Rheinmetall (Germany)

Developing a reliable and robust finite element model of a carbon fiber-reinforced plastic (CFRP) composite structure is investigated by using the LS-DYNA solver and Python. This study tries to provide a systematic numerical approach to cover the principal impediment to adaptation of composite energy absorbers, that is the lack of a reliable predictive method. The proposed procedure aims to further the understanding of advanced composite structures’ behavior during the crash phenomenon by developing an accurate finite element model. To do so, the mechanical properties of the material were extracted from American Society for Testing and Materials (ASTM) standard test methods, followed by experimental investigation of circular CFRP tubes undergoing quasi-static loading. A numerical simulation framework was then utilized to scrutinize the effectiveness of simulation parameters on the crushing mechanism. Finally, a systematic approach based on machine learning techniques was performed to adjust non-physical modeling parameters for further calibration and validation. In this regard, a versatile Python code was developed to automate pre-processing, processing, and post-processing steps. The code also provides a groundwork to perform machine learning techniques. Interestingly, the numerical and experimental results were highly correlated with a correlation coefficient of almost 90%. Additionally, several non-physical numerical parameters were found to be inactive, while some else were identified as effective parameters, and their corresponding effectiveness was quantitatively extracted and discussed for the first time in the literature.

Image dehazing is a method to reduce the effects of haze, dust, or fog in images in order to provide a clear view of the observed scene. A large variety of traditional and machine learning-based approaches exists in the literature. However, these approaches mostly consider color images in the visual-optical spectrum. Apparently, the thermal infrared spectrum is much less affected by haze due to its longer wavelength. But atmospheric perturbations during long-range observation can cause image quality degradation in the thermal infrared (TIR) spectrum as well. In this paper, we propose a method to generate synthetic haze for TIR images. Then, we analyze the already existing blind image quality assessment measure Fog Aware Density Evaluator (FADE) for its applicability to the TIR spectrum. We further provide a comprehensive overview of the current state-of-the-art in image dehazing and empirically show that many approaches originally designed for visual-optical images perform surprisingly well when applied to the TIR spectrum. This is shown in experiments conducted on the recently published M3FD dataset.

Ramjet propulsion for gun-launched projectiles has received substantial attention since the invention of the ramjet engine concept in 1913. After the second World War, projectiles using solid-fuel were gaining interest due to their inherent simplicity and robustness. Throughout the world, research and development programs have been and still are being executed on the topic of solid fuel ramjet projectiles. This paper intends to give an overview of the worldwide activities in this field. To this end an extensive literature review is executed which gives a clear view on the activities performed over the years by the research institutes and companies of the different countries. The paper is concluded by a summary of the main achievements of all the work on solid fuel ramjet projectiles as well as of the remaining challenges. In addition, an outlook of this technology field into the future is given by indicating some opportunities that may be within reach with the present state-of-the-art of the solid fuel ramjet projectile technology.

The chapter starts with a brief introduction about corrosion, which is defined as the interdependency between a metal, a corrosive environment, and the respective component design. The second section introduces the most important forms of aqueous electrochemical corrosion (uniform corrosion, galvanic corrosion, selective and intergranular corrosion, and finally pitting and crevice corrosion in the case of passive layer forming metals). In addition, electrochemical corrosion under applied mechanical load is introduced (stress corrosion cracking, hydrogen-assisted cracking, corrosion fatigue), as well as special forms of corrosion (erosion, fretting, and microbiologically induced corrosion). The third section of this chapter introduces (mostly dry) chemical corrosion and high-temperature corrosion (oxidation, carburization, high-temperature hydrogen attack, sulfurization, nitriding, halogenation). As in the case of electrochemical corrosion, chemical corrosion can also be superimposed by mechanical loads. Finally, general facts on the testing of corrosion are introduced.

Electrophoretic separations are of growing interest to tackle complex analytical challenges. Nevertheless, capillary electrophoresis, as the most common mode, still suffers from insufficient detection limits due to low capillary loadability. ITP is of growing interest as preconcentration method for capillary electrophoresis and is also interesting to be applied as an independent analytical method. While mass spectrometric detection is common for capillary electrophoresis, the combination of ITP with MS is still a niche technique. In this work, we want to give an overview on isotachophoretic effects in CE-MS and ITP-MS methods, as well as coupling techniques of ITP with CE-MS. The challenges and possibilities associated with mass spectrometric detection in ITP and its coupling to capillary electrophoresis are critically discussed.

