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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 1,009 records · Page 56
All sourcesNASA NTRSDOE OSTI
DOE OSTI2021

Does Bayesian model averaging improve polynomial extrapolations? Two toy problems as tests

We assess the accuracy of Bayesian polynomial extrapolations from small parameter values, x, to large values of x. We consider a set of polynomials of fixed order, intended as a proxy for a fixed-order effective field theory (EFT) description of data. We employ Bayesian model averaging (BMA) to combine results from different order polynomials (EFT orders). Our study considers two 'toy problems' where the underlying function used to generate data sets is known. We use Bayesian parameter estimation to extract the polynomial coefficients that describe these data at low x. A 'naturalness' prior is imposed on the coefficients, so that they are $\mathcal{O}(1)$. We BMA different polynomial degrees by weighting each according to its Bayesian evidence and compare the predictive performance of this BMA with that of the individual polynomials. In conclusion, the credibility intervals on the BMA forecast have the stated coverage properties more consistently than does the highest evidence polynomial, though BMA does not necessarily outperform every polynomial.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2021

Model predictive combustion control of a Gasoline Compression Ignition engine

Gasoline Compression Ignition is a novel combustion concept that derives its superiority from the high compression ratio of a compression ignition engine as well as the properties of gasoline fuel, such as longer ignition delay and higher volatility compared to diesel. Here, this combustion concept was experimentally tested on a 12.4L Class 8 truck engine. Based on these experimental data, prior efforts by the authors focused on the development of an engine model for a heavy-duty engine operating on a low-reactivity fuel. This engine model was leveraged within this study to investigate a combustion control strategy at different engine conditions and injection methods and was augmented to incorporate cycle-to-cycle combustion variations. State estimation is performed by means of a Kalman filter which feeds into a model predictive controller. The model predictive controller chooses control actions based on a predefined cost function under consideration of bounds reflecting physical constraints. The engine model was utilized to establish a state-space model that serves the Kalman filter and model predictive controller for estimation and prediction. A comparative study investigating control actions and engine behavior was performed with and without limiting in-cylinder peak pressure as well as combustion noise, which is of particular interest for early pilot injection strategies. In addition, the proposed control architecture was investigated at two different levels of cycle-to-cycle variations and compared to the performance of a control structure with input disturbance rejection. For increased cycle-to-cycle variations, disturbance estimation reduces state fluctuations and control effort. In general, this investigation highlights control aspects specific to a compression-ignited combustion regime with low-reactivity fuel. The control algorithm is able to maintain the desired references for brake mean effective pressure and combustion phasing while controlling peak in-cylinder pressure and combustion noise.

42 ENGINEERING↗
DOE OSTI2026

Ultrasonic-Assisted Extrusion Processing for Enhancing Physical Properties of High-Density Polyethylene by Flow-Induced Crystallization

The evolution of crystallinity resulting from stress imposed on a melt, known as flow-induced crystallinity, can strongly influence the mechanical and physical properties of semicrystalline polymers. This study investigates shear-induced crystallization by applying an ultrasonic field to the melt flow as it passes through dies with various geometries. A custom-built sonication die is employed for controlling the dynamic temperature and shear environment, resulting in molecular alignment and potential for flow-induced crystallization. Application of both conventional and ultrasonic shear rates at the equilibrium melt temperature of high-density polyethylene (HDPE) was investigated to accelerate crystallinity and manipulate the crystal morphology across the film in pursuit of improved mechanical and gas barrier properties without the need for additives or other polymer layers. The relationships among ultrasonic-assisted extrusion processing, polymer structure, and performance were analyzed using wide- and small-angle X-ray scattering (WAXS and SAXS), tensile testing, and oxygen transmission rate (OTR) analysis. Multiple linear regression models were implemented to predict the correlation among HDPE structure, process, and properties. Structural analysis revealed that both conventional and ultrasonic shear rates had the most significant influence on lamellar spacing and redistribution of rigid and soft amorphous fractions within the crystalline domains, ultimately dictating the mechanical and physical properties of the films. The goal is to explore the potential of the ultrasonic-assisted high crystallinity monolayer that can replace some of the functionality of complex, heterogeneous multilayer packaging with a single-material film having enhanced oxygen barrier properties.

