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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 253 records · Page 14
All sourcesNASA NTRSDOE OSTI
DOE OSTI2024

Adaptive Fault Current-Limiting Control of MMC for Protection of Multiterminal HVDC Systems

A crucial requirement of the protection system for multi-terminal high-voltage DC (MTDC) transmission is that it is capable of selectively isolating the faulty area from the healthy part of the network using DC circuit breakers (DCCBs), while ensuring continuous operation of converter stations in the healthy part of the network. But in the half-bridge modular multilevel converters (HB-MMCs) based MTDC system, since HB-MMCs do not have fault current absorption capability, when a DC fault occurs, the rising fault currents can quickly reach the blocking threshold within a few milliseconds and disrupt the operation of the MMCs in the healthy part. To facilitate fault-ride-through capability of MTDC system, large DC reactors are often considered in series with DCCBs to reduce the rate of rise of the fault current and prevent blocking the MMCs in the healthy part of the DC networks; however, large DC reactors prohibitively increase the cost of the system, introduce stability issues and can create post-fault oscillations. This paper presents an adaptive fault current-limiting control method for MMCs to avoid their blocking and enable the continuous operation of the healthy part of MTDC systems. The method contains two parts: The first part is based on circulating current feedforward control, which emulates virtual reactors in each arm of an MMC, and is immediately activated when the fault current starts to increase to reduce the rate of rise of the fault current. The second part temporarily bypasses all the submodules when the fault current exceeds a preset threshold, complementing the fault current-limiting effect of the first part. Neither part requires fault detection signals, and they are automatically activated during faults. Simulation case studies of a four-terminal bipolar MMC-based high-voltage DC system are presented to demonstrate the effectiveness of the proposed control methods.

active fault current limiting↗
DOE OSTI2024

MUSE adaptive-optics spectroscopy confirms dual active galactic nuclei and strongly lensed systems at sub-arcsec separation

The novel Gaia multi peak (GMP) technique has proven to be able to successfully select dual and lensed active galactic nuclei (AGN) candidates at sub-arcsecond separations. Both populations are important because dual AGN represent one of the central, still largely untested, predictions of ΛCDM cosmology, and compact lensed AGN allow us to probe the central regions of the lensing galaxies. In this work, we present high-spatial-resolution spectroscopy of 12 GMP-selected systems. We used the adaptive-optics assisted integral-field spectrograph MUSE at the VLT to resolve each system and investigate the nature of each component. All targets show the presence of two components confirming the GMP selection. We classify 4 targets as dual AGN, 3 as lensed quasar candidates, and 5 as a chance alignment of a star and an AGN. With separations ranging from 0.30″ to 0.86″, these dual and lensed systems are among the most compact systems discovered to date at z > 0.5. This is the largest sample of distant dual AGN with sub-arcsecond separations ever presented in a single paper.

Scialpi, M. (ORCID:0009000651004986)↗
DOE OSTI2025

Many-body perturbation theory with hybrid density functional theory starting points accelerated by adaptively compressed exchange

We report on the use of the adaptively compressed exchange (ACE) operator to accelerate many-body perturbation theory (MBPT) calculations, including G 0 W 0 and the Bethe–Salpeter equation (BSE), for hybrid density functional theory starting points. We show that by approximating the exact exchange operator with the low-rank ACE operator, substantial computational savings can be achieved with systematically controllable errors in the quasiparticle energies computed with full-frequency G 0 W 0 and the optical absorption spectra and vertical excitation energies computed by solving the BSE within density matrix perturbation theory. Our implementation makes use of the ACE-accelerated electronic Hamiltonian to carry out both G 0 W 0 and BSE without explicitly computing empty states. We show the robustness of the approach and present the computational gains obtained on both the central processing unit and graphics processing unit nodes. In conclusion, our work will facilitate the exploration and evaluation of fine-tuned hybrid starting points aimed at enhancing the accuracy of MBPT calculations without involving computationally demanding self-consistency in Hedin’s equations.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2026

Control Algorithms for Dual-wavefront Sensor Single-conjugate Adaptive Optics

High-contrast imaging systems using active control with adaptive optics (AO) are often limited by non-common path (NCP) aberrations that are seen only at the final science image. AO systems employing focal-plane wavefront sensors (FP-WFSs) are able to simultaneously correct NCP aberrations and measure science images, but they typically require a second stage of control that adds system cost and complexity. We present control algorithms to augment AO systems with FP-WFSs within their existing control setup. We demonstrate inter-arm NCP aberration transfer can be mitigated through temporal filtering, present frequency- and time-domain validation of controller stability and performance, and discuss the optimality of the chosen controllers. This work will enable the development, testing, and installation of FP-WFS technologies for direct imaging of exoplanets.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2025

