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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 55 records · Page 3
All sourcesNASA NTRSDOE OSTI
DOE OSTI2024

Enabling "Fuel" Switching through On-Demand Wind Control

This work demonstrates that classical shear-flow stability theory can be successfully applied to modify wind turbine wakes and also explains the success of several emerging, empirically-arrived control methods (i.e., dynamic induction and helix control). Linear stability theory predictions are extended to include the effects of non-axisymmetric inflow profiles, such as wind shear, which is shown to not strongly affect the primary forcing frequency. The predictions, as well as idealized large-eddy simulations using actuator-line representation of the turbine blades, agree that the n = 0 and ±1 modes have faster initial growth rates than higher-order modes, suggesting the lower-order modes are more appropriate for wake control. Exciting the lower-order modes with periodic pitching of the blades produces higher entrainment into the wake and consequently faster wake recovery.

17 WIND ENERGY↗
DOE OSTI2024

Practical Insights on Applying Simulation-Based Control Methods in Experimental Studies

Advanced nuclear reactors are crucial to the future of energy both in the United States and around the globe. In contrast to the current operating fleet, they are characterized as being deployable in remote locations and able to operate in semi-autonomous or autonomous fashion. This leap forward necessitates a new reactor control paradigm. Because advanced nuclear reactors are still under development in the United States, the creation of new control methods to achieve autonomous operations has been based on systems modeling and simulation. However, an important factor in successfully deploying these new control methods is the ability to seamlessly transition from simulation environments to real-world settings. Control methods tested in both simulation and experimental settings need to be investigated in the context of advanced reactor applications. This work developed a series of simple controllers for Idaho National Laboratory (INL)’s Microreactor Applications Research Validation and Evaluation (MARVEL) microreactor operating in load-following scenarios. These controllers were tested in both simulation and experimental settings, and a comparative performance analysis was performed. The simulation tests leveraged the Control and Optimization Modular Modeling Application for Nuclear Deployment (COMMAND) software developed in a previous stage of the current effort, along with the MARVEL Reactor Excursion and Leak Analysis Program (RELAP5-3D) and Monte Carlo N-Particle (MCNP) models. The experimental tests leveraged the COMMAND software, MARVEL models, and the U.S. Department of Energy Microreactor Program’s Microreactor Automated Control System (MACS). MACS was developed to serve as a control method testbed. It was customized to mirror the MARVEL microreactor, and COMMAND enabled MACS to emulate the physics of MARVEL. The load-following controller was developed using the simulation platform, with efforts to emulate real systems by introducing actuator saturation and noise. These factors were incrementally accounted for in the controller design. After finalizing the controller design, it was implemented with the experimental setup. The experimental conditions tested included an initial test under conditions similar to the final simulation test, and two additional scenarios. The first scenario introduced additional actuator saturation to account for equipment aging over time, which was unknown to the controller. The second scenario introduced sensor delay, a phenomenon anticipated with the use of remote operations or wireless communication in advanced reactors. These tests revealed several notable differences. While the controller performed well in simulation, it exhibited several limitations when transitioning to hardware. The main challenges involved maintaining the steady-state target power, as evidenced by larger error values between the true reactor power and setpoint power, as well as persistent oscillations in controlled reactor power. These issues could lead to unacceptable transient conditions in real reactor testing. Introducing actuator aging and stochastic delays in the experimental setup significantly impacted controller performance, resulting in increased overshoot and undershoot, and exacerbated error and oscillations previously mentioned. These findings underscore the importance of experimental testbeds for testing and validating control methods, as controllers developed using only theory and/or simulation may perform unexpectedly when applied to actual hardware. This research emphasizes the need for an experimental testbed for achieving such validation.

