Laboratory Directed Research and Development Annual Report for 2012
This report documents progress made on all LDRD-funded projects during fiscal year 2012.
Advanced Sensors and Instrumentation↗
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Laboratory Directed Research and Development Annual Report (FY 2013)
The mission of Pacific Northwest National Laboratory is to transform the world through courageous discovery and innovation. Every day, scientists and engineers at PNNL set out to fulfill the promise of that mission by advancing scientific discovery and delivering critical solutions to the challenges that face our nation and the world. The science and technology we work with, create, and improve upon inspires and enables the world to live prosperously, safely, and securely. Our investments in Laboratory Directed Research and Development are essential to our ability to advance our strategy and realize this mission. LDRD investments help us nurture science and technology capabilities while capitalizing on the breadth and depth of talent that our staff possess. This report describes how we conduct our LDRD program in compliance with DOE objectives and guidelines. We use rigorous internal and external peer review to maintain the scientific value and soundness of the research enabled by our LDRD program.
advanced sensors and instrumentation↗
Laboratory Directed Research and Development Annual Report: Fiscal Year 2014
This report documents progress made on all LDRD-funded projects during fiscal year 2014.
96 KNOWLEDGE MANAGEMENT AND PRESERVATION↗
The Path to Nitrate Salt Disposition
The topic is presented in a series of slides arranged according to the following outline: LANL nitrate salt incident as thermal runaway (thermally sensitive surrogates, full-scale tests), temperature control for processing, treatment options and down selection, assessment of engineering options, anticipated control set for treatment, and summary of the overall steps for RNS.
12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗
Laboratory Directed Research and Development Annual Report for 2015
This report documents progress made on all LDRD_funded projects during fiscal year 2015.
99 GENERAL AND MISCELLANEOUS↗
Microbes as engines of ecosystem function: When does community structure enhance predictions of ecosystem processes?
In this study, microorganisms are vital in mediating the earth’s biogeochemical cycles; yet, despite our rapidly increasing ability to explore complex environmental microbial communities, the relationship between microbial community structure and ecosystem processes remains poorly understood. Here, we address a fundamental and unanswered question in microbial ecology: ‘When do we need to understand microbial community structure to accurately predict function?’ We present a statistical analysis investigating the value of environmental data and microbial community structure independently and in combination for explaining rates of carbon and nitrogen cycling processes within 82 global datasets. Environmental variables were the strongest predictors of process rates but left 44% of variation unexplained on average, suggesting the potential for microbial data to increase model accuracy. Although only 29% of our datasets were significantly improved by adding information on microbial community structure, we observed improvement in models of processes mediated by narrow phylogenetic guilds via functional gene data, and conversely, improvement in models of facultative microbial processes via community diversity metrics. Our results also suggest that microbial diversity can strengthen predictions of respiration rates beyond microbial biomass parameters, as 53% of models were improved by incorporating both sets of predictors compared to 35% by microbial biomass alone. Our analysis represents the first comprehensive analysis of research examining links between microbial community structure and ecosystem function. Taken together, our results indicate that a greater understanding of microbial communities informed by ecological principles may enhance our ability to predict ecosystem process rates relative to assessments based on environmental variables and microbial physiology.
respiration↗
Crystal engineering with DNA
This Review chronicles over two decades of research into creating a genetic code for crystal engineering. Rather than directing biological processes, this code uses synthetic forms of DNA to programme the assembly of nanoparticles and microparticles into 1D, 2D and 3D crystalline architectures, in which almost every aspect of the resultant structures can be systematically controlled. Within this conceptual framework, the structural and functional advances are described in an effort to define the present level of sophistication and to predict future directions of the platform. These advances include exotic structures with programmable lattice symmetries and well-defined crystal habits, responsive materials that leverage the intrinsic properties of nucleic acids to manipulate structures on demand, nanoparticle superlattices grown epitaxially from surfaces and colloidal crystals that offer insights into light–matter interactions. Looking forward, we challenge the community to leverage the extraordinary structural control afforded by crystal engineering with DNA to synthesize classes of functional materials that push beyond what has been possible with naturally occurring crystalline materials or those made by more conventional strategies.
