Engineering Papers⌕ Search

SEARCH · Engineering Papers

Results for “SCIENTISTS”

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.

At least 1,693 records · Page 94
All sourcesNASA NTRSDOE OSTI
DOE OSTI2024

30 years of semiconductor nanowire research: A Personal Journey

This project is about flux line pinning in high T c superconductors using columnar defects as the pinning centers. The basic idea is that linear structural defects can pin the flux lines within HTSCs so that their critical current density (the maximal current density that these HTSCs can carry with zero resistance) can be enhanced. The question is how to create such linear structural defects within crystalline cuprate thin films. Scientists have already successfully demonstrated the concept by irradiating HTSC crystals with high-energy ions at synchrotron beamline facilities. However, such heavy ion irradiation is practically not viable for large-scale processes. Finally, we reasoned that maybe we could introduce some sort of linear nanoscopic objects into HTSC to form a composite, and then these linear nanoscopic objects would serve as the magnetic flux line pinning centers.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2021

Perceived Conflicts of Systems Engineering in Early-Stage Research and Development 1

Early Systems Research and Development (ESR&D) is one of the most crucial phases in the product development process. It both blends and blurs the lines between science and engineering, and requires a risk-based, disciplined, and graded approach to effectively manage scope, cost, and complexity of the final product. Many leaders, program managers, and scientists are unwilling to involve systems engineering because of the perception that systems engineering is heavily process oriented, adds unnecessary costs, and should be applied only to mature technologies. The value of systems engineering as applied to ESR&D is unclear to these key individuals. The unfortunate result is that system engineering is not applied to ESR&D. This results in R&D efforts that may have solved the wrong problem, selected the wrong architecture, require technical rework, have difficulty transitioning later maturity levels, and result in higher R&D costs and extended development timelines. This work discusses the difficulty of introducing systems engineering to the research and early development process and their inclination perspectives of researchers, engineers, and managers. The article shall offer potential means to manage the cultural transformation of early adoption of right-sized systems engineering in ESR&D and reverse the attitudinal positions.

42 ENGINEERING↗
DOE OSTI2017

Prediction of seebeck coefficient for compounds without restriction to fixed stoichiometry: A machine learning approach

The regression model-based tool is developed for predicting the Seebeck coefficient of crystalline materials in the temperature range from 300 K to 1000 K. The tool accounts for the single crystal versus polycrystalline nature of the compound, the production method, and properties of the constituent elements in the chemical formula. Here we introduce new descriptive features of crystalline materials relevant for the prediction the Seebeck coefficient. To address off-stoichiometry in materials, the predictive tool is trained on a mix of stoichiometric and nonstoichiometric materials. The tool is implemented into a web application to assist field scientists in the discovery of novel thermoelectric materials.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2022

Effects of random forest modeling decisions on biogeochemical time series predictions

Abstract Random forests (RF) are an increasingly popular machine learning approach used to model biogeochemical processes in the Earth system. While RF models are robust to many assumptions that complicate deterministic models, there are several important parameterization decisions for appropriate use and optimal model fit. We explored the role that parameter decisions, including training/testing data splitting strategies, variable selection, and hyperparameters play on RF goodness‐of‐fit by constructing models using 1296 unique parameter combinations to predict concentrations of nitrate, a key nutrient for biogeochemical cycling in aquatic ecosystems. Models were built on long‐term, publicly available water quality and meteorology time series collected by the National Estuarine Research Reserve monitoring network for two contrasting ecosystems representing freshwater and brackish estuaries. We found that accounting for temporal dependence when splitting data into training and testing subsets was key for avoiding over‐estimation of model predictive power. In addition, variable selection, the ratio of training to testing data, and to a lesser degree, variables per split and number of trees, were significant parameters for optimizing RF goodness‐of‐fit. We also explored how model parameter decisions influenced interpretation of the relative importance of predictors to the model, and model predictor‐dependent variable relationships, with results suggesting that both data structure and model parameterization influence these factors. Because much of the current RF literature is written for the computational and statistical science communities, the primary goal of this study is to provide guidelines for aquatic scientists new to machine learning to apply RF techniques appropriately to aquatic biogeochemical datasets.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2022

