Dark Matter & Dark Energy from a single scalar field: CMB spectrum and matter transfer function

The dual axion model (DAM), yielding bot DM and DE form a PQ-like scalar field solving the strong CP problem, is known to allow a fair fit of CMB data. Recently, however, it was shown that its transfer function exhibits significant anomalies, causing difficulties to fit deep galaxy sample data. Here we show how DAM can be modified to agree with the latter data set. The modification follows the pattern suggested to reconcile any PQ-like approach with gravity. Modified DAM allows precise predictions which can be testable against future CMB and/or deep sample data.Comment: 15 pages, 8 figures, accepted for publication in JCA

A machine learning enabled multi-fidelity platform for the integrated design of aircraft systems

The push toward reducing the aircraft development cycle timemotivates the development of collaborative frameworks that enable themore integrated design of aircraft and their systems. The ModellIng and Simulation tools for Systems IntegratiONon Aircraft (MISSION) project aims to develop an integratedmodelling and simulation framework. This paper focuses on some recent advancements in theMISSION project and presents a design framework that combines a filtering process to down-select feasible architectures, amodeling platformthat simulates the power system of the aircraft, and a machine learning-based clustering and optimization module. This framework enables the designer to prioritize different designs and offers traceability on the optimal choices. In addition, it enables the integration of models at multiple levels of fidelity depending on the size of the design space and the accuracy required. It is demonstrated for the electrification of the Primary Flight Control System (PFCS) and the landing gear braking system using different electric actuation technologies. The performance of different architectures is analyzed with respect to key performance indicators (fuel burn, weight, power). The optimization process benefits from a data-driven localization step to identify sets of similar architectures. The framework demonstrates the capability of optimizing across multiple, different system architectures in an efficient way that is scalable for larger design spaces and larger dimensionality problems

Thermal and Economic Efficiency of Progressive Retrofit Strategies for School Buildings by a Statistical Analysis based Tool

Design alternatives in air conditioned buildings may be easily compared just by summing the hourly consumption of primary energy, while quantitative approachs for bioclimatic design strategies are difficult to be assessed and compared. A actively heated and passively cooled school building is considered as an application field of a novel methodology to promote an informed choice about the retrofit strategies to be adopted for buildings, defined as the Gained Comfort Cost (GCC). A functional and significant unit (i.e. a classroom), is used to test different energy retrofit solutions and their performances were compared with a baseline, in terms of the capacity to reduce the indoor air temperature variation. The novel methodology is a visual tool allowing to understand the “distance” of indoor conditions from comfort; the retrofit strategies are promoted to reduce this distance considering however the associated costs (LCC) to deal with actual feasibility

Dark Matter and Dark Energy from a single scalar field and CMB data

Axions are likely to be the Dark Matter (DM) that cosmological data require. They arise in the Peccei Quinn solution of the strong CP problem. In a previous work we showed that their model has a simple and natural generalization which yields also Dark Energy (DE), in fair proportions, without tuning any parameter: DM and DE arise from a single scalar field and are weakly coupled in the present era. In this paper we extend the analysis of this dual-axion cosmology and fit it to WMAP data, by using a Markov chain technique. We find that LCDM, dynamical DE with a SUGRA potential, DE with a SUGRA potential and a constant DE-DM coupling, as well as the dual-axion model with a SUGRA potential, fit data with a similar accuracy. The best-fit parameters are however fairly different, although consistency is mostly recovered at the 2-sigma level. A peculiarity of the dual-axion model with SUGRA potential is to cause more stringent constraints on most parameters and to favor high values of the Hubble parameter.Comment: 34 pages, 15 figures, replaced with accepted versio