SEARCH · Search NASA

Results for “STEM Evaluations”

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.

SourceResearch connectionFrom yearThrough year

Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.

At least 163 records · Page 9

All sources NASA NTRS DOE OSTI

DOE OSTI2024

Leveraging hyperspectral imaging to identify drought tolerant Populus species and genotypes within species

The aim of this study was to identity variation in drought tolerance across genotypes of Populus deltoides, Populus trichocarpa, and hybrids of the two species. A panel of 102 Populus genotypes, comprising 37 genotypes of P. trichocarpa, 37 of P. deltoides and 28 unique hybrid genotypes (P. trichocarpa x P. deltoides and P. deltoides x P. trichocarpa) were evaluated in the greenhouse under two treatments, well-watered (WW) and drought (DS). Plant physiological data were collected throughout the experiment once the drought treatment began. Throughout the experiment, we tracked soil volumetric water content, pot weight, stomatal conductance, quantum yield of photosystem II, and electron transport rate. In addition to those measurements, upon completion of the experiment, we assessed above and belowground plant biomass, plant height and stem diameter, leaf number, specific leaf area, relative water content, total protein, and total chlorophyll. We obtained hyperspectral signatures of one leaf from each plant at the end of the experiment. Columns BC – LL are hyperspectral averages for one leaf from each plant at each wavelength as described in the column header.

Hyper-spectral imaging, Populus, plant stress tole↗

DOE OSTI2024

Quantifying Investment Risk: Analysis of the Purchase Decision of a Nuclear Power Plant (Presentation)

Cost overruns are an ill-fated part of the deployment history of nuclear power plants (NPPs) in the United States, and yet studies increasingly show the important role nuclear technologies must play in decarbonizing the U.S. economy. Paradoxically, then, a key piece of a coherent decarbonization strategy depends on attracting investor action to a purchase where historical cost overruns have been sizable. To address this challenge, this study aims to develop a financial model that quantifies the risk of cost overruns in the decision-making process for purchasing advanced reactor concepts. Using the concept of value at risk (VaR), the model is built to evaluate financial risk nuclear construction with the aim to identify risk mitigation strategies. The objective is to identify strategies to mitigate cost-risk challenges and assess the potential reduction in investor risk exposure. This paper presents the initial development and preliminary verification of the financial risk analysis model. The development of this model involved a comprehensive approach to estimating financial risk over the operating life of NPP that stems from construction uncertainties. By utilizing net present value (NPV) with discounted cash flows, the model captures the complex interconnections of project costs, construction timelines, revenue, and uncertainties. Verifying the model involved testing historical data from previous reactor construction projects against the construction project of Vogtle 3 and 4. This paper’s results present the comparison of the preconstruction cost overrun prediction with the current cost estimates from a nearly complete Vogtle 3 and 4.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗

DOE OSTI2026

TEM Approaches for Microstructure-Informed Prediction of Mechanical Properties in Structural Alloys