catalysis (homogeneous), solar (photovoltaic), bio↗
High Load Expansion of Catalytic EGR-Loop Reforming under Stoichiometric Conditions for Increased Efficiency in Spark Ignition Engines
The use of fuel reformate from catalytic processes is known to have beneficial effects on the spark-ignited (SI) combustion process through enhanced dilution tolerance and decreased combustion duration, but in many cases reformate generation can incur a significant fuel penalty. In a previous investigation, the researchers showed that, by controlling the boundary conditions of the reforming catalyst, it was possible to minimize the thermodynamic expense of the reforming process, and in some cases, realize thermochemical recuperation (TCR), a form of waste heat recovery where exhaust heat is converted to usable chemical energy. The previous work, however, focused on a relatively light-load engine operating condition of 2000 rpm, 4 bar brake mean effective pressure (BMEP). The present investigation demonstrates that this operating strategy is applicable to higher engine loads, including boosted operation up to 10 bar BMEP. By controlling the reforming catalyst boundary conditions, it is possible to achieve fuel reforming without experiencing high temperature exotherms that could be damaging to the catalyst. Additionally, the thermodynamic air handling consequences of operating a highly dilute strategy at high loads is quantified. Furthermore the results confirm that this operating strategy provides an efficiency benefit at all conditions investigated, with relative efficiency increases of 3-6%, and is therefore applicable over wider regions of the engine operating map.
42 ENGINEERING↗
Implementation and Verification of the Sesame Equation of State Database into Abaqus
Towards ability to perform advanced engineering calculations of shock driven processes requiring an accurate prediction of temperature inside the shock, the Sesame equation of state database has been implemented into Abaqus explicit, a finite element analysis code commonly used in the analysis community. The interface algorithm and supporting details are discussed, and results from a simple benchmark problem comparing Abaqus predictions to other previously validated calculations and published data are presented. Features of the Sesame-Abaqus interface are also discussed and a sample input file is provided for the interested user.
42 ENGINEERING↗
DEEP CELLULAR RECURRENT NEURAL ARCHITECTURE FOR EFFICIENT MULTIDIMENSIONAL TIME-SERIES DATA PROCESSING
Efficient processing of time series data is a fundamental yet challenging problem in pattern recognition. Though recent developments in machine learning and deep learning have enabled remarkable improvements in processing large scale datasets in many application domains, most are designed and regulated to handle inputs that are static in time. Many real-world data, such as in biomedical, surveillance and security, financial, manufacturing and engineering applications, are rarely static in time, and demand models able to recognize patterns in both space and time. Current machine learning (ML) and deep learning (DL) models adapted for time series processing tend to grow in complexity and size to accommodate the additional dimensionality of time. Specifically, the biologically inspired learning based models known as artificial neural networks that have shown extraordinary success in pattern recognition, tend to grow prohibitively large and cumbersome in the presence of large scale multi-dimensional time series biomedical data such as EEG. Consequently, this work aims to develop representative ML and DL models for robust and efficient large scale time series processing. First, we design a novel ML pipeline with efficient feature engineering to process a large scale multi-channel scalp EEG dataset for automated detection of epileptic seizures. With the use of a sophisticated yet computationally efficient time-frequency analysis technique known as harmonic wavelet packet transform and an efficient self-similarity computation based on fractal dimension, we achieve state-of-the-art performance for automated seizure detection in EEG data. Subsequently, we investigate the development of a novel efficient deep recurrent learning model for large scale time series processing. For this, we first study the functionality and training of a biologically inspired neural network architecture known as cellular simultaneous recurrent neural network (CSRN). We obtain a generalization of this network for multiple topological image processing tasks and investigate the learning efficacy of the complex cellular architecture using several state-of-the?art training methods. Finally, we develop a novel deep cellular recurrent neural network (CDRNN) architecture based on the biologically inspired distributed processing used in CSRN for processing time series data. The proposed DCRNN leverages the cellular recurrent architecture to promote extensive weight sharing and efficient, individualized, synchronous processing of multi-source time series data. Experiments on a large scale multi-channel scalp EEG, and a machine fault detection dataset show that the proposed DCRNN offers state-of-the-art recognition performance while using substantially fewer trainable recurrent units.
