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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 955 records · Page 53
All sourcesNASA NTRSDOE OSTI
DOE OSTI2022

Automated Control for Nuclear Thermal Propulsion Start-Up using MOOSE-based Applications

This report presents a Griffin/Bison/RELAP-7 numerical model of a prototypical NTP system that features fuel assemblies arranged in rings, and which was designed to simulate rapid startup transients. The physics modeled include full-core neutronics, assembly-wise heat conduction, and conjugate heat transfer, with the balance of plant mainly imposed through boundary conditions. In addition, various forms of automated reactivity control were deployed by using the MOOSE to autonomously drive the model and simulate the reactor transitioning from assumed initial conditions to nominal power in a fraction of a minute. To generate the cross-sections of the neutronics model, and in an effort to simultaneously account for the tremendous axial temperature gradients in the reactor and to limit the number of state points required for cross-section generation, the average component temperatures and hydrogen densities in the cooling channels were correlated to the average fuel and moderator temperatures, and fixed axial profiles were derived for nominal conditions and then used during the transient. With this approximation, a tractable cross-section library tabulated with fuel/moderator temperatures and CD angles was generated using Serpent. The full-core SPH correction procedure and the CD decusping technology in Griffin, respectively, ensure preservation of the multiplication factor and reaction rates at state points, along with a reasonably accurate reactivity worth between tabulated CD angles, despite using a coarse mesh. Feedback from other physics was calculated by modeling one representative fuel assembly per ring, along with the corresponding fuel and moderator cooling channels. To limit power overshoots during startup, another layer of multiphysics coupling was added to the model in order to automatically control the drums. Two different technologies presented herein showed outstanding performance in this regard: (1) a novel hybrid PID controller based on both power and reactivity signals, and (2) a PGC that relies on kinetics parameters and reactivity coefficients to predict future behavior and adjust the desired signal accordingly. A challenging benchmark was devised, featuring a power demand curve that exponentially increases by a factor of 500 within 30 seconds, then levels out after that. Both control approaches create a simulated power curve that closely follows the power demand curve and limits power overshoots to 1% or less. While the former approach requires more tuning of the internal parameters, the latter requires additional knowledge of the reactivity feedback coefficients and rates of change of the corresponding variables, including fuel and moderator temperature, which could be difficult to dynamically measure for a real NTP system. Fortunately, some inaccuracy in these quantities will not drastically degrade the PGC performance. Subsequently, a more realistic startup sequence was considered, in which the mass flow rate and outlet pressures are ramped up to model bootstrap and thrust build-up phases prior to reaching steady-state conditions, demonstrating the ability of the hybrid PID and PGCs to handle such transients, with both types of controllers exhibiting very similar behavior. Nevertheless, a significant chamber temperature overshoot was observed, caused by the demanded power signal and assumed mass flow rate. This issue could be mitigated by deploying a reactor controller that follows the chamber temperature signal and actuates both the control valves and drums (rather than using a power signal based solely on the drums to control reactivity). Enhancement of the hydrogen fluid properties available in MOOSE, as well as a better understanding of prototypical initial conditions, are also needed to further enhance this startup model. Finally, a study was performed to model decay heat post-shutdown, and to prepare for extending this model to predict shutdown behavior and post-shutdown pulsed cooling requirements.

33 ADVANCED PROPULSION SYSTEMS↗
DOE OSTI2020

Tuning spin-orbit coupling towards enhancing photocurrent in hybrid organic-inorganic perovskites by using mixed organic cations

The presence of the heavy elements leads to strong spin-orbit coupling (SOC) in hybrid organic-inorganic perovskites (HOIPs), which plays an important role in the photovoltaic performance of HOIPs by changing the populations between bright and dark states. The organic cation is a critical composition and affects SOC via the interaction with the inorganic cage. In this work, we use magneto-photocurrent measurement to explore the SOC effect upon using mixed organic cations (methylammonium (MA) and formamidinium (FA)) with different dipole moments in Pb based perovskites. Magneto-photocurrent measurements showed that the internal magnetic parameter B0 is decreased from 402.41 mT to 180.18 mT and 104.58 mT with decreasing the MA/FA ratio. This provides evidence that changing the internal dipole moment by using mixed organic cations can essentially change the SOC in HOIPs. Simultaneously, the photocurrent is increased from 21.46 mA/cm 2 to 22.60 mA/cm 2 and 23.08 mA/cm 2 when the MA/FA ratio is changed from 1:0 to 0.9:0.1 and 0.7:0.3. Above all, our results indicate that the change in SOC by different organic cations can also be a factor affecting the performance of perovskite solar cells.