Predicting the mechanical performance of structural alloys from their evolving microstructure remains a major challenge in materials science, particularly for nuclear structural materials, where irradiation-induced defects span multiple types and length scales and interact through complex mechanisms. The dispersed barrier hardening (DBH) [1] and Friedel–Kroupa–Hirsch (FKH) [2,3] models have been widely used to evaluate the hardening contributions of individual obstacles and to estimate tensile strength from quantified microstructures; however, when multiple size-dependent obstacles coexist and evolve, predicting temperature-dependent tensile strength becomes significantly more complex, and a fully consistent hardening model is still lacking. Transmission electron microscopy (TEM) plays a central role in refining hardening models and enabling predictive assessments of tensile strength evolution by providing quantitative characterization of dislocations, irradiation-induced defects (e.g., dislocation loops and cavities), precipitates, and grain structure (Fig. 1.). These experimentally measured defect densities are incorporated into physically based hardening models with size- and shape- dependent obstacle strengths [4], using root-sum-square superposition for obstacles of comparable strength and linear superposition for dissimilar ones [5]. In addition, recent advances in TEM [6-8], including high-resolution imaging, 4D-STEM strain mapping, EDS/EELS elemental analysis, and flash-polishing-based TEM specimen preparation and extraction-replica methods (Fig. 2), further improve the accuracy of microstructural quantification. By comparison with prior studies as well as our own results, we show that when TEM-derived microstructural information is carefully integrated with physically grounded hardening models, yield strength (or irradiation-induced hardening) measured at room temperature can be predicted with good quantitative agreement across multiple alloy classes. In-situ TEM combined with high-temperature mechanical testing represents an important next step for refining hardening models by directly probing dislocation–obstacle interactions across varying irradiation doses and temperatures [9]. Because the barrier strength factor (α) depends on both temperature and obstacle size, it should not be treated as a constant fitting parameter; rather, it must be explicitly evaluated to achieve physically meaningful predictions of mechanical behaviour at operating temperatures. This presentation therefore discusses why all strengthening contributions (e.g., Peierls stress, solid-solution strengthening, voids, bubbles, dislocation loops, dislocation lines, and grain boundaries) must be considered collectively, why appropriate superposition methods are essential when obstacles possess different barrier strength factors, how hardness measurements can be meaningfully related to tensile properties, and how TEM-derived microstructural information can be systematically incorporated into hardening models. More broadly, it outlines a pathway toward microstructure-informed prediction of mechanical properties and supports the goal of establishing science-based tools for evaluating structural materials in extreme environments [10].

Lin, Yan-Ru [ORNL] (ORCID:0000000339991473)↗

DOE OSTI2024

Scalable Risk Assessment of Rare Events in Power Systems With Uncertain Wind Generation and Loads

Risk assessment of rare events has become increasingly important in power system planning and operation with the increasing integration of renewable energy and the presence of system uncertainties. However, quantifying the risk posed by rare events via the traditional method, i.e., Monte Carlo sampling (MCS), incurs substantial computational expense stemming from the vast ensemble of power flow simulations. To accelerate the assessment, this paper proposes a Deep Neural Network (DNN)-kernelized vector-valued Gaussian Process (VVGP) approach with excellent computational efficiency while maintaining high accuracy. Consequently, serving as a surrogate model for the power flow solver, the DNN-kernelized VVGP enables significantly faster but accurate risk assessment compared to the power flow solver. The developed surrogate model evaluates low-order N - k events that contain more than 90% instances by adeptly capturing the topological features while the high-order N - k events are assessed via a power flow solver, thereby striking a balance between computational efficiency and uncertainty quantification accuracy. Moreover, the model incorporates a Support Vector Machine (SVM) classifier to resample concerning low-probability tail events to counteract the biases potentially introduced during the DNN-kernelized VVGP evaluations. Simulations conducted on the modified IEEE 24-bus, 118-bus, and European 1354-bus systems demonstrate that the proposed method maintains the accuracy benchmark set by MCS while significantly reducing computational demands in large-scale power systems as compared to other state-of-the-art methods.

17 WIND ENERGY↗

DOE OSTI2024

A Hybrid Heavy Duty Diesel Power System for Off-Road Applications—Concept Validation

A multiyear power system R&D program was completed with the objective of developing an off-road hybrid heavy duty diesel engine with front end accessory drive-integrated energy storage. This system was validated to deliver 10.5–25.6% reduction in fuel consumption over current Tier 4 Final-based 18L diesel engines, over various off-road machine application cycles. The power system consisted of a downsized heavy-duty diesel 13L engine containing advanced combustion technologies, capable of elevated peak cylinder pressures and thermal efficiencies, thermal barrier coatings, exhaust waste heat recovery via SuperTurbo™ turbocompounding, and hybrid energy assisting and recovery through both mechanical and electrical systems. Following the concept definition, design, and analysis phases of the program, the final phase focused on building and validating the performance and efficiency in laboratory tests. While aspects of the system such as start/stop and reduced off-road cooling package energy losses were only analytically evaluated, the main 13L concept engine with full hybrid system was successfully built and tested in steady-state and in transient certification and real-world application cycles. Extensive simulations in Caterpillar's DYNASTY™ software environment utilized the validation test data to assess performance more fully and confidently over varied cycles and strategies. An average fuel consumption reduction of 17.9% was realized, and the majority (~13%) of the benefit stemmed from the core concept 13L engine. In conclusion, a total cost of ownership analysis provides context to commercial viability and where adoption focus should be placed.

