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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 379 records · Page 21
All sourcesNASA NTRSDOE OSTI
DOE OSTI2024

Integration and Optimization of a Waste Heat Driven Organic Rankine Cycle for Power Generation in Wastewater Treatment Plants

The study focuses on achieving energy self-sufficiency in Wastewater Treatment Plants by proposing a comprehensive model for integrating, sizing, and optimizing an Organic Rankine Cycle system. The Organic Rankine Cycle system is designed to utilize waste heat from the gensets at As Samra Wastewater Treatment Plant in Jordan, where it will contribute to the overall electrical energy supply of the plant. Real data from As Samra Wastewater Treatment Plant is used to model and calculate the available waste heat using TRNSYS® software. The Organic Rankine Cycle model is then developed using ASPEN PLUS® software to explore the impact of operational parameters and determine their optimal values for maximizing the plant's energy profile. An economic analysis is conducted to assess the feasibility of the proposed model, considering system components, installation, operation, and maintenance costs. To optimize the Organic Rankine Cycle system, the study employs the Multi-Output Support Vector Regression technique to capture nonlinear relationships between independent variables (fluid type, turbine inlet pressure, turbine inlet temperature, turbine outlet pressure, and mass flow rate) and dependent variables (pump power input, waste heat input, and turbine specific work). The Osprey optimization algorithm is used to address the multi-objective optimization problem, with the proposed Pareto-based Osprey Optimization Algorithm and the Multi-Objective Particle Swarm Optimization technique being employed to evaluate critical performance and economic parameters such as system thermal efficiency, net power output, and the levelized cost of electricity. The results of the optimization strategies indicate that the M-SVR model's prediction accuracy is significantly improved after parameter optimization, with the model returning high R 2 and low Mean Square Error values of 0.991 and 0.00216, respectively. The Pareto-Based Osprey Optimization Algorithm optimizer identifies the best working fluid as Isobutane/Isopentane in a ratio of 66:34, with optimal turbine inlet pressure and temperature of 15 bars and 218 °C, respectively. In conclusion, the Organic Rankine Cycle model at these optimal conditions achieves a cycle efficiency of 19.93% and an Levelized Cost of Electricity value of 0.0353 USD/kWh.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2024

Bayesian calibration and uncertainty quantification of a rate-dependent cohesive zone model for polymer interfaces

In this work we present a rate-dependent cohesive zone model for the fracture of polymeric interfaces and performs a Bayesian calibration, an uncertainty quantification, and a sensitivity analysis for the model. The proposed cohesive zone model accounts for both reversible elastic and irreversible rate-dependent separation sliding deformation at the interface. The viscous dissipation due to the irreversible opening at the interface is modeled using elastic-viscoplastic kinematics that incorporates the effects of strain rate. Inverse calibration of parameters for such complex models through trial and error is challenging due to the large number of parameters of the model. Moreover, the calibrated parameter values are often non-unique and uncertain when the available experimental data is limited. To tackle this challenge, we employ a Bayesian calibration approach to identify parameters from experimental data, the resulting parameters significantly enhance the accuracy of the model. To quantify the uncertainty associated with the inverse parameter estimation, a modular Bayesian approach is employed to calibrate the unknown model parameters, accounting for the parameter uncertainty of the cohesive zone model. The advantages of the Bayesian calibration over a deterministic parameter fit are demonstrated. Further, to quantify the model uncertainties, such as incorrect assumptions or missing physics, a discrepancy function is introduced, which significantly improves the model’s prediction. Finally, the total uncertainty of the model is quantified in a predictive setting. A sensitivity analysis is performed to assess how changes in the input variables of the model affect the peak load, facilitating the identification of a concise set of highly influential parameters. The present approach can be used for calibration and uncertainty quantification for other complex computational mechanics models. It should also facilitate the designing of interface materials under uncertainty.