36 MATERIALS SCIENCE↗
DOE OSTI2024

Analyzing the effect of doping concentration in split-well resonant-phonon terahertz quantum cascade lasers

The effect of doping concentration on the temperature performance of the novel split-well resonant-phonon (SWRP) terahertz quantum-cascade laser (THz QCL) scheme supporting a clean 4-level system design was analyzed using non-equilibrium Green’s functions (NEGF) calculations. Experimental research showed that increasing the doping concentration in these designs led to better results compared to the split-well direct-phonon (SWDP) design, which has a larger overlap between its active laser states and the doping profile. However, further improvement in the temperature performance was expected, which led us to assume there was an increased gain and line broadening when increasing the doping concentration despite the reduced overlap between the doped region and the active laser states. Through simulations based on NEGF calculations we were able to study the contribution of the different scattering mechanisms on the performance of these devices. We concluded that the main mechanism affecting the lasers’ temperature performance is electron-electron (e-e) scattering, which largely contributes to gain and line broadening. Interestingly, this scattering mechanism is independent of the doping location, making efforts to reduce overlap between the doped region and the active laser states less effective. Optimization of the e-e scattering thus could be reached only by fine tuning of the doping density in the devices. By uncovering the subtle relationship between doping density and e-e scattering strength, our study not only provides a comprehensive understanding of the underlying physics but also offers a strategic pathway for overcoming current limitations. This work is significant not only for its implications on specific devices but also for its potential to drive advancements in the entire THz QCL field, demonstrating the crucial role of e-e scattering in limiting temperature performance and providing essential knowledge for pushing THz QCLs to new temperature heights.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2024

Non-Invasive Biophysical Techniques to Monitor the Structural Plasticity of the Photosynthetic Machinery of Live Diatom Cells

The photosynthetic performance of diatoms depends largely on the organization and structural flexibility of their thylakoid membranes, the densely packed, highly organized membrane vesicles in which light reactions of photosynthesis occur. Different regulatory mechanisms that fine tune the photosynthetic functions affect the organization of the photosynthetic machinery at different levels of structural complexity, from the level of individual protein complexes to the macroarray of membrane proteins and the remodeling of the entire thylakoid membrane system. To monitor these reorganizations, non-invasive techniques are of special value. In this chapter, we focus our attention on three of these techniques, which have been demonstrated to provide unique and useful information on the structure and structural and functional plasticity of live diatom cells: (i) circular dichroism (CD) spectroscopy, which has provided unique information on the chiral (macro-)organization of protein complexes and on their rapid, reversible reorganizations, fine-tuning the light-harvesting processes, as well as on variations in the short-range excitonic interactions in the antenna complexes; (ii) small-angle neutron scattering (SANS), which has been used to determine the periodic organization of the thylakoid membranes and to monitor reversible ultrastructural changes on the time-scale of minutes, induced by variations in the environmental conditions such as changes in temperature or light intensity; and (iii) electrochromic shift absorbance transients (ΔA ECS ), a spectroscopic tool which has been shown to be capable of identifying distinct functional groups of the light-harvesting carotenoid fucoxanthin in different diatoms and in cells exposed to different light intensities. Future use of these techniques will most certainly contribute to the deeper understanding of key regulatory mechanisms of photosynthesis in diatoms.

