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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 325 records · Page 18

Design and fabrication of TiO 2 -based dye sensitized solar cells using plant-derived organic dyes

The utilization of solar energy, an abundant and renewable resource, shows great promise. Various technologies have been employed to capture this vast energy potential. In this study, the effectiveness of natural dye-sensitized solar cells (DSSCs) was explored through experimental and computational methods. Organic dyes derived from specific plant species were examined, with a solvent mixture of ethanol, methanol, and tetrahydrofuran used for extraction. A UV–vis spectrophotometer was utilized to measure the sensitizers’ optical characteristics. The study employed density functional theory (DFT), implemented in Gaussian 09 W software, to perform computational calculations. The B3LYP method and a basis set of 6-31G++(d,p) were chosen for optimizing the geometries and energetics of dye molecules. The DFT results indicated that the dye molecules had a bandgap in the range of 2.16–2.38 eV. The photochemical performance of all four fabricated DSSC-based devices was evaluated to be between 0.33% and 1.04%, and the Euryops pectinatus sensitizer demonstrated the highest efficiency of 1.04% among the studied sensitizers.

36 MATERIALS SCIENCE↗

Sea-surface temperature pattern effects have slowed global warming and biased warming-based constraints on climate sensitivity

The observed rate of global warming since the 1970s has been proposed as a strong constraint on equilibrium climate sensitivity (ECS) and transient climate response (TCR)—key metrics of the global climate response to greenhouse-gas forcing. Using CMIP5/6 models, we show that the inter-model relationship between warming and these climate sensitivity metrics (the basis for the constraint) arises from a similarity in transient and equilibrium warming patterns within the models, producing an effective climate sensitivity (EffCS) governing recent warming that is comparable to the value of ECS governing long-term warming under CO 2 forcing. However, CMIP5/6 historical simulations do not reproduce observed warming patterns. When driven by observed patterns, even high ECS models produce low EffCS values consistent with the observed global warming rate. The inability of CMIP5/6 models to reproduce observed warming patterns thus results in a bias in the modeled relationship between recent global warming and climate sensitivity. Correcting for this bias means that observed warming is consistent with wide ranges of ECS and TCR extending to higher values than previously recognized. These findings are corroborated by energy balance model simulations and coupled model (CESM1-CAM5) simulations that better replicate observed patterns via tropospheric wind nudging or Antarctic meltwater fluxes. Because CMIP5/6 models fail to simulate observed warming patterns, proposed warming-based constraints on ECS, TCR, and projected global warming are biased low. The results reinforce recent findings that the unique pattern of observed warming has slowed global-mean warming over recent decades and that how the pattern will evolve in the future represents a major source of uncertainty in climate projections.

58 GEOSCIENCES↗

Development of a Low-Cost, Sensitivity-Optimized Fluorescence Sensor for Visible Spectrum Analysis

This paper presents an economically viable, sensitivity-optimized fluorescence sensor for detecting low concentrations of fluorophores in the visible spectrum range. The sensor employs a deep ultraviolet (DUV) 275-nm LED and a dichroic mirror to establish a co-axial optical path for excitation and emission. Instead of utilizing a general-purposed spectrometer optimized for high spectral resolution, the sensor strikes a balance between resolution and sensitivity by using an appropriate aperture size and optimized optical design tailored for the spectral characteristics of fluorophores. Here, the manuscript demonstrates measurements of ppb-level rare earth element (REE) samples containing terbium (Tb), europium (Eu), dysprosium (Dy), and samarium (Sm) in aqueous solutions. Through balanced design that optimizes collection of fluorescence emission without losing spectral information, this work shows the feasibility of developing a low-cost, compact, and highly sensitive fluorescence sensors with improved detection sensitivity when compared to bench-top commercial spectrofluorometers at far higher costs.

47 OTHER INSTRUMENTATION↗

Local Power-Voltage Sensitivity and Thevenin Impedance Estimation from Phasor Measurements

This paper describes how to use voltage phasor measurements to produce a sensitivity matrix that describes how real and reactive power injections at a node on a distribution network affect the local voltage magnitude and angle. Rather than estimating the sensitivity directly, the voltage phasor measurements and power commands/measurements are used to estimate the unbalanced, three-phase Thevenin impedance. The Thevenin impedance estimation is conducted using recursive least squares on temporal difference measurements. The Thevenin impedance and voltage phasor measurement are then used to build the local power-voltage sensitivity matrix with the closed form expression for the Jacobian of the power flow manifold. Hardware-in-the-loop simulations with phasor measurement units providing real phasor measurements are used to evaluate the recursive temporal difference Thevenin impedance estimation and Thevenin-based power-voltage sensitivity methods.