Vidyaratne, Lasitha S.↗
Results for the First, Second, and Third Quarter Calendar Year 2015 Tank 50H WAC slurry samples chemical and radionuclide contaminants
This report details the chemical and radionuclide contaminant results for the characterization of the Calendar Year (CY) 2015 First, Second, and Third Quarter sampling of Tank 50H for the Saltstone Waste Acceptance Criteria (WAC) in effect at that time. Information from this characterization will be used by Defense Waste Processing Facility (DWPF) & Saltstone Facility Engineering (D&S-FE) to support the transfer of low-level aqueous waste from Tank 50H to the Salt Feed Tank in the Saltstone Facility in Z-Area, where the waste will be immobilized. This information is also used to update the Tank 50H Waste Characterization System. Previous memoranda documenting the WAC analyses results have been issued for these three samples.
12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗
Development of a Sustainable Green Chemistry Platform for Production of Acetone and Downstream Drop-in Fuel and Commodity Products Directly From Biomass Syngas Via a Novel Energy Conserving Route in Engineered Acetogenic Bacteria
LanzaTech and Oak Ridge National Laboratory have developed and scaled up a process for sustainable production of acetone and downstream drop-in fuel and commodity products directly from biomass syngas via a novel energy conserving route in engineered acetogenic bacteria. This process offers a safer and more environmentally-friendly production method for acetone production than the current phenol-dependent method, and the product has significantly lower GHG emissions. The developed process offers a cost competitive route to Acetone and enables biofuels at or below DOE’s $3 gge target. In addition, it also provides an attractive biological alternative to traditional sugar-based ABE fermentation, by enabling utilization of non-food biomass resources as fermentation feedstocks. Challenges overcome: • By-products formation: Byproduct 2,3-butandiol and 3-hydroxybutyrate reduce yield and stability. Addressed by elimination of both pathways in our chassis acetone production strain. • Cost competitiveness: Addressed by developing an integrated acetone strain and eliminating by-products to increase yield and stability. Optimized co-selectivity and coproductivity of acetone and ethanol instead of titer, based on technoeconomic analysis. • Limited enzyme variety: Addressed by genome mining of over 300 industrial strains in LanzaTech collection, identifying unique enzyme sequences, and refactoring these unique enzyme variants through LanzaTech’s engineering platform. Resulted in over 10x improvement in acetone production from gas. • Continuous process (stability): Identified bottleneck through detailed omics studies, addressed by integration of the acetone pathway on the chromosome. • Novel process to scale up: Addressed by demonstrating stable acetone production in a 80- L pilot reactor, with acetone productivity and selectivity comparable to those observed under the same conditions in 2-L reactors. We have demonstrated stable acetone production for over 7 days at commercial target rate and selectivity in 2-L reactors and have piloted the process.