46 - INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AN↗
DOE OSTI2025

Dynamic Modeling, Trajectory Optimization, and Linear Control of Cable-Driven Parallel Robots for Automated Panelized Building Retrofits

The construction industry faces a growing need for automation to reduce costs, improve accuracy and productivity, and address labor shortages. One area that stands to benefit significantly from automation is panelized prefabricated building envelope retrofits, which can improve a building’s energy efficiency in heating and cooling interior spaces. In this paper, we propose using cable-driven parallel robots (CDPRs), which can effectively lift and handle large objects, to install these panels. However, implementing CDPRs presents significant challenges because of their nonlinear dynamics, complex trajectory planning, and precise control requirements. To tackle these challenges, this work focuses on a new application of established control and trajectory optimization theories in a CDPR simulation of a building envelope retrofit under real-world conditions. We first model the dynamics of CDPRs, highlighting the critical role of damping in system behavior. Building on this dynamic model, we formulate a trajectory optimization problem to generate feasible and efficient motion plans for the robot under operational and environmental constraints. Given the high precision required in the construction industry, accurately tracking the optimized trajectory is essential. However, challenges such as partial observability and external vibrations complicate this task. To address these issues, a Linear Quadratic Gaussian control framework is applied, enabling the robot to track the optimized trajectories with precision. Simulation results show that the proposed controller enables precise end effector positioning with errors under 4 mm, even in the presence of external wind disturbances. Through comprehensive simulations, our approach allows for an in-depth exploration of the system’s nonlinear dynamics, trajectory optimization, and control strategies under controlled yet highly realistic conditions. The results demonstrate the feasibility of CDPRs for automating panel installation and provide insights into their practical deployment.

CDPR↗
DOE OSTI2026

Point defect energetics in gallium arsenide, a comprehensive density functional theory study

In materials, point defects often control or modify functional properties. To predict the performance of materials intended for application in optoelectronic devices, it is imperative to understand the properties of those point defects. For the first time, all six intrinsic defects of GaAs, a key optoelectronics material, and their charge transition levels are calculated using density functional theory with the HSE06 functional. For comparison, both PBE and r 2 SCAN calculations are also carried out. The HSE06 results are found to be in better agreement with experimental data than previous calculations. In conclusion, the importance of using the exact electron exchange present in hybrid functionals and larger supercells to accurately determine defect levels and ground state defect configurations is demonstrated.

36 MATERIALS SCIENCE↗
DOE OSTI2025

Measuring the growth of Alfvén wave parametric decay instability using counter-propagating waves: Theory and simulations

The parametric decay instability (PDI) of Alfvén waves—where a pump Alfvén wave decays into a backward-propagating child Alfvén wave and a forward ion acoustic wave—is a fundamental nonlinear wave-wave interaction and holds significant implications for space and laboratory plasmas. However, to date there has been no direct experimental measurement of PDI. Here, we propose a novel and experimentally viable scheme to quantify the growth of Alfvén wave PDI on a linear device using a large pump Alfvén wave and a small counter-propagating seed Alfvén wave, with the seed-wave frequency tuned to match the backward Alfvén wave generated by standard PDI. Using hybrid simulations, we show that energy transfer from the pump to the seed reduces the latter's spatial damping. By comparing seed-wave amplitudes with and without the pump wave, this damping reduction can be used as a direct and reliable proxy for PDI growth. The method is validated in our simulations across a range of plasma and wave parameters and agrees well with theoretical predictions. Notably, the scheme exhibits no threshold for PDI excitation and is, in principle, readily implementable under current laboratory conditions. Finally, this scheme is a critical step toward solving the challenge of experimentally accessing Alfvén wave PDI and provides an elegant method that may be used to validate fundamental theories of parametric instabilities in controlled laboratory settings.

heliosphere↗
DOE OSTI2024

Interaction Effects on the Dynamical Anderson Metal-Insulator Transition Using Kicked Quantum Gases

