med att batteriet åldras. En uppdaterad modellkan också fungera som ett diagnostiskt verktyg eftersom det speglar batteriets förändrade egenskaper. Den här avhandlingen undersöker identifieringav fysikbaserade och empiriska modeller från kördata. Arbetet delas in i treseparata studier. 1) En global känslighetsanalys utfördes på parametrarna i en fysikbaseradmodell av hög ordning. Som inputsignal användes uppmätt ström från riktigaelfordon i drift. Parametrarnas effekt på både cellspänning och interna batteritillstånd analyserades. Studien visade att alla modellparametrar exciteradesav strömmen från ett helelektriskt fordon. Anledningen var att batteriernaanvändes inom ett brett SOC spann samt att den dragna strömmen var stor.I fordon med snävare SOC span och lägre strömmar var inte alla parametrarkänsliga. 2) Dynam, QC 20220516

The battery is a component with significant impact on both the cost and environmental footprint of a full electric vehicle (EV). Consequently, there is a strong motivation to maximize its degree of utilization. Usage limits are enforced by the battery management system (BMS) to ensure safe operation and limit battery degradation. The limits tend to be conservative to account for uncertainty in battery state estimation as well as changes in the battery's characteristics due to aging. To improve the utilization degree, aging sensitive battery control is necessary. This refers to control that a) adjusts during the battery's life based on its state and b) balances the trade-off between utilization and degradation according to requirements from the specific application. In state-of-the-art battery installations, only three signals are measured; current, voltage and temperature. However, the battery's behaviour is governed by other states that must be estimated such as its state-of-charge (SOC) or local concentrations and potentials. The BMS therefore relies on models to estimate states and to perform control actions. In order to realize points a) and b), the models that are used for state estimation and control must be updated onboard. An updated model can also serve the purpose of diagnosing the battery, since it reflects the changing properties of an aging battery. This thesis investigates identification of physics-based and empirical battery models from operational EV data. The work is divided into three main studies. 1) A global sensitivity analysis was performed on the parameters of a high-order physics-based model. Measured current profiles from real EV:s were used as input and the parameters' impact on both modelled cell voltage and other internal states was assessed. The study revealed that in order to excite all model parameters, an input with high current rates, large SOC span and longer charge or discharge periods was required. This was only present in the da, Elekriska fordon utgör en allt större andel av världens fordonsflotta. Batteriet är en komponent med betydande påverkan både på fordonets kostnadoch dess miljö- och klimatpåverkan. Det är därför viktigt att försöka maximera batteriets utnytjandegrad. Användargränser upprätthålls av batterietsstyrsystem, såkallad BMS, för att garantera säker drift samt för att begränsabatteriets åldrande. Användargränserna tenderar att vara konservativa för attta höjd för osäkerhet i tillståndsestimeringen samt batteriets förändrade egenskaper under dess livstid. För att utöka utnyttjandegraden är ålderskänsligstyrning nödvändig. Med detta avses styrning som a) justeras under batterietslivstid och b) balancerar utnyttjande och prestanda på ett sätt som passar enspecifik applikation. Ombord på fordon mäts typiskt tre signaler; ström, spänning och temperatur. Batteriets beteende bestäms dock av andra tillstånd som måste estimeras, såsom dess laddnivåeller lokala koncentrationer och potentialer. BMS:enförlitar sig därför på modeller för att estimera interna tillstånd och utföra styrning. För att uppfylla punkterna a) och b) måste modellerna som användsuppdateras ombord i takt med att batteriet åldras. En uppdaterad modellkan också fungera som ett diagnostiskt verktyg eftersom det speglar batteriets förändrade egenskaper. Den här avhandlingen undersöker identifieringav fysikbaserade och empiriska modeller från kördata. Arbetet delas in i treseparata studier. 1) En global känslighetsanalys utfördes på parametrarna i en fysikbaseradmodell av hög ordning. Som inputsignal användes uppmätt ström från riktigaelfordon i drift. Parametrarnas effekt på både cellspänning och interna batteritillstånd analyserades. Studien visade att alla modellparametrar exciteradesav strömmen från ett helelektriskt fordon. Anledningen var att batteriernaanvändes inom ett brett SOC spann samt att den dragna strömmen var stor.I fordon med snävare SOC span och lägre strömmar var inte alla parametrarkänsliga. 2) Dynam, QC 20220516

