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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 739 records · Page 41
All sourcesNASA NTRSDOE OSTI
DOE OSTI2020

Approximating Trajectory Constraints With Machine Learning – Microgrid Islanding With Frequency Constraints

Here, we introduce deep earning aided constraint encoding to tackle the frequency-constraint microgrid scheduling problem. The nonlinear function between system operating condition and frequency nadir is approximated by using a neural network, which admits an exact mixed-integer formulation (MIP). This formulation is then integrated with the scheduling problem to encode the frequency constraint. With the stronger representation power of the neural network, the resulting commands can ensure adequate frequency response in a realistic setting in addition to islanding success. The proposed method is validated on a modified 33-node system. Successful islanding with a secure response is simulated under the scheduled commands using a detailed three-phase model in Simulink. The advantages of our model are particularly remarkable when the inertia emulation functions from wind turbine generators are considered.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2024

Robust Iterative Method for Symmetric Quantum Signal Processing in All Parameter Regimes

Here, this paper addresses the problem of solving nonlinear systems in the context of symmetric quantum signal processing (QSP), a powerful technique for implementing matrix functions on quantum computers. Symmetric QSP focuses on representing target polynomials as products of matrices in SU(2) that possess symmetry properties. We present a novel Newton’s method tailored for efficiently solving the nonlinear system involved in determining the phase factors within the symmetric QSP framework. Our method demonstrates rapid and robust convergence in all parameter regimes, including the challenging scenario with ill-conditioned Jacobian matrices, using standard double precision arithmetic operations. For instance, solving symmetric QSP for a highly oscillatory target function α cos(1000x) (polynomial degree ≈ 1433) takes 6 iterations to converge to machine precision when α = 0.9, and the number of iterations only increases to 18 iterations when α = 1 – 10 -9 with a highly ill-conditioned Jacobian matrix. Leveraging the matrix product state structure of symmetric QSP, the computation of the Jacobian matrix incurs a computational cost comparable to a single function evaluation. Moreover, we introduce a reformulation of symmetric QSP using real-number arithmetics, further enhancing the method’s efficiency. Extensive numerical tests validate the effectiveness and robustness of our approach, which has been implemented in the QSPPACK software package.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2022

VQE method: a short survey and recent developments

Abstract The variational quantum eigensolver (VQE) is a method that uses a hybrid quantum-classical computational approach to find eigenvalues of a Hamiltonian. VQE has been proposed as an alternative to fully quantum algorithms such as quantum phase estimation (QPE) because fully quantum algorithms require quantum hardware that will not be accessible in the near future. VQE has been successfully applied to solve the electronic Schrödinger equation for a variety of small molecules. However, the scalability of this method is limited by two factors: the complexity of the quantum circuits and the complexity of the classical optimization problem. Both of these factors are affected by the choice of the variational ansatz used to represent the trial wave function. Hence, the construction of an efficient ansatz is an active area of research. Put another way, modern quantum computers are not capable of executing deep quantum circuits produced by using currently available ansatzes for problems that map onto more than several qubits. In this review, we present recent developments in the field of designing efficient ansatzes that fall into two categories—chemistry–inspired and hardware–efficient—that produce quantum circuits that are easier to run on modern hardware. We discuss the shortfalls of ansatzes originally formulated for VQE simulations, how they are addressed in more sophisticated methods, and the potential ways for further improvements.