Understanding the interplay of interaction and disorder in quantum transport poses long-standing scientific challenges for theory and experiment. While highly controlled ultracold atomic platforms combining atomic interactions with spatially disordered lattices have led to remarkable advances, the extension of such controlled studies to phenomena in high-dimensional disordered systems, such as the three-dimensional Anderson metal-insulator transition has been limited. Kicked quantum gases provide an alternate experimental platform that captures the Anderson model in momentum space and features dynamical localization as the analog of Anderson localization. Here, we utilize a momentum space lattice platform using quasiperiodically kicked ultracold atomic gases to experimentally investigate interaction effects on the three-dimensional dynamical Anderson metal-insulator transition. Here, we observe interaction-driven subdiffusion and a divergence of delocalization onset time on approaching the phase boundary. Mean-field numerical simulations show qualitative agreement with experimental observations, but with significant quantitative deviations.

74 ATOMIC AND MOLECULAR PHYSICS↗
DOE OSTI2024

Krash-ESL-Final report

During the period of the current Grant, the effort of the UCSD researchers was focused on: i) The application of the gyrokinetic code COGENT code to study the kinetic drift wave instability and its impact on blob dynamics, and ii) The modeling of edge plasma transport in collisional and semi-collisional regimes, and iii) Impurity transport in edge plasma and the incorporation of plasma-impurity collision operator (hereafter unlike particle collision operator) into COGENT. Over the years four UCSD graduate students (J. Angus, W. Lee, Y. Zhang, and A. Knyazev), funded under this Grant, contributed to the COGENT development/utilization and defended their PhD theses, which are based, partly, on their research related to the COGENT. Three of them (J. Angus, W. Lee, and Y. Zhang) have received “The Best MAE Graduate Student of the Year” awards. Whereas W. Lee after defending his PhD thesis returned back to his home country (South Korea), J. Angus, and Y. Zhang are continuing their research in plasma physics in the LLNL and LANL, whereas A. Knyazev currently is a postdoctoral fellow at Columbia University working on the theoretical aspects of advanced stellarators. The results obtained in our studies were disseminated in many papers published in first-class refereed scientific journals and presented on prestigious scientific meetings including the Annual APS DPP Meeting, Sherwood Fusion Theory Conference, IAEA Fusion Energy Conferences, International Conferences on Plasma-Surface Interactions in Controlled Fusion Devices, International Workshops on Edge Plasma Theory in Fusion Devices, etc. In what follows we highlight the major accomplishments of the UCSD Team in the main areas of our research.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2025

Kβ X-ray Emission Spectra Analysis Using Bayesian Optimization

The Kβ X-ray emission spectrum of 3 d transition metals is rich with electronic and structural information due to strong exchange interactions with the valence shell of the metal, and has become crucial for understanding their spin and oxidation states. The spectrum is commonly treated using crystal-field multiplet theory, a semi-empirical theory that uses tunable parameters to control the strength of the effects present in X-ray emission spectroscopy (XES). However, determining the experimental values of these parameters remains a challenge. We present a methodology that applies Bayesian optimization to crystal-field multiplet theory to determine parameter values. The algorithm is tested on the X-ray emission spectra of a collection of Mn, Co, and Ni oxides. We are able to find optimal values for the four most impactful parameters: Slater−Condon reduction factors F dd , F pd , and G pd , and crystal field splitting 10 Dq . The algorithm produces significantly improved accuracy compared to current analysis methods, and probes interparameter dependencies by modeling the error landscape. This advancement enhances XES analysis by offering an approach of obtaining quantitative electronic structural information on 3 d transition metal valence shells, facilitating applications across various scientific fields.

Bayesian optimization↗
DOE OSTI2026

Control and Real-Time Simulation of Microgrids with EV Fast-Charging and Grid-Forming Resources

Fast charging stations (FCSs) for electric vehicles (EVs) can behave as constant power loads, which is problematic for the operation of grid-forming (GFM) inverter-based resources (IBRs). To tackle this problem, this paper proposes a control suite that makes FCSs responsive to ac voltage and frequency disturbances. To test the performance of the FCS controls, the paper also sets forth theory to compensate for time-delays appearing in the real-time simulation of microgrid models divided into several central processing units (CPUs). Furthermore, these contributions are showcased via multi-CPU real-time simulations of a faulted microgrid having EV FCSs and GFM IBRs powered by photovoltaic solar arrays as well as power hardware-in-the-loop experiments. These contributions are significant to address NERC recommendations and IEEE standards.

