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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 199 records · Page 11
All sourcesNASA NTRSDOE OSTI
DOE OSTI2012

High-bandwidth Modulation of H2/Syngas Fuel to Control Combustion Dynamics in Micro-Mixing Lean Premix Systems

The goal of this program was to develop and demonstrate fuel injection technologies that will facilitate the development of cost-effective turbine engines for Integrated Gasification Combined Cycle (IGCC) power plants, while improving efficiency and reducing emissions. The program involved developing a next-generation multi-point injector with enhanced stability performance for lean premix turbine systems that burn hydrogen (H2) or synthesis gas (syngas) fuels. A previously developed injector that demonstrated superior emissions performance was improved to enhance static flame stability through zone staging and pilot sheltering. In addition, piezo valve technology was implemented to investigate the potential for enhanced dynamic stability through high-bandwidth modulation of the fuel supply. Prototype injector and valve hardware were tested in an atmospheric combustion facility. The program was successful in meeting its objectives. Specifically, the following was accomplished: Demonstrated improvement of lean operability of the Parker multi-point injector through staging of fuel flow and primary zone sheltering; Developed a piezo valve capable of proportional and high-bandwidth modulation of gaseous fuel flow at frequencies as high as 500 Hz; The valve was shown to be capable of effecting changes to flame dynamics, heat release, and acoustic signature of an atmospheric combustor. The latter achievement indicates the viability of the Parker piezo valve technology for use in future adaptively controlled systems for the mitigation of combustion instabilities, particularly for attenuating combustion dynamics under ultra-lean conditions.

08 HYDROGEN↗
DOE OSTI2026

Adaptive Reinforcement Learning Control for Power Distribution in Multi-Output Resonant Converters

This paper presents an adaptive reinforcement learning (ARL)-based control framework for efficient power distribution in a multi-output resonant converter for UAV applications. The proposed system is based on a high-frequency isolated resonant architecture, where a single energy source supplies multiple propulsion loads through independently controlled output rectifiers, addressing the need for coordinated multi-motor power management. The ARL framework dynamically allocates output power by learning optimal phase-shift control actions under varying load demands and operating conditions. The agent autonomously determines control parameters that maximize conversion efficiency while ensuring accurate power sharing among multiple outputs. In addition, the proposed approach enables adaptive operation without requiring detailed system modeling or manual tuning. Experimental results demonstrate stable and efficient performance over a wide range of operating conditions, confirming the effectiveness and robustness of the learning-based control strategy for multi-output resonant converter system.

Asa, Erdem [ORNL] (ORCID:0000000190884812)↗
DOE OSTI2015

Designing Light-Activated Charge-Separating Proteins with a Naphthoquinone Amino Acid

The first principles design of manmade redox-protein maquettes is used to clarify the physical/chemical engineering supporting the mechanisms of natural enzymes with a view to recapitulate and surpass natural performance. Herein, we use intein-based protein semisynthesis to pair a synthetic naphthoquinone amino acid (Naq) with histidine-ligated photoactive metal–tetrapyrrole cofactors, creating a 100 μs photochemical charge separation unit akin to photosynthetic reaction centers. By using propargyl groups to protect the redox-active para-quinone during synthesis and assembly while permitting selective activation, we gain the ability to employ the quinone amino acid redox cofactor with the full set of natural amino acids in protein design. Direct anchoring of quinone to the protein backbone permits secure and adaptable control of intraprotein electron-tunneling distances and rates.

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

Inertia estimation for power grids: A review of methods, challenges, and future prospects

