High-precision studies of domain-wall properties in the 2D Gaussian Ising spin glass

In two dimensions, short-range spin glasses order only at zero temperature, where efficient combinatorial optimization techniques can be used to study these systems with high precision. The use of large system sizes and high statistics in disorder averages allows for reliable finite-size extrapolations to the thermodynamic limit. Here, we use a recently introduced mapping of the Ising spin-glass ground-state problem to a minimum-weight perfect matching problem on a sparse auxiliary graph to study square-lattice samples of up to 10 000 × 10 000 spins. We propose a windowing technique that allows to extend this method, that is formally restricted to planar graphs, to the case of systems with fully periodic boundary conditions. These methods enable highly accurate estimates of the spin-stiffness exponent and domain-wall fractal dimension of the 2D Edwards-Anderson spin-glass with Gaussian couplings. Studying the compatibility of domain walls in this system with traces of stochastic Loewner evolution (SLE), we find a strong dependence on boundary conditions and compatibility with SLE only for one out of several setups

Hygrothermal durability of bond in FRP-strengthened masonry

Fiber reinforced polymers (FRPs) are accepted as an efficient material for external strengthening of masonry structures. Previous researches have shown that the bond between FRP and the substrate plays an important role in the effectiveness of this strengthening technique. Extensive investigations have been devoted to the characterization of the short-term bond behavior, while its durability and long-term performance requires further studies. In this regard, a full experimental program for investigating the environmental durability of bond in FRP-strengthened masonry is crucial for understanding the degrading mechanisms. This paper presents the results of an experimental program aimed at investigating the hygrothermal durability of bond in FRP-strengthened bricks. Accelerated ageing tests were performed on the FRP-strengthened brick elements and the bond degradation was periodically investigated by visual inspection and by conventional single-lap shear bond tests. The changes in the properties of material constituents have also been monitored. The obtained results are presented and critically discussed.This work was developed within the framework of the RILEM Technical Committee "223-MSC: Masonry Strengthening with Composite Materials". The financial support from the project FP7-ENV-2009-1-244123-NIKER of the 7th Framework Program of the European Commission is gratefully acknowledged. The first author also acknowledges the financial support of the Portuguese Science Foundation (Fundacao de Ciencia e Tecnologia, FCT), through grant SFRH/BD/80697/2011

Efficiency and emissions mapping of a light duty diesel - natural gas engine operating in conventional diesel and RCCI modes

Reactivity Controlled Compression Ignition (RCCI) is a promising dual-fuel Low Temperature Combustion (LTC) mode with significant potential for reducing NOx and particulate emissions while improving or maintaining thermal efficiency compared to Conventional Diesel Combustion (CDC) engines. The large reactivity difference between diesel and Natural Gas (NG) fuels provides a strong control variable for phasing and shaping combustion heat release. In this work, the Brake Thermal Efficiencies (BTE), emissions and combustion characteristics of a light duty 1.9L, four-cylinder diesel engine operating in single fuel diesel mode and in Diesel-NG RCCI mode are investigated and compared. The engine was operated at speeds of 1300 to 2500 RPM and loads of 1 to 7 bar BMEP. Operation was limited to 10 bar/deg Maximum Pressure Rise Rate (MPRR) and 6% Coefficient of Variation (COV) of IMEP. The engine performance was investigated using a combination of RCCI control variables including NG/diesel Blend Ratio (BR), diesel injection fuel split, and Start of Injection (SOI) timing for diesel injections. The RCCI map was generated using different injection strategies (single and double injections) and up to 20% EGR Exhaust Gas Recirculation (EGR) at higher loads to obtain the best brake thermal efficiency. In addition, the majority of the required energy (more than 80%) in RCCI operating points was provided from NG. The results showed a maximum of 5% increase in brake thermal efficiency and 92% reduction in NOx in RCCI combustion mode compared to the CDC mode

Data-driven modeling and predictive control of combustion phasing for RCCI engines

Reactivity controlled compression ignition (RCCI) engines center on a combustion strategy with higher thermal efficiency, lower particulate matter (PM), and lower oxides of nitrogen (NOx) emissions compared to conventional diesel combustion (CDC) engines. However, real time optimal control of RCCI engines is challenging during transient operation due to the need for high fidelity combustion models. Development of a simple, yet accurate control-oriented RCCI model from physical laws is time consuming and often requires substantial calibrations. To overcome these challenges, data-driven models can be developed. In this paper, a data-driven linear parameter-varying (LPV) model for an RCCI engine is developed. An LPV state space model is identified to predict RCCI combustion phasing as a function of multiple RCCI control variables. The results show that the proposed method provides a fast and reliable route to identify an RCCI engine model. The developed model is then used for the design of a model predictive controller (MPC) to control crank angle for 50% fuel burnt (CA50) for varying engine conditions. The experimental results show that the designed MPC with the data-driven LPV model can track desired CA50 with less than 1 crank angle degree (CAD) error against changes in engine load