24 POWER TRANSMISSION AND DISTRIBUTION↗

Generalized Analytical Estimation of Sensitivity Matrices in Unbalanced Distribution Networks

Fast and accurate estimation of sensitivity matrices is significant for the enhancement of distribution system modeling and automation. Analytical estimations have mainly focused on voltage magnitude sensitivity to active/reactive power injections for unbalanced networks with Wye-connected loads and neglecting DERs' smart inverter functionality. Hence, this paper enhances the scope of analytical estimation of sensitivity matrices for unbalanced networks with 1- Φ, 2- Φ, and 3- Φ Delta/Wye-connected loads, DERs with smart inverter functionality, and substation/line step-voltage regulators (SVR). A composite bus model comprising of DER, Delta- and Wye-connected load is proposed to represent a generic distribution bus, which can be simplified to load, PV, or voltage-controlled bus as required. Furthermore, the proposed matrix-based analytical method consolidates voltage magnitude and angle sensitivity to active/reactive power injection and tap-position of all SVRs into a single algorithm. Extensive case studies on IEEE and EPRI networks show the accuracy and wide scope of the proposed algorithm compared to the existing benchmark method.

24 POWER TRANSMISSION AND DISTRIBUTION↗

Combining Emergent Constraints for Climate Sensitivity

A method is proposed for combining information from several emergent constraints into a probabilistic estimate for a climate sensitivity proxy Y such as equilibrium climate sensitivity (ECS). The method is based on fitting a multivariate Gaussian PDF for Y and the emergent constraints using an ensemble of global climate models (GCMs); it can be viewed as a form of multiple linear regression of Y on the constraints. The method accounts for uncertainties in sampling this multidimensional PDF with a small number of models, for observational uncertainties in the constraints, and for overconfidence about the correlation of the constraints with the climate sensitivity. Its general form (Method C) accounts for correlations between the constraints. Method C becomes less robust when some constraints are too strongly related to each other; this can be mitigated using regularization approaches such as ridge regression. An illuminating special case, Method U, neglects any correlations between constraints except through their mutual relationship to the climate proxy; it is more robust to small GCM sample size and is appealingly interpretable. These methods are applied to ECS and the climate feedback parameter using a previously published set of 11 possible emergent constraints derived from climate models in the Coupled Model Intercomparison Project (CMIP). The ±2σ posterior range of ECS for Method C with no overconfidence adjustment is 4.3 ± 0.7 K. For Method U with a large overconfidence adjustment, it is 4.0 ± 1.3 K. This study adds confidence to past findings that most constraints predict higher climate sensitivity than the CMIP mean.

54 ENVIRONMENTAL SCIENCES↗

Proposal and application of ROM-Lasso method for sensitivity coefficient evaluation

We propose a novel method for evaluating sensitivity coefficients of neutronics parameters to cross sections, so-called the reduced-order modeling technique ROM-Lasso. In this method, cross sections of interest are randomly sampled, and corresponding perturbed core analyses are performed. Then, the sensitivity coefficient vector of the higher-level model is expanded via the active subspace bases obtained with the lower-level model whose dimensional complexity is smaller than that of the higher-level model, and the expansion coefficients are estimated by the Lasso regression. A unique feature of the ROM-Lasso method allows the use of different bases optimized for each neutronics parameter. We conducted a verification calculation for an accelerator-driven system and demonstrated that the ROM-Lasso method can reproduce the sensitivity coefficients with a much smaller number of forward calculations than the direct method. The proposed method can be used to practically evaluate sensitivity coefficients. (authors)

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗

Sensitivity analyses of a homogeneous model and a RZ model of the MYRRHA reactor in its critical configuration

