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This publication highlights achievements in nuclear security, science, technology and engineering, mission operations, and community relations at Los Alamos National Laboratory.
In support of DOE’s move toward transformational whole-building upgrades and enclosure solutions, the Pacific Northwest National Laboratory (PNNL) and National Renewable Energy Laboratory (NREL) are partnering and collaborating with leading building science researchers and home-performance entities to identify and characterize technical and economic barriers to façade retrofits in an effort to identify market-viable façade solutions and opportunities for an actionable plan to transform the market. The project includes partnerships with Building Science Corporation (BSC) and a combination of strategic implementation partners with home-performance and retrofit expertise and industry contacts. The project will include expert advisory and review consultation from Lawrence Berkeley National Laboratory’s (LBNL’s) Residential Windows & Attachments team and Oak Ridge National Laboratory’s Building Envelope team. The project consists of three parts: 1. a market analysis that captures the current state of the façade retrofit market and includes housing characteristics and retrofit costs, façade retrofit approaches and materials, contractor business models and workforce requirements to support advanced façade approaches; 2. an economic analysis focused on the viability of advanced façade retrofit approaches and materials; and 3. field demonstration of façade retrofits that include enhanced insulation/air-sealing and window technologies in multiple climate zones. This report represents the market analysis, as outlined in item 1 above. The goal of this analysis is to provide a techno-economic study that supports comprehensive retrofits of residential enclosures that include traditional approaches, and integrated wall assemblies and windows that result in durable, energy efficient, and marketable strategies. This study will provide a better knowledge base regarding the viable market for façade retrofit strategies, identify the barriers to uptake, analyze economic opportunities, and develop documentation specifically aimed to overcome technical and market barriers associated with installation.
The effect of above-band gap photons on the domains of the BiFeO3 (BFO) thin film was investigated via piezoresponse force microscopy and Kelvin probe force microscopy. It is found that under above-band gap illumination, the relaxation time of the polarization state was significantly extended, while the effective polarizing voltage for the pristine domains was reduced. We propose that this photoinduced domain stabilization can be attributed to the interaction between photogenerated surface charges and domains. Importantly, a similar phenomenon is observed in other ferroelectric (FE) materials with an internal electric field once they are illuminated by above-band gap light, indicating that this photoinduced stabilization is potentially universal rather than specific to BFO. Furthermore, this study will not only contribute to the knowledge of photovoltaic (PV) phenomena but also provide a new route to promote the stability of PV and FE materials.
Execution of complex analytic queries on massive semantic graphs is a challenging problem in big-data analytics that requires high-performance parallel computing. In a semantic graph, vertices and edges carry attributes of various types and the analytic queries typically depend on the values of these attributes. Thus, the computation must view the graph through a filter that passes only those individual vertices and edges of interest. Previous investigations have developed Knowledge Discovery Toolbox (KDT), a sophisticated Python library for parallel graph computations. In KDT, the user can write custom graph algorithms by specifying operations between edges and vertices ( semiring operations ). The user can also customize existing graph algorithms by writing filters. While the high-level language for this customization enables domain scientists to productively express their graph analytics requirements, the customized queries perform poorly due to the overhead of having to call into the Python virtual machine for each vertex and edge. In this work, we use the Selective Embedded Just-In-Time Specialization (SEJITS) approach to automatically translate semiring operations and filters defined by programmers into a lower-level efficiency language, bypassing the upcall into Python. We evaluate our approach by comparing it with the high-performance Combinatorial BLAS engine and show that our approach combines the benefits of programming in a high-level language with executing in a low-level parallel environment. We increase the system's flexibility by developing techniques that provide users with the ability to define new vertex and edge types from Python. We also present a new Roofline model for graph traversals and show that we achieve performance that is significantly closer to the bounds suggested by the Roofline. Finally, to further understand the complex interaction with the underlying architecture, we present an analysis using performance counters that quantifies the improvement in hardware behavior in the context our SEJITS methodology. Comprehensively, we demonstrate the first known solution to the problem of obtaining high performance from a productivity language when applying graph algorithms selectively on semantic graphs with hundreds of millions of edges and scaling to thousands of processors for graphs.