Fedorov, Dmitry A. (ORCID:0000000316598580)↗
DOE OSTI2022

Marine Renewable Energy Applications for Restorative Ocean Farming: Kelp

Kelp farming and kelp forest restoration have both been proposed as a solution to locally decrease the impacts of ocean acidification and eutrophication, often with co-benefits to other forms of aquaculture and mariculture. Compared to global markets, the kelp industry in the United States is still in its early phases, with the first commercial kelp farm founded in Casco Bay, Maine in 2010. Since then, interest and effort in kelp production has been increasing, with farms now present in Maine, New Hampshire, Connecticut, Rhode Island, Massachusetts, New York, Washington, and Alaska. Many research projects are underway in the United States to explore benefits of 3D ocean farming, tackle logistical problems of working in the ocean, autonomous farming, and explore viable end uses for kelp products. In seaweed farming, to remove the stored carbon or excess nutrients from the system, the biomass needs to be harvested at the optimal time to avoid the release of CO 2 that comes with decomposition. Timing of the harvest is also important for maximum crop yield, which can vary based on the final product. Additional monitoring needs can include a variety of water quality metrics, growth measurements, and visuals to ensure the health of the farm, comply with permits, support operations and maintenance functions. The variables measured may vary by desired end use of the product, location of farm, and operational design. Monitoring all of these parameters requires specialized devices that can be costly and challenging to maintain. Monitoring devices often face power and logistical constraints that could prevent kelp farmers from adopting these technologies or receiving accurate, efficient monitoring to assess ecosystem benefits and valuation. Marine energy has been identified as a possible power source for these devices. This project investigates the power needs for conducting kelp farm environmental monitoring compared with the available marine energy resource to evaluate if locally generated ocean energy could provide a solution to these monitoring challenges and benefit kelp farmers. This process was structured as follows: 1. Define what data is needed for farmers and their communities through desk research and interviews with end users. 2. Identify sensors and power requirements currently in use or available for commercial purchase. 3. Analyze current kelp and other mariculture farm locations for the potential marine energy resource. 4. Analyze farm designs and associated structures to make recommendations for marine energy design. 5. Quantify value that investment in sensors could provide in terms of carbon credit possibilities.

09 BIOMASS FUELS↗
DOE OSTI2023

Variance Reduction within Implicit Monte Carlo Thermal Radiation Transport using the Local Importance Function Transform [Slides]

System of equations derived by Fleck and Cummings to model time-dependent thermal radiative transfer (TRT) problems: Solution of radiation specific intensity (I), non-linearly coupled to material internal energy (U); Absorption and re-emission approximated by effective scattering events. Useful for high-energy density physics simulations: i.e., astrophysics, inertial confinement fusion (ICF). Whereas deterministic methods are fully discretized and free of statistical noise, Monte Carlo methods allow for dynamic sampling of the phase space at the price of statistical noise; Monte Carlo methods are also characterized by a slow $\frac{1}{√Ν}$ convergence rate.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2023

Emergent Behaviors and the Limits of Large Language Model Generalization

Over the past couple years, large language models (LLMs) have rapidly grown in size and performance across wide numbers of tasks, leading some to herald them as machine learning models with truly “general” capabilities. We address some of the ways that the language used to describe these models can be both deceptive and a poor framing for making progress towards understanding their capabilities, and also review arguments on to what extent these models can represent and interact with the meaning of language. Emergent behaviors in these highly complex models are unpredictable and research into understanding them is still in early phases, which limits the strength of claims of generality about them. Moreover, while these models demonstrate surprising emergent reasoning capabilities on many tasks, there are still many limitations in generalization that overall high benchmark performance can hide. We review the literature showcasing limitations of LLMs as well as techniques to mitigate or overcome these challenges, while also highlighting how fundamental problems in model evaluation may prevent true claims of generality. We conclude with a practical section on using LLMs on real world problems while appropriately evaluating and validating these tasks to prevent or mitigate the downstream impacts of incorrect results.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2020

Optimal polynomial based quantum eigenstate filtering with application to solving quantum linear systems

We present a quantum eigenstate filtering algorithm based on quantum signal processing (QSP) and minimax polynomials. The algorithm allows us to efficiently prepare a target eigenstate of a given Hamiltonian, if we have access to an initial state with non-trivial overlap with the target eigenstate and have a reasonable lower bound for the spectral gap. We apply this algorithm to the quantum linear system problem (QLSP), and present two algorithms based on quantum adiabatic computing (AQC) and quantum Zeno effect respectively. Both algorithms prepare the final solution as a pure state, and achieves the near optimal $\mathcal{\tilde{O}}$(dκlog(1/ϵ)) query complexity for a d -sparse matrix, where κ is the condition number, and ϵ is the desired precision. Neither algorithm uses phase estimation or amplitude amplification.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2021

Multi-Area Distribution System State Estimation Using Decentralized Physics-Aware Neural Networks

