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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 703 records · Page 39
All sourcesNASA NTRSDOE OSTI
DOE OSTI2022

Production of high fluence laser beams using ion wave plasma optics

Optical components for laser beams with high peak and averaged powers are being developed worldwide using stimulated plasma scattering that occurs when plasmas interact with intense, coherent light. After decades of pursuit of pulse compressors, mirrors, and other plasma based components that can be created by stimulated scattering from electron density perturbations forming on ultra-short time scales (e.g., via Stimulated Raman Scattering), more recent work has produced optical components on longer time scales allowing ion motion as well [via Stimulated Brillouin Scattering (SBS)]. In the most recent work, ion wave plasma optics have had success in producing pulses of focusable coherent light with high energy and fluence by operating on ns time scales and now promise to enable numerous applications. Experiments have further shown that in some parameter regimes, even simple plasma response models can describe the output of such optics with sufficient accuracy that they can be used as engineering tools to design plasma optics for future applications, as is already being done to control power deposition in fusion targets. In addition, the development of more sophisticated models promises to enable still higher performance from SBS driven plasma optical components under a wider range of conditions. The present status and most promising directions for future development of ion wave plasma optic techniques are discussed here.

47 OTHER INSTRUMENTATION↗
NASA NTRS2000

Nucleate Boiling Heat Transfer Studied Under Reduced-Gravity Conditions

Boiling is known to be a very efficient mode of heat transfer, and as such, it is employed in component cooling and in various energy-conversion systems. In space, boiling heat transfer may be used in thermal management, fluid handling and control, power systems, and on-orbit storage and supply systems for cryogenic propellants and life-support fluids. Recent interest in the exploration of Mars and other planets and in the concept of in situ resource utilization on the Martian and Lunar surfaces highlights the need to understand how gravity levels varying from the Earth's gravity to microgravity (1g = or > g/g(sub e) = or > 10(exp -6)g) affect boiling heat transfer. Because of the complex nature of the boiling process, no generalized prediction or procedure has been developed to describe the boiling heat transfer coefficient, particularly at reduced gravity levels. Recently, Professor Vijay K. Dhir of the University of California at Los Angeles proposed a novel building-block approach to investigate the boiling phenomena in low-gravity to microgravity environments. This approach experimentally investigates the complete process of bubble inception, growth, and departure for single bubbles formed at a well-defined and controllable nucleation site. Principal investigator Professor Vijay K. Dhir, with support from researchers from the NASA Glenn Research Center at Lewis Field, is performing a series of pool boiling experiments in the low-gravity environments of the KC 135 microgravity aircraft s parabolic flight to investigate the inception, growth, departure, and merger of bubbles from single- and multiple-nucleation sites as a function of the wall superheat and the liquid subcooling. Silicon wafers with single and multiple cavities of known characteristics are being used as test surfaces. Water and PF5060 (an inert liquid) were chosen as test liquids so that the role of surface wettability and the magnitude of the effect of interfacial tension on boiling in reduced gravity can be investigated.

Chao, David F.↗
NASA NTRS1986

Space Station power distribution and control

A general description of the Space Station is given with the basic requirements of the power distribution and controls system presented. The dual bus and branch circuit concepts are discussed and a computer control method presented.

Willis, A. H.↗
NASA NTRS1987

Automotive Stirling engine system component review

The design and testing of the power and combustion control system for the basic Stirling engine, Mod II, are examined. The power control system is concerned with transparent operation, and the Mod II uses engine working gas pressure variation to control the power output of the engine. The main components of the power control system, the power control valve, the pump-down system, and the hydrogen stable system, are described. The combustion control system consists of a combustion air supply system and an air/fuel ratio control system, and the system is to maintain constant heater head temperature, and to maximize combustion efficiency and to minimize exhaust emissions.

