Optimizing parallel particle tracking in Brownian motion using machine learning

In this paper, we present a generic, scalable and adaptive load balancing parallel Lagrangian particle tracking approach in Wiener type processes such as Brownian motion. The approach is particularly suitable in problems involving particles with highly variable computation time, like deposition on boundaries that may include decay, when particle lifetime obeys exponential distribution. At first glance, Lagranginan tracking is highly suitable for a distributed programming model due to the independence of motion of separate particles. However, the commonly employed Decomposition Per Particle (DPP) method, where each process is in charge of a certain number of particles, actually displays poor parallel efficiency due to the high particle lifetime variability when dealing with a wide set of deposition problems that optionally include decay. The proposed method removes DPP defects and brings a novel approach to discrete particle tracking. The algorithm introduces master/slave model dubbed Partial Trajectory Decomposition (PTD), in which a certain number of processes produce partial trajectories and put them into the shared queue, while the remaining processes simulate actual particle motion using previously generated partial trajectories. Our approach also introduces meta-heuristics for determining the optimal values of partial trajectory length, chunk size and the number of processes acting as producers/consumers, for the given total number of participating processes (Optimized Partial Trajectory Decomposition, OPTD). The optimization process employs a surrogate model to estimate the simulation time. The surrogate is based on historical data and uses a coupled machine learning model, consisting of classification and regression phases. OPTD was implemented in C, using standard MPI for message passing and benchmarked on a model of 220 Rn progeny in the diffusion chamber, where particle motion is characterized by an exponential lifetime distribution and Maxwell velocity distribution. The speedup improvement of OPTD is approximatelly 320% over standard DPP, reaching almost ideal speedup on up to 256 CPUs.

Global and regional impacts of HONO on the chemical composition of clouds and aerosols

Recently, realistic simulation of nitrous acid (HONO) based on the HONO / NOx ratio of 0.02 was found to have a significant impact on the global budgets of HOx (OH + HO2) and gas phase oxidation products in polluted regions, especially in winter when other photolytic sources are of minor importance. It has been reported that chemistry-transport models underestimate sulphate concentrations, mostly during winter. Here we show that simulating realistic HONO levels can significantly enhance aerosol sulphate (S(VI)) due to the increased formation of H2SO4. Even though in-cloud aqueous phase oxidation of dissolved SO2 (S(IV)) is the main source of S(VI), it appears that HONO related enhancement of H2O2 does not significantly affect sulphate because of the predominantly S(IV) limited conditions, except over eastern Asia. Nitrate is also increased via enhanced gaseous HNO3 formation and N2O5 hydrolysis on aerosol particles. Ammonium nitrate is enhanced in ammonia-rich regions but not under ammonia-limited conditions. Furthermore, particle number concentrations are also higher, accompanied by the transfer from hydrophobic to hydrophilic aerosol modes. This implies a significant impact on the particle lifetime and cloud nucleating properties. The HONO induced enhancements of all species studied are relatively strong in winter though negligible in summer. Simulating realistic HONO levels is found to improve the model-measurement agreement of sulphate aerosols, most apparent over the US. Our results underscore the importance of HONO for the atmospheric oxidizing capacity and corroborate the central role of cloud chemical processing in S(IV) formation.

Global and regional impacts of HONO on the chemical composition of clouds and aerosols

Nitrous acid (HONO) photolysis can significantly increase HOx (OH+HO2) radical formation, enhancing organic and inorganic oxidation products in polluted regions, especially during winter. It has been reported that chemistry-transport models underestimate sulphate concentrations, mostly during winter. Here we show that HONO can significantly enhance aerosol sulphate (S(VI)), mainly due to the increased formation of H2SO4. Even though in-cloud aqueous phase oxidation of dissolved SO2 (S(IV)) is the main source of S(VI), it appears that HONO related enhancement of H2O2 does not significantly affect sulphate because of the predominantly S(IV) limited conditions, except over eastern Asia. Nitrate is also increased via enhanced gaseous HNO3 formation and N2O5 hydrolysis on aerosol particles. Ammonium nitrate is enhanced in ammonia-rich regions but not under ammonia-limited conditions. Furthermore, particle number concentrations are also higher, accompanied by the transfer from hydrophobic to hydrophilic aerosol modes. This implies a significant impact on the particle lifetime and cloud nucleating properties. The HONO induced enhancements of all species studied are relatively strong in winter though negligible in summer. Simulating realistic HONO levels is found to improve the model-measurement agreement of sulphate aerosols, most apparent over the US. Our results underscore the importance of HONO for the atmospheric oxidizing capacity and the central role of cloud chemical processing in aerosol formation.

Monte Carlo simulation of positronium thermalization in gases

In this paper we present the results of Monte Carlo simulations of positronium (Ps) swarm thermalization in helium (He) and water vapour. We have investigated the temporal evolution of energy and spatial parameters of the swarm and their sensitivity to the shape of the cross-section and the initial energy distribution. Positron anihilation spectroscopy (PAS) and positron emission tomography (PET) are techniques that depend on anihilation of positronium in materials and tissue. The results obtained point that the Monte Carlo technique shows good agreement with experimental results and is capable of accurately describing the behaviour of Ps particles including the energy, particle lifetime and the moment and location of the anihilation.