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Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

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At least 1,279 records · Page 71
All sourcesNASA NTRSDOE OSTI
DOE OSTI2015

Cellobionic acid utilization: from Neurospora crassa to Saccharomyces cerevisiae

Background: Economical production of fuels and chemicals from plant biomass requires the efficient use of sugars derived from the plant cell wall. Neurospora crassa, a model lignocellulosic degrading fungus, is capable of breaking down the complex structure of the plant cell wall. In addition to cellulases and hemicellulases, N. crassa secretes lytic polysaccharide monooxygenases (LPMOs), which cleave cellulose by generating oxidized sugars—particularly aldonic acids. However, the strategies N. crassa employs to utilize these sugars are unknown. Results: We identified an aldonic acid utilization pathway in N. crassa, comprised of an extracellular hydrolase (NCU08755), cellobionic acid transporter (CBT-1, NCU05853) and cellobionic acid phosphorylase (CAP, NCU09425). Extracellular cellobionic acid could be imported directly by CBT-1 or cleaved to gluconic acid and glucose by a β-glucosidase (NCU08755) outside the cells. Intracellular cellobionic acid was further cleaved to glucose 1-phosphate and gluconic acid by CAP. However, it remains unclear how N. crassa utilizes extracellular gluconic acid. The aldonic acid pathway was successfully implemented in Saccharomyces cerevisiae when N. crassa gluconokinase was coexpressed, resulting in cellobionic acid consumption in both aerobic and anaerobic conditions. Conclusions: We successfully identified a branched aldonic acid utilization pathway in N. crassa and transferred its essential components into S. cerevisiae, a robust industrial microorganism.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2021

Overexpression of the rice BAHD acyltransferase AT10 increases xylan-bound p-coumarate and reduces lignin in Sorghum bicolor

Abstract Background The development of bioenergy crops with reduced recalcitrance to enzymatic degradation represents an important challenge to enable the sustainable production of advanced biofuels and bioproducts. Biomass recalcitrance is partly attributed to the complex structure of plant cell walls inside which cellulose microfibrils are protected by a network of hemicellulosic xylan chains that crosslink with each other or with lignin via ferulate (FA) bridges. Overexpression of the rice acyltransferase OsAT10 is an effective bioengineering strategy to lower the amount of FA involved in the formation of cell wall crosslinks and thereby reduce cell wall recalcitrance. The annual crop sorghum represents an attractive feedstock for bioenergy purposes considering its high biomass yields and low input requirements. Although we previously validated the OsAT10 engineering approach in the perennial bioenergy crop switchgrass, the effect of OsAT10 expression on biomass composition and digestibility in sorghum remains to be explored. Results We obtained eight independent sorghum ( Sorghum bicolor (L.) Moench) transgenic lines with a single copy of a construct designed for OsAT10 expression. Consistent with the proposed role of OsAT10 in acylating arabinosyl residues on xylan with p -coumarate ( p CA), a higher amount of p -coumaroyl-arabinose was released from the cell walls of these lines upon hydrolysis with trifluoroacetic acid. However, no major changes were observed regarding the total amount of p CA or FA esters released from cell walls upon mild alkaline hydrolysis. Certain diferulate (diFA) isomers identified in alkaline hydrolysates were increased in some transgenic lines. The amount of the main cell wall monosaccharides glucose, xylose, and arabinose was unaffected. The transgenic lines showed reduced lignin content and their biomass released higher yields of sugars after ionic liquid pretreatment followed by enzymatic saccharification. Conclusions Expression of OsAT10 in sorghum leads to an increase of xylan-bound p CA without reducing the overall content of cell wall FA esters. Nevertheless, the amount of total cell wall p CA remains unchanged indicating that most p CA is ester-linked to lignin. Unlike other engineered plants overexpressing OsAT10 or a phylogenetically related acyltransferase with similar putative function, the improvements of biomass saccharification efficiency in sorghum OsAT10 lines are likely the result of lignin reductions rather than reductions of cell wall-bound FA. These results also suggest a relationship between xylan-bound p CA and lignification in cell walls.