Szabo, Milan↗
DOE OSTI2021

A generalized machine learning workflow to visualize mechanical discontinuity

Accurate detection and mapping of mechanical discontinuity in materials has widespread industrial and research applications. Herein, we developed a generalized machine-learning framework for visualizing single mechanical discontinuity embedded in material of any composition, velocity, density, porosity, and size with limited data. The proposed visualization of discontinuity requires accurate estimations of the length, location, and orientation of the embedded discontinuity by processing multipoint wave-transmission measurements. k-Wave simulator is used to create a large dataset of elastic waveforms recorded during multi-point wave-transmission measurements through materials containing single mechanical discontinuity. k-Wave simulator considers the wave attenuation, dispersion, and mode conversion in wave motion. Discrete wavelet transform (DWT) and statistical feature extraction are essential for data preprocessing prior to the data-driven model development. DWT also minimizes the effect of noise. Using hyper-parameter tuning and cross validation, gradient boosting regression can visualize the mechanical discontinuity with an accuracy of 0.85, in terms of coefficient of determination. A double-layered neural network-based regression has better performance with an accuracy of 0.95. Use of convolutional neural network converts the predictive task from a waveform processing to an image processing problem. Convolutional neural network achieved a generalization performance of 0.91. The proposed generalized workflow requires robust simulation of wave propagation, signal processing, feature engineering, and model evaluation. Sensors closest to the source and those located opposite the source are the most significant for the desired visualization. Notably, the sensors closest to the source capture the non-linear associations, whereas the sensor on the border opposite to the source capture the linear associations between the measured waveforms and the properties of the mechanical discontinuity.

42 ENGINEERING↗
DOE OSTI2020

In Silico Discovery of Covalent Organic Frameworks for Carbon Capture

We screen a database of more than 69 000 hypothetical covalent organic frameworks (COFs) for carbon capture using parasitic energy as a metric. To compute CO 2 –framework interactions in molecular simulations, we develop a genetic algorithm to tune the charge equilibration method and derive accurate framework partial charges. Nearly 400 COFs are identified with parasitic energy lower than that of an amine scrubbing process using monoethanolamine; more than 70 are better performers than the best experimental COFs and several perform similarly to Mg-MOF-74. We analyze the effect of pore topology on carbon capture performance to guide the development of improved carbon capture materials.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2023

Dark matter trigger for early dark energy coincidence

Current cosmological measurements present a persistent tension in the value of the current cosmic expansion rate, the Hubble constant, as inferred from cosmic microwave background (CMB) and large-scale structure (LSS) data compared to that inferred from the classical distance ladder. Early dark energy (EDE), whose cosmological role is localized in time around the epoch of matter-radiation equality just prior to the release of the CMB photons, is designed to resolve this "Hubble tension". However, the model introduces a new coincidence problem: Why should the EDE dynamics occur near matter-radiation equality if EDE is decoupled from both matter and radiation? The resolution of this problem may lie in an early dark sector (EDS), wherein the dark matter mass is dependent on the EDE scalar field. In this work, we construct such an EDS model and show that it naturally resolves the EDE coincidence problem at the background level without any fine-tuning of the coupling to dark matter or of the initial conditions. When fitting to current cosmological data, including that from the local distance ladder, CMB, and LSS, our EDS maximum-likelihood model performs comparably to EDE for resolving the Hubble tension. However, fitting the Planck CMB data requires a specific range of initial field positions to balance the scalar field fluctuations that drive acoustic oscillations, providing testable differences with other EDE models and a platform for future model-building.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2026

PowerModelsGAT-AI: Physics-Informed Graph Attention for Multi-System Power Flow With Continual Learning

Solving the alternating current power flow equations in real time is essential for secure grid operation, yet classical Newton–Raphson solvers can be slow under stressed conditions. Existing graph neural networks for power flow are typically trained on a single system and often degrade on different systems. We present PowerModelsGAT-AI, a physics-informed graph attention network that predicts bus voltages and generator injections. The model uses bus-type-aware masking to handle different bus types and balances multiple loss terms, including a power-mismatch penalty, using learned weights. We evaluate the model on 14 benchmark systems (4 to 6,470 buses) and train a unified model on 13 of these under contingency conditions with up to two branch outages, achieving an average normalized mean absolute error of 0.89% for voltage magnitudes and R 2 >0.99 for voltage angles. We also show continual learning: when adapting a base model to a new 1,354-bus system, standard fine-tuning causes severe forgetting with error increases exceeding 1000% on base systems, while our experience replay and elastic weight consolidation strategy keeps error increases below 2% and in some cases improves base-system performance. Interpretability analysis shows that learned attention weights correlate with physical branch parameters (susceptance: r=0.38 ; thermal limits: r=0.22 ), and feature importance analysis supports that the model captures established power flow relationships.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2024