New capabilities to predict, design and control the chemistries of carbon could answer a global imperative to transition from fossil-based to sustainable transportation fuels. While the use of inexpensive hydrocarbons has been an unparalleled achievement and enabler of economic prosperity for many nations, singular dependence upon crude oil has given rise to systemic vulnerabilities in climate, energy, economic, and national security. Lignocellulosic biomass, a renewable and carbon-neutral resource, has the potential to displace an estimated annual equivalent of three billion barrels of oil in the U.S. alone (National Research Council 2009, U. S. Department of Energy, 2011). However, biomass has only one-third the energy density of crude oil (Agrawal and Singh 2009, Richard 2010) and lacks petroleum’s versatility as a feedstock for fuels and chemicals. These limitations keep biomass conversion below the efficiency level needed for strategic impact while the scientific challenge of routing carbon from one molecular context to another remains unmet. In 2009, the Center for Direct Catalytic Conversion of Biomass to Biofuels (C3Bio) recognized the potential of chemical catalysis and fast pyrolysis to overcome such limitations by transforming the main components of biomass (cellulose, xylan, and lignin) from grasses and trees directly to liquid hydrocarbons and aromatic co-products. Enabled by the EFRC high-risk, high-reward approach to grand challenge science, C3Bio researchers have been key national players in disrupting the conventional paradigm of the cellulosic biorefinery into a new future of “no carbon left behind”—the full utilization of carbon from plant cell walls in energy-dense fuels (Fig. 1). We identified catalytic and fast-pyrolytic pathways that utilize cellulose, xylan and, most significantly, lignin. We developed catalytic processes that deoxygenate and transform monomers and isolated polymers into useful products and tested their use with intact biomass. We gained control of lignin synthesis within plants and initiated tailoring biomass to its end-use through the tools of plant molecular biology and genetic engineering. C3Bio breakthroughs have increased the energy density of biomass-derived substrates via catalytic and pyrolytic conversions into products such as benzoquinones, furfural and hydroxymethylfurfural, levoglucosan and levulinic acid, methoxypropylphenols and propylbenzene. Such advances in biomass conversion would not have been possible without simultaneous advances in analytical instrumentation and methodologies, and imaging technologies and applications. The legacy science developed by C3Bio enables design and control strategies for achieving a targeted product portfolio of fuel and chemical feedstocks from a diverse range of native and tailored biomass. Our research provides the knowledge base required for a bio-economy with product streams as diverse in functionality as those of the petrochemical industry. Coupling targeted computational modeling with experimentation, we achieved: (1) fundamental understanding of biopolymers and cell wall architecture assembly, (2) discovery of new chemistries that allow the development of highly selective pathways to fuels and desirable chemicals, and (3) an integrated systems-level understanding to control catalytic and pyrolytic pathways.
Efficient conversion of inexpensive feedstocks to valuable chemicals by microbes is critical for a robust bioeconomy, but the ability to rationally design bacteria is hampered by insufficient knowledge of how post translational modifications (PTMs) control bacterial protein function and thus bioproduction phenotypes. Our study will focus on the lysine acetylation, a ubiquitous bacterial PTM that can affect the function of enzymes in central metabolism that are often critical for bioproduction processes, disrupt transcriptional regulation, and reduce translation. However, most lysine acetylation data is observational, which means that we do not know when, how, and what specific acetylated residues affect protein function and bacterial physiology. For our model host, we will use a Pseudomonas putida strain that we previously engineered to convert lignocellulosic feedstocks into chemicals such as itaconic acid (ITA). With this strain, we use a dynamic two-stage bioproduction process in which ITA is produced during a non-growth associated production phase. Production is highest during growth stages when lysine acetylation is low in other organisms (early stationary phase) and stalls in conditions where acetylation is highest (late stationary phase). The switch from high to stalled ITA production is also correlated with an unexpected increase in acetate levels – the precursor to non-enzymatic lysine acetylation. As such, we predict that lysine acetylation plays a substantial role in regulating the metabolic pathways required for ITA production. We will develop a generalizable approach that combines high-throughput genetic screens and cutting-edge genome engineering with state-of-the-art proteomics, metabolomics, and genetic code expansion methods to identify and modulate lysine acetylation patterns in bacteria. Ultimately, these strategies aim to manipulate protein expression and acetylation patterns to enhance bioproduction phenotypes (e.g., sustained ITA production in late stationary phase).