14 SOLAR ENERGY↗
DOE OSTI2025

Nonparametric extensions of nuclear equations of state: Probing the breakdown scale of relativistic mean-field theory

Phenomenological calculations of the properties of dense matter, such as relativistic mean-field theories, represent a pathway to predicting the microscopic and macroscopic properties of neutron stars. However, such theories do not generically have well-controlled uncertainties and may break down within neutron stars. To faithfully represent the uncertainty in this breakdown scale, we develop a hybrid representation of the dense-matter equation of state, which assumes the form of a relativistic mean-field theory at low densities, while remaining agnostic to any nuclear theory at high densities. To achieve this, we use a nonparametric equation of state model to incorporate the correlations of the underlying relativistic mean-field theory equation of state at low pressures and transition to more flexible correlations above some chosen pressure scale. We perform astrophysical inference under various choices of the transition pressure between the theory-informed and theory-agnostic models. Here, we further study whether the chosen relativistic mean-field theory breaks down above some particular pressure and find no such evidence. Using simulated data for future astrophysical observations at about two-to-three times the precision of current constraints, we show that our method can identify the breakdown pressure associated with a potential strong phase transition.

Equations of state of nuclear matter↗
DOE OSTI2025

First-Principles Assessment of ZnTe and CdSe as Prospective Tunnel Barriers at the InAs/Al Interface

Majorana zero modes are predicted to emerge in semiconductor/ superconductor interfaces, such as InAs/Al. Majorana modes could be utilized for fault tolerant topological qubits. However, their realization is hindered by materials challenges. The coupling between the superconductor and the semiconductor may be too strong for Majorana modes to emerge, due to effective doping of the semiconductor by the metallic contact. This could be mediated by adding a tunnel barrier of controlled thickness. We use density functional theory (DFT) with Hubbard U corrections, whose values are machine-learned via Bayesian optimization (BO), to assess ZnTe and CdSe as prospective tunnel barriers for the InAs/Al interface. The results of DFT +U(BO) for ZnTe are validated by comparison to angle resolved photoemission spectroscopy (ARPES). We then study bilayer interfaces of the three semiconductors with each other and with Al, as well as trilayer interfaces with a varying number of ZnTe or CdSe layers inserted between InAs and Al. We find that 16 atomic layers of either material completely insulate the InAs from metal induced gap states (MIGS). However, ZnTe and CdSe differ significantly in their band alignment, such that ZnTe forms an effective barrier for electrons, whereas CdSe forms a barrier for holes. Because of Fermi level pinning in the conduction band at the interface, only electron transport is relevant for InAs-based Majorana devices. Therefore, ZnTe is the better choice. Based on the results of our simulations, we suggest conducting experiments with ZnTe barriers in the thickness range of 6–18 atomic layers.

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

Synthetic Tuning of Exciton–Phonon Coupling in Janus WS 2(1-x) Se 2x Monolayers Revealed by Resonant Raman Excitation Spectroscopy for Optoelectronic Applications

