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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 721 records · Page 40
All sourcesNASA NTRSDOE OSTI
DOE OSTI2011

Adsorption of Potassium on the MoS2(100) Surface: A First-Principles Investigation

Periodic density functional theory calculations were performed to investigate the interaction that potassium with the Mo and S edges of the MoS2(100) surface. Both neutral and cationic (+1) charged potassium-promoted systems at different sulfur coverages were considered. Our calculations indicate that the potassium atom readily donates its single 4s valence electron to the MoS2 structure for the neutral potassium-promoted system, and the neutral and cationic potassium-promoted systems demonstrate a similar adsorption behavior. Moreover, potassium changes the magnetic properties known to occur at the metallic edge surface, which have implications for electron spin dependent surface characterization methods (i.e., electron spin/paramagnetic spectroscopy). Potassium in both the neutral and cationic systems tends to maximize its interactions with the available sulfur atoms at the edge surface, preferring sites over four-fold S hollows on fully sulfided Mo and S edges and over the interstitial gap where two to four edge surface S atoms are available for coordination. As the potassium coverage increases, the adsorption energy per potassium atom, surface work function, and transfer of the K 4s electron to the MoS2(100) surface decreases, which is in line with an increased metallization of the potassium adlayer. The potassium adlayer tends to form chains along the interstitial with K-K distances ~1 Å, which is notably less than those of bulk bcc K metal (4.61 Å). Density of states for the potassium-saturated surface suggests enhanced involvement of broad K 3d states beginning just above the Fermi level. Potassium-promotion of MoS2(100) has implications for alcohol catalysis: increasing the surface basicity by increasing the electron charge of the surface, providing hydrogenation-promoting CO site, blocking edge surface that dissociate CO and lead to methanation, and limiting H2 dissociative adsorption to the edge surface and possibly inhibiting the H2 dissociative adsorption via s character electron repulsion. This research was performed in part using the Molecular Science Computing Facility in the William R. Wiley Environmental Molecular Sciences Laboratory, a U.S. Department of Energy (DOE) national scientific user facility located at the Pacific Northwest National Laboratory (PNNL). PNNL is operated by Battelle for DOE.

10 SYNTHETIC FUELS↗
DOE OSTI2012

Building Agent™ Software (BA) [SWRs 12-05 and 17-21]

Energy interfaces provide a new level of motivational information. Many studies have reported energy savings after dashboards for operators are installed, but these provide little actionable information to the occupant and the occupant cannot report individual comfort. The Building Agent™ (BA), in development at the National Renewable Energy Laboratory (NREL), provides an interface for the occupant to converse with the building control system and aims to actively engage and affect occupant behavior to achieve positive energy outcomes. Energy savings achieved via occupant behavioral changes could be as high as 25%, and could rival savings achieved by technological measures. For example, energy audits often show that more than 50% of the energy used in a building is consumed at night. Occupants leaving equipment and lighting on after they leave work is a large contributing factor. Similar savings opportunities have been identified when occupants were away from their desks 50% of the time, while most of their computers, lights, and other equipment remained powered on. Building Agent aims to create cohesion between building automation system (BAS) data, local measurements, and occupant feedback to provide visualizations, empowering occupants and building engineers to take diagnostic, proactive, energy-saving, and comfort-improving actions. The multilayer BA architecture consists of central server, measurement, and distributed application layers. The server layer aggregates global and localized measurements with occupant feedback to correlate perceived comfort with actual data throughout the building. The measurement layer complements BAS data with localized, environmental condition measurements at workstations. The application layer provides a bidirectional communications mechanism between the building and the occupants that accepts comfort feedback and provides energy performance information. The interface layer also uses three displays to influence building energy use: empathetic and personalized gauges, graphical data, and competitive rankings. Building Agent was implemented at NREL in the Research Support Facility (RSF), a LEED Platinum and Net Zero office building, which has received many awards. The constant feedback from occupants has enabled BA to better enhance the BAS data controls. Continued work is being done on BA to increase visualization of data and enhanced the user experience.

Long, Nicholas↗
DOE OSTI2018

Two-Scale 13 C Metabolic Flux Analysis for Metabolic Engineering

Accelerating the Design-Build-Test-Learn (DBTL) cycle in synthetic biology is critical to achieving rapid and facile bioengineering of organisms for the production of, e.g., biofuels and other chemicals. The Learn phase involves using data obtained from the Test phase to inform the next Design phase. As part of the Learn phase, mathematical models of metabolic fluxes give a mechanistic level of comprehension to cellular metabolism, isolating the principle drivers of metabolic behavior from the peripheral ones, and directing future experimental designs and engineering methodologies. Furthermore, the measurement of intracellular metabolic fluxes is specifically noteworthy as providing a rapid and easy-to-understand picture of how carbon and energy flow throughout the cell. Here, we present a detailed guide to performing metabolic flux analysis in the Learn phase of the DBTL cycle, where we show how one can take the isotope labeling data from a 13 C labeling experiment and immediately turn it into a determination of cellular fluxes that points in the direction of genetic engineering strategies that will advance the metabolic engineering process.For our modeling purposes we use the Joint BioEnergy Institute (JBEI) Quantitative Metabolic Modeling (jQMM) library, which provides an open-source, python-based framework for modeling internal metabolic fluxes and making actionable predictions on how to modify cellular metabolism for specific bioengineering goals. It presents a complete toolbox for performing different types of flux analysis such as Flux Balance Analysis, 13 C Metabolic Flux Analysis, and it introduces the capability to use 13 C labeling experimental data to constrain comprehensive genome-scale models through a technique called two-scale 13 C Metabolic Flux Analysis (2S- 13 C MFA) [1]. In addition to several other capabilities, the jQMM is also able to predict the effects of knockouts using the MoMA and ROOM methodologies. The use of the jQMM library is illustrated through a step-by-step demonstration, which is also contained in a digital Jupyter Notebook format that enhances reproducibility and provides the capability to be adopted to the user's specific needs. As an open-source software project, users can modify and extend the code base and make improvements at will, providing a base for future modeling efforts.

