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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 577 records · Page 32
All sourcesNASA NTRSDOE OSTI
DOE OSTI2021

Investigation of grid-based vorticity-velocity large eddy simulation off-body solvers for application to overset CFD

Accurately predicting unsteady wakes and vortex-dominated flows is essential to a wide range of engineering applications, including aircraft, rotorcraft, shipboard operations, bio-inspired unsteady flight and propulsion, wind turbines, and urban flows. While current CFD software can model the complete flow field and wake system, the computational costs incurred in high Reynolds number unsteady turbulent flow simulations often remain prohibitive for routine engineering use, particularly for applications involving moving components. Prior work has demonstrated that by adopting a vorticity-velocity formulation in a grid-based off-body flow solver (VorTran-M and VorTran-M2) one can lower these costs by several orders of magnitude when compared to conventional approaches. This paper describes the extensions made to VorTran-M2 to support turbulent flows, and associated benchmarking activity to assess its performance for problems involving strong stretching and diffusion processes, whose competing contributions to the vorticity field are core drivers of turbulent flow evolution. Predictions are presented for: (i) the Kida-Pelz problem whose inviscid form is of mathematical interest due to its apparent formation of singular flow in finite time; and (ii) the Taylor Green vortex arrangement, which has been extensively studied as a fundamental simulation challenge in the turbulent modeling community. Here, the results are used to evaluate the overall predictive ability and performance of two sub-grid scale models incorporated into VorTran-M2. Results indicate that the computational cost savings seen previously for inviscid and convection dominated problems extend to turbulent flow simulations supporting the viability of VorTran-M2 as a low cost means for accurately modeling the far-field and background flow, particularly when long duration vorticity evolution is of interest.

42 ENGINEERING↗
DOE OSTI2020

Robust galactomannan/graphene oxide film with ultra-flexible, gas barrier and self-clean properties

Here, this study examines a facile technology to manufacture food packaging alternatives with superior mechanical, ultra-flexible, gas barrier and self-cleaning properties, based on green and benign starting materials. A novel galactomannan (GM) was isolated from the Sesbania cannabina seeds, which was used as a matrix for the fabrication of GM-based films. Inspired by the brick-and-mortar structure of natural nacre, a facile method was adopted to fabricate an artificial nacre based on the self-assembly of GM and borate crosslinked graphene oxide (GO). These GM/GO composites are ultra-flexible, which can be folded into various shapes. The tensile strength reached 135.54 MPa which is 2.40 times that of the pure GM film. In addition, after coating with the poly(dimethylsiloxane) (PDMS), these films became hydrophobic (WCA around 120°) with self-cleaning properties. Our study further revealed that the oxygen and water vapor permeabilities were improved with the introduction of GO and PDMS coating.

36 MATERIALS SCIENCE↗
DOE OSTI2023

Polyester-based epoxy vitrimer integrating spent coffee ground as a natural filler

Coffee is a widely consumed beverage, but approximately 80% mass of coffee cherry is discarded as waste. Here, in this study, spent coffee grounds (SCG) were recycled and used as a natural filler in a polyester-based epoxy matrix made from reactions of diglycidyl ethers of Bisphenol A and polyethylene glycol with methyl nadic anhydride. The properties of SCG/epoxy composites with various SCG contents up to 40% w/w were evaluated using various techniques such as FT-IR spectroscopy, tensile testing, optical microscopy, differential scanning calorimetry, and rheology. The SCG filled polyester-based epoxy matrix demonstrated excellent dimensional stability and retained a permanently crosslinked network, but re-shaping was possible at ~150 °C through transesterification, which occurred due to a dynamic chain exchange reaction between the ester group of the epoxy matrix and the hydroxyl group of the SCG fillers. The transesterification also improved the interfacial adhesion between the matrix and SCG fillers, resulting in tensile strength of over 20 MPa and a modulus of around 2.5 GPa, even with high SCG contents up to 40% w/w. While neat, cured epoxy shows thermoset behavior, filling the matrix with SCG enables reforming and origami-inspired spontaneous change in shape.