Development of a Superconducting Adaptive Gap Undulator Prototype for NSLS-II

The photon flux and brightness of synchrotron radiation, crucial parameters for any light source, vary significantly depending on the type of source employed. Among the 23 Insertion Device (ID) sources at the National Synchrotron Light Source II (NSLS-II) at Brookhaven National Lab (BNL), the 12-year-old 3m-long In-Vacuum Undulator (IVU20) stands out for its superior performance, although it no longer represents the cutting edge of technology. Recently, there has been a shift in focus towards developing next-generation sources, particularly Superconducting Undulators (SCUs), characterized by smaller gaps, shorter periods, and maximum lengths. However, despite ongoing research and development efforts, SCUs have yet to surpass their predecessors, the Cryogenic Permanent Magnet Undulators (CPMUs), in terms of performance. This is largely attributed to the limitations posed by traditional superconducting wire, as well as challenges in the design of the magnetic structure and vacuum chamber. In this paper, we aim to overcome such limitations through the development of a unique prototype Superconducting Adaptive Gap Undulator (SC-AGU) magnet core and vacuum chamber design. This paper will outline a novel technical approach aimed at constructing a compact prototype magnet array utilizing state-of-the-art superconducting wire technology. This approach provides a more efficient magnetic structure, allowing for enhanced magnetic field strength and stability.

36 MATERIALS SCIENCE↗
DOE OSTI2024

Normalizing flows for domain adaptation when identifying Λ hyperon events

Here this study focuses on the application of a normalizing flow as a method of domain adaptation when classifying physics data. Normalizing flows offer a way to transform data points between two different distributions. The present study investigates a novel method of transforming latent representations of physics data to a normal distribution and then to a physics distribution again. The final distribution models a simulated distribution. After being transformed, the data can be classified by a neural network trained on labeled simulation data. The present study succeeds in training two normalizing flows that can transform between data (or simulation) and a Gaussian distribution.

47 OTHER INSTRUMENTATION↗
DOE OSTI2025

Identification of tau leptons using a convolutional neural network with domain adaptation

A tau lepton identification algorithm,DeepTau, based on convolutional neural network techniques, has been developed in the CMS experiment to discriminate reconstructed hadronic decays of tau leptons (τ h ) from quark or gluon jets and electrons and muons that are misreconstructed as τ h candidates. The latest version of this algorithm, v2.5, includes domain adaptation by backpropagation, a technique that reduces discrepancies between collision data and simulation in the region with the highest purity of genuine τh candidates. Additionally, a refined training workflow improves classification performance with respect to the previous version of the algorithm, with a reduction of 30–50% in the probability for quark and gluon jets to be misidentified as τ h candidates for given reconstruction and identification efficiencies. This paper presents the novel improvements introduced in theDeepTau algorithm and evaluates its performance in LHC proton-proton collision data at √(s) = 13 and 13.6 TeV collected in 2018 and 2022 with integrated luminosities of 60 and 35 fb -1 , respectively. Techniques to calibrate the performance of the τ h identification algorithm in simulation with respect to its measured performance in real data are presented, together with a subset of results among those measured for use in CMS physics analyses.

Large detector-systems performance↗
DOE OSTI2025

SymbolNet: neural symbolic regression with adaptive dynamic pruning for compression

Abstract Compact symbolic expressions have been shown to be more efficient than neural network (NN) models in terms of resource consumption and inference speed when implemented on custom hardware such as field-programmable gate arrays (FPGAs), while maintaining comparable accuracy (Tsoi et al 2024 EPJ Web Conf. 295 09036). These capabilities are highly valuable in environments with stringent computational resource constraints, such as high-energy physics experiments at the CERN Large Hadron Collider. However, finding compact expressions for high-dimensional datasets remains challenging due to the inherent limitations of genetic programming (GP), the search algorithm of most symbolic regression (SR) methods. Contrary to GP, the NN approach to SR offers scalability to high-dimensional inputs and leverages gradient methods for faster equation searching. Common ways of constraining expression complexity often involve multistage pruning with fine-tuning, which can result in significant performance loss. In this work, we propose S y m b o l N e t , a NN approach to SR specifically designed as a model compression technique, aimed at enabling low-latency inference for high-dimensional inputs on custom hardware such as FPGAs. This framework allows dynamic pruning of model weights, input features, and mathematical operators in a single training process, where both training loss and expression complexity are optimized simultaneously. We introduce a sparsity regularization term for each pruning type, which can adaptively adjust its strength, leading to convergence at a target sparsity ratio. Unlike most existing SR methods that struggle with datasets containing more than O ( 10 ) inputs, we demonstrate the effectiveness of our model on the LHC jet tagging task (16 inputs), MNIST (784 inputs), and SVHN (3072 inputs).

