Hybrid energy converter based on swirling combustion chambers: the hydrocarbon feeding analysis

This manuscript reports the latest investigations about a miniaturized hybrid energy power source, compatible with thermal/electrical conversion, by a thermo-photovoltaic cell, and potentially useful for civil and space applications. The converter is a thermally-conductive emitting parallelepiped element and the basic idea is to heat up its emitting surfaces by means of combustion, occurred in swirling chambers, integrated inside the device, and/or by the sun, which may work simultaneously or alternatively to the combustion. The current upgrades consist in examining whether the device might fulfill specific design constraints, adopting hydrocarbons-feeding. Previous papers, published by the author, demonstrate the hydrogen-feeding effectiveness. The project's constraints are: 1) emitting surface dimensions fixed to 30 × 30 mm, 2) surface peak temperature T > 1000 K and the relative ΔT < 100 K (during the combustion mode), 3) the highest possible delivered power to the ambient, and 4) thermal efficiency greater than 20% when works with solar energy. To this end, a 5 connected swirling chambers configuration (3 mm of diameter), with 500 W of injected chemical power, stoichiometric conditions and detailed chemistry, has been adopted. Reactive numerical simulations show that the stiff methane chemical structure obliges to increase the operating pressure, up to 10 atm, and to add hydrogen, to the methane fuel injection, in order to obtain stable combustion and efficient energy conversion

Energy Converter with Inside Two, Three, and Five Connected H2/Air Swirling Combustor Chambers: Solar and Combustion Mode Investigations

This work reports the performance of an energy converter characterized by an emitting parallelepiped element with inside two, three, or five swirling connected combustion chambers. In particular, the idea is to adopt the heat released by H2/air combustion, occurring in the connected swirling chambers, to heat up the emitting surfaces of the thermally-conductive emitting parallelepiped brick. The final goal consists in obtaining the highest emitting surface temperature and the highest power delivered to the ambient environment, with the simultaneous fulfillment of four design constraints: dimension of the emitting surface fixed to 30 30 mm2, solar mode thermal efficiency greater than 20%, emitting surface peak temperature T > 1000 K, and its relative DT 99.9%, and high peak temperature, but the emitting surface DT is strongly sensitive to the geometrical configuration. The present work is related to the “EU-FP7-HRC-Power” project, aiming at developing micro-meso hybrid sources of power, compatible with a thermal/electrical conversion by thermo-photovoltaic cells

Quantum interference effects in resonant Raman spectroscopy of single- and triple-layer MoTe2_2 from first principles

We present a combined experimental and theoretical study of resonant Raman spectroscopy in single- and triple-layer MoTe$_2$. Raman intensities are computed entirely from first principles by calculating finite differences of the dielectric susceptibility. In our analysis, we investigate the role of quantum interference effects and the electron-phonon coupling. With this method, we explain the experimentally observed intensity inversion of the $A^\prime_1$ vibrational modes in triple-layer MoTe2 with increasing laser photon energy. Finally, we show that a quantitative comparison with experimental data requires the proper inclusion of excitonic effects.Comment: Main Text (5 Figures, 1 Tables) + Supporting Information (6 Figures

Breadth First Search Vectorization on the Intel Xeon Phi

Breadth First Search (BFS) is a building block for graph algorithms and has recently been used for large scale analysis of information in a variety of applications including social networks, graph databases and web searching. Due to its importance, a number of different parallel programming models and architectures have been exploited to optimize the BFS. However, due to the irregular memory access patterns and the unstructured nature of the large graphs, its efficient parallelization is a challenge. The Xeon Phi is a massively parallel architecture available as an off-the-shelf accelerator, which includes a powerful 512 bit vector unit with optimized scatter and gather functions. Given its potential benefits, work related to graph traversing on this architecture is an active area of research. We present a set of experiments in which we explore architectural features of the Xeon Phi and how best to exploit them in a top-down BFS algorithm but the techniques can be applied to the current state-of-the-art hybrid, top-down plus bottom-up, algorithms. We focus on the exploitation of the vector unit by developing an improved highly vectorized OpenMP parallel algorithm, using vector intrinsics, and understanding the use of data alignment and prefetching. In addition, we investigate the impact of hyperthreading and thread affinity on performance, a topic that appears under researched in the literature. As a result, we achieve what we believe is the fastest published top-down BFS algorithm on the version of Xeon Phi used in our experiments. The vectorized BFS top-down source code presented in this paper can be available on request as free-to-use software

On the handover security key update and residence management in LTE networks

In LTE networks, key update and residence management have been investigated as an effective solution to cope with desynchronization attacks in mobility management entity (MME) handovers. In this paper, we first analyse the impacts of the key update interval (KUI) and MME residence interval (MRI) on the handover performance in terms of the number of exposed packets (NEP) and signaling overhead rate (SOR). By deriving the bounds of the NEP and SOR over the KUI and MRI, it is shown that there exists a tradeoff between the NEP and the SOR, while our aim is to minimise both of them simultaneously. This accordingly motivates us to propose a multiobjective optimisation problem to find the optimal KUI and MRI that minimise both the NEP and SOR. By introducing a relative importance factor between the SOR and NEP along with their derived bounds, we further transform the proposed optimisation problem into a single-objective optimisation problem which can be solved via a simple numerical method. In particular, the results show that a higher accuracy of up to 1 second is achieved with the proposed approach while requiring a lower complexity compared to the conventional approach employing iterative searches

On the Overhead of Topology Discovery for Locality-aware Scheduling in HPC

International audienceThe increasing complexity of parallel computing platforms requires a deep knowledge of the hardware and of the application needs. Locality a key criteria for performance optimization. It involves software tools to expose information about the hardware topology to high performance runtime libraries. We show that the overhead of gathering such information from the operating system is significant on large computing nodes that run Linux. This overhead also increases more than linearly with the number of processes that perform it simultaneously. We then study the actual needs of the HPC software ecosystem in terms of topology information. We propose some ways to avoid multiple expensive topology discovery and to share topology information between components such as the resource manager or the runtime libraries

Know your enemies and know yourself in the real-time bidding function optimisation

Real-time bidding (RTB) is a popular method to sell online ad space inventory using real-time auctions to determine which advertiser gets to make the ad impression. Advertisers can take user information into account when making their bids and get more control over the process. The goal of an optimal bidding function is to maximise the overall effectiveness of the ad campaigns defined by the advertisers under a certain budget constraint. A straightforward solution would be to model the bidding function in an explicit form. However, such functional solutions lack generality in practice and are insensitive to the stochastic behaviour of other bidders in the environment. In this paper, we propose to formulate the online auctions into a general mean field multi-agent framework, in which the agents compete with each other and each agent's best response strategy depends on its opponents' actions. We firstly introduce a novel Deep Attentive Survival Analysis (DASA) model to estimate the opponent's action distribution on the ad impression level which outperforms state-of-the-art survival analysis. Furthermore, we introduce the DASA model as the opponent model into the Mean Field Deep Deterministic Policy Gradients (DDPG) algorithm for each agent to learn the optimal bidding strategy and converge to the mean field equilibrium. The experiments have shown that with the inference of the market, the market converges to the equilibrium faster while playing against both fixed strategy agents and dynamic learning agents