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Load bearing capacity of thin shell structures made of elastoplastic material by direct methods
(2008)
Limit loads can be calculated with the finite element method (FEM) for any component, defect geometry, and loading. FEM suggests that published long crack limit formulae for axial defects under-estimate the burst pressure for internal surface defects in thick pipes while limit loads are not conservative for deep cracks and for pressure loaded crack-faces. Very deep cracks have a residual strength, which is modelled by a global collapse load. These observations are combined to derive new analytical local and global collapse loads. The global collapse loads are close to FEM limit analyses for all crack dimensions.
Objective
In local SAR compression algorithms, the overestimation is generally not linearly dependent on actual local SAR. This can lead to large relative overestimation at low actual SAR values, unnecessarily constraining transmit array performance.
Method
Two strategies are proposed to reduce maximum relative overestimation for a given number of VOPs. The first strategy uses an overestimation matrix that roughly approximates actual local SAR; the second strategy uses a small set of pre-calculated VOPs as the overestimation term for the compression.
Result
Comparison with a previous method shows that for a given maximum relative overestimation the number of VOPs can be reduced by around 20% at the cost of a higher absolute overestimation at high actual local SAR values.
Conclusion
The proposed strategies outperform a previously published strategy and can improve the SAR compression where maximum relative overestimation constrains the performance of parallel transmission.
Logic-based robot control in highly dynamic domains / Ferrein, Alexander ; Lakemeyer, Gerhard
(2008)
In comparison to crude oil, biorefinery raw materials are challenging in concerns of transport and storage. The plant raw materials are more voluminous, so that shredding and compacting usually are necessary before transport. These mechanical processes can have a negative influence on the subsequent biotechnological processing and shelf life of the raw materials. Various approaches and their effects on renewable raw materials are shown. In addition, aspects of decentralized pretreatment steps are discussed. Another important aspect of pretreatment is the varying composition of the raw materials depending on the growth conditions. This problem can be solved with advanced on-site spectrometric analysis of the material.
Electronic cigarettes (e-cigarettes) have become popular worldwide with the market growing exponentially in some countries. The absence of product standards and safety regulations requires urgent development of analytical methodologies for the holistic control of the growing diversity of such products. An approach based on low-field nuclear magnetic resonance (LF-NMR) at 80 MHz is presented for the simultaneous determination of key parameters: carrier solvents (vegetable glycerine (VG), propylene glycol (PG) and water), total nicotine as well as free-base nicotine fraction. Moreover, qualitative and quantitative determination of fourteen weak organic acids deliberately added to enhance sensory characteristics of e-cigarettes was possible. In most cases these parameters can be rapidly and conveniently determined without using any sample manipulation such as dilution, extraction or derivatization steps. The method was applied for 37 authentic e-cigarettes samples. In particular, eight different organic acids with the content up to 56 mg/mL were detected. Due to its simplicity, the method can be used in routine regulatory control as well as to study release behaviour of nicotine and other e-cigarettes constituents in different products.
Humic substances (HS), as important environmental components, are essential to soil health and agricultural sustainability. The usage of low-rank coal (LRC) for energy generation has declined considerably due to the growing popularity of renewable energy sources and gas. However, their potential as soil amendment aimed to maintain soil quality and productivity deserves more recognition. LRC, a highly heterogeneous material in nature, contains large quantities of HS and may effectively help to restore the physicochemical, biological, and ecological functionality of soil. Multiple emerging studies support the view that LRC and its derivatives can positively impact the soil microclimate, nutrient status, and organic matter turnover. Moreover, the phytotoxic effects of some pollutants can be reduced by subsequent LRC application. Broad geographical availability, relatively low cost, and good technical applicability of LRC offer the advantage of easy fulfilling soil amendment and conditioner requirements worldwide. This review analyzes and emphasizes the potential of LRC and its numerous forms/combinations for soil amelioration and crop production. A great benefit would be a systematic investment strategy implicating safe utilization and long-term application of LRC for sustainable agricultural production.