The capability of high-resolution modeling of crystals subjected to large plastic strain is essential in predicting many important phenomena occurring in polycrystalline materials, such as microstructure, deformation localization, and in-grain texture evolution. However, due to the heterogeneity of the plastic deformation in polycrystals, the simulation mesh gets distorted during the deformation. In this work, an adaptive remeshing approach is introduced for simulating large deformation of 3D polycrystals with high resolution under periodic boundary conditions. In the next step, high-resolution three-dimensional crystal plasticity simulations are used to investigate deformation heterogeneity and microstructure evolution during the cold rolling of interstitial free (IF-) steel. A Fast Fourier Transform (FFT)-based spectral solver is used to conduct crystal plasticity simulations using a dislocation-density-based crystal plasticity model. The in-grain texture evolution and misorientation spread are consistent with experimental results obtained using electron backscatter diffraction (EBSD) experiments. Crystal plasticity simulation shows that two types of strain localization develop during the large deformation of IF-steel. The first type forms band-like areas with large strain accumulation that appear as river patterns extending across the specimen. In addition to these river-like patterns, a second type of strain localization with rather sharp and highly localized in-grain shear bands is identified. These localized features are dependent on the crystallographic orientation of the grain and extend within a single grain. In addition to the strain localization, the evolution of in-grain orientation gradients, dislocation density, kernel average misorientation, and stress in major texture components are discussed., Team Jilt Sietsma

In this paper, we propose a parametric system identification approach for a class of continuous-time Lur'e-type systems. Using the Mixed-Time-Frequency (MTF) algorithm, we show that the steady-state model response and the gradient of the model response with respect to its parameters can be computed in a numerically fast and efficient way, allowing efficient use of global and local optimization methods to solve the identification problem. Furthermore, by enforcing the identified model to be inside the set of convergent models, we certify a stability property of the identified model, which allows for reliable generalized usage of the model also for other excitation signals than those used to identify the model. The effectiveness and benefits of the proposed approach are demonstrated in a simulation case study. Furthermore, we have experimentally shown that the proposed approach provides fast identification of both medical equipment and patient parameters in mechanical ventilation and, thereby, enables improved patient treatment.

Physics-based crystal plasticity models rely on certain statistical assumptions about the collective behavior of dislocation populations on one slip system and their interactions with the dislocations on the other slip systems. One main advantage of using such physics-based constitutive dislocation models in crystal plasticity kinematic frameworks is their suitability for predicting the mechanical behavior of polycrystals over a wide range of deformation temperatures and strain rates with the same physics-based parameter set. In this study, the ability of a widely used temperature-dependent dislocation-density-based crystal plasticity formulation to reproduce experimental results, with a main focus on the yield stress behavior, is investigated. First, the material parameters are identified from experimental macroscopic stress–strain curves using a computationally efficient optimization methodology that uses a genetic algorithm along with the response surface methodology. For this purpose, a systematic set of compression tests on interstitial free (IF) steel samples is performed at various temperatures and strain rates. Next, the influence of the individual parameters on the observed behavior is analyzed. Based on mutual interactions between various parameters, the ability to find a unique parameter set is discussed. This allows identifying shortcomings of the constitutive law and sketch ideas for possible improvements. Particular attention is directed toward identifying possibly redundant material parameters, narrowing the acceptable range of material parameters based on physical criteria, and modifying the crystal plasticity formulation numerically for high-temperature use., Team Jilt Sietsma, Team Maria Santofimia Navarro

Ultrasound acoustic energy transfer (UAET) is an emerging contactless technology that offers the capability to safely and efficiently power sensors and devices while eliminating the need to replace batteries, which is of interest in many applications. It has been proposed to recharge and communicate with implanted medical devices, thereby eliminating the need for invasive and expensive surgery and also to charge sensors inside enclosed metal containers typically found in automobiles, nuclear power plants, space stations, and aircraft engines. In UAET, energy is transferred through the reception of acoustic waves by a piezoelectric receiver that converts the energy of acoustic waves to electrical voltage. It has been shown that UAET outperforms the conventional CET technologies that use electromagnetic waves to transfer energy, including inductive coupling and capacitative coupling. To date, the majority of research on UAET systems has been limited to modeling and proof-of-concept experiments, mostly in the linear regime, i.e., under small levels of acoustic pressure that result in small amplitude longitudinal vibrations and linearized piezoelectricity. Moreover, existing models are based on the "piston-like" deformation assumption of the transmitter and receiver, which is only accurate for thin disks and does not accurately account for radiation effects. The linear models neglect nonlinear effects associated with the nonlinear acoustic wave propagation as well as the receiver's electroelastic nonlinearities on the energy transfer characteristics, which become significant at high source strengths. In this dissertation, we present experimentally-validated analytical and numerical multiphysics modeling approaches aimed at filling a knowledge gap in terms of considering resonant acoustic-piezoelectric structure interactions and nonlinear effects associated with high excitation levels in UAET systems. In particular, we develop a reduced-order model that can accurately ac