Abundance and Migration Success of Overshoot Steelhead in the Upper Columbia River

Abstract Summer steelhead Oncorhynchus mykiss may enter freshwater almost a year before spawning and potentially make long migrations (>1,000 km) to interior headwater habitats. However, in response to suboptimal freshwater habitat conditions (e.g., warmer water temperatures), adult summer steelhead may exhibit complex behaviors during upstream migration in the Columbia River basin. Steelhead may migrate upstream of their natal tributary (hereafter, referred to as “overshoot”) and spend days to several months before subsequently migrating downstream (hereafter, referred to as “fallback”) to their natal tributary to spawn. An expansion of an existing Bayesian patch occupancy model, derived from observations of adult steelhead that were PIT-tagged to estimate population-specific abundance upstream of the tagging location, incorporated downstream detection locations to estimate the abundance of overshoot fallbacks. Overshoot steelhead abundance at the tagging location was estimated based on the relationship between the number of known overshoot fallbacks (i.e., the number of steelhead that overshot and successfully migrated downstream to their natal tributary) and their model-estimated abundance. During the study period (2010–2017), the annual mean proportion of overshoot steelhead that successfully migrated downstream of the tagging location (Priest Rapids Dam) was 0.59 (SD = 0.14). The number of dams encountered by overshoot steelhead during their downstream migration was negatively correlated with their downstream migration success probability. Improved downstream passage survival for adult steelhead will increase the abundance of affected populations while reducing potential genetic introgression of upstream populations (i.e., strays). This is the first study to estimate the abundance of overshoot and fallback steelhead, providing the data necessary for scientists to estimate potential conservation benefits of improved downstream survival. For example, surface flow passage routes (e.g., sluiceways and temporary spillway weirs) are very effective in guiding and passing adult steelhead downstream of Columbia River hydroelectric projects and data from this assessment show that changes in dam operations throughout the downstream migration period may maximize conservation benefits.

Murdoch, Andrew R. (ORCID:0000000324827689)↗
DOE OSTI2023

Status of genome function annotation in model organisms and crops

Abstract Since the entry into genome‐enabled biology several decades ago, much progress has been made in determining, describing, and disseminating the functions of genes and their products. Yet, this information is still difficult to access for many scientists and for most genomes. To provide easy access and a graphical summary of the status of genome function annotation for model organisms and bioenergy and food crop species, we created a web application ( https://genomeannotation.rheelab.org ) to visualize, search, and download genome annotation data for 28 species. The summary graphics and data tables will be updated semi‐annually, and snapshots will be archived to provide a historical record of the progress of genome function annotation efforts. Clear and simple visualization of up‐to‐date genome function annotation status, including the extent of what is unknown, will help address the grand challenge of elucidating the functions of all genes in organisms.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2021

Monomer-scale design of functional protein polymers using consensus repeat sequences

Protein-based polymers possess chemically defined sequences that can encode diverse properties and functions into a new class of biopolymeric materials. However, sequence variation that emerges from evolution can obscure the sequence–function relationships of naturally derived polymers. One strategy to clarify these relationships is to identify common sequences between proteins with similar functions. These conserved sequences often emerge from repeat proteins, and “consensus repeat sequences” provide a convenient platform for systematic investigations of biopolymer sequence–property relationships. In this review, we highlight recent approaches to engineer tunable polymeric materials using monomer-scale design of consensus repeat proteins. Here, we explore established and emerging protein-based materials with mechanical resilience, thermodynamic phase behavior, chemical responsiveness, biomolecular transport, and hierarchical structure. Overall, recent advances in the monomer-scale design of repetitive protein polymers present exciting fundamental and translational opportunities for polymer scientists and engineers.