42 ENGINEERING↗
DOE OSTI2023

Control oriented model order reduction for the hydraulic system of an autonomous wheel loader

Control design for an autonomous wheel loader requires a high-fidelity and low-order dynamics model. The objective of order reduction is to decrease the number of states in the model while maintaining performance comparable to the original model. Of all the power components in a wheel loader, the model order reduction for steering and working hydraulic systems is most demanding due to its nonlinear and stiff dynamical nature. This paper describes a physics-inspired model order reduction method that can reduce the model order by close to thirty percent. It is achieved by discarding orders with fast dynamics and consolidating multiple states into less new states. Furthermore, this method can also be extended to other types of off-road vehicles such as excavators, bulldozers, etc. Simulation results demonstrate that the reduced-order model exhibits very similar performance to the full-order model under typical operating conditions of wheel loaders, with an output error of less than six percent.

42 ENGINEERING↗
DOE OSTI2025

Optimizing Cupriavidus necator H16 as a host for aerobic C1 conversion

Biological systems capable of converting CO 2 or CO 2 -derived, single-carbon (C1) compounds can be used to reduce or reverse carbon emissions while establishing a circular bioeconomy to provide sustainable sources of the fuels, foods, and materials humanity relies on. A robust bioeconomy will rely upon a variety of microorganisms capable of assimilating C1 compounds and converting them to valuable products at industrial scale. While anaerobic microbes are ideal hosts for production of short-chain acids and alcohols, microbes capable of aerobic respiration are well suited for biosynthesis of higher molecular weight products. One such organism is the gram-negative soil bacterium Cupriavidus necator, which has been utilized in commercial production of biopolymers for decades. More recently, its capability of robust, aerobic growth on CO 2 has inspired research efforts that have advanced it toward becoming one of the leading bacterial hosts for C1-based biomanufacturing. This review highlights those efforts in the context of the characteristics that have historically made C. necator an excellent host for industrial bioconversion processes: its metabolic versatility, ability to grow rapidly to high cell densities, and genetic amenability.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2020

Parallel three-dimensional simulations of quasi-static elastoplastic solids

Hypo-elastoplasticity is a flexible framework for modeling the mechanics of many hard materials under small elastic deformation and large plastic deformation. Under typical loading rates, most laboratory tests of these materials happen in the quasi-static limit, but there are few existing numerical methods tailor-made for this physical regime. Here, we extend to three dimensions a recent projection method for simulating quasi-static hypo-elastoplastic materials. The method is based on a mathematical correspondence to the incompressible Navier–Stokes equations, where the projection method of Chorin (1968) is an established numerical technique. We develop and utilize a three-dimensional parallel geometric multigrid solver employed to solve a linear system for the quasi-static projection. Our method is tested through simulation of three-dimensional shear band nucleation and growth, a precursor to failure in many materials. As an example system, we employ a physical model of a bulk metallic glass based on the shear transformation zone theory, but the method can be applied to any elastoplasticity model. We consider several examples of three-dimensional shear banding, and examine shear band formation in physically realistic materials with heterogeneous initial conditions under both simple shear deformation and boundary conditions inspired by friction welding.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2022

Time-discretization of a plasma-neutral MHD model with a semi-implicit leapfrog algorithm

The semi-implicit leapfrog time-discretization is a workhorse algorithm for initial-value MHD codes to bridge between vastly separated time scales. Inclusion of atomic interactions with neutrals breaks the functional structure of the MHD equations that exploited by the leapfrog. In this work, we address how to best integrate atomic physics into the semi-implicit leapfrog. Following the Crank-Nicolson method, one approach is to time-center the atomic interactions in the linear solver and use a Newton method to include the nonlinear contributions. Alternatively, another family of methods are based on operator-splitting the terms associated with the atomic interactions using a Strang-splitting technique. These methods naturally break equations into constituent ODE and PDE parts and preserve the structure exploited by the semi-implicit leapfrog. We study the accuracy and efficiency of these methods through a battery of 0D and 1D cases and show that a second-order-in-time Douglas-Rachford inspired coupling between the ODE and PDE advances is effective in reducing the time-discretization error to be comparable to that of Crank-Nicolson with Newton iteration of the nonlinear terms. Splitting ODE and PDE parts results in independent matrix solves for each field which reduces the computational cost considerably and provides parallelization over species relative to Crank-Nicolson.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2024