Two homogenized models of the MYRRHA (Multi-purpose hybrid Research Reactor for High-tech Applications) reactor in its critical configuration are presented in this study. Both models will be verified by comparing the main reactor parameters (K{sub eff}, β{sub eff} and Λ) with those of the heterogeneous model and a sensitivity analysis will be performed. A sensitivity analysis of some important parameters related to reactivity, namely the vacuum coefficient, the Doppler and the power peaking factors, is also presented. The study of the vacuum coefficient has been carried out by analyzing two possible scenarios: 50% vacuum in the coolant (lead bismuth eutectic) and 100%, obtaining a list of the 10 reactions and nuclides that most affect the k{sub eff} value. With respect to the Doppler coefficient, it will be seen that a 300 degree increase in fuel temperature (starting at 800 K) results in a reduction of the total reactivity of the system by 134 pcm.. Finally, the map of the power peaking factors will be shown, as well as a sensitivity analysis. For making the sensitivity calculations Serpent-2 reactor physics Monte Carlo code has been used and JEFF-3.3 library has been chosen in order to continue with the OECD/NEA WPEC SG46 work evaluating the nuclear data of this library. (authors)

21 SPECIFIC NUCLEAR REACTORS AND ASSOCIATED PLANTS↗

Evaluation of Oxygen Consumption as a Sensitive Measure of Electrical Cable Polymer Insulation Degradation

Polymers used as dielectric materials in nuclear grade electrical cables, such as cross-linked polyethylene (XLPE) and ethylene-propylene rubber (EPR), are long-lived and durable. Estimating the decades-long useful service life of polymers experimentally in a feasible timeframe of weeks or months requires greatly accelerated aging in the laboratory at elevated temperatures and/or gamma dose rates. Many limitations of accelerated aging, such as due to artefacts of the process including diffusion limited oxidation and dose rate effects, have been identified that can make accurate lifetime prediction at service conditions challenging. This can be due to differences in degradation mechanisms between rapid and extended aging or to the presence of induction periods during which early aging is undetectable using common metrics of polymer insulation characterization such as tensile elongation at break. Conceptually, an ideal measure of aging would track clearly and predictably with material changes throughout the lifecycle of the evolving material state from early aging through end of useful life. With such a metric accelerated aging could be performed at much milder conditions, conditions closer to those found in long term service, and end of life calculated from the early data. Oxygen consumption has previously been proposed as a sensitive measure of insulation polymer oxidation that might meet such desirable characteristics. In this work, nuclear cable insulation samples of the two most common types, XLPE and EPR, from two of the most sourced manufacturers, RSCC and The Okonite Company, were subjected to gamma irradiation at dose rates of 100, 200, and 300 Gy/hr for up to 42 days at 26°C. The aged samples were used to evaluate oxygen consumption as a sensitive measure of aging for these materials and these conditions. For comparison and validation, the aged samples were also characterized using the more conventional tests of mass change, density, carbonyl index, and yellowness index. For both materials, oxygen consumption during irradiation was found to track consistently with applied dose—oxygen consumption rate was linear with time. Mass change and carbonyl index were also clearly proportional to exposure dose. Yellowness index, a surface sensitive measure, was observed to increase linearly with dose at low exposure levels before plateauing. Sample density was not observed to change under the conditions explored. Oxygen consumption was found to be a particularly sensitive metric to measure the degradation of polymeric cable insulation exposed to radiation. Oxygen consumption was compared to Arrhenius and time-temperature-superposition methods for activation energy calculation using literature date and found to be a useful and viable alternative for lifetime prediction. The techniques developed in this work are anticipated to be valuable in the estimation of insulation aging rates under mild conditions approaching service conditions. Direction comparison of useful life prediction by oxygen consumption with prediction via Arrhenius methodology for thermal aging and existing methods for radiation and combined thermal/radiation aging would serve to further validate this promising approach and refine our understanding of material-specific aging rates.

