SEARCH · Engineering Papers
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.
Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.
The Environmental Brand Guidelines have been prepared by the CEA-CAS Visual Design team for use primarily by IFPROG, but they apply to all LANL employees ordering signage for new and existing buildings. By adhering to this guidance, you will be a responsible steward of the “One Lab, one voice” mentality and successfully implement the Lab’s brand on all of your communications.
Class A burning brand (UL 790) tests are used to determine the roof covering's effectiveness against severe exposure to external fire. Unfortunately, these tests can be costly to repeatedly perform when iteratively developing new materials/designs. The NREL team will work with the R&D Lab to develop and deploy computational tools to facilitate rapid development of roofing materials for the UL 790 standard test.
The goal is to revitalize the Lab’s identity so that it reflects a focused and forward-looking approach to solving national security challenges. This goal is achieved through design that is dynamic, accessible, and optimized for 21st century communications.
The goal is to revitalize the Lab’s identity so that it reflects a focused and forward-looking approach to solving national security challenges. This goal is achieved through design that is dynamic, accessible, and optimized for 21st century communications.
Commercial PV modules have various packaging choices nowadays, which influence their long-term reliability. This study compared the degradation behaviors of sixteen module variants from two brands with varying encapsulant materials (EVA or POE), encapsulant types, module architectures (GB or DG), and cell types (monofacial or bifacial) using null hypothesis testing to determine statistical significant findings. The modules were exposed for 2,520 hours under two accelerated exposures: modified damp heat (mDH) and modified damp heat with full-spectrum light (mDH+FSL). For both brands, two DG module variants with UV-Cutoff rear encapsulant are found to have significantly lower average power loss than the module variants of EVA+GB with opaque rear encapsulant after each accelerated exposure. Metallization interconnect corrosion is identified as the primary degradation mechanism. Furthermore, unsupervised hierarchical clustering finds that the degradation behaviors of modules from one brand with a more strict manufacturing quality control depends on module architectures only.
Brand names can be used to hold plastic companies accountable for their items found polluting the environment. We used data from a 5-year (2018–2022) worldwide (84 countries) program to identify brands found on plastic items in the environment through 1576 audit events. We found that 50% of items were unbranded, calling for mandated producer reporting. The top five brands globally were The Coca-Cola Company (11%), PepsiCo (5%), Nestlé (3%), Danone (3%), and Altria (2%), accounting for 24% of the total branded count, and 56 companies accounted for more than 50%. There was a clear and strong log-log linear relationship production (%) = pollution (%) between companies’ annual production of plastic and their branded plastic pollution, with food and beverage companies being disproportionately large polluters. Phasing out single-use and short-lived plastic products by the largest polluters would greatly reduce global plastic pollution.
A full-scale understanding of the dynamics of the Chinese vehicle market can benefit stakeholders with respect to rational decision-making and effective long-term investment. This study attempts to discover the common vehicle pricing patterns in the Chinese market by quantifying statistical correlations among critical vehicle features from intrinsic powertrain systems to extrinsic market positioning. The data samples involve almost all passenger vehicle models sold in 2013 to 2019. After comparing multiple statistical methodologies, a log-transformation variant of the multinomial linear regression model was found to be the best one, and the goodness of fit shows that this model can offer stable estimates, which were validated using 2019 market data. The insights achieved are: (1) The price and major performance features of SUVs/crossovers are similar to those of sedans; (2) If all other explicit features remain the same, the price of a Japanese midsize sedan is 62% higher than that of a Chinese midsize sedan, and European midsize vehicles have the highest prices overall. (3) The incremental price of fuel consumption varies by vehicle class and fuel economy. For example, from 30 to 50 MPG, the vehicle price increases by $119 for a Chinese brand sedan vehicle, by $69 for a Chinese brand SUV.
As a brand new two-dimensional (2D) material with promising electronic properties, monolayer I-VII silver iodide (AgI) has the potential for future 2D electronic devices. To advance the development of such devices, the exploration of n-type and p-type conductivities of AgI is indispensable. With first-principles calculations, we systematically investigate the properties of intrinsic defects and extrinsic dopants in monolayer AgI, including atomic structural pictures, formation energies, and ionization energies to offer carriers. Considering the divergence in energies of charged defects in 2D materials when the traditional jellium scheme is used, we adopt an extrapolation approach to overcome the problem. The Ag vacancy (VAg) and Be substitution on Ag site (BeAg) are found to be the most promising p-type and n-type doping candidates, respectively. They could provide bound carriers for transport through the defect-bound band edge states, although the ionization energies are still larger than thermal energy at room temperature. Furthermore, negative-U behaviors are demonstrated in I vacancy (VI), Zn substitution on Ag site (ZnAg), and Cd substitution on Ag site (CdAg). The present work, for the first time, offers a detailed study of the defect physics in 2D I-VII monolayer semiconductor, laying the foundation for subsequent physics and device explorations based on these brand new 2D materials.