42 ENGINEERING↗
DOE OSTI2022

Why is it still too warm or cold in my house? Examining the relationships between energy efficient capital and household energy insecurity

Here, this paper examines the relationships between energy efficient (EE) capital technology and household energy insecurity in the United States. The theoretical model of these relationships employs household production theory to capture the demand for and production of household energy services, and a stochastic production frontier approach to describe how having access to and the usage intensity of EE capital technology could help alleviate inefficiency in the production of household energy services. A working hypothesis formulated from the theoretical model posits that having EE capital technology in the home will reduce the level of household energy insecurity experienced. The extent of energy insecurity experienced is inferred from an energy insecurity index value assigned to each household, generated via the application of a dichotomous Rasch model to questions contained in the 2015 Residential Energy Consumption Survey. Noting the potential simultaneous relationship that exists between a household having access to and the usage intensity of EE capital technology and the experience of being energy insecure, an instrumental variables approach was employed to estimate a series of ordered logit models. Results suggest access to EE capital technology in the form of Energy Star® appliances, Energy Star® windows, or a SMART thermostat does not reduce the probability of experiencing a greater level of energy insecurity. Nor does the usage intensity of EE capital. Thus, policy instruments designed to alleviate household energy insecurity may need to go beyond simply helping households obtain EE capital technology.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2022

Numerical analysis of soot emissions from gasoline-ethanol and gasoline-butanol blends under gasoline compression ignition conditions

In the present work, computational fluid dynamics (CFD) simulations of a single-cylinder gasoline compression ignition (GCI) engine were performed to investigate the impact of blending two biofuels, ethanol and n-butanol, with gasoline on the trade-off between combustion phasing and soot emissions under low load conditions. Here, in order to represent market gasoline (RD5-87), a four-component toluene primary reference fuel (TPRF) + ethanol (ETPRF) surrogate (with 20% ethanol by mole; E20) was formulated using a neural network based octane predictor such that the surrogate had the same ethanol content, Research Octane Number (RON) and Octane Sensitivity (S). In addition, a novel skeletal kinetic mechanism for ETPRF and TPRF + n-butanol (BTPRF) blends, incorporating polycyclic aromatic hydrocarbon (PAH) chemistry, was developed. A three-dimensional (3D) engine CFD formulation employing the skeletal mechanism, adaptive mesh refinement (AMR), finite-rate chemistry approach, and hybrid method of moments (HMOM) was adopted to capture the in-cylinder combustion phenomena and soot emissions. The engine CFD model was validated against RD5-87 experimental data for a broad range of start-of-injection (SOI) timings (-21/-27/-36/-45 crank angle degrees (CAD) after top-dead center (aTDC)), with respect to in-cylinder pressure, heat release rate, combustion phasing, and soot emissions. The closed-cycle simulation results were analyzed to elucidate the non-monotonic trend of soot emissions versus SOI timing: SOI-36 > SOI-45 > SOI-21 > SOI-27. Thereafter, the validated CFD model was employed to simulate the combustion of a gasoline-ethanol blend with 45% (by mole) ethanol (E45) and a gasoline-butanol blend with 45% (by mole) n-butanol (B45) under the same operating conditions to study the effects of fuel composition and SOI timing on combustion phasing and soot emissions. The sooting propensity followed the trend: B45 > E20 > E45 at all SOI timings. Overall, it was observed that the autoignition propensity was primarily related to fuel chemistry. On the other hand, sooting propensity showed strong coupling with both fuel chemistry and physical properties, with greater impact of fuel physical properties at advanced SOI timings.

30 DIRECT ENERGY CONVERSION↗
DOE OSTI2025

Quasi-optical beam tracing module development for millimeter-wave high-wavenumber collective scattering on the NSTX-U and EAST tokamaks

A Python3-based beam tracing code utilizing Quasi-Optics has been developed to track both incident and receiving beams in high-k collective millimeter wave scattering systems within magnetic fusion plasmas. In contrast to existing ray tracing codes that solely consider refraction, this beam tracing code incorporates diffraction phenomena, providing a more comprehensive calculation. Here, this enhanced capability allows for a more accurate calculation of the scattering volume and spatial resolution in high-k collective scattering systems, crucial for evaluating system performance and facilitating data analysis. Unlike Geometrical Optics, Quasi-Optics employs the complex eikonal method, representing a Gaussian beam as a collection of coupled rays to accurately preserve diffraction characteristics. The developed code is intended for application in NSTX-Upgrade and EAST high-k beam tracing analyses, targeting frequencies of 693 GHz and 270 GHz, respectively. The high-k system's primary objective is the observation of electron-scale instabilities. Employing a symplectic integrator, the code ensures numerical accuracy, assessed through the conservation of the Hamiltonian. With its precision and efficiency, the code facilitates rapid inter-shot analyses.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2024

Value of abstraction in performance assessment – When is a higher level of detail necessary?