Principal Landau determinants

We reformulate the Landau analysis of Feynman integrals with the aim of advancing the state of the art in modern particle-physics computations. We contribute new algorithms for computing Landau singularities, using tools from polyhedral geometry and symbolic/numerical elimination. Inspired by the work of Gelfand, Kapranov, and Zelevinsky (GKZ) on generalized Euler integrals, we define the principal Landau determinant of a Feynman diagram. We illustrate with a number of examples that this algebraic formalism allows to compute many components of the Landau singular locus. We adapt the GKZ framework by carefully specializing Euler integrals to Feynman integrals. For instance, ultraviolet and infrared singularities are detected as irreducible components of an incidence variety, which project dominantly to the kinematic space. We compute principal Landau determinants for the infinite families of one-loop and banana diagrams with different mass configurations, and for a range of cutting-edge Standard Model processes. Furthermore, our algorithms build on the Julia package this http URL and are implemented in the new open-source package this http URL available at this https URL.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2025

CONFLUX: A standardized framework to calculate reactor antineutrino flux

Nuclear fission reactors are abundant sources of antineutrinos for neutrino physics experiments. The flux and spectrum of antineutrinos emitted by a reactor can indicate its activity and composition, suggesting potential applications of neutrino measurements beyond fundamental scientific studies that may be valuable to society. The utility of reactor antineutrinos for applications and fundamental science is dependent on the availability of precise predictions of these emissions. For example, in the last decade, disagreements between reactor antineutrino measurements and models have inspired revision of reactor antineutrino calculations and standard nuclear databases as well as searches for new fundamental particles not predicted by the Standard Model of particle physics. Past predictions and descriptions of the methods used to generate them are documented to varying degrees in the literature, with different modeling teams incorporating a range of methods, input data, and assumptions. The resulting difficulty in accessing or reproducing past models and reconciling results from differing approaches complicates the future study and application of reactor antineutrinos. The CONFLUX (Calculation Of Neutrino FLUX) software framework is a neutrino prediction tool built with the goal of simplifying, standardizing, and democratizing the process of reactor antineutrino flux calculations. CONFLUX includes three primary methods for calculating the antineutrino emissions of nuclear reactors or individual beta decays that incorporate common nuclear data and beta decay theory. The software is prepackaged with the current nuclear databases, including ENDF.B/VIII, JEFF-3.3, and ENSDF, and it includes the capability to predict time-dependent reactor emissions, adjust nuclear database or beta decay inputs/assumptions, and propagate related sources of uncertainty. Here, this paper describes the CONFLUX software structure, details the methods used for flux and spectrum calculations, and provides examples of potential use cases.

Zhang, Xianyi [Lawrence Livermore National Laborat↗
DOE OSTI2024

Testing α -attractor quintessential inflation against CMB and low-redshift data

Due to universality and attractor properties, α-attractor quintessential inflation establishes direct relations between inflationary observables such as the scalar tilt n s and the tensor-to-scalar ratio r, and late-time dark energy equation of state parameters w 0 and w a . In this work, we examine three different physically motivated regimes, considering complete freedom in the parameter α, models inspired by supergravity where α takes on values up to α = 7/3, and Starobinsky inflation (α = 1). We investigate the consistency and constraints imposed by Cosmic Microwave Background measurements from the Planck satellite, B-mode polarization data from the BICEP/Keck collaboration, and low-redshift observations. Additionally, we consider small-scale CMB measurements released by the Atacama Cosmology Telescope, which give results approaching the Harrison– Zel’dovich spectrum (n s ≈ 1). Here α-attractors lead to an improved fit over $\Lambda$CDM. For the large-scale CMB measurements, α ≳ 2 models can provide equally good fits as $\Lambda$CDM

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2025

A neural-network-enhanced parameter-varying framework for multi-objective model predictive control applied to buildings

Management of the electrical grid is becoming more complex due to the increased penetration of alternative energy generation technologies and a broadening diversity of electric loads. This complexity creates challenges in balancing demand and generation that can increase the potential for grid instabilities. One effective way to address this issue is to leverage previously unexploited demand flexibility through advanced control strategies. In this work, we propose an advanced control method, called adaptive neural parameter-varying model predictive control (ANPV-MPC), to control the temperature and energy consumption of a building via its Heating, Ventilation, and Air Conditioning system. ANPV-MPC combines key ideas in parameter-varying control, adaptive control, and online learning strategies to bridge the gap between computationally efficient linear model predictive control and more accurate nonlinear model predictive control. The novelty in ANPV-MPC is the use of a physics-inspired Bayesian neural network to estimate the coefficients of the parameter-varying linear control model. The Bayesian neural network additionally provides uncertainty estimates, triggering online training to capture evolving building system conditions. We show that ANPV-MPC can approximate the building system dynamics with a 28.39% higher accuracy than traditional linear model predictive control, resulting in 36.23% better control performance without increasing complexity of the optimal control problem. ANPV-MPC also adapts in real time to previously unseen conditions using online learning, further improving its performance.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2023

