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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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The demand for a more capable electric power grid has increased due to greater energy generation and consumption. • Controlling energy flow bidirectionally with new technologies such as solid state transformers can meet this demand, improving power quality, efficiency, and grid security. • A human machine interface was created to control a medium voltage solid state transformer.
In accordance with one embodiment is a transformer with a core comprising a perimeter portion and central intervening portion. The central intervening portion is separated from the perimeter portion by air gaps, creating an opening on either side of the intervening portion. A primary winding and secondary winding are wound around the central intervening portion of the core. The primary winding is capable of electromagnetic interaction with the secondary winding. A pair of ferrite members arranged outward from a central axis of the central intervening portion of the core and increases a series inductance with the primary winding. In accordance with another aspect of the disclosure, each ferrite member may have an air gap associated with the core to facilitate heat dissipation from the transformer.
This paper presents a novel approach to addressing the long-sequence problem in high-resolution medical images for Vision Transformers (ViTs). Using smaller patches as tokens can enhance ViT performance, but quadratically increases computation and memory requirements. Therefore, the common practice for applying ViTs to high-resolution images is either to: (a) employ complex sub-quadratic attention schemes or (b) use large to medium-sized patches and rely on additional mechanisms within the model to capture the spatial hierarchy of details. We propose Symmetrical Hierarchical Forest (SHF), a lightweight approach that adaptively patches the input image to increase token information density and encode hierarchical spatial structures into the input embedding. We then apply a reverse depatching scheme to the output embeddings of the transformer encoder, eliminating the need for convolution-based decoders. Unlike previous methods that modify attention mechanisms or use a complex hierarchy of interacting models, SHF can be retrofitted to any ViT model to allow it to learn the hierarchical structure of details in high-resolution images without requiring architectural changes. Experimental results demonstrate significant gains in computational efficiency and performance: on the PAIP WSI dataset, we achieved a 3∼32×speedup or a 2.95%∼7.03% increase in accuracy (measured by Dice score) at a 64K2 resolution with the same computational budget, compared to state-of-the-art production models. On the 3D medical datasets BTCV and KiTS, training was 6×faster, with accuracy gains of 6.93% and 5.9%, respectively, compared to models without SHF.
Real-time jet tagging is critical for identifying short-lived particle decays in the high-throughput detectors of the Large Hadron Collider, where real-time trigger systems responsible for deciding which collision events to store impose strict latency and accuracy constraints. While transformer architectures achieve the highest jet tagging accuracy when compute is unconstrained, their quadratic self-attention cost makes inference restrictive on trigger budget. Existing efficient variants reduce the computational cost, but hinder the classification performance. To address this limitation, we introduce the Patch Hierarchical Attention Transformer (PHAT-JeT), which combines two mechanisms: a physics-inspired geometric message-passing module that encodes local detector-plane structure, and a hierarchical patch-based attention scheme that computes exact attention within small particle groups while preserving global context through lightweight patch-token communication. Within a restricted budget, PHAT-JeT achieves state-of-the-art accuracy and background rejection among all resource-constrained jet tagging models on four benchmarks (\textsc{hls4ml}, JetClass, Top Tagging, and Quark--Gluon). Our code is available at https://github.com/aaronw5/PHAT-JeT.
This work presents a theoretical framework for the propagation of partially coherent Gaussian radiation in a modified Mach–Zehnder interferometer designed for Fourier transform spectroscopy (FTS) applications. Using the Wigner function formalism, we analytically propagate the radiation through the system and benchmark our approach by comparing the resulting interference pattern and interferogram with previous works in the diffraction limit. Our analysis reveals that the transverse coherence length requirement of the incident light field for detectable modulation is less stringent than previously assumed. Additionally, we provide theoretical demonstrations of FTS performance across various wavelengths using the proposed setup. These findings underscore the potential of this interferometer to achieve high-resolution FTS in the soft X-ray regime.
This project successfully developed and verified a new approach for medium voltage (MV) planar transformers for use in emerging, utility-scale, modular PV systems. This approach simplifies their manufacture, improves their isolation capability, lowers their cost, and reduces their volume and loss, while also creating flexibility in the materials that can be used.