36 MATERIALS SCIENCE↗
DOE OSTI2021

Picturing the future of food

Abstract High‐throughput phenotyping (HTP) has emerged as one of the most exciting and rapidly evolving spaces within plant science. The successful application of phenotyping technologies will facilitate increases in agricultural productivity. High‐throughput phenotyping research is interdisciplinary and may involve biologists, engineers, mathematicians, physicists, and computer scientists. Here we describe the need for additional interest in HTP and offer a primer for those looking to engage with the HTP community. This is a high‐level overview of HTP technologies and analysis methodologies, which highlights recent progress in applying HTP to foundational research, identification of biotic and abiotic stress, breeding and crop improvement, and commercial and production processes. We also point to the opportunities and challenges associated with incorporating HTP across food production to sustainably meet the current and future global food supply requirements.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2019

Therapeutic recombinant protein production in plants: Challenges and opportunities

Societal Impact Statement Therapeutic protein production in plants is an area of great potential for increasing and improving the production of proteins for the treatment or prevention of disease in humans and other animals. There are a number of key benefits of this technique for scientists and society, as well as regulatory challenges that need to be overcome by policymakers. Increased public understanding of the costs and benefits of therapeutic protein production in plants will be instrumental in increasing the acceptance, and thus the medical and veterinary impact, of this approach. Summary Therapeutic recombinant proteins are a powerful tool for combating many diseases which have previously been hard to treat. The most utilized expression systems are Chinese Hamster Ovary cells and Escherichia coli , but all available expression systems have strengths and weaknesses regarding development time, cost, protein size, yield, growth conditions, posttranslational modifications and regulatory approval. The plant industry is well established and growing and harvesting crops is easy and affordable using current infrastructure. Growth conditions are generally simple: sunlight, water, and the addition of cheap, available fertilizers. There are multiple options for plant expression systems, including species, genetic constructs and protein targeting, each best suited to a particular type of therapeutic protein production. Transient expression systems provide a mechanism to rapidly transfect plants and produce therapeutic protein in a matter of weeks, rather than the months it can take for many competing expression systems, while proteins targeted to cereal seeds can be harvested, stored and potentially purified much more easily than in competing systems. Current challenges for plant expression systems include a lack of regulatory approval, environmental containment concerns and nonhuman glycosylation, which may limit the scope of the type of therapeutic proteins that can be manufactured in plants. The specific strengths of plant expression systems could facilitate the production of certain therapeutic proteins quickly and cheaply in the near future.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2022

Publishing Challenges in Energetic Materials Science

The editorial addressed the ethical dilemma that energetic materials scientists face in advancing their field for societal good and not providing information that could be used for nefarious purposes. The reaction to the editorial was heated, both pro and con. Here we concluded that PEP had done a good job in catalyzing a thoughtful debate in our community about what information should or should not be published.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2020

The Table of Standard Atomic Weights—an exercise in consensus

The present Table of Standard Atomic Weights (TSAW) of the elements is perhaps one of the most familiar datasets in science. Unlike most parameters in physical science whose values and uncertainties are evaluated using the “Guidelines for Uncertainty in Measurement” (GUM), the majority of standard atomic-weight values and their uncertainties are consensus values, not GUM-evaluated values. The Commission on Isotopic Abundances and Atomic Weights of the International Union of Pure and Applied Chemistry (IUPAC) regularly evaluates the literature for new isotopic-abundance measurements that can lead to revised standard atomic-weight values, A r (E) for element E. The Commission strives to provide utmost clarity in products it disseminates, namely the TSAW and the Table of Isotopic Compositions of the Elements (TICE). In 2016, it was recognized by the Commission that a guideline recommending expression of uncertainty listed in parentheses following the standard atomic-weight value, e.g. A r (Se) = 78.971(8), is not in accord with the GUM, which suggests that this parenthetic notation be reserved to express standard uncertainty, not the expanded uncertainty used in the TSAW and TICE. In 2017, to eliminate this noncompliance with the GUM, a new format was adopted in which the uncertainty value is specified by the plus-minus symbol, e.g. A r (Se) = 78.971 ± 0.008. To clarify the definition of uncertainty, a new footnote has been added to the TSAW. This footnote emphasizes that an atomic-weight uncertainty is a consensus (decisional) uncertainty. Not only has the Commission shielded users of the TSAW and TICE from unreliable measurements that appear in the literature as a result of unduly small uncertainties, but the aim of IUPAC has been fulfilled by which any scientist, taking any natural sample from commerce or research, can expect their sample atomic weight to lie within A r (E) ± its uncertainty almost all of the time.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2023