33 ADVANCED PROPULSION SYSTEMS↗

DOE OSTI2024

Quantifying Investment Risk: Analysis of the Purchase Decision of a Nuclear Power Plant

Cost overruns are an ill-fated part of the deployment history of nuclear power plants (NPPs) in the United States, and yet studies increasingly show the important role nuclear technologies must play in decarbonizing the U.S. economy. Paradoxically, then, a key piece of a coherent decarbonization strategy depends on attracting investor action to a purchase where historical cost overruns have been sizeable. To address this challenge, this study aims to develop a financial model that quantifies risk of cost overruns in the decision-making process for purchasing advanced reactor concepts. Using the concept of Value at Risk (VaR), the model is built to evaluate financial risk nuclear construction with the aim to identify risk mitigation strategies. The objective is to identify strategies to mitigate cost-risk challenges and to assess the potential reduction in investor risk exposure. The paper presents the initial development and preliminary verification of the financial risk analysis model. The development of this model involved a comprehensive approach to estimating financial risk over the operating life of NPP that stems from construction uncertainties. By utilizing net present value (NPV) with discounted cash flows, the model captures the complex interconnections of project costs, construction timelines, revenue, and uncertainties. Verification of the model involved testing historical data from previous reactor construction projects against the construction project of Vogtle 3 and 4. The results of this paper present the comparison of the preconstruction cost overrun prediction with the current cost estimates from a nearly complete Vogtle 3 and 4.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗

DOE OSTI2023

Triacylglycerol, total fatty acid, and biomass accumulation of metabolically engineered energycane grown under field conditions confirms its potential as feedstock for drop‐in fuel production

Abstract Metabolic engineering for hyperaccumulation of lipids in vegetative tissues of high biomass crops promises a step change in oil yields for the production of advanced biofuels. Energycane is the ideal feedstock for this approach due to its exceptional biomass production and persistence under marginal conditions. Here, we evaluated metabolically engineered energycane with constitutive expression of the lipogenic factors WRINKLED 1 ( WRI 1), DIACYLGLYCEROL ACYLTRANSFERASE 1 ( DGAT 1), and OLEOSIN 1 ( OLE 1) for the accumulation of triacylglycerol (TAG), total fatty acid (TFA), and biomass under field conditions at the University of Florida‐IFAS experiment station near Citra, Florida. TAG and TFA accumulation were highest in leaves (up to 9.9% and 12.9% of DW, respectively), followed by juice from crushed stems, stems, and roots. TAG and TFA accumulation increased up to harvest time and correlated highest with OLE 1 and DGAT 1 expression. Biomass dry weight, TAG, and TFA content differed greatly depending on DGAT 1 and OLE 1 expression in transgenic lines with similar WRI 1 expression. Biomass did not significantly differ between WT and line L2 with DAGT 1 and OLE 1 expressed at low levels and TAG and TFA accumulating to 12‐ and 1.6‐fold that of WT leaves, respectively. In contrast, line L13, with intron‐mediated enhancement of DGAT 1 expression, displayed a 245‐ to 330‐fold increase in TAG and a 4.75‐ to 6.45‐fold increase in TFA content compared with WT leaves and a biomass reduction of 52%. These results provide the basis for developing novel feedstocks for expanding plant lipid production and point to new prospects for advanced biofuels.