13C Metabolic flux analysis↗
DOE OSTI2022

Implement and Test 3D Mortar Contact in BISON

We leverage the extension of the generation of mortar segment meshes to three dimensions in MOOSE’s framework to extend thermomechanical modeling capabilities to problems with three dimensions. A modular approach to gap heat transfer physics using the mortar finite element method was created and documented, mechanical contact was extended to three dimensions—including frictional behavior, performance and ease of use were improved, and steps towards scalability of solid mechanics problems involving contact were taken. Many of these new developments are demonstrated in the simulation of 3D light-water reactor (LWR) problems, where the thermomechanical interface problem is solved using the mortar finite element method. Usage of the mortar framework has improved convergence in 2D problems and has enabled employing friction in 3D problems, of which we show results of a short, local stack of 3D pellets. Consequently, the benefits of mortar in terms of solution convergence and quality are extended to three dimensions. Section 2 discusses fundamental developments that enabled the simulation of practical mortar problems in three dimensions and other general improvements, including the reduction of the derivative container size, the modification of dual basis computations when edge dropping (lack of secondary element projection) takes place, the improvement of conditioning when employing the VCP in-edge dropping conditions, and code usability and quality improvements. These latter code enhancements include the migration of tests using “old” mortar contact constraints to using dual mortar with a semi-smooth Newton solution strategy and the reuse of lower dimensional domains for straightforwardly setting up a mortar thermomechanical LWR problem, i.e. the MOOSE action is employed for mechanical contact and the thermal LWR action is employed to capture the gas conductance, contact, and radiation components of gap heat transfer physics. Independently of the mortar LWR thermal action, we developed a modular approach to gap heat transfer that resides in MOOSE and can be leveraged, e.g., in metallic fuel problems. This approach, whose code design based on MOOSE’s user objects to model specific physics was proposed by the maintenance activity, is detailed in Section 3. Based on the dual mortar finite element method, the frictional contact constraints were extended to three dimensions. A block sheared in two directions in and out of contact with a rigid plane is employed in Section 4 to show the way the approach handles changes in frictional states (e.g. stick to slip) within a competitive number of Newton iterations. Equations and numerical results on the use of the VCP with Cartesian Lagrange multipliers, whose combination enables their direct condensation, are described in Section 5.3. Two-dimensional and three-dimensional BISON LWR simulations are discussed in Section 6. Particularly, a stack of five eccentric pellets with a surface defect is simulated and the effect of pellet-cladding friction is assessed. Finally, conclusions are outlined in Section 7.

42 ENGINEERING↗
DOE OSTI2015

Photochemical Outcomes of Adsorbed Oxygen: Desorption, Dissociation and Passivation by Coadsorbed Water

A mixed Fe+Cr oxide surface was used to explore the photochemical fate of adsorbed O2 under ultrahigh vacuum (UHV) conditions. This mixed oxide surface possessed a magnetite-like (111) structure based on low energy electron diffraction (LEED), with its chemical behavior resembling that of Fe3O4(111). Oxygen adsorption at 40 K resulted in two chemisorption states, a strongly bound form that desorbed in temperature programmed desorption (TPD) at 230 K and a weakly bound form that evolved at 100 K. The former was assigned to charge transfer adsorption at Fe2+ sites and the latter to electrostatic binding at Fe3+ sites. A minority state of O2 was also detected in TPD at ~160 K, and tentatively assigned to adsorption at Cr3+ sites. The 230 K O2 state was the focus of photochemical studies employing four wavelengths of light from the red to the UV. Irrespective of wavelength, O2 molecules in the 230 K state preferentially photodesorbed when irradiated, with comparable rates across the visible and an order of magnitude greater in the UV. Also, approximately 10% of adsorbed O2 irreversibly photodissociated, irrespective of wavelength, with the resulting fragments blocking access to cation sites for subsequent O2 adsorption. Preadsorbed water also blocked O2 adsorption, but post-adsorbed water stabilized O2 at Fe2+ sites in TPD to 285 K. However, water-stabilized O2 molecules were insensitive to photodesorption. O2 photodissociation and photo-passivation both represent potentially adverse outcomes in the release of O2 during the heterogeneous water photooxidation reaction. The author thanks Drs. Sara Chamberlin and Scott Chambers for supplying the film used in this work. This work was supported by the US Department of Energy, Office of Science, Office of Basic Energy Sciences, Division of Chemical Sciences, Geosciences & Biosciences. Pacific Northwest National Laboratory (PNNL) is a multiprogram national laboratory operated for DOE by Battelle. The research reported here was performed using EMSL, a national scientific user facility sponsored by the Department of Energy's Office of Biological and Environmental Research and located at Pacific Northwest National Laboratory.