In this study, different approaches in performance assessment (PA) of the long-term safety of a repository for radioactive waste were examined. This investigation was carried out as part of the DECOVALEX-2023 project, an international collaborative effort for research and model comparison. One specific task of the DECOVALEX-2023 project was the Salt Performance Assessment Modelling task (Salt PA), which aimed at comparing various models and methods employed in the performance assessment of deep geological repositories in salt. In the context of the Salt PA task, three distinct teams from SNL (United States), Quintessa Ltd (United Kingdom), and GRS (Germany) examined the consequences of employing different levels of abstractions when modelling the repository's geometry and implementing various features and processes, using the example of a simple hypothetical repository structure in domal salt. Each team applied their own tools: PFLOTRAN (SNL), QPAC (Quintessa) and LOPOS (GRS). These differ essentially regarding numerical concept and degree of detail in the representation of the underlying physical processes. The discussion focused on when simplifications can be appropriately applied and what consequences result from them. Furthermore, it was explored when and if a higher level of fidelity in geometry or physical processes is required.

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗
DOE OSTI2026

Extraction of lithium from battery recycling wastewater using synergistic D2EHPA and TBP

The recycling of spent lithium-ion batteries (LIBs) poses significant challenges, including the generation of large volumes of chemically complex wastewater. The composition of this wastewater is influenced by both the intrinsic chemistry of the batteries and the specific recycling processes employed. Notably, this wastewater contains economically valuable components, such as lithium, which can be recovered. Here, in this study, a solvent extraction (SX) process was investigated as a method to recover lithium from battery recycling wastewater, especially from anode-washing stage. Initially, various commercial extractants were evaluated, including di(2-ethylhexyl)phosphoric acid (D2EHPA), mono-2-ethylhexyl (2-ethylhexyl)phosphonate (PC88A), bis(2,4,4-trimethylpentyl)phosphinic acid (Cyanex 272), 2-hydroxy-5-nonylacetophenone oxime (LIX 84-I), tri-butyl phosphate (TBP), and their combinations. Among these, D2EHPA + TBP demonstrated a synergism to advance and maximize the lithium extraction. Subsequently, the effects of key parameters, including D2EHPA concentration, TBP concentration, contact time, initial pH, and aqueous-to-organic (A/O) phase ratio, were systematically investigated and optimized. A two-stage SX approach was employed to enhance lithium recovery. Under the optimized conditions of 30.0 vol% D2EHPA, 10.0 vol% TBP, 10 min of contact time, and a 1:1 A/O phase ratio, a lithium extraction efficiency of more than 88% in a two-stage solvent extraction was achieved. Lithium was subsequently stripped from the loaded organic solution using sulfuric acid (H 2 SO 4 ). Using 2.0 M H 2 SO 4 , lithium stripping was achieved after two counter-current stripping stages at an organic-to-aqueous (O/A) phase ratio of 6:1. This stripping process enriched the lithium concentration by a factor of four compared to the original lithium concentration in the anode-washing wastewater. The recyclability of the synergistic D2EHPA + TBP system was also evaluated over four extraction-stripping cycles. The results demonstrated that the system maintained high extraction and stripping efficiencies.