59 BASIC BIOLOGICAL SCIENCES↗

DOE OSTI2021

Field evaluation of transgenic hybrid poplars with desirable wood properties and enhanced growth for biofuel production by bicistronic expression of PdGA20ox1 and PtrMYB3 in wood-forming tissue

Background: To create an ideotype woody bioenergy crop with desirable growth and biomass properties, we utilized the viral 2A-meidated bicistronic expression strategy to express both PtrMYB3 ( MYB46 ortholog of Populus trichocarpa , a master regulator of secondary wall biosynthesis) and PdGA20ox1 (a GA20-oxidase from Pinus densiflora that produces gibberellins) in wood-forming tissue (i.e., developing xylem). Results: Transgenic Arabidopsis plants expressing the gene construct DX15::PdGA20ox1-2A-PtrMYB3 showed a significant increase in both stem fresh weight (threefold) and secondary wall thickening (1.27-fold) relative to wild-type (WT) plants. Transgenic poplars harboring the same gene construct grown in a greenhouse for 60 days had a stem fresh weight up to 2.6-fold greater than that of WT plants. In a living modified organism (LMO) field test conducted for 3 months of active growing season, the stem height and diameter growth of the transgenic poplars were 1.7- and 1.6-fold higher than those of WT plants, respectively, with minimal adverse growth defects. Although no significant changes in secondary wall thickening of the stem tissue of the transgenic poplars were observed, cellulose content was increased up to 14.4 wt% compared to WT, resulting in improved saccharification efficiency of the transgenic poplars. Moreover, enhanced woody biomass production by the transgenic poplars was further validated by re-planting in the same LMO field for additional two growing seasons. Conclusions: Taken together, these results show considerably enhanced wood formation of our transgenic poplars, with improved wood quality for biofuel production.

09 BIOMASS FUELS↗

DOE OSTI2025

Evidence for In-Plane Electrical Polarization in 3R-β’-In 2 Se 3 Thin Films Grown by Molecular Beam Epitaxy

β-In 2 Se 3 has been identified as a potential ferroelectric. This work describes the growth of β-In 2 Se 3 via molecular beam epitaxy along with a description of its properties, including the search for switchable polarization in 3R-β’-In2Se3. The thin-film morphology, crystal structure, and phase maps of β-In 2 Se 3 on Si(111) and Al 2 O 3 (0001) were evaluated by atomic force microscopy, X-ray diffraction, and Raman spectroscopy as a function of the atomic Se/In flux ratio and growth temperature. Smooth β-In 2 Se 3 thin films were successfully realized on Si(111) at a substrate temperature of 150 °C using a Se/In flux ratio of 5.7, as well as on Al 2 O 3 (0001) at 450 °C using a Se/In flux ratio of 5.5. Scanning transmission electron microscopy (STEM) confirms the 3R polytype of β-In 2 Se 3 in films on both substrates, with a minor disorder associated with the 2H polytype at the interface. Indications of in-plane Se atom displacements characteristic of the 3R-β’-In 2 Se 3 polytype were found by STEM and second harmonic generation analysis in films on Al 2 O 3 (0001), but not in films on Si(111). Furthermore, attempts at electrical polarization switching did not produce compelling evidence for ferroelectricity. Instead, electrical transport measurements demonstrated locally varying anisotropic responses with the applied electric fields along different in-plane directions, with some hysteresis associated with the trapping of charges.

36 MATERIALS SCIENCE↗

DOE OSTI2020

Non-autoregressive time-series methods for stable parametric reduced-order models

