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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 451 records · Page 25
All sourcesNASA NTRSDOE OSTI
DOE OSTI2022

MatPhase: Material phase prediction for Li-ion Battery Reconstruction using Hierarchical Curriculum Learning

Li-ion Batteries (LIB), one of the most efficient energy storage devices, are used extensively in many industrial applications. These batteries consist of electrodes that are put together with heterogeneous material compositions. Imaging data of these battery electrodes obtained from X-ray tomography can explain the distribution of material constituents and allow reconstructions to study electron transport pathways. Such reconstructions of material constituents help quantify various associated properties of electrodes (e.g., volume-specific surface area, porosity) which determine the performance of batteries. These images often suffer from low image contrast between multiple material constituents, hence making it difficult for humans to distinguish and characterize these constituents through visual inspection. A minor error in detecting distributions of the material constituents can lead to magnified errors in the calculated parameters of material properties (e.g., porosity). We present MatPhase, a novel hierarchical curriculum learning technique to address the complex task of estimating material constituent distribution in battery electrodes. MatPhase comprises three modules: (i) an uncertainty-aware global model trained to yield inferences conditioned upon global knowledge of material distribution, (ii) a local model to capture relatively more fine-grained (local) distributional signals, (iii) an aggregator model to appropriately fuse the local and global effects towards obtaining the final distribution. On average, MatPhase improves prediction up to 8.5% relative to other sophisticated modeling pipelines and state-of-the-arts (SOTA) object detection models employed in the performance comparison.

Tabassum, Anika↗
DOE OSTI2020

Electronic Scanning Strategies in Adaptive Electrical Capacitance Volume Tomography: Tradeoffs and Prospects

Electrical Capacitance Volume Tomography (ECVT) has been applied for imaging of multiphase flows found in industrial applications. The ill-posed nature of the image reconstruction problem in ECVT and the consequent low resolution can be alleviated by employing electronic scanning enabled by electrode segmentation and reconfiguration during data acquisition. Here we study electronic scanning strategies that mimic physical rotation and shifting of the sensor along its symmetry axis. First, we study the feasibility of electronic scanning by analyzing the capacitance transducer circuit in SPICE. Then, we simulate electronic scanning using the finite element method for different electrode shapes, for which we compare the image reconstruction results and acquisition time. We find a noticeable improvement in image resolution for the scanning cases over conventional ECVT. Lastly, among the scanning cases, we emphasize a particular electrode shape that provides the best image resolution along with the minimal amount of acquisition time.

47 OTHER INSTRUMENTATION↗
DOE OSTI2020

Toward Water Volume Fraction Calculation in Multiphase Flows Using Electrical Capacitance Tomography Sensors

Real-time monitoring of water volume fraction in multiphase flows is an important problem for a number of industrial applications. The water phase in the multiphase flows may correspond to either the dispersed phase or the continuous phase. In the past, several lowcost and nonintrusive techniques based on the electrical capacitance tomography (ECT) has been developed to image and monitor in real-time multiphase flows containing water. Furthermore, such monitoring becomes increasingly challenging for high salinity levels, and no reliable ECTbased method is presently available which could work for obtaining water volume fraction in multiphase flows for all water salinity levels. In this paper, we propose a new approach based on the Hanai’s formula for complex dielectric constant and taking advantage of the Maxwell- Wagner-Sillars effect to obtain, to a good approximation, water volume fractions in multiphase flows containing water as either dispersed or continuous phase.