Robust and Simple ADMM Penalty Parameter Selection

We present a new method for online selection of the penalty parameter for the alternating direction method of multipliers (ADMM) algorithm. ADMM is a widely used method for solving a range of optimization problems, including those that arise in signal and image processing. In its standard form, ADMM includes a scalar hyperparameter, known as the penalty parameter, which usually has to be tuned to achieve satisfactory empirical convergence. In this work, we develop a framework for analyzing the ADMM algorithm applied to a quadratic problem as an affine fixed point iteration. Using this framework, we develop a new method for automatically tuning the penalty parameter by detecting when it has become too large or small. We analyze this and several other methods with respect to their theoretical properties, i.e., robustness to problem transformations, and empirical performance on several optimization problems. Our proposed algorithm is based on a theoretical framework with clear, explicit assumptions and approximations, is theoretically covariant/invariant to problem transformations, is simple to implement, and exhibits competitive empirical performance.

42 ENGINEERING↗
DOE OSTI2025

Learning-Based Quantum Compilation: Translating QASM to QIR with CodeBERT

We propose a learning-based approach to quantum compilation by translating OpenQASM to Quantum Intermediate Representation (QIR) using a fine-tuned CodeBERT model. Trained on 10,000 synthetic QASM-QIR pairs, the model captures code semantics while addressing QIR verbosity and the 512-token limit via a custom token compression scheme. Finetuning was performed on the Frontier supercomputer, with results showing syntactic correctness and stable validation loss reduction. Our method moves toward enabling flexible, language-modeldriven quantum software tools. It also introduces syntax error handling and the possibility of incorporating classical control constructs, addressing limitations in existing rule-based compilers like qBraid-QIR. While the current model has been validated on quantum-only circuits, we propose future evaluations on hybrid quantum-classical examples. This poster will provide architecture insights, compression examples, training loss plots, and QIR outputs. Our work highlights the potential for scalable, adaptable compilation in future quantum toolchains.

Afrose, Sharmin [ORNL]↗
DOE OSTI2024

Self-Healing Films for Vacuum Insulation Panels (Final Report)

Vacuum insulation panels (VIPs) have an extremely high thermal resistance of around R35/inch, which makes them ideal for building envelope retrofits and prefabricated construction with space constraints. However, the barrier film that maintains the vacuum and thermal performance of the panel can be easily damaged during transportation, installation, and service life. Oak Ridge National Laboratory (ORNL) has developed a self-healable barrier film for VIPs that instantly self-heals damages caused by punctures. The multi-layer barrier film is manufactured using roll-to-roll (R2R) methods. The self-healing barrier film prevents loss of vacuum in VIPs and maintains the exceptional thermal insulation performance. Enhanced durability of VIP by self-healable barrier film and establishing the commercialization path will increase the use of VIPs, thus reducing overall energy usage of buildings. This project has fine-tuned the slurry chemistry of the self-healing components and transitioned the R2R manufacturing trials from mid-scale lab equipment to large-scale industrial equipment for de-risking the technology for commercialization with our TCF CRADA partner, FLEXcon.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2023

A comparison of mechanical properties from natural and process-induced interfaces in filament extrusion AM of polymer blends

Polymer blends are commonly tuned for specific applications to achieve desired properties otherwise inaccessible or prohibitively expensive to obtain via homopolymers. The interfacial characteristics of the polymer A-polymer B interface and resultant domain sizes govern key performance properties. Micro- and meso-scale morphology forms through the interplay of surface forces between the polymers and between each polymer and the surrounding atmosphere. Analogously, the layer-layer and road-road interfaces of material extrusion (MEX) additive manufacturing (AM) govern key performance properties of printed parts. This work explores the effect of layer height on the thermomechanical performance of polystyrene (PS)-polycarbonate (PC) blends. Filament is prepared from a 50/50 weight ratio of the two polymers and compared against dual-nozzle printing where every layer alternates between PS or PC homopolymer forming a part with an overall 50/50 polymer ratio. Typical indicators of polymer blend compatibility are also studied.