crystallinity↗
DOE OSTI2023

Electrodynamic Shaker Capability Estimation Through Experimental Dynamic Substructuring [Thesis]

Electrodynamic shaker systems are an essential tool in shock and vibration testing of dynamic environments. However, the specific performance capability of these systems is difficult to characterize. The dynamics of the shaker itself, the device under test and the specific test configuration used all couple to create a dynamic response unique to each test. Poorly predicted limitations in shaker capability affect the ability to achieve test specifications, delay testing schedules, and create difficulties for choosing test equipment. To predict shaker capability for a specific test configuration prior to setup, a lumped parameter model of the shaker system and a modal model of a device under test was developed. These models were then analytically coupled using LaGrange multiplier frequency based substructuring to estimate their coupled frequency response functions. The coupled frequency response functions were used to predict electrical inputs required to meet a given test specification. These input requirements were finally compared to a validation test using the specification and setup. Input requirements estimated using the substructuring estimated frequency response functions showed significant error. However, results using an ideal frequency response function showed very little error. These results indicate that with a better method of experimental dynamic substructuring employed it would be possible to accurately predict shaker capability for a given test configuration prior to setup.

42 ENGINEERING↗
DOE OSTI2022

Monitoring of Temperature Measurements for Different Flow Regimes in Water and Galinstan with Long Short-Term Memory Networks and Transfer Learning of Sensors

Temperature sensing is one of the most common measurements of a nuclear reactor monitoring system. The coolant fluid flow in a reactor core depends on the reactor power state. We investigated the monitoring and estimation of the thermocouple time series using machine learning for a range of flow regimes. Measurement data were obtained, in two separate experiments, in a flow loop filled with water and with liquid metal Galinstan. We developed long short-term memory (LSTM) recurrent neural networks (RNNs) for sensor predictions by training on the sensor’s own prior history, and transfer learning LSTM (TL-LSTM) by training on a correlated sensor’s prior history. Sensor cross-correlations were identified by calculating the Pearson correlation coefficient of the time series. The accuracy of LSTM and TL-LSTM predictions of temperature was studied as a function of Reynolds number (Re). The root-mean-square error (RMSE) for the test segment of time series of each sensor was shown to linearly increase with Re for both water and Galinstan fluids. Using linear correlations, we estimated the range of values of Re for which RMSE is smaller than the thermocouple measurement uncertainty. For both water and Galinstan fluids, we showed that both LSTM and TL-LSTM provide reliable estimations of temperature for typical flow regimes in a nuclear reactor. The LSTM runtime was shown to be substantially smaller than the data acquisition rate, which allows for performing estimation and validation of sensor measurements in real time.

Pantopoulou, Stella↗
DOE OSTI2022

Precision QCD, Hadronic Structure & Forward QCD, Heavy Ions: Report of Energy Frontier Topical Groups 5, 6, 7 submitted to Snowmass 2021

This report was prepared on behalf of three Energy Frontier Topical Groups of the Snowmass 2021 Community Planning Exercise. It summarizes the status and implications of studies of strong interactions in high-energy experiments and QCD theory. We emphasize the rich landscape and broad impact of these studies in the decade ahead. Hadronic interactions play a central role in the high-luminosity Large Hadron Collider (LHC) physics program, and strong synergies exist between the (HL-)LHC and planned or proposed experiments at the U.S. Electron-Ion Collider, CERN forward physics experiments, high-intensity facilities, and future TeV-range lepton and hadron colliders. Prospects for precision determinations of the strong coupling and a variety of nonperturbative distribution and fragmentation functions are examined. We also review the potential of envisioned tests of new dynamical regimes of QCD in high-energy and high-density scattering processes with nucleon, ion, and photon initial states. The important role of the high-energy heavy-ion program in studies of nuclear structure and the nuclear medium, and its connections with QCD involving nucleons are summarized. We address ongoing and future theoretical advancements in multi-loop QCD computations, lattice QCD, jet substructure, and event generators. Cross-cutting connections between experimental measurements, theoretical predictions, large-scale data analysis, and high-performance computing are emphasized.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2022

