Small-body deflection techniques using spacecraft: techniques in simulating the fate of ejecta

We define a set of procedures to numerically study the fate of ejecta produced by the impact of an artificial projectile with the aim of deflecting an asteroid. Here we develop a simplified, idealized model of impact conditions that can be adapted to fit the details of specific deflection-test scenarios, such as what is being proposed for the AIDA project. Ongoing studies based upon the methodology described here can be used to inform observational strategies and safety conditions for an observing spacecraft. To account for ejecta evolution, the numerical strategies we are employing are varied and include a large N-Body component, a smoothed-particle hydrodynamics (SPH) component, and an application of impactor scaling laws. Simulations that use SPH-derived initial conditions show high-speed ejecta escaping at low angles of inclination, and very slowly moving ejecta lofting off the surface at higher inclination angles, some of which re-impacts the small-body surface. We are currently investigating the realism of this and other models' behaviors. Next steps will include the addition of solar perturbations to the model and applying the protocol developed here directly to specific potential mission concepts such as the proposed AIDA scenario.Comment: 19 pages, 11 figures, accepted for publication in Advances in Space Research, Special Issue: Asteroids & Space Debri

Impact of Service Sector Loads on Renewable Resource Integration

Urban areas consist of a mix of households and services, such as offices, shops, schools, etc. Yet most urban energy models only consider household load profiles, omitting the service sector. Realistic assessment of the potential for renewable resource integration in cities requires models that include detailed demand and generation profiles. Detailed generation profiles are available for many resources. Detailed demand profiles, however, are currently only available for households and not for the service sector. This paper addresses this gap. The paper (1) proposes a novel approach to devise synthetic service sector demand profiles based on a combination of a large number of different data sources, and (2) uses these profiles to study the impact of the service sector on the potential for renewable resource integration in urban energy systems, using the Netherlands as a case study. The importance of the service sector is addressed in a broad range of solar and wind generation scenarios, and in specific time and weather conditions (in a single scenario). Results show that including the service sector leads to statistically significantly better estimations of the potential of renewable resource integration in urban areas. In specific time and weather conditions, including the service sector results in estimations that are up to 33% higher than if only households are considered. The results can be used by researchers to improve urban energy systems models, and by decision-makers and practitioners for grid planning, operation and management}.Comment: 32 pages, 7 figures, 4 table

Space-irrelevant scaling law for fish school sizes

Universal scaling in the power-law size distribution of pelagic fish schools is established. The power-law exponent of size distributions is extracted through the data collapse. The distribution depends on the school size only through the ratio of the size to the expected size of the schools an arbitrary individual engages in. This expected size is linear in the ratio of the spatial population density of fish to the breakup rate of school. By means of extensive numerical simulations, it is verified that the law is completely independent of the dimension of the space in which the fish move. Besides the scaling analysis on school size distributions, the integrity of schools over extended periods of time is discussed.Comment: 23 pages, 12 figures, to appear in J. Theor. Bio

Non-linear Evolution of f(R) Cosmologies III: Halo Statistics

The statistical properties of dark matter halos, the building blocks of cosmological observables associated with structure in the universe, offer many opportunities to test models for cosmic acceleration, especially those that seek to modify gravitational forces. We study the abundance, bias and profiles of halos in cosmological simulations for one such model: the modified action f(R) theory. In the large field regime that is accessible to current observations, enhanced gravitational forces raise the abundance of rare massive halos and decrease their bias but leave their (lensing) mass profiles largely unchanged. This regime is well described by scaling relations based on a modification of spherical collapse calculations. In the small field regime, enhanced forces are suppressed inside halos and the effects on halo properties are substantially reduced for the most massive halos. Nonetheless, the scaling relations still retain limited applicability for the purpose of establishing conservative upper limits on the modification to gravity.Comment: 12 pages, 10 figures; v2: revised version accepted by Phys. Rev.

Investigation of LSTM Based Prediction for Dynamic Energy Management in Chip Multiprocessors

In this paper, we investigate the effectiveness of using long short-term memory (LSTM) instead of Kalman filtering to do prediction for the purpose of constructing dynamic energy management (DEM) algorithms in chip multi-processors (CMPs). Either of the two prediction methods is employed to estimate the workload in the next control period for each of the processor cores. These estimates are then used to select voltage-frequency (VF) pairs for each core of the CMP during the next control period as part of a dynamic voltage and frequency scaling (DVFS) technique. The objective of the DVFS technique is to reduce energy consumption under performance constraints that are set by the user. We conduct our investigation using a custom Sniper system simulation framework. Simulation results for 16 and 64 core network-on-chip based CMP architectures and using several benchmarks demonstrate that the LSTM is slightly better than Kalman filtering

Investigation of LSTM Based Prediction for Dynamic Energy Management in Chip Multiprocessors

In this paper, we investigate the effectiveness of using long short-term memory (LSTM) instead of Kalman filtering to do prediction for the purpose of constructing dynamic energy management (DEM) algorithms in chip multi-processors (CMPs). Either of the two prediction methods is employed to estimate the workload in the next control period for each of the processor cores. These estimates are then used to select voltage-frequency (VF) pairs for each core of the CMP during the next control period as part of a dynamic voltage and frequency scaling (DVFS) technique. The objective of the DVFS technique is to reduce energy consumption under performance constraints that are set by the user. We conduct our investigation using a custom Sniper system simulation framework. Simulation results for 16 and 64 core network-on-chip based CMP architectures and using several benchmarks demonstrate that the LSTM is slightly better than Kalman filtering