Afterburner Performance of Circular V-Gutters and a Sector of Parallel V-Gutters for a Range of Inlet Temperatures to 1255 K (1800 F)

Combustion tests of two V-gutter types were conducted in a 19.25-in. diameter duct using vitiated air. Fuel spraybars were mounted in line with the V-gutters. Combustor length was set by flame-quench water sprays which were part of a calorimeter for measuring combustion efficiency. Although the levels of performance of the parallel and circular array afterburners were different, the trends with geometry variations were consistent. Therefore, parallel arrays can be used for evaluating V-gutter geometry effects on combustion performance. For both arrays, the highest inlet temperature produced combustion efficiencies near 100 percent. A 5-in. spraybar - to - V-gutter spacing gave higher efficiency and better lean blowout performance than a spacing twice as large. Gutter durability was good

Effect of Extrusion Parameters on Properties of Powder Coatings Determined by Infrared Spectroscopy

In polymer extrusion, compounding is a continuous mixing process that is also used to produce highly reactive powder coatings. A premixed batch of powder coating is added to the feeding section and extruded, preferably by a co-rotating twin-screw extruder. One essential parameter in the processing of highly reactive materials is the melt temperature: If it is too high, pre-reactions occur during the extrusion process, which may cause high rejection rates. We studied the melt temperature of an epoxy/carboxyl-based powder coating using a retractable thermocouple at 3 different axial positions along the barrel of a ZSK34 co-rotating twin-screw extruder. The influence of different processing conditions on the reactivity of a highly reactive powder coating was examined by infrared spectroscopy and differential scanning calorimetry. Furthermore, the specific energy input and the color change in the finished powder coating at different processing points were investigated. Multivariate data analysis was used to correlate mid-infrared spectra, melt temperatures, specific energy inputs, enthalpies of reaction and changes in color

Microscopic Analysis of Thermodynamic Parameters from 160 MeV/n - 160 GeV/n

Microscopic calculations of central collisions between heavy nuclei are used to study fragment production and the creation of collective flow. It is shown that the final phase space distributions are compatible with the expectations from a thermally equilibrated source, which in addition exhibits a collective transverse expansion. However, the microscopic analyses of the transient states in the reaction stages of highest density and during the expansion show that the system does not reach global equilibrium. Even if a considerable amount of equilibration is assumed, the connection of the measurable final state to the macroscopic parameters, e.g. the temperature, of the transient ''equilibrium'' state remains ambiguous.Comment: 13 pages, Latex, 8 postscript figures, Proceedings of the Winter Meeting in Nuclear Physics (1997), Bormio (Italy

STONE 6: Artificial Sedimentary Meteorites in Space

The STONE 6 experiment demonstrated the survivability of carbonaceous and microfossiliferous martian analogue sediments during atmospheric re-entry. Doped endoliths died but their carbonised cells remained

Quantum Optical Experiments Modeled by Long Short-Term Memory

We demonstrate how machine learning is able to model experiments in quantum physics. Quantum entanglement is a cornerstone for upcoming quantum technologies such as quantum computation and quantum cryptography. Of particular interest are complex quantum states with more than two particles and a large number of entangled quantum levels. Given such a multiparticle high-dimensional quantum state, it is usually impossible to reconstruct an experimental setup that produces it. To search for interesting experiments, one thus has to randomly create millions of setups on a computer and calculate the respective output states. In this work, we show that machine learning models can provide significant improvement over random search. We demonstrate that a long short-term memory (LSTM) neural network can successfully learn to model quantum experiments by correctly predicting output state characteristics for given setups without the necessity of computing the states themselves. This approach not only allows for faster search but is also an essential step towards automated design of multiparticle high-dimensional quantum experiments using generative machine learning models

Extracting the equation of state from a microscopic non-equilibrium model

We study the thermodynamic properties of infinite nuclear matter with the Ultrarelativistic Quantum Molecular Dynamics (URQMD), a semiclassical transport model, running in a box with periodic boundary conditions. It appears that the energy density rises faster than $T^4$ at high temperatures of $T\approx 200-300$~MeV. This indicates an increase in the number of degrees of freedom. Moreover, We have calculated direct photon production in Pb+Pb collisions at 160~GeV/u within this model. The direct photon slope from the microscopic calculation equals that from a hydrodynamical calculation without a phase transition in the equation of state of the photon source.Comment: Proceedings of the XIV International Conference on Particles and Nuclei (PANIC'96), 22-28 May 1996, Williamsburg, Virginia, USA, to be published by World Scientific Publ. Co. (3 pages

Signatures of dense hadronic matter in ultrarelativistic heavy ion reactions

The behavior of hadronic matter at high baryon densities is studied within Ultrarelativistic Quantum Molecular Dynamics (URQMD). Baryonic stopping is observed for Au+Au collisions from SIS up to SPS energies. The excitation function of flow shows strong sensitivities to the underlying equation of state (EOS), allowing for systematic studies of the EOS. Dilepton spectra are calculated with and without shifting the $\rho$ pole. Except for S+Au collisions our calculations reproduce the CERES data.Comment: Invited talk at RHIC-theory workshop at BNL july 8-1