Hindes, Chip↗
DOE OSTI2026

Deciphering the Dynamic Balance Between Solvation Strength and Polysulfides Reaction Heterogeneity in Practical Lithium‐Sulfur Batteries

Achieving stable interfacial chemistry in lithium–sulfur batteries under practical conditions remains a key barrier to commercialization. Here, we demonstrate that interfacial dynamics can be effectively regulated by coupling solvation-power control with intrinsic heterogeneity of sulfur redox chemistry through the introduction of a weakly solvating fluorinated cosolvent, LIB 1200ET (1200ET). Compared with conventional fluorinated ethers, 1200ET efficiently shifts Li + solvation environment toward a more non-coordinated configuration at low volume fractions, enabling substantial solvation modulation without significantly impairing sulfur redox kinetics. This solvation transition weakens Li + –solvent interactions while strengthening Li + –anion and Li + –lithium polysulfide (LPS) coordination, suppressing LPS solubility and promoting reconstruction of solid–electrolyte interphase (SEI). Regulated LPS chemistry, together with 1200ET, leads to formation of a S 4+ -rich, LiF-reinforced SEI with enhanced ionic conductivity and mechanical robustness. Spatially resolved sulfur K-edge X-ray absorption spectroscopy on pouch cells reveals pronounced current-density-dependent chemical heterogeneity, distinguishing kinetically dominated and solvation-controlled regions. Under practical conditions (3.7 mg cm −2 sulfur loading, E/S = 6 µL mg −1 ), a single-layer pouch cell delivers 527 mAh g −1 over 200 cycles at C/3, while an Ah-level multilayer pouch cell achieves an energy density of 358 Wh kg −1 . These results establish non-coordinating cosolvent-driven solvation engineering as a scalable strategy for practical Li–S batteries.

36 MATERIALS SCIENCE↗
DOE OSTI2021

Large-scale additive manufacturing of self-heating molds

Large-scale material extrusion additive manufacturing technology is becoming the new mainstream technology for scaled-up composite mold and die applications. This paradigm shift in composite processing technology is primarily driven by out-of-autoclave tooling applications, in which fiber reinforced composite molds with scaled-up sizes and embedded heating elements are attractive. The present research describes the design, manufacturing, and testing of self-heating composite molds fabricated via a large-scale pellet extrusion 3D printing machine with an integrated wire co-extrusion tool. Polycarbonate (PC) composites reinforced with carbon fiber (PC/CF; 20 wt.%) and glass fiber (PC/GF; 20 wt.%) were used to fabricate mold parts. Joule heating thermal test results showed that uniform temperatures (~100 °C) were achieved for both PC/CF and PC/GF mold surfaces, using a custom-made feedback control power supply and infrared thermography. Mechanical characterizations, including tensile and flexural testing were performed on the wire-embedded and un-wired PC/CF and PC/GF base specimens to investigate the impact of the fiber reinforcement as well as the embedded wires. In the direction of extrusion, the ultimate tensile stress of PC/CF was 105 MPa, and that of PC/GF was 73 MPa, while the neat PC value was 64 MPa. Inner-bead voids and interfacial gaps were observed and characterized via optical and scanning electron microscopy. The embedded wires and inner bead impacted the mechanical properties of the composites. Furthermore, the stiffness of the wire-embedded mold was still satisfactory, proving that the technology can be used to fabricate additively manufactured out-of-oven/autoclave molds.

36 MATERIALS SCIENCE↗
DOE OSTI2024

Aligning lithium metal battery research and development across academia and industry

Successful integration of metallic lithium anodes into secondary batteries could enhance energy density and enable new forms of electrified transportation. However, the outlook for widespread lithium metal adoption in energy storage devices remains mixed. This comes in part from existing gaps in our understanding of the relationships connecting the initial state of lithium, its evolution with cycling, and end-of-life state. It remains important to develop standardized protocols for material and cell characterization, cycling performance, safety, and recycling procedures for lithium metal-based batteries. In February 2023 a cohort of scientists and engineers from academia, national laboratories, and industry gathered to converge on a list of critical challenges and action items to provide better understanding of lithium metal evolution and to enhance academic, governmental, and industrial partnerships to address these challenges. Here, we highlight the major discussion topics revolving around the manufacturing of lithium metal, its related metrology and integration into battery form factors, and best practices testing its electrochemical performance relevant to automotive applications. We introduce a power-controlled discharge testing protocol for research and development cells, in alignment between major automotive stakeholders, that may reveal lithium metal battery dynamics closer to practical driving behavior.