With growing global commitment to renewable energy generation, the role of energy storage systems has become a central issue in traction power applications, such as electric vehicles, trains, and elevators. To achieve the optimal integration of batteries in such applications, without unnecessary oversizing, improvements in the process of battery selection are needed. Specifically, it is necessary to develop models able to predict battery performance for each particular application. In this paper, a methodology for the parametrization of a battery equivalent circuit model (ECM) based on capacity and pulse tests is presented. The model can be extrapolated to different battery technologies, and was validated by comparing simulations and experimental tests with lead-acid and lithium-ion batteries.

A bounded cost path planning method is developed for underwater vehicles assisted by a data-driven flow modeling method. The modeled flow field is partitioned as a set of cells of piece-wise constant flow speed. A flow partition algorithm and a parameter estimation algorithm are proposed to learn the flow field structure and parameters with justified convergence. A bounded cost path planning algorithm is developed taking advantage of the partitioned flow model. An extended potential search method is proposed to determine the sequence of partitions that the optimal path crosses. The optimal path within each partition is then determined by solving a constrained optimization problem. Theoretical justification is provided for the proposed extended potential search method generating the optimal solution. The path planned has the highest probability to satisfy the bounded cost constraint. The performance of the algorithms is demonstrated with experimental and simulation results, which show that the proposed method is more computationally efficient than some of the existing methods. © Copyright © 2021 Hou, Cho, Zhou, Edwards and Zhang.

This dissertation examines the challenge of (i) estimating the internal states of lithium-sulfur (Li-S) batteries based on an experimentally-parameterized multi-physics model, and (ii) optimizing the discharge trajectory to maximize the energy release of a Li-S battery over a fixed time horizon. This research is motivated both by the potential of Li-S batteries to provide higher energy densities compared to traditional lithium-ion batteries and the potential of model-based estimation/control to improve the performance of a Li-S battery. Previous work in the literature optimizes the materials used in Li-S batteries for better performance, and also develops multiple types of models for these batteries, ranging from simple equivalent circuit models (ECMs) to sophisticated diffusion-reaction models. The dissertation builds on the insights from the existing literature, and focuses on the control-oriented study/analysis of Li-S batteries. From the battery management perspective, the essential components include a computationally tractable model, an internal state estimator, and eventually an optimal control strategy. This research addresses the gaps corresponding these key components. First, this dissertation explores the problem of parameterizing a zero-dimensional physics-based Li-S model. Due to the dependency between the parameters, a simulation-based sensitivity study is performed to provide guidance on the choice of to-be-identified parameters. These parameters are identified by fitting the simulated voltage profile to the experimental data for four models considering different reaction chains. The best fitted model is suggested for the following state estimation study. Second, there is a need for online state estimation algorithms that take into account the multiplicity of active species in Li-S batteries. This dissertation addresses this gap by developing a model-based unscented Kalman filter for state estimation in Li-S batteries, using the parameterized zero-dim

Simulation models of the ship propulsion system play an increasingly important role, for instance in controller design and condition monitoring. However, creation of such simulation models requires significant time and effort. In this paper, the application of deterministic identification techniques on a DC-electric ship drive train is explored as an alternative for data-driven identification techniques that require extensive measured data sets collected over long periods of ship operation. First, a nonlinear and a linear simulation model that represent the dynamic behavior of the propulsion plant are developed, and the main parameters to be identified are defined. Then, a set of experiments on a model scale boat in the bollard pull condition are conducted using an ad hoc experimental setup and data acquisition system. Subsequently, various types of identification techniques are applied, aiming to determine the unknown model parameters. Eventually, a comparison is made between experimental and simulated results, using the different sets of the estimated parameters. The value of the demonstrated approaches lies in the fast determination of unknown system parameters. These parameters can be used in simulation models, which in turn can be used for various purposes such as system controller development and tuning. Furthermore, periodic determination of system parameters can support condition monitoring to detect faults or degradation of the system. The latter point directly deals with the condition-based maintenance issue; in fact, monitoring the propulsion plant parameters over time could allow for better management (and timing) of maintenance. Although the developed ideas are far from ready to be used on the full-scale, the authors believe that the methodologies are promising enough to be developed further towards a full-scale application., Ship Design, Production and Operations