Driving mode analysis—How uncertain functional inputs propagate to an output

Abstract Driving mode analysis elucidates how correlated features of uncertain functional inputs jointly propagate to produce uncertainty in the output of a computation. Uncertain input functions are decomposed into three terms: the mean functions, a zero‐mean driving mode, and zero‐mean residual. The random driving mode varies along a single direction, having fixed functional shape and random scale. It is uncorrelated with the residual, and under linear error propagation, it produces an output variance equal to that of the full input uncertainty. Finally, the driving mode best represents how input uncertainties propagate to the output because it minimizes expected squared Mahalanobis distance amongst competitors. These characteristics recommend interpretation of the driving mode as the single‐degree‐of‐freedom component of input uncertainty that drives output uncertainty. We derive the functional driving mode, show its superiority to other seemingly sensible definitions, and demonstrate the utility of driving mode analysis in an application. The application is the simulation of neutron transport in criticality experiments. The uncertain input functions are nuclear data that describe how Pu reacts to bombardment by neutrons. Visualization of the driving mode helps scientists understand what aspects of correlated functional uncertainty have effects that either reinforce or cancel one another in propagating to the output of the simulation.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2024

Characterizing climate pathways using feature importance on echo state networks

The 2022 National Defense Strategy of the United States listed climate change as a serious threat to national security. Climate intervention methods, such as stratospheric aerosol injection, have been proposed as mitigation strategies, but the downstream effects of such actions on a complex climate system are not well understood. The development of algorithmic techniques for quantifying relationships between source and impact variables related to a climate event (i.e., a climate pathway) would help inform policy decisions. Data-driven deep learning models have become powerful tools for modeling highly nonlinear relationships and may provide a route to characterize climate variable relationships. In this paper, we explore the use of an echo state network (ESN) for characterizing climate pathways. ESNs are a computationally efficient neural network variation designed for temporal data, and recent work proposes ESNs as a useful tool for forecasting spatiotemporal climate data. However, ESNs are noninterpretable black-box models along with other neural networks. The lack of model transparency poses a hurdle for understanding variable relationships. We address this issue by developing feature importance methods for ESNs in the context of spatiotemporal data to quantify variable relationships captured by the model. We conduct a simulation study to assess and compare the feature importance techniques, and we demonstrate the approach on reanalysis climate data. In the climate application, we consider a time period that includes the 1991 volcanic eruption of Mount Pinatubo. This event was a significant stratospheric aerosol injection, which acts as a proxy for an anthropogenic stratospheric aerosol injection. Furthermore, we are able to use the proposed approach to characterize relationships between pathway variables associated with this event that agree with relationships previously identified by climate scientists.

black-box models↗
DOE OSTI2018

Morphology Characterization of Bulk Heterojunction Solar Cells

Abstract Revealing the nanostructure of bulk‐heterojunction (BHJ) photovoltaic blends is a critical task in the field of organic photovoltaics. The complicated morphology, ranging from binary blends to ternary mixtures, shows quite varied structural details that need to be fully characterized in terms of correlating them with device performance. The nonequilibrium nature of the BHJ thin‐film morphology makes the structure optimization difficult, and fundamental principles that relate to chemical structure, processing methods, and device performances have not yet been fully understood. On the other hand, various high‐powered techniques ranging from in‐house instrumentation to synchrotron techniques have been applied to this tremendously difficult research topic. More structural details, including length scale of phase separation, structural ordering, chain/molecular orientation, surface topology, etc., can be obtained in a quantitative manner, which finally allows the structure–property relationships to be determined. Here, the most important characterization techniques in understanding the morphology of BHJ solar cells are summarized, targeting chemists and materials scientists.

