The sandfish lizard, Scincus scincus (Squamata: Scincidae), spends nearly its whole life in aeolian sand and only comes to the surface for foraging, defecating and mating. It is not yet understood how the animal can respire without sand particles entering its respiratory organs when buried under thick layers of sand. In this work, we integrated biological studies, computational calculations and physical experiments to understand this phenomenon. We present a 3D model of the upper respiratory system based on a detailed histological analysis. A 3D-printed version of this model was used in combination with characteristic ventilation patterns for computational calculations and fluid mechanics experiments. By calculating the velocity field, we identified a sharp decrease in velocity in the anterior part of the nasal cavity where mucus and cilia are present. The experiments with the 3D-printed model validate the calculations: particles, if present, were found only in the same area as suggested by the calculations. We postulate that the sandfish has an aerodynamic filtering system; more specifically, that the characteristic morphology of the respiratory channel coupled with specific ventilation patterns prevent particles from entering the lungs.

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Accurate flow measurement is a ubiquitous task in fields such as industry, medical technology, or chemistry; it remains however challenging due to small measurement ranges or erosive flows. Inspiration for possible measurement methods can come from nature, for example from the lateral line organ of fish, which is comprised of hair cells embedded in a gelatinous cupula. When the cupula is deflected by water movement, the hair cells generate neural signals from which the fish gains an accurate representation of its environment. We built a flow sensor mimicking a hair cell, but coupled it with an optical detection method. Light is coupled into a PDMS waveguide that consists of a core and a cladding with a low refractive index contrast to ensure high bending sensitivity. Fluid flow bends the waveguide; this leads to a measurable light loss. The design of our sensory system allows flow measurement in opaque and corrosive fluids while keeping production costs low. To prove the measurement concept, we evaluated the light loss while (a) reproducibly bending the fiber with masses, and (b) exposing the fiber to air flow. The results demonstrate the applicability of an optical fiber as a flow sensor.

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In modern mechanical engineering and steelwork the use of cold-rolled steel sections is a standard method. These sections should be mechanically stable on the one hand and cost efficient on the other hand. To decide what profile suits for a certain case is a constrained optimization problem which is in general non convex, i.e. several local optima exist. To solve this non trivial problem we used genetic algorithms, search heuristics that mimic the process of natural evolution. For the specific application some additional problems had to be solved: First, an adaptive mutation control was implemented. Second, a mixed asexual and sexual reproduction was applied with an inbreed avoiding method based on the genetic distance of the individuals. Third, the restrictions were handled flexible, dependent on the mutation strength. This means that under the conditions of strong mutations (r-strategy), violations of the restrictions are allowed within some limits corresponding to reduced evolutionary pressure. Later on when approaching an optimum and the algorithm changes eventually to K-strategy, the restrictions become more severe corresponding to stabilising selection. The presented algorithm was tested on some cases; we found that significant improvement of cost efficiency was reached while mechanical stability was still granted. In comparison to hard restriction implementations like constant penalty functions or Lagrange-multipliers due to the flexible restrictions the algorithm tends significantly less to sustain in local optima. This approach could help to find cost efficient and light weight steel structures for mechanical engineering in the near future. case. Usually, the required mechanical parameters are calculated from the loads expected. Then either the cheapest profile from a palette of standard products is selected just fulfilling the mechanical requirements (including some safety-factor) or some experienced engineer uses inspiration and perspiration to design a profile where the material is "optimally" exploited. The optimization problem could be solved using a method from the field of nonlinear programming, where the problem is defined by a system of equalities and inequalities, collectively termed constraints, over a set of unknown real variables, along with an objective function to be maximized or minimized, where some of the constraints or the objective function are nonlinear [2]. Formally: Let n, m, and p be positive integers. Let X be a subset of ℝ n , let f, g i , and h j be real-valued functions on X for each i in {1, …, m} and each j in {1, …, p}.

The pollutant emissions from vehicles are forming major sources of metallic nanoparticles into the environment and surrounding atmosphere. In this research we spectrochemicaly analyse chemical composition of Particle Matter emissions from in-use Diesel engine passenger vehicles. We have extracted Diesel Particulate Matter from the end part of the tail pipe, from more than seventy different vehicles. And in laboratory we have used the high resolution laser induced plasma spectroscopy (LIBS) spectrochemical analytical technique to sensitively analyse chemical elements in different DPM. We have found that PM is composed of major, minor and trace chemical elements. The major compound of PM is not strictly Carbon element but rather other adsorbed metallic nanoparticles such as Iron, Chromium, Magnesium, Zinc, Calcium. Beside the major elements of DPM there are also minor elements: Silicon, Nickel, Titan, Potassium, Strontium, Molybdenium and others. Additionally in DPM are adsorbed atomic trace elements like Barium, Boron, Cobalt, Copper, Phosphorus, Manganese and Platinum. All these chemical elements are forming significant atomic composition of real PM from in-use Diesel engine vehicles.

Particulate air pollution has an adverse effect on cardiovascular and respiratory health. Air filtration systems are therefore essential in closed indoor environments. While mechanical filtration is described as an efficient technology, particle filters may act as a source of pollution if not correctly installed and frequently maintained. The sandfish lizard, a sand swimmer that spends nearly its whole life in fine desert sand, inspired us to rethink traditional filtering systems due to its unique ability of filtering sand from its nasal cavity. During a slow, prolonged inhalation, strong cross-flow velocities develop in a certain region of the upper respiratory tract; these cross-flows enhance gravitational settling and force inhaled sand grains towards the wall where they adhere to mucus, which covers the walls in this region. During an intense, cough-like exhalation the particles are blasted out. In this work, the sandfish’s aerodynamic filtering system was analyzed experimentally and by computational fluid dynamics simulations to study the flow profile and particle trajectories. Based on these findings, we discuss the development of a biomimetic filtering system, which could have the following advantages: due to the absence of a membrane, total pressure losses can be reduced. The mucus-covered surface would be mimicked by a specifically treated surface to trap particulate matter. Also, the device would contain a self-cleaning mechanism that simulates the lizard’s exhalation. This biomimetic filtering system would therefore have an enhanced life-time and it would be low-maintenance and therefore economical and sustainable.