The electric power grid is undergoing a significant transformation, shifting from traditional synchronous generators to inverter-based resources (IBRs) such as solar photovoltaics, wind turbines, and energy storage systems. This evolution leads to a reduction in system inertia, a critical attribute for maintaining frequency stability in response to disturbances. Consequently, the ability to monitor and estimate system inertia has become increasingly essential. This paper provides a comprehensive review of existing inertia estimation methodologies, analyzing them from multiple perspectives, including the types of data utilized, underlying estimation principles, operational modes, and system-wide applicability. A comparative summary table is included to distill commonalities and key characteristics across various studies. In addition, the paper examines practical implementations of inertia estimation across several major power systems worldwide, including the U.S. interconnections, the Nordic power system, and the U.K. grid. Key challenges are identified, particularly in estimating contributions from virtual inertia sources and load-induced inertia in increasingly converter-dominated networks. To address these emerging challenges, the paper proposes an integrated framework for real-time inertia estimation and monitoring. This framework encompasses critical components such as data acquisition, inertia estimation from both synchronous and non-synchronous sources, load-induced effects, optimization techniques, forecasting, and virtual inertia scheduling. Collectively, these elements enable dynamic, system-wide monitoring and adaptive control of grid inertia.

Inertia estimation↗
DOE OSTI2024

Recent and future developments in pultrusion technology with consideration for curved geometries: A review

Herein this paper examined the current state and future developments in pultrusion with particular emphasis on its application in curved part manufacturing. The relationship between factors such as resin chemistry, fiber characteristics, and die geometry that influences the properties of pultruded product were highlighted. Moreover, the specific challenges associated with pultruding curved parts such as the complexities in achieving uniformity and structural integrity in such geometries were discussed. The review emphasized mold design, process improvement, adaptive control systems for precise resin impregnation and material selection to address these challenges. Additionally, the paper suggests the integration of real-time monitoring and data analytics as ways to enhance quality control during curved parts pultrusion. These advancements will help to overcome challenges specific to curved pultrusion and make the process more efficient. Other manufacturing techniques such as filament winding, thermoforming, pulforming were mentioned as alternatives to curved parts pultrusion. The review also explores pultruded variable curvature processes, highlighting some notable patents and article related to this subject matter. Production of pultruded variable curvature parts was seen as a key driver that can shape the future of pultrusion. Finally, the paper anticipates future trends, with sustainability, customization, integration of advanced materials, and development of techniques for pultrusion of composites parts.

42 ENGINEERING↗
DOE OSTI2011

Construction of low dissipative high-order well-balanced filter schemes for non-equilibrium flows

The goal of this paper is to generalize the well-balanced approach for non-equilibrium flow studied by Wang et al. (2009) to a class of low dissipative high-order shock-capturing filter schemes and to explore more advantages of well-balanced schemes in reacting flows. More general 1D and 2D reacting flow models and new examples of shock turbulence interactions are provided to demonstrate the advantage of well-balanced schemes. The class of filter schemes developed by Yee et al. (1999) , Sjoegreen and Yee (2004) and Yee and Sjoegreen (2007) consist of two steps, a full time step of spatially high-order non-dissipative base scheme and an adaptive non-linear filter containing shock-capturing dissipation. A good property of the filter scheme is that the base scheme and the filter are stand-alone modules in designing. Therefore, the idea of designing a well-balanced filter scheme is straightforward, i.e. choosing a well-balanced base scheme with a well-balanced filter (both with high-order accuracy). A typical class of these schemes shown in this paper is the high-order central difference schemes/predictor-corrector (PC) schemes with a high-order well-balanced WENO filter. The new filter scheme with the well-balanced property will gather the features of both filter methods and well-balanced properties: it can preserve certain steady-state solutions exactly; it is able to capture small perturbations, e.g. turbulence fluctuations; and it adaptively controls numerical dissipation. Thus it shows high accuracy, efficiency and stability in shock/turbulence interactions. Numerical examples containing 1D and 2D smooth problems, 1D stationary contact discontinuity problem and 1D turbulence/shock interactions are included to verify the improved accuracy, in addition to the well-balanced behavior.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2018

Exploring How Changing Monsoonal Dynamics and Human Pressures Challenge Multireservoir Management for Flood Protection, Hydropower Production, and Agricultural Water Supply