Advection-dominated dynamical systems, characterized by partial differential equations, are found in applications ranging from weather forecasting to engineering design where accuracy and robustness are crucial. There has been significant interest in the use of techniques borrowed from machine learning to reduce the computational expense and/or improve the accuracy of predictions for these systems. These rely on the identification of a basis that reduces the dimensionality of the problem and the subsequent use of time series and sequential learning methods to forecast the evolution of the reduced state. Often, however, machine-learned predictions after reduced-basis projection are plagued by issues of stability stemming from incomplete capture of multiscale processes as well as due to error growth for long forecast durations. To address these issues, we have developed a non-autoregressive time series approach for predicting linear reduced-basis time histories of forward models. In particular, we demonstrate that non-autoregressive counterparts of sequential learning methods such as long short-term memory (LSTM) considerably improve the stability of machine-learned reduced-order models. Further, we evaluate our approach on the inviscid shallow water equations and show that a non-autoregressive variant of the standard LSTM approach that is bidirectional in the principal component directions obtains the best accuracy for recreating the nonlinear dynamics of partial observations. Moreover-and critical for many applications of these surrogates-inference times are reduced by three orders of magnitude using our approach, compared with both the equation-based Galerkin projection method and the standard LSTM approach.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2020

Analyzing the Performance Trade-Off in Implementing User-Level Threads

User-level threads have been widely adopted as a means of achieving lightweight concurrent execution without the costs of OS-level threads. Nevertheless, the costs of managing user-level threads represent a performance barrier that dictates how fine grained the concurrency exposed by an application can be without incurring significant overheads; this in turn may translate into insufficient parallelism to exploit highly parallel systems. This article is a deep dive into the fundamental costs in implementing user-level threads. We first identify that one of the highest sources of fork-join overheads stems from deviations, events that incur context switching during the execution of a thread and disrupt a run-to-completion execution. We then conduct an in-depth investigation of a wide spectrum of methods with respect to how they handle deviations while covering both parent- and child-first scheduling policies. Our methodology involves a comprehensive instruction- and cache-level analysis of all methods on several modern CPU architectures. Finally, the primary finding of our evaluation is that dynamic promotion methods that assume the absence of deviation and dynamically provide context-switching support offer the best trade-off between performance and capability when the likelihood of deviation is low.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2023

Passive Safety System Reliability Analysis: Lessons Learned and Open Items

Passive safety systems have multiple benefits, largely stemming from their high functional reliability due to their simplicity and lack of dependencies. However, accurately assessing the reliability of passive systems can be challenging due in large part to the possibility of functional failures. Argonne National Laboratory (Argonne) has been involved in several recent efforts concerning the reliability assessment of passive safety systems for advanced, non-light water reactors (non-LWRs). These efforts have focused on the development and application of mechanistic methods for the evaluation of passive system reliability, which rely on system modelling and uncertainty analyses to derive reliability values for use in probabilistic safety assessments (PSAs). Further information on these efforts is provided in ref [1]. The current paper reviews key lessons learned through these projects, along with outstanding open items regarding the reliability assessment of passive systems.

Grabaskas, David↗

DOE OSTI2021

Comparative establishment and yield of bioenergy sorghum and maize following pre–emergence waterlogging

Biofuel feedstocks grown on marginal lands, such as those that experience ephemeral waterlogging, reduce interference with food agriculture. As early-season extreme precipitation events in the U.S. Midwest continue to increase, waterlogging tolerance may play an important role in the productivity of annual biofuel cropping systems. We assessed the establishment and yield of photoperiod-sensitive sorghum [Sorghum bicolor (L.) Moench] and maize (Zea mays L.) after extreme early-season rainfall events in central Illinois. We used paired sorghum and maize transects, spanning from low-lying poorly drained areas to higher better drained areas, to evaluate the response of both cropping systems throughout the 2020 growing season. Sorghum maintained 25% mean emergence rates in areas that experienced the most severe waterlogging, but maize failed to establish under the same conditions. Despite the low establishment, sorghum yields in the poorly drained areas were upwards of 50% of those found in the better drained, whereas maize yields were zero areas in the poorly drained locations. The yield compensation in sorghum resulted from increased tillering, higher productivity per stem, and subsequently higher productivity per plant, which illustrates the greater phenotypic plasticity of sorghum compared to maize. Land managers who are currently seeking to grow annual cellulosic feedstocks on soils that are vulnerable to transient soil waterlogging will likely have more success with photoperiod-sensitive bioenergy sorghum than maize.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2026