02 PETROLEUM↗
DOE OSTI2025

A Miniaturized, High-Bandwidth Optical Fiber Fabry–Perot Cavity Vibration Sensor Demonstrated up to 800 °C

A typical structural health monitoring technique involves measuring the vibrational characteristics of components or systems to detect signs of degradation or damage. Many industrial applications require engineered systems to safely operate under extreme, high-temperature environments that pose challenges not only to materials but also to sensors that would be used for structural health monitoring. Here, in this study, miniaturized optical Fabry-Perot cavities (FPCs) were developed and tested as a means of measuring the resonant frequencies of metal components that are most relevant to extreme-environment applications. Two of the three candidate FPC designs tested up to 800 ° C provided accurate measurements (validated by theoretical models and laser Doppler vibrometry) of the fundamental vibrational mode of the specimen to which each was bonded, although both sensors failed during thermal cycling. An analysis of the reflected optical spectrum from the FPC and X-ray computed tomography revealed two opportunities to improve the sensor reliability. First, the Cu optical fiber coating that was used could either be replaced with a more oxidation-resistant material or protected with commercially available films. Second, the adhesives used to bond the fibers to metal capillaries and establish the FPC could be replaced with a more robust solution, although the Resbond 907TS adhesive appeared to outperform Resbond 907.

Birri, Anthony [Oak Ridge National Laboratory (ORN↗
DOE OSTI2025

A Novel Secondary Frequency Regulation with Optimal Priority Selection of AGC Contributions

Automatic generation control (AGC) is used to maintain acceptable frequencies during operation owing to fluctuations in load and variable resources. In conventional industry applications, the AGC signal is allocated to each generator according to the predispatched frequency regulation capacity or the order of economic efficiency. However, with the increasing integration of inverter-based resources (IBRs), the retirement of conventional synchronous generators (SGs) has posed new challenges to frequency control schemes because fewer of them are optional for AGC regulation. In this paper, we propose a novel model predictive control (MPC)-based frequency regulation model to reduce control cost and ensure stability, by considering different critical dynamic factors when optimally selecting the AGC units. The proposed control model – developed in a general form – comprehensively embeds characteristics such as generator ramping rates, reserve capacity, and operation cost. The model predictive control–based two-timescale AGC scheme enhances the capability of immunizing the power disturbance from types of resources by coordinating the control signals between faster IBRs and slower SGs. The case study’s proposed model is verified to be effective in synergistically enforcing different dynamic properties of AGC units into the frequency regulation scheme.

Jiang, Sufan [The University of North Carolina at ↗
DOE OSTI2022

Signal-Compensation-Based Adaptive PID Control for Fused Magnesia Smelting Processes

For fused magnesia smelting processes, the proportional-integral-derivative (PID) controller is difficult to regulate the smelting currents within desired ranges due to unexpected variations of the dynamics caused by material feeding operations. To solve this problem, we first proposed its dynamic model composed of a linear model and an uncertain nonlinear dynamic system that describes the unknown time-varying dynamics of nonlinearity and strong couplings among controlled variables. Then, using the accurately calculated value of the uncertain part at the previous sampling time instant and the tracking error that reflects the influence of its change rate, we developed a feedforward and a one-step optimal control to obtain two signal compensators to eliminate the impact of frequent variations of the uncertainties. For the compensators, we proposed a weight selection method to minimize the sum of the squared tracking error with ensured closed-loop stability. Based on the abovementioned modeling and control structure, we finally developed a novel adaptive PID controller augmented with signal compensators to control the whole process. Industrial applications have shown that the proposed method can always keep the tracking error and the control input within their targeted ranges, leading to a significantly reduced energy consumption per ton and increased fused magnesia production.