09 BIOMASS FUELS↗
DOE OSTI2021

Metagenomic insights into the taxonomy, function, and dysbiosis of prokaryotic communities in octocorals

Background. In octocorals (Cnidaria Octocorallia), the functional relationship between host health and its symbiotic consortium has yet to be determined. Here, we employed comparative metagenomics to uncover the distinct functional and phylogenetic features of the microbiomes of healthy Eunicella gazella, Eunicella verrucosa, and Leptogorgia sarmentosa tissues, in contrast with the microbiomes found in seawater and sediments. We further explored how the octocoral microbiome shifts to a pathobiome state in E. gazella. Results. Multivariate analyses based on 16S rRNA genes, Clusters of Orthologous Groups of proteins (COGs), Protein families (Pfams), and secondary metabolite-biosynthetic gene clusters annotated from 20 Illumina-sequenced metagenomes each revealed separate clustering of the prokaryotic communities of healthy tissue samples of the three octocoral species from those of necrotic E. gazella tissue and surrounding environments. While the healthy octocoral microbiome was distinguished by so-far uncultivated Endozoicomonadaceae, Oceanospirillales, and Alteromonadales phylotypes in all host species, a pronounced increase of Flavobacteriaceae and Alphaproteobacteria, originating from seawater, was observed in necrotic E. gazella tissue. Increased abundances of eukaryotic-like proteins, exonucleases, restriction endonucleases, CRISPR/Cas proteins, and genes encoding for heat-shock proteins, inorganic ion transport, and iron storage distinguished the prokaryotic communities of healthy octocoral tissue regardless of the host species. An increase of arginase and nitric oxide reductase genes, observed in necrotic E. gazella tissues, suggests the existence of a mechanism for suppression of nitrite oxide production by which octocoral pathogens may overcome the host’s immune system. Conclusions. This is the first study to employ primer-less, shotgun metagenome sequencing to unveil the taxonomic, functional, and secondary metabolism features of prokaryotic communities in octocorals. Our analyses reveal that the octocoral microbiome is distinct from those of the environmental surroundings, is host genus (but not species) specific, and undergoes large, complex structural changes in the transition to the dysbiotic state. Host-symbiont recognition, abiotic-stress response, micronutrient acquisition, and an antiviral defense arsenal comprising multiple restriction endonucleases, CRISPR/Cas systems, and phage lysogenization regulators are signatures of prokaryotic communities in octocorals. We argue that these features collectively contribute to the stabilization of symbiosis in the octocoral holobiont and constitute beneficial traits that can guide future studies on coral reef conservation and microbiome therapy.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2019

Establishment and Characterization of Two Novel Cholangiocarcinoma Cell Lines

Background: Cholangiocarcinoma (CCA) is a rare, aggressive and highly lethal tumor. The disease is very difficult to diagnose and multi-modality treatments are ineffective. To improve our understanding of the biological characteristics of CCA, and facilitate the identification of valid treatments, an in-depth characterization of two novel Chinese patient-derived primary CCA cell lines was performed. Methods: Two CCA cell lines were developed and labelled ZJU-0826 and -1125. The two cell lines were characterized with respect to phenotypic, molecular, biomarker, functional and histological properties. Results: Two novel cell lines were cultured for 2 years, and maintained for more than 100 passages. They retained their typical biliary epithelial morphology and ultrastructure. The population doubling times of ZJU-0826, and -1125 were 63.84 h and 44.73 h, respectively. The cells exhibited near-triploid karyotypes with complex structural aberrations. ZJU-1125 cells had mutations in TP53 exons. Short tandem repeats genotyping confirmed the human origin and difference between lines. An immunophenotype analysis showed that ZJU-0826 is positive for CD44, CD29, Pdx1, CD236, FoxA1, FoxA2, and Nanog, and ZJU-1125 positive for CD44, CD29, CD133, Pdx1, FoxA1, FoxA2, and Nanog. ZJU-1125 had greater invasion ability in vitro and tumorigenicity than those of ZJU-0826. Conclusions: Our results confirm the validity of the ZJU-0826 and -1125 as representative models for the elucidation of the molecular pathogenesis of perihilar CCA, and intrahepatic CCA in both in vitro and in vivo studies, respectively.

62 RADIOLOGY AND NUCLEAR MEDICINE↗
DOE OSTI2024

Data for Microbial-Explicit Processes and Refined Perennial Plant Traits Improve Modeled Ecosystem Carbon Dynamics