Prelithiated SiO x /Graphite-NMC811 Cells: Capacity Loss, Impedance Rise and Hidden Degradation Pathways Revealed Using Three-Electrode Diagnostics

Electrodes containing SiO x /graphite (Gr) materials are attractive as anodes for high-energy lithium-ion batteries. However, their mechanical deformation, electrochemical response, and impedance evolution during long-term cycling are strongly coupled, complicating accurate diagnosis of performance fade mechanisms. In this work, the behavior of electrochemically prelithiated SiO x /Gr anodes paired with NMC811 cathodes is systematically investigated using techniques that include in-situ dilatometry, three-electrode electrochemistry, and multiscale post-cycling microscopy. The SiO x /Gr electrode exhibits a maximum expansion of 49% upon lithiation to 10 mV vs Li + /Li, with 84% of the expansion and 91% of the capacity being reversible. In full cells, relatively stable cycling with only 12% capacity fade over 500 cycles is observed. Three-electrode experiments reveal cell-level impedance growth, which is dominated by the NMC811 cathode: the SiO x /Gr anode exhibits minimal net impedance rise and an initial impedance decrease at low potentials. Despite this apparent electrochemical stability, cross-sectional SEM, PFIB tomography, and cryo-STEM reveal irreversible anode thickening caused by the accumulation of an inorganic-rich solid electrolyte interphase (SEI) permeating the anode bulk. Electrode potential-shift analysis further demonstrates that Li + ions released from lithium reservoirs in the prelithiated anode mask true lithium inventory loss during aging. These results demonstrate that low-expansion SiO x /Gr anodes can simultaneously exhibit favorable cycling and impedance metrics while undergoing substantial, hidden degradation, underscoring the importance of electrode-resolved diagnostics for evaluating prelithiated silicon-based anodes.

25 ENERGY STORAGE↗

DOE OSTI2020

Uptake and speciation of zinc in edible plants grown in smelter contaminated soils

Heavy metal accumulation in edible plants grown in contaminated soils poses a major environmental risk to humans and grazing animals. This study focused on the concentration and speciation of Zn in different edible plants grown in soils contaminated with smelter wastes (Spelter, WV, USA) containing high levels of the metals Zn, Cu, Pb, Cd. Their accumulation was examined in different parts (roots, stem, and leaves) of plants and as a function of growth stage (dry seed, sprouting seed, cotyledon, and leaves) in the root vegetables radish, the leafy vegetable spinach and the legume clover. Although the accumulation of metals varied significantly with plant species, the average metal concentrations were [Zn] > [Pb] > [Cu] > [Cd]. Metal uptake studies were complemented with bulk and micro X-ray absorption spectroscopy (XAS) at Zn K-edge and micro X-ray fluorescence (μXRF) measurements to evaluate the speciation and distribution of Zn in these plant species. Dynamic interplay between the histidine and malate complexation of Zn was observed in all plant species. XRF mapping of spinach leaves at micron spatial resolution demonstrated the accumulation of Zn in vacuoles and leaf tips. Radish root showed accumulation of Zn in root hairs, likely as ZnS nanoparticles. At locations of high Zn concentration in spinach leaves, μXANES suggests Zn complexation with histidine, as opposed to malate in the bulk leaf. These findings shed new light on the dynamic nature of Zn speciation in plants.

59 BASIC BIOLOGICAL SCIENCES↗

DOE OSTI2026

Stream Chemistry, Synoptic Surveys, East Fork Poplar Creek Watershed, TN, USA; April 2023 to February 2025