Not Available
Metagenomic analysis of food is becoming more routine and can provide important information pertaining to the shelf life potential and the safety of these products. However, less information is available on the microbiomes associated with low water activity foods. Pine nuts and sesame seeds, and food products which contain these ingredients, have been associated with recalls due to contamination with bacterial foodborne pathogens. The objective of this study was to identify the microbial community of pine nuts and sesame seeds using targeted 16S rRNA sequencing technology. Ten different brands of each seed type were assessed, and core microbiomes were determined. A total of 21 and 16 unique taxa with proportional abundances >1% in at least one brand were identified in the pine nuts and sesame seeds, respectively. Members of the core pine nut microbiome included the genera Alishewanella, Aminivibrio, Mycoplasma, Streptococcus, and unassigned OTUs in the families of Desulfobacteraceae and Xanthomonadaceae. For sesame seeds, the core microbiome included Aminivibrio, Chryseolina, Okibacterium, and unassigned OTUs in the family Flavobacteriaceae. The microbiomes of these seeds revealed that these products are dominated by environmental bacterial genera commonly isolated from soil, water, and plants; bacterial genera containing species known as commensal organisms were also identified. Understanding these microbiomes can aid in the risk assessment of these products by identifying food spoilage potential and community members which may co-enrich with foodborne bacterial pathogens.
The installation of marine energy systems may affect marine environments, and by extension, marine fish communities. Therefore, biomonitoring is an integral part of assessing impacts on species. Environmental DNA (eDNA) provides a noninvasive alternative to conventional monitoring surveys and the possibility of a more accurate assessment of species richness. Yet, its cost efficiency compared to traditional methods of monitoring is relatively unknown, especially when applied to monitoring around tidal, wave, and offshore wind energy installations. For this study, 202 peer-reviewed journal articles were dissected to inventory the diversity of supplies used for collecting and processing eDNA samples and to compile the average cost of eDNA surveys. Information collected included the type, volume, and brand of containers used in sampling; material, size, and brand of filters; and extraction methods. Cost information was gathered for the most common supplies, and a total cost was estimated for a hypothetical eDNA survey in Sequim Bay, WA, to compare with traditional methods of surveying such as beach seining and scuba surveys. The results showed a higher-than-expected diversity of supplies to collect and process eDNA samples. The most common supplies were 1 L Nalgene bottles at an average cost of 7.96 USD for collecting samples, 0.45 µm glass fiber Merck Millipore filters at an average cost of 1.51 USD for filtering samples, and the Qiagen DNeasy Blood and Tissue kit at 3.54 USD per sample for extracting DNA. When compared to beach seine and scuba surveys, eDNA surveys undertaken by senior researchers are less expensive for both initial surveys with all new materials as well as for follow-up surveys reusing some of the supplies. However, when surveys are done solely by students, eDNA surveys are more expensive than scuba surveys when no prior supplies are available and more than both beach seine and scuba surveys for follow-up surveys reusing supplies. In a professional sphere, where surveys are less often conducted by teams of students only, eDNA surveys are an effective and less-costly alternative to conventional methods. We anticipate that the development and refinement of eDNA methodology will continue to decrease surveying costs.
Problem: Remanufacturers want to increase consumer interest in refurbished products, which motivates the need to understand which product features are important to buyers of refurbished products such as mobile phones. Research Questions: This study addresses two questions. First, which product features are most important for buyers of refurbished iPhones? Second, how do those preferences differ from the preferences of buyers of new iPhones? Methods: Online reviews of iPhones are obtained and converted into a document–term matrix. Using this text model, three subsets of features are identified using statistical analysis of frequency of mention: most frequent, average, and least frequent. A logistic regression (LR) model is then used to identify which features are most predictive of whether a review is for a new or refurbished phone. Results: Buyers of refurbished phones mention battery health, screen/display, shell condition, and brand significantly more often than other features. Directly contrasting reviews of refurbished versus new phones shows that shell condition, brand, speaker, and charger are found to be the most predictive product features indicated in reviews for refurbished phones. Of those, the shell condition is significantly more predictive than the others. Implications: The results identify product features that remanufacturers of iPhones can emphasize to increase customer demand.
China's plug-in electric vehicle (PEV) market with stocks at 7.8 million is the world's largest in 2021, and it accounts for half of the global PEV growth in 2021. The PEV market in China has dramatically evolved since the pandemic in 2020: over 20% of all new PEV sales are from China by mid-2022. Recent features of PEV market dynamics, consumer acceptance, policies, and infrastructure have important implications for both the global energy market and manufacturing stakeholders. From the perspective of demand pull-supply push, this study analyzes China's PEV industry with a market dynamics framework by reviewing sales, product and brand, infrastructure, and government policies from the last few years and outlooking the development of the new government’s 14th Five-Year Plan (2021-2025). From the demand side, small-sized sedans and compact sport utility vehicles with increased electric ranges are both popular for PEVs, and the electric range of over 60% of new battery electric vehicles in 2021 has been longer than 400 km. From the supply side, although foreign brands like Tesla are still competitive, the products by Chinese domestic automakers like BYD are becoming more attractive and cannibalizing the high-end market. However, the production capacity and cost of PEVs may be limited by the upstream of the supply chain – the battery manufacturing and supply chain inflation. In addition, it is also uncertain how much sales demand impacts will be caused by the potential global economic recession. The government firmly supports electrification and decarbonization of the vehicle industry by emphasizing the importance of the vehicle industry for promoting the greenhouse gas net zero by 2060. The dual-credit policy is regarded as the most critical regulation in a bid to restrain fuel consumption and promote PEV share. Still, the market is facing some technological obstacles, such as battery safety and driving range anxiety, before real prosperity. In addition, the Chinese electric vehicle market is seeing a trend toward the development of new technologies such as vehicle-to-X, autonomous driving, and connected vehicles.