Wang, Weizhou↗
DOE OSTI2021

Pressure-Driven Fiber-Optic Sensor for Online Corrosion Monitoring

For many industrial applications, corrosion is a life-limiting phenomenon and therefore requires careful consideration and rigorous experimental testing before structural materials can be reliably deployed, particularly in harsh chemical environments. The traditional approach to measuring corrosion requires exposing many samples to the intended environment and then extracting them individually at discrete intervals for postexposure characterization. This approach does not provide a high degree of temporal resolution, nor does it provide any real-time information regarding dynamic changes in corrosion rates. Additionally, developing an online corrosion monitor capable of surviving harsh chemical environments could provide valuable information regarding the structural health of components and changing process conditions that could accelerate corrosion. To this end, a corrosion sensor was developed based on a pressure-driven Fabry-Pérot cavity (FPC). This sensor uses a pressure control system to internally pressurize the FPC formed between the sensor’s housing and a metal-embedded singlemode optical fiber. Simultaneous measurement of the change in FPC length using low-coherence interferometry and the applied pressure enables the calculation of the relative changes in the sensor’s diaphragm thickness due to corrosion on its outer surface. Measurements were made in situ while actively corroding the sensor and were validated against surveillance specimens that were corroded simultaneously. The uncertainties in the measured corrosion were analyzed using error propagation of the uncertainties in the measured pressures and displacements and were found to be <1% of the initial diaphragm thickness.

42 ENGINEERING↗
DOE OSTI2025

Adaptation and Plasticity of Nannochloropsis sp. in Response to Seasonal and Geographic Climate Variation

Phytoplankton respond to their environment through genetic adaptation and plasticity to maintain fitness. This poses challenges when growing microalgae for industrial applications because, inherently, outdoor mass cultivation may lead to adaptations that alter desirable phenotypic traits and environmental niches. Here, we used common garden experiments to quantify the plasticity and adaptive responses to seasonal and geographic climate variation of Nannochloropsis, a microalga commonly used in biotechnology. An initially monoclonal strain was grown outdoors across four locations in Hawaii, Texas, California, and New Mexico. Following 17 and 22 months of cultivation outdoors, we collected samples during winter and summer, respectively, and we compared strains' growth from the four sites across temperature and light gradients in the laboratory. Despite hundreds of generations of exposure to divergent climates, with ~20°C and three-fold differences in daily light intensity, strains showed only minor differences in performance. Thermal performance varied more among seasons than sites, whereas light performance varied with both season and site. Our study indicates that Nannochloropsis exhibits broad plasticity in response to light and temperature, which may inhibit genetic adaptation in space or time. Highly variable field conditions, with daily and seasonal climate fluctuations, may favor plasticity and prevent the rapid adaptation often seen in laboratory studies of microorganisms in constant environments.

common garden experiment↗
DOE OSTI2026

Leveraging Inequality-Constrained Data for Enhanced Liquidus Temperature Prediction in Nuclear Waste Glass Melts

Inequality-constrained data are frequently discarded in engineering, leading to significant information loss in data-scarce domains like glass characterization in nuclear waste vitrification. This paper presents a nonparametric censored-data regression framework based on an l1-norm optimization criterion that leverages slack variables to integrate left-, right-, and interval-constrained observations into training without distributional assumptions. Validated on synthetic data and a Physics-Informed Neural Network (PINN) for predicting liquidus temperature (TL), the method improved R2 from 0.60 to 0.89 and reduced Mean Absolute Error (MAE) by 48% (51.46 to 26.89?rC) on deterministic values. The traditional models failed to satisfy any inequality constraints while the proposed l1-norm PINN satisfies 81.25% of the constraints. The proposed framework effectively extracts actionable information from previously unusable data to enhance predictive accuracy, reduce epistemic uncertainty, and ensure physical consistency in complex industrial applications.

Garcia-Morado, Erick↗
DOE OSTI2023

Simultaneous suppression of lignin, tricin and wall‐bound phenolic biosynthesis via the expression of monolignol 4‐ O ‐methyltransferases in rice