mixed oxide, photodesorption, oxygen, temperature ↗
DOE OSTI2020

Deep Learning for Automated Identification of Eels in Sonar Data

Freshwater eels, such as the American eel (Anguilla rostrata) present numerous challenges related to safe downstream fish passage at hydroelectric facilities. One of those challenges is effective monitoring of their abundance, movements, and behavior to facilitate design and operation of eel protection and passage facilities. A previous EPRI study documented the ability of human analysts to reliably identify American eels in data obtained with a 1100/1800 kHz, multibeam sonar. This report describes a project to develop deep learning (a subset of artificial intelligence) tools to automate the time-consuming, subjective process of eel identification in multibeam sonar data. The project exploited new data collected in the laboratory and the existing data from the prior EPRI field study to develop and test deep learning and other data analytic tools, including wavelet filtering, differencing for static object removal, and convolutional neural network analysis. The analysis of the laboratory data demonstrated feasibility of the approach, revealed object characteristics observed with the sonar that distinguish eels from similarly sized and shaped acoustic targets, and provided additional data for algorithm selection and training. Deep learning algorithms trained and tested on the laboratory data alone achieved accuracy rates of greater than 98% when classifying acoustic images of eels and similar-sized neutrally buoyant sticks. The algorithm trained and tested on the pre-existing field data alone, and yielded classification accuracy of 9.3% false positives and 13.3% false negatives when distinguishing between eels and sticks/PVC pipes based on video clips (i.e., multiple, consecutive images). This performance is comparable to the classification accuracy achieved by human analysts in the prior study. The deep learning algorithm trained on a combination of video clips obtained in the laboratory and the field and tested on video clips from the field, was able to distinguish eels from sticks and PVC pipes (a river debris analog) of similar size with 100% accuracy. Outreach to the hardware, software, and end-user communities early in the project helped to identify needs and specify the application space. Outreach to those communities at the end of the project communicated project results and opportunities for further development. The project achieved proof of concept for automated identification of eel in multibeam sonar data. Future work should focus on acquisition of additional data for more robust algorithm training and testing; modification of the software tools to accommodate multiple acoustic targets in the acoustic field at a given time; identification of additional object classes; incorporation of motion in the object identification and classification algorithms; operationalizing the software tools, including integration with other existing sonar data analysis tools; and partnering with hardware and software providers for distribution of the software tools with their commercial products.

13 HYDRO ENERGY↗
DOE OSTI2026

SAM Code Enhancements for Fission Product Tracking of Noble Gases and Metals in MSRs

This report documents fiscal year 2026 enhancements to the System Analysis Module (SAM) for modeling fission product transport in liquid-fueled molten salt reactors (MSRs). The work advances three principal areas: noble gas transport, noble metal deposition, and user interface improvements. The noble gas transport capability integrates drift-flux gas transport, Henry’s law two-film interphase mass transfer with pressure-based nucleation suppression, Knudsen-regime pore diffusion into porous graphite with a conjugate salt-graphite interface constraint, built-in material properties, five Sherwood-number mass transfer correlations including three derived from high-fidelity NekRS simulations, and xenon-135 reactivity feedback through SAM’s point-kinetics model. This work also presents a comprehensive verification test suite, including new analytically verified cases for pressure-dependent onset of interphase gas transfer in a stagnant vertical pipe, a postulated FLiBe-graphite Xe extraction permeator, a gravity riser with a fission-product source, and a descending pipe with gas redissolution driven by hydrostatic pressure. A machine learning framework for bubble rise velocity prediction in molten salt systems is developed and benchmarked on molten-salt and diverse aqueous bubble datasets. The best-performing fine-tuned transfer-learning networks achieve an 82% reduction in RMSE relative to the Clift correlation, and is implemented directly in SAM. The noble metal transport capability is developed, including a liquid-wall deposition model and a gas-surface flotation mechanism that transfers insoluble particles entrained by sparging gas to wetted structures. Verification tests and demonstration cases cover the surface deposition, flotation efflux, and flotation shedding. Finally, a new [SpeciesTransport] input structure replaces positional global vectors with selfcontained, order-independent, named species blocks, simplifies the specification of multiphase species and decay chains, and remains fully compatible with existing SAM input files. Together, these developments improve the physical fidelity, verification basis, and usability of SAM for system-level analyses of fissionproduct behavior in MSRs.