Interplanetary trajectories for low-thrust spacecraft are often characterized by multiple revolutions around the sun. Unfortunately, the convergence of traditional trajectory optimizers that are based on numerical optimal control methods depends strongly on an adequate initial guess for the control function (if a direct method is used) or for the starting values of the adjoint vector (if an indirect method is used). Especially when many revolutions around the sun are re-
quired, trajectory optimization becomes a very difficult and time-consuming task that involves a lot of experience and expert knowledge in astrodynamics and optimal control theory, because an adequate initial guess is extremely hard to find. Evolutionary neurocontrol (ENC) was proposed as a smart method for low-thrust trajectory optimization that fuses artificial neural networks and evolutionary algorithms to so-called evolutionary neurocontrollers (ENCs) [1]. Inspired by natural archetypes, ENC attacks the trajectoryoptimization problem from the perspective of artificial intelligence and machine learning, a perspective that is quite different from that of optimal control theory. Within the context of ENC, a trajectory is regarded as the result of a spacecraft steering strategy that maps permanently the actual spacecraft state and the actual target state onto the actual spacecraft control vector. This way, the problem of searching the optimal spacecraft trajectory is equivalent to the problem of searching (or "learning") the optimal spacecraft steering strategy. An artificial neural network is used to implement such a spacecraft steering strategy. It can be regarded as a parameterized function (the network function) that is defined by the internal network parameters. Therefore, each distinct set of network parameters defines a different network function and thus a different steering strategy. The problem of searching the optimal steering strategy is now equivalent to the problem of searching the optimal set of network parameters. Evolutionary algorithms that work on a population of (artificial) chromosomes are used to find the optimal network parameters, because the parameters can be easily mapped onto a chromosome. The trajectory optimization problem is solved when the optimal chromosome is found. A comparison of solar sail trajectories that have been published by others [2, 3, 4, 5] with ENC-trajectories has shown that ENCs can be successfully applied for near-globally optimal spacecraft control [1, 6] and that they are able to find trajectories that are closer to the (unknown) global optimum, because they explore the trajectory search space more exhaustively than a human expert can do. The obtained trajectories are fairly accurate with respect to the terminal constraint. If a more accurate trajectory is required, the ENC-solution can be used as an initial guess for a local trajectory optimization method. Using ENC, low-thrust trajectories can be optimized without an initial guess and without expert attendance.
Here, new results for nuclear electric spacecraft and for solar sail spacecraft are presented and it will be shown that ENCs find very good trajectories even for very difficult problems. Trajectory optimization results are presented for 1. NASA's Solar Polar Imager Mission, a mission to attain a highly inclined close solar orbit with a solar sail [7] 2. a mission to de ect asteroid Apophis with a solar sail from a retrograde orbit with a very-high velocity impact [8, 9] 3. JPL's \2nd Global Trajectory Optimization Competition", a grand tour to visit four asteroids from different classes with a NEP spacecraft
After a brief introduction of conventional laboratory structures, this work focuses on an innovative and universal approach for a setup of a training laboratory for electric machines and drive systems. The novel approach employs a central 48 V DC bus, which forms the backbone of the structure. Several sets of DC machine, asynchronous machine and synchronous machine are connected to this bus. The advantages of the novel system structure are manifold, both from a didactic and a technical point of view: Student groups can work on their own performance level in a highly parallelized and at the same time individualized way. Additional training setups (similar or different) can easily be added. Only the total power dissipation has to be provided, i.e. the DC bus balances the power flow between the student groups. Comparative results of course evaluations of several cohorts of students are shown.
