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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 109 records · Page 6
All sourcesNASA NTRSDOE OSTI
DOE OSTI2021

Network Based Estimation of Wind Farm Power and Velocity Data Under Changing Wind Direction

This paper describes an estimation algorithm for velocity and power output signals in a wind farm under changing wind direction. A graph-theoretic definition describes the wind farm as a collection of nodes (turbines) and time-varying weighted edges (inter-turbine wake propagation) that change as a function of incoming wind direction. The velocity at each turbine is determined through a discrete input-output model. Changes in wind direction serve as the input and the output is defined in terms of a time-varying weighted adjacency matrix that depends on the time-delay of information propagation between turbines. These delays, which are defined in terms of the advection speed of the wind and the distance between the turbines, capture the delayed effect of wind direction changes on the inter-connectivity of the graph as the wind conditions at the farm inlet propagate through the turbine array. An event-based update framework is employed to capture time-dependent topology changes due to shifts in wind direction. Simulation results for dynamically changing wind inlet directions to a circular wind farm are compared to predictions from both the static and dynamic versions of the FLOw Redirection and Induction in Steady State (FLORIS) model. The approach is shown to enable real-time tracking of dynamic changes to wind farm power output within a framework that can be easily integrated into real-time, horizon-based, control strategies that typically do not account for wind direction changes.

distributed↗
DOE OSTI2024

Closed-loop electron-beam-induced spectroscopy and nanofabrication around individual quantum emitters

Color centers in diamond play a central role in the development of quantum photonic technologies, and their importance is only expected to grow in the near future. For many quantum applications, high collection efficiency from individual emitters is required, but the refractive index mismatch between diamond and air limits the optimal collection efficiency with conventional diamond device geometries. While different out-coupling methods with near-unity efficiency exist, many have yet to be realized due to current limitations in nanofabrication methods, especially for mechanically hard materials like diamond. Here, we leverage electron-beam-induced etching to modify Sn-implanted diamond quantum microchiplets containing integrated waveguides with a width and thickness of 280 nm and 200 nm, respectively. This approach allows for simultaneous high-resolution imaging and modification of the host matrix with an open geometry and direct writing. When coupled with the cathodoluminescence signal generated from the electron–emitter interactions, we can monitor the enhancement of the quantum emitters in real-time with nanoscale spatial resolution. The operando cathodoluminescence measurement and fabrication around single photon emitters demonstrated here provide a new foundation for the potential control of emitter–cavity interactions in integrated quantum photonics.

36 MATERIALS SCIENCE↗
DOE OSTI2026

Real-Time GPU-Accelerated OFDR With an Integrated Auxiliary Interferometer

A GPU-accelerated optical frequency domain reflectometry (OFDR) system with an improved integrated auxiliary interferometer is proposed. Unlike conventional approaches that require separate auxiliary interferometers and multiple detection channels, the proposed OFDR system embeds this functionality directly into the signal via an intentional beat component. This enables self-calibration of laser nonlinearity while maintaining a cost-effective hardware configuration. Building on this simplified configuration, the system leverages GPU acceleration with an NVIDIA RTX 4070 Ti to achieve real-time performance, delivering high-throughput signal processing for continuous OFDR interrogation. The signal processing pipeline comprises signal capture, resampling for nonlinearity compensation, and frequency shift computation, all optimized for parallel execution. Hardware benchmarking demonstrates substantial acceleration over CPU implementations, achieving up to a 45× speedup for resampling and frequency shift computations and enabling processing latencies below 30 ms. Thermal response validation is conducted under two complementary scenarios: localized heating using a water bath and cryogenic-temperature conditions using liquid nitrogen. Under localized heating, the system achieves an accuracy of 0.249 °C with a thermal sensitivity of 5.971 GHz/°C, while cryogenic-temperature validation demonstrates a frequency shift response with a sensitivity of 2.383 GHz/°C and an accuracy of 2.04 °C. The high acceleration of the proposed GPU-accelerated OFDR system and its accuracy are achieved by exploiting CUDA-based stride indexing, enabling efficient parallel segmentation and processing of large datasets without additional memory copies. The benchmarking results confirm the robustness, accuracy, and deployability of the proposed OFDR system across a wide temperature range, establishing it as a practical platform for real-time distributed fiber sensing in structurally dynamic environments.