D2EHPA↗
DOE OSTI2022

Rolling Optimization of Transmission Network Recovery and Load Restoration Considering Hybrid Wind-Storage System and Cold Load Pickup

A common solution to deal with the stochasticity introduced by fast-ramping wind power integration is to equip wind farms (WFs) with energy storage systems (ESSs) to formulate hybrid WF-ESSs. In addition to leveling off wind power fluctuations during normal operations, a hybrid WF-ESS can be a flexible power source to accumulate the power system restoration. In this paper, we propose a rolling optimization model for transmission network recovery and load restoration considering the contributions of WF-ESSs. The proposed model is formulated as a mixed integer linear programming problem that simultaneously optimizes the amount and location of restorable load blocks as well as the restoration lines. The cold load pickup features of interrupted loads considering the outage duration are modeled in detail. A chance-constrained method is employed to deal with the uncertainty of wind power, and a rolling horizon-based framework is adopted to reduce the influence of forecast error. Case studies are conducted on both New England 39-bus system and part of a provincial power system in China. The results show that the load restoration process can be significantly accelerated by employing the proposed method and contributions of hybrid WF-ESSs to power system restoration are validated.

chance-constrained optimization↗
DOE OSTI2021

An automated system for continuous monitoring of CO 2 geosequestration using multi-well offset VSP with permanent seismic sources and receivers: Stage 3 of the CO2CRC Otway Project

Time-lapse seismic is an essential tool for monitoring CO 2 injection into the subsurface but suffers from high cost and long intervals between surveys. An alternative approach is continuous monitoring using permanent sources and receivers capable of mapping CO 2 plume evolution on an almost daily basis and drastically reduce human effort, cost and survey turnaround. As part of the CO2CRC Otway project, a continuous monitoring system is employed to monitor a small (15,000 tonnes) CO 2 injection within the CO2CRC Otway Project. The permanent monitoring system employs distributed acoustic sensors (DAS) installed in boreholes as seismic receivers and seismic orbital vibrators (SOV) as seismic sources. The monitoring approach is based on vertical seismic profiling using five wells equipped with DAS cemented behind the casing and nine SOVs and is acquiring the data continuously in an autonomous manner. The data is then automatically processed on-site producing a seismic image every 2.5 days, which is then transmitted to a remote office. The data has good repeatability with NRMS of about ~10-15%.

58 GEOSCIENCES↗
DOE OSTI2022

Deep learning-accelerated 3D carbon storage reservoir pressure forecasting based on data assimilation using surface displacement from InSAR

Fast forecasting of the reservoir pressure distribution during geologic carbon storage (GCS) by assimilating monitoring data is a challenging problem. Due to high drilling cost, GCS projects usually have spatially sparse measurements from few wells, leading to high uncertainties in reservoir pressure prediction. To address this challenge, we use low-cost Interferometric Synthetic-Aperture Radar (InSAR) data as monitoring data to infer reservoir pressure build up. We develop a deep learning-accelerated workflow to assimilate surface displacement maps interpreted from InSAR and to forecast dynamic reservoir pressure. Employing an Ensemble Smoother Multiple Data Assimilation (ES-MDA) framework, the workflow updates three-dimensional (3D) geologic properties and predicts reservoir pressure with quantified uncertainties. We use a synthetic commercial-scale GCS model with bimodally distributed permeability and porosity to demonstrate the efficacy of the workflow. A two-step CNN-PCA approach is employed to parameterize the bimodal fields. The computational efficiency of the workflow is boosted by two residual U-Net based surrogate models for surface displacement and reservoir pressure predictions, respectively. The workflow can complete data assimilation and reservoir pressure forecasting in half an hour on a personal computer.

25 ENERGY STORAGE↗
DOE OSTI2021

Data-driven enhancement of coherent structure-based models for predicting instantaneous wall turbulence