Accurate knowledge of acid dew point is essential for industrial and applied combustion. Sulfur in the fuel or raw materials is converted to sulfur dioxide (SO2) during combustion, and a portion of the SO2 is oxidized to sulfur trioxide (SO3). The SO3 will react to form H2SO4 vapor when in the presence of water vapor. Even with just trace levels of H2SO4 vapor in the gas phase (1-10 ppm), the dew point can reach 100°C and higher. To avoid acid condensation and the resulting corrosion on heat recovery equipment, plant engineers must ensure that surface temperatures are above the acid dew point, but this decreases the efficiency of the thermal energy recovery. Thus, there is a trade-off between minimizing equipment corrosion and maximizing thermal energy recovery efficiency, and the acid dew point is a key parameter for this optimization. In this work, we analyze and discuss the theory of the sulfuric acid condensation process and develop a novel dew point meter based on this analysis. The meter consists of a novel optical instrument that is designed to monitor the slightest appearance of condensation on a hydrophobic window surface, and the surface temperature of the window is slowly decreased until condensation is first observed, yielding an accurate measurement of the dew point. The basis of the instrument is that a collimated beam from a diode laser generates scattered light when encountering surface condensate, and a sophisticated array detector is used to sensitively monitor the light scattering. The measurement procedures are established to rapidly find the acid dew point, while minimizing error. Further, we propose a calibration system based on a liquid bubbler, which can generate a stable gas flow with known sulfuric acid dew point, to test the dew point meter. Test results show that the dew point meter can accurately measure the acid dew point over a wide range.
The incorporation of Al and increased curing temperature promotes the crystallization and cross-linking of calcium (alumino)silicate hydrate (C-(A-)S-H), which is the primary binding phase in most contemporary concrete materials. However, the influence of Al-induced structural changes on the mechanical properties at atomistic scale is not well understood. Herein, synchrotron radiation-based high-pressure X-ray diffraction is used to quantify the influence of dreierketten chain cross-linking on the anisotropic mechanical behavior of C-(A-)S-H. We show that the ab-planar stiffness is independent of dreierketten chain defects, e.g. vacancies in bridging tetrahedra sites and Al for Si substitution. The c-axis of non-cross-linked C-(A-)S-H is more deformable due to the softer interlayer opening but stiffens with decreased spacing and/or increased zeolitic water and Ca 2+ of the interlayer. Dreierketten chain cross-links act as ‘columns’ to resist compression, thus increasing the bulk modulus of C-(A-)S-H. We provide the first experimental evidence on the influence of the Al-induced atomistic configurational change on the mechanical properties of C-(A-)S-H. Our work advances the fundamental knowledge of C-(A-)S-H on the lowest level of its hierarchical structure, and thus can impact the way that innovative C-(A-)S-H-based cementitious materials are developed using a ‘bottom-up’ approach.
The emergence of microreactor technology has helped to drive supporting nuclear materials qualification and acceptance processes. One essential component in these small reactors is a solid moderator, which typically consists of metal hydride and cladding. While the behavior and performance of metal-hydride moderators go back to early advanced reactor development for nuclear-powered aviation and space propulsion, there remains a knowledge gap in the understanding of hydrogen transport–related phenomena and irradiation performance for hydride moderators. This impacts the acceptance/qualification of hydride moderators for microreactors. The goal of this technical note is to lay out a potential path forward for advanced moderator qualification and acceptance for designers and developers of microreactors. The proposed approach has benefited from a model microreactor core with the design parameters of a hydride moderator. Based on the model core and design parameters, a simple chart was developed for the major challenges of hydride moderators where potential incidents, causes, effects, and resolutions are described. The relation between the offered resolutions and the maturity of the metal-hydride moderator technology was emphasized using technological readiness. Technological readiness levels (TRLs) were clustered to three sets: physical phenomena related, reactor irradiations, and system demonstration. Some essential needs to fill the knowledge gaps are discussed for physical phenomena–related TRLs. For reactor irradiations, the importance of identifying goals and priorities is stressed to reach certain TRLs. For system demonstration, it is noted that metal-hydride moderator qualification must comply with the overall microreactor design.
Planet is a vertical-lift assembly machine currently located at the National Criticality Experiments Research Center (NCERC) at the Nevada National Security Site. In the past, Planet resided at Technical Area-18 in Los Alamos, New Mexico, as part of the Los Alamos Critical Experiments Facility (LACEF). Following the de-inventorying of LACEF, the Planet assembly was relocated to NCERC in 2008 and became fully operational in June of 2011. The Class Foils experiment, which involves stacking highly enriched uranium foils to obtain a critical configuration, was the first critical experiment performed on Planet. As a major component of the Nuclear Criticality Safety Class taught for the U.S. Department of Energy (DOE) Nuclear Criticality Safety Program, the Class Foils experiment allows personnel from all over the DOE complex to handle nuclear material and to complete the approach to critical safely and successfully. This paper describes the Planet vertical assembly and recent engineering upgrade and a selection of the experiments that have been performed on Planet since its transition to NCERC 10 years ago.