Globally, soils hold approximately half of ecosystem carbon and can serve as a source or sink depending on climate, vegetation, management, and disturbance regimes. Understanding how soil carbon dynamics are influenced by these factors is essential to evaluate proposed natural climate solutions and policy regarding net ecosystem carbon balance. Soil microbes play a key role in both carbon fluxes and stabilization. However, biogeochemical models often do not specifically address microbial-explicit processes. Here, we incorporated microbial-explicit processes into the DayCent biogeochemical model to better represent large perennial grasses and mechanisms of soil carbon formation and stabilization. We also take advantage of recent model improvements to better represent perennial grass structural complexity and life-history traits. Specifically, this study focuses on: 1) a plant sub-model that represents perennial phenology and more refined plant chemistry with downstream implications for soil organic matter (SOM) cycling though litter inputs, 2) live and dead soil microbe pools that influence routing of carbon to physically protected and unprotected pools, 3) Michaelis-Menten kinetics rather than first-order kinetics in the soil decomposition calculations, and 4) feedbacks between decomposition and live microbial pools. We evaluated the performance of the plant sub-model and two SOM cycling sub-models, Michaelis-Menten (MM) and first-order (FO), using observations of net ecosystem production, ecosystem respiration, soil respiration, microbial biomass, and soil carbon from long-term bioenergy research plots in the mid-western United States. The MM sub-model represented seasonal dynamics of soil carbon fluxes better than the FO sub-model which consistently overestimated winter soil respiration. While both SOM sub-models were similarly calibrated to total, physically protected, and physically unprotected soil carbon measurements, the models differed in future soil carbon response to disturbance and climate, most notably in the protected pools. Adding microbial-explicit mechanisms of soil processes to ecosystem models will improve model predictions of ecosystem carbon balances but more data and research are necessary to validate disturbance and climate change responses and soil pool allocation.

Field Data↗
DOE OSTI2020

A computational model tracks whole-lung Mycobacterium tuberculosis infection and predicts factors that inhibit dissemination

Mycobacterium tuberculosis (Mtb), the causative infectious agent of tuberculosis (TB), kills more individuals per year than any other infectious agent. Granulomas, the hallmark of Mtb infection, are complex structures that form in lungs, composed of immune cells surrounding bacteria, infected cells, and a caseous necrotic core. While granulomas serve to physically contain and immunologically restrain bacteria growth, some granulomas are unable to control Mtb growth, leading to bacteria and infected cells leaving the granuloma and disseminating, either resulting in additional granuloma formation (local or non-local) or spread to airways or lymph nodes. Dissemination is associated with development of active TB. It is challenging to experimentally address specific mechanisms driving dissemination from TB lung granulomas. Herein, we develop a novel hybrid multi-scale computational model, MultiGran, that tracks Mtb infection within multiple granulomas in an entire lung. MultiGran follows cells, cytokines, and bacterial populations within each lung granuloma throughout the course of infection and is calibrated to multiple non-human primate (NHP) cellular, granuloma, and whole-lung datasets. We show that MultiGran can recapitulate patterns of in vivo local and non-local dissemination, predict likelihood of dissemination, and predict a crucial role for multifunctional CD8+ T cells and macrophage dynamics for preventing dissemination.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2016

Assessment of Membrane Fluidity Fluctuations during Cellular Development Reveals Time and Cell Type Specificity

Cell membrane is made up of a complex structure of lipids and proteins that diffuse laterally giving rise to what we call membrane fluidity. During cellular development, such as differentiation cell membranes undergo dramatic fluidity changes induced by proteins such as ARC and Cofilin among others. In this study we used the generalized polarization (GP) property of fluorescent probe Laurdan using two-photon microscopy to determine membrane fluidity as a function of time and for various cell lines. A low GP value corresponds to a higher fluidity and a higher GP value is associated with a more rigid membrane. Four different cell lines were monitored such as hN2, NIH3T3, HEK293 and L6 cells. Membrane fluidity was measured at 12h, 72h and 92 h. Our results show significant changes in membrane fluidity among all cell types at different time points. GP values tend to increase significantly within 92 h in hN2 cells and 72 h in NIH3T3 cells and only at 92 h in HEK293 cells. L6 showed a marked decrease in membrane fluidity at 72 h and starts to increase at 92 h. As expected, NIH3T3 cells have more rigid membrane at earlier time points. On the other hand, neurons tend to have the highest membrane fluidity at early time points emphasizing its correlation with plasticity and the need for this malleability during differentiation. This study sheds light on the involvement of membrane fluidity during neuronal differentiation and development of other cell lines.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2019

Mg[(UO2)2(Ge2O6(OH)2]·(H2O)4.4, a novel compound with mixed germanium coordination: cation disordering and topological features of β-U3O8 type sheets