Mui, Travis (ORCID:0000000303736470)↗
DOE OSTI2024

Innovating the next generation of commercial smart building software

Nearly 30% of commercial building energy use is wasted due to equipment faults and HVAC controls problems. The result is increased emissions, compromised comfort and productivity, and less reliable coordination of building power needs with a clean grid. The energy impact alone represents $17 billion in potential savings. Today’s smart building software provides a robust solution to address these operational deficiencies. Energy management and information systems (EMIS) are saving up to 9% on average, with two-year paybacks. They are being incorporated into energy management processes, commissioning services, and utility programs. As effective as they are, two barriers prevent even deeper benefits; limited personnel to fix problems once they are identified, and the expense and time to manually implement changes in control systems. In partnership with the research community, the EMIS industry is developing new capabilities to overcome these barriers. Moving beyond siloed products for either fault detection and diagnostics, or optimal control, these new capabilities empower users to not only automatically identify faults, but also to push corrective action, and control improvements to their buildings. In this paper, several areas for enhancements are documented: ‘one-time’ correction of faults such as setpoints, schedules, and economizer lockouts; short-term active testing for automated proportional integral derivative (PID) loop tuning and functional testing; and continuous supervisory control for demand flexibility and year-round efficiency. Results are presented from a pair of partner implementations out of a dozen providers integrating these enhancements into their products, including field tests from across the country, and insights into operator acceptance and integration into operations and maintenance practices.

Casillas, Armando↗
DOE OSTI2013

Structural and Spectroscopic Characterization of 17- and 18-Electron Piano-Stool Complexes of Chromium. Thermochemical Analyses of Weak Cr–H Bonds

The 17-electron radical CpCr(CO)2(IMe)• (IMe = 1,3-dimethylimidazol-2-ylidene) was synthesized by the reaction of IMe with [CpCr(CO)3]2, and characterized by single crystal X-ray diffraction and by EPR, IR and variable temperature 1H NMR spectroscopy. The metal-centered radical is monomeric under all conditions and exhibits Curie paramagnetic behavior in solution. An electrochemically reversible reduction to 18-electron CpCr(CO)2(IMe)- takes place at E½ = -1.89(1) V vs Cp2Fe+•/0 in MeCN, and was accomplished chemically with KC8 in THF. The salts K+(18-crown-6)[CpCr(CO)2(IMe)]- • ½THF and K+[CpCr(CO)2(IMe)]- • ¾THF were crystallographically characterized. Monomeric ion pairs are found in the former, whereas the latter has a polymeric structure due to a network of K∙∙∙O(CO) interactions. Protonation of K+(18-crown-6)[CpCr(CO)2(IMe)]- • ½THF gives the hydride CpCr(CO)2(IMe)H, which could not be isolated, but was characterized in solution; a pKa of 27.2(4) was determined in MeCN. A thermochemical analysis provides the Cr-H bond dissociation free energy (BDFE) for CpCr(CO)2(IMe)H in MeCN solution as 47.3(6) kcal mol-1. This value is exceptionally low for a transition metal hydride, and implies that the reaction 2 [Cr-H] → 2 [Cr•] + H2 is exergonic (ΔG = -9.0(8) kcal mol-1). This analysis explains the experimental observation that generated solutions of the hydride produce CpCr(CO)2(IMe)• (typically on the timescale of days). By contrast, CpCr(CO)2(PCy3)H has a higher Cr-H BDFE (52.9(4) kcal mol-1), is more stable with respect to H2 loss, and is isolable. We thank the U.S. Department of Energy, Office of Science, Office of Basic Energy Sciences, Division of Chemical Sciences, Geosciences and Biosciences for support. M.L.H. carried out the crystallographic studies and was supported as part of the Center for Molecular Electrocatalysis, an Energy Frontier Research Center funded by the U.S. Department of Energy, Office of Science, Office of Basic Energy Sciences. The EPR studies were performed at the William R. Wiley Environmental Molecular Sciences Laboratory (EMSL), a national scientific user facility sponsored by the Department of Energy’s Office of Biological and Environmental Research located at PNNL. Pacific Northwest National Laboratory is a multiprogram national laboratory operated by Battelle for the U.S. Department of Energy.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2018

Bison Improvements for Robustness and Speed

Early in its development, the primary emphasis of efforts to improve the Bison fuel performance code were focused on initial development of new capabilities. As Bison has matured and become adopted by a wider set of users, it has become clear that a number of robustness issues needed to be addressed to ensure that the code can reliably produce a converged solution. This need became particularly evident when a recent effort by Westinghouse Electric Company to simulate all of the fuel rods in a reactor using Bison resulted in approximately 30% of the models failing to run to completion because of lack of convergence or other errors that were encountered. It is important to emphasize that there was no evidence that Bison generated results that were incorrect for the set of models exercised. In many cases, issues such as those encountered could be addressed by adjusting solver-related parameters. However, there is no question that for Bison to become widely adopted for production work, it needs to be inherently far more robust than it was. Because of this, a concerted effort was undertaken to improve Bison's robustness, or its ability to reliably obtain converged solutions under a wider variety of conditions. As a result of this work, a number of high-priority areas for improvement for modeling light water reactor (LWR) fuel rods have been identified and addressed. As a result of this work, the number of failing Bison analyses in the aforementioned set of simulations for a full core has been reduced to a fraction of a percent. In addition, the robustness of Bison models using the VERA core simulator has been significantly improved. This is important because all of the Bison models representing individual fuel rods must run to completion successfully for the full core model to run to completion. This report provides summaries of the identified sources of robustness issues in Bison and the development that was done to resolve these issues. It also summarizes the addition of new capabilities to address deficiencies in Bison's ability to include needed aspects of LWR fuel behavior, and improvements that were made to improve Bison's solution efficiency.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
DOE OSTI2025