As a promising candidate for nonvolatile memory devices, the hafnia-based ferroelectric system has recently been a hot research topic. Although significant progress has been made over the past decade, the endurance problem is still an obstacle to its final application. In perovskite-based ferroelectrics, such as the well-studied Pb[Zr x Ti 1-x ]O 3 (PZT) family, polarization fatigue has been discussed within the framework of the interaction of charged defects (such as oxygen vacancies) with the moving domains during the switching process, particularly at the electrode-ferroelectric interface. Armed with this background, a hypothesis is set out to test that a similar mechanism can be in play with the hafnia-based ferroelectrics. The conducting perovskite La-Sr-Mn-O is used as the contact electrode to create La 0.67 Sr 0.33 MnO 3 / Hf 0.5 Zr 0.5 O 2 (HZO)/ La 0.67 Sr 0.33 MnO 3 capacitor structures deposited on SrTiO 3 -Si substrates. Nanoscale X-ray diffraction is performed on single capacitors, and a structural phase transition from polar o-phase toward non-polar m-phase is demonstrated during the bipolar switching process. The energy landscape of multiphase HZO has been calculated at varying oxygen vacancy concentrations. Based on both theoretical and experimental results, it is found that a polar to non-polar phase transformation caused by oxygen vacancy redistribution during electric cycling is a likely explanation for fatigue in HZO.
The first in-situ X-ray diffraction (XRD) study of the evolution of the crystallite size and dislocation density in phases during plastic strain-induced phase transformation (PT) is performed utilizing α-ω PT in strongly pre-deformed commercially pure Zr as an important example. Rough diamond anvils (rough-DA) are introduced to intensify all occurring processes during heterogeneous compression of Zr in a diamond anvil cell (DAC). The main rule is found that during α-ω PT the crystallite size and dislocation density in ω-Zr depend solely on the volume fraction of ω-Zr and are independent of pressure, plastic strain tensor, its path, and initial nanostructure. Crystallite size in ω-Zr increases from 10 to 60 nm during the PT, while dislocation density reduces from 1.83×10 15 /m 2 to 0.6×10 15 /m 2 . Rough-DA produce a steady nanostructure in α-Zr before PT with smaller crystallite size and larger dislocation density than smooth-DA, leading to a reduction of the minimum pressure for α-ω PT to a record value 0.67 GPa, 9 times smaller than under hydrostatic loading and 5.1 times lower than the phase equilibrium pressure. In addition to strain, the kinetics of strain-induced PT unexpectedly depends on time. Also, strain-controlled part of kinetics is zero order, in contrast to the first-order kinetics with smooth-DA. The obtained results open a new window for understanding the mutual effects of nanostructure evolution and PT during severe plastic flow in various technological and natural processes. In conclusion, they may bring up economic strategies of producing nanocomposites and single-phase nanostructured materials with optimal properties.
Protonic ceramic cells (PCCs) have emerged as a promising technology for power generation, energy storage, and value-added chemical synthesis, offering benefits such as fuel flexibility, low emissions, and efficient operation at intermediate temperatures (300–600 °C). Recently, significant breakthroughs in materials and manufacturing methods have markedly enhanced the performance of PCCs. However, establishing a fundamental understanding of their electrocatalytic reactions has gained less attention. As a fast and cost-effective method for physicochemical fingerprinting, diffuse reflectance infrared Fourier transform spectroscopy (DRIFTS) has proven to be a surface-sensitive analytical tool for structural and functional studies. This review critically examines the most up-to-date applications of DRIFTS for characterizing key components of PCCs, including oxygen electrodes, protonic electrolytes, and hydrogen electrodes for different applications, with a focus on revealing hydration properties and catalytic reactions, and guiding rational material design. The challenges for advancing DRIFTS, including quantitative capabilities and operando applications for PCC investigations, are highlighted and strategies to tackle these challenges are discussed. Ultimately, this review underscores the critical role of DRIFTS in accelerating the development of high-performance and durable PCCs for next-generation energy solutions, offering methodologies and insights broadly applicable to a wide range of electrochemical energy conversion and storage technologies.