Abstract A novel hydrated magnesium uranyl germanate, Mg[(UO 2 ) 2 (Ge 2 O 6 (OH) 2 )]·(H 2 O) 4.4 , has been synthesized under hydrothermal conditions at 200 °C. The orthorhombic unit-cell parameters are a =10.829(6), b =7.625(4), c =16.888(10) Å, V =1394.5(1) Å 3 , space group Cmcm , Z =4. The crystal structure is based on β-U 3 O 8 -type sheets of corner- and edge-sharing U 6+ O 7 pentagonal bipyramids. The GeO 3 (OH) tetrahedra and GeO 4 (OH) trigonal bipyramids are linked to form [Ge 2 φ 8 ] diortho groups that fill the hexagonal-shaped windows within the sheets. The uranyl germanate layers are connected through Mgφ 6 octahedra. The disorder of the [Ge 2 φ 8 ] diortho groups leads to different local structure types with layered- and framework-like characters. A review of the crystal structures of uranyl minerals and actinide-bearing synthetic compounds based on β-U 3 O 8 topological-type sheets is provided. Structural complexity parameters ( I G,total =176.19 bits/unit cell) indicate that the title compound is one of the simplest actinyl compounds among this family.

nuclear (including radiation effects), materials a↗
DOE OSTI2024

Advanced and functional composite materials via additive manufacturing: Trends and perspectives

Additive manufacturing (AM) has many advantages over conventional subtractive manufacturing methods. The cost-effective AM allows for precise fabrication of complex structures with less material waste, making it a popular manufacturing process in many applications. The recent development of AM materials has advanced significantly. Further, by precisely controlling material distribution and microstructural features, AM facilitates the creation of new composites with specific requirements. AM techniques have contributed considerably to integrating unique functionalities for various applications. This review emphasizes multiple categories of materials, including metal alloys, polymer-based composites, and sustainable composites, as well as applications of sensing materials and strategies and emerging artificial intelligence and machine learning.

36 MATERIALS SCIENCE↗
DOE OSTI2023

Experimental Observations of the Topology of Convolutional Neural Network Activations

Topological data analysis (TDA) is a branch of computational mathematics, bridging algebraic topology and data science, that provides compact, noise-robust representations of complex structures. Deep neural networks (DNNs) learn millions of parameters associated with a series of transformations defined by the model architecture resulting in high-dimensional, difficult to interpret internal representations of input data. As DNNs become more ubiquitous across multiple sectors of our society, there is increasing recognition that mathematical methods are needed to aid analysts, researchers, and practitioners in understanding and interpreting how these models' internal representations relate to the final classification. In this paper we apply cutting edge techniques from TDA with the goal of gaining insight towards interpretability of convolutional neural networks used for image classification. We use two common TDA approaches to explore several methods for modeling hidden layer activations as high-dimensional point clouds, and provide experimental evidence that these point clouds capture valuable structural information about the model's process. First, we demonstrate that a distance metric based on persistent homology can be used to quantify meaningful differences between layers and discuss these distances in the broader context of existing representational similarity metrics for neural network interpretability. Second, we show that a mapper graph can provide semantic insight as to how these models organize hierarchical class knowledge at each layer. These observations demonstrate that TDA is a useful tool to help deep learning practitioners unlock the hidden structures of their models.

topological data analysis, deep learning↗
DOE OSTI2016

AmeriFlux US-Wrc Wind River Crane Site

This is the AmeriFlux version of the carbon flux data for the site US-Wrc Wind River Crane Site. Site Description - Wind River Field Station flux tower site is located in the T.T. Munger Research Area of the Wind River Ranger District in the Gifford Pinchot National Forest. Protected since 1926, the T.T. Munger Research Natural Area (RNA) is administered by the USDA Forest Service Pacific Northwest Research Station and Gifford Pinchot National Forest. The Douglas-fir/western hemlock dominant stand is approximately 500 years old and represents end points of several ecological gradients including age, biomass, structural complexity, and density of the dominant overstory species. A complete stand replacement fire, approximately 450-500 years ago, resulted in the initial establishment. No significant disturbances have occurred since the fire aside from those confined to small groups of single trees, such as overturn from high wind activity and mechanical damage from winter precipitation.