Janus monolayers, such as WSSe, have broken out-of-plane symmetry and an intrinsic dipole moment, impacting exciton transport, lifetime, and phonon interactions while imbuing piezoelectric, photocatalytic, and Rashba spin-splitting properties to transition metal dichalcogenides (TMDs). The new properties of this atomically thin material can be used for optoelectronic device applications. As TMDs are converted into Janus monolayers, e.g., top selenization of WS2 to WSSe, the bandgap and structure smoothly evolve, impacting not only the formation of excitons but also their complex interactions with different phonon modes. Resonant Raman excitation profiles (REPs) are uniquely well-suited to reveal both excitonic transitions and exciton–phonon coupling. Here, the resonant REPs of $A^{'}_{1}$ WS 2 and A 1 WSSe modes are measured to understand the strength of their coupling with the A, B, and C excitonic bands of a WS2 monolayer throughout its stepwise transformation into Janus WSSe by pulsed laser deposition (PLD) of energetic selenium species. In situ Raman spectroscopy during deposition is used to controllably prepare stable intermediate Janus structures, WS 2(1-x) Se 2x (0 ≤ x ≤ 0.5), for ex situ measurement of their resonant REPs. As x increases, REPs reveal not only pronounced excitonic bands that gradually shift toward lower photon energies but also strong, mode-selective exciton–phonon coupling. First-principles resonant Raman simulations independently predict this spectral behavior and are shown capable of matching the spectrally broadened, experimentally observed REP profiles in this model system, indicating their strong predictive capability for future experiments. The combination of controlled synthesis, REP characterization, and predictive theory employed here demonstrates a powerful pathway to understand and ultimately tune exciton–phonon interactions for future quantum optical devices.

Janus monolayers↗
DOE OSTI2025

Degrees of Rate Control in Interconnected Reaction Networks

Overall reactions in interconnected networks exhibit net, forward, and reverse rates that are governed by both constitutive elementary steps in the pathway of interest and branching elementary steps that lead to alternative products. Accordingly, steps in branching pathways exhibit negative net, forward, and reverse degrees of rate control, as they reduce reaction flux to the desired product. We here contextualize the forward and reverse degrees of rate control in terms of kinetic resistances (inverse of rates) and leverage the additive nature of kinetic resistance to decouple kinetic driving forces contributed by constitutive elementary steps and branching points (nodal species) in interconnected networks. Regardless of the network connectivity, forward and reverse degrees of rate control are shown to converge at equilibrium. Away from equilibrium, we identify two critical features of interconnected networks: stoichiometric regularity─condition where all stoichiometric numbers are unity─and pathway symmetry around nodal species─condition where branching pathways share the same rate constants, stoichiometry, and species concentrations/activities─that result in (i) equal forward, reverse, and consequently net degrees of rate control and (ii) forward and reverse degrees of rate control that exhibit constant offsets, respectively, across all extents of reaction. Furthermore, our discourse further provides a mathematical description for the influence of stoichiometric irregularity and pathway asymmetry on forward and reverse degrees of rate control. Altogether, the presented work details the effects of network (inter)connectivity and stoichiometry on reaction kinetics and, in doing so, establishes general protocols for capturing these effects as additive terms in the formulation of forward and reverse degrees of rate control.

10 SYNTHETIC FUELS↗
DOE OSTI2024

Robustness of quantum correlation in quantum energy teleportation

In this article, we explore a feedback-control protocol in different quantum field theories (QFTs) to study the quantum correlation in nonunitary evolution of quantum systems. Traditional studies on QFTs focus on quantum entanglement of pure states under the unitary evolution, however, we examine quantum correlation in mixed states using quantum energy teleportation (QET), which is an energy transfer protocol by utilizing ground state entanglement, and introduce quantum discord as a measure. QET involves a midcircuit measurement, which disrupts pure state entanglement. Despite this, our analysis demonstrates that quantum discord maintains the correlation throughout the QET process. We conducted numerical analyses with benchmark models including the Nambu-Jona-Lasinio (NJL) model, revealing that quantum discord consistently acts as an order parameter for phase transitions. The model is extended in a way that it has both the chiral chemical potential and the chemical potential, which are useful to study the phase structures mimicking the chiral imbalance between left- and right- quarks coupled to the chirality density operator. In all cases we studied, the quantum discord behaved as an order parameter of the phase transition. Published by the American Physical Society 2024

Ikeda, Kazuki (ORCID:0000000338212669)↗
DOE OSTI2026

Lyapunov-Based Iterative Learning of Regions of Attraction for Autonomous Systems