Abstract Multireservoir systems require robust and adaptive control policies capable of managing hydroclimatic variability and human demands across a range of time scales. This is especially true for river basins with high intraannual and interannual variability, such as monsoonal systems that need to buffer against seasonal droughts while also managing extreme floods. Moreover, the timing, intensity, duration, and frequency of these hydrologic extremes may evolve with deeply uncertain changes in socioeconomic and climatic pressures. This study contributes an innovative method for exploring how possible changes in the timing and magnitude of the monsoonal cycle impact the robustness of reservoir operating policies designed assuming stationary hydrologic and socioeconomic conditions. We illustrate this analysis on the Red River basin in Vietnam, where reservoirs and dams serve as important sources of hydropower production, multisectoral water supply, and flood protection for the capital city of Hanoi. Applying our scenario discovery approach, we find that reservoir operations designed assuming stationarity provide robust hydropower performance in the Red River but that increased mean streamflow, amplification of the within‐year monsoonal cycle, and increased interannual variability all threaten their ability to manage flood risk. Additionally, increased agricultural water demands can only be tolerated if they are accompanied by greater mean flow, exacerbating food‐flood trade‐offs in the basin. These findings highlight the importance of exploring the impacts of a wide range of deeply uncertain socioeconomic and hydrologic factors when evaluating system robustness in monsoonal river basins, considering in particular both lower‐order moments of annual streamflow and intraannual monsoonal behavior.

Quinn, J. D.↗
DOE OSTI2011

Environmental proteomics of microbial plankton in a highly productive coastal upwelling system

Metaproteomics is one of a suite of new approaches providing insights into the activities of microorganisms in natural environments. Proteins, the final products of gene expression, indicate cellular priorities, taking into account both transcriptional and posttranscriptional control mechanisms that control adaptive responses. Here, we report the proteomic composition of the o 1.2 lm fraction of a microbial community from Oregon coast summer surface waters, detected with two-dimensional liquid chromatography coupled with electrospray tandem mass spectrometry. Spectra corresponding to proteins involved in protein folding and biosynthesis, transport, and viral capsid structure were the most frequently detected. A total of 36% of all the detected proteins were best matches to the SAR11 clade, and other abundant coastal microbial clades were also well represented, including the Roseobacter clade (17%), oligotrophic marine gammaproteobacteria group (6%), OM43 clade (1%). Viral origins were attributed to 2.5% of proteins. In contrast to oligotrophic waters, phosphate transporters were not highly detected in this nutrient-rich system. However, transporters for amino acids, taurine, polyamines and glutamine synthetase were among the most highly detected proteins, supporting predictions that carbon and nitrogen are more limiting than phosphate in this environment. Intriguingly, one of the highly detected proteins was methanol dehydrogenase originating from the OM43 clade, providing further support for recent reports that the metabolism of one-carbon compounds by these streamlined methylotrophs might be an important feature of coastal ocean biogeochemistry.

10 SYNTHETIC FUELS↗
DOE OSTI2024

Highest fusion performance without harmful edge energy bursts in tokamak

The path of tokamak fusion and International thermonuclear experimental reactor (ITER) is maintaining high-performance plasma to produce sufficient fusion power. This effort is hindered by the transient energy burst arising from the instabilities at the boundary of plasmas. Conventional 3D magnetic perturbations used to suppress these instabilities often degrade fusion performance and increase the risk of other instabilities. This study presents an innovative 3D field optimization approach that leverages machine learning and real-time adaptability to overcome these challenges. Implemented in the DIII-D and KSTAR tokamaks, this method has consistently achieved reactor-relevant core confinement and the highest fusion performance without triggering damaging bursts. This is enabled by advances in the physics understanding of self-organized transport in the plasma edge and machine learning techniques to optimize the 3D field spectrum. The success of automated, real-time adaptive control of such complex systems paves the way for maximizing fusion efficiency in ITER and beyond while minimizing damage to device components.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2024

Programmed cell death regulator BAP2 is required for IRE1-mediated unfolded protein response in Arabidopsis