Employing Eye Trackers to Reduce Nuisance Alarms

When process operators anticipate an alarm prior to its annunciation, that alarm loses information value and becomes a nuisance. This study investigated using eye trackers to measure and adjust the salience of alarms with three methods of gaze-based acknowledgement (GBA) of alarms that estimate operator anticipation. When these methods detected possible alarm anticipation, the alarm’s audio and visual salience was reduced. A total of 24 engineering students (male = 14, female = 10) aged between 18 and 45 were recruited to predict alarms and control a process parameter in three scenario types (parameter near threshold, trending, or fluctuating). The study evaluated whether behaviors of the monitored parameter affected how frequently the three GBA methods were utilized and whether reducing alarm salience improved control task performance. The results did not show significant task improvement with any GBA methods (F(3,69) = 1.357, p = 0.263, partial η 2 = 0.056). However, the scenario type affected which GBA method was more utilized (X 2 (2, N = 432) = 30.147, p < 0.001). Alarm prediction hits with gaze-based acknowledgements coincided more frequently than alarm prediction hits without gaze-based acknowledgements (X 2 (1, N = 432) = 23.802, p < 0.001, OR = 3.877, 95% CI 2.25–6.68, p < 0.05). Participant ratings indicated an overall preference for the three GBA methods over a standard alarm design (F(3,63) = 3.745, p = 0.015, partial η 2 = 0.151). This study provides empirical evidence for the potential of eye tracking in alarm management but highlights the need for additional research to increase validity for inferring alarm anticipation.

99 - GENERAL AND MISCELLANEOUS↗
DOE OSTI2013

Standardizng Data

To enable the rational design of nanomaterials for improved efficacy and safety, it is critical to understand and exploit the physicochemical properties that drive biological behavior (Morris, 2010). Data mining and computer simulation are essential for deriving information about nanomaterial behavior; however, the datasets needed to support such studies are sparse and stored across a variety of repositories and resources. Schrurs and Lison (2012) have expressed the need for more coherence and structure in the conduct of nanotechnology research. Additionally, the lack of common reporting standards and non-uniformity of information reported have proven to be significant barriers to such data sharing and re-use. The Nanotechnology Working Group (Nano WG), of the US National Institutes of Health National Cancer Informatics Program, has been focused on addressing these barriers. The Nano WG - which includes representatives from over 20 organizations including government agencies, academia, industry, standards organizations, and alliances -has developed ISA-TAB-Nano (Thomas et al, 2013), a general framework for representing and integrating diverse types of data related to the description and characterization of nanomaterials. Recognizing that nanoparticle characterization studies have many of the same challenges as ‘omics-based assays, the Nano WG joined the ISA Commons (Sansone et al., 2012) to leverage lessons learned in ‘omics data sharing. The ISA Commons community brings together 50 collaborators at over 30 scientific organizations around the globe, including regulatory and industrial participants in an increasingly diverse set of life science domains. At the core of the ISA Commons is the ISA metadata tracking framework which forms the basis for the ISA-TAB-Nano extension. The extension of the ISA framework to nanotechnology domain illustrates the power of a synergistic approach that seeks the interoperability of data across multiple research disciplines. To increase adoption, especially in the commercial arena from vendors and manufactures, the ISA-Tab-Nano data-sharing specification has also been submitted for consideration as a standard to the American Society for Testing and Materials (ASTM). Delivering a community-driven specification is only the first phase of the process. To be useful and used, ISA-TAB-Nano must be implemented by tools and databases to assist researchers in reporting their data accordingly, shielding them from unnecessary complexity. Our next step is to extend the open source ISA Software Suite to provide user-oriented tools for the collection, curation, and storage of data compliant with the ISA-TAB-Nano specification. Future work will also focus on the application of the ISA-TAB-Nano format to support emerging standards on minimal information about nanomaterials in biological research (Ostraat et al, 2012; MinChar). ISA-TAB-Nano development is a community-driven effort and we welcome new contributions, collaborations and domain expertise. We invite researchers, software developers, vendors, and other stakeholders to work with us to implement the ISA-Tab-Nano format in their existing systems and research. Likewise, we welcome engagement of regulators, funding agencies, editors, and other policy makers to discuss how this standard can be used to facilitate the sharing and reuse of nanotechnology data across a wide range of disciplines. More information about the ISA-TAB-Nano project can be found online at https://wiki.nci.nih.gov/display/ICR/ISA-TAB-Nano.