The purpose of this study was to investigate whether sprint performance is related to lower leg musculoskeletal geometry within a homogeneous group of highly trained 100-m sprinters. Using a cluster analysis, eighteen male sprinters were divided into two groups based on their personal best (fast: N = 11, 10.30 ± 0.07 s; slow: N = 7, 10.70 ± 0.08 s). Calf muscular fascicle arrangement and Achilles tendon moment arms (calculated by the gradient of tendon excursion versus ankle joint angle) were analyzed for each athlete using ultrasonography. Achilles tendon moment arm, foot and ankle skeletal geometry, fascicle arrangement as well as the ratio of fascicle length to Achilles tendon moment arm showed no significant (p > 0.05) correlation with sprint performance, nor were there any differences in the analyzed musculoskeletal parameters between the fast and slow sprinter group. Our findings provide evidence that differences in sprint ability in world-class athletes are not a result of differences in the geometrical design of the lower leg even when considering both skeletal and muscular components.
In product development, numerous design decisions have to be made. Multi-domain virtual prototyping provides a variety of tools to assess technical feasibility of design options, however often requires substantial computational effort for just a single evaluation. A special challenge is therefore the optimal design of product families, which consist of a group of products derived from a common platform. Finding an optimal platform configuration (stating what is shared and what is individually designed for each product) and an optimal design of all products simultaneously leads to a mixed-integer nonlinear black-box optimization model. We present an optimization approach based on metamodels and a metaheuristic. To increase computational efficiency and solution quality, we compare different types of Gaussian process regression metamodels adapted from the domain of machine learning, and combine them with a genetic algorithm. We illustrate our approach on the example of a product family of electrical drives, and investigate the trade-off between solution quality and computational overhead.
In this paper we investigate the use of deep neural networks for 3D object detection in uncommon, unstructured environments such as in an open-pit mine. While neural nets are frequently used for object detection in regular autonomous driving applications, more unusual driving scenarios aside street traffic pose additional challenges. For one, the collection of appropriate data sets to train the networks is an issue. For another, testing the performance of trained networks often requires tailored integration with the particular domain as well. While there exist different solutions for these problems in regular autonomous driving, there are only very few approaches that work for special domains just as well. We address both the challenges above in this work. First, we discuss two possible ways of acquiring data for training and evaluation. That is, we evaluate a semi-automated annotation of recorded LIDAR data and we examine synthetic data generation. Using these datasets we train and test different deep neural network for the task of object detection. Second, we propose a possible integration of a ROS2 detector module for an autonomous driving platform. Finally, we present the performance of three state-of-the-art deep neural networks in the domain of 3D object detection on a synthetic dataset and a smaller one containing a characteristic object from an open-pit mine.
Magnetic nanoparticles (MNP) are investigated with great interest for biomedical applications in diagnostics (e.g. imaging: magnetic particle imaging (MPI)), therapeutics (e.g. hyperthermia: magnetic fluid hyperthermia (MFH)) and multi-purpose biosensing (e.g. magnetic immunoassays (MIA)). What all of these applications have in common is that they are based on the unique magnetic relaxation mechanisms of MNP in an alternating magnetic field (AMF). While MFH and MPI are currently the most prominent examples of biomedical applications, here we present results on the relatively new biosensing application of frequency mixing magnetic detection (FMMD) from a simulation perspective. In general, we ask how the key parameters of MNP (core size and magnetic anisotropy) affect the FMMD signal: by varying the core size, we investigate the effect of the magnetic volume per MNP; and by changing the effective magnetic anisotropy, we study the MNPs’ flexibility to leave its preferred magnetization direction. From this, we predict the most effective combination of MNP core size and magnetic anisotropy for maximum signal generation.
Magnetic detection structure for Lab-on-Chip applications based on the frequency mixing technique
(2018)
A magnetic frequency mixing technique with a set of miniaturized planar coils was investigated for use with a completely integrated Lab-on-Chip (LoC) pathogen sensing system. The system allows the detection and quantification of superparamagnetic beads. Additionally, in terms of magnetic nanoparticle characterization ability, the system can be used for immunoassays using the beads as markers. Analytical calculations and simulations for both excitation and pick-up coils are presented; the goal was to investigate the miniaturization of simple and cost-effective planar spiral coils. Following these calculations, a Printed Circuit Board (PCB) prototype was designed, manufactured, and tested for limit of detection, linear response, and validation of theoretical concepts. Using the magnetic frequency mixing technique, a limit of detection of 15 µg/mL of 20 nm core-sized nanoparticles was achieved without any shielding.