25 ENERGY STORAGE↗
DOE OSTI2025

Photoredox Catalysis with Spin Magnetic Field Effects: A Scheme for Chiral Resolution

Quantities that break both mirror symmetry and time-reversal symmetry, such as the orbital angular momentum, are known to connect molecular chirality with an applied magnetic field. This concept has led to observations such as magneto-chiral dichroism and chirality-induced spin selectivity (CISS). However, being small, these effects often require additional amplification procedures such as flow chemistry to achieve bulk enantioseparation. In this work, we demonstrate how the magnetic field effect on photogenerated radical pairs, which also breaks time-reversal symmetry, can be harnessed for enantiopurification. Fundamental to this process is the collective decay of the singlet and triplet radical-pair states made possible by an applied magnetic field. Because opposite enantiomers exhibit spin-orbit coupling matrix elements of opposite signs, the singlet and triplet decay channels interfere constructively in one enantiomer. Meanwhile, molecules of the other enantiomer are funnelled into the first enantiomer through excited-state chirality inversion, achieving (dynamic kinetic) chiral resolution. Using an axially chiral binaphthyl derivative and a borane photosensitizer as prototype, we predict an appreciable enantiomeric excess (e.e.) of 90% to be possible at steady state, attained within hundreds of milliseconds when irradiated by a laser. Importantly, our analytical results showcase regimes of perfect enantioselectivity (100% e.e.), accessible by further chemical optimization of the photosensitizer for which general strategies are discussed. Altogether, this work illustrates a so-far untapped but powerful control knob for photoredox catalysis based on spin chemistry principles.

Magnetic properties↗
DOE OSTI2024

A magnetic diagnostic suite for the Pegasus-III experiment

Pegasus-III is an ultralow aspect ratio spherical tokamak providing a dedicated US experiment for comparative solenoid-free startup studies. A new magnetic diagnostic suite for equilibrium and low frequency (<200 kHz) magnetohydrodynamic mode analysis has been installed. These new diagnostics address the significant challenges of measuring magnetic field in a high noise environment with the majority constrained to fit in an 8 mm diagnostic gap on the high field side. Electrostatic switching noise generated by the 16 independent current feedback-controlled power supplies produces dV cm /dt ~ 1 kV/μs and volt level common mode noise on the magnetics. Immunity to this switching noise is accomplished through differential signal runs and signal processing, along with end-to-end electromagnetic interference shielding. The magnetic measurements are simultaneously digitized at 1 MHz and conditioned by precision 8 pole Butterworth filters with a corner frequency of 200 kHz to prevent aliasing down to the 16-bit level over the full passband. Ex-vessel calibrations of the B p coils were completed with a typical uncertainty of <0.5%. Stray toroidal field pickup from coil misalignment or positioning errors is corrected using a physics-based model. Comparisons of the corrected measurements to modeling agree to within 1.3% on average. This is within the 1.5% measurement uncertainty that a sensitivity analysis determined is needed for accurate fast boundary and equilibrium reconstruction.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2025

Polarized and unpolarized gluon PDFs: Generative machine learning applications for lattice QCD matrix elements at short distance and large momentum

Lattice quantum chromodynamics (QCD) calculations share a defining challenge by requiring a small finite range of spatial separation z between quark/gluon bilinears for controllable power corrections in the perturbative QCD factorization, and a large hadron boost p z for a successful determination of collinear parton distribution functions (PDFs). However, these two requirements make the determination of PDFs from lattice data very challenging. We present the application of generative machine learning algorithms to estimate the polarized and unpolarized gluon correlation functions utilizing short-distance data and extending the correlation up to z p z ≲ 14 , surpassing the current capabilities of lattice QCD calculations. We train physics-informed machine learning algorithms to learn from the short-distance correlation at z ≲ 0.36 fm and take the limit, p z → ∞ , thereby minimizing possible contamination from the higher-twist effects for a successful reconstruction of the polarized gluon PDF. We also expose the bias and problems with underestimating uncertainties associated with the use of model-dependent and overly constrained functional forms, such as x α ( 1 − x ) β and its variants to extract PDFs from the lattice data. We propose the use of generative machine learning algorithms to mitigate these issues and present our determination of the polarized and unpolarized gluon PDFs in the nucleon. Published by the American Physical Society 2025