In a fast changing environment that is affected by global megatrends, disruptive technological developments and a growing number of new market players, global tier-one suppliers to the automotive industry have to adapt their technology strategy planning by considering current trends in innovation management to maintain their prevailing leading position. This paper presents Rheinmetall Automotive’s approach to adopt modern Open Innovation paradigms with a focus on outside-in innovation through Corporate Venturing. Embedded in their Technological Direction Development Process, Rheinmetall Automotive has developed a strategic model to plan startup engagement to improve the current product development and enhance further diversification. Here we describe the several steps how this company-specific strategic model was designed and what prerequisites have to be taken into account to introduce the model in a corporate context.

Radio communication of civil emergency services, like police, fire service, etc. will be changed in Europe to the digital broadcast networks TETRA (“terrestrial trunked radio”) and TETRAPOL. Potential threats and IEMI effects of such radio networks against HPEM sources will be investigated and evaluated. The test setup contains a base station and two mobile handhelds. Transfer functions are documented. At higher exposition levels the base station temporarily showed failures. Higher levels of effects like degradation or mission kill can be achieved by using pulsed LPEM/HPEM sources. Further susceptibility investigations with HPEM sources are useful to determine the resilience of such a potential critical infrastructure (necessary for emergency services) against EM threats.

A six degrees of freedom flight dynamics module was coupled to DLR’s CFD solver TAU. The algorithms implemented in the flight dynamics module for solving the 6-DoF equations of motion are explained in detail. The coupling is validated using one analytic test case and two store release experiments. The effects on the prediction accuracy of a strong coupling approach using an error-based coupling convergence criterion are compared to classical weak coupling.

In Alex Proyas’s science fiction movie “I, Robot” (2004) a detective suspects a robot as murderer. This robot is a representative of a new generation of personal assistants that help and entertain people during daily life activities. In opposition to the public opinion the detective proclaimed that the robot is able to follow his own will and is not forced to Isaac Asimov’s three main rules of robotics (Asimov, 1991). In the end this assumption turned out to be the truth. Even though the technological part of this story is still far beyond realization, the idea of a personal robotic assistant is still requested. Experts predicted robotic solutions to be ready to break through in domestic and other non-industrial domains (Engelberger, 1989) within the next years. But up to now, only rather simple robotic assistants like lawn mowers and vacuum cleaners are available on the market. As stated in (Grafe & Bischoff, 2003), all these systems have in common that they only show traces of intelligence and are specialists, designed for mostly a particular task. Robots being able to solve more complex tasks have not yet left the prototypical status. This is due to the large number of scientific and technical challenges that have to be coped with in the domain of robots acting and interacting in human environments (Kemp et al., 2007). The focus of this paper is to describe a tool based process model, called the “FRIEND::Process”1, which supports the development of intelligent robots in the domain of personal assistants. The paper concentrates on the interaction and close relation between the FRIEND::Process and configurable task-knowledge, the so called process-structures. Process-structures are embedded in different layers of abstraction within the layered control architecture MASSiVE2 (Martens et al., 2007). Even though the usage of layered control architectures for service robots is not a novel idea and has been proposed earlier (Schlegel &

C = wave celerity similarity number Cf = friction coefficient Cp = pressure coefficient Cp diff = differential pressure coefficient D = diameter of the body, m J = momentum flux similarity number JR = momentum flux ratio M = mass flux similarity number Mth = thruster ejection Mach number M1 = freestream Mach number NCR = nozzle celerity ratio NJR = nozzle momentum flux ratio NMR = nozzle mass flux ratio NPR = nozzle pressure ratio NrTR = nozzle temperature ratio P = pressure similarity number PR = total pressure ratio Pj = nozzle exit static pressure, Pa Po, P1 = freestream static pressure, Pa Pt = total pressure, Pa Pti = thruster stagnation pressure, Pa ReD = diameter-based Reynolds number rth = specific thruster gas constant, J kg 1 K 1 r1 = specific freestream gas constant, J kg 1 K 1 T = temperature similarity number Tj = nozzle exit static temperature, K Tt = total temperature, K Tti = thruster stagnation temperature, K T1 = freestream static temperature, K U = velocity component along the x axis, m s 1 Uo, U1 = freestream velocity component along the x axis, m s 1 uf = friction velocity component along the x axis, m s 1 W = velocity component along the mean ejection direction, m s 1 Wj = nozzle exit velocity component along the mean ejection direction, m s 1 x = horizontal coordinate along the body axis, m y = horizontal coordinate perpendicular to the body axis, m y = dimensionless wall distance z = vertical coordinate, m th = thruster gas isentropic coefficient 1 = freestream gas isentropic coefficient = kinematic viscosity, m s 1 = density, kg m 3 j = nozzle exit density, kg m 3 1 = freestream density, kg m 3 w = wall shear, Pa