SIERRA/Aero is a compressible fluid dynamics program intended to solve a wide variety compressible fluid flows including transonic and hypersonic problems. This document describes the commands for assembling a fluid model for analysis with this module, henceforth referred to simply as Aero for brevity. Aero is an application developed using the SIERRA Toolkit (STK). The intent of STK is to provide a set of tools for handling common tasks that programmers encounter when developing a code for numerical simulation. For example, components of STK provide field allocation and management, and parallel input/output of field and mesh data. These services also allow the development of coupled mechanics analysis software for a massively parallel computing environment.
Nielsen, Erik N.; Mills, Adam R.; Guinn, Charles R.; Gullans, Michael J.; Sigillito, Anthony J.; Feldman, Mayer M.; Petta, Jason R.
Silicon spin qubits satisfy the necessary criteria for quantum information processing. However, a demonstration of high-fidelity state preparation and readout combined with high-fidelity single- and two-qubit gates, all of which must be present for quantum error correction, has been lacking. We use a two-qubit Si/SiGe quantum processor to demonstrate state preparation and readout with fidelity greater than 97%, combined with both singleand two-qubit control fidelities exceeding 99%. The operation of the quantum processor is quantitatively characterized using gate set tomography and randomized benchmarking. Our results highlight the potential of silicon spin qubits to become a dominant technology in the development of intermediate-scale quantum processors.
Corynebacterium glutamicum has been successfully employed for the industrial production of amino acids and other bioproducts, partially due to its native ability to utilize a wide range of carbon substrates. We demonstrated C. glutamicum as an efficient microbial host for utilizing diverse carbon substrates present in biomass hydrolysates, such as glucose, arabinose, and xylose, in addition to its natural ability to assimilate lignin-derived aromatics. As a case study to demonstrate its bioproduction capabilities, L-lactate was chosen as the primary fermentation end product along with acetate and succinate. C. glutamicum was found to grow well in different aromatics (benzoic acid, cinnamic acid, vanillic acid, and p-coumaric acid) up to a concentration of 40 mM. Besides, 13C-fingerprinting confirmed that carbon from aromatics enter the primary metabolism via TCA cycle confirming the presence of β-ketoadipate pathway in C. glutamicum. 13C-fingerprinting in the presence of both glucose and aromatics also revealed coumarate to be the most preferred aromatic by C. glutamicum contributing 74 and 59% of its carbon for the synthesis of glutamate and aspartate respectively. 13C-fingerprinting also confirmed the activity of ortho-cleavage pathway, anaplerotic pathway, and cataplerotic pathways. Finally, the engineered C. glutamicum strain grew well in biomass hydrolysate containing pentose and hexose sugars and produced L-lactate at a concentration of 47.9 g/L and a yield of 0.639 g/g from sugars with simultaneous utilization of aromatics. Succinate and acetate co-products were produced at concentrations of 8.9 g/L and 3.2 g/L, respectively. Our findings open the door to valorize all the major carbon components of biomass hydrolysate by using C. glutamicum as a microbial host for biomanufacturing.
Wind energy can provide renewable, sustainable electricity to rural Native homes and power schools and businesses. It can even provide tribes with a source of income and economic development. The purpose of this research is to determine the potential for deploying community and utility-scale wind renewable technologies on Turtle Mountain Band of Chippewa tribal lands. Ideal areas for wind technology development were investigated, based on wind resources, terrain, land usage, and other factors. This was done using tools like the National Renewable Energy Laboratory Wind Prospector, in addition to consulting tribal members and experts in the field. The result was a preliminary assessment of wind energy potential on Turtle Mountain lands, which can be used to justify further investigation and investment into determining the feasibility of future wind technology projects.
We demonstrate SONOS (silicon-oxide-nitride-oxide-silicon) analog memory arrays that are optimized for neural network inference. The devices are fabricated in a 40nm process and operated in the subthreshold regime for in-memory matrix multiplication. Subthreshold operation enables low conductances to be implemented with low error, which matches the typical weight distribution of neural networks, which is heavily skewed toward near-zero values. This leads to high accuracy in the presence of programming errors and process variations. We simulate the end-To-end neural network inference accuracy, accounting for the measured programming error, read noise, and retention loss in a fabricated SONOS array. Evaluated on the ImageNet dataset using ResNet50, the accuracy using a SONOS system is within 2.16% of floating-point accuracy without any retraining. The unique error properties and high On/Off ratio of the SONOS device allow scaling to large arrays without bit slicing, and enable an inference architecture that achieves 20 TOPS/W on ResNet50, a > 10× gain in energy efficiency over state-of-The-Art digital and analog inference accelerators.