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2024

Tokamak Plasmas with Density up to 10 Times the Greenwald Limit

Current-carrying, toroidal laboratory plasmas typically cannot be sustained with an electron density above the empirical Greenwald limit. Presented here are tokamak experiments in the Madison Symmetric Torus with a density up to an unprecedented level about ten times this limit. This is thought to be made possible in part by a thick, stabilizing, conductive wall, and a high-voltage, feedback-controlled power supply driving the plasma current. The radial profile of the toroidal current flattens around twice the limit, without the edge collapse routinely observed in other experiments.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2025

Dynamical Generation of Higher-Order Spin-Orbit Coupling, Topology, and Persistent Spin Texture in Light-Irradiated Altermagnets

Altermagnets have been identified as the third category of magnetic materials, exhibiting momentum-dependent spin splitting characterized by even powers of momentum. Here, in this Letter, we show that when subjected to elliptically polarized light, these materials serve as an exemplary framework for the dynamic generation of topological bands featuring higher-order spin-orbit coupling (SOC). Notably, while the generated Zeeman field remains invariant to the particular altermagnetic ordering, the induced higher-order SOCs are related to the magnitude and symmetry of the altermagnetic order. Specifically, we show that an altermagnet exhibiting 𝐤 2⁢𝑛 -spin splitting can generate odd-in-𝐤 spin-orbit couplings up to 𝐤 2⁢𝑛−1 . In the limit of circularly polarized light, the only correction is 𝑘 2⁢𝑛−1 , with all lower-order contributions being nullified. Interestingly, light-induced SOCs significantly impact the low-energy band topology, where their Chern numbers change by Δ⁢𝐶 = ±1, 2, 3 for 𝑑, 𝑔, 𝑖-wave altermagnets. Finally, we find a critical field in which a persistent spin texture is realized, a highly desirable state with predicted infinite spin lifetime. Our Letter showcases light as a powerful, controllable tool for engineering complex and exciting phenomena in altermagnets.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
DOE OSTI2022

Data-driven cyber-attack detection for photovoltaic systems: A transfer learning approach

With increasing exposure to software-based sensing and control, power systems are facing higher risks of cyber/physical attacks. Here, to ensure system stability and minimize the potential economic losses, it is imperative to monitor the operating states and detect those attacks at the early stage. In this paper, a transfer learning method is proposed to detect cyber-attacks in photovoltaic (PV) systems with much less training data. First of all, two PV systems with a different number of PV inverters and power ratings are analyzed and their attack models are studied. Next, an attack detection Convolutional Neural Network (CNN) model was trained with rich amount of data from PV #1. Then, transfer learning was proposed to transfer the well-trained features from PV #1 to PV #2. Lastly, the attack detection model on PV #2 was trained based on the transferred CNN model. The experiment results show that the proposed transfer learning method achieves better accuracy and a faster convergence rate with a much less training dataset than conventional deep learning.

14 SOLAR ENERGY↗
DOE OSTI2022

3D Frequency Domain Reflectometry Digital Twin of an Electrical Cable: A First Glance

