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At least 829 records · Page 46
All sourcesNASA NTRSDOE OSTI
DOE OSTI2025

A tri-level distribution locational marginal price-based demand response framework

Here, in this paper, we propose a tri-level, nested, two-stage price-based demand response (PBDR) framework that considers distribution locational marginal price (DLMP) as DR enabler between load-serving entities (LSE), demand response providers (DRPs), and customers in the day-ahead distribution market. It enables LSE and customer interactions by using multiple DRPs, positioned in-between, and independently optimizes their objectives. The problem is formulated using linear power flow with approximated power losses and its application in DLMP as DR pricing. The tri-level problem is solved using a nested reformulation & decomposition (R&D) method and tested on the real Indian-108 bus distribution system under various dynamic pricings. Further, the temporal–spatial variations in DLMPs are assessed using fairness criteria. Numerical analyses demonstrate that DLMP applications can effectively improve economic efficiency, and transparency in DR programs valuation with a favorable fairness margin. The results show that DLMP as DR pricing signal induces (0-2) % variation in DLMP for DR participation up to 10 %. Further, it gives over 90 % fairness over temporal–spatial variation for all the customers.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2025

Circularly Polarized Light-Induced Microwave Conductivity Measurement: Rapid Screening Technique of Electronic Conductivity in Chiral Molecular Materials

Here, we developed circularly polarized light-time-resolved microwave conductivity (CPL-TRMC) for investigation of the CPL-dependent photoinduced charge carrier dynamics in chiral materials with chiroptical properties. Chiral R- or S-perylenediimide (PDI) molecular thin films were paired with handedness-sorted (6,5) and (11,-5) single-walled carbon nanotube (SWCNT) films to compose a donor (D)-acceptor (A) system for the spin-dependent charge separation process, and the D-A system was examined through linear and circular polarization-dependent steady-state and time-resolved measurements. The R-PDI-(6,5) film exhibited strong enhancement in circular dichroism (CD) and revealed a reversed transient conductivity signal, relative to the polarity of CD in CPL-TRMC measurement upon excitation of the E 11 state, which is interpreted as arising from a spin-dependent initial charge separation process. Through linear polarization-dependent flash photolysis TRMC and circular polarization-resolved femtosecond transient absorption, we could deduce that sub-picosecond intertubular charge separation upon E 11 excitation in SWCNT was responsible for the spin-dependent photoconductivity transients observed in CPL-TRMC measurements.

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

Deep material network via a quilting strategy: visualization for explainability and recursive training for improved accuracy

Recent developments integrating micromechanics and neural networks offer promising paths for rapid predictions of the response of heterogeneous materials with similar accuracy as direct numerical simulations. The deep material network is one such approaches, featuring a multi-layer network and micromechanics building blocks trained on anisotropic linear elastic properties. Once trained, the network acts as a reduced-order model, which can extrapolate the material’s behavior to more general constitutive laws, including nonlinear behaviors, without the need to be retrained. However, current training methods initialize network parameters randomly, incurring inevitable training and calibration errors. Here, we introduce a way to visualize the network parameters as an analogous unit cell and use this visualization to “quilt” patches of shallower networks to initialize deeper networks for a recursive training strategy. The result is an improvement in the accuracy and calibration performance of the network and an intuitive visual representation of the network for better explainability.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2024

SAM: A Modern System Code for Advanced Non-LWR Safety Analysis

The System Analysis Module (SAM), developed at Argonne National Laboratory and by collaborators at other organizations, is for advanced non–light water reactor safety analysis. SAM aims to provide fast-running, modest-fidelity, whole-plant transient analysis capabilities that are essential for fast-turnaround design scoping and engineering analyses of advanced reactor concepts. To facilitate code development, SAM utilizes the MOOSE object-oriented application framework, its underlying finite element library, and linear and nonlinear solvers to leverage modern advanced software environments and numerical methods. SAM aims to solve tightly coupled physical phenomena, including fission reaction, heat transfer, fluid dynamics, and thermal-mechanical responses in advanced reactor structures, systems, and components with high accuracy and efficiency. Finally, this paper gives an overview of the SAM code development, including goals and functional requirements, physical models, current capabilities, verification and validation, software quality assurance, and examples of simulations for advanced nuclear reactor applications.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2021

Restoring Critical Loads In Resilient Distribution Systems using A Curriculum Learned Controller