Harb, Salah [Lawrence Berkeley National Laboratory↗
DOE OSTI2023

A Review on Progress, Challenges, and Prospects of Material Jetting of Copper and Tungsten

Copper (Cu) and tungsten (W) possess exceptional electrical and thermal conductivity properties, making them suitable candidates for applications such as interconnects and thermal conductivity enhancements. Solution-based additive manufacturing (SBAM) offers unique advantages, including patterning capabilities, cost-effectiveness, and scalability among the various methods for manufacturing Cu and W-based films and structures. In particular, SBAM material jetting techniques, such as inkjet printing (IJP), direct ink writing (DIW), and aerosol jet printing (AJP), present a promising approach for design freedom, low material wastes, and versatility as either stand-alone printers or integrated with powder bed-based metal additive manufacturing (MAM). Thus, this review summarizes recent advancements in solution-processed Cu and W, focusing on IJP, DIW, and AJP techniques. The discussion encompasses general aspects, current status, challenges, and recent research highlights. Furthermore, this paper addresses integrating material jetting techniques with powder bed-based MAM to fabricate functional alloys and multi-material structures. Finally, the factors influencing large-scale fabrication and potential prospects in this area are explored.

36 MATERIALS SCIENCE↗
DOE OSTI2021

Direct Optical Lithography of Colloidal Metal Oxide Nanomaterials for Diffractive Optical Elements with 2π Phase Control

Spatially patterned dielectric materials are ubiquitous in electronic, photonic, and optoelectronic devices. These patterns are typically made by subtractive or additive approaches utilizing vapor-phase reagents. On the other hand, recent advances in solution-phase synthesis of oxide nanomaterials have unlocked a materials library with greater compositional, microstructural, and interfacial tunability. However, methods to pattern and integrate these nanomaterials in real-world devices are less established. In this work, we directly optically pattern oxide nanoparticles (NPs) by mixing them with photosensitive diazo-2-naphthol-4-sulfonic acid and irradiating with widely available 405 nm light. We demonstrate the direct optical lithography of ZrO 2 , TiO 2 , HfO 2 , and ITO NPs and investigate the chemical and physical changes responsible for this photoinduced decrease in solubility. Micron-thick layers of amorphous ZrO 2 NPs were patterned with micron resolution and shown to allow 2 pi phase control of visible light. We also show multilayer patterning and use it to fabricate features with different thicknesses and distinct structural colors. Upon annealing at 400 degrees C, the deposited ZrO 2 structures have excellent optical transparency across a wide wavelength range (0.3-10 mu m), a high refractive index (n = 1.84 at 633 nm), and are optically smooth. We then fabricate diffractive optical elements, such as binary phase diffraction gratings, that show efficient diffractive behavior and good thermal stability. Different oxide NPs can also be mixed prior to patterning, providing a high level of material tunability. This work demonstrates a general patterning approach that harnesses the processability and diversity of colloidal oxide nanomaterials for use in photonic applications.

2𝜋 phase shift↗
DOE OSTI2022

Neural Lyapunov Differentiable Predictive Control

We present a learning-based predictive control methodology using the differentiable programming framework with sufficient Lyapunov-based guarantees. The neural Lyapunov differentiable predictive control (NLDPC) learns the policy by constructing a computational graph encompassing the system dynamics, state, and input constraints, and the necessary Lyapunov certification constraints, and thereafter using the automatic differentiation to update the neural policy parameters. In conjunction, our approach jointly learns a Lyapunov function that certifies the regions of state-space with stable dynamics. We also provide an initial condition sampling-based statistical guarantee for the training of NLDPC. Our offline training approach provides a computationally efficient and scalable alternative to classical model predictive control solutions, which now directly comes with Lyapunov based guarantees as shown in this paper. We substantiate the advantages of the proposed approach with simulations to stabilize the double integrator model and on an example of controlling an aircraft model.