Environmental and physiological situations can challenge the balance between protein synthesis and folding capacity of the endoplasmic reticulum (ER) and cause ER stress, a potentially lethal condition. The unfolded protein response (UPR) restores ER homeostasis or actuates programmed cell death (PCD) when ER stress is unresolved. The cell fate determination mechanisms of the UPR are not well understood, especially in plants. Here, we integrate genetics and ER stress profiling with natural variation and quantitative trait locus analysis of 350 natural accessions of the model species Arabidopsis thaliana . Our analyses implicate a single nucleotide polymorphism to the loss of function of the general PCD regulator BON-ASSOCIATED PROTEIN2 (BAP2) in UPR outcomes. We establish that ER stress-induced BAP2 expression is antagonistically regulated by the UPR master regulator, inositol-requiring enzyme 1 (IRE1), and that BAP2 controls adaptive UPR amplitude in ER stress and ignites pro-death mechanisms in conditions of UPR insufficiency.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2020

Mineral Fabric as a Hidden Variable in Fracture Formation in Layered Media

Two longstanding goals in subsurface science are to induce fractures with a desired geometry and to adaptively control the interstitial geometry of existing fractures in response to changing subsurface conditions. Here, we demonstrate that microscopic mineral fabric and structure interact with macroscopic strain fields to generate emergent meso-scale geometries of induced fractures. These geometries define preferential directions of flow. Using additively manufactured rock, we demonstrate that highly conductive flow paths can be formed in tensile fractures by creating corrugated surfaces. Generation, suppression and enhancement of corrugations depend on the relative orientation between mineral fabric and layering. These insights into the role of micro-scale structure on macro-scale flow provide a new method for designing subsurface strategies to maximize potential production or to inhibit flow.

36 MATERIALS SCIENCE↗
DOE OSTI2014

Uncertainty in least-squares fits to the thermal noise spectra of nanomechanical resonators with applications to the atomic force microscope

Thermal noise spectra of nanomechanical resonators are used widely to characterize their physical properties. These spectra typically exhibit a Lorentzian response, with additional white noise due to extraneous processes. Least-squares fits of these measurements enable extraction of key parameters of the resonator, including its resonant frequency, quality factor, and stiffness. Here, we present general formulas for the uncertainties in these fit parameters due to sampling noise inherent in all thermal noise spectra. Good agreement with Monte Carlo simulation of synthetic data and measurements of an Atomic Force Microscope (AFM) cantilever is demonstrated. These formulas enable robust interpretation of thermal noise spectra measurements commonly performed in the AFM and adaptive control of fitting procedures with specified tolerances.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2014

Passivity analysis for a winged re-entry vehicle

Application of simple adaptive control (SAC) theory to the design of guidance and control systems for winged re-entry vehicles has been proven successful. To apply SAC to these non-linear and non-stationary systems, it needs to be Almost Strictly Passive (ASP), which is an extension of the Almost Strictly Positive Real (ASPR) condition for linear, time-invariant systems. To fulfill the ASP condition, the controlled, non-linear system has to be minimum-phase (i.e., the zero dynamics is stable), and there is a specific condition for the product of output and input matrix. Earlier studies indicate that even the linearised system is not ASPR. The two problems at hand are: 1) the system is non-minimum phase when flying with zero bank angle, and 2) whenever there is hybrid control, e.g., yaw control is established by combined reaction and aerodynamic control for the major part of flight, the second ASPR condition cannot be met. In this paper we look at both issues, the former related to the guidance system and the latter to the attitude-control system. It is concluded that whenever the nominal bank angle is zero, the passivity conditions can never be met, and guidance should be based on nominal commands and a redefinition of those whenever the error becomes too large. For the remaining part of the trajectory, the passivity conditions are marginally met, but it is proposed to add feedforward compensators to alleviate these conditions. The issue of hybrid control is avoided by redefining the controls with total control moments and adding a so-called control allocator. Deriving the passivity conditions for rotational motion, and evaluating these conditions along the trajectory shows that the (non-linear) winged entry vehicle is ASP. The sufficient conditions to apply SAC for attitude control are thus met.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2010

Laser-induced modification of transparent crystals and glasses

We analyse the processes taking place in transparent crystals and glasses irradiated by ultrashort laser pulses in the regimes typical of various applications in optoelectronics and photonics. We consider some phenomena, which have been previously described by the authors within the different model representations: charging of the dielectric surface due to electron photoemission resulting in a Coulomb explosion; crater shaping by using an adaptive control of the laser pulse shape; optimisation of the waveguide writing in materials strongly resistant to laser-induced compaction under ordinary irradiation conditions. The developed models and analysis of the processes relying on these models include the elements of the solid-state physics, plasma physics, thermodynamics, theory of elasticity and plasticity. Some important experimental observations which require explanations and adequate description are summarised. (photonics and nanotechnology)