36 MATERIALS SCIENCE↗

Parametric and Sensitivity Analysis of a Steam Generator Model Using Python and Machine-Learning Tools

For this study, we used Python and machine-learning tools to perform a comprehensive parametric and sensitivity analysis on a steam generator (SG) model. (The Python model was based on a previously completed MATLAB framework for the Holtec SMR-160 SG.) We investigated the influence of various input parameters (e.g., heat transfer coefficient [HTC], Nusselt number, and heat exchanger effectiveness) on the system’s output. With machine-learning tools such as the Risk Analysis Virtual Environment (RAVEN), which was developed at Idaho National Laboratory, we were then able to perform an automated analysis of the SG inputs’ effect on the HTC. The analysis results give valuable insights into the performance and optimization of SG systems. We found the inlet mass flow rate (MFR) to have the greatest impact on the HTC, followed closely by the inlet temperature, and then pressure. Shifting of the input parameters causes the location of the maximum HTC along the SG length to change incrementally. The cold leg (CL) MFR was also found to impact the HTC magnitude as well as the location of the maximum HTC. At between 0.4–0.9 of the total SG length, the input parameters experience maximum impact on the HTC, leading us to suggest that sensors be efficiently placed on the SG so as to closely and effectively monitor thermal-hydraulic properties during reactor operation. We also found that the sensitivity data calculated manually agrees with the RAVEN – based data, confirming the same range of maximum sensitivity. However, the RAVEN-based analysis showed that cold leg pressure and hot leg temperature have a greater impact on the heat transfer coefficient than the mass flow rate, implying that a manual sensitivity study taking only two samples is not accurate.

20 FOSSIL-FUELED POWER PLANTS↗

Aerodynamic Sensitivities over Separable Shape Tensors

Here, we present a comprehensive aerodynamic sensitivity analysis of airfoil parameterization informed by separable shape tensors. This parameterization approach uniquely benefits the design process by isolating various well-studied shape characteristics, such as airfoil thickness, and providing a well-regulated low-dimensional parameter domain for aerodynamic designs. Exploring the aerodynamic sensitivities of this novel parameterization can provide valuable insights for more robust designs and future manufacturing efforts. We construct a data-driven parameter space of airfoils using principal geodesic analysis of separable shape tensors informed by a curated database containing almost 20,000 suitable engineering airfoils. Analyzing the shape reconstruction error and the maximum mean discrepancy between joint distributions of aerodynamic quantities, we study the dimensionality of the learned parameter space. This simple numerical experiment demonstrates a dramatic dimension reduction that retains design effectiveness and promotes regularity of the shape representations. Finally, we generate new airfoils and use the HAM2D Reynolds-averaged Navier–Stokes solver to predict lift, drag, and moment coefficients. We compute multiple sensitivity metrics to quantify and assert the consistency of parameter influence on the aerodynamic quantities. We also explore low-dimensional polynomial ridge approximations to motivate physical intuitions and offer explanations of the approximated sensitivities.

17 WIND ENERGY↗

Assessing Strain Rate Sensitivity of Nanotwinned Al–Zr Alloys through Nanoindentation

Nanotwinned metals have exhibited many enhanced physical and mechanical properties. Twin boundaries have recently been introduced into sputtered Al alloys in spite of their high stacking fault energy. These twinned Al alloys possess unique microstructures composed of vertically aligned Σ3(112) incoherent twin boundaries (ITBs) and have demonstrated remarkable mechanical strengths and thermal stability. However, their strain rate sensitivity has not been fully assessed. A modified nanoindentation method has been employed here to accurately determine the strain rate sensitivity of nanotwinned Al–Zr alloys. The hardness of these alloys reaches 4.2 GPa while simultaneously exhibiting an improved strain rate sensitivity. The nanotwinned Al–Zr alloys have shown grain size-dependent strain rate sensitivity, consistent with previous findings in the literature. This work provides insight into a previously unstudied aspect of nanotwinned Al alloys.

36 MATERIALS SCIENCE↗

Mitigation of Cu-Induced Grain Boundary Sensitization in Steel Wire Rods Through a Desensitization Heat Treatment

Steel wire rods are essential for manufacturing high-strength steel tire cords. Yet, the presence of residual copper (Cu) in recycled steel can cause grain-boundary sensitization, embrittlement, and deterioration of the mechanical performance of the final product. This study introduces a desensitization heat treatment step designed to redistribute Cu away from austenite grain boundaries after sensitization occurs. The treatment consists of a 10 min dwell at 1000 °C in a 5%H 2 -Ar reducing atmosphere followed by quench. The temperature and hold time were selected based on diffusion calculations to promote solid-state back diffusion of Cu without altering grain morphology. Experimental validation showed that the dwell step reduced the length of Cu-rich sensitized zones of steel wire rod samples containing 0.21 wt.% Cu by approximately 89% and restored the mechanical properties to nearly 95–98% relative to low-Cu baseline steel (0.01 wt.% Cu). Compared with sensitized and as-obtained samples, these results highlight the effectiveness of the proposed method in improving both the microstructure and tensile performance of recycled steel wire rods, enabling their potential application in tire manufacturing.