Wharton, Sonia↗
DOE OSTI2021

Humming Trains in Seismology: An Opportune Source for Probing the Shallow Crust

Abstract Seismologists are eagerly seeking new and preferably low-cost ways to map and track changes in the complex structure of the top few kilometers of the crust. By understanding it better, they can build on what is known regarding important, practical issues. These include telling us whether imminent earthquakes and volcanic eruptions are generating telltale underground signs of hazard, about mitigation of induced seismicity such as from deep injection of wastewater, how the Earth and its atmosphere couple, and where accessible natural resources are. Passive seismic imaging usually relies on blind correlations within extended recordings of Earth’s ceaseless “hum” or coda of well-mixed, small vibrations. In this article, we propose a complementary approach. It is seismic interferometry using opportune sources—specifically ones not stationary in time and moving in a well-understood configuration. Its interpretation relies on an accurate understanding of how these sources radiate seismic waves, precise timing, careful placement of pairs of listening stations, and seismic phase differentiation (surface and body waves). Massive freight trains were only recently recognized as such a persistent, powerful cultural (human activity-caused) seismic source. One train passage may generate a tremor with an energy output of a magnitude 1 earthquake and be detectable for up to 100 km from the track. We discuss the source mechanisms of train tremors and review the basic theory on sources. Finally, we present case studies of body- and surface-wave retrieval as an aid to mineral exploration in Canada and to monitoring of a southern California fault zone. We believe noise recovery from this new signal source, together with dense data acquisition technologies such as nodes or distributed acoustic sensing, will deeply transform our ability to monitor activity in the shallow crust at sharpened resolution in time and space.

Geochemistry & Geophysics↗
DOE OSTI2025

Basin-Scale Structural Features Database

The Basin-Scale Structural Features database provides spatial datasets of faults, fractures, folds, and earthquakes compiled from public, authoritative sources (e.g., U.S. Geological Survey and State Geological Surveys) and aggregated into derivative forms to support subsurface assessments. Recognizing that characterizing basin-scale structural features requires interpreting data that are often ambiguous or lack key information, the source data were evaluated using a knowledge-data framework and geospatial fuzzy logic method (Justman et al., 2020) to represent both measured (observed) and predicted (inferred or potential) structural features as derivative datasets. This workflow employs conceptual models for known structural features and predicted structural features, incorporating geospatial data to estimate potential, even with limited data. The aim is to aid and support an understanding of basin-scale features and identify potential gaps in data and knowledge. As of 4/30/2025, the database includes resources for nine sedimentary basins: Appalachian, Denver, U.S. Gulf Coast, Illinois, Michigan, Permian, Sacramento, San Joquin and Williston. The database is organized by basin and then data category: 1) Faults, fractures, folds, 2) Earthquakes, 3) Topographic, 4) Structural contours and isopachs, 5) Geophysical, and 6) Structural feature density assessment maps.

basin scale↗
DOE OSTI2019

(1,0) gauge theories on the six-sphere

We construct gauge theories with a vector multiplet and hypermultiplets of (1,0) ( 1 , 0 ) supersymmetry on the six-sphere. The gauge coupling on the sphere depends on the polar angle. This has a natural explanation in terms of the tensor branch of (1,0) ( 1 , 0 ) theories on the six-sphere. For the vector multiplet we give an off-shell formulation for all supersymmetries. For hypermultiplets we give an off-shell formulation for one supersymmetry. We show that the path integral for the vector multiplet localizes to solutions of the Hermitian-Yang-Mills equation, which is a generalization of the (anti-)self duality condition to higher dimensions. For the hypermultiplet, the path integral localizes to configurations where the field strengths of two complex scalars are related by an almost complex structure.

Naseer, Usman↗
DOE OSTI2013

Hierarchy-Direction Selective Approach for Locally Adaptive Sparse Grids

We consider the problem of multidimensional adaptive hierarchical interpolation. We use sparse grids points and functions that are induced from a one dimensional hierarchical rule via tensor products. The classical locally adaptive sparse grid algorithm uses an isotropic refinement from the coarser to the denser levels of the hierarchy. However, the multidimensional hierarchy provides a more complex structure that allows for various anisotropic and hierarchy selective refinement techniques. We consider the more advanced refinement techniques and apply them to a number of simple test functions chosen to demonstrate the various advantages and disadvantages of each method. While there is no refinement scheme that is optimal for all functions, the fully adaptive family-direction-selective technique is usually more stable and requires fewer samples.