The development of active distribution grids requires more accurate and lower computational cost state estimation. In this paper, the authors investigate a decentralized learning-based distribution system state estimation (DSSE) approach for large distribution grids. The proposed approach decomposes the feeder-level DSSE into subarea-level estimation problems that can be solved independently. The proposed method is decentralized pruned physics-aware neural network (D-P2N2). The physical grid topology is used to parsimoniously design the connections between different hidden layers of the D-P2N2. Monte Carlo simulations based on one-year of load consumption data collected from smart meters for a three-phase distribution system power flow are developed to generate the measurement and voltage state data. The IEEE 123-node system is selected as the test network to benchmark the proposed algorithm against the classic weighted least squares and state-of-the-art learning-based DSSE approaches. Numerical results show that the D-P2N2 outperforms the state-of-the-art methods in terms of estimation accuracy and computational efficiency.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2020

Halogenation-Dependent Effects of the Chlorosulfolipids of Ochromonas danica on Lipid Bilayers

The chlorosulfolipids are amphiphilic natural products with stereochemically complex patterns of chlorination and sulfation. Despite their role in toxic shellfish poisoning, potential pharmacological activities, and unknown biological roles, they remain understudied due to the difficulties in purifying them from natural sources. The structure of these molecules, with a charged sulfate group in the middle of the hydrophobic chain, appears incompatible with the conventional lipid bilayer structure. Questions about chlorosulfolipids remain unanswered partly due to the unavailability of structural analogues with which to conduct structure–function studies. Here, we approach this problem by combining enantioselective total synthesis and membrane biophysics. Using a combination of Langmuir pressure–area isotherms of lipid monolayers, fluorescence imaging of vesicles, mass spectrometry imaging, natural product isolation, small-angle X-ray scattering, and cryogenic electron microscopy, we show that danicalipin A (1) likely inserts into lipid bilayers in the headgroup region and alters their structure and phase behavior. Specifically, danicalipin A (1) thins the bilayer and fluidizes it, allowing even saturated lipid to form fluid bilayers. Lipid monolayers show similar fluidizing upon insertion of danicalipin A (1). Furthermore, we show that the halogenation of the molecule is critical for its membrane activity, likely due to sterically controlled conformational changes. Synthetic unchlorinated and monochlorinated analogues do not thin and fluidize lipid bilayers to the same extent as the natural product. Overall, this study sheds light on how amphiphilic small molecules interact with lipid bilayers and the importance of stereochemistry and halogenation for this interaction.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2024

Improved machine learning algorithm for predicting ground state properties

Finding the ground state of a quantum many-body system is a fundamental problem in quantum physics. In this work, we give a classical machine learning (ML) algorithm for predicting ground state properties with an inductive bias encoding geometric locality. The proposed ML model can efficiently predict ground state properties of an n-qubit gapped local Hamiltonian after learning from only $\mathcal{O}$(log(n)) data about other Hamiltonians in the same quantum phase of matter. This improves substantially upon previous results that require $\mathcal{O}$(n c ) data for a large constant c. Furthermore, the training and prediction time of the proposed ML model scale as $\mathcal{O}$(n log n) in the number of qubits n. Numerical experiments on physical systems with up to 45 qubits confirm the favorable scaling in predicting ground state properties using a small training dataset.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2020

GaN‐based split phase transformer‐less PV inverter with auxiliary ZVT circuit

This paper explores performance enhancement of the common ground dynamic dc‐link (CGDL) inverter for single phase photovoltaic (PV) applications by a combination of gallium nitride (GaN) devices, split phase topology, coupled inductors, and zero voltage transition (ZVT) scheme. The CGDL inverter has the inherent advantage of minimised dc‐link capacitance and negligible leakage current due to the common ground configuration, but its reported efficiency was usually lower because of the higher dc‐link voltage used for the reduction of decoupling capacitance to a great extent. To solve the efficiency problem, in this study, a soft switching circuit is proposed for the first stage, while a coupled inductor integrated magnetics is incorporated in the second stage to reduce inductor loss, volume, and cost. Both of these topological improvements combined with the use of GaN devices facilitate in achieving high efficiency without compromising converter power density. Extensive experimental results are provided from a GaN based 1 kVA hardware prototype to demonstrate the superior performance of the CGDL inverter attaining a peak efficiency of 98.7% and a California Energy Commission efficiency of 98.5% at 75/50 kHz switching frequency.

