Hybrid State Estimation in a Semitrailer for Different Loading Conditions

For state and parameter estimation in vehicles, Kalman filters, especially nonlinear extensions like the extended Kalman filter (EKF) and unscented Kalman filter (UKF), are very common. However, the estimation accuracy is highly dependent on the quality of the model used in the process update of the Kalman filter. Model errors can result from non-modeled dynamics that are either unknown or very difficult to describe. In recent years data-driven approaches for state estimation are the subject of research with promising results in estimation accuracy and reduced implementation effort. In this work, both a model-based method with an UKF and a data-driven approach based on recurrent neural networks (RNN) are implemented and combined to two hybrid methods for the application of state and parameter estimation in a truck-semitrailer for three different loading conditions. Hybrid estimation architectures promise to combine the advantages of model-based and data-driven methods to achieve better estimation accuracy than their standalone components. To the best knowledge of the authors, this work is the first to extend the field of hybrid state estimation to semitrailers estimating the truck steering angle, articulation angle, and the trailer's lateral and vertical tire forces. Four estimation architectures (an UKF, one purely data-driven method, and two hybrid methods) are optimized and compared to each other regarding estimation accuracy. The UKF is optimized with a particle swarm optimization (PSO) while the hyperparameters of the data-driven method are tuned with the asynchronous successive halving algorithm (ASHA) to result in a fair comparison. All methods are developed and compared based on an experimental data set from a test vehicle

Multi-dimensional summation-by-parts operators for general function spaces: Theory and construction

Summation-by-parts (SBP) operators allow us to systematically develop energy-stable and high-order accurate numerical methods for time-dependent differential equations. Until recently, the main idea behind existing SBP operators was that polynomials can accurately approximate the solution, and SBP operators should thus be exact for them. However, polynomials do not provide the best approximation for some problems, with other approximation spaces being more appropriate. We recently addressed this issue and developed a theory for one-dimensional SBP operators based on general function spaces, coined function-space SBP (FSBP) operators. In this paper, we extend the theory of FSBP operators to multiple dimensions. We focus on their existence, connection to quadratures, construction, and mimetic properties. A more exhaustive numerical demonstration of multi-dimensional FSBP (MFSBP) operators and their application will be provided in future works. Similar to the one-dimensional case, we demonstrate that most of the established results for polynomial-based multi-dimensional SBP (MSBP) operators carry over to the more general class of MFSBP operators. Our findings imply that the concept of SBP operators can be applied to a significantly larger class of methods than is currently done. This can increase the accuracy of the numerical solutions and/or provide stability to the methods.Comment: 28 pages, 9 figure

Summation-by-parts operators for general function spaces: The second derivative

Many applications rely on solving time-dependent partial differential equations (PDEs) that include second derivatives. Summation-by-parts (SBP) operators are crucial for developing stable, high-order accurate numerical methodologies for such problems. Conventionally, SBP operators are tailored to the assumption that polynomials accurately approximate the solution, and SBP operators should thus be exact for them. However, this assumption falls short for a range of problems for which other approximation spaces are better suited. We recently addressed this issue and developed a theory for first-derivative SBP operators based on general function spaces, coined function-space SBP (FSBP) operators. In this paper, we extend the innovation of FSBP operators to accommodate second derivatives. The developed second-derivative FSBP operators maintain the desired mimetic properties of existing polynomial SBP operators while allowing for greater flexibility by being applicable to a broader range of function spaces. We establish the existence of these operators and detail a straightforward methodology for constructing them. By exploring various function spaces, including trigonometric, exponential, and radial basis functions, we illustrate the versatility of our approach. We showcase the superior performance of these non-polynomial FSBP operators over traditional polynomial-based operators for a suite of one- and two-dimensional problems, encompassing a boundary layer problem and the viscous Burgers' equation. The work presented here opens up possibilities for using second-derivative SBP operators based on suitable function spaces, paving the way for a wide range of applications in the future.Comment: 20 pages, 7 figure

Design of a green chemoenzymatic cascade for scalable synthesis of bio-based styrene alternatives