Tsoi, Ho Fung (ORCID:0000000225502184)↗
DOE OSTI2025

Simulation driven adaptive sampling for neutron-diffraction based strain mapping of additively manufactured parts

Neutron diffraction based strain mapping is a useful technique for measuring residual strains in additively manufactured (AM) metal parts. The measurement is traditionally done by scanning the sample in a point-wise raster pattern to extract the strain at each position. Since the overall scan can span several hours, adaptive sampling approaches using Bayesian optimization based on Gaussian process (BO-GP) regression have been introduced—demonstrating that even with a fraction of the typically made measurements the dominant strain patterns in the sample can be reconstructed. However, the parameters of the BO-GP algorithm have to be carefully chosen for best performance, and the movement time between arbitrary points can offset the time savings from a reduced number of measurement locations. In this paper, we propose algorithms to refine the BO-GP based methods by using simulations of strain patterns in AM parts based on the materials and the process used to print them. We demonstrate that the simulated strain patterns can be used to help choose better parameters for the BO-GP based framework—leading to low reconstruction error for the final strain pattern. Furthermore, we show that the strain mapping experiment can be initialized with a sampling pattern learnt from the simulation data and ordered to reduce movement time, dramatically enabling reduction in the overall time required to run the baseline BO-GP method.

Gaussian process regression↗
DOE OSTI2026

Towards a self-driving trigger at the LHC: adaptive response in real time

Real-time data filtering and selection—or trigger—systems at high-throughput scientific facilities such as the experiments at the Large Hadron Collider must process extremely high-rate data streams under stringent bandwidth, latency, and storage constraints. Yet these systems are typically designed as static, hand-tuned menus of selection criteria grounded in prior knowledge and simulation. In this work, we further explore the concept of a self-driving trigger, an autonomous data-filtering framework that reallocates resources and adjusts thresholds dynamically in real-time to optimize signal efficiency, rate stability, and computational cost as instrumentation and environmental conditions evolve. We introduce a benchmark ecosystem to emulate realistic collider scenarios and demonstrate real-time optimization of a menu including canonical energy sum triggers as well as modern anomaly-detection algorithms that target non-standard event topologies using machine learning. Using simulated data streams and publicly available collision data from the Compact Muon Solenoid experiment, we demonstrate the capability to dynamically and automatically optimize trigger performance under specific cost objectives without manual retuning. Our adaptive strategy shifts trigger design from static menus with heuristic tuning to intelligent, automated, data-driven control, unlocking greater flexibility and discovery potential in future high-energy physics analyses.

Emami, Shaghayegh [Michigan U.] (ORCID:00090007589↗
DOE OSTI2025

The genome of the polyextremophilic yeast, Naganishia friedmannii, reveals adaptations involved in stress response pathways, carbohydrate metabolism expansion, and a limited DNA repair repertoire

Here we report the draft genome sequence of Naganishia friedmannii (formerly Cryptococcus friedmannii) isolate, a Basidiomycota yeast commonly found in some of the most extreme environments of the Earth's cryosphere. We isolated N. friedmannii strain Llullensis from soils at 6000 m above sea level on Volcán Llullaillaco, Argentina. The genome was 22.2 Mb with 6251 identified protein coding genes. Proteins known to be associated with thermal, osmotic, and radiation stress were identified in the genome. Comparative analysis with seven other Naganishia genomes revealed unique features underlying its polyextremophilic lifestyle. Naganishia friedmannii showed an expansion of genes involved in breaking down plant-derived carbohydrates, supporting the hypothesis that it survives at high elevations by metabolizing wind-deposited organic matter. Surprisingly, many genes involved in cell-cycle checkpoints and DNA repair were missing, as in several other Naganishia species. This extensive loss may be adaptive in extreme environments prone to abiotic stress, where a high mutation rate could generate advantageous traits, and reduced cell-cycle control may allow for faster reproduction that would be advantageous for rapid growth during brief periods of soil wetting following rare snow events.

