Modelica - A Language for Physical System Modeling, Visualization and Interaction

Modelica is an object-oriented language for modeling of large, complex and heterogeneous physical systems. It is suited for multi-domain modeling, for example for modeling of mechatronics including cars, aircrafts and industrial robots which typically consist of mechanical, electrical and hydraulic subsystems as well as control systems. General equations are used for modeling of the physical phenomena, No particular variable needs to be solved for manually. A Modelica tool will have enough information to do that automatically. The language has been designed to allow tools to generate efficient code automatically. The modeling effort is thus reduced considerably since model components can be reused and tedious and error-prone manual manipulations are not needed. The principles of object-oriented modeling and the details of the Modelica language as well as several examples are presented

Getting grip on complex water issues? : a case study: Rotterdam Mainport, Appropriate Assessment Wadden Sea

In Dutch water systems many human interventions are carried out. These interventions are designed to achieve management goals, like increase protection against flooding, improve environmental quality and/or stimulate the national economy. Decision-makers involved in these kinds of plans have to deal with ecological effects, physical effects, economic costs and benefits and technical feasibility. Furthermore, they operate within a complex web of interactions between policy, regulations, and social and political processes. This PhD-project aims at assisting decision-makers in constructing assessments of the impact of future human interventions in water systems

Knowledge and perceptions in participatory policy processes: lessons from the delta-region in the Netherlands

Water resources management issues tend to affect a variety of uses and users. Therefore, they often exhibit complex and unstructured problems. The complex, unstructured nature of these problems originates from uncertain knowledge and from the existence of divergent perceptions among various actors. Consequently, dealing with these problems is not just a knowledge problem; it is a problem of ambiguity too. This paper focuses on a complex, unstructured water resources management issue, the sustainable development—for ecology, economy and society—of the Delta-region of the Netherlands. In several areas in this region the ecological quality decreased due to hydraulic constructions for storm water safety, the Delta Works. To improve the ecological quality, the Dutch government regards the re-establishment of estuarine dynamics in the area as the most important solution. However, re-establishment of estuarine dynamics will affect other uses and other users. Among the affected users are farmers in the surrounding areas, who use freshwater from a lake for agricultural purposes. This problem has been addressed in a participatory decision-making process, which is used as a case study in this paper. We investigate how the dynamics in actors’ perceptions and the knowledge base contribute to the development of agreed upon and valid knowledge about the problem–solution combination, using our conceptual framework for problem structuring. We found that different knowledge sources—expert and practical knowledge—should be integrated to create a context-specific knowledge base, which is scientifically valid and socially robust. Furthermore, we conclude that for the convergence of actors’ perceptions, it is essential that actors learn about the content of the process (cognitive learning) and about the network in which they are involved (strategic learning). Our findings form a plea for practitioners in water resources management to adopt a problem structuring approach in order to deal explicitly with uncertainty and ambiguity

Testing and validating the CERES-wheat (Crop Estimation through Resource and Environment Synthesis-wheat) model in diverse environments

CERES-Wheat is a computer simulation model of the growth, development, and yield of spring and winter wheat. It was designed to be used in any location throughout the world where wheat can be grown. The model is written in Fortran 77, operates on a daily time stop, and runs on a range of computer systems from microcomputers to mainframes. Two versions of the model were developed: one, CERES-Wheat, assumes nitrogen to be nonlimiting; in the other, CERES-Wheat-N, the effects of nitrogen deficiency are simulated. The report provides the comparisons of simulations and measurements of about 350 wheat data sets collected from throughout the world

3-D printing of microwave components for 21st century applications

Additive manufacturing using 3-D printing is an emerging technology for the production of high performance microwave and terahertz components. Traditionally, these components are made by (micro-)machining. However, recent advances in rapid prototyping technology have led to its use in creating high performance and low weight RF components. In this review paper ten state-of-the-art exemplars are described, covering a wide variety of applications (absorbers, waveguides, antennas and lenses) operating over a broad range of frequencies, from 8 to 330 GHz

Tungsten resonance integrals and Doppler coefficients First quarterly progress report, Jul. - Sep. 1965

Resonance integrals and Doppler coefficients of samples of natural tungsten, tungsten isotopes, and uranium oxide tungsten fue

3D printed waveguides: A revolution in low volume manufacturing for the 21st century

3D printing is a disruptive technology, offering the inherent capabilities for creating truly arbitrary 3D structures, with low manufacturing costs associated with low volume production runs. This paper provides an overv iew of the current progress in 3D printing of metal - pipe rectangular waveguide (MPRWG) components, from 10 GHz to 1 THz, at Imperial College London. First, measurements performed at the UK National Physical Laboratory demonstrate that 3D printed MPRWG perf ormance is comparable to standard commercial waveguides at X - band and W - band. Then, a fully 3D printed X - band dielectric flap tuneable phase shifter and W - band 6th - order inductive iris bandpass filter are demonstrated experimentally. Finally, an optically - controlled 500 GHz IQ vector modulator will also be presented; packaged laser diodes and high resistivity silicon implants are integrated within a hybrid 3D printed split - block module, representing a paradigm shift in additive manufacturing for realizing t uneable THz applications

Sparse Nerves in Practice

Topological data analysis combines machine learning with methods from algebraic topology. Persistent homology, a method to characterize topological features occurring in data at multiple scales is of particular interest. A major obstacle to the wide-spread use of persistent homology is its computational complexity. In order to be able to calculate persistent homology of large datasets, a number of approximations can be applied in order to reduce its complexity. We propose algorithms for calculation of approximate sparse nerves for classes of Dowker dissimilarities including all finite Dowker dissimilarities and Dowker dissimilarities whose homology is Cech persistent homology. All other sparsification methods and software packages that we are aware of calculate persistent homology with either an additive or a multiplicative interleaving. In dowker_homology, we allow for any non-decreasing interleaving function $\alpha$. We analyze the computational complexity of the algorithms and present some benchmarks. For Euclidean data in dimensions larger than three, the sizes of simplicial complexes we create are in general smaller than the ones created by SimBa. Especially when calculating persistent homology in higher homology dimensions, the differences can become substantial