Search NASA⌕ Search

SEARCH · Search NASA

Results for “certification”

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 1,063 records · Page 59
All sourcesNASA NTRSDOE OSTI
DOE OSTI2022

Radiochronometric analysis of an historic Cs-137 activity standard

In this work, a 137 Cs activity standard discovered during routine inventory was analyzed to determine its model age using radiochronometry. The aqueous activity standard was separated using an established separation method that employs commercially available Sr resin. The method was also tested against a nuclear forensics reference material developed specifically for benchmarking 137 Cs radiochronometry methods. Results of the analyses showed good agreement between the results and the certified values of the reference material. Analysis of the discovered activity standard were also in good agreement with the activity certification date, though uncertainty was higher due to natural Ba contaminating the sample and the stable 133 Cs used as a carrier during the standard’s production.

38 RADIATION CHEMISTRY, RADIOCHEMISTRY, AND NUCLEA↗
DOE OSTI2016

Real-Time Considerations for Rugged Embedded Systems

This chapter introduces the characterizing aspects of embedded systems, and discusses the specific features that a designer should address to an embedded system “rugged”, i.e., able to operate reliably in harsh environments. The chapter addresses both the hardware and the less obvious software aspect. After presenting a current list of certifications for ruggedization, the chapters present a case study that focuses on the interaction of the hardware and software layers in reactive real-time system. In particular, it shows how the use of fast FPGA prototyping could provide insights on unexpected factors that influence the performance and thus responsiveness to events of a scheduling algorithm for multiprocessor systems that manages both periodic, hard real-time task, and aperiodic tasks. The main lesson is that to make the system “rugged”, a designer should consider these issues by, for example, overprovisioning resources and/or computation capabilities.

FPGA↗
DOE OSTI2020

Correlations between thermal history and keyhole porosity in laser powder bed fusion

Additive manufacturing has the potential to revolutionize the production of metallic components as it yields near net shape parts with complex geometries and minimizes waste. At the present day, additively manufactured components face qualification and certification challenges due to the difficulty in controlling defects. This has driven a significant research effort aimed at better understanding and improving processing controls – yielding a plethora of in-situ measurements aimed at correlating defects with material quality metrics of interest. In this work, we develop machine-learning methods to learn correlations between thermal history and subsurface porosity for a variety of print conditions in laser powder bed fusion. Un-normalized surface temperatures (in the form of black-body radiances) are obtained using high-speed infrared imaging and porosity formation is observed in the sample cross-section through synchrotron x-ray imaging. To demonstrate the predictive power of these features, we present four statistical machine-learning models that correlate temperature histories to subsurface porosity formation in laser fused Ti-6Al4V powder.

36 MATERIALS SCIENCE↗
DOE OSTI2021

Effects of spatial energy distribution-induced porosity on mechanical properties of laser powder bed fusion 316L stainless steel

Laser powder bed fusion (LPBF) additive manufacturing (AM) offers a variety of advantages over traditional manufacturing, however its usefulness for manufacturing of high-performance components is currently hampered by internal defects (porosity) created during the LPBF process that have an unknown impact on global mechanical performance. By inducing porosity distributions through variations in print energy density and inspecting the resulting tensile samples using computed tomography, nearly 50,000 pores across 75 samples were identified. Porosity characteristics were quantitatively extracted from inspection data and compared with mechanical properties to understand the strength of relationships between porosity and global tensile performance. Useful porosity characteristics were identified for prediction of part performance. Results indicate that ductility and strain at ultimate tensile strength are the global tensile properties most significantly impacted by porosity and can be predicted with reasonable accuracy using simple porosity shape descriptors such as volume, diameter, and surface area. Moreover, it was found that the largest pores influenced behavior most significantly. Specifically, pores in excess of 125 µm in diameter were found to be a sufficient threshold for property estimation. These results establish an initial understanding of the complex defect-performance relationship in AM 316L stainless steel and can be leveraged to develop certification standards and improve confidence in part quality and reliability for the broader set of engineering alloys.