Mukherjee, Sayak↗
DOE OSTI2025

Accelerating the discovery of low-energy structure configurations: A computational approach that integrates first-principles calculations, Monte Carlo sampling, and Machine Learning

Finding Minimum Energy Configurations (MECs) is essential in fields such as physics, chemistry, and materials science, as they represent the most stable states of the systems. In particular, identifying such MECs in multi-component alloys considered candidate PFMs is key because it determines the most stable arrangement of atoms within the alloy, directly influencing its phase stability, structural integrity, and thermo-mechanical properties. However, since the search space grows exponentially with the number of atoms considered, obtaining such MECs using computationally expensive first-principles DFT calculations often results in a cumbersome task. To escape the above compromise between physical fidelity and computational efficiency, we have developed a novel physics-based data-driven approach that combines Monte Carlo sampling, first-principles DFT calculations, and Machine Learning to accelerate the discovery of MECs in multi-component alloys. More specifically, we have leveraged well-established Cluster Expansion (CE) techniques with Local Outlier Factor models to establish strategies that enhance the reliability of the CE method. In this work, we demonstrated the capabilities of the proposed approach for the particular case of a tungsten-based quaternary high-entropy alloy. However, the method is applicable to other types of alloys and enables a wide range of applications.

36 MATERIALS SCIENCE↗
DOE OSTI2024

Improved Characterization of Soil Organic Matter by Integrating FT-ICR MS, Liquid Chromatography Tandem Mass Spectrometry, and Molecular Networking: A Case Study of Root Litter Decay under Drought Conditions

Understanding of how soil organic matter (SOM) chemistry is altered in a changing climate has advanced considerably; however, most SOM components remain unidentified, impeding the ability to characterize a major fraction of organic matter and predict what types of molecules, and from which sources, will persist in soil. Here we present a novel approach to better characterize SOM extracts by integrating information from three types of analyses, and we deploy this method to characterize decaying root-detritus soil microcosms subjected to either drought or normal conditions. To observe broad differences in composition, we employed direct infusion Fourier-transform ion cyclotron resonance mass spectrometry (DI-FT-ICR MS). We complemented this with liquid chromatography tandem mass spectrometry (LC-MS/MS) to identify components by library matching. Since libraries contain only a small fraction of SOM components, we also used fragment spectral cosine similarity scores to relate unknowns and library matches through molecular networks. This integrated approach allowed us to corroborate DI-FT-ICR MS molecular formulas using library matches, which included fungal metabolites and related polyphenolic compounds. We also inferred structures of unknowns from molecular networks and improved LC-MS/MS annotation rates from ~5 to 35% by considering DI-FT-ICR MS molecular formula assignments. Under drought conditions, we found greater relative amounts of lignin-like vs condensed aromatic polyphenol formulas and lower average nominal oxidation state of carbon, suggesting reduced decomposition of SOM and/or microbes under stress. Our integrated approach provides a framework for enhanced annotation of SOM components that is more comprehensive than performing individual data analyses in parallel.

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

Fourier Neural Networks as Function Approximators and Differential Equation Solvers

We present a Fourier neural network (FNN) that can be mapped directly to the Fourier decomposition. The choice of activation and loss function yields results that replicate a Fourier series expansion closely while preserving a straightforward architecture with a single hidden layer. The simplicity of this network architecture facilitates the integration with any other higher-complexity networks, at a data pre- or postprocessing stage. We validate this FNN on naturally periodic smooth functions and on piecewise continuous periodic functions. We showcase the use of this FNN for modeling or solving partial differential equations with periodic boundary conditions. The main advantages of the current approach are the validity of the solution outside the training region, interpretability of the trained model, and simplicity of use.