36 MATERIALS SCIENCE↗
DOE OSTI2024

Overview of the KSTAR experiments toward fusion reactor

The Korean Superconducting Tokamak Advanced Research has been focused on exploring the key physics and engineering issues for future fusion reactors by demonstrating the long pulse operation of high beta steady-state discharge. Advanced scenarios are being developed with the goal for steady-state operation, and significant progress has been made in high ℓ i , hybrid and high beta scenarios with β N of 3. In the new operation scenario called fast ion regulated enhanced (FIRE), fast ions play an essential role in confinement enhancement. GK simulations show a significant reduction of the thermal energy flux when the thermal ion fraction decreases and the main ion density gradient is reversed by the fast ions in FIRE mode. Optimization of 3D magnetic field techniques, including adaptive control and real-time machine learning control algorithm, enabled long-pulse operation and high-performance ELM-suppressed discharge. Symmetric multiple shattered pellet injections (SPIs) and real-time disruption event characterization and forecasting are being performed to mitigate and avoid the disruptions associated with high-performance, long-pulse ITER-like scenarios. Finally, the near-term research plan will be addressed with the actively cooled tungsten divertor, a major upgrade of the NBI and helicon current drive heating, and transition to a full metallic wall.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2022

Quantum advantages for Pauli channel estimation

We show that entangled measurements provide an exponential advantage in sample complexity for Pauli channel estimation, which is both a fundamental problem and a practically important subroutine for benchmarking near-term quantum devices. The specific task we consider is to simultaneously learn all the eigenvalues of an n-qubit Pauli channel to ±ε precision. We give an estimation protocol with an n-qubit ancilla that succeeds with high probability using only O(n/ε 2 ) copies of the Pauli channel, while proving that any ancilla-free protocol (possibly with adaptive control and channel concatenation) would need at least Ω(2 n/3 ) rounds of measurement. We further study the advantages provided by a small number of ancillas. For the case that a k-qubit ancilla (k≤n) is available, we obtain a sample complexity lower bound of Ω(2 (n-k)/3 ) for any nonconcatenating protocol, and a stronger lower bound of Ω(n 2n-k ) for any nonadaptive, nonconcatenating protocol, which is shown to be tight. We also show how to apply the ancilla-assisted estimation protocol to a practical quantum benchmarking task in a noise-resilient and sample-efficient manner, given reasonable noise assumptions. Our results provide a practically interesting example for quantum advantages in learning and also bring insights for quantum benchmarking.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2024

Machine learning surrogate for charged particle beam dynamics with space charge based on a recurrent neural network with aleatoric uncertainty

In this work, we develop a machine learning (ML) model with aleatoric uncertainty for the low energy beam transport (LEBT) region of the LANSCE linear accelerator in which we model the transport of a space-charge-dominated 750 keV proton beam through a lattice of 22 quadrupole magnets. Our ML model is developed based on data generated by a Kapchinsky–Vladimirsky (KV) envelope model of beam transport. We show that a recurrent neural network can be used as a dynamical surrogate model for fast prediction of the LEBT beam envelope. Furthermore, we endow the model with the prediction of aleatoric uncertainty and compare three different approaches. We demonstrate that the ML-based uncertainty quantification models are well calibrated and produce good estimates of the regions where the model is less certain about its predictions. This ML framework is a necessary step in the development of a real-time virtual diagnostic tool with uncertainty quantification that can be integrated into more complex downstream tasks (e.g., adaptive control or learning flexible control policies via reinforcement learning) for improved efficiency in beam operations. In future work, we plan to expand on this preliminary study by considering more realistic envelope models that include longitudinal momentum spread and dispersive effects in bending magnets, as well as particle tracking codes with 3D space charge (such as and ). Published by the American Physical Society 2024

43 PARTICLE ACCELERATORS↗