36 MATERIALS SCIENCE↗
DOE OSTI2024

ThermoPore: Predicting part porosity based on thermal images using deep learning

Part qualification is often a critical and labor-intensive process in additive manufacturing, particularly in the detection of defects such as porosity, which stands to benefit significantly from advancements in machine learning. We present a deep learning approach for quantifying and localizing ex-situ porosity within Laser Powder Bed Fusion fabricated samples utilizing in-situ thermal image monitoring data. Our goal is to build the real time porosity map of parts based on thermal images acquired during the build. The quantification task builds upon the established Convolutional Neural Network model architecture to predict pore count and the localization task leverages the spatial and temporal attention mechanisms of the novel Video Vision Transformer model to indicate areas of expected porosity. Our model for porosity quantification achieved a R 2 score of 0.57 and our model for porosity localization produced an average Intersection over Union (IoU) score of 0.32 and a maximum of 1.0. This work is setting the foundations of part porosity “Digital Twins” based on additive manufacturing monitoring data and can be applied downstream to reduce time-intensive post-inspection and testing activities during part qualification and certification. In addition, we seek to accelerate the acquisition of crucial insights normally only available through ex-situ part evaluation by means of machine learning analysis of in-situ process monitoring data.

Deep learning↗
DOE OSTI2017

Mechanistic materials modeling for nuclear fuel performance

Fuel performance codes are critical tools for the design, certification, and safety analysis of nuclear reactors. However, their ability to predict fuel behavior under abnormal conditions is severely limited by their considerable reliance on empirical materials models correlated to burn-up (a measure of the number of fission events that have occurred, but not a unique measure of the history of the material). In this paper, we propose a different paradigm for fuel performance codes to employ mechanistic materials models that are based on the current state of the evolving microstructure rather than burn-up. In this approach, a series of state variables are stored at material points and define the current state of the microstructure. The evolution of these state variables is defined by mechanistic models that are functions of fuel conditions and other state variables. The material properties of the fuel and cladding are determined from microstructure/property relationships that are functions of the state variables and the current fuel conditions. Multiscale modeling and simulation is being used in conjunction with experimental data to inform the development of these models. Finally, this mechanistic, microstructure-based approach has the potential to provide a more predictive fuel performance capability, but will require a team of researchers to complete the required development and to validate the approach.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
DOE OSTI2019

Review of environmental metrics used across multiple sectors and geographies to evaluate the effects of hydropower development

In this paper, as U.S. hydropower development continues to expand, a thorough inventory of potentially relevant environmental metrics will provide a comprehensive and transparent starting point for stakeholders seeking to evaluate the sustainability and environmental effects of this anticipated expansion. To address this need, we conducted a literature review to extract and analyze a swath of environmental metrics related to previous hydropower projects. Our examination of several U.S. Federal Energy Regulatory Commission licensing documents, several certification documents prepared by the Low Impact Hydropower Institute and the International Hydropower Association, and a set of peer-reviewed journal articles led to creation of a database of > 3000 current environmental metrics related to hydropower projects in 231 locations worldwide. Information recorded for each environmental metric was used to map the comprehensiveness and consistency of environmental measurements gathered across primary categories, dam life cycle stages, spatial scales and locations. We found that a majority of environmental metrics were related to water quantity and water quality, a large number of metrics were related to biota and biodiversity, and relatively few metrics were related to the categories of geomorphology, land cover, connectivity and fragmentation, and infrastructure and design. However, the categories of environmental metrics represented in each type of literature varied significantly. Forty-five subcategories of environmental metrics emerged from our analysis, thereby suggesting a potential envelope of environmental measurements that might be used to improve efficiency in evaluating the potential environmental effects of new U.S. hydropower projects.