Impacts of developed land cover on stream chemistry can be difficult to discern from natural variability, particularly in carbonate watersheds where weathering of urban infrastructure and lithology generate similar signatures. We evaluated how spatial patterns of stream chemistry varied across perennial and non-perennial tributaries spanning an urban-to-forested gradient in a mid-order, carbonate-dominated watershed. This data package contains a processed and compiled summary of stream chemistry and properties obtained from 12 synoptic surveys of 54 stream sites across the East Fork Poplar Creek watershed located near Oak Ridge, TN, United States. The sites include non-perennial tributaries, perennial tributaries, and the main stem and span forested to urban (highly developed) land cover gradients. The data package includes the processed and flagged chemical data (WaDE_SynopticSummary_FinalChemistry), metadata describing data flagging and analysis (WaDE_SynopticSummary_Metadata), information about each site and its contributing subcatchment (WaDE_SynopticSummary_SiteInformation), and a comparison of instrument and field detection limits used to determine method detection limits for the study (WaDE_SynopticSummary_DetectionLimitComparison). Stream chemistry includes stream parameters measured in situ using multiparameter probes (dissolved oxygen, pH, specific conductance, temperature) and solutes including nutrients (nitrate, ammonium, soluble reactive phosphorus), dissolved organic carbon, dissolved inorganic carbon, major cations (calcium, magnesium, potassium, sodium), major anions (chloride, sulfate), and a broad suite of minor and trace elements.

EARTH SCIENCE > TERRESTRIAL HYDROSPHERE > SURFACE ↗

DOE OSTI2021

Fixed Bit Rotary Drilling Failure Criteria Effect on Drilling Vibration

Polycrystalline diamond compact (PDC) bit use has been expanded to geothermal drilling applications due to their improved performance in drilling compared to roller cone bits. Multiple vibration models have been developed yet and there are limited use of field data to study the ability of numerical vibration model to predict recorded vibration in the field. The objective of this paper is to evaluate the capabilities of two different rock-bit interaction models with two different ROP formulations for predicting the actual behavior seen in the field. The first model (Model-I) considers the rock-bit interaction, incorporating the effect of subsurface formation characteristics. The second model (Model-II) is a PDC bit model that uses calculated threshold weight-on-cutter (WOCt) to study the interaction of the bit with the rock surface. The models, which couple the drill stem axial and torsional motions, are solved numerically using the fourth-order Runge-Kutta method and compared with recorded downhole drilling data from the Chocolate Mountain Aerial Gunnery Range (CMAGR) geothermal well located in California. An 8–½" PDC bit with 8 blades was modeled and vibration simulation was performed to investigate stick-slip vibrations at 1400 ft. depth. Simulation results using field data indicate that Model-II encounters a low range of stick-slip severity (SSV) as compared to Model-I, which is in the moderate range. Comparison with the field value for SSV shows Model-II is the best representative of the field case for this depth. The sensitivity analysis shows that results are sensitive to model constants making prediction of SSV from models uncertain.

15 GEOTHERMAL ENERGY↗

DOE OSTI2022

Size Dependent Optical Properties and Structure of ZnS Nanocrystals Prepared from a Library of Thioureas

Here, ZnS nanocrystals (λ max (1S e -1S 3/2h ) = 260 - 320 nm, d = 1.7 - 10.0 nm) are synthesized from Zn(O 2 CR) 2 (O 2 CR = tetra-decanoate, oleate and 2-hexyldecanoate), N,N'-disubstituted and N,N',N'-trisubstituted thioureas, and P,P,N-trisubstituted phos-phanecarbothioamides. The influence of precursor substitution, ligand sterics, and reaction temperature on the final nanocrystal size were evaluated. By using saturated hydrocarbon solvents and saturated aliphatic carboxylate ligands, polymeric byproducts could be avoided and pure ZnS nanocrystals isolated. Elevated temperatures, slower precursor conversion reactivity and branched zinc 2-hexyldecanoate yield the largest ZnS nanocrystals. Carefully purified zinc carboxylate, rapidly converting precursors, and cooling the synthesis mixture following complete precursor conversion provide quasi spherical nanocrystals with the narrowest shape dispersity. Nanocrystal sizes were measured using pair distribution function (PDF) analysis of X-ray scattering and scanning transmission electron microscopy (STEM) and plotted versus the energy of their first excitonic optical absorption. The resulting empirical relationship provides a useful method to characterize the nanocrystal size from 1.7-4.0 nm using optical absorption spectroscopy.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