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Due to the increasing complexity of robot swarm algorithms, ana- lyzing their performance theoretically is often very difficult. Instead, simulators are often used to benchmark the performance of robot swarm algorithms. However, we are not aware of simulators that take advantage of the naturally highly parallel nature of distributed robot swarms. This paper presents ParSwarm, a parallel C++ frame- work for simulating robot swarms at scale on multicore machines. We demonstrate the power of ParSwarm by implementing two applications, task allocation and density estimation, and running simulations on large numbers of agents. 

Abstract Magnesium, the lightest structural metal, usually exhibits limited ambient plasticity when compressed along its crystallographic c -axis (the “hard” orientation of magnesium). Here we report large plasticity in c -axis compression of submicron magnesium single crystal achieved by a dual-stage deformation. We show that when the plastic flow gradually strain-hardens the magnesium crystal to gigapascal level, at which point dislocation mediated plasticity is nearly exhausted, the sample instantly pancakes without fracture, accompanying a conversion of the initial single crystal into multiple grains that roughly share a common rotation axis. Atomic-scale characterization, crystallographic analyses and molecular dynamics simulations indicate that the new grains can form via transformation of pyramidal to basal planes. We categorize this grain formation as “deformation graining”. The formation of new grains rejuvenates massive dislocation slip and deformation twinning to enable large plastic strains. 

High elevation mountain watersheds are undergoing rapid warming and declining snow fractions worldwide, causing earlier and quicker snowmelt. Understanding how this hydrologic shift affects subsurface flow paths, biogeochemical reactions, and solute export has been challenging due to the entanglement of hydrological and biogeochemical processes. Coal Creek, a high-elevation catchment (2,700 3,700 m, 53 km2) in Colorado, is experiencing a higher rate of warming than surrounding low-lying areas. This warming corresponds with dynamic and increased responses from biogenic solutes and dissolved organic carbon (DOC), whereas the behavior of geogenic solutes and dissolved inorganic carbon (DIC) has remained relatively unchanged. DOC has experienced the largest concentration increase (>3x), with annual average flow weighted concentrations positively correlated to average annual temperature. This suggests temperature is the main driver of increasing DOC levels. Although DOC and DIC response to warming is influenced by many drivers, the relative contribution of each remains unknown. DOC and DIC were analyzed to incorporate both carbon component products of soil respiration (DOC and CO2) and to represent high solute concentrations transported by shallow (DOC) versus deep (DIC) subsurface flow. The contrasting behavior of these carbon solutes indicates climate change and warming are driving changes in organic matter decomposition and soil respiration. Modeling results from the process-based model HBV-BioRT show increased temperatures cause earlier snowmelt and streamflow generation and lower peak discharge. As stream flow generation occurs earlier, so do DOC flushing and DIC dilution events. Additionally, post-snowmelt periods show greater DOC production and concentrations under warming scenarios. Results indicated increased production of DOC in post-snowmelt periods. DOC is then flushed out by earlier snowmelt partitioned through the shallow soil zone. Most process-based studies lack a watershed-scale understanding of carbon transformation and flow path alterations. This work demonstrates complex hydrologic and biogeochemical coupling at the watershed scale to illustrate how water flow paths and chemistry are responding to a changing climate in highelevation mountain watersheds. 

Abstract. Watersheds are the fundamental Earth surface functioning units that connect the land to aquatic systems. Many watershed-scale models represent hydrological processes but not biogeochemical reactive transport processes. This has limited our capability to understand and predict solute export, water chemistry and quality, and Earth system response to changing climate and anthropogenic conditions. Here we present a recently developed BioRT-Flux-PIHM (BioRT hereafter) v1.0, a watershed-scale biogeochemical reactive transport model. The model augments the previously developed RT-Flux-PIHM that integrates land-surface interactions, surface hydrology, and abiotic geochemical reactions. It enables the simulation of (1) shallow and deep-water partitioning to represent surface runoff, shallow soil water, and deeper groundwater and of (2) biotic processes including plant uptake, soil respiration, and nutrient transformation. The reactive transport part of the code has been verified against the widely used reactive transport code CrunchTope. BioRT-Flux-PIHM v1.0 has recently been applied in multiple watersheds under diverse climate, vegetation, and geological conditions. This paper briefly introduces the governing equations and model structure with a focus on new aspects of the model. It also showcases one hydrology example that simulates shallow and deep-water interactions and two biogeochemical examples relevant to nitrate and dissolved organic carbon (DOC). These examples are illustrated in two simulation modes of complexity. One is the spatially lumped mode (i.e., two land cells connected by one river segment) that focuses on processes and average behavior of a watershed. Another is the spatially distributed mode (i.e., hundreds of cells) that includes details of topography, land cover, and soil properties. Whereas the spatially lumped mode represents averaged properties and processes and temporal variations, the spatially distributed mode can be used to understand the impacts of spatial structure and identify hot spots of biogeochemical reactions. The model can be used to mechanistically understand coupled hydrological and biogeochemical processes under gradients of climate, vegetation, geology, and land use conditions. 

Abstract Accurate control and measurement of real-time sample temperature are critical for the understanding and interpretation of the experimental results from in situ heating experiments inside environmental transmission electron microscope (ETEM). However, quantifying the real-time sample temperature remains a challenging task for commercial in situ TEM heating devices, especially under gas conditions. In this work, we developed a home-made micro-electrical-mechanical-system (MEMS) heater with unprecedented small temperature gradient and thermal drift, which not only enables the temperature evolution caused by gas injection to be measured in real-time but also makes the key heat dissipation path easier to model to theoretically understand and predict the temperature decrease. A new parameter termed as “gas cooling ability ( H )”, determined purely by the physical properties of the gas, can be used to compare and predict the gas-induced temperature decrease by different gases. Our findings can act as a reference for predicting the real temperature for in situ heating experiments without closed-loop temperature sensing capabilities in the gas environment, as well as all gas-related heating systems.