13 HYDRO ENERGY↗
DOE OSTI2015

Investigation of a tripropylene-glycol monomethyl ether and diesel blend for soot-free combustion in an optical direct-injection diesel engine

Natural luminosity and chemiluminescence imaging diagnostics were employed to investigate if a 50/50 blend by volume of tripropylene-glycol monomethyl ether (TPGME) and ultra-low sulfur #2 diesel certification fuel (CF) could enable leaner-lifted flame combustion (LLFC), a non-sooting mode of mixing-controlled combustion associated with equivalence ratios below approximately 2. The experiments were performed in a singlecylinder heavy-duty optical compression-ignition engine at three injection pressures and three dilution levels. Results indicate that TPGME addition effectively eliminated engine-out smoke emissions by curtailing soot production and/or increasing soot oxidation during and after the end of fuel injection. TPGME greatly reduced soot luminosity when compared with neat CF, but did not enable LLFC because the equivalence ratios at the lift-off length, $\phi$(H), never reached the non-sooting limit. Nevertheless, this study showed that TPGME addition has the potential to enable LLFC under different experimental conditions that would further decrease $\phi$(H) to ~ 2 and below. Concerning other engine-out emissions, injection pressure influenced the effects of TPGME addition on NO x emissions. Finally, HC and CO emissions were higher compared to baseline fuel likely due to the lower net heat of combustion of TPGME and the need to limit fuel-injection duration for valid optical measurements.

33 ADVANCED PROPULSION SYSTEMS↗
DOE OSTI2020

Characterization of 244 Cm Neutron Sources

There is a need to find a replacement for the 252 Cf sources currently used in Standards-based performance testing of neutron instrumentation. This is a result of its relatively short half-life, concerns about future availability and recent increases in cost. A potential replacement source, 244 Cm is evaluated in this study where two commercially available sources have been acquired and their neutron emission spectra measured using a pair of spectrometers. Both instruments showed the 244 Cm spontaneous neutron spectrum to be fully consistent with a MCNP6®-calculated spectrum using published Watt fission parameters for 244 Cm. In addition, the emission rate of the weaker 244 Cm source was established through a direct count rate comparison against a calibrated and similarly encapsulated 252 Cf source. The 244 Cm source emission rate was found to be in excellent agreement with value stated on the manufacturer's source certificate. It is concluded that 244 Cm would be an ideal replacement for 252 Cf based on its longer half-life (18.1 y) and its essentially identical neutron emission spectrum. In addition, 244 Cm sources are commercially available at reasonable cost.

244Cm↗
DOE OSTI2022

Evaluation of artificial neural network performance for classification of potato plants infected with potato virus Y using spectral data on multiple varieties and genotypes

Potato virus Y (Potyviridae, PVY) is a plant virus that poses a significant threat to potato producers on a global basis. The pathogen has disrupted seed potato supplies and negatively impacted yield and quality of commercial potato crops. The potato industry currently manages PVY infection levels via insecticide applications, regional seed certification programs that rely on field scouting to visually assess individual plants for infection status, and destructive and costly tissue sampling coupled with laboratory assays. Despite these efforts, PVY continues to confound potato industry stakeholders resulting in economic harm. Remote sensing and machine learning provide for the development of new tools to more accurately detect and spatially quantify PVY-infected plants versus the current state of the art. However, there is a need to understand how the occurrence of many different potato varieties impact the dynamics of developing models to detect potato plants impacted with PVY and their potential effectiveness. This study evaluates classification modelling outcomes using spectral datasets collected in different temporal and spatial environments (greenhouse and a production field) on multiple potato varieties consisting of labelled instances of plants infected with PVY and those not infected with the virus. A modelling framework was developed to support iterative modelling runs using artificial neural network (ANN) architectures configured as binary classifiers to develop sample populations to support statistical analysis on model performance using specific spectral subsets. When using spectral data to detect PVY-infected plants, ANN models achieved the highest mean accuracy of 0.894 on a single variety. Conversely, the same ANN model architecture only achieved a mean accuracy of 0.575 on a spectral data set representing 29 potato breeding lines. Additionally, statistical analysis indicates spectral regions including the red edge, near infrared and shortwave infrared contain more important spectral features for the ANN classifier introduced in this research.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2017