In the paper the results obtained from experiments at a modelled reinforced building in case of a direct lightning strike are compared with calculations. The comparison includes peak values of the magnetic field Hmax, its derivative (dH/dt)max and of induced voltages umax in typical cable routings. The experiments are performed at a 1:6 scaled building and the results are extrapolated using the similarity relations theory. The calculations are based on the approximate formulae given in IEC 62305-4 and have to be supplemented by a rough estimation of the additional shielding effect of a second reinforcement layer. The comparison shows, that the measured peak values of the magnetic field and its derivative are mostly lower than the calculated. The induced voltages are in good agreement. Hence, calculations of the induced voltages based on IEC 62305-4 are a good method for lightning protection studies of buildings, where the reinforcement is used as a grid-like electromagnetic shield.
For the application of the concept of Lightning Protection Zones (LPZ), the knowledge of the magnetic fields and induced voltages inside a structure is necessary. Laboratory experiments have been conducted at a downscaled model of a building (scale factor 1:6) to determine these electromagnetic quantities in case of a direct strike to the structure. The model (3 m x 2 m x 2 m) represented a small industrial building using the reinforcement of the concrete as electromagnetic shield. The magnetic fields and magnetic field derivatives were measured at several location inside the scaled model. Further, the voltages induced on three typical cable routes inside the model was determined. The influence of the lightning current waveshape, point-of-strike, bonding of the cable routes, and bridging of an expansion joint in the middle of the building on these quantities was studied.
Magnetic nanoparticle relaxation in biomedical application: focus on simulating nanoparticle heating
(2021)
Magnetic nanoparticles (MNPs) are used as therapeutic and diagnostic agents for local delivery of heat and image contrast enhancement in diseased tissue. Besides magnetization, the most important parameter that determines their performance for these applications is their magnetic relaxation, which can be affected when MNPs immobilize and agglomerate inside tissues. In this letter, we investigate different MNP agglomeration states for their magnetic relaxation properties under excitation in alternating fields and relate this to their heating efficiency and imaging properties. With focus on magnetic fluid hyperthermia, two different trends in MNP heating efficiency are measured: an increase by up to 23% for agglomerated MNP in suspension and a decrease by up to 28% for mixed states of agglomerated and immobilized MNP, which indicates that immobilization is the dominant effect. The same comparatively moderate effects are obtained for the signal amplitude in magnetic particle spectroscopy.
Hydrophobic magnetic nanoparticles (NPs) consisting of undecanoate-capped magnetite (Fe3O4, average diameter ca. 5 nm) are used to control quantized electron transfer to surface-confined redox units and metal NPs. A two-phase system consisting of an aqueous electrolyte solution and a toluene phase that includes the suspended undecanoatecapped magnetic NPs is used to control the interfacial properties of the electrode surface. The attracted magnetic NPs form a hydrophobic layer on the electrode surface resulting in the change of the mechanisms of the surface-confined electrochemical processes. A quinone-monolayer modified Au electrode demonstrates an aqueous-type of the electrochemical process (2e-+2H+ redox mechanism) for the quinone units in the absence of the hydrophobic magnetic NPs, while the attraction of the magnetic NPs to the surface results in the stepwise single-electron transfer mechanism characteristic of a dry nonaqueous medium. Also, the attraction of the hydrophobic magnetic NPs to the Au electrode surface modified with Au NPs (ca. 1.4 nm) yields a microenvironment with a low dielectric constant that results in the single-electron quantum charging of the Au NPs.