As renewable lignin building blocks, hydroxystyrenes are particularly appealing as either a replacement or addition to styrene-based polymer chemistry. These monomers are obtained by decarboxylation of phenolic acids and often subjected to chemical modifications of their phenolic hydroxy groups to improve polymerization behaviour. Despite efforts, a simple, scalable, and purely (chemo)catalytic synthesis of acetylated hydroxystyrenes remains elusive. We thus propose a custom-made chemoenzymatic route that utilizes a phenolic acid decarboxylase (PAD). Our process development strategy encompasses a computational solvent assessment informing about solubilities and viable reactor operation modes, experimental solvent screening, cascade engineering, heterogenization of biocatalyst, tailoring of acetylation conditions, and reaction upscale in a rotating bed reactor. By this means, we established a clean one-pot two-step process that uses the renewable solvent CPME, bio-based phenolic acid educts and reusable immobilised PAD. The overall chemoenzymatic reaction cascade was demonstrated on a 1 L scale to yield 18.3 g 4-acetoxy-3-methoxystyrene in 96% isolated yield. © 2022 The Royal Society of Chemistry

Simulation and Validation of an Incremental Bending Process for Cylindrical Fuselage Components

In the aviation industry, a large number of processes are not digitalised. Simultaneously, many special processes are used in production, such as incremental bending. In order to model and efficiently design multi-stage processes with methods such as FEM, automation and linking of the individual simulations are necessary. This paper therefore presents a method for automatically simulating and evaluating a complete incremental bending process with 24 strokes in LS-Dyna using a Python framework with cfiles. The final validation of the force–displacement relationships and inner radii of the generated scaled fuselage shell show high prediction accuracies of about 90%. Thus, the presented methodology enables a FEM-based process design of incremental bending in the aviation industry

Experiences and Future of Using VR in the Construction Sector

Living in the era of digitalization shapes more or less all the aspects of one's life. The multitude of available technologies extends the range of tools, established processes, and available affordances in many spheres. Cities of the future will not only impact the living patterns of their inhabitants but also require special conditions and requirements for their planning and design. Virtual reality as an interactive tool for visualization and urban planning is no more tomorrow’s technology, as it can be seen from the appearance of cheaper and portable virtual reality devices. However, we still lack established routine and multidisciplinary best practices for designing VR educational applications. There are also not enough “visionary approaches” attempting to cross-sectoral exploitation of technologies. In this paper we will try to extrapolate and extend learning use cases of construction and mechatronics to the broader areas of construction and planning sector. We will discuss our experiences and use-cases of integrating innovative visualizations tools in the learning context of construction and planning related fields. Based on this, we will discuss potential applications and links to other disciplines and their integration into the construction and planning sector

Optimized Tuning of an EKF for State and Parameter Estimation in a Semitrailer

The Extended Kalman Filter (EKF) is a well-known method for state and parameter estimation in vehicle dynamics. However, for tuning the EKF, knowledge about the process and measurement noise is needed, which is usually unknown. Tuning the noise parameters manually is very time consuming, especially for systems with many states. Automated optimization based on the filtering errors promises less application time and better estimation performance, but also requires computing resources. This work presents two approaches for estimating the noise parameters of an EKF: A particle swarm optimization (PSO) and a gradient-based optimization. The EKF is applied to a nonlinear vehicle model of a tractor-semitrailer for estimating the steering and articulation angle as well as lateral and vertical tire forces based on real measurement data with different trailer loadings. Both methods are compared to each other to achieve the best estimation performance

Deep Eutectic Solvents for the Enzymatic Synthesis of Sugar Esters: A Generalizable Strategy?

Sugar (fatty acid) esters are industrially relevant compounds, with a cumbersome production process due to the solubility issues of the substrates, which forces the use of environmentally unfriendly reaction media. Herein, deep eutectic solvents (DESs) are considered as a promising solution: several literature examples use glucose and different acyl donors to illustrate the efficient synthesis of sugar esters in classic DESs like choline chloride/urea (ChCl/U). However, this paper discloses that when sugars like lactose or other disaccharides are used, enzymes cannot efficiently perform (trans)esterifications in DESs, while the same reaction can proceed in mixtures like pyridine/tetrahydrofuran (Py/THF). This could be explained by computational solubility studies and molecular dynamics simulations of both reaction media, showing two effects: (i) on the one hand, large acyl donors (more than C10) display poor solubility in DESs and (ii) on the other hand, disaccharides interact with DES components. Thus, the DES affects the conformation of lactose (compared to the conformation observed in the Py/THF mixture), in such a way that the enzymatic reaction results impaired. Despite that classic DESs (e.g., ChCl/U) may not be useful for generalizing their use in saccharide ester syntheses, the achieved theoretical understanding of the reaction may enable the design of future DESs that can combine enzyme compatibility with eco-friendliness and efficiency in sugar chemistry