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Diversity is increasingly being addressed as an innovation-promoting factor. For this reason, companies and institutions tackle the integration of a diversity management approach that enables a heterogenic perspective on innovation development. However, system-theoretical frameworks state that the implementation of diversity measures that are not tailored to the needs of the organization often leads to a rejection or reactivity with regard to the management approach. In this context, especially organizations, which are characterized by a specific hierarchical structure, a dominant habitus or specialist culture, must face the challenge of realizing a sustainable change of the corporate culture that sets the basis for implementing diversity management approaches. The presented research project focuses on analyzing the situation in a huge scientific collaborative project - so called Cluster of Excellence (CoE) - with the aim to implement a diversity - and innovation management strategy. Considering the influencing determinants, the CoE is characterized by its embeddedness in the scientific system, a complex organizational structure, and a high fluctuation rate. The paper presents a systemic approach of reflecting these factors in order to develop a diversity- and innovation management strategy. In this frame, the results of a quantitative survey of CoE employees and derived mindset-types are presented. The results show a need for taking different mindset-types into account, to be able to develop a tailored management strategy. The aim of the project is to give recommendations for developing a sustainable management concept that promotes both diversity and innovation by drawing on the persisting mindsets of organization members while reflecting top down as well as bottom up factors of implementation processes as well as the psychology of change. This paper addresses all who are concerned with the management of human resources in innovation processes and are striving for a cultural change within the framework of complex organizations.
Asteroid mining has the potential to greatly reduce the cost of in-space manufacturing, production of propellant for space transportation and consumables for crewed spacecraft, compared to launching the required resources from Earth’s deep gravity well. This paper discusses the top-level mission architecture and trajectory design for these resource-return missions, comparing high-thrust trajectories with continuous low-thrust solar-sail trajectories. This work focuses on maximizing the economic Net Present Value, which takes the time-cost of finance into account and therefore balances the returned resource mass and mission duration. The different propulsion methods will then be compared in terms of maximum economic return, sets of attainable target asteroids, and mission flexibility. This paper provides one more step towards making commercial asteroid mining an economically viable reality by integrating trajectory design, propulsion technology and economic modelling.
In parallel to the evolution of the Planetary Defense Conference, the exploration of small solar system bodies has advanced from fast fly-bys on the sidelines of missions to the planets to the implementation of dedicated sample-return and in-situ analysis missions. Spacecraft of all sizes have landed, touch-and-go sampled, been gently beached, or impacted at hypervelocity on asteroid and comet surfaces. More have flown by close enough to image their surfaces in detail or sample their immediate environment, often as part of an extended or re-purposed mission. And finally, full-scale planetary defense experiment missions are in the making. Highly efficient low-thrust propulsion is increasingly applied beyond commercial use also in mainstream and flagship science missions, in combination with gravity assist propulsion. Another development in the same years is the growth of small spacecraft solutions, not in size but in numbers and individual capabilities. The on-going NASA OSIRIS-REx and JAXA HAYABUSA2 missions exemplify the trend as well as the upcoming NEA SCOUT mission or the landers MINERVA-II and MASCOT recently deployed on Ryugu. We outline likely as well as possible and efficient routes of continuation of all these developments towards a propellant-less and highly efficient class of spacecraft for small solar system body exploration: small spacecraft solar sails designed for carefree handling and equipped with carried landers and application modules, for all asteroid user communities –planetary science, planetary defence, and in-situ resource utilization. This projection builds on the experience gained in the development of deployable membrane structures leading up to the successful ground deployment test of a (20 m)² solar sail at DLR Cologne and in the 20 years since. It draws on the background of extensive trajectory optimization studies, the qualified technology of the DLR GOSSAMER-1 deployment demonstrator, and the MASCOT asteroid lander. These enable ‘now-term’ as well as near-term hardware solutions, and thus responsive fast-paced development. Mission types directly applicable to planetary defense include: single and Multiple NEA Rendezvous ((M)NR) for mitigation precursor, target monitoring and deflection follow-up tasks; sail-propelled head-on retrograde kinetic impactors (RKI) for mitigation; and deployable membrane based methods to modify the asteroid’s properties or interact with it. The DLR-ESTEC GOSSAMER Roadmap initiated studies of missions uniquely feasible with solar sails such as Displaced L1 (DL1) space weather advance warning and monitoring and Solar Polar Orbiter (SPO) delivery which demonstrate the capability of near-term solar sails to achieve NEA rendezvous in any kind of orbit, from Earth-coorbital to extremely inclined and even retrograde orbits. For those mission types using separable payloads, such as SPO, (M)NR and RKI, design concepts can be derived from the separable Boom Sail Deployment Units characteristic of DLR GOSSAMER solar sail technology, nanolanders like MASCOT, or microlanders like the JAXA-DLR Jupiter Trojan Asteroid Lander for the OKEANOS mission which can shuttle from the sail to the asteroids visited and enable multiple NEA sample-return missions. These are an ideal match for solar sails in micro-spacecraft format whose launch configurations are compatible with ESPA and ASAP secondary payload platforms.
The paper industry is the industry with the third highest energy consumption in the European Union. Using recycled paper instead of fresh fibers for papermaking is less energy consuming and saves resources. However, adhesive contaminants in recycled paper are particularly problematic since they reduce the quality of the resulting paper-product. To remove as many contaminants and at the same time obtain as many valuable fibres as possible, fine screening systems, consisting of multiple interconnected pressure screens, are used. Choosing the best configuration is a non-trivial task: The screens can be interconnected in several ways, and suitable screen designs as well as operational parameters have to be selected. Additionally, one has to face conflicting objectives. In this paper, we present an approach for the multi-criteria optimization of pressure screen systems based on Mixed-Integer Nonlinear Programming. We specifically focus on a clear representation of the trade-off between different objectives.
Water suppliers are faced with the great challenge of achieving high-quality and, at the same time, low-cost water supply. In practice, the focus is set on the most beneficial maintenance measures and/or capacity adaptations of existing water distribution systems (WDS). Since climatic and demographic influences will pose further challenges in the future, the resilience enhancement of WDS, i.e. the enhancement of their capability to withstand and recover from disturbances, has been in particular focus recently. To assess the resilience of WDS, metrics based on graph theory have been proposed. In this study, a promising approach is applied to assess the resilience of the WDS for a district in a major German City. The conducted analysis provides insight into the process of actively influencing the
resilience of WDS
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 order to maximize the possible travel distance of battery electric vehicles with one battery charge, it is mandatory to adjust all components of the powertrain carefully to each other. While current vehicle designs mostly simplify the powertrain rigorously and use an electric motor in combination with a gearbox with only one fixed transmission ratio, the use of multi-gear systems has great potential. First, a multi-speed system is able to improve the overall energy efficiency. Secondly, it is able to reduce the maximum momentum and therefore to reduce the maximum current provided by the traction battery, which results in a longer battery lifetime. In this paper, we present a systematic way to generate multi-gear gearbox designs that—combined with a certain electric motor—lead to the most efficient fulfillment of predefined load scenarios and are at the same time robust to uncertainties in the load. Therefore, we model the electric motor and the gearbox within a Mixed-Integer Nonlinear Program, and optimize the efficiency of the mechanical parts of the powertrain. By combining this mathematical optimization program with an unsupervised machine learning algorithm, we are able to derive global-optimal gearbox designs for practically relevant momentum and speed requirements.