Vimercati, Lara↗
DOE OSTI2026

Microbial inoculants for soil restoration: A Risk-Proportional Stewardship Framework Integrating Strain-Resolved Genomics and Adaptive Governance

Global soil degradation and increasing reliance on chemical inputs threaten agricultural sustainability, driving interest in microbial inoculants as tools for soil restoration. These biological products have the potential to enhance nutrient cycling, improve soil structure, and support plant resilience, but their environmental release raises important safety and stewardship considerations. Here, we propose a risk-proportional framework for the responsible deployment of microbial inoculants grounded in release-based stewardship. The framework integrates genome-resolved strain identification, exclusionary hazard screening, bioassay-based risk triage, ecological testing under realistic conditions, and monitored field deployment. Drawing on evidence from microbial ecology and invasion biology, we highlight how inoculants can alter resident microbial communities, influence ecosystem function, and, in some cases, facilitate gene flow, underscoring the need for risk assessment. We further outline a federated, genome-informed data infrastructure to support traceability, cross-jurisdiction learning, and adaptive management. Together, this approach provides a scalable and scientifically grounded pathway to balance innovation and safety, enabling microbial technologies to contribute to soil restoration and climate-resilient agriculture.

Edlund, Anna [OATH Inc]↗
DOE OSTI2025

Unraveling Bacterial Adaptation Strategies in the Microbiome Shaped by the Chemical Environment of the Plant Rhizosphere

The rhizosphere is a dynamic environment where rhizodeposits that include primary and secondary metabolites and mucilage serve as nutrient sources for soil microorganisms, attracting them toward plant roots. However, understanding how these microbes specifically respond to plant root chemical signals has been hindered by the challenges of disentangling physical and chemical interactions between the microbes and plant roots. To address this, we implemented an innovative filter-based experimental setup on plant roots that creates a physical barrier while facilitating the exchange of chemical signals. The proteomic analysis of 10 Populus root-associated bacterial strains grown in the presence or absence of a plant in either individual or mixed community conditions provided detailed insights into the functional responses of these strains to the root chemical environment. Additionally, this approach allowed us to discern the impact of root exudates on overall community dynamics. In particular, metaproteomic analyses revealed that each of these 10 microbial members responds uniquely to the presence of the plant, with Bacillus and Pantoea exhibiting the most dramatic favorable impact. Proteomic examination revealed the details of metabolism fine-tuning, including processes such as chemotaxis and ATP-binding cassette transporter proteins. This study demonstrates the application of a filter-based experimental setup to study microbial responses to plant chemicals and sheds light on adaptation strategies employed by various bacterial strains for survival in the rhizosphere.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2025

Axion Mass Prediction from Adaptive Mesh Refinement Cosmological Lattice Simulations

The quantum chromodynamics (QCD) axion arises as the pseudo-Goldstone mode of a spontaneously broken Abelian Peccei-Quinn (PQ) symmetry. If the scale of PQ symmetry breaking occurs below the inflationary reheat temperature and the domain wall number is unity, then there is a unique axion mass that gives the observed dark matter (DM) abundance. Computing this mass has been the subject of intensive numerical simulations for decades since the mass prediction informs laboratory experiments. Axion strings develop below the PQ symmetry-breaking temperature, and as the string network evolves, it emits axions that go on to become the DM. A key ingredient in the axion mass prediction is the spectral index of axion radiation emitted by the axion strings. We compute this index in this Letter using the most precise and accurate large-scale simulations to date of the axion-string network leveraging adaptive mesh refinement to achieve the precision that would, otherwise, require a static lattice with 262,144 3 lattice sites. We find a scale-invariant axion radiation spectrum to within 1% precision and find no evidence that the spectral index of radiation evolves with time. Accounting for axion production from strings prior to the QCD phase transition leads us to predict that the axion mass should be approximately 𝑚 𝑎 ∈ (45, 65) μ ⁢eV. However, we provide preliminary evidence that axions are produced in greater quantities from the string-domain-wall network collapse during the QCD phase transition, potentially increasing the mass prediction to as much as 300 μ ⁢eV.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2024

Ab initio leading order effective potential for elastic proton scattering based on the symmetry-adapted no-core shell model