For the development of reliable stretchable electronic systems, it is essential to comprehend and predict their mechanical behavior. It is important to test and analyze original as-processed samples, as opposed to standard tests on bulk material. Dedicated analysis methods are necessary for obtaining the material properties from the tests, as complex 3D deformations complicate the use of existing methods. This paper presents an integrated digital height correlation (IDHC) method for the mechanical characterization of a recently developed ultra-stretchable freestanding interconnect. Height maps from an out-of-plane loading experiment are correlated to a numerical model, with the aim to identify the material parameters in the plastic regime. The IDHC method is tested on a virtual test case, where it is shown that the algorithm converges for the considered three plasticity parameters. For the real experiment, simultaneous correlation of all three parameters is not possible due to an inherently flat residual landscape with many local minima. However, the initial yield strength and hardening exponent were still identified and estimated at 225-300 MPa and 0.15-0.2 respectively. Despite the moderate accuracy of the identification, the potency of the IDHC method for this extremely challenging case of micron-sized delicate freestanding stretchable electronic interconnects is demonstrated.

Pantographic structures are examples of metamaterials with such a microstructure that higher-gradient terms' role is increased in the mechanical response. In this work, we aim for validating parameters of a reduced-order model for a pantographic structure. Experimental tests are carried out by applying forced oscillation to 3D-printed specimens for a range of frequencies. A second-gradient coarse-grained nonlinear model is utilized for obtaining a homogenized 2D description of the pantographic structure. By inverse analysis and through an automatized optimization algorithm, the parameters of the model are identified for the corresponding pantographic structure. By comparing the displacement plots, the performance of the model and the identified parameters are assessed for dynamic regime. Qualitative and quantitative analyses for different frequency ranges are performed. A good agreement is present far away from the eigenfrequencies. The discrepancies near the eigenfrequencies are a possible indication of the significance of higher-order inertia in the model.

Pantographic structures are examples of metamaterials with such a microstructure that higher-gradient terms' role is increased in the mechanical response. In this work, we aim for validating parameters of a reduced-order model for a pantographic structure. Experimental tests are carried out by applying forced oscillation to 3D-printed specimens for a range of frequencies. A second-gradient coarse-grained nonlinear model is utilized for obtaining a homogenized 2D description of the pantographic structure. By inverse analysis and through an automatized optimization algorithm, the parameters of the model are identified for the corresponding pantographic structure. By comparing the displacement plots, the performance of the model and the identified parameters are assessed for dynamic regime. Qualitative and quantitative analyses for different frequency ranges are performed. A good agreement is present far away from the eigenfrequencies. The discrepancies near the eigenfrequencies are a possible indication of the significance of higher-order inertia in the model.

Pantographic structures are examples of metamaterials with such a microstructure that higher-gradient terms' role is increased in the mechanical response. In this work, we aim for validating parameters of a reduced-order model for a pantographic structure. Experimental tests are carried out by applying forced oscillation to 3D-printed specimens for a range of frequencies. A second-gradient coarse-grained nonlinear model is utilized for obtaining a homogenized 2D description of the pantographic structure. By inverse analysis and through an automatized optimization algorithm, the parameters of the model are identified for the corresponding pantographic structure. By comparing the displacement plots, the performance of the model and the identified parameters are assessed for dynamic regime. Qualitative and quantitative analyses for different frequency ranges are performed. A good agreement is present far away from the eigenfrequencies. The discrepancies near the eigenfrequencies are a possible indication of the significance of higher-order inertia in the model.

Pantographic structures are examples of metamaterials with such a microstructure that higher-gradient terms' role is increased in the mechanical response. In this work, we aim for validating parameters of a reduced-order model for a pantographic structure. Experimental tests are carried out by applying forced oscillation to 3D-printed specimens for a range of frequencies. A second-gradient coarse-grained nonlinear model is utilized for obtaining a homogenized 2D description of the pantographic structure. By inverse analysis and through an automatized optimization algorithm, the parameters of the model are identified for the corresponding pantographic structure. By comparing the displacement plots, the performance of the model and the identified parameters are assessed for dynamic regime. Qualitative and quantitative analyses for different frequency ranges are performed. A good agreement is present far away from the eigenfrequencies. The discrepancies near the eigenfrequencies are a possible indication of the significance of higher-order inertia in the model.