From PROTAC to inhibitor: Structure-guided discovery of potent and orally bioavailable BET inhibitors

An X-ray structure of a CLICK chemistry-based BET PROTAC bound to BRD2(BD2) inspired synthesis of JQ1 derived heterocyclic amides. This effort led to the discovery of potent BET inhibitors displaying overall improved profiles when compared to JQ1 and birabresib. A thiadiazole derived 1q (SJ1461) displayed excellent BRD4 and BRD2 affinity and high potency in the panel of acute leukaemia and medulloblastoma cell lines. A structure of 1q co-crystalised with BRD4-BD1 revealed polar interactions with the AZ/BC loops, in particular with Asn140 and Tyr139, rationalising the observed affinity improvements. In addition, exploration of pharmacokinetic properties of this class of compounds suggest that the heterocyclic amide moiety improves drug-like features. Finally, our study led to the discovery of potent and orally bioavailable BET inhibitor 1q (SJ1461) as a promising candidate for further development.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2020

State-of-the-art on research and applications of machine learning in the building life cycle

Fueled by big data, powerful and affordable computing resources, and advanced algorithms, machine learning has been explored and applied to buildings research for the past decades and has demonstrated its potential to enhance building performance. This study systematically surveyed how machine learning has been applied at different stages of building life cycle. By conducting a literature search on the Web of Knowledge platform, we found 9579 papers in this field and selected 153 papers for an in-depth review. The number of published papers is increasing year by year, with a focus on building design, operation, and control. However, no study was found using machine learning in building commissioning. There are successful pilot studies on fault detection and diagnosis of HVAC equipment and systems, load prediction, energy baseline estimate, load shape clustering, occupancy prediction, and learning occupant behaviors and energy use patterns. None of the existing studies were adopted broadly by the building industry, due to common challenges including (1) lack of large scale labeled data to train and validate the model, (2) lack of model transferability, which limits a model trained with one data-rich building to be used in another building with limited data, (3) lack of strong justification of costs and benefits of deploying machine learning, and (4) the performance might not be reliable and robust for the stated goals, as the method might work for some buildings but could not be generalized to others. Finally, findings from the study can inform future machine learning research to improve occupant comfort, energy efficiency, demand flexibility, and resilience of buildings, as well as to inspire young researchers in the field to explore multidisciplinary approaches that integrate building science, computing science, data science, and social science.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2022

A reinforcement learning approach to long-horizon operations, health, and maintenance supervisory control of advanced energy systems

In this work, we develop a Reinforcement Learning (RL) approach to the supervisory control problem for advanced energy systems, such as novel nuclear reactors and other demand-driven, mission-critical, and component-health-sensitive energy plants. The inclusive problem landscape considered captures the stochastic confluence of plant performance, component health evolution, power demand from the grid, diverse maintenance actions, and operator-defined goals and constraints, all considered over meaningfully long-enough reasoning horizons. Key aspects of the proposed approach are a receding horizon control-inspired technique dictating time- or event-triggered supervisory policy (re-)constructions, as well as additional capability-enabling contributions such as timescale compression, to handle long reasoning horizons and uncertainty in parts of the problem, and practical yet demonstrably-effective handling of hybrid action spaces with continuous and discrete decision variables. The resulting algorithm consists of a simulation-based RL agent constructing stochastic supervisory control policies over nontrivial action spaces and for long horizons, applying the learned policy to the system for a much shorter interval, and perpetually repeating, to construct the next long-horizon policy. That next policy will only be applied, again, for a short interval, yet originally far-in-time events move progressively closer, their associated uncertainty decreases, and new events and aspects enter the reasoning horizon. The proposed methodology bridges fundamental receding horizon concepts with the unequivocally stronger and more scalable reasoning of contemporary RL. Numerical examples using Soft Actor–Critic Deep RL illustrate the operation and efficacy of the proposed technique for a power plant tasked with health-aware load following missions in a dynamic electricity market landscape.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2023