Predictions of the spatial representation of instantaneous wall-bounded flows, via coherent structure-based models, are highly sensitive to the geometry of the representative structures employed by them. In this study, we propose a methodology to extract the three-dimensional (3-D) geometry of the statistically significant eddies from multi-point wall-turbulence datasets, for direct implementation into these models to improve their predictions. The methodology is employed here for reconstructing a 3-D statistical picture of the inertial wall coherent turbulence for all canonical wall-bounded flows, across a decade of friction Reynolds number (Re T ). These structures are responsible for the Re T -dependence of the skin-friction drag and also facilitate the inner-outer interactions, making them key targets of structure-based models. The empirical analysis brings out the geometric self-similarity of the large-scale wall-coherent motions and also suggests the hairpin packet as the representative flow structure for all wall-bounded flows, thereby aligning with the framework on which the attached eddy model (AEM) is based. The same framework is extended here to also model the very-large-scaled motions, with a consideration of their differences in internal versus external flows. Implementation of the empirically-obtained geometric scalings for these large structures into the AEM is shown to enhance the instantaneous flow predictions for all three velocity components. Finally, an active flow control system driven by the same geometric scalings is conceptualized, towards favourably altering the influence of the wall coherent motions on the skin-friction drag.

42 ENGINEERING↗
DOE OSTI2020

CFD-DEM and PR-DNS studies of low-temperature densely packed beds

Over the past few decades, granular media is gaining attention as a viable option for heat transfer fluids (HTFs). Several research efforts are studying the use of particle-based heat transfer fluids in a wide variety of applications. With this motivation, the current work focusses on analyzing the different heat transfer mechanisms in low-temperature mono-sized densely packed granular media. To study the heat transfer behavior of granular media at different scales, the current work employs a two-way coupled computational strategy. The motion of particles is solved using the Discrete Element Method (DEM) and the interstitial air is solved using a Finite-Volume (CFD) approach. The Open-Source library CFDEM Coupling® is used in the current study to join the Finite Volume PISO solver of OpenFOAM® and the DEM solver of LIGGGHTS®. Typically, particle-particle contact conduction and particle-air convection are the most popular closure models. But recent research identified a different heat transfer phenomenon in packed beds that cannot be identified by conduction or convection models. While closure models were developed to implement this on a CFD-DEM framework, they did not capture the effect of intra-particulate thermal gradients on this phenomenon. Hence the current work also employs Particle-Resolved Direct Numerical Simulations (PR-DNS) to gain valuable insights allowing for the modification of existing models. A new closure model is then proposed here and is implemented in the CFD-DEM framework. This model provides key insights into the different heat transfer mechanism of packed beds.

42 ENGINEERING↗
DOE OSTI2024

A phase-field diffraction model for thermo-hydro-mechanical propagating fractures

This paper introduces a novel diffraction based thermo-hydraulic–mechanical (THM) model for fracture propagation using a phase-field fracture (PFF) approach. The key innovation of the THM-PFF model lies in its integrated treatment of four solution variables—displacements, phase-field, pressure, and temperature—each governed by a combination of conservation of momentum (mechanics problem), a variational inequality (constrained minimization problem), mass conservation (pressure problem), and energy conservation (temperature problem). This leads to a new formulation of a coupled variational inequality system. A major advancement is the development of an extended fixed-stress algorithm, where displacements, phase-field, pressures, and temperatures are solved in a staggered sequence. An important aspect of this work is the global coupling of pressures and temperatures across the domain using diffraction systems, with diffraction coefficients defined by material parameters weighted by the diffusive phase-field variable. To ensure robust local mass conservation, we employ enriched Galerkin finite elements (EG) for both pressure and temperature diffraction equations. By enriching the continuous Galerkin basis functions with discontinuous piecewise constants, EG accurately represents solution and parameter discontinuities while preserving local mass and energy conservation—crucial aspects for THM problems and realistic behavior. Moreover, the use of a predictor–corrector local mesh adaptivity scheme is employed, allowing the model to handle small phase-field length-scale parameters while maintaining high numerical accuracy and reasonable computational cost. Furthermore, these new model and algorithmic developments represent significant advances in the field and have been substantiated through rigorous numerical tests.