Calculating microscopic optical potentials for elastic scattering at intermediate energies from light nuclei in an ab initio fashion within the Watson expansion has been established within the last few years. Based on the Watson expansion of the multiple scattering series, we employ a nonlocal translationally invariant nuclear density derived within the symmetry-adapted no-core shell model (SA-NCSM) framework from a chiral next-to-next-to-leading order (NNLO) nucleon-nucleon interaction and the very same interaction for a consistent full-folding calculation of the effective (optical) potential for nucleon-nucleus scattering for medium-heavy nuclei. The leading order effective (optical) folding potential is computed by integrating over a translationally invariant SA-NCSM one-body scalar density, spin-projected momentum distribution, and the Wolfenstein amplitudes 𝐴, 𝐶, and 𝑀. The resulting nonlocal potentials serve as input for a momentum space Lippmann-Schwinger equation. In the SA-NCSM, the model space is systematically up-selected using Sp⁡(3,ℝ) symmetry considerations. For the light nucleus of 6 He, we establish a systematic selection scheme in the SA-NCSM for scattering observables. Then, we apply this scheme to calculations of scattering observables, such as differential cross sections, analyzing powers, and spin rotation functions for elastic proton scattering from 20 Ne and 40 Ca in the energy regime between 65 and 200 MeV, and compare to available data. Furthermore, our calculations show that the leading order effective nucleon-nucleus potential in the Watson expansion of multiple scattering theory obtained from an up-selected SA-NCSM model space describes 40 Ca elastic scattering observables reasonably well to about 60 degrees in the center-of-mass frame, which coincides roughly with the validity of the NNLO chiral interaction used to calculate both the nucleon-nucleon amplitudes and the one-body scalar and spin nuclear densities.

Ab initio calculations↗
DOE OSTI2025

Quasiprobabilistic Readout Correction of Midcircuit Measurements for Adaptive Feedback via Measurement Randomized Compiling

Quantum measurements are a fundamental component of quantum computing. However, on present-day quantum computers, measurements can be more error prone than quantum gates and are susceptible to nonunital errors as well as nonlocal correlations due to measurement crosstalk. While readout errors can be mitigated in postprocessing, this is inefficient in the number of qubits due to a combinatorially large number of possible states that need to be characterized. In this work, we show that measurement errors can be tailored into a simple stochastic error model using randomized compiling, enabling the efficient mitigation of readout errors via quasiprobability distributions reconstructed from the measurement of a single preparation state in an exponentially large confusion matrix. We demonstrate the scalability and power of this approach by correcting readout errors without matrix inversion on a large number of different preparation states applied to a register of eight superconducting transmon qubits. Moreover, we show that this method can be extended to midcircuit measurements used for active feedback via quasiprobabilistic error cancellation, and we demonstrate the correction of measurement errors on an ancilla qubit used to detect and actively correct bit-flip errors on an entangled memory qubit. Our approach enables the correction of readout errors on large numbers of qubits and offers a strategy for correcting readout errors in adaptive circuits in which the results of midcircuit measurements are used to perform conditional operations on nonlocal qubits in real time.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2026

Adaptively coupled phase retrieval in multi-peak Bragg coherent diffraction imaging

Recent advances in Bragg coherent diffraction imaging (BCDI) experimental techniques permit routine measurement of multiple Bragg peaks from a single crystalline grain. The resulting images contain the full lattice distortion vector field which can be differentiated to provide lattice strain and rotation. With the advent of fourth-generation synchrotron light sources, such multi-peak datasets are produced at high rates, facilitating the need for rapid phase retrieval of the multiple peaks and subsequent image analysis. Here we describe and demonstrate a new implementation of a coupled phase retrieval technique for multi-peak BCDI which simultaneously treats each Bragg peak of the dataset and produces a three-dimensional image of the crystal's morphology and lattice distortion field. In addition, this method uses the redundant information contained in the various Bragg diffraction patterns to detect and suppress spurious signal appearing on the detector in a subset of the measurements. Compared with manual data editing, adaptive coupling produces a more consistent phase profile in reciprocal space and sharper surfaces in direct space, with no significant difference in computational cost. These improvements reduce the need for manual preprocessing and enable robust high-throughput analysis of multi-peak BCDI data, supporting near-real-time strain microscopy at modern synchrotron facilities.

36 MATERIALS SCIENCE↗
DOE OSTI2025

Adaptive PID Gain Scheduling Control for Hydropower Turbine Using Neural CDE and Stochastic Distribution Shaping

This paper introduces a gain-scheduling PID controller design strategy for hydroturbine frequency control mode. This scheme first uses real data to learn the nonlinear dynamics of the hydroturbine using neural controlled differential equations and then perturbs the obtained nonlinear system at different equilibrium points, based on which a static output feedback adaptive dynamic programming algorithm is then used to optimize the PID gains for each equilibrium point. Moreover, a continuous-time version of stochastic distribution control is proposed to further fine-tune the optimized PID gains. Finally, the controller is obtained by implementing linear interpolation between the optimized PID control gains. The simulation results show that the proposed gain-scheduling PID controller can control a larger range of operation points compared with the given fixed PID controller and the baseline method. Compared with the given fixed PID controller, the proposed gain-scheduling PID controller can regulate hydroturbine frequency against disturbances induced by power-load variation with over 50% less overshoot for some operation points.

13 HYDRO ENERGY↗