Xia, Yinglai↗
DOE OSTI2023

Floating Block Method for Quantum Monte Carlo Simulations

Quantum Monte Carlo simulations are powerful and versatile tools for the quantum many-body problem. In addition to the usual calculations of energies and eigenstate observables, quantum Monte Carlo simulations can in principle be used to build fast and accurate many-body emulators using eigenvector continuation or design time-dependent Hamiltonians for adiabatic quantum computing. Furthermore, these new applications require something that is missing from the published literature, an efficient quantum Monte Carlo scheme for computing the inner product of ground state eigenvectors corresponding to different Hamiltonians. In this work, we introduce an algorithm called the floating block method, which solves the problem by performing Euclidean time evolution with two different Hamiltonians and interleaving the corresponding time blocks. We use the floating block method and nuclear lattice simulations to build eigenvector continuation emulators for energies of 4 He, 8 Be, 12 C, and 16 O nuclei over a range of local and nonlocal interaction couplings. From the emulator data, we identify the quantum phase transition line from a Bose gas of alpha particles to a nuclear liquid.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2025

Toward Stable, High‐Energy, Partially Disordered Mn‐Rich Spinel Cathodes by Revealing and Mitigating Surface Degradation

Mn-rich cathodes balance performance and sustainability but suffer from limited cyclability due to Mn dissolution and cathode-to-anode crosstalk. The Jahn-Teller (J-T) effect of Mn 3+ is often linked to the above phenomena, such as in spinel LiMn 2 O 4 . However, in typical voltage ranges, significant Mn 3+ only appears near the end of discharge, highlighting the need to reassess its role in driving Mn dissolution, structural degradation, and battery performance. Here, the spinel cathode's degree of disorder is tailored to expand the Mn redox range, enabling segmentation into J-T active and less active voltage ranges. Cycling at segmented voltage windows reveals surface degradation mechanisms with and without the major J-T effect. Despite a stronger J-T effect below 3.6 V vs. Li/Li + , Mn dissolution is less significant than above 3.6 V. Expanding the cycling window to 2.0–4.3 V causes severe degradation as the J-T active range induces a tetragonal phase and Mn 2+ -rich surface, driving Mn dissolution and consuming Li-ion inventory in full cells. Reducing electrolyte acidity minimizes Mn 3+ disproportionation, enabling a stable dopant-free Mn-only cathode with a 250 mAh g −1 specific capacity. These findings demonstrate that full cells using Mn-rich cathodes have the potential to avoid the notorious crosstalk problem through electrolyte engineering.

25 ENERGY STORAGE↗
DOE OSTI2022

Hydroxyl Conducting Hydrogels Enable Low-Maintenance Commercially Sized Rechargeable Zn–MnO2 Batteries for Use in Solar Microgrids

Zinc (Zn)–manganese dioxide (MnO2) rechargeable batteries have attracted research interest because of high specific theoretical capacity as well as being environmentally friendly, intrinsically safe and low-cost. Liquid electrolytes, such as potassium hydroxide, are historically used in these batteries; however, many failure mechanisms of the Zn–MnO2 battery chemistry result from the use of liquid electrolytes, including the formation of electrochemically inert phases such as hetaerolite (ZnMn2O4) and the promotion of shape change of the Zn electrode. This manuscript reports on the fundamental and commercial results of gel electrolytes for use in rechargeable Zn–MnO2 batteries as an alternative to liquid electrolytes. The manuscript also reports on novel properties of the gelled electrolyte such as limiting the overdischarge of Zn anodes, which is a problem in liquid electrolyte, and finally its use in solar microgrid applications, which is a first in academic literature. Potentiostatic and galvanostatic tests with the optimized gel electrolyte showed higher capacity retention compared to the tests with the liquid electrolyte, suggesting that gel electrolyte helps reduce Mn3+ dissolution and zincate ion migration from the Zn anode, improving reversibility. Cycling tests for commercially sized prismatic cells showed the gel electrolyte had exceptional cycle life, showing 100% capacity retention for >700 cycles at 9.5 Ah and for >300 cycles at 19 Ah, while the 19 Ah prismatic cell with a liquid electrolyte showed discharge capacity degradation at 100th cycle. We also performed overdischarge protection tests, in which a commercialized prismatic cell with the gel electrolyte was discharged to 0 V and achieved stable discharge capacities, while the liquid electrolyte cell showed discharge capacity fade in the first few cycles. Finally, the gel electrolyte batteries were tested under IEC solar off-grid protocol. It was noted that the gelled Zn–MnO2 batteries outperformed the Pb–acid batteries. Additionally, a designed system nameplated at 2 kWh with a 12 V system with 72 prismatic cells was tested with the same protocol, and it has entered its third year of cycling. This suggests that Zn–MnO2 rechargeable batteries with the gel electrolyte will be an ideal candidate for solar microgrid systems and grid storage in general.