The Floquet exponents of periodic field lines are studied through the variations of the magnetic action on the magnetic axis, which is assumed to be elliptical. The near-axis formalism developed by Mercier, Solov'ev and Shafranov is combined with a Lagrangian approach. The on-axis Floquet exponent is shown to coincide with the on-axis rotational transform. A discrete solution suitable for numerical implementation is introduced, which gives the Floquet exponents as solutions to an eigenvalue problem. This discrete formalism expresses the exponents as the eigenvalues of a $6$ X $6$ matrix.
Cyanobacteria are promising microbial platforms for a diverse set of biotechnology applications, from living materials to photosynthetic chemical production, but are less well characterized than commonly engineered microbes such as Escherichia coli. This study facilitates genetic engineering in Synechococcus sp. PCC 7002, a fast-growing, halotolerant, and naturally competent strain, by identifying ten native methylation motifs and designing shuttle strains that mimic the native methylation state by expressing a subset of heterologous methyltransferases. DNA methylation in E. coli with as few as two active methyltransferases increased transformation efficiency up to 30-fold across four distinct integration sites in PCC 7002. This work provides an experimental framework to bypass native restriction-modification systems for efficient genome editing and metabolic engineering in nonmodel bacteria.
The impacts of natural hazards on infrastructure, enhanced by climate change, are increasingly more severe emphasizing the necessity of resilient energy grids. Microgrids, tailored energy systems for specific neighbourhoods and districts, play a pivotal role in sustaining energy supply during main grid outages. These solutions not only mitigate economic losses and well-being disruptions against escalating hazards but also enhance city resilience in alignment with Sustainable Development Goal (SDG) 11. However, disregarding socioeconomic factors in defining microgrid boundaries risks perpetuating inequalities and impeding progress towards other SDG 11 targets, including fair democratic participation. Our approach integrates social and technical indicators to bolster urban microgrid planning. Through a case study in a US county, we illustrate how integrated microgrid planning effectively intertwines urban resilience, well-being and equity while promoting sustainable development. This study underscores the importance of integrated microgrid planning for sustainable and resilient urban transformation amid environmental and societal challenges.
A study on the neutron production from single and double gas puff Z-pinches on the CESZAR linear transformer driver with ~0.45 MA current and 170 ns rise time is presented. Total neutron yield measurements made with a LaBr activation detector are compared for three configurations, using a double nozzle setup. When a single, hollow, deuterium gas shell was used, reliable implosions could only be attained at higher load mass than the optimal value to match implosion time with the driver rise time, with neutron yields of ~10 6 per pulse. The use of a double gas puff configuration with a deuterium center jet allowed a reduction in the shell density and operation closer to machine-matched conditions, recording up to (4.1 ± 0.3) × 10 7 neutrons/pulse when either Kr or D 2 was used in the shell. For a comparable mass and implosion time, using a higher atomic-number gas in the outer shell results in more unstable plasma surface and smaller plasma radius at the location of instability bubbles, which, however, do not seem to consistently correlate with a higher neutron yield. Comparing implosion dynamics with models and neutron yields with literature scaling suggests that the machine current is not well coupled to the plasma during the final stages of compression. Optimizing current and energy coupling to the pinched plasma is critical to improving performance, particularly in low-impedance drivers.
The numerical calculation of electromagnetic far fields in the time-domain requires a near to far field transformation (NTFF) method. While time-domain NTFF methods for popular finite-difference time-domain (FDTD) approaches are well established, there is little discourse on NTFF methods for finite-element time-domain (FETD) codes. Here, this work is concerned with the development of an NTFF method for the Empire FETD code, which utilizes curl and divergence conforming elements. This discretization presents a difficulty in obtaining the equivalent electric current for the NTFF. Straightforward finite element interpolation of the fields is shown to give poor accuracy. Alternative interpolation methods are recommended. An expanding magnetic quadrupole pulse benchmark problem, which is fully developed in the appendices, provides the basis for quantitative comparison.