Abstract In this paper, we explored different ways to write the algebraic version of betweenness centrality algorithm. Particularly, we focused on Brandes' algorithm. We aimed for algebraic betweenness centrality that can be parallelized easily. We proposed 3-tuple geodetic semiring as an extension to the usual geodetic semiring with 2-tuples. Using the 3-tuple geodetic semiring, Dijkstra's and Brandes' algorithm, we wrote more concise and general algebraic betweenness centrality (ABC) algorithm which is valid for weighted and directed graphs. We also proposed an alternative version of ABC using the usual geodetic semiring with 2-tuple where we used a simple way to construct shortest path tree after computing shortest path distances in the usual geodetic semiring. This allows us to avoid computational complexity of ABC implementation using 3-tuple geodetic semiring. We used numba to optimize and parallelize ABC. We evaluated the performance of ABC using 2-tuple geodetic semiring as compared to NetworkX, a common python package for graph algorithms. We did scalability experiments on parallel ABC and showed its total speedup. We also showed that with small modification, ABC can be adapted to algebraicly compute other centrality measures such as percolation centrality.
This work investigates the potential induced degradation-polarization (PID-p) of bifacial passivated emitter and rear contact (PERC) modules installed in the field at the Florida Solar Energy Center. 22 modules were stressed over 1 year under various voltage (-1500V, -3500, +1500V) and mounting configurations (close to ground, 2 meters from ground, close to roof). The positively stressed modules were anticipated to have experiences PID-p on their front side. However, no degradation was observed, as the light-induced recovery effect effectively counteracted the degradation. The negatively stressed modules experienced Pmax drops during 2 weeks before stabilizing at 5-6% loss. I-V and EL characteristics suggest a PID-p degradation on the rear side. Lower degradation rates are observed with lower voltage and when the mounting configuration allows more albedo light on the rear side to compensate for the degradation. PID-p is observed to stabilize at similar level for the voltage and mounting configuration tested with slightly lower Pmax in winter. EL imaging reveals that cells are not degrading at the same rate across a module. Some cells get darker and then brighter which corresponds to the silicon/passivation interface going through depletion and then inversion when enough positive charges are accumulated. After full light-recovery, the modules were tested again along with 22 brand-new ones to assess the impact of the module history. Results confirmed the trends observed in the first year with PID-p occurring at voltage as low as -600V and recovered modules degrading faster than brand-new ones.
The Security Starts With You Campaign Guidelines have been prepared by the CEA-CAS Visual Design team for use primarily by DSOs, FODs, and/or any LANL employee who wishes to extend the messaging to new and existing buildings. By adhering to this guidance, you will be a responsible steward of the “One Lab, one voice” mentality and successfully implement the Lab’s brand and security messaging on all of your communications.
This presentation summarizes progress to date of the U.S. Department of Energy project, "Development of a computational framework for modeling golden eagles (Aquila chrysaetos) near wind farms," which focuses on stochastic behavioral modeling of soaring raptors across landscape, facility, and turbine spatiotemporal scales. This publicly available, open-source modeling framework includes behavioral models based on three different underlying principles: energy minimization at landscape scale, behavioral heuristics at landscape-facility scale, and data-driven behaviors at the facility-micro-scale. We will briefly overview the key advancements in the behavioral modeling state of the art, which leverages multiple high-resolution telemetry data sources combined with high-fidelity atmospheric flow modeling insights. We then present preliminary results from a validation study in Altamont, California. This new study involves a novel application of the Stochastic Soaring Raptor Simulator (SSRS), in a new geographic locale, to understand facility scale eagle movement patterns over time scales representative of a wind project's lifetime. For this desktop analysis (that does not depend on any high-performance computing resources), SSRS simultaneously considers a variety of wind conditions and eagle approach vectors toward a project site of interest. This work demonstrates the integration of publicly available landscape-scale atmospheric datasets, our recently improved engineering updraft models (see presentation from Thedin et al.), and our energy minimization behavioral models within the SSRS framework. While we only present results from a single behavioral model, the integration of these three modeling components forms the foundation for our more sophisticated behavioral models (see presentations from Brandes et al., Sandhu et al.) that are under active development. Results are presented in the form of presence maps, which may be applied to estimate risk to wildlife, augment ground survey data, inform wind-plant operations, or incorporated into wind-plant designs.