Diffraction systems↗
DOE OSTI2024

Hierarchical Bayesian modeling for Inverse Uncertainty Quantification of system thermal-hydraulics code using critical flow experimental data

The best estimate plus uncertainty methodology in nuclear system thermal-hydraulic studies necessitates a comprehensive understanding of uncertainties in system code predictions. The forward uncertainty quantification (UQ) process involves the propagation of input uncertainties through the computational models to obtain uncertainties in the outputs. To this end, achieving an accurate estimation of input uncertainties is important, which is the focus of inverse UQ (IUQ). Traditionally, research in Bayesian IUQ within the nuclear engineering domain has largely relied on single-level Bayesian inference. While being effective for relatively small datasets, this approach encounters limitations for cases with large datasets. The use of a single-level model may prove inefficient, as the resultant posterior distributions can significantly differ when distinct subsets of data are employed. To address this issue, we employ an hierarchical Bayesian model for IUQ. Furthermore, this approach involves organizing observations into different groups based on the test conditions, thereby accommodating varying calibration parameters across these distinct groups. In this study, we developed and implemented a hierarchical Bayesian IUQ method to consider the grouping effect of critical flow measurement data from various geometries. Comparing the outcomes of IUQ under different selections of test data using hierarchical Bayesian IUQ against those obtained from single-level Bayesian IUQ, the forward propagation of hierarchical Bayesian IUQ results demonstrates a notably improved agreement with the experimental data.

42 ENGINEERING↗
DOE OSTI2021

Data requirements for improving the Quantitative Risk Assessment of liquid hydrogen storage systems

Quantitative Risk Assessment (QRA) supports the development of risk-informed safety codes and standards which are employed to enable the safe deployment of hydrogen technologies essential to decarbonize the transportation sector. System reliability data is a necessary input for rigorous QRA. Here, the lack of reliability data for bulk liquid hydrogen (LH 2 ) storage systems located on site at fueling stations limits the use of QRAs. In turn, this hinders the ability to develop the necessary safety codes and standards that enable worldwide deployment of these stations. Through a QRA-based analysis of a LH 2 storage system, this work focuses on identifying relevant scenario and probability data currently available and ascertaining future data collection requirements regarding risks specific to liquid hydrogen releases. The work developed consists of the analysis of a general bulk LH 2 storage system design located at a hydrogen fueling station. Failure Mode and Effect Analysis (FMEA) and traditional QRA modeling tools such as Event Sequence Diagrams (ESD) and Fault Tree Analysis (FTA) are employed to identify, rank, and model risk scenarios related to the release of LH 2 . Based on this analysis, scenario and reliability data needs to add LH 2 -related components to QRA are identified with the purpose of improving the future safety and risk assessment of these systems.

08 HYDROGEN↗
DOE OSTI2022

Switching from batch to continuous granulation: A case study of metoprolol succinate ER tablets

Continuous manufacturing (CM) has been used to produce several immediate release drug products. No extended-release (ER) product manufactured employing CM technology has been approved yet. Herein this study investigated the critical aspects of switching from the batch mode of high shear granulation to the continuous operation of twin-screw granulation for extended-release tablets. Metoprolol succinate ER tablets was used as a model ER formulation for this purpose. A central composite design (CCD) was employed to determine the effects of high shear granulator (HSG) parameters, namely impeller speed, granulation time, and binder liquid feeding rate, on the critical granulation characteristics important for product performance. These critical granulation characteristics served as a guide for switching from the batch processing to the continuous operation for achieving the same breaking strength and dissolution for this ER metoprolol tablets. The granulation time was the most critical factor affecting the bulk properties of granules which contributed to tablet dissolution. The higher density and lower compressibility of granules were attained at the longest granulation time of 5.4 min with the fastest liquid feeding rate of 75 g/min. The granules’ density was the primary factor negatively affecting the dissolution of metoprolol tablets. However, the breaking strength of tablets confounded the effect of granules density on metoprolol dissolution. Switching the processing parameters of high shear granulation to twin-screw granulation achieved similar dissolution profiles (F2 greater than 50). The screw speed was not found to affect bulk properties of granules. The root cause of granulation failures in twin-screw granulation, such as premature consolidation, excessive swelling, poor cohesion, inconsistent shearing effects, and formation of deformed agglomerates, were identified. In conclusion, the use of critical granulation characteristics through a performance-based approach of ER tablets facilitated the switching of manufacturing of an ER formulation form batch to continuous operation.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