Three parallel algorithms for classical molecular dynamics are presented. The first assigns each processor a fixed subset of atoms; the second assigns each a fixed subset of inter-atomic forces to compute; the third assigns each a fixed spatial region. The algorithms are suitable for molecular dynamics models which can be difficult to parallelize efficiently—those with short-range forces where the neighbors of each atom change rapidly. They can be implemented on any distributed-memory parallel machine which allows for message-passing of data between independently executing processors. The algorithms are tested on a standard Lennard-Jones benchmark problem for system sizes ranging from 500 to 100,000,000 atoms on several parallel supercomputers--the nCUBE 2, Intel iPSC/860 and Paragon, and Cray T3D. Comparing the results to the fastest reported vectorized Cray Y-MP and C90 algorithm shows that the current generation of parallel machines is competitive with conventional vector supercomputers even for small problems. For large problems, the spatial algorithm achieves parallel efficiencies of 90% and a 1840-node Intel Paragon performs up to 165 faster than a single Cray C9O processor. Trade-offs between the three algorithms and guidelines for adapting them to more complex molecular dynamics simulations are also discussed.

Fast parallel algorithms for short-range molecular dynamics

Review of passive PCM latent heat thermal energy storage systems towards buildings’ energy efficiency

The most significant threat that mankind faces in the 21th century is global warming. Buildings, which account for 40% of global energy consumption and greenhouse gas emissions, play a pivotal role in global warming. Estimates show that their destructive impact will grow by 1.8% per year through 2050, which indicates that future consumption and emissions will be worse than today. Therefore, the impact of cooling systems cannot be ignored, as they, along with ventilation and heating systems, account for 60% of the energy consumed in buildings. Passive cooling techniques are a promising alternative to conventional cooling systems. Of the various passive cooling strategies, thermal energy storage by means of latent heat is an efficient way to increase the thermal inertia of building envelopes, which would reduce temperature fluctuations, leading to the improved thermal comfort of occupants. Phase change materials (PCMs) with high density for thermal energy storage can be efficiently employed to this purpose. This paper reviews recent studies of the application of PCMs for passive cooling in buildings. From the literature, a comprehensive list of different organic, inorganic and eutectic PCMs appropriate for passive cooling in buildings are reviewed. Full-scale testing and numerical modeling were found to be the most popular investigative methods used for experimental and theoretical analysis of PCMs. The combination of these two methods can provide a detailed and valid technique for PCM investigations. Finally, incorporating PCMs into building walls with macro encapsulation was also a dominant interest in previous studies.

A review on phase change material (PCM) for sustainable passive cooling in building envelopes

Developments in organic solid–liquid phase change materials and their applications in thermal energy storage

WHO Guidelines for Indoor Air Quality: Selected pollutants.

Combined experimental and numerical evaluation of a prototype nano-PCM enhanced wallboard☆

Evaluation of weather datasets for building energy simulation

Insulation materials for commercial buildings in North America: An assessment of lifetime energy and environmental impacts

Nano-size confinement induces many intriguing non-Fourier heat conduction phenomena, such as nonlinear temperature gradients, temperature jumps near the contacts, and size-dependent thermal conductivity. Over the past decades, these effects have been studied and interpreted by nonequilibrium molecular dynamics (NEMD) and phonon Boltzmann transport equation (BTE) simulations separately, but no theory that unifies these two methods has ever been established. In this work, we unify these methods using a quantitative mode-level comparison and demonstrate that they are equivalent for various thermostats. We show that different thermostats result in different non-Fourier thermal transport characteristics due to the different mode-level phonon excitations inside the thermostats, which explains the different size-dependent thermal conductivities calculated using different reservoirs, even though they give the same bulk thermal conductivity. Specifically, the Langevin thermostat behaves like a thermalizing boundary in phonon BTE and provides mode-level thermal-equilibrium phonon outlets, while the Nose-Hoover chain thermostat and velocity rescaling method behave like biased reservoirs, which provide a spatially uniform heat generation and mode-level nonequilibrium phonon outlets. These findings explain why different experimental measurement methods can yield different size-dependent thermal conductivity. They also indicate that the thermal conductivity of materials can be tuned for various applications by specifically designing thermostats. The unification of NEMD and phonon BTE will largely facilitate the study of thermal transport in complex systems in the future by, e.g., replacing computationally unaffordable first-principles NEMD simulations with computationally less expensive spectral BTE simulations.

/pdf/unification-of-nonequilibrium-molecular-dynamics-and-the-3fx6m7iu2y.pdf

Unification of nonequilibrium molecular dynamics and the mode-resolved phonon Boltzmann equation for thermal transport simulations

This paper introduces a novel “Autotune” methodol- ogy under development for calibrating building energy models (BEM). It is aimed at developing an automated BEM tuning methodology that enables models to reproduce measured data such as utility bills, sub-meter, and/or sensor data accurately and robustly by selecting best- match E+ input parameters in a systematic, automated, and repeatable fashion. The approach is applicable to a building retrofit scenario and aims to quantify the trade- offs between tuning accuracy and the minimal amount of “ground truth” data required to calibrate the model. Au- totune will use a suite of machine-learning algorithms de- veloped and run on supercomputers to generate calibra- tion functions. Specifically, the project will begin with a de-tuned model and then perform Monte Carlo simula- tions on the model by perturbing the “uncertain” parame- ters within permitted ranges. Machine learning algorithms will then extract minimal perturbation combinations that result in modeled results that most closely track sensor data. A large database of parametric EnergyPlus (E+) simulations has been made publicly available. Autotune is currently being applied to a heavily instrumented residen- tial building as well as three light commercial buildings in which a “de-tuned” model is autotuned using faux sensor data from the corresponding target E+ model.

http://ibpsa-usa.org/index.php/ibpusa/article/download/440/426

Som S Shrestha

Papers

Combined experimental and numerical evaluation of a prototype nano-PCM enhanced wallboard☆

Evaluation of weather datasets for building energy simulation

Insulation materials for commercial buildings in North America: An assessment of lifetime energy and environmental impacts

Unification of nonequilibrium molecular dynamics and the mode-resolved phonon Boltzmann equation for thermal transport simulations

Autotune e+ building energy models