The revised IEEE 1547 standard (IEEE Std 1547-2018) contains eleven chapters (clauses) and eight annexes that comprise 136 pages. Each clause specifies information or requirements that apply to certain aspects important to the interconnection of distributed energy resources to the electric power system. Implementation of the requirements needs a careful study of the underlying technical concept and also needs the appropriate information that may be required to calculate various settings and configurations. This document aims to provide an introductory text to assist users of IEEE Std 1547-2018 in identifying the following questions: what is the primary audience of the clause/subclause? What knowledge is required to understand the technical subject matter? What information is required to apply/implement the requirements specified? What level of fluency with the subject matter is required to apply/implement the requirements?
The Science Resource Office-Peer Review and Metrics (SRO-PRM) Team hosts and maintains LANL's central repository for Science, Technology, and Engineering (STE) data. The Team tracks, integrates, analyzes, and reports qualitative and quantitative data measuring performance, quality, impact, and productivity. These metrics are indicators of the effectiveness and impact of STE efforts.
The term “artificial intelligence” (AI) - essentially programming machines to think like humans - conjures up different emotions in different people. Some view it with fear, imagining a malevolent AI similar to Skynet in the Terminator movies. Others see something benevolent, such as the character Data in Star Trek: The Next Generation . Still others see it as a means to advance the frontiers of science, engineering and technology to new levels not possible through traditional means. At the National Security Research Center (NSRC or the Center), which is the classified library at Los Alamos National Laboratory, we see AI as a tool to help us go through the monumental tasks we have in digitizing, cataloging, and searching our collections.
Canadian Nuclear Laboratories' (CNL) National Research Universal (NRU) reactor located at Chalk River Laboratories (CRL) first attained criticality in 1957. Outside of the reactor core, the safe handling and storage of fissile material including experimental fuel components is required to support NRU's ongoing missions of research and medical isotope production [1]. Management of this fissionable material relies heavily on experienced operating staff, who perform work in accordance with a defined set of limits and restrictions as laid out in a Criticality Safety Document (CSD). Historically, the development of CSDs has relied heavily upon criticality safety analysts to gain sufficient knowledge about the facility. This has resulted in significant labor costs due to heavy use of computational methods. This paper describes a new approach for CNL to identify potential criticality scenarios, a 'Criticality Hazard Identification' (CHI) technique, developed from traditional process-oriented hazard and operability analysis, to identify and define operating conditions, identify limits and restrictions for the handling of fissionable materials outside of the core. The approach permits thorough evaluation of potential criticality hazards, for both well-understood historical and proposed future activities, by better leveraging the knowledge of operations personnel as well as other subject matter experts. The resulting document provides a detailed record of the various scenarios that were considered. These improvements to the CSD development and documentation process provide an additional benefit in terms of ease of modification to include new or changed activities within the limits and conditions. (authors)
It has been conventional wisdom in the criticality safety discipline that for spent fuel systems like water pit storage and transportation, modeling water at its peak density will maximize reactivity. Thus, modeling water at 3.96 deg. C will produce a conservatively bounding k{sub eff} for that configuration. This assumption has been found to be incorrect for certain high-leakage water-isolated array configurations. In fact, temperatures up to the boiling point of water can in fact be more reactive, and the most reactive temperature is highly dependent upon the specific fuel configuration being analyzed. In light of the accident at Fukushima-Daiichi - where the spent fuel pool temperature increased - knowledge of this unexpected behavior is essential to ensuring that criticality safety evaluations are conservatively bounding. To illustrate the reflector temperature effect, MC21 calculations are provided for a sensitivity study based on LEU-COMP-THERM-044, which models the IPEN/MB-01 reactor at various temperatures between 3.9 deg. C and 93.3 deg. C. (authors)