Fourier decomposition↗
DOE OSTI2024

Neural network interatomic potential-driven analysis of phase stability in Ti–V alloys at the atomistic scale

The evolution of the ω phase in titanium–vanadium (Ti–V) alloys is critical for their mechanical properties, particularly in aerospace and biomedical applications. Here, this study employs a Rapid Artificial Neural Network (RANN) potential to model the ω phase evolution at the atomistic level, demonstrating a high degree of consistency with experimental observations, unlike the Modified Embedded Atom Method (MEAM), which fails to capture this phase transformation accurately. RANN simulations replicate key phenomena such as the nucleation of α precipitates at ω/β interfaces and accurate lattice orientations, enhancing our understanding of phase stability and transformation kinetics. The findings affirm that RANN potentials can significantly improve the prediction accuracy of complex material behaviors, offering a powerful tool for designing advanced materials with tailored properties such as solute effect in various stacking fault energies. This approach not only bridges the gap between theoretical predictions and empirical data but also sets a new direction for future research in materials science, emphasizing the integration of machine learning techniques in the development and optimization of new alloys.

36 MATERIALS SCIENCE↗
DOE OSTI2024

Markov Decision Processes for Intelligent, Risk-Informed Asset-Management Decision-Making

Advanced nuclear reactors are a promising option for aiding the world in achieving its net-zero carbon emission goals, however, there are significant challenges to attaining and maintaining economic competitiveness with other sources of electricity. To improve the economic competitiveness of advanced reactor designs, a project was initiated to explore the use of Markov Decision Processes (MDPs) to guide asset-management decision-making during advanced reactor operation. MDPs are a powerful tool for optimizing decision-making in complex environments and their application to advanced reactors can aid in planning maintenance and repair activities to minimize downtime and maximize generation. The described approach expands on previous work regarding the use of MDPs for operational decision-making through the direct incorporation of real-time plant information. The integral MDP analysis includes information from online component diagnostic tools and the plant’s real-time generation risk assessment (GRA) and probabilistic risk assessment (PRA), which evaluate plant risk from both an economic and safety perspective. The result is an asset-management optimization framework that is based on real-time data regarding plant component status and the current best-estimate of plant risk. The paper presents an overview of the theoretical framework to incorporate the different information pathways into an integral MDP analysis, along with example analyses.

Grabaskas, David↗
DOE OSTI2026

Fidelity-preserving enhancement of ptychography with foundational text-to-image models

Ptychographic phase retrieval enables high-resolution imaging of complex samples but often suffers from artifacts such as grid pathology and multislice crosstalk, which degrade reconstructed images. We propose a plug-and-play (PnP) framework that integrates physics model-based phase retrieval with text-guided image editing using foundational diffusion models. By employing the alternating direction method of multipliers, our approach ensures consensus between data fidelity and artifact removal subproblems, maintaining physical consistency while enhancing image quality. Artifact removal is achieved using a text-guided diffusion image editing method (LEDITS++) with a pre-trained foundational diffusion model, allowing users to specify artifacts for removal in natural language. Demonstrations on simulated and experimental datasets show significant improvements in artifact suppression and structural fidelity, validated by metrics such as peak signal-to-noise ratio and diffraction pattern consistency. This work highlights the combination of text-guided generative models and model-based phase retrieval algorithms as a transferable and fidelity-preserving method for high-quality diffraction imaging.

image editing↗
DOE OSTI2024

Enhancing Camera Calibration for Traffic Surveillance with an Integrated Approach of Genetic Algorithm and Particle Swarm Optimization

Recent advancements in sensor technologies, coupled with signal processing and machine learning, have enabled real-time traffic control systems to effectively adapt to changing traffic conditions. Cameras, as sensors, offer a cost-effective means to determine the number, location, type, and speed of vehicles, aiding decision-making at traffic intersections. However, the effective use of cameras for traffic surveillance requires proper calibration. This paper proposes a new optimization-based method for camera calibration. In this approach, initial calibration parameters are established using the Direct Linear Transformation (DLT) method. Then, optimization algorithms are applied to further refine the calibration parameters for the correction of nonlinear lens distortions. A significant enhancement in the optimization process is achieved through the integration of the Genetic Algorithm (GA) and Particle Swarm Optimization (PSO) into a combined Integrated GA and PSO (IGAPSO) technique. The effectiveness of this method is demonstrated through the calibration of eleven roadside cameras at three different intersections. The experimental results show that when compared to the baseline DLT method, the vehicle localization error is reduced by 22.30% with GA, 22.31% with PSO, and 25.51% with IGAPSO.