Overexpression of microRNA-125b inhibits human acute myeloid leukemia cells invasion, proliferation and promotes cells apoptosis by targeting NF-κB signaling pathway

microRNA-125b has been reported to play an novel biological function in the progression and development of several kinds of leukemia. However, the detail role of miR-125b in acute myeloid leukemia (AML) is remains largely unknown. The present study aimed to investigate the biological role of miR-125b in AML and the potential molecular mechanism involved in this process. Our results showed that overexpression of miR-125b suppressed AML cells proliferation, invasion and promotes cells apoptosis in a dose-dependent manner, while the miR-NC did not show the same effect. In addition, miR-125b induced AML cells G2/M cell cycle arrest in vitro. Overexpression of miR-125b resulted in a significant decrease of the expression of p-IκB-α and inhibition of IκB-α degradation, and the nuclear translocation of NF-κB subunit p65 was abrogated by miR-125b simutaneously. To further verify that miR-125b targeted NF-κB signaling pathway, the NF-κB-regulated downstream genes that were associated with cell cycle arrest and apoptosis was also determined. The results showed that, miR-125b also affect NF-κB-regulated genes expression involved in cell cycle arrest and apoptosis. In conclusion, the present work certificates that miR-125b can significantly inhibit human AML cells invasion, proliferation and promotes cells apoptosis by targeting the NF-κB signaling pathway, and thus it can be viewed as an promising therapeutic target for AML. - Highlights: • Overexpression of miR-125b suppressed AML cells proliferation, invasion and promotes cells apoptosis. • miR-125b induced AML cells G2/M cell cycle arrest in vitro. • miR-125b suppressed AML cells tumorigenicity and promoted cells apoptosis by targeting NF-κB pathway.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2020

A multiscale cohesive law for carbon fiber networks

Better predictive models of mechanical failure in low-weight heat shield composites would aid material certification for missions with aggressive atmospheric entry conditions. In this study, we develop such a model for the rapid engineering analysis of the failure limits of phenolic impregnated carbon ablator (PICA) - a leading heat shield material whose structural component is a carbon fiber network. We hypothesize inelastic deformation failure mechanisms and model their behavior using molecular dynamics simulations to calculate the binding energy. We then upscale this binding energy to the macroscale using a renormalization argument. The approach delivers insightful and reasonably accurate macroscale predictions that compare favorably to experiments. In application, the model is validated for a particular variety of PICA by comparison to experiment and would then be used to study design scenarios in different entry conditions.

36 MATERIALS SCIENCE↗
DOE OSTI2020

Autoignition of CRC diesel surrogates at low temperature combustion conditions: Rapid compression machine experiments and modeling

As federal programs require increasingly stringent engine emissions and fuel economy standards, these ambitions can only be met if next-generation combustion technology is developed focusing on high-efficiency and low-emissions engines. Recent research has indicated the need to operate engines at higher compression ratios and with low temperature combustion (LTC) to achieve the needed gains in engine efficiency and reductions in emissions. Because there is a lack of understanding of the chemistry of diesel fuel components and their mixtures at these LTC conditions, this limits the ability to develop predictive chemical kinetic models that can be used to optimize engine combustion. The current study aims to fill in gaps in fundamental combustion data on surrogate fuel mixtures relevant to diesel fuels. Specifically, four multicomponent diesel surrogates formulated by the Coordinating Research Council (CRC) to emulate an ultra-low-sulfur research-grade #2 certification diesel fuel (CFA), namely V0a (4 components), V0b (5 components), V1 (8 components), and V2 (9 components), have been investigated in a rapid compression machine (RCM) through determination of total and first-stage ignition delay times. Autoignition characteristics of lean to rich fuel/O 2 /N 2 mixtures, for the four CRC surrogates and CFA, have been measured using an RCM at LTC relevant pressures and temperatures, in the ranges of 10–20 bar and 650–1000 K, respectively. The equivalence ratios have been varied by independently changing the oxygen mole fraction and the fuel mole fraction in the test mixtures, thereby illustrating the individual effects of oxygen concentration and fuel loading on diesel autoignition. Autoignition results of these four CRC surrogates are compared among them and with those of CFA. Some degree of agreement in autoignition response between each CRC surrogate and CFA is observed, while discrepancies are also identified and discussed. In addition, a detailed chemical kinetic model for diesel surrogates has been developed and validated against these newly-acquired RCM data. We find that this model shows reasonable agreement with the overall ignition delay time results of the current RCM experiments. Chemical kinetic analyses of the developed model were further conducted to help identify the reactions controlling the autoignition processes and the consumption of fuel components in CRC surrogates.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2017