Electrical cables within nuclear power plants (NPPs) are critical components required for power, control, and instrumentation systems which may be exposed to stressors, such as elevated temperatures and gamma radiation. Such stressors can lead to a reduction in the remaining useful life of electrical cables, jeopardizing the safety of NPP systems. To evaluate the effect of stressors on the degradation of electrical cables, electrical reflectometry methods are commonly employed. Frequency domain reflectometry (FDR) is a non-destructive electrical reflectometry method that uses transmission line theory to detect degradation or impedance changes within electrical cables. However, in most cases FDR is only applied to de-energized cables, limiting the application in NPPs as the cable system must be taken offline. In this work, we explore the development of an FDR digital twin to predict the degradation of an electrical cable exposed to elevated temperature, which is expected to reduce the need for offline FDR. A 3-conductor low-voltage electrical cable was selected for evaluation of the digital twin. The fully three-dimensional digital twin was developed in COMSOL using the RF module. A cable length of 30-m and frequency bandwidth of 400 MHz was selected to mimic real-world application of FDR. Over a 1-m region, the permittivity of the insulation was varied by up to 20% to model thermal degradation. The results demonstrate accurate detection of the insulation damage region, supporting further investigation of the FDR digital twin using real-world data and machine learning for predictive damage estimation or remaining lifetime.

Spencer, Mychal P.↗
DOE OSTI2022

Medium Voltage Energy Hub Based on Multilevel Cascaded H Bridge-Dual Active Bridge Back-to-Back Converter for Power Distribution Feeders Interconnection and Multiple Simultaneous Grid Services

This paper presents a medium voltage energy hub based on a modular design of a multilevel cascaded H bridge (CHB)-dual active bridge (DAB) converter. The energy hub composed of the two CHB-DAB modules with a back-to-back topology can be utilized as a grid interconnection component between distribution feeders. The energy hub can provide multiple simultaneous grid services such as voltage regulation, power factor correction, as well as active power flow control between the connected feeders, contributing to grid flexibility, efficiency, reliability, and resilience. Circuit structure and control schemes for the energy hub including both local controllers for each converter and outer loop controllers are proposed to enable simultaneous grid services with coordination to prevent the overloading of the hub. To validate the performance of the hub, the hub system is interconnected between two IEEE 4-bus feeders and provides voltage regulation for both feeders, while controlling active power flow between them. The system and the feeders are implemented in Real-Time Digital Simulator (RTDS) for real-time simulation verification.

Choi, Jongchan↗
DOE OSTI2024

Structured Neural Network Modeling for Developing Digital Twins Models of Hydropower Generation Units

Dynamic modeling is a key part in the development of digital twin (DT) for dynamic systems. This is true for hydropower systems, where whole system modeling including penstock, turbine and generators, etc is important in realizing actuate modeling for the real systems. On the other hand, in response to the large variations of the power demand due to increased penetration of renewables such as wind and solar, hydropower systems are now required to operate in a large power generation range. This situation triggers the nonlinear characteristics of the generation unit with respect to its models. As such, it is imperative to use data driven modeling such as neural networks to learn the nonlinear dynamics of the hydropower generation unit. To achieve this objective, this study constructs a modeling and learning algorithm integrated with multiple structured neural network models for the modeling of turbine shaft speed, penstock pressure, and generator power output based on the generator power control setpoint, field current, and field voltage. In addition, the study uses the hydropower data from Tacoma Public Utilities to train and validate the proposed neural network algorithm. The results have shown that this structured neural network modeling approach can learn the system dynamics effectively by using the real-time data collected from the hydropower system with the desired modeling results.

Wang, Hong↗
DOE OSTI2023

Characterizing HVDC Transmission Flexibility under Extreme Operating Conditions

System operators rely on system flexibility, traditionally mainly from generation, to handle unexpected reliability and resilience events, ranging from excessive resource forecast errors to extreme events like heatwaves, earthquakes, and cyberattacks. Flexible transmission, such as controllable high voltage direct current (HVDC) transmission systems present an opportunity to increase overall system flexibility to accommodate operational challenges. This paper provides a methodology to study contributions to system flexibility by controllable, power electronics based transmission. The Western Electricity Coordinating Council (WECC) system is used as an example to study contributions from existing and future HVDC lines. Under extreme system conditions, it is identified that HVDC transmission flexibility can contribute with 24.8 to 28% of avoided unserved energy, and in some areas, the benefits amount to 50 to 70%.

HVDC transmission, PCM, Balancing authorities↗