47 OTHER INSTRUMENTATION↗
DOE OSTI2023

Decarbonization of the Chemical Industry Through Electrification: Barriers and Opportunities

The chemical industry is a major source of economic productivity and employment globally and among the top 3 industrial sources of greenhouse gas (GHG) emissions, along with steel and cement. As global demand for chemical products continues to grow, there is an urgency to develop and deploy sustainable chemical production pathways and to reconsider continued investment in current emission-intensive production technologies. This perspective describes the challenges and opportunities to decarbonize the chemical industry via electrification powered by low-carbon electricity supply, both in the near term and long term, and it discusses four technological pathways ranging from the more mature direct substitution of heat with electricity and use of hydrogen to technologically less mature, yet potentially more selective, approaches based on electrochemistry and plasma. Finally, we highlight the key elements of integrating an electrified industrial process with the power sector to leverage process flexibility to reduce energy costs of chemical production and provide valuable power grid support services. Unlocking such plant-to-grid coordination and the four electrification pathways has significant potential to facilitate rapid and deep decarbonization of the chemical industry sector.

chemical synthesis↗
DOE OSTI2022

Gaussian approximation of dispersion potentials for efficient featurization and machine-learning predictions of metal–organic frameworks

Energy-related descriptors in machine learning are a promising strategy to predict adsorption properties of metal–organic frameworks (MOFs) in the low-pressure regime. Interactions between hosts and guests in these systems are typically expressed as a sum of dispersion and electrostatic potentials. The energy landscape of dispersion potentials plays a crucial role in defining Henry’s constants for simple probe molecules in MOFs. To incorporate more information about this energy landscape, we introduce the Gaussian-approximated Lennard-Jones (GALJ) potential, which fits pairwise Lennard-Jones potentials with multiple Gaussians by varying their heights and widths. The GALJ approach is capable of replicating information that can be obtained from the original LJ potentials and enables efficient development of Gaussian integral (GI) descriptors that account for spatial correlations in the dispersion energy environment. GI descriptors would be computationally inconvenient to compute using the usual direct evaluation of the dispersion potential energy surface. Here, we show that these new GI descriptors lead to improvement in ML predictions of Henry’s constants for a diverse set of adsorbates in MOFs compared to previous approaches to this task.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2020

Assessing the Additional Carbon Savings with Biofuel

A recent study claims that corn used for ethanol should not be considered to be inherently biogenically carbon-neutral because not all that corn was grown additional to the level otherwise. By assessing the extent of carbon neutrality of corn for ethanol using the reference point baseline approach and historical data that study concluded that the carbon intensity of US corn ethanol is 27% higher than that of gasoline. We develop a framework to determine the carbon neutrality of corn for ethanol by assessing the additional carbon uptake by crops using an anticipated baseline approach. We also apply this framework to determine the additional corn produced for ethanol and include the direct life cycle carbon emissions of only that portion of corn in the direct life cycle carbon intensity of corn ethanol. We implement this framework by integrating an economic model of the agricultural sector in the USA with a biogenic carbon model and life cycle analysis to quantify biogenic carbon uptake and direct life cycle emissions with and without corn ethanol expansion over the 2007–2027 period. We find that the combined biogenic carbon emissions and direct life cycle carbon emission intensity of corn ethanol (not including indirect land use related emissions) is 21% lower than gasoline. In conclusion, the lower value of this carbon intensity of corn ethanol compared with gasoline is robust to a wide range of parametric assumptions.

09 BIOMASS FUELS↗