Summary Grass lignocelluloses feature complex compositions and structures. In addition to the presence of conventional lignin units from monolignols, acylated monolignols and flavonoid tricin also incorporate into lignin polymer; moreover, hydroxycinnamates, particularly ferulate, cross‐link arabinoxylan chains with each other and/or with lignin polymers. These structural complexities make grass lignocellulosics difficult to optimize for effective agro‐industrial applications. In the present study, we assess the applications of two engineered monolignol 4‐ O ‐methyltransferases (MOMTs) in modifying rice lignocellulosic properties. Two MOMTs confer regiospecific para ‐methylation of monolignols but with different catalytic preferences. The expression of MOMTs in rice resulted in differential but drastic suppression of lignin deposition, showing more than 50% decrease in guaiacyl lignin and up to an 90% reduction in syringyl lignin in transgenic lines. Moreover, the levels of arabinoxylan‐bound ferulate were reduced by up to 50%, and the levels of tricin in lignin fraction were also substantially reduced. Concomitantly, up to 11 μmol/g of the methanol‐extractable 4‐ O ‐methylated ferulic acid and 5–7 μmol/g 4‐ O ‐methylated sinapic acid were accumulated in MOMT transgenic lines. Both MOMTs in vitro displayed discernible substrate promiscuity towards a range of phenolics in addition to the dominant substrate monolignols, which partially explains their broad effects on grass phenolic biosynthesis. The cell wall structural and compositional changes resulted in up to 30% increase in saccharification yield of the de‐starched rice straw biomass after diluted acid‐pretreatment. These results demonstrate an effective strategy to tailor complex grass cell walls to generate improved cellulosic feedstocks for the fermentable sugar‐based production of biofuel and bio‐chemicals.

4-O-methylated↗
DOE OSTI2020

Measurements of Density and Sound Speed in Mixtures Relevant to Supercritical CO2 Cycles

Abstract The objective of this research is to validate properties of mixtures relevant to supercritical carbon dioxide (sCO2) power cycles. Direct-fired sCO2 cycles are a promising technology for the future power generation systems. The working fluid of sCO2 cycles will be near and above critical point of CO2. One of the challenges is that the simulation of mixtures should consider real gas behavior. Expected operating conditions of Allam cycles reach up to 300 bar and 1000 °C. Characterizing the mixtures at the extreme conditions is an important issue in current researches and industrial applications. Thermophysical properties of mixtures may be beyond the valid range of the widely used database such as NIST REFPROP. Experimental data of mixture properties in the literature are limited which is necessary to develop high-fidelity design tools for sCO2 power cycles. We measured the density and sound speed of several multi-component mixtures. A temperature-controlled high-pressure test cell was used for the density measurements. Sound speed was measured by resonant frequency detection using an external speaker and a piezoelectric pressure sensor. Mixtures studied in this work include carbon dioxide, methane, oxygen, and water vapor. Properties of pure CO2 were measured to show the validity of our technique. Compositions were selected to be close to frozen mixtures at the inlet, mid-progress, and exhaust conditions of a model sCO2 combustor in the previous numerical simulation work. Corresponding reaction progress variables (RPV) were RPV = 0, 0.5, and 1. Temperature and pressure conditions of experiments are 310–450 K and 0–150 bar. In our study, density and sound speed from the NIST REFPROP database agree with experimental measurements within the range of our measurement uncertainties.

Energy & Fuels↗
DOE OSTI2021

Fuel Stratification Influence on NO x Emission in a Premixed Axial Reacting Jet-in-Crossflow at High Pressure

Three reacting jet-in-crossflow (JiC) methane/air flames were numerically investigated in a lean axially staged combustor at a pressure of five atmospheres. A detailed chemistry Star-CCM+ computational fluid dynamics (CFD) model was used with 53 species considered and the result of turbulence-governed finite-rate modeling was validated with in-house experimental data. An optically accessible test section features three side windows, allowing local flow and flame analysis with particle image velocimetry (PIV) and CH* chemiluminescence as well as pressure, temperature, and species exit measurements. The research objective was to predict and verify NO x formation of the premixed 12.7 mm axial jet. Three headend temperature levels were investigated along with three premixed jets at lean (φ Jet = 0.75), near-stoichiometric (φ Jet = 1.07), and rich (φ Jet = 1.78) axial fuel line equivalence ratio. Based on the matching exit emission concentration, global emission benefits were investigated by adjustment of the fuel stratification. The perfectly premixed methane/air flames of this study were shown to ignite at the lee-side of the jet. For the elevated headend temperature level T = 1800 K, the flame extended beyond the windward jet trajectory and caused high axial NO production. For industry application, a firing temperature of 1920 K was achieved with a NO x optimized fuel split of 25%, combining a lean headend (φ Headend = 0.61) with a rich (φ Jet = 1.78) jet equivalence ratio. As a result, this operating point allowed minimization of the combustor residence time at temperatures above 1700 K as well as combustion in a compact flame at the jet lee-side along the counter rotating vortex pair.