Song, Jingnan↗
DOE OSTI2019

Rainfall trends in the African Sahel: Characteristics, processes, and causes

Abstract Sahel rainfall is dynamically linked to the global Hadley cell and to the regional monsoon circulation. It is therefore susceptible to forcings from remote oceans and regional land alike. Warming of the oceans enhances the stability of the tropical atmosphere and weakens deep ascent in the Hadley circulation. Warming of the Sahara and of the nearby oceans changes the structure and position of the regional shallow circulation and allows more of the intense convective systems that determine seasonal rain accumulation. These processes can explain the observed interannual to multidecadal variability. Sea surface temperature anomalies were the dominant forcing of the drought of the 1970s and 1980s. In most recent decades, seasonal rainfall amounts have partially recovered, but rainy season characteristics have changed: rainfall is more intense and intermittent and wetting is concentrated in the late rainy season and away from the west coast. Similar subseasonal and subregional differences in rainfall trends characterize the simulated response to increased greenhouse gases, suggesting an anthropogenic influence. While uncertainty in future projections remains, confidence in them is encouraged by the recognition that seasonal mean rainfall depends on large‐scale drivers of atmospheric circulations that are well resolved by current climate models. Nevertheless, observational and modeling efforts are needed to provide more refined projections of rainfall changes, expanding beyond total accumulation to metrics of intraseasonal characteristics and risk of extreme events, and coordination between climate scientists and stakeholders is needed to generate relevant information that is useful even under deep uncertainty. This article is categorized under: Paleoclimates and Current Trends > Modern Climate Change

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2022

Metrics as tools for bridging climate science and applications

In climate science and applications, the term “metric” is used to describe the distillation of complex, multifaceted evaluations to summarize the overall quality of a model simulation, or other data product, and/or as a means to quantify some response to climate change. Metrics provide insights into the fidelity of processes and outcomes from climate models and can assist with both differentiating models' representation of variables or processes and informing whether models are “fit for purpose.” Metrics can also provide a valuable reference point for co-production of knowledge between climate scientists and climate impact practitioners. Although continued metric developments enable model developers to better understand the impacts of decisions made in the model design process, metrics also have implications for the characterization of uncertainty and facilitating analyses of underlying physical processes. As a result, comprehensive evaluation with multiple metrics enhances usability of climate information by both scientific and stakeholder communities. Here, this paper presents examples of insights gained from the development and appropriate use of metrics, and provides examples of how metrics can be used to engage with stakeholders and inform decision-making.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2019

Comparing the Efficiency of In Situ Visualization Paradigms at Scale

This work compares the two major paradigms for doing in situ visualization: in-line, where the simulation and visualization share the same resources, and in-transit, where simulation and visualization are given dedicated resources. Our runs vary many parameters, including simulation cycle time, visualization frequency, and dedicated resources, to study how tradeoffs change over configuration. In particular, we consider simulations as large as 1,024 nodes (16,384 cores) and dedicated visualization resources with as many as 512 nodes (8,192 cores). We draw conclusions about when each paradigm is superior, such as in-line being superior when the simulation cycle time is very fast. Surprisingly, we also find that in-transit can minimize the total resources consumed for some configurations, since it can cause the visualization routines to require fewer overall resources when they run at lower concurrency. For example, one of our scenarios finds that allocating 25% more resources for visualization allows the simulation to run 61% faster than its in-line comparator. Finally, we explore various models for quantifying the cost for each paradigm, and consider transition points when one paradigm is superior to the other. Our contributions inform design decisions for simulation scientists when performing in situ visualization.

Kress, James M.↗
DOE OSTI2020

ContourNet: Salient Local Contour Identification for Blob Detection in Fusion Plasma Simulation Data

We present ContourNet, a deep learning approach to identify salient local isocontours as blobs in large-scale 5D gyrokinetic tokamak simulation data. Blobs-regions of high turbulence that run along the edge wall down toward the diverter and can damage the tokamak-are non-well-defined features but have been empirically localized by isocontours in 2D normalized fluctuating density fields. The key of our study is to train ContourNet to follow the empirical rules to detect blobs over the time-varying simulation data. The architecture of ContourNet is a convolutional neural segmentation network: the inputs are the density field and a rasterized isocontour; the output is a set of isocontour encircling blobs. At the training stage, we feed the network with manually identified isocontours and propagated labels. At the inference stage, we extract isocontours from the segmented blob regions. Results show that our approach can achieve both high accuracy and performance, which enables scientists to understand the blob dynamics influencing the confinement of the plasma.

Blob detection↗