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A GPU-based Approach for Path Planning Optimization via Travel Length Reduction
Typically, before constructing an object with an additive manufacturing system, the 3D object must be sent through a process called slicing. Slicing converts a 3D object commonly in the form of an STL file into a set of layers by horizontally intersecting a plane with the object at various heights. At each height, called a layer, multiple 2D polygons can be generated. Each polygon represents a boundary for solid geometry and is called an island. Each island is then comprised of multiple path types in an attempt to optimally fill the polygon. To move between each island and each islands’ paths, travels are inserted. Travels are simply motion by the system to move from one area of construction to another. Travels do not contribute to the construction of the object, and so, are considered wasted motion. In large-scale additive manufacturing, objects can be quite large and the distance between islands can be large as well. As a result, these travels can waste a significant amount of time. Ideally, travels would be as short as possible, however, computing global minimal travel paths is computationally expensive. To combat this problem, researchers at Oak Ridge National Lab developed a GPU-based approach to travel insertion based on a unique factoradic representation. This representation was then utilized by the GPU to solve the Traveling Salesman Problem (TSP). This algorithm was able to compute global minimal travel paths quickly resulting in faster object construction. A general investigation was also carried out to determine when a GPU vs CPU implementation would be beneficial.
Lattice Anharmonicity of Stereochemically Active Lone Pairs Controls Thermochromic Band Gap Reduction of PbVO3Cl
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First and Second Reductions in an Aprotic Solvent: Comparing Computational and Experimental One-Electron Reduction Potentials for 345 Quinones
Not Available
Selective Electrocatalytic Reduction of Nitrous Oxide to Dinitrogen with an Iron Porphyrin Complex
Nitrous oxide (N 2 O) is the third largest contributor to anthropogenic greenhouse gas emissions and plays a detrimental role in the depletion of ozone. Despite the contribution of N 2 O as an atmospheric pollutant, there are currently only a few examples of electrochemical N 2 O remediation to N 2 . Herein we report the electrocatalytic deoxygenation of N 2 O by iron tetraphenylporphyrin (FeTPP) to form N 2 with quantitative Faradaic efficiency and an observed rate of 1.91 s –1 . No degradation was observed after controlled potential electrolysis. Mechanistic investigation of the proposed catalytic steps determined that hydrogen-bond donors are critical for accelerating N 2 O activation and that proton transfer is involved in the rate-determining step.
Intersubunit Coupling Enables Fast CO 2 -Fixation by Reductive Carboxylases
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In Situ X-ray Absorption Spectroscopy of PtNi-Nanowire/Vulcan XC-72R under Oxygen Reduction Reaction in Alkaline Media
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Tandem Reactions of CO2 Reduction and Ethane Aromatization
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Isotopic Effect on Electrochemical CO2 Reduction Activity and Selectivity in H2O- and D2O-based Electrolytes over Palladium
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One electron reduction transforms high-valent low-spin cobalt alkylidene into high-spin cobalt( ii ) carbene radical
Synthesis, electronic structure, and reactivity of a novel Co( ii ) carbene radical in the bis(alkoxide) ligand environment are reported.
W-Doped TiO 2 for photothermocatalytic CO 2 reduction
TiO 2 is one of the most widely used photocatalysts and photothermocatalysts.
Crystallographic characterization of the metal-organic framework Fe[subscript 2](bdp)[subscript 3] upon reductive cation insertion
Abstract not provided
Discrete Empirical Interpolation Method Based Dynamic Load Model Reduction
Dynamic load models add significant complexity to bulk power system time-domain simulations. The complexity is due to the large number of ordinary differential equations (ODEs) introduced by the dynamic load components such as induction motors. It is challenging to derive reduced-order models (ROMs) for dynamic loads due to the nonlinear functions in their governing equations. This paper applies the discrete empirical interpolation method enhanced proper orthogonal decomposition (DEIM-POD) to approximate the full dynamic load model with the ROM that minimizes the projection error of the nonlinear functions in dynamic load ODEs onto their dominant modes. This approach only requires evaluation of nonlinear functions at selected observation points. The observation points selected by DEIM also provide information for screening critical load buses where dynamic load model parameters contribute the most to the accuracy of ROM across multiple contingencies. The proposed approach is validated on IEEE 9-bus, WECC 179-bus and 2384-bus Polish systems.
Techno-economic Analysis of Novel PV Plant Designs for Extreme Cost Reductions
A techno-economic analysis is underway examining the cost and performance of future large-scale photovoltaic (PV) plant components, including bifacial modules, tandem modules, increased plant voltage architectures, and module-level power electronics. Integration of these components into PV plant designs is compared with current PV technologies based on levelized cost of electricity (LCOE). Baseline models are developed and validated against recorded PV plant performance data. Expected cost and performance data of future PV technologies are incorporated into the baseline models. An evolutionary algorithm is utilized to optimize PV plant configuration, technology combination, and LCOE. Furthermore, this paper focuses on the bifacial module analysis.