informatics↗
DOE OSTI2023

An Analytic Formula for Entraining CAPE in Midlatitude Storm Environments

Abstract This article introduces an analytic formula for entraining convective available potential energy (ECAPE) with an entrainment rate that is determined directly from an environmental sounding, rather than prescribed by the formula user. Entrainment is connected to the background environment using an eddy diffusivity approximation for lateral mixing, updraft geometry assumptions, and mass continuity. These approximations result in a direct correspondence between the storm-relative flow and the updraft radius and an inverse scaling between the updraft radius squared and entrainment rate. The aforementioned concepts, combined with the assumption of adiabatic conservation of moist static energy, yield an explicit analytic equation for ECAPE that depends entirely on state variables in an atmospheric profile and a few constant parameters with values that are established in past literature. Using a simplified Bernoulli-like equation, the ECAPE formula is modified to account for updraft enhancement via kinetic energy extracted from the cloud’s background environment. CAPE and ECAPE can be viewed as predictors of the maximum vertical velocity w max in an updraft. Hence, these formulas are evaluated using w max from past numerical modeling studies. Both of the new formulas improve predictions of w max substantially over commonly used diagnostic parameters, including undiluted CAPE and ECAPE with a constant prescribed entrainment rate. The formula that incorporates environmental kinetic energy contribution to the updraft correctly predicts instances of exceedance of by w max , and provides a conceptual explanation for why such exceedance is rare among past simulations. These formulas are potentially useful in nowcasting and forecasting thunderstorms and as thunderstorm proxies in climate change studies. Significance Statement Substantial mixing occurs between the upward-moving air currents in thunderstorms (updrafts) and the surrounding comparatively dry environmental air, through a process called entrainment. Entrainment controls thunderstorm intensity via its diluting effect on the buoyancy of air within updrafts. A challenge to representing entrainment in forecasting and predictions of the intensity of updrafts in future climates is to determine how much entrainment will occur in a given thunderstorm environment without a computationally expensive high-resolution simulation. To address this gap, this article derives a new formula that computes entrainment from the properties of a single environmental profile. This formula is shown to predict updraft vertical velocity more accurately than past diagnostics, and can be used in forecasting and climate prediction to improve predictions of thunderstorm behavior and impacts.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2019

Detection of Spatiotemporal Prescription Opioid Hot Spots With Network Scan Statistics: Multistate Analysis

Background: Overuse and misuse of prescription opioids have become significant public health burdens in the United States. About 11.5 million people are estimated to have misused prescription opioids for nonmedical purposes in 2016. This has led to a significant number of drug overdose deaths in the United States. Previous studies have examined spatiotemporal clusters of opioid misuse, but they have been restricted to circular shaped regions. Objective: The goal of this study was to identify spatiotemporal hot spots of opioid users and opioid prescription claims using Medicare data. Methods: We examined spatiotemporal clusters with significantly higher number of beneficiaries and rate of prescriptions for opioids using Medicare payment data from the Centers for Medicare & Medicaid Services. We used network scan statistics to detect significant clusters with arbitrary shapes, the Kulldorff scan statistic to examine the significant clusters for each year (2013, 2014, and 2015) and an expectation-based version to examine the significant clusters relative to past years. Regression analysis was used to characterize the demographics of the counties that are a part of any significant cluster, and data mining techniques were used to discover the specialties of the anomalous providers. Results: We examined anomalous spatial clusters with respect to opioid prescription claims and beneficiary counts and found some common patterns across states: the counties in the most anomalous clusters were fairly stable in 2014 and 2015, but they have shrunk from 2013. In Virginia, a higher percentage of African Americans in a county lower the odds of the county being anomalous in terms of opioid beneficiary counts to about 0.96 in 2015. For opioid prescription claim counts, the odds were 0.92. This pattern was consistent across the 3 states and across the 3 years. A higher number of people in the county with access to Medicaid increased the odds of the county being in the anomalous cluster to 1.16 in both types of counts in Virginia. A higher number of people with access to direct purchase of insurance plans decreased the odds of a county being in an anomalous cluster to 0.85. The expectation-based scan statistic, which captures change over time, revealed different clusters than the Kulldorff statistic. Providers with an unusually high number of opioid beneficiaries and opioid claims include specialties such as physician’s assistant, nurse practitioner, and family practice. Conclusions: Our analysis of the Medicare claims data provides characteristics of the counties and provider specialties that have higher odds of being anomalous. The empirical analysis identifies highly refined spatial hot spots that are likely to encounter prescription opioid misuse and overdose. The methodology is generic and can be applied to monitor providers and their prescription behaviors in regions that are at a high risk of abuse.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2024

Assessment of Readout Techniques for Passive Monitors

This fiscal year (FY) 2023 report on passive temperature sensors covers two main objectives: to demonstrate that the optical dilatometer can successfully process disc shaped silicon carbide (SiC) temperature monitors (TMs), and to demonstrate proof of concept for using the capacitance readout method to read printed melt wires. The SiC objective was successfully met by annealing and analyzing, via optical dilatometry, all eight 3-mm SiC discs provided by the Nuclear Science User Facilities (NSUF) Idaho State University (ISU) Nanostructured Steels for Enhanced Radiation Tolerance (N SERT) experiment, which was irradiated at Idaho National Laboratory (INL)’s Advanced Test Reactor (ATR). Per the ISU N SERT experiment, capsule 1 (KGT 3828 1 and KGT 3828-2) had a design temperature of 300°C +/- 50°C and an exposure of 2 dpa +/- 10%; capsule 2 (KGT 4600 and KGT 4609) had a design temperature of 300°C +/- 50°C and an exposure of 6 dpa +/- 10%; capsule 3 (KGT 4639 C and KGT 4639-D) had a design temperature of 500°C +/- 50°C and an exposure of 6 dpa +/- 10%; and capsule 4 (KGT 3841 3 and KGT 3841 4) had a design temperature of 500°C +/- 50°C and an exposure of 2 dpa +/- 10%. The target exposure rates, in dpa, are the neutron damage for various types of nanostructured steels. All but three SiC TMs (KGT 4600, KGT 4639 D, and KGT 3841 4) revealed averaged peak irradiation temperatures that fell within the design temperature ranges. The three SiC TMs that did not fall within the design temperatures ranges were at least 100°C below that target temperature. Furthermore, SiC TM KGT 3841 C revealed two irradiation regimes: one closer to the 300°C design temperature, and the other closer to the 500°C design temperature. Also, all the SiC TMs’ averaged peak irradiation temperatures came in anywhere between 20°C and 240°C below the irradiation temperatures predicted by the thermal models. This showed the optical dilatometry method to be a reliable and less time intensive process for determining averaged peak irradiation temperatures from passive SiC TMs such as rods and discs. Under the Advanced Sensors and Instrumentation (ASI) program in FY-23, Boise State University (BSU) proposed to demonstrate proof of concept for using a capacitance readout technique applicable to printed melt wires; however, they were stymied by the complexity of the capacitance readout method. In support of the BSU work, INL developed an additively manufactured (AM) ceramic package for encapsulating the new melt wires. Inks were synthesized at BSU that used new protocols rather than following previously established protocols implemented at INL, and testing of various temperatures was conducted at BSU to evaluate the melting behaviors of the printed melt wires. The result was that the capacitance readout technique showed promise but also created more challenges than originally anticipated. For example, the tin ink synthesized at BSU showed unusual melting behaviors that did nothing to enhance the performance of the final printed melt wire prototype in terms of the capacitance readout method. To make the proof of concept work when applied to the printed melt wires, the ASI program would need to invest further resources and time. Consequently, the program is not planning to continue this proof of concept work in FY-24, based on the progress and findings achieved in FY-23.