Abstract Coastal dunes are important protective features against sea level rise and coastal storms. Interactions between dune plant aboveground structures and sediment trapping that allow for dune building and maintenance are well established. More recently, studies documenting belowground biomass for promoting erosion resistance in dominant dune species have been conducted, yet a knowledge gap remains regarding species‐specific characterization of whole plants, specifically with respect to roots, rhizomes, and belowground stems. Our objective was to quantify above‐ and belowground traits of four dominant dune grasses to document the potential for species‐specific effects on dune growth, maintenance, and erosion resistance. We examined above‐ and belowground traits among four prominent dune grasses of the Atlantic and Gulf Coasts of North America: Ammophila breviligulata , Panicum amarum , Spartina patens , and Uniola paniculata . Whole plant samples of each species were collected from the foredune at the US Army Engineer Research and Development Center's Field Research Facility in Duck, North Carolina, USA, and quantified for several above‐ and belowground traits (e.g., stem height, rhizome number and length, root surface area by diameter class, root tensile strength, and mycorrhizal percent infection). Belowground factors known to impact important dune processes, such as rhizome length, mycorrhizal percent infection, and root traits, differed substantially among species. When visualized in multivariate space, all species significantly differed in suites of above‐ and belowground traits. When considering belowground only, Ammophila and Spartina were similar, despite differences in biomass allocation. Species separated along axes related to mycorrhizal association, biomass allocation, and root construction. The four co‐occurring dune grass species were dissimilar in suites of plant traits. Belowground trait differences were driven by those describing root construction, biomass allocation, and mycorrhizal infection. Dissimilarity in above‐ and belowground suites of traits may demonstrate different approaches for surviving the dune environment. Incorporating belowground traits into modeling will enhance predictions of dune response to climate change through interactions between vegetation and dune dynamics that facilitate coastal resistance and resilience.
Accurate knowledge of acid dew point is essential for industrial and applied combustion applications. Sulfur in the fuel or raw materials is converted to sulfur dioxide (SO2) during combustion, and a portion of the SO2 is oxidized to sulfur trioxide (SO3). The SO3 will react to form H2SO4 vapor when in the presence of water vapor. Even with just trace levels of H2SO4 vapor in the gas phase (1-10 ppm), the dew point can reach 100°C and higher. To avoid acid condensation and the resulting corrosion on heat recovery equipment, plant engineers must ensure that surface temperatures are above the acid dew point, but this decreases the efficiency of thermal energy recovery. Thus, there is a trade-off between minimizing equipment corrosion and maximizing thermal energy recovery, and the acid dew point is a key parameter for this optimization. Commercially available acid dew point meters use electric conductivity sensors. These sensors are known to greatly underestimate the dew point due to their low sensitivity. In addition, no validation testing has been reported for these units and they are often expensive. In this work, we analyze the theory of the sulfuric acid condensation and develop a novel dew point meter based on this analysis. The meter consists of a novel optical instrument that is designed to monitor the slightest appearance of condensation on a hydrophobic window surface as the surface temperature of the window is slowly decreased. In this way, an accurate measurement of the dew point is obtained under a wide range of concentrations. The basis of the instrument is that a collimated beam from a diode laser will generate forward scattered light when the beam encounters surface condensate, and a sophisticated array detector is used to sensitively monitor the onset of light scattering. The measurement procedures are established to rapidly find the acid dew point, while minimizing error. Further, to calibrate the dew point meter we developed a calibration system based on a liquid bubbler that can generate a stable gas flow with a known sulfuric acid dew point. Test results show that the dew point meter can accurately measure acid dew point over a wide range. For H2SO4 vapor concentrations as low as 6 ppm the acid dew point is measured with an error of only ~1°C. To demonstrate the versatility of this instrument, the dew point meter was adapted for use with a high-pressure flow cell to allow for measurements of the dew point of flue gas from pressurized oxy-fuel combustion in a 100 kWth pressurized reactor.
Open-bottom permeameters (OBP) are commonly used for estimating the hydraulic properties of sediment beds under water bodies and fluxes across the sediment-water interface. The specific shape factor for an OBP flow situation is necessary for data interpretation. It is determined by the ratio of the permeameter tube radius to the tube's penetration depth within the sediment. Previous permeameter designs favored small radii which allowed for deeper penetration and stability of the tube. The shape factor is provided here for large footprint OBPs that allows for shallow penetration and greater radius. Through a numerical and analytical modeling study of the OBP hydrodynamics, previous knowledge on the OBP shape factor is generalized to the entire range of radius-to-penetration ratios that might be practically applied. The results reveal the advantages of the large footprint design, including better sensitivity due to the ability to impose larger discharge over a shorter test duration.