Existence and uniqueness of Bowen-York Trumpets

We prove the existence of initial data sets which possess an asymptotically flat and an asymptotically cylindrical end. Such geometries are known as trumpets in the community of numerical relativists.Comment: This corresponds to the published version in Class. Quantum Grav. 28 (2011) 24500

Distributional sources for black hole initial data

Black hole initial data is usually produced using Bowen-York type puncture initial data or by applying an excision boundary condition. The benefits of the Bowen-York initial data are the ability to specify the spin and momentum of the system as parameters of the initial data. In an attempt to extend these benefits to other formulations of the Einstein constraints, the puncture method is reformulated using distributions as source terms. It is shown how the Bowen-York puncture black hole initial data and the trumpet variation is generated by distributional sources. A heuristic argument is presented to argue that these sources are the general sources of spin and momentum. In order to clarify the meaning of other distributional sources, an exact family of initial data with generalized sources to the Hamiltonian constraint are studied; spinning trumpet black hole initial data and black hole initial data with higher order momentum sources are also studied.Comment: Code available at https://github.com/SwampWalker/LeapingMonke

Proof of the area-angular momentum-charge inequality for axisymmetric black holes

We give a comprehensive discussion, including a detailed proof, of the area-angular momentum-charge inequality for axisymmetric black holes. We analyze the inequality from several viewpoints, in particular including aspects with a theoretical interest well beyond the Einstein-Maxwell theory.Comment: 31 pages, 2 figure

Forecasting Thermoacoustic Instabilities in Liquid Propellant Rocket Engines Using Multimodal Bayesian Deep Learning

The 100 MW cryogenic liquid oxygen/hydrogen multi-injector combustor BKD operated by the DLR Institute of Space Propulsion is a research platform that allows the study of thermoacoustic instabilities under realistic conditions, representative of small upper stage rocket engines. We use data from BKD experimental campaigns in which the static chamber pressure and fuel-oxidizer ratio are varied such that the first tangential mode of the combustor is excited under some conditions. We train an autoregressive Bayesian neural network model to forecast the amplitude of the dynamic pressure time series, inputting multiple sensor measurements (injector pressure/ temperature measurements, static chamber pressure, high-frequency dynamic pressure measurements, high-frequency OH* chemiluminescence measurements) and future flow rate control signals. The Bayesian nature of our algorithms allows us to work with a dataset whose size is restricted by the expense of each experimental run, without making overconfident extrapolations. We find that the networks are able to accurately forecast the evolution of the pressure amplitude and anticipate instability events on unseen experimental runs 500 milliseconds in advance. We compare the predictive accuracy of multiple models using different combinations of sensor inputs. We find that the high-frequency dynamic pressure signal is particularly informative. We also use the technique of integrated gradients to interpret the influence of different sensor inputs on the model prediction. The negative log-likelihood of data points in the test dataset indicates that predictive uncertainties are well-characterized by our Bayesian model and simulating a sensor failure event results as expected in a dramatic increase in the epistemic component of the uncertainty

Early detection of thermoacoustic instabilities in a cryogenic rocket thrust chamber using combustion noise features and machine learning

We present a data-driven method for the early detection of thermoacoustic instabilities. Recurrence quantification analysis is used to calculate characteristic combustion features from short-length time series of dynamic pressure sensor data. Features like recurrence rate are used to train support vector machines to detect the onset of instability a few hundred milliseconds in advance. The performance of the proposed method is investigated on experimental data from a representative LOX/H research thrust chamber. In most cases, the method is able to timely predict two types of thermoacoustic instabilities on test data not used for training. The results are compared with state-of-the-art early warning indicators

ASCenSIon: An innovative network to train the space access leaders of tomorrow

The trend towards smaller satellites and mega-constellations has enormously changed the space sector and its utilisation in the last decades, allowing new players to enter the market and to introduce stringent requirements to enable a variety of novel applications. Alongside, also the launcher market is undergoing a transformation epoch: the development, manufacturing and integration of launcher systems is being shifted from the hands of governmental institutions to commercial industry. Moreover, nations like Unites States, China, India and New Zealand are increasing the competition and pressure on Europe, urging the goal to ensure European autonomy in accessing and using space in a safe and secure environment. Europe does not only need innovations, but primarily a new generation of engineers, capable of understanding the full complexity of launcher development and trained to create and realise the necessary innovations. In this context, ASCenSIon is a multidisciplinary training programme involving 15 Early Stage Researchers (ESRs) from anywhere in the world, focused on several specific areas of cutting-edge space access research, particularly on launcher systems that are (partially) reusable and capable of injecting multiple payloads into multiple orbits. The network aims to identify and advance critical technologies in the space access field, and prove their feasibility. ASCenSIon, whose acronym stands for “Advancing Space Access Capabilities -Reusability and Multiple Satellite Injection”, is a consortium of 11 beneficiaries and 17 partners across Europe, eager to contribute to the establishment of an ecologically and economically sustainable space access for Europe, oriented towards user needs. Unlike other single-aspect research projects, the core objective of ASCenSIon is not only to train 15 PhD students to become excellent specialists in their respective field, but also to provide them a thorough understanding of the complexity, multidisciplinary and internationality of launcher development, in order to become leaders in the European effort of utilising space. This will be achieved through secondments, events and lessons from experts, but mostly through strong interconnections among the ESRs, who will work on Individual Research Projects with a multi-disciplinal and multi-sectoral approach. This paper aims to provide an overview of ASCenSIon programme. Its values and core objectives will be introduced, together with the innovative aspects and content structure. An overview of the research methodology and recruitment strategy will be given, with a particular focus on the contributions and synergies of all participating organisations, core of such a novel training approach

ASCenSIon: An Innovative Network to Train the Space Access Leaders of Tomorrow

reserved15simixedGloder, A.; Apel, U.; Bianchi, D.; Bonetti, D.; Deeken, J.; Hendrick, P.; Hijlkema, J.; Lavagna, M.; Pasini, A.; Prevereaud, Y.; Sippel, M.; Stoll, E.; Waxenegger-Wilfing, G.; Tajmar, M.; Bach, C.Gloder, A.; Apel, U.; Bianchi, D.; Bonetti, D.; Deeken, J.; Hendrick, P.; Hijlkema, J.; Lavagna, M.; Pasini, A.; Prevereaud, Y.; Sippel, M.; Stoll, E.; Waxenegger-Wilfing, G.; Tajmar, M.; Bach, C