36 MATERIALS SCIENCE↗
DOE OSTI2024

IDAES-PSE 2.6.0 Release

The Institute for the Design of Advanced Energy Systems (IDAES) Integrated Platform is a versatile computational environment offering extensive process systems engineering (PSE) capabilities for optimizing the design and operation of complex, interacting technologies and systems. IDAES enables users to efficiently search vast, complex design spaces to discover the lowest cost solutions while supporting the full process modeling lifecycle, from conceptual design to dynamic optimization and control. The extensible, open platform empowers users to create models of novel processes and rapidly develop custom analyses, workflows, and end-user applications. IDAES-PSE 2.6.0 Release Highlights Upcoming Changes IDAES will be switching to the new Pyomo solver interface in the next release. Whilst this will hopefully be a smooth transition for most users, there are a few important changes to be aware of. The new solver interface uses a different version of the IPOPT writer (“ipopt_v2”) and thus any custom configuration options you might have set for IPOPT will not carry over and will need to be reset. By default, the new Pyomo linear presolver will be activated with ipopt_v2. Whilst are working to identify any bugs in the presolver, it is possible that some edge cases will remain. IDAES will begin deploying a new set of scaling tools and APIs over the next few releases that make use of the new solver writers. The old scaling tools and APIs will remain for backward compatibility but will begin to be deprecated. New Models, Tools and Features New Intersphinx extension automatically linking Jupyter notebook examples to project documentation New end-to-end diagnostics example demonstrated on a real problem New complementarity formulation for VLE with cubic equations of state, backward compatibility for old formulation New solver interface with presolve (ipopt_v2) in support of upcoming changes to the initialization and APIs methods, with default set to ipopt to maintain backwards compatibility; this will deprecate once all examples have been updated New forecaster and parameterized bidder methods within grid integration library Updated surrogates API and examples to support Keras 3, with backwards compatibility for older formats such as TensorFlow SavedModel (TFSM) Updated costing base dictionary to include the 2023 cost year index value Updated ProcessBlock to include information on the constructing block class Updated Flowsheet Visualizer to allow visualize() method to return value and functions Bug Fixes Fixed bug in the Modular Property Framework that would cause errors when trying to use phase-based material balances with phase equilibria. Fixed bug in Modular Properties Framework that caused errors when initializing models with non-vapor-liquid phase equilibria. Fixed typos flagged by June update to crate-ci/typos and removed DMF-related exceptions Minor corrections of units of measurement handling in power plant waste/transport costing expressions, control volume material holdup expressions, and BTX property package parameters Fixed throwing >7500 numpy deprecation warnings by replacing scalar value assignment with element extraction and item iteration calls Testing and Robustness Migrated slow tests (>10s) to integration, impacting test coverage but also yielding a nearly 30% decrease in local test runtime Pinned pint to avoid issues with older supported Python versions Pinned codecov versions to avoid tokenless upload behavior with latest version Bumped extensions to version 3.4.2 to allow pointing to non-standard install location Deprecations and Removals Python 3.8 is no longer supported. The supported Python versions are 3.9 through 3.12 The Data Management Framework (DMF) is no longer supported. Importing idaes.core.dmf will cause a deprecation warning to be displayed until the next release The SOFC Keras surrogates have been removed. The current version of the SOFC surrogate model in the examples repository is a PySMO Kriging model.