Examining professional emergency managers in Korea

Although the number of emergency managers has risen in South Korea (hereafter referred to as Korea) over the years, their role is not yet as defined and noteworthy compared to other professions because of its unidisciplinary approach. This article investigates how Korea has to improve emergency managers' disciplinary approach to ultimately contribute to the goal of effective transnational disaster management. This study uses qualitative content analysis of government policies, college curricula, nongovernmental organizations' (NGOs') emergency-manager certification, and mass media coverage to compare emergency managers' unidisciplinary and multidisciplinary approaches. The key tenet is that Korea must change its emergency managers' unidisciplinary approach into a multidisciplinary approach because the former is less effective when dealing with complicated disaster management systems. To achieve this change, the stakeholders must carry out their assigned responsibilities under risk-oriented management. As for the study's international implications, developing nations may consider the enhancement of related educational curricula, collaborative learning, continuous evaluation, disaster awareness, and disaster prevention for the emergency managers' multidisciplinary approach.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2022

Policy choices and outcomes for offshore wind auctions globally

Offshore wind energy is rapidly expanding, facilitated largely through auctions run by governments. We provide a detailed quantified overview of utilised auction schemes, including geographical spread, volumes, results, and design specifications. Our comprehensive global dataset reveals heterogeneous designs. Although most auction designs provide some form of revenue stabilisation, their specific instrument choices vary and include feed-in tariffs, one-sided and two-sided contracts for difference, mandated power purchase agreements, and mandated renewable energy certificates. We review the schemes used in all eight major offshore wind jurisdictions across Europe, Asia, and North America and evaluate bids in their jurisdictional context. We analyse cost competitiveness, likelihood of timely construction, occurrence of strategic bidding, and identify jurisdictional aspects that might have influenced auction results. We find that auctions are embedded within their respective regulatory and market design context, and are remarkably diverse, though with regional similarities. Auctions in each jurisdiction have evolved and tend to become more exposed to market price risks over time. Less mature markets are more prone to make use of lower-risk designs. Still, some form of revenue stabilisation is employed for all auctioned offshore wind energy farms analysed here, regardless of the specific policy choices. Our data confirm a coincidence of declining costs and growing diffusion of auction regimes.

17 WIND ENERGY↗
DOE OSTI2024

Short-Run Marginal Emission Factors Neglect Impactful Phenomena and are Unsuitable for Assessing the Power Sector Emissions Impacts of Hydrogen Electrolysis

This comment reacts to Ruhnau and Schiele's (2023) assessment of the cost and emissions impacts of electrolytic hydrogen production operating under different green hydrogen certification requirements in the EU. We critique the paper's use of short-run marginal emissions rates to estimate emissions impacts, a methodology which the literature has shown to be inadequate for assessing the full lifecycle emissions impacts of electricity sector interventions. We hope that our response clarifies the need to consider induced structural change when assessing the greenhouse gas emissions impacts of electricity sector decisions at all scales.

electricity↗