copper↗

MIRC-X: A Highly Sensitive Six-telescope Interferometric Imager at the CHARA Array

Michigan InfraRed Combiner-eXeter (MIRC-X) is a new highly sensitive six-telescope interferometric imager installed at the CHARA Array that provides an angular resolution equivalent of up to a 330 m diameter baseline telescope in J- and H-band wavelengths ((λ/(2B))∼0.6 mas). We upgraded the original Michigan InfraRed Combiner (MIRC) instrument to improve sensitivity and wavelength coverage in two phases. First, a revolutionary sub-electron noise and fast-frame-rate C-RED ONE camera based on an SAPHIRA detector was installed. Second, a new-generation beam combiner was designed and commissioned to (i) maximize sensitivity, (ii) extend the wavelength coverage to J band, and (iii) enable polarization observations. A low-latency and fast-frame-rate control software enables high-efficiency observations and fringe tracking for the forthcoming instruments of the CHARA Array. Since mid-2017, MIRC-X has been offered to the community and has demonstrated best-case H-band sensitivity down to 8.2 correlated magnitude. MIRC-X uses single-mode fibers to coherently combine the light from six telescopes simultaneously with an image-plane combination scheme and delivers a visibility precision better than 1%, and closure phase precision better than 1°. MIRC-X aims at (i) imaging protoplanetary disks, (ii) detecting exoplanets with precise astrometry, and (iii) imaging stellar surfaces and starspots at an unprecedented angular resolution in the near-infrared. In this paper, we present the instrument design, installation, operation, and on-sky results, and demonstrate the imaging capability of MIRC-X on the binary system ι Peg. The purpose of this paper is to provide a solid reference for studies based on MIRC-X data and to inspire future instruments in optical interferometry.

79 ASTRONOMY AND ASTROPHYSICS↗

Sensitization of 316L Stainless Steel made by Laser Powder Bed Fusion Additive Manufacturing

Additively manufactured (AM) 316L stainless steel (SS) manufactured by laser powder bed fusion (L-PBF) and wrought 316L SS were subjected to sensitization heat treatments at 700°C up to 100 h. Using two evaluation methods, double-loop electrochemical potentiokinetic reactivation (DL-EPR) and ditching tests, degree of sensitization (DOS) and intergranular corrosion (IGC) susceptibility was evaluated. It was found that the wrought samples showed slightly lower IGC susceptibility compared to their AM counterpart. DOS and IGC attacks increased with sensitization time for all samples. Dislocation cellular structures were found to have little to no impact on DOS and IGC for the AM samples. Sensitized at 100 h, the AM sample showed significant Cr depletion along high-angle grain boundaries (12.35 wt% on average) and exhibited Cr carbide precipitation. Mo-rich particles along grain boundaries were also observed. The DL-EPR test attacks the surface oxide film and grain boundaries while the ditching test attacks the melt pool boundaries and grain boundaries (IGC and pitting). Changes to the DL-EPR and ditching standards for AM application have been proposed in this work.

316L stainless steel↗

Sensitivity analysis of numerical modeling input parameters on floating offshore wind turbine loads