Accurate knowledge of the atomistic transition pathways in materials and material surfaces is crucial for many material science problems. However, conventional simulation techniques used to find these transitions are extremely computationally intensive. Even with large-scale, accelerated material simulations, the computational cost constrains the applicable domain in practice. Machine learning models, with the potential to learn the complex emergent behaviors governing atomistic transitions as a fast surrogate model, have great promise to predict transitions with a vastly reduced computational cost. Here, we demonstrate how transformers can be trained to predict atomistic transitions in nano-clusters. We show how we evaluate physical validity of the predictions and how a multitude of additional, different microstates can be generated by slightly varying the data provided to the model.
The determination of the dielectric breakdown strengths of microstructurally heterogeneous materials has been a primarily experimental endeavor. We report the development of a microstructure-level model for computationally predicting the breakdown strength and analyzing the interactions between electromagnetic pulses (EMP) and the constituents in a composite of cellulose-based paper and mineral oil found in electrical transformers. The model allows explicit simulation of the material breakdown process by tracking the transition of dielectric constituents from non-conductive to conductive states. The focus is on the electric fields induced in the materials and the overall conditions for dielectric breakdown (defined as the onset of avalanche) caused by the electric field induced in the composite. Responses to three distinct pulse shapes, i.e., Steep Front (SF), Lightning (L), and AC with spectra spanning 60–9 × 105 Hz are considered. It is found that the breakdown strength of the material is significantly affected by microstructure heterogeneities, the spatial variations of the constituent properties, and the pulse shapes. A probabilistic characterization of the breakdown strength is computationally obtained and compared with experimental measurements. Although one particular material is analyzed, the model and approach are applicable to other heterogeneous materials as well.
Accurate representation of the multiscale features in spatiotemporal physical systems using vision transformer architectures requires extremely long, computationally prohibitive token sequences. To address this issue, we propose two novel adaptive tokenization schemes that dynamically adjust patch sizes based on local features: one ensures convergent behavior to uniform patch refinement, while the other offers better computational efficiency. Moreover, we present a set of spatiotemporal attention schemes, where the temporal or axial spatial dimensions are decoupled, to evaluate their baseline computational and data efficiencies and to determine whether adaptive tokenization can improve this performance. We assess the performance of the proposed multiscale adaptive model, MATEY, in a sequence of experiments. Compared to a full spatiotemporal attention scheme or a scheme that decouples only the temporal dimension, we find that fully decoupled axial attention is less efficient and expressive, requiring more training time and model parameters to achieve the same accuracy. The experiments on the adaptive tokenization schemes show that, compared to a uniformly refined model, the proposed schemes achieve comparable or improved accuracy at a much lower cost in the tested two-dimensional settings. While the asymptotic analysis suggests the potential for favorable scaling, empirical validation at substantially longer sequence lengths remains to be performed in future work. Finally, we demonstrate in two fine-tuning tasks featuring different physics that models pretrained on PDEBench data outperform the ones trained from scratch, especially in the low data regime with frozen attention.
The ability to anticipate global technical expertise and capability evolution trends is essential for national and global security, especially in safety-critical domains such as nuclear nonproliferation (NN) and rapidly emerging fields like artificial intelligence (AI). Here, in this work, we extend traditional statistical relational learning approaches (e.g., link prediction in collaboration networks) and formulate a problem of anticipating technical expertise and capability evolution using dynamic heterogeneous graph representations. We develop novel capabilities to forecast collaboration patterns, authorship behavior, and technical capability evolution at different granularities (e.g., scientist and institution levels) in two distinct research fields. We implement a dynamic graph transformer (DGT) neural architecture, which pushes the state-of-the-art graph neural network models by: 1) forecasting heterogeneous (rather than homogeneous) nodes and edges; and 2) relying on both discrete- and continuous-time inputs. We demonstrate that our DGT models predict collaboration, partnership, and expertise patterns with 0.26, 0.73, and 0.53 mean reciprocal rank values for AI and 0.48, 0.93, and 0.22 for NN domains. DGT model performance exceeds the best-performing static graph baseline models by 30%–80% across AI and NN domains. Our findings demonstrate that DGT models boost inductive task performance when previously unseen nodes appear in the test data for the domains with emerging collaboration patterns (e.g., AI). Specifically, models accurately predict which established scientists will collaborate with early career scientists and vice versa in the AI domain.