09 BIOMASS FUELS↗
LES and RANS Spray Combustion Analysis of OME 3-5 and n-Dodecane
Clean-burning oxygenated and synthetic fuels derived from renewable power, so-called e-fuels, are a promising pathway to decarbonize compression–ignition engines. Polyoxymethylene dimethyl ethers (PODEs or OMEs) are one candidate of such fuels with good prospects. Their lack of carbon-to-carbon bonds and high concentration of chemically bound oxygen effectively negate the emergence of polycyclic aromatic hydrocarbons (PAHs) and even their precursors like acetylene (C 2 H 2 ), enabling soot-free combustion without the soot-NO x trade-off common for diesel engines. The differences in the spray combustion process for OMEs and diesel-like reference fuels like n-dodecane and their potential implications on engine applications include discrepancies in the observed ignition delay, the stabilized flame lift-off location, and significant deviations in high-temperature flame morphology. For CFD simulations, the accurate modeling and prediction of these differences between OMEs and n-dodecane proved challenging. This study investigates the spray combustion process of an OME 3 − 5 mixture and n-dodecane with advanced optical diagnostics, Reynolds-Averaged Navier–Stokes (RANS), and Large-Eddy Simulations (LESs) within a constant-volume vessel. Cool-flame and high-temperature combustion were measured simultaneously via high-speed (50 kHz) imaging with formaldehyde (CH 2 O) planar laser-induced fluorescence (PLIF) representing the former and line-of-sight OH* chemiluminescence the latter. Both RANS and LES simulations accurately describe the cool-flame development process with the formation of CH 2 O. However, CH 2 O consumption and the onset of high-temperature reactions, signaled by the rise of OH* levels, show significant deviations between RANS, LES, and experiments as well as between n-dodecane and OME. A focus is set on the quality of the simulated results compared to the experimentally observed spatial distribution of OH*, especially in OME fuel-rich regions. The influence of the turbulence modeling is investigated for the two distinct ambient temperatures of 900 K and 1200 K within the Engine Combustion Network Spray A setup. The capabilities and limitations of the RANS simulations are demonstrated with the initial cool-flame propagation and periodic oscillations of CH 2 O formation/consumption during the quasi-steady combustion period captured by the LES.
CFD↗
Small-scale pilot plant for gasification of coal and coal/biomass blends and conversion of derived syngas to liquid fuels via Fischer-Tropsch synthesis
This report describes the second phase of a project to design, construct and commission an integrated coal/biomass-to-liquids facility at a capacity of 1 bbl. /day at the University of Kentucky Center for Applied Energy Research (UK-CAER) – specifically for construction, commissioning and operating of the downstream process units for water-gas-shift, Fischer-Tropsch synthesis and balance of plant [BOP]. The deliverables from the operation of this pilot plant is firstly the liquid F-T products and finished fuels which are of interest to UK-CAER’s academic, government and industrial research partners. Going forward the facility will produce research quantities of F-T liquids and finished fuels for subsequent Fuel Quality Testing, Performance and Acceptability. Moreover, the facility is expected to be employed for a range of research and investigations related to: Feed Preparation, Characteristics and Quality; Coal and Biomass Gasification; Gas Clean-up/ Conditioning; Gas Conversion by -F-T Synthesis; Product Work-up and Refining; Systems Analysis and Integration; and Scale-up and Demonstration. Environmental Considerations - particularly how to manage and reduce carbon dioxide emissions from CBTL facilities and from use of the fuels will be a primary research objective. The results of this second phase study include the FEED study and detailed engineering design of the downstream processing units and BOP, the selection of a range of technologies and technology vendors, the as-built plant - its equipment and capabilities, and the results of two trial production runs, one with coal only and one with a coal/biomass blend. These are described in detail in this report.
01 COAL, LIGNITE, AND PEAT↗
Development of a Sustainable Green Chemistry Platform for Production of Acetone and Downstream Drop-in Fuel and Commodity Products directly from Biomass Syngas via a Novel Energy Conserving Route in Engineered Acetogenic Bacteria
LanzaTech and Oak Ridge National Laboratory have developed and scaled up a process for sustainable production of acetone and downstream drop-in fuel and commodity products directly from biomass syngas via a novel energy conserving route in engineered acetogenic bacteria. This process offers a safer and more environmentally-friendly production method for acetone production than the current phenol-dependent method, and the product has significantly lower GHG emissions. The developed process offers a cost competitive route to Acetone and enables biofuels at or below DOE’s $3/gge target. In addition, it also provides an attractive biological alternative to traditional sugar-based ABE fermentation, by enabling utilization of non-food biomass resources as fermentation feedstocks. Challenges overcome: (1) By-products formation: Byproduct 2,3-butanediol and 3-hydroxybutyrate reduce yield and stability. Addressed by elimination of both pathways in the chassis acetone production strain. (2) Cost competitiveness: Addressed by developing an integrated acetone strain and eliminating by-products to increase yield and stability. Optimized co-selectivity and co-productivity of acetone and ethanol, based on technoeconomic analysis. (3) Limited enzyme variety: Addressed by genome mining of over 300 industrial strains in LanzaTech collection, identifying unique enzyme sequences, and refactoring these unique enzyme variants through LanzaTech’s engineering platform. Resulted in over 10x improvement in acetone production from gas. (4) Continuous process (stability): Identified bottleneck through detailed omics studies, addressed by integration of the acetone pathway on the chromosome. (5) Novel process to scale up: Addressed by demonstrating stable acetone production in an 80-L pilot reactor, with acetone productivity and selectivity comparable to those observed under the same conditions in 2-L reactors. LanzaTech has demonstrated stable acetone production for over 7 days at commercial target rate and selectivity in a 2-L reactor and have piloted the process.
