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.
The inverse methods team provides a set of tools for solving inverse problems in structural dynamics and thermal physics, and also sensor placement optimization via Optimal Experimental Design (OED). These methods are used for designing experiments, model calibration, and verfication/validation analysis of weapons systems. This document provides a user's guide to the input for the three apps that are supported for these methods. Details of input specifications, output options, and optimization parameters are included.
The nuclear accident consequence analysis code MACCS has traditionally modeled dispersion during downwind transport using a Gaussian plume segment model. MACCS is designed to estimate consequence measures such as air concentrations and ground depositions, radiological doses, and health and economic impacts on a statistical basis over the course of a year to produce annualaveraged output measures. The objective of this work is to supplement the Gaussian atmospheric transport and diffusion (ATD) model currently in MACCS with a new option using the HYSPLIT model. HYSPLIT/MACCS coupling has been implemented, with HYSPLIT as an alternative ATD option. The subsequent calculations in MACCS use the HYSPLIT-generated air concentration, and ground deposition values to calculate the same range of output quantities (dose, health effects, risks, etc.) that can be generated when using the MACCS Gaussian ATD model. Based on the results from the verification test cases, the implementation of the HYSPLIT/MACCS coupling is confirmed. This report contains technical details of the HYSPLIT/MACCS coupling and presents a benchmark analysis using the HYSPLIT/MACCS coupling system. The benchmark analysis, which involves running specific scenarios and sensitivity studies designed to examine how the results generated by the traditional MACCS Gaussian plume segment model compare to the new, higher fidelity HYSPLIT/MACCS modeling option, demonstrates the modeling results that can be obtained by using this new option. The comparisons provided herein can also help decision-makers evaluate the potential benefit of using results based on higher fidelity modeling with the additional computational burden needed to perform the calculations. Three sensitivity studies to investigate the potential impact of alternative modeling options, regarding 1) input meteorological data set, 2) method to estimate stability class, and 3) plume dispersion model for larger distances, on consequence results were also performed. The results of these analyses are provided and discussed in this report.
The objective of this project is the demonstration, and validation of hydrogen fuel cells in the marine environment. The prototype generator can be used to guide commercial development of a fuel cell generator product. Work includes assessment and validation of the commercial value proposition of both the application and the hydrogen supply infrastructure through third-party hosted deployment as the next step towards widespread use of hydrogen fuel cells in the maritime environment.
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.
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.