Component Thermodynamical Selection Based Gene Expression Programming for Function Finding

Gene expression programming (GEP), improved genetic programming (GP), has become a popular tool for data mining. However, like other evolutionary algorithms, it tends to suffer from premature convergence and slow convergence rate when solving complex problems. In this paper, we propose an enhanced GEP algorithm, called CTSGEP, which is inspired by the principle of minimal free energy in thermodynamics. In CTSGEP, it employs a component thermodynamical selection (CTS) operator to quantitatively keep a balance between the selective pressure and the population diversity during the evolution process. Experiments are conducted on several benchmark datasets from the UCI machine learning repository. The results show that the performance of CTSGEP is better than the conventional GEP and some GEP variations

Assessing gaps and needs for integrating building performance optimization tools in net zero energy buildings design

This paper summarizes a study undertaken to reveal potential challenges and opportunities for integrating optimization tools in net zero energy buildings (NZEB) design. The paper reviews current trends in simulation-based building performance optimization (BPO) and outlines major criteria for optimization tools selection and evaluation. This is based on analyzing user's needs for tools capabilities and requirement specifications. The review is carried out by means of a literature review of 165 publications and interviews with 28 optimization experts. The findings are based on an inter-group comparison between experts. The aim is to assess the gaps and needs for integrating BPO tools in NZEB design. The findings indicate a breakthrough in using evolutionary algorithms in solving highly constrained envelope, HVAC and renewable optimization problems. Simple genetic algorithm solved many design and operation problems and allowed measuring the improvement in the optimality of a solution against a base case. Evolutionary algorithms are also easily adapted to enable them to solve a particular optimization problem more effectively. However, existing limitations including model uncertainty, computation time, difficulty of use and steep learning curve. Some future directions anticipated or needed for improvement of current tools are presented.Peer reviewe

A Holistic Take on Simulating Battery Electrolytes

As powering a sustainable future is a global goal, interest in battery research and technology is at an all-time high. In order to enable a transition to green-tech, many industries, such as the automotive industry, urge for batteries with higher power and energy densities, longer life-times, and that are safer. All these properties are fundamentally limited by the materials employed. Hence humanity’s ability to create new energy storage materials need to improve. The way energy storage materials have been developed up until now have mainly been in the lab. With many other industries benefiting from IT tools the battery industry is seeing a need for new better computational tools to aid in developing new materials. Many put their faith in machine learning algorithms to provide the solution, but those methods are not flawless, and are especially hard to work with when modeling electrolytes. This thesis focuses on physics-based methods to model battery electrolytes, such as DFT, AIMD, and classical MD, and makes a holistic retake on how these methods could be used in unison to better help material developers screen their materials. Novel electrolyte concepts such as highly concentrated electrolytes and localized highly concentrated electrolytes, both for lithium and calcium batteries, are studied using the aforementioned tools. This thesis also presents how the newly developed CHAMPION software and methods can be used to tie the dierent methods together and possibly also extend their use by mapping forces on identified interatomic interactions, which may enable much faster turn-around in the simulation protocols

Near-infrared scattered light properties of the HR 4796 A dust ring A measured scattering phase function from 13.6° to 166.6°

Context. HR 4796 A is surrounded by a debris disc, observed in scattered light as an inclined ring with a high surface brightness. Past observations have raised several questions. First, a strong brightness asymmetry detected in polarised reflected light has recently challenged our understanding of scattering by the dust particles in this system. Secondly, the morphology of the ring strongly suggests the presence of planets, although no planets have been detected to date. Aims. We aim here at measuring with high accuracy the morphology and photometry of the ring in scattered light, in order to derive the phase function of the dust and constrain its near-infrared spectral properties. We also want to constrain the presence of planets and set improved constraints on the origin of the observed ring morphology. Methods. We obtained high-angular resolution coronagraphic images of the circumstellar environment around HR 4796 A with VLT/SPHERE during the commissioning of the instrument in May 2014 and during guaranteed-time observations in February 2015. The observations reveal for the first time the entire ring of dust, including the semi-minor axis that was previously hidden either behind the coronagraphic spot or in the speckle noise. Results. We determine empirically the scattering phase function of the dust in the H band from 13.6° to 166.6°. It shows a prominent peak of forward scattering, never detected before, for scattering angles below 30°. We analyse the reflectance spectra of the disc from the 0.95 μm to 1.6 μm, confirming the red colour of the dust, and derive detection limits on the presence of planetary mass objects. Conclusions. We confirm which side of the disc is inclined towards the Earth. The analysis of the phase function, especially below 45°, suggests that the dust population is dominated by particles much larger than the observation wavelength, of about 20 μm. Compact Mie grains of this size are incompatible with the spectral energy distribution of the disc, however the observed rise in scattering efficiency beyond 50° points towards aggregates which could reconcile both observables. We do not detect companions orbiting the star, but our high-contrast observations provide the most stringent constraints yet on the presence of planets responsible for the morphology of the dust

Model Predictive Control (MPC) for Enhancing Building and HVAC System Energy Efficiency: Problem Formulation, Applications and Opportunities

In the last few years, the application of Model Predictive Control (MPC) for energy management in buildings has received significant attention from the research community. MPC is becoming more and more viable because of the increase in computational power of building automation systems and the availability of a significant amount of monitored building data. MPC has found successful implementation in building thermal regulation, fully exploiting the potential of building thermal mass. Moreover, MPC has been positively applied to active energy storage systems, as well as to the optimal management of on-site renewable energy sources. MPC also opens up several opportunities for enhancing energy efficiency in the operation of Heating Ventilation and Air Conditioning (HVAC) systems because of its ability to consider constraints, prediction of disturbances and multiple conflicting objectives, such as indoor thermal comfort and building energy demand. Despite the application of MPC algorithms in building control has been thoroughly investigated in various works, a unified framework that fully describes and formulates the implementation is still lacking. Firstly, this work introduces a common dictionary and taxonomy that gives a common ground to all the engineering disciplines involved in building design and control. Secondly the main scope of this paper is to define the MPC formulation framework and critically discuss the outcomes of different existing MPC algorithms for building and HVAC system management. The potential benefits of the application of MPC in improving energy efficiency in buildings were highlighted

Cosmological Parameters from Observations of Galaxy Clusters

Studies of galaxy clusters have proved crucial in helping to establish the standard model of cosmology, with a universe dominated by dark matter and dark energy. A theoretical basis that describes clusters as massive, multi-component, quasi-equilibrium systems is growing in its capability to interpret multi-wavelength observations of expanding scope and sensitivity. We review current cosmological results, including contributions to fundamental physics, obtained from observations of galaxy clusters. These results are consistent with and complementary to those from other methods. We highlight several areas of opportunity for the next few years, and emphasize the need for accurate modeling of survey selection and sources of systematic error. Capitalizing on these opportunities will require a multi-wavelength approach and the application of rigorous statistical frameworks, utilizing the combined strengths of observers, simulators and theorists.Comment: 53 pages, 21 figures. To appear in Annual Review of Astronomy & Astrophysic

HYDRODYNAMICAL SIMULATIONS OF GALAXY CLUSTERS: THERMODYNAMICS AND CHEMICAL ENRICHMENT

2002/2003XVI Ciclo1975Versione digitalizzata della tesi di dottorato cartacea

Measurements, Models, Systems and Design

531 s.