Many gas-phase chemical reactions proceed via reaction intermediates, supported by potential wells. The characteristics of such complex-forming reactions differ drastically from those for direct ...reactions that involve barriers. For example, the reaction path for a complex-forming reaction is often barrierless, which results in weak and sometimes negative temperature dependence for its rate constant. The product angular and internal distributions of such reactions also bear clear signatures. Specifically, the angular distribution (i.e. differential cross-section) of a complex-forming reaction is often dominated by scattering in the forward and backward directions, and the product rotational state distribution usually peaks near the highest accessible rotational state, while vibrational state distribution often decays monotonically. While the quantum dynamics of direct reactions is well established, our understanding of complex-forming reactions is still far from complete. Given the importance of such reactions in interstellar, atmospheric and combustion chemistry, much research effort has recently been devoted to understand their dynamics. In this review, we survey the recent progress in the quantum dynamics of several prototypical complex-forming reactions, particularly those involving three or four atoms. We will focus on methodological advances in quantum scattering theory, quasi-classical trajectory methods and statistical models.
Partial Differential Equations (PDEs) are fundamental to model different phenomena in science and engineering mathematically. Solving them is a crucial step towards a precise knowledge of the ...behavior of natural and engineered systems. In general, in order to solve PDEs that represent real systems to an acceptable degree, analytical methods are usually not enough. One has to resort to discretization methods. For engineering problems, probably the best-known option is the finite element method (FEM). However, powerful alternatives such as mesh-free methods and Isogeometric Analysis (IGA) are also available. The fundamental idea is to approximate the solution of the PDE by means of functions specifically built to have some desirable properties. In this contribution, we explore Deep Neural Networks (DNNs) as an option for approximation. They have shown impressive results in areas such as visual recognition. DNNs are regarded here as function approximation machines. There is great flexibility to define their structure and important advances in the architecture and the efficiency of the algorithms to implement them make DNNs a very interesting alternative to approximate the solution of a PDE. We concentrate on applications that have an interest for Computational Mechanics. Most contributions explore this possibility have adopted a collocation strategy. In this work, we concentrate on mechanical problems and analyze the energetic format of the PDE. The energy of a mechanical system seems to be the natural loss function for a machine learning method to approach a mechanical problem. In order to prove the concepts, we deal with several problems and explore the capabilities of the method for applications in engineering.
•Proof of concept for the possibility of approximating the solution of BVPs using concepts and tools coming from deep machine learning.•The energy is the basis for the construction of the loss function.•The approximation space is defined by the architecture of the neural network.•The approach is applied to several engineering problems including linear elasticity, elastodynamics, nonlinear hyperelasticity, plate bending, piezoelectricity and phase field modeling of fracture.
•An integrated fuzzy entropy-weight MCDA method is proposed.•It is firstly applied to risk assessment of hydraulic projects in the Xiangxi River.•It avoids subjective effects on the weights.•The ...comprehensive risk levels of hydropower stations are ranked.
An integrated fuzzy entropy-weight multiple criteria decision making (IFEMCDM) method was proposed and applied to risk assessment of hydropower stations in the Xiangxi River. The IFEMCDM integrates the fuzzy set theory, the entropy weight method and the multiple criteria decision making method within a risk assessment framework. It can quantify uncertainties presented in fuzzy sets and assess multi-criteria decision problems in a more objective manner through avoiding subjective effects on the weights. The detailed computational procedures were provided to illustrate the integration process of the above methods. The performance of IFEMCDM was analyzed in terms of relative closeness and α-cut levels. The comprehensive assessment results demonstrated that, all of the ten hydropower stations can be divided into four degree ranges in accordance with the relative closeness. Most of the hydropower stations along the Gaolan River and the Gufu River would have lower risk. Decision makers can conduct flexible and variable response programs for the ten hydropower stations under different α-cut levels. The application of the IFEMCDM revealed its superiority in solving complicated multi-criteria assessment problems more objectively under fuzzy uncertainty. This study was the first application of the IFEMCDM model to risk assessment of hydropower stations, which indicated that it can also be applied to other environmental problems under uncertainties.
Indoor volatile organic compound (VOC) data obtained in 100 Hong Kong homes were analyzed to investigate the nature of emission sources and their contributions to indoor concentrations. A principal ...component analysis (PCA) showed that off-gassing of building materials, household products, painted wood products, room freshener, mothballs and consumer products were the major sources of VOCs in Hong Kong homes. The source apportionments were then evaluated by using an absolute principal component scores (APCS) technique combined with multiple linear regressions. The results indicated that 76.5
±
1% (average
±
standard error) of the total VOC emissions in Hong Kong homes attributes to the off-gassing of building materials, followed by the room freshener (8
±
4%), household products (6
±
2%), mothballs (5
±
3%) and painted wood products (4
±
2%). Analysis on the source strength in the monitored homes revealed that although six indoor sources were identified and quantified in the Hong Kong homes, only some homes were responsible for the elevated concentrations of target VOCs emitted from these sources. The findings provide us the mechanism of reducing levels of indoor VOCs and ultimately lead to cost effective reduction in population exposures.
Dissipative solitons are self-localised structures resulting from the double balance of dispersion by nonlinearity and dissipation by a driving force arising in numerous systems. In Kerr-nonlinear ...optical resonators, temporal solitons permit the formation of light pulses in the cavity and the generation of coherent optical frequency combs. Apart from shape-invariant stationary solitons, these systems can support breathing dissipative solitons exhibiting a periodic oscillatory behaviour. Here, we generate and study single and multiple breathing solitons in coherently driven microresonators. We present a deterministic route to induce soliton breathing, allowing a detailed exploration of the breathing dynamics in two microresonator platforms. We measure the relation between the breathing frequency and two control parameters-pump laser power and effective-detuning-and observe transitions to higher periodicity, irregular oscillations and switching, in agreement with numerical predictions. Using a fast detection, we directly observe the spatiotemporal dynamics of individual solitons, which provides evidence of breather synchronisation.Dissipative Kerr solitons enable optical frequency comb generation in microresonators, but these solitons can undergo a breathing transition which impacts the stability of such microcombs. Here, Lucas et al. deterministically induce soliton breathing and directly observe the spatiotemporal dynamics.
PDF