AS↗
DOE OSTI2011

The Elusive “Life” of LEDs: How TM-21 Contributes to the Solution

This magazine article discusses the issue of LED 'lifetime' and explains where TM-21 plays as part of the solution to this issue. In August 2011, the Illuminating Engineering Society (IES) published the TM-21 document entitled 'Lumen degradation lifetime estimation method for LED light sources.' TM-21 is the IES-recommended method for projecting lumen degradation of an LED package, array or module based on data collected according to LM-80. The lighting community expects TM-21 to become the standard method for projecting useful LED lighting product life at realistic operating temperature. This article presents the development process behind TM-21, and clarifies how and when to apply the lifetime extrapolation method to arrive at reasonable and useful estimations. Why TM-21 and why now? We are all familiar with the very real but sometimes exaggerated long-life attributes of LED technology. Not the least of these is the potential for very long life that helps make it an attractive design choice. The trick has been and continues to be how to measure or estimate this longevity to provide assurance to users of this technology's reliability (life) compared to other options. We also understand that the overall reliability of a complete LED lighting fixture can be affected by the reliability of individual product components (driver, lens, etc.) and should be accounted for in lifetime estimations. The useful life of standard lighting technologies is defined as the time to filament or cathode failure. For most of these lamps, the time period prior to failure exhibits acceptable levels of light output, as shown with the solid lines in Fig. 1. This makes it easy to determine when to replace the lamp. However, LEDs do not have filament burn-out that conveniently announces the end of life (dashed line in Fig. 1). Further, the rapid development of the technology and the desire to bring products to market in a timely manner does not allow for actual testing verification of the long lives claimed (100,000 or even 35,000 hours). As a result, the industry has come to accept a definition of the end of the useful life of an LED as the point when it no longer provides a specified level of light output. And finally, the life and performance of LED lighting products depends greatly on excess heat retained at the diode. This is why LEDs require testing at multiple temperatures such that when a source is installed in a luminaire, its actual operating temperature can be measured and lumen depreciation of the product can be derived. Therefore, to serve the solid-state lighting industry, the Technical Procedures Committee (TPC) of the IES proceeded to develop appropriate tests for use in rating LED product longevity. The initial need was a measure of the basic lumen degradation of LED source components identified by a module, package, or array of diodes and this came in the form of LM-80. Importantly, LM-80 only specifies how to measure lumen depreciation to a minimum of 6000 hours (but recommends testing to 10,000 hours or longer). LM-80 stops short of using that data to estimate any depreciation after that, which is where TM-21 comes in. The TM-21 working group (WG) as part of the IES TPC was formed to develop the lumen depreciation projection method and spent over three years exploring many options. The WG evaluated various projection options starting with an analysis of various mathematical, engineering-based models to provide effective depreciation fit and a useful projection method. Next, the WG analyzed LED lumen maintenance behavior using over 40 sets of LM-80-08 test data (20 sets with 10,000 hours or more) collected from four major LED manufacturers. The working group also examined the accuracy of proposed projections using various proposed models and LM-80 data that extended up to 15,000 hours.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2013

Exploratory Nuclear Reactor Safety Analysis and Visualization via Integrated Topological and Geometric Techniques

A recent trend in the nuclear power engineering field is the implementation of heavily computational and time consuming algorithms and codes for both design and safety analysis. In particular, the new generation of system analysis codes aim to embrace several phenomena such as thermo-hydraulic, structural behavior, and system dynamics, as well as uncertainty quantification and sensitivity analyses. The use of dynamic probabilistic risk assessment (PRA) methodologies allows a systematic approach to uncertainty quantification. Dynamic methodologies in PRA account for possible coupling between triggered or stochastic events through explicit consideration of the time element in system evolution, often through the use of dynamic system models (simulators). They are usually needed when the system has more than one failure mode, control loops, and/or hardware/process/software/human interaction. Dynamic methodologies are also capable of modeling the consequences of epistemic and aleatory uncertainties. The Monte-Carlo (MC) and the Dynamic Event Tree (DET) approaches belong to this new class of dynamic PRA methodologies. The major challenges in using MC and DET methodologies (as well as other dynamic methodologies) are the heavier computational and memory requirements compared to the classical ET analysis. This is due to the fact that each branch generated can contain time evolutions of a large number of variables (about 50,000 data channels are typically present in RELAP) and a large number of scenarios can be generated from a single initiating event (possibly on the order of hundreds or even thousands). Such large amounts of information are usually very difficult to organize in order to identify the main trends in scenario evolutions and the main risk contributors for each initiating event. This report aims to improve Dynamic PRA methodologies by tackling the two challenges mentioned above using: 1) adaptive sampling techniques to reduce computational cost of the analysis and 2) topology-based methodologies to interactively visualize multidimensional data and extract risk-informed insights. Regarding item 1) we employ learning algorithms that aim to infer/predict simulation outcome and decide the coordinate in the input space of the next sample that maximize the amount of information that can be gained from it. Such methodologies can be used to both explore and exploit the input space. The later one is especially used for safety analysis scopes to focus samples along the limit surface, i.e. the boundaries in the input space between system failure and system success. Regarding item 2) we present a software tool that is designed to analyze multi-dimensional data. We model a large-scale nuclear simulation dataset as a high-dimensional scalar function defined over a discrete sample of the domain. First, we provide structural analysis of such a function at multiple scales and provide insight into the relationship between the input parameters and the output. Second, we enable exploratory analysis for users, where we help the users to differentiate features from noise through multi-scale analysis on an interactive platform, based on domain knowledge and data characterization. Our analysis is performed by exploiting the topological and geometric properties of the domain, building statistical models based on its topological segmentations and providing interactive visual interfaces to facilitate such explorations.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