09 BIOMASS FUELS↗
A Guide for Selecting Appropriate Human Factors Methods and Measures in Control Room Modernization Efforts in Nuclear Power Plants
Abstract. Many of the U.S. nuclear power plants are approaching the end of their 60-year licensing period. The U.S. Department of Energy Light Water Reactor Sustainability Program is conducting targeted research to extend the lives and ensure long-term reliability, productivity, safety, and security of these plants through targeted research, such as with integrating advanced digital instrumentation and control technologies in the main control room. There are many challenges to this, one being the integration of human factors engineering in the design and evaluation of these upgrades. This paper builds upon recent efforts in developing utility-specific guidance for integrating human factors engineering in the control room modernization process by providing commonly used data collection methods that are applicable at various phases of the upgrade process. Advantages and disadvantages of each method is provided for consideration of an optimal human factors evaluation plan to be used throughout the lifespan of the upgrade process.
99 GENERAL AND MISCELLANEOUS↗
Evaluation of an LED Retrofit Project at Princeton University’s Carl Icahn Laboratory
The LED lighting retrofit at the Carl Icahn Laboratory of the Lewis-Sigler Institute for Integrative Genomics was the first building-wide interior LED project at Princeton University, following the University’s experiences from several years of exterior and small-scale interior LED implementation projects. The project addressed three luminaire types – recessed 2x2 troffers, cove and other luminaires using linear T8 fluorescent lamps, and CFL downlights - which combined accounted for over 564,000 kWh of annual energy, over 90% of the lighting energy used in the facility. The Princeton Facilities Engineering staff used a thorough process of evaluating product alternatives before selecting an acceptable LED retrofit solution for each luminaire type. Overall, 815 2x2 luminaires, 550 linear fluorescent luminaires, and 240 downlights were converted to LED as part of this project. Based solely on the reductions in wattage in converting from the incumbent fluorescent lamps to LED retrofit kits, the annual energy savings from the project was over 190,000 kWh, a savings of 37%. An additional 125,000 kWh of energy savings is expected from the implementation of occupancy and task-tuning control solutions, which will bring the total savings for the project to 62%.
32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
Surface engineering to enhance heat generation and joint strength in dissimilar materials AZ31 and DP590 ultrasonic welding
A multiscale simulation approach was developed and employed to optimize the sheet surface conditions for higher interfacial temperature and joint strength in ultrasonic welding of magnesium alloy AZ31 and dual-phase steel DP590. First, a mesoscale model was used to study the relationship between friction coefficient and surface roughness, which can be modified by various engineering methods. Then a macroscopic process model was employed to study the effects of surface roughness on heat generation, indicating that a temperature increase can be achieved with rougher surfaces on two sides of both DP590 and AZ31 sheets. Samples prepared by sanding and filing, as well as grinding, were first characterized for surface roughness and then welded under ultrasonic vibration. An infrared camera was used to measure temperatures in situ for model validation. Overall, lap shear test results for the welded joint showed that the joint strength can be improved by 10~25% using filing and round grinding methods as a result of the enhanced heat generation and mechanical interlocking on the interface.
42 ENGINEERING↗