Abstract. Floating wind turbines must withstand a unique and challenging set of loads from the wind and ocean environment. To de-risk development, accurate predictions of these loads are necessary. Uncertainty in modeling predictions leads to larger required safety factors, increasing project costs and the levelized cost of energy. Complex aero-hydro-elastic modeling tools use many input parameters to represent the wind, waves, current, aerodynamic loads, hydrodynamic loads, and structural properties. It is helpful to understand which of these parameters ultimately drives a design. In this work, an ultimate and fatigue-proxy load sensitivity analysis was performed with 35 different input parameters, using an elementary effects approach to identify the most influential parameters for a case study involving the National Renewable Energy Laboratory (NREL) 5 MW baseline wind turbine atop the OC4-DeepCwind semisubmersible during normal operation. The importance of each parameter was evaluated using 14 response quantities of interest across three operational wind speed conditions. The study concludes that turbulent wind velocity standard deviation is the parameter with the strongest sensitivity; this value is important not just for turbine loads, but also for the global system response. The system center of mass in the wind direction is found to have the highest impact on the system rotation and tower loads. The current velocity is found to be the most dominating parameter for the system global motion and consequently the mooring loads. All tested wind turbulence parameters in addition to the standard deviation are also found to be influential. Wave characteristics are influential for some fatigue-proxy loading but do not significantly impact the extreme ultimate loads in these operational load cases. The required number of random seeds for stochastic environmental conditions is considered to ensure that the sensitivities are due to the input parameters and not due to the seed. The required number of analysis points in the parameter space is identified so that the conclusions represent a global sensitivity. The results are specific to the platform, turbine, and choice of parameter ranges, but the demonstrated approach can be applied widely to guide focus in parameter uncertainty.

17 WIND ENERGY↗

Implications for Climate Sensitivity from the Response to Individual Forcings

Climate sensitivity to doubled CO2 is a widely-used metric of the large-scale response to external forcing. Climate models predict a wide range for two commonly used definitions: the transient climate response (TCR: the warming after 70 years of CO2 concentrations that riseat 1 per year), and the equilibrium climate sensitivity (ECS: the equilibrium temperature change following a doubling of CO2 concentrations). Many observational datasets have been used to constrain these values, including temperature trends over the recent past 16, inferences from paleo-climate and process-based constraints from the modern satellite eras. However, as the IPCC recently reported different classes of observational constraints produce somewhat incongruent ranges. Here we show that climate sensitivity estimates derived from recent observations must account for the efficacy of each forcing active during the historical period. When we use single forcing experiments to estimate these efficacies and calculate climate sensitivity from the observed twentieth-century warming, our estimates of both TCR and ECS are revised upward compared to previous studies, improving the consistency with independent constraints.

Sensitivity↗

Quantifying PM2.5-Meteorology Sensitivities in a Global Climate Model

Climate change can influence fine particulate matter concentrations (PM2.5) through changes in air pollution meteorology. Knowledge of the extent to which climate change can exacerbate or alleviate air pollution in the future is needed for robust climate and air pollution policy decision-making. To examine the influence of climate on PM2.5, we use the Geophysical Fluid Dynamics Laboratory Coupled Model version 3 (GFDL CM3), a fully-coupled chemistry-climate model, combined with future emissions and concentrations provided by the four Representative Concentration Pathways (RCPs). For each of the RCPs, we conduct future simulations in which emissions of aerosols and their precursors are held at 2005 levels while other climate forcing agents evolve in time, such that only climate (and thus meteorology) can influence PM2.5 surface concentrations. We find a small increase in global, annual mean PM2.5 of about 0.21 micro-g/cu m3 (5%) for RCP8.5, a scenario with maximum warming. Changes in global mean PM2.5 are at a maximum in the fall and are mainly controlled by sulfate followed by organic aerosol with minimal influence of black carbon. RCP2.6 is the only scenario that projects a decrease in global PM2.5 with future climate changes, albeit only by -0.06 micro-g/cu m (1.5%) by the end of the 21st century. Regional and local changes in PM2.5 are larger, reaching upwards of 2 micro-g/cu m for polluted (eastern China) and dusty (western Africa) locations on an annually averaged basis in RCP8.5. Using multiple linear regression, we find that future PM2.5 concentrations are most sensitive to local temperature, followed by surface wind and precipitation. PM2.5 concentrations are robustly positively associated with temperature, while negatively related with precipitation and wind speed. Present-day (2006-2015) modeled sensitivities of PM2.5 to meteorological variables are evaluated against observations and found to agree reasonably well with observed sensitivities (within 10e50% over the eastern United States for several variables), although the modeled PM2.5 is less sensitive to precipitation than in the observations due to weaker convective scavenging. We conclude that the hypothesized "climate penalty" of future increases in PM2.5 is relatively minor on a global scale compared to the influence of emissions on PM2.5 concentrations.

PM2.5↗