Application of a deep learning semantic segmentation model to helium bubbles and voids in nuclear materials

Imaging nanoscale radiation-induced defects using the transmission electron microscope (TEM) is a key factor in the successful implementation of materials for nuclear energy structural applications. Analyzing each defect in a TEM micrograph is currently a manual task. To identify the defects in a single image can take anywhere from 15 min to an hour and a project can require the analysis of anywhere from tens to ≥ 100 images. Here, we use artificial intelligence (AI) models to automate this task. For simplification, we evaluated images with only a single type of defect; helium bubbles. Additionally, we performed semantic segmentation of these helium bubble defects in electron microscopy images of irradiated FeCrAl alloys using a deep learning DefectSegNet model. This model, which was previously used to classify crystal defects, is inspired by the classic DenseNet and U-Net image segmentation models. It claims high spatial resolution, but has poor performance at object boundaries. Our paper improves the DefectSegNet model’s application by adding two new features. First, the DefectSegNet model is applied not only to perform calculation pixel-wise but also object (or feature) wise. Because object-wise metrics are directly relevant to our final goal of detecting bubbles, whereas pixel-wise classification is only an intermediate step, it is an important part of our study. Second, a distance map loss (DML) function has been added to increase its performance at object boundaries. It is crucial to accurately represent defects boundaries, especially bubbles, in order to track the bubble-induced swelling caused by irradiation. The boundary-focused DML function is also compared to other loss functions like Cross-entropy, Weighted Binary Cross Entropy (WBCE), Dice and Intersection over Union (IOU). Finally, by incorporating new features, we found a marked improvement on segmentation quality and better shape preservation at the boundaries and areas of the bubbles.

42 ENGINEERING↗
DOE OSTI2023

Deep learning-enhanced design for functionally graded auxetic lattices

Auxetic materials with counterintuitive negative Poisson’s ratio have been of significant interest due to potential applications across diverse engineering fields. Functionally grading such auxetics further enables customization of the structural response and harnesses the potential for multi-functional applications. However, a critical challenge in designing functionally graded lattices is to efficiently determine the spatial variation of the functional gradient and the corresponding geometric designs to achieve the desired response. In this paper, a highly efficient deep learning-based inverse design framework for functionally graded tetra-petal auxetics with spatially tailored properties is presented. This framework significantly improves the efficiency of tailoring functionally graded auxetics where many unit cells need to be tailor-designed. The graded tetra-petal auxetics obtained from the inverse design framework are additively manufactured and subjected to impact tests. The results show superior impact performance compared with uniform designs, demonstrating the effectiveness of the proposed inverse design framework, which can be inspirable to promote advanced structures/materials with enhanced impact resistance.

42 ENGINEERING↗
DOE OSTI2021

Development of cathode-electrolyte-interphase for safer lithium batteries

Accompanied by the adoption of aggressive cathodes to continuously improve batteries energy density, enhancing their safety is becoming increasingly urgent for the electric vehicle development. In-situ controllable formation of robust cathode-electrolyte interphase (CEI) with high inorganic content seems to be the most promising strategy to address the thermal runaway concerns. Moreover, the in-situ formation strategy via suitable electrolyte replacement or electrolyte additives is extremely simple yet effective, especially for industrial manufacture of batteries. Here, this paper briefly reviews recent advanced CEIs formed by conventional carbonate-based electrolytes, fluorinated electrolytes, concentrated electrolytes, and solid state electrolytes. The focus on the thermal stability of the cathodes after CEI modification, and at the same time conduct safety tests at the material, cell, and module levels for comprehensive evaluation are encouraged. The review will provide inspiration for future developments in battery safety and push forward the practical applications of newly developed high-energy density batteries.

25 ENERGY STORAGE↗
DOE OSTI2024

Ameliorating the sodium storage performance of hard carbon anode through rational modulation of binder

Hard carbon anodes have emerged as promising candidates for sodium-ion batteries due to their inherent advantages. Nevertheless, the surface imperfections in these materials often culminate in irreversible electrolyte consumption, fostering the development of a heterogeneous and fragile solid electrolyte interface (SEI), thereby compromising the initial Coulombic efficiency (ICE). Here, drawing inspiration from the catalytic potential of C