03 NATURAL GAS↗
DOE OSTI2021

Economic, Exergy, and Environmental Analyses of the Energy Assessments for U.S. Industries

Abstract This paper highlights the expected versus actual outcomes of 152 energy assessments that were performed between 2011 and 2020. The 1317 energy-assessment recommendations (ARs) are grouped into eight categories. This study adopted four measures per each category of recommendations: annual electricity savings, annual gas savings, annual cost savings, and annual CO2 emission reduction. The first part of the analysis compares the expected to the actually implemented values of the measures applied to each recommendation’s category. It was found that the percentages of the actual to the expected electricity, gas, and cost savings are 26.6%, 11.4%, and 17.1%, respectively, while the percentage of the actual to the expected CO2 reduction is 22%. Moreover, the second part of the analysis presents each category's implementation rate and the reasons for rejecting the unimplemented ARs. Cash flow and expensive initial investment resulted in rejecting 25% of ARs. Furthermore, the study proposes techniques and strategies to increase ARs’ implementation rate and improve private energy services companies’ implementation rate. Finally, exergy analysis is added to show the improvement that energy assessment achieves regarding exergy and exergy efficiencies of different industrial applications.

Energy & Fuels↗
DOE OSTI2023

Experimental Performance of a Nonlinear Control Strategy to Regulate Temperature of a High-Temperature Solar Reactor

Abstract Despite the significant potential of solar thermochemical process technology for storing solar energy as solid-state solar fuel, several challenges have made its industrial application difficult. It is important to note that solar energy has a transient nature that causes instability and reduces process efficiency. Therefore, it is crucial to implement a robust control system to regulate the process temperature and tackle the shortage of incoming solar energy during cloudy weather. In our previous works, different model-based control strategies were developed namely a proportional integral derivative controller (PID) with gain scheduling and adaptive model predictive control (MPC). These methods were tested numerically to regulate the temperature inside a high-temperature tubular solar reactor. In this work, the proposed control strategies were experimentally tested under various operation conditions. The controllers were challenged to track different setpoints (500 °C, 1000 °C, and 1450 °C) with different amounts of gas/particle flowrates. Additionally, the flow controller was tested to regulate the reactor temperature under a cloudy weather scenario. The ultimate goal was to produce 5 kg of reduced solar fuel magnesium manganese oxide (MgMn2O4) successfully, and the controllers were able to track the required process temperature and reject disturbances despite the system's strong nonlinearity. The experimental results showed a maximum error in the temperature setpoint of less than 0.5% (6 °C), and the MPC controller demonstrated superior performance in reducing the control effort and rejecting disturbances.

Energy & Fuels↗
DOE OSTI2023

A Physics-Based Data-Driven Approach for Modeling of Environmental Degradation in Elastomers