Chatham, Camden A.↗
DOE OSTI2021

Q-VR: System-Level Design for Future Mobile Collaborative Virtual Reality

High Quality Mobile Virtual Reality (VR) is what the incoming graphics technology era demands: users around the world, regardless of their hardware and network conditions, can all enjoy the immersive virtual experience. However, the state-of-the-art software-based mobile VR designs cannot fully satisfy the realtime performance requirements due to the highly interactive nature of user's actions and complex environmental constraints during VR execution. Inspired by the unique human visual system effects and the strong correlation between VR motion features and realtime hardware-level information, we propose Q-VR, a novel dynamic collaborative rendering solution via software-hardware co-design for enabling future low-latency high-quality mobile VR. At software-level, Q-VR provides flexible high-level tuning interface to reduce network latency while maintaining user perception. At hardware-level, Q-VR accommodates a wide spectrum of hardware and network conditions across users by effectively leveraging the computing capability of the increasingly powerful VR hardware. Extensive evaluation on real-world games demonstrates that Q-VR can achieve an average end-to-end performance speedup of 3.4x (up to 6.7x) over the traditional local rendering design in commercial VR devices, and a 4.1x frame rate improvement over the state-of-the-art static collaborative rendering.

Xie, Chenhao↗
DOE OSTI2025

Tuning Cation (Dis)Order in Cr‐Based Li‐Excess Oxide Cathode Materials to Improve Li+ Transport Properties

Abstract Li‐excess disordered rocksalts (DRXs) hold promise as next‐generation cathodes for Li‐ion batteries due to their high capacity and energy density, along with the potential to eliminate the need for Co and Ni. However, due to their low Li + diffusivity, DRXs need to be pulverized into nanoparticles to achieve high performance. Herein, a new strategy for overcoming this limitation is demonstrated, involving the design of as‐synthesized partially disordered oxides with a structure that lies between ordered layered and fully disordered, exhibiting a varying degree of local (dis)order. This unique structure activates new Li + diffusion channels, improving percolation and transport properties. This strategy allows a large content of Li + to be accessed in material with large, micron‐sized particles through highly reversible Cr 3+/6+ and O redox, yielding a first discharge capacity of 286 mAh g −1 (881 Wh kg −1 ). The Li + percolation network is further improved by substituting Ti with a mixture of multiple metals, which appears to locally decrease the migration barrier through lattice distortion. Proper tuning of the chemical composition, especially the content of metals with empty d orbitals, is established as a crucial factor for controlling the degree of disorder and mitigating voltage fade and hysteresis growth upon cycling.

Moździerz, Maciej↗
DOE OSTI2021

Band Edge Control of Quasi–2D Metal Halide Perovskites for Blue Light–Emitting Diodes with Enhanced Performance

Perovskite light–emitting diodes (PeLEDs) have received great attention for their potential as next–generation display technology. While remarkable progress has been achieved in green, red, and near–infrared PeLEDs with external quantum efficiencies (EQEs) exceeding 20%, obtaining high performance blue PeLEDs remains a challenge. Poor charge balance due to large charge injection barriers in blue PeLEDs has been identified as one of the major roadblocks to achieve high efficiency. Here band edge control of perovskite emitting layers for blue PeLEDs with enhanced charge balance and device performance is reported. By using organic spacer cations with different dipole moments, that is, phenethyl ammonium (PEA), methoxy phenethyl ammonium (MePEA), and 4–fluoro phenethyl ammonium (4FPEA), the band edges of quasi–2D perovskites are tuned without affecting their band gaps. Furthermore, detailed characterization and computational studies have confirmed the effect of dipole moment modification to be mostly electrostatic, resulting in changes in the ionization energies of ≈0.45 eV for MePEA and ≈ –0.65 eV for 4FPEA based thin films relative to PEA–based thin films. With improved charge balance, blue PeLEDs based on MePEA quasi–2D perovskites show twofold increase of the EQE as compared to the control PEA based devices.