In this paper, we propose a curriculum learned reinforcement learning (RL) controller to facilitate distribution system critical load restoration (CLR), leveraging RL's fast online response and its outstanding optimal sequential control capability. Like many grid control problems, CLR is complicated due to the large control action space and renewable uncertainty in a heavily constrained non-linear environment with strong intertemporal dependency. The nature of the problem oftentimes causes the RL policy to converge to a poor-performing local optimum if learned directly. To overcome this, we design a two-stage curriculum in which the RL agent will learn generation control and load restoration decision under different scenarios progressively. Via curriculum learning, the trained RL controller is expected to achieve a better control performance, with critical loads restored as rapidly and reliably as possible. Using the IEEE 13-bus test system, we illustrate the performance of the RL controller trained by the proposed curriculum-based method.

curriculum learning↗
DOE OSTI2022

Calibration and Localization of Optically Pumped Magnetometers Using Electromagnetic Coils

In this paper, we propose a method to estimate the position, orientation, and gain of a magnetic field sensor using a set of (large) electromagnetic coils. We apply the method for calibrating an array of optically pumped magnetometers (OPMs) for magnetoencephalography (MEG). We first measure the magnetic fields of the coils at multiple known positions using a well-calibrated triaxial magnetometer, and model these discreetly sampled fields using vector spherical harmonics (VSH) functions. We then localize and calibrate an OPM by minimizing the sum of squared errors between the model signals and the OPM responses to the coil fields. We show that by using homogeneous and first-order gradient fields, the OPM sensor parameters (gain, position, and orientation) can be obtained from a set of linear equations with pseudo-inverses of two matrices. The currents that should be applied to the coils for approximating these low-order field components can be determined based on the VSH models. Computationally simple initial estimates of the OPM sensor parameters follow. As a first test of the method, we placed a fluxgate magnetometer at multiple positions and estimated the RMS position, orientation, and gain errors of the method to be 1.0 mm, 0.2°, and 0.8%, respectively. Lastly, we calibrated a 48-channel OPM array. The accuracy of the OPM calibration was tested by using the OPM array to localize magnetic dipoles in a phantom, which resulted in an average dipole position error of 3.3 mm. The results demonstrate the feasibility of using electromagnetic coils to calibrate and localize OPMs for MEG.

47 OTHER INSTRUMENTATION↗
DOE OSTI2021

Restoring Critical Loads in Resilient Distribution Systems Using a Curriculum Learned Controller: Preprint

In this paper, we propose a curriculum learned reinforcement learning (RL) controller to facilitate distribution system critical load restoration (CLR), leveraging RL's fast online response and its outstanding optimal sequential control capability. Like many grid control problems, CLR is complicated due to the large control action space and renewable uncertainty in a heavily constrained non-linear environment with strong intertemporal dependency. The nature of the problem oftentimes causes the RL policy to converge to a poor-performing local optimum if learned directly. To overcome this, we design a two-stage curriculum in which the RL agent will learn generation control and load restoration decision under different scenarios progressively. Via curriculum learning, the trained RL controller is expected to achieve a better control performance, with critical loads restored as rapidly and reliably as possible. Using the IEEE 13-bus test system, we illustrate the performance of the RL controller trained by the proposed curriculum-based method.

61 RADIATION PROTECTION AND DOSIMETRY↗
DOE OSTI2021

Flow and Arching of Biomass Particles in Wedge-Shaped Hoppers

Poor understanding of the flow behavior of granular biomass material poses great challenges for the bioenergy industry, as the equipment functioning time is significantly reduced by handling issues like screw feeder clogging and hopper arching. In this work, the flow behavior of loblolly pine chips, including the mass flow rate and the critical outlet width, in a wedge-shaped hopper is investigated by combining physical experiments and numerical simulations. Comprehensive characterization of the flow response affected by the two material attributes (initial packing, particle density) and the three operational parameters (hopper outlet width, hopper inclination, and surcharge) is conducted. The results show that the hopper outlet width linearly controls the mass flow rate while the hopper inclination angle controls the critical outlet size. The packing determines whether the flow is smooth or surging, and the surcharge-induced compaction creates flow impedance. The magnitude of these influences varies from a slender hopper with a low inclination angle to a flat-bottom silo. These findings provide guidance for hopper operation in the material handling industry and shed light on the construction of a novel design method for material handling equipment in biorefineries.