Stoyanov, Miroslav K↗
DOE OSTI2015

Using The Corngrass1 Gene To Enhance The Biofuel Properties Of Crop Plants

The development of novel plant germplasm is vital to addressing our increasing bioenergy demands. The major hurdle to digesting plant biomass is the complex structure of the cell walls, the substrate of fermentation. Plant cell walls are inaccessible matrices of macromolecules that are polymerized with lignin, making fermentation difficult. Overcoming this hurdle is a major goal toward developing usable bioenergy crop plants. Our project seeks to enhance the biofuel properties of perennial grass species using the Corngrass1 (Cg1) gene and its targets. Dominant maize Cg1 mutants produce increased biomass by continuously initiating extra axillary meristems and leaves. We cloned Cg1 and showed that its phenotype is caused by over expression of a unique miR156 microRNA gene that negatively regulates SPL transcription factors. We transferred the Cg1 phenotype to other plants by expressing the gene behind constitutive promoters in four different species, including the monocots, Brachypodium and switchgrass, and dicots, Arabidopsis and poplar. All transformants displayed a similar range of phenotypes, including increased biomass from extended leaf production, and increased vegetative branching. Field grown switchgrass transformants showed that overall lignin content was reduced, the ratio of glucans to xylans was increased, and surprisingly, that starch levels were greatly increased. The goals of this project are to control the tissue and temporal expression of Cg1 by using different promoters to drive its expression, elucidate the function of the SPL targets of Cg1 by generating gain and loss of function alleles, and isolate downstream targets of select SPL genes using deep sequencing and chromatin immunoprecipitation. We believe it is possible to control biomass accumulation, cell wall properties, and sugar levels through manipulation of either the Cg1 gene and/or its SPL targets.

09 BIOMASS FUELS↗
DOE OSTI2016

A Group Contribution Method for Estimating Cetane and Octane Numbers

Much of the research on advanced biofuels is devoted to the study of novel chemical pathways for converting nonfood biomass into liquid fuels that can be blended with existing transportation fuels. Many compounds under consideration are not found in the existing fuel supplies. Often, the physical properties needed to assess the viability of a potential biofuel are not available. The only reliable information available may be the molecular structure. Group contribution methods for estimating physical properties from molecular structure have been used for more than 60 years. The most common application is estimation of thermodynamic properties. More recently, group contribution methods have been developed for estimating rate dependent properties including cetane and octane numbers. Often, published group contribution methods are limited in terms of types of function groups and range of applicability. In this study, a new, broadly-applicable group contribution method based on an artificial neural network was developed to estimate cetane number research octane number, and motor octane numbers of hydrocarbons and oxygenated hydrocarbons. The new method is more accurate over a greater range molecular weights and structural complexity than existing group contribution methods for estimating cetane and octane numbers.

09 BIOMASS FUELS↗
DOE OSTI2016

Workshop Report on Additive Manufacturing for Large-Scale Metal Components - Development and Deployment of Metal Big-Area-Additive-Manufacturing (Large-Scale Metals AM) System

Additive manufacturing (AM) is considered an emerging technology that is expected to transform the way industry can make low-volume, high value complex structures. This disruptive technology promises to replace legacy manufacturing methods for the fabrication of existing components in addition to bringing new innovation for new components with increased functional and mechanical properties. This report outlines the outcome of a workshop on large-scale metal additive manufacturing held at Oak Ridge National Laboratory (ORNL) on March 11, 2016. The charter for the workshop was outlined by the Department of Energy (DOE) Advanced Manufacturing Office program manager. The status and impact of the Big Area Additive Manufacturing (BAAM) for polymer matrix composites was presented as the background motivation for the workshop. Following, the extension of underlying technology to low-cost metals was proposed with the following goals: (i) High deposition rates (approaching 100 lbs/h); (ii) Low cost (<$10/lbs) for steel, iron, aluminum, nickel, as well as, higher cost titanium, (iii) large components (major axis greater than 6 ft) and (iv) compliance of property requirements. The above concept was discussed in depth by representatives from different industrial sectors including welding, metal fabrication machinery, energy, construction, aerospace and heavy manufacturing. In addition, DOE’s newly launched High Performance Computing for Manufacturing (HPC4MFG) program was reviewed. This program will apply thermo-mechanical models to elucidate deeper understanding of the interactions between design, process, and materials during additive manufacturing. Following these presentations, all the attendees took part in a brainstorming session where everyone identified the top 10 challenges in large-scale metal AM from their own perspective. The feedback was analyzed and grouped in different categories including, (i) CAD to PART software, (ii) selection of energy source, (iii) systems development, (iv) material feedstock, (v) process planning, (vi) residual stress & distortion, (vii) post-processing, (viii) qualification of parts, (ix) supply chain and (x) business case. Furthermore, an open innovation network methodology was proposed to accelerate the development and deployment of new large-scale metal additive manufacturing technology with the goal of creating a new generation of high deposition rate equipment, affordable feed stocks, and large metallic components to enhance America’s economic competitiveness.

36 MATERIALS SCIENCE↗