This paper proposes a novel algorithm for estimating the region of attraction of equilibrium points for nonlinear discrete-time autonomous systems. The method iteratively expands an initial estimate of the region of attraction by constructing unions of sublevel sets of learned functions parametrized as neural networks. Unlike conventional techniques that rely on a single global Lyapunov function, the proposed approach provides a collection of local Lyapunov-like functions, enabling richer representations and potentially larger region of attraction estimates. These functions are trained using sampled state-space data, and their Lipschitz continuity ensures that desirable properties extend beyond the training samples. The devised strategy is tested via numerical simulations, demonstrating the effectiveness of the proposed approach.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2026

Lyapunov-Based Iterative Learning of the Region of Attraction for Autonomous Systems

This presentation introduces a novel algorithm for estimating the region of attraction of equilibrium points for nonlinear discrete-time autonomous systems. The method iteratively expands an initial estimate of the region of attraction by constructing unions of sublevel sets of learned functions parametrized as neural networks. Unlike conventional techniques that rely on a single global Lyapunov function, the proposed approach provides a collection of local Lyapunov-like functions, enabling richer representations and potentially larger region of attraction estimates. These functions are trained using sampled state-space data, and their Lipschitz continuity ensures that desirable properties extend beyond the training samples. The devised strategy is tested via numerical simulations, demonstrating the effectiveness of the proposed approach.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2026

Continental-Scale Controls on Hyporheic Respiration Revealed by Knowledge-Guided Machine Learning

Hyporheic zone sediments regulate organic matter turnover and in-stream respiration, yet controls on sediment respiration remain poorly constrained across heterogeneous river networks, limiting prediction of stream metabolism and carbon processing at continental scales. Here, we integrate observations from ~90 river corridors across the United States in the WHONDRS consortium with a knowledge-guided machine learning (KGML) framework that couples thermodynamic rate theory with machine learning to identify dominant controls on hyporheic respiration. Diagnostic analyses show that organic matter concentration and thermodynamic favorability define an upper bound on respiration potential, whereas biological catalytic capacity and physical accessibility jointly govern realized respiration rates through interaction effects. To represent unmeasurable accessibility constraints, we use the mechanistic model as a scaffold for KGML, allowing machine learning to target residual structure not explained by process theory. This hybrid framework improves predictive skill relative to both the mechanistic model alone and fully data-driven models while preserving interpretability. These results indicate that variability in hyporheic respiration is largely mechanistically structured and demonstrate how integrating process theory with explainable AI enhances predictive performance while enabling scalable synthesis of river corridor observations.

Zheng, Jianqiu↗
DOE OSTI2026

Compositional Reasoning for Hierarchical State Machines

Harel statecharts and its derivatives are popular graphical languages for specifying discrete control systems via hierarchical state machines. Separately, there has been a long line of work on specifying concurrent systems with process calculi which come equipped with an algebraic theory, the ability reason compositionally about various temporal properties, and strong type systems. While these two approaches to modeling systems are tantalizingly similar, the integrated reasoning principles that exist for process calculi have not been demonstrated in hierarchical state machines. A key issue is that operational theories for process calculi do not behave like control systems, and thus, there is virtually no tool support for modeling control systems with such languages. For a control system designer, bringing the integrated, more scalable reasoning from the process calculi to state-machine languages would enable the specification of more complex systems and a more modular systems development process. Our insight is that we can recover many important results from the process calculi in hierarchical state machines with local scope. We employ a structural operational semantics, which is ubiquitous in process and 𝜆-calculi but uncommon in hierarchical statemachine formalizations, to enable inductive reasoning about behavior. Taking inspiration from the structure of process calculi metatheories, we define a calculus of refinement and equivalence that we prove sound with respect to local notion of (bi)simulation. Furthermore, we prove that the calculus preserves the behavioral properties of reactivity, observational determinism, traces, and linear temporal properties. Our results are mechanized in the Rocq proof assistant.

97 MATHEMATICS AND COMPUTING↗