09 BIOMASS FUELS↗
DOE OSTI2020

High-speed x-ray phase contrast imaging and digital image correlation analysis of microscale shock response of an additively manufactured energetic material simulant

The performance of energetic materials subjected to dynamic loading significantly depends on their micro- and meso-scale structural morphology. The geometric versatility offered by additive manufacturing opens new pathways to tailor the performance of these materials. Additively manufactured energetic materials (AMEMs) have a wide range of structural characteristics with a hierarchy of length scales and process-inherent heterogeneities, which are hitherto difficult to precisely control. It is important to understand how these features affect AMEMs’ response under dynamic/shock loading. Therefore, temporally and spatially resolved measurements of both macroscopic behavior and micro- and meso-level processes influencing macroscopic behavior are required. In this paper, we analyze the shock compression response of an AMEM simulant loaded under several impact conditions and orientations. Furthermore, x-ray phase contrast imaging (PCI) is used to track features across the observed shock front and determine the linear shock velocity vs particle velocity equation of state, as well as to quantify the interior deformation fields via digital image correlation (DIC) analyses. Photon Doppler velocimetry is simultaneously used to measure the particle velocities of the specimens, which are consistent with those obtained from x-ray PCI. The DIC analyses provide an assessment of the average strain fields inside the material, showing that the average axial strain depends on the loading intensity and reaches as high as 0.23 for impact velocities up to 1.5 km/s. The overall results demonstrate the utility of x-ray PCI for probing “in-material” equation of state and interior strains associated with dynamic shock compression behavior of the AMEM simulant.

3D printing↗
DOE OSTI2026

Non-resonant Raman optical activity from phase-space electronic structure theory

In order to model experimental non-resonant Raman optical activity, chemists must compute a host of second-order response tensors (e.g., the electric-dipole–magnetic-dipole polarizability) and their nuclear derivatives along a set of vibrational modes. While these response functions are almost always computed within a Born–Oppenheimer (BO) framework, here we provide a natural interpretation of the electric-dipole–magnetic-dipole polarizability within phase space electronic structure theory, a beyond-BO model whereby the electronic structure depends on nuclear momentum (P) in addition to nuclear position (R). By coupling to nuclear momentum, phase space electronic structure theory is able to capture the asymmetric response of the electronic properties to an external field, in so far as for a vibrating (non-stationary) molecule, $\frac{∂μ}{∂B}$≠$\frac{∂m}{∂F}$, where μ and m are the electrical linear and magnetic dipoles, and F and B are electric and magnetic fields. As an example, for a prototypical methyloxirane molecule, we show that phase space electronic structure theory is able to deliver a reasonably good match with experimental results in a manner that is formally invariant to gauge origin G 0 —provided that one uses a complete basis or, alternatively, gauge invariant atomic orbitals.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2022

Scaled Vecchia Approximation for Fast Computer-Model Emulation

Many scientific phenomena are studied using computer experiments consisting of multiple runs of a computer model while varying the input settings. Gaussian processes (GPs) are a popular tool for the analysis of computer experiments, enabling interpolation between input settings, but direct GP inference is computationally infeasible for large datasets. We adapt and extend a powerful class of GP methods from spatial statistics to enable the scalable analysis and emulation of large computer experiments. Specifically, we apply Vecchia’s ordered conditional approximation in a transformed input space, with each input scaled according to how strongly it relates to the computer-model response. The scaling is learned from the data by estimating parameters in the GP covariance function using Fisher scoring. Our methods are highly scalable, enabling estimation, joint prediction, and simulation in near-linear time in the number of model runs. In several numerical examples, our approach substantially outperformed existing methods.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2021