25 ENERGY STORAGE↗
DOE OSTI2025

Investigating the crust of neutron stars with neural-network quantum states

An accurate description of low-density nuclear matter is crucial for explaining the physics of neutron star crusts. In the density range between approximately 0.01 fm −3 and 0.1 fm −3 , matter transitions from neutron-rich nuclei to various higher-density pasta shapes, before ultimately reaching a uniform liquid. In this work, we introduce a variational Monte Carlo method based on a neural Pfaffian-Jastrow quantum state, which allows us to model the transition from the liquid phase to neutron-rich nuclei microscopically. At low densities, nuclear clusters dynamically emerge from the microscopic interactions among protons and neutrons, which we model based on pionless effective field theory. Our variational Monte Carlo approach represents a significant improvement over the state-of-the-art auxiliary-field diffusion Monte Carlo method, which is severely hindered by the fermion-sign problem in this low-density regime and cannot capture the onset of clusters. In addition to computing the energy per particle of symmetric nuclear matter and pure neutron matter, we analyze an intermediate isospin-asymmetry configuration to elucidate the formation of nuclear clusters. We also provide evidence that the presence of such nuclear clusters influences the amount of protons in the crust compared to protons in beta-equilibrated, neutrino-transparent matter.

Nuclear astrophysics↗
DOE OSTI2020

Methods and algorithms for computer synthesis of holographic elements to obtain a complex impulse response of optical information processing systems based on modern spatial light modulators

The possibilities of designing optical devices for data processing and imaging based on the manipulation of coherent light beams by means of spatial light modulators (SLMs) are investigated. A review of commercially available SLMs is presented and the limitations of their complex modulation characteristics are analysed. The main problem of using present-day SLMs is the lack of the ability to modulate directly all states within a unit circle in the complex plane. In this regard, the characteristics of current methods for the synthesis of holographic elements are described that implement a given complex impulse response of the optical system and are optimal for using SLMs with purely amplitude, purely phase, and hybrid amplitude – phase modulation. (paper)

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2021

Reversible function switching of Ag catalyst in Mg/S battery with chloride-containing electrolyte

Rechargeable Mg/S batteries suffer from fast capacity decay because of the difficult re-oxidation of MgS. To tackle this problem, we used Ag catalyst in Mg/S cells with Cl- containing electrolyte, and achieved a greatly improved specific capacity of ~1200 mAh•g -1 and a long cycling life of 100 cycles. To understand the mechanism behind this improvement, we employed in-situ synchrotron radiation X-ray diffraction and in-situ X-ray absorption spectroscopy tools to study the reversible phase transitions during charge/discharge cycling. Additionally, the in-situ experiment results revealed that, at deeply charged state, Ag reacted with Cl- in the electrolyte to form the AgCl interfacial layer which prevented the physical contact of Ag with elemental S and avoided the formation of Ag 2 S; at early discharged state, AgCl transformed back to metallic Ag, which guaranteed its catalytic effectiveness for MgS decomposition. This reversible function switching mechanism of Ag catalyst is completely different from that in Mg/S cells using other catalysts or electrolytes.

25 ENERGY STORAGE↗