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Limit and shakedown theorems are exact theories of classical plasticity for the direct computation of safety factors or of the load carrying capacity under constant and varying loads. Simple versions of limit and shakedown analysis are the basis of all design codes for pressure vessels and pipings. Using Finite Element Methods more realistic modeling can be used for a more rational design. The methods can be extended to yield optimum plastic design. In this paper we present a first implementation in FE of limit and shakedown analyses for perfectly plastic material. Limit and shakedown analyses are done of a pipe–junction and a interaction diagram is calculated. The results are in good correspondence with the analytic solution we give in the appendix.
Direct methods comprising limit and shakedown analysis is a branch of computational mechanics. It plays a significant role in mechanical and civil engineering design. The concept of direct method aims to determinate the ultimate load bearing capacity of structures beyond the elastic range. For practical problems, the direct methods lead to nonlinear convex optimization problems with a large number of variables and onstraints. If strength and loading are random quantities, the problem of shakedown analysis is considered as stochastic programming. This paper presents a method so called chance constrained programming, an effective method of stochastic programming, to solve shakedown analysis problem under random condition of strength. In this our investigation, the loading is deterministic, the strength is distributed as normal or lognormal variables.
7th International Conference on Reliability of Materials and Structures (RELMAS 2008). June 17 - 20, 2008 ; Saint Petersburg, Russia. pp 354-358. Reprint with corrections in red Introduction Analysis of advanced structures working under extreme heavy loading such as nuclear power plants and piping system should take into account the randomness of loading, geometrical and material parameters. The existing reliability are restricted mostly to the elastic working regime, e.g. allowable local stresses. Development of the limit and shakedown reliability-based analysis and design methods, exploiting potential of the shakedown working regime, is highly needed. In this paper the application of a new algorithm of probabilistic limit and shakedown analysis for shell structures is presented, in which the loading and strength of the material as well as the thickness of the shell are considered as random variables. The reliability analysis problems may be efficiently solved by using a system combining the available FE codes, a deterministic limit and shakedown analysis, and the First and Second Order Reliability Methods (FORM/SORM). Non-linear sensitivity analyses are obtained directly from the solution of the deterministic problem without extra computational costs.
Traglast- und Einspielanalysen sind vereinfachte doch exakte Verfahren der Plastizität, die neben ausreichender Verformbarkeit keine einschränkenden Voraussetzungen beinhalten. Die Vereinfachungen betreffen die Beschaffung der Daten und Modelle für Details der Lastgeschichte und des Stoffverhaltens. Anders als die klassische Behandlung nichtlinearer Probleme der Strukturmechanik führt die Methode auf Optimierungsprobleme. Diese sind bei realistischen FEM-Modellen sehr groß. Das hat die industrielle Anwendung der Traglast- und Einspielanalysen stark verzögert. Diese Situation wird durch das Brite-EuRam Projekt LISA grundlegend geändert. In LISA entsteht auf der Basis des industriellen FEM-Programms PERMAS ein Verfahren zur direkten Berechnung der Tragfähigkeit duktiler Strukturen. Damit kann der Betriebsbereich von Komponenten und Bauwerken auf den plastischen Bereich erweitert werden, ohne den Aufwand gegenüber elastischen Analysen wesentlich zu erhöhen. Die beachtlichen Rechenzeitgewinne erlauben Parameterstudien und die Berechnung von Interaktionsdiagrammen, die einen schnellen Überblick über mögliche Betriebsbereiche vermitteln. Es zeigt sich, daß abhängig von der Komponente und ihren Belastungen teilweise entscheidende Sicherheitsgewinne zur Erweiterung der Betriebsbereiche erzielt werden können. Das Vorgehen erfordert vom Anwender oft ein gewisses Umdenken. Es werden keine Spannungen berechnet, um damit Sicherheit und Lebensdauer zu interpretieren. Statt dessen berechnet man direkt die gesuchte Sicherheit. Der Post-Prozessor wird nur noch zur Modell- und Rechenkontrolle benötigt. Das Vorgehen ist ähnlich der Stabilitätsanalyse (Knicken, Beulen). Durch namhafte industrielle Projektpartner werden Validierung und die Anwendbarkeit auf eine breite Palette technischer Probleme garantiert. Die ebenfalls in LISA entwickelten Zuverlässigkeitsanalysen sind nichlinear erst auf der Basis direkter Verfahren effektiv möglich. Ohne Traglast- und Einspielanalyse ist plastische Strukturoptimierung auch heute kaum durchführbar. Auf die vorgesehenen Erweiterungen der Werkstoffmodellierung für nichtlineare Verfestigung und für Schädigung konnte hier nicht eingegangen werden. Es herrscht ein deutlicher Mangel an Experimenten zum Nachweis der Grenzen zwischen elastischem Einspielen und dem Versagen durch LCF oder durch Ratchetting.
Traglast- und Einspielanalysen sind vereinfachte doch exakte Verfahren der Plastizität, die neben ausreichender Verformbarkeit keine einschränkenden Voraussetzungen beinhalten. Die Vereinfachungen betreffen die Beschaffung der Daten und Modelle für Details der Lastgeschichte und des Stoffverhaltens. Anders als die klassische Behandlung nichtlinearer Probleme der Strukturmechanik führt die Methode auf Optimierungsprobleme. Diese sind bei realistischen FEM-Modellen sehr groß. Das hat die industrielle Anwendung der Traglast- und Einspielanalysen stark verzögert. Diese Situation wird durch das Brite-EuRam Projekt LISA grundlegend geändert. Die Autoren möchten der Europäischen Kommission an dieser Stelle für die Förderung ausdrücklich danken. In LISA entsteht auf der Basis des industriellen FEM-Programms PERMAS ein Verfahren zur direkten Berechnung der Tragfähigkeit duktiler Strukturen. Damit kann der Betriebsbereich von Komponenten und Bauwerken auf den plastischen Bereich erweitert werden, ohne den Aufwand gegenüber elastischen Analysen wesentlich zu erhöhen. Die beachtlichen Rechenzeitgewinne erlauben Parameterstudien und die Berechnung von Interaktionsdiagrammen, die einen schnellen Überblick über mögliche Betriebsbereiche vermitteln. Es zeigt sich, daß abhängig von der Komponente und ihren Belastungen teilweise entscheidende Sicherheitsgewinne zur Erweiterung der Betriebsbereiche erzielt werden können. Das Vorgehen erfordert vom Anwender oft ein gewisses Umdenken. Es werden keine Spannungen berechnet, um damit Sicherheit und Lebensdauer zu interpretieren. Statt dessen berechnet man direkt die gesuchte Sicherheit. Der Post-Prozessor wird nur noch zur Modell- und Rechenkontrolle benötigt. Das Vorgehen ist änhlich der Stabilitätsanalyse (Knicken, Beulen). Durch namhafte industrielle Projektpartner werden Validierung und die Anwendbarkeit auf eine breite Palette technischer Probleme garantiert. Die ebenfalls in LISA geplante Zuverlässigkeitsanalyse ist erst auf der Basis direkter Verfahren effektiv möglich. Ohne Traglast- und Einspielanalyse ist plastische Strukturoptimierung auch heute kaum durchführbar.
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
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.