Removing the Effects of Tropical Dynamics from North Pacific Climate Variability

Teleconnections from the Tropics energize variations of the North Pacific climate, but detailed diagnosis of this relationship has proven difficult. Simple univariate methods, such as regression on El Niño-Southern Oscillation (ENSO) indices, may be inadequate since the key dynamical processes involved -- including ENSO diversity in the Tropics, re-emergence of mixed layer thermal anomalies, and oceanic Rossby wave propagation in the North Pacific -- have a variety of overlapping spatial and temporal scales. Here we use a multivariate Linear Inverse Model to quantify tropical and extra-tropical multi-scale dynamical contributions to North Pacific variability, in both observations and CMIP6 models. In observations, we find that the Tropics are responsible for almost half of the seasonal variance, and almost three quarters of the decadal variance, along the North American coast and within the subtropical front region northwest of Hawaii. SST anomalies that are generated by local dynamics within the Northeast Pacific have much shorter time scales, consistent with transient weather forcing by Aleutian low anomalies. Variability within the Kuroshio-Oyashio Extension (KOE) region is considerably less impacted by the Tropics, on all time scales. Consequently, without tropical forcing the dominant pattern of North Pacific variability would be a KOE pattern, rather than the Pacific Decadal Oscillation (PDO). In contrast to observations, most CMIP6 historical simulations produce North Pacific variability that maximizes in the KOE region, with amplitude significantly higher than observed. Correspondingly, the simulated North Pacific in all CMIP6 models is shown to be relatively insensitive to the Tropics, with a dominant spatial pattern generally resembling the KOE pattern, not the PDO.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2020

The Future of LANSCE [Slides]

LANSCE is a NNSA Center for Materials and Nuclear Research. An aging stockpile presents unique challenges in materials and nuclear science. The grand challenge of a predictive materials capability includes design to manufacture and born certified. This requires capability to study manufacture to structure relationships and capability to study structure to performance relationships. LANSCE has the capabilities needed for this mission. We leverage our capability to provide broader benefit to the Nation including production of medical isotopes, industrial irradiation facility for electronics certification, and fundamental materials and physics research. The LANSCE linear accelerator is nearly 50 years old. The severity of maintenance issues is increasing. We must reinvest in LANSCE to ensure mission delivery into the future. We must develop new capability to be responsive to changing mission needs. Ultimately this will require a Dynamic Mesoscale Materials Science Capability. Now we must develop a pathway to MaRIE that ensures continued operation of LANSCE.

43 PARTICLE ACCELERATORS↗
DOE OSTI2024

Mechanochromic Palettes of Cholesteric Liquid Crystal Elastomers for Visual Signaling

Mechanochromic photonic materials present promising prospects for enhancing diverse facets of human life. Cholesteric liquid crystal elastomers (CLCEs) present immediate mechanochromic properties, allowing their utility in various fields, including camouflages, textiles, and anticounterfeiting. Nonetheless, it is essential to provide differentiated color information corresponding to diverse types and degrees of deformation for practical use in real life. Herein, we investigate the mechanochromic responses of CLCEs using their optical rotation under compression and uniaxial stretching. The CLCEs exhibit the same reflection colors indistinguishable for compression and uniaxial stretching due to their contracted pitches. In contrast, the linearly polarized light passing through deformed CLCEs can be modulated by optical rotation, resulting in distinct transmission colors that can be discerned through mechanical deformations. We demonstrate a visual signaling system with color information derived from optical rotation. By comprehending and manipulating the mechanoresponsive behavior of CLCEs, we can advance these materials into innovative solutions with enhanced functionality and performance, thereby broadening their applications for human life enhancement.

36 MATERIALS SCIENCE↗
DOE OSTI2025

Machine Learning-Based Process Control for Injection Molding of Recycled Polypropylene

The increased interest in artificial intelligence in manufacturing has driven the adoption of machine learning to optimize processes and improve efficiency. A key challenge in injection molding is the variability of recycled materials, which affects part quality and processing stability. This study presents a novel closed-loop process control approach for injection molding, leveraging machine learning to adaptively predict processing inputs and quality outcomes. The methodology was tested on five blends of recycled polypropylene (rPP), using artificial neural networks (ANNs), linear regression, and polynomial regression to model the relationships between material properties and process parameters. The dataset was split 80/20 into training and testing sets. The ANN model was implemented using TensorFlow and Keras, with six hidden layers of 32 neurons per layer, ReLU activation, and an Adam optimizer. Empirical tuning and early stopping were used to optimize performance and prevent overfitting. Predictions were evaluated based on mean absolute error (MAE), mean squared error (MSE), and percentage error. The results showed that yield stress, ultimate elongation, and part weight were accurately predicted within a 5% error for linear and polynomial regression models and within a 10% error for the ANN. However, modulus predictions were less reliable, with errors of ~11% for ANN and linear regression and ~40% for polynomial regression, reflecting the inherent variability of this property in rPP blends. Predictions of processing inputs had errors ranging from 3% to 25%, depending on the model and response variable. No single modeling approach was consistently superior across all responses, highlighting the complexity of the relationship between material properties, process parameters, and quality metrics. Overall, the work demonstrates that closed-loop process control, powered by machine learning, can effectively predict key quality parameters in injection molding of recycled materials. The proposed approach can improve process stability and material utilization, facilitating increased adoption of sustainable materials.