36 MATERIALS SCIENCE↗
DOE OSTI2022

Deconvoluting the benefits of porosity distribution in layered electrodes on the electrochemical performance of Li-ion batteries

Thick Lithium Ion Battery (LIB) electrodes suffer from poor rate capability and high ionic impedance due to their thickness and mesostructure. Therefore, optimizing thick electrode architectures becomes crucial. In this work, we report a systematic assessment of the ionic resistance in heterogeneous porous electrodes through the combination of computational simulations using a 4D-resolved model and experimental measurements. The first part of the study is devoted to a general assessment of Electrochemical Impedance Spectroscopy (EIS) spectra, mapping the impact of ionic and electronic resistances on the overall impedances of uncalendered and calendered LiNi 1/3 Mn 1/3 Co 1/3 O 2 , LiFePO 4 and graphite electrodes. In the second part, in silico-generated electrodes with different porosities are used in computational EIS simulations to analyze the impact of the electrode porosity on the ionic impedance. As expected, the results show that a lower porosity leads to a higher ionic impedance because of a higher electrode tortuosity factor. Furthermore, in silico-generated electrodes with different porosities were stacked and assembled to create heterogeneities of porosity along the thickness, and used in computational EIS and galvanostatic discharge simulations. The computational results show that the porosity heterogeneity along the electrode thickness has a significant effect on the ionic impedance and capacity of the electrode. The electrode architecture with progressively decreasing porosity from separator to current collector shows the highest performance, a trend validated by our in house experimental EIS and galvanostatic discharge also reported in this manuscript. Overall, we conclude that the ionic resistance in a thick electrode can be effectively reduced through proper tuning of the porosity heterogeneity. The proposed heterogeneous electrode architectures presented here could enormously help building efficient thick electrodes for LIBs.

25 ENERGY STORAGE↗
DOE OSTI2020

Gradient microstructure induced shear band constraint, delocalization, and delayed failure in CuZr nanoglasses

The mechanical properties of metallic glasses (MGs) can be significantly tuned by introducing a nanoscale glass-glass interface microstructure. Here, we design three types of Cu 64 Zr 36 MG seamless gradient microstructures by combining glassy grain sizes from 3 to 15 nm. We perform tensile loading molecular dynamics simulations on the named gradient nanoglasses to characterize their mechanical behavior. In all cases, results indicate the generation of gradient plasticity following the gradient in grain size, i.e. local plasticity increases from large to small grain size regions. Loading parallel to the gradient direction results in initial broad necking followed by catastrophic failure. In contrast, loading perpendicular to the gradient direction results in diffused shear band propagation from large to small grain size regions, delaying the generation of a dominant shear band and further delocalizing plastic deformation. In conclusion, the results demonstrate the synergistic effects generated by heterogeneous gradient designs in the mechanical behavior of metallic glasses.

36 MATERIALS SCIENCE↗
DOE OSTI2024

Understanding the effect of refractory metal chemistry on the stacking fault energy and mechanical property of Cantor-based multi-principal element alloys

Multi-principal-element alloys (MPEAs) based on 3d-transition metals show remarkable mechanical properties. In this study, the stacking fault energy (SFE) in face-centered cubic (fcc) alloys is a critical property that controls underlying deformation mechanisms and mechanical response. Here, we present an exhaustive density-functional theory study on refractory- and copper-reinforced Cantor-based systems to ascertain the effects of refractory metal chemistry on SFE. We find that even a small percent change in refractory metal composition significantly changes SFEs, which correlates favorably with features like electronegativity variance, size effect, and heat of fusion. For fcc MPEAs, we also detail the changes in mechanical properties, such as bulk, Young's, and shear moduli, as well as yield strength. A Labusch-type solute-solution-strengthening model was used to evaluate the temperature-dependent yield strength, which, combined with SFE, provides a design guide for high-performance alloys. We also analyzed the electronic structures of two down-selected alloys to reveal the underlying origin of optimal SFE and strength range in refractory-reinforced fcc MPEAs. These new insights on tuning SFEs and modifying composition-structure-property correlation in refractory- and copper-reinforced MPEAs by chemical disorder, provide a chemical route to tune twinning- and transformation-induced plasticity behavior in fcc MPEAs.

36 MATERIALS SCIENCE↗