Abstract Elastomers are now commonly used in a number of industries, including aerospace, structure, transportation, shipbuilding, and automotive, due to their excellent workability, formability, and flexibility. During their activity, elastomers are subjected to harsh environmental conditions, which decreases their resilience. False predictions made early in their lives can have major financial and environmental implications. Elastomers’ performance and properties, such as strength, durability, and density, are influenced by chemical changes in these materials, known as degradation, which occurs over time. This process can alter the morphology of a polymer matrix as well as cause chain scission and cross-linking, resulting in different behaviors than that of the unaged material. To demonstrate the effect of thermaloxidative aging on the mechanical behavior of elastomers, several experimental and theoretical models have been proposed. In view of the large volume of experimental data available on micro-structural evolution in the course of aging, we propose a physics-based data-driven approach to overcome the shortcomings of both phenomenological and micro-mechanical models. This work presents a novel thermodynamically consistent, multiagent machine-learned model for predicting the constitutive behavior of cross-linked elastomers during environmental aging, such as thermo-oxidative and hydrolytic aging for various states of deformation. Single mechanism degradation changes the polymer matrix over time where it is causing chain scission, reduction of cross-links, and morphology change. To capture the idealized Mullins effect and permanent set due to the effect of single aging mechanisms on nonlinear mechanical responses of elastomers, we propose a data-driven model for simulating inelastic elements in a polymer matrix. By using a sequential order reduction, we were able to reduce the 3D stress-strain tensor mapping problem to a small number of super-constrained 1D mapping problems. To systematically classify such mapping problems into a few categories, an assembly of multiple replicated conditional neural network learning agents (L-agents) is used based on our recent work. Each category is represented by a different type of agent. The effect of deformation history, aging time, and aging temperature is captured by this model. The model is validated using a broad collection of data, ranging from our experimental results to data from the literature. In addition, thermodynamic consistency and frame independence are investigated. The most significant achievements of this model are its precision, simplicity, and prediction of inelasticity under various states of deformation. The model’s accuracy and simplicity make it a good option for commercial and industrial applications. Conveniently, due to the model modular nature, it can be expanded in the future to include viscoelasticity and non-isotropic formation for better precision.

Ghaderi, Aref↗
DOE OSTI2023

Comparing Experimental Results for Large Particle Separation from Non-Newtonian Slurries Using Full and Tapered Bump Arrays

To separate particles from carrier fluids arrays of staggered posts inserted into the flow stream can be employed. The smaller particles follow the flow stream while the larger particles move laterally to one side. During flow, the concentration of particulate increases in one direction and decreases in the other crosswise direction. This technique separates the particles from the carrier fluid. Experiments using a slurry with non-Newtonian rheology were conducted to evaluate large particle separation from the non-Newtonian carrier fluid using bump arrays. The bentonite kaolin clay slurry with non-Newtonian rheology was spiked with large diameter inert glass particles. Experiments with flow through lateral displacement arrays evaluated the performance of particle separation using two array configurations: a full array with all posts and a tapered array with posts removed in a triangular portion of the array past the rows of anticipated particle of separation. The posts are staggered posts to promote particle segregation to one side of the flow channel. Using this type of array for removing particles from non-Newtonian yield stress slurries is novel and we present unique results. In this paper, the performance of full and tapered arrays are compared. These results have many industrial applications including removing particles from slurries of nuclear waste. Large particle removal is an important step in waste processing.

slurry, solids removal, deterministic lateral disp↗
DOE OSTI2022

Tailoring sub-3.3 Å ultramicropores in advanced carbon molecular sieve membranes for blue hydrogen production

Carbon molecular sieve (CMS) membranes prepared by carbonization of polymers containing strongly size-sieving ultramicropores are attractive for high-temperature gas separations. However, polymers need to be carbonized at extremely high temperatures (900° to 1200°C) to achieve sub-3.3 Å ultramicroporous channels for H 2 /CO 2 separation, which makes them brittle and impractical for industrial applications. Here, we demonstrate that polymers can be first doped with thermolabile cross-linkers before low-temperature carbonization to retain the polymer processability and achieve superior H 2 /CO 2 separation properties. Specifically, polybenzimidazole (PBI) is cross-linked with pyrophosphoric acid (PPA) via H bonding and proton transfer before carbonization at ≤600°C. The synergistic PPA doping and subsequent carbonization of PBI increase H 2 permeability from 27 to 140 Barrer and H 2 /CO 2 selectivity from 15 to 58 at 150°C, superior to state-of-the-art polymeric materials and surpassing Robeson’s upper bound. This study provides a facile and effective way to tailor subnanopore size and porosity in CMS membranes with desirable molecular sieving ability.

42 ENGINEERING↗