Krantz, Joshua↗
DOE OSTI2024

Swift heavy ion irradiation effects in zirconium and hafnium carbides

The behavior of microcrystalline zirconium carbide (ZrC) and hafnium carbide (HfC) was studied under highly ionizing irradiation conditions at room temperature. The induced structural modifications were characterized via synchrotron-based X-ray diffraction experiments. Unit-cell expansion and buildup of microstrain were determined across a wide fluence range and linked to chemical compositions of the target material. The observed swelling resulting from irradiation with 198 MeV Xe ions in both carbide materials is characterized by two distinct mechanisms that operate within different fluence regimes. Unit-cell expansion initially proceeds by a direct-impact behavior that reaches saturation, followed at higher fluences by a second, linear swelling regime. The overall behavior, particularly the direct-impact regime, is similar for ZrC and HfC, with a more pronounced second defect accumulation process in HfC. Swelling in ZrC shows the same two distinct mechanisms upon irradiation with 198 MeV Xe ions and 946 MeV Au ions, but swelling induced by the lower-energy ions is greater across the entire fluence series. Accounting for the difference in energy deposition density between the two irradiation conditions reveals that the first swelling mechanism (direct-impact behavior) is likely related to the formation of more simple defects. In contrast, the second damaging mechanism at higher fluences (linear increase) cannot be fully explained by the induced energy density, and swelling remains somewhat higher for the low-velocity Xe irradiation. This may suggest that more complex defects and defect clusters are responsible for this swelling regime, with either their size and/or morphology modified at different energy densities.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
DOE OSTI2021

Nonlinear Optical Microscopy with Ultralow Quantum Light

Nonlinear optical (NLO) microscopy relies on multiple light–matter interactions to provide unique contrast mechanisms and imaging capabilities that are inaccessible to traditional linear optical imaging approaches, making them versatile tools to understand a wide range of complex systems. However, the strong excitation fields that are necessary to drive higher-order optical processes efficiently are often responsible for photobleaching, photodegradation, and interruption in many systems of interest. This is especially true for imaging living biological samples over prolonged periods of time or in accessing intrinsic dynamics of electronic excited-state processes in spatially heterogeneous materials. This perspective outlines some of the key limitations of two NLO imaging modalities implemented in our lab and highlights the unique potential afforded by the quantum properties of light, especially entangled two-photon absorption based NLO spectroscopy and microscopy. Here, we further review some of the recent exciting advances in this emerging filed and highlight some major challenges facing the realization of quantum-light-enabled NLO imaging modalities.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2022

A visible to near-infrared nanocrystalline organic photodetector with ultrafast photoresponse

Organic photoelectron conversion devices, due to their solution process and wide use in portable devices, have attracted intensive interest. In this work, a newly developed active layer of D18:Y6 is used to fabricate an organic photodetector and the device of ITO/PEDOT:PSS/D18:Y6/Phen-NaDPO/Ag shows a responsivity of 680 mA W –1 and a detectivity of 6.35 × 10 13 Jones (1 Jones = 1 cm Hz 1/2 W –1 ) at a wavelength of 850 nm. Additionally, this device shows a wide linear dynamic range of 120 dB, indicating a potential near-infrared region (NIR) detector system. Owing to the minimal exciton binding energy of 37.6 meV for Y6, a desirable energy offset for efficient electron–hole pair dissociation and an ultrafast charge transfer of ~58 ps at the interface of D18:Y6, the obtained organic photodetector has demonstrated a photoresponse time as fast as 1.8 μs upon laser light excitation without an external driven voltage. More importantly, D18 shows a preferred out-of-plane orientation with strong intensities and Y6 exhibits a preferred orientation along the in-plane direction on lamellar stacking, which facilitates charge transport. This work has demonstrated significant progress in exploring D18:Y6 NIR organic devices with a wide photosensitivity linearity and ultrafast photoresponse.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