High-Temperature Latent Heat Storage System using Transportable Heat Pipes for Versatile Integration with Emerging Microreactors

With the growing interest in the development of micro nuclear reactors and their application to microgrid, high-temperature thermal energy storage (TES) has also been receiving great attention as a technology to reinforce the energy utilization and profitability of this class of reactors. Given the importance and research needs of the TES technology, a research project is underway at Idaho National Laboratory (INL) to develop high-temperature TES that can be easily interfaced with emerging micro nuclear reactors. Currently under development is the Heat pipe-Integrated Thermal Battery (HITB), a novel latent heat storage design that enables flexible and robust integration with any design of currently considered micro nuclear reactors. The development of detailed design based on the proposed TES concept, HITB, requires not only development and validation of modeling and simulation capabilities, but also research that spans thermal performance analysis, component- and system-scale design optimization, and experimental demonstrations including high-temperature feasibility testing. The current research efforts (and plans) are divided into the following four areas: (i) Thermal and structural modeling and analysis at both system- and component-scales for evaluating performance and conducting design optimization, (ii) Small-scale experimental demonstrations for the key components of the proposed HITB design, (iii) Modelica-based dynamic model development for developing a control strategy under dynamic operation within integrated energy systems configuration, (iv) Integrated experimental testing and demonstration. This paper introduces the overall research efforts that have been and are being conducted to develop and demonstrate the novel design of the high-temperature latent heat storage device, HITB. The key design features, ongoing research efforts including the development of modeling and simulation capabilities, small-scale demonstrations of the HITB’s key functionalities prior to scaled-up experimental demonstration, are discussed. Then, this paper concludes with a remark on the future research plans for this project.

25 ENERGY STORAGE↗
DOE OSTI2024

Drop Analysis of a Department of Energy Standard Canister Containing Fort Saint Vrain SNF– 24138

DOE manages over 300 types of SNF, many of which are located at the INL site. Managing this large variety of SNF for storage, transportation, and disposal poses a challenge to DOE. The Idaho Cleanup Project and INL are collaborating on the DOE SNF Road-Ready Demonstration (“Road-Ready Demonstration”), which will develop and demonstrate the designs, technology, processes, and regulatory framework for packaging DOE-managed SNF for “road-ready dry storage.” Road-ready dry storage (RRDS) is an SNF management concept in which SNF is packaged into dry, sealed canisters that are then placed in on-site storage in anticipation of later transport and disposition. The forward-looking goal of the Road-Ready Demonstration is to establish the foundation for a large-scale RRDS program at the INL site. One critical aspect of RRDS is the ability to certify the DOE Standard Canister and its associated transportation package in accordance with 10 CFR 71 for offsite transportation. Depending on the SNF type and transportation strategy, DOE Standard Canisters may be required to maintain structural integrity under hypothetical accident scenarios (e.g., drop events). The DOE Standard Canisters have been tested and analyzed under various SNF loading configurations and accident drop events in support of the Idaho Spent Fuel Facility and other DOE programs; however, no analysis has yet been completed in support of the recently initiated Road-Ready Demonstration. This paper presents preliminary results from a finite element analysis of the Ø45.7 cm × 4.6 m (Ø18 in. × 15 ft) DOE Standard Canister under the 9 m drop at 80 degrees off-vertical drop scenario considered in previous INL tests and analyses. It considers the Fort St. Vrain spent nuclear fuel loading configuration proposed for the Road-Ready Demonstration, uses updated material properties, and applies the strain-based acceptance criteria established in ASME Boiler and Pressure Vessel Code’s Section III, Division 3 rules for storage and transportation spent nuclear fuel containments. This updated analysis is compared to previous DOE Standard Canister drop analyses. Preliminary results from the updated analysis show that certain regions of the containment exceed the allowable limits during the accidental drop event. However, these regions are limited to components performing a non-structural function. While further work on this analysis will be pursued, this analysis serves as the foundation for formal calculations used to support applicable certification efforts of the RRDS system at INL.

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗
DOE OSTI2024

High-Performance Iridium–Molybdenum Oxide Electrocatalysts for Water Oxidation in Acid: Bayesian Optimization Discovery and Experimental Testing

Ir oxides are costly and scarce catalysts for oxygen evolution reaction (OER) in acid. There has been extensive interest in developing alternatives that are either Ir-free or require smaller amounts of Ir to drive the reactions at acceptable rates. One design strategy is to identify Ir-based mixed oxides that achieve similar performance while requiring smaller amounts of Ir. The obstacle to this strategy has been a very large phase space of the Ir-based mixed metal oxides, in terms of the metals combined with Ir and the different crystallographic structures of the mixed oxides, which prevents a thorough exploration of possible materials. In this work, we developed a workflow that uses machine-learning-aided Bayesian optimization in combination with density functional theory to make the exploration of this phase space plausible. This screening identified Mo as a promising dopant for forming acid-tolerant Ir-based oxides for the OER. We synthesized and characterized the Ir–Mo mixed oxides in the form of thin-film electrocatalysts with a known surface area. We show that these mixed oxides exhibited overpotentials ~30 mV lower than a pure Ir control while maintaining 24% lower Ir dissolution rates than the Ir control. Furthermore, these findings suggest that Mo is a promising dopant and highlight the promise of machine learning to guide the experimental exploration and optimization of catalytic materials.

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

Glucuronidase Function in Clinical Samples

The purpose of this CRADA is to expand upon the work previously performed through CRADA No. PNNL/381. As explained in the previous CRADA, Glucuronidase enzymes are important for contributing to enterohepatic recycling of numerous therapeutic drug compounds, resulting in impaired or prolonged drug activity. Further, other pathways within the gut, such as bile acid metabolism through the action of bile salt hydrolase enzymes (BSH), have shown to impact both the development of colon cancers and impact recovery. Adapting the activity-based protein profiling (ABPP) technologies developed at PNNL to a microtiter plate-based chemical probe assay was the first step in demonstrating the clinical usability of the technology. With assay proof-of-concept complete through CRADA No. PNNL/381, clinical cohort testing using the developed assay is the next step towards commercialization of this technology. Some chemotherapeutic agents are mediated by gut-produced microbiota through enterohepatic circulation. For example, irinotecan, the first line treatment for many colon and other cancers, can be recycled into a patient’s system through gut microbiota produced glucuronidase enzymes. A significant percentage of patients subjected to irinotecan treatment experience dose-limiting diarrhea and gastric distress that limits treatment options and delays effective intervention. Relatedly, bile salt hydrolases modify ratios of bile acids in the gut, which alter nutrient and pharmaceutical uptake by the intestines and impact patient response to treatment. Coupled together these gut-mediated disorders can significantly decrease patient outcomes. In this project, we propose to investigate a clinical cohort of samples from colon cancer patients to better understand gut microbiome activities surrounding microbiota produced glucuronidases to aid clinician decisions and enhance therapeutic intervention. Our project will measure glucuronidase activity of individual patients and compare this activity to a variety of factors, including patient medical history, treatment response, and overall outcomes. Data will be compared with metagenomic, metabolomic, and proteomic analyses to fully defined the entire gut microbiome space and correlate results with the glucuronidase activity. Resultant analysis is expected to provide actionable information to direct decision making towards therapeutic intervention.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2020

Multi-Gas Sensors for Enhanced Reliability of SOFC Operation

GE Research, in partnership with SUNY Polytechnic Institute (SUNY Poly), designed, built, and tested gas sensors for in situ monitoring of H 2 and CO anode tail gases produced with on-site steam reforming in solid oxide fuel cell (SOFC) systems. The knowledge of the H 2 /CO ratio of these anode tail gases should allow accurate determination and control of the efficiency of the reforming process in the SOFC system and should deliver a lower operating cost for SOFC customers. The project objectives were to achieve multi-gas monitoring capability with a single multivariable sensor, and to sustain this performance in the presence of gaseous interferences and a potential poison for the sensor. The duration of the project was 24 months with the project structure that included three technical tasks such as (1) development of design rules of photonic nanostructures for H 2 and CO gas detection, (2) laboratory validation of photonic nanostructures for selective H 2 and CO gas detection, and (3) validation of photonic nanostructures for initial stability and poison-resistance against H 2 S. To build multivariable sensors for selective H 2 and CO gas detection in the presence of interferences, we expanded our earlier knowledge of multi-gas sensors into new fabrication and functionalization methodologies as well as into new methodologies for the spectral data analysis of multi-gas responses. We have advanced our design rules of the three-dimensional (3D) photonic nanostructures that allowed detection of H 2 and CO at high temperatures as individual gases and as their mixtures and rejection of interferences such as CO 2 , H 2 O, CH 4 , and other hydrocarbons for SOFC applications. Our advanced design rules should be attractive for building the new generation of cost-effective industrial sensors. Stability and poison resistance of our 3D photonic nanostructures was tested in the laboratory conditions. While initially we utilized conventional machine learning data analysis tools, we have found that they were unable to correct for the sensor drift. Thus, we have implemented new methods of machine learning for the analysis of our spectral data. These learnings pave the way to move the future studies into advanced testing of effects of interferences, aging and field tests. In future, our work will continue to advance our sensing designs to operate in conditions with known and unknown interferences by implementing nanostructures with enhanced spectral diversity of responses to gaseous species of interest and interferences. Our systematic reduction of technical risks in this completed project and in future studies will ensure transition of this sensing technology to commercialization.

03 NATURAL GAS↗
DOE OSTI2022

Predicting the Cetane Number, Sooting Tendency, and Energy Density of Terpene Fuel Additives

Abstract Discovering renewable fuels and fuel additives is paramount in reducing carbon emissions from internal combustion engines. Terpenes, a group of compounds that can be synthesized from plant matter and microorganisms, have gained significant interest in recent years as promising candidates for fuels/additives. Terpenes are a diverse class of compounds that contain rings and methyl branches, resulting in high energy densities and optimal cold weather behavior. Their variation in bond order, carbon chains, and functional groups lead to varying degrees of soot formation and performance in existing engines. The present work leverages predictive models, namely artificial neural networks, to predict the cetane number (CN), sooting tendency (quantified with yield sooting index, YSI), and energy density (quantified with lower heating value, LHV) of terpenes and hydrogenated terpenes whose sooting propensities were previously determined through experimental means. Predicted sooting propensities of these terpenes are compared with experimental values, and predicted cetane numbers and energy densities are used to comment on the compounds’ ability to act as fuels/additives. Expected prediction errors for CN, YSI, and LHV, defined by blind test set median absolute error, are within 5.56 cetane units, 3.63 yield sooting index units, and 0.77 MJ/kg respectively. Additionally, the present work investigates a variety of correlation/dependence metrics for property-property relationships, furthering our understanding of how combustion-relevant properties are related.

09 BIOMASS FUELS↗
DOE OSTI2020

Experiments at Sandia to Measure the Effect of Temperature on Critical Systems

Estimation of the uncertainty in a critical experiment attributable to uncertainties in the measured experiment temperature is done by calculating the variation of the eigenvalue of a benchmark configuration as a function of temperature. In the low-enriched water-moderated critical experiments performed at Sandia, this is done by 1) estimating the effects of changing the water temperature while holding the UO 2 fuel temperature constant, 2) estimating the effects of changing the UO 2 temperature while holding the water temperature constant, and 3) combining the two results. This assumes that the two effects are separable. The results of such an analysis are nonintuitive and need experimental verification. Critical experiments are being planned at Sandia National Laboratories (Sandia) to measure the effect of temperature on critical systems and will serve to test the methods used in estimating the temperature effects in critical experiments.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2020

Heat Loss Correction Factor for Fireball Yield Measurements

Fireball yield calculations are performed using Taylor’s radius equation. However, at early times, the x-ray diffusion process has a significant effect on the size of the fireball, and at late times the shock wave degenerates into an acoustic wave. Thus, earlier research determined that the range of validity for Taylor’s equation was in the scaled time regime of 0.004–0.008 s. So, when performing a yield calculation, the only values that are analyzed are those that lie within that range of validity. While Taylor’s equations offer an approximation of the scaled yield, they also rely on several assumptions. For example, the equations assume that the shock wave is adiabatic, when, in fact, the first light pulse on the nuclear test films, which occurs as the shock wave is forming, suggests that the shock wave is not adiabatic. Furthermore, the amount of heat loss from the shock wave that occurs prior to the scaled time regime of 0.004–0.008 s can change significantly as a function of air density. This may cause the shock wave radius to be smaller relative to the assumed adiabatic shock wave. Therefore, the objective of this project was to determine the correction factor for heat transfer, which should be applied in order to produce a more accurate approximation of the weapon’s yield.

45 MILITARY TECHNOLOGY, WEAPONRY, AND NATIONAL DEF↗
DOE OSTI2020

Experiments at Sandia to Measure the Effect of Temperature on Critical Systems [Slides]

Estimation of the uncertainty in a critical experiment attributable to uncertainties in the measured experiment temperature is done by calculating the variation of the eigenvalue of a benchmark configuration as a function of temperature. In the low-enriched water-moderated critical experiments performed at Sandia, this is done by 1) estimating the effects of changing the water temperature while holding the UO 2 fuel temperature constant, 2) estimating the effects of changing the UO 2 temperature while holding the water temperature constant, and 3) combining the two results. This assumes that the two effects are separable. The results of such an analysis are nonintuitive and need experimental verification. Critical experiments are being planned at Sandia National Laboratories (Sandia) to measure the effect of temperature on critical systems and will serve to test the methods used in estimating the temperature effects in critical experiments.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2022

CORRTEX Analysis Techniques

Continuous Reflectometry for Radius versus Time Experiments, or CORRTEX, is a diagnostic that was developed during nuclear testing to measure the distance at which rock walls were disrupted by an explosive device as a function of the time at which disruption occurred. Distances recorded are dynamic measurements of cable length as the shockwaves from an explosive event crush the experiment cables, reducing their usable length. Applications of the CORRTEX diagnostic have been expanded in recent times to include instrumentation of the explosive device itself, to record phenomena such as the speed of the detonation wave in the explosive charge and the crushing or disassembly of material in the charge housing. The diagnostic has likewise been used to monitor explosive performance in rock blasting and oil well hole-clearing activities.

42 ENGINEERING↗
DOE OSTI2026

Laboratory and field assessment of mid-infrared absorption (MIRA) instrument performance for methane and ethane dry mole fractions

Concurrent measurements of methane (CH 4 ) and ethane (C 2 H 6 ) can be used to identify and separate methane sources, as ethane is present in thermogenic sources (e.g., oil and natural gas) but not in biogenic sources (e.g., agriculture). In this study, we evaluated the performance of multiple Aeris MIRA Ultra instruments (Versions 1 and 2) through controlled laboratory tests and tower-based deployments under field conditions. The systems were modified with an external pump, flow control, a Nafion dryer, and a custom-built auxiliary box to automate the system and transmit near real-time data. We determined the best calibration approach for our application, given practical limitations, to be a full calibration cycle (with ambient and high calibration cylinders) about once per day and an ambient calibration cylinder sampled hourly. Measurement uncertainty was assessed, including the uncertainty due to instrument noise as a function of calibration frequency, uncertainty in the water vapor correction, and cylinder assignment uncertainty. Instrument noise was the dominant source of uncertainty for C 2 H 6 , while the water vapor correction dominated the CH 4 uncertainty. For Version 2 systems with hourly calibrations and a Nafion dryer with counterflow, the mean total uncertainty, including both systematic errors and noise, of hourly averages was 0.8–3.0 ppb CH 4 and 0.35–0.37 ppb C 2 H 6 . Laboratory intercomparisons showed network compatibility within 1.2 ppb CH 4 and 0.23 ppb C 2 H6, and a collocated deployment with a NOAA Picarro system agreed within 1.8 ppb CH 4 . Instrument noise varied substantially amongst the instruments, with errors reaching up to 11 ppb CH 4 and 2 ppb C 2 H 6 for hourly means, with similar variability indicated in a 50 h cylinder test. With appropriate engineering and calibration, the Aeris MIRA Ultra has the potential to distinguish regional methane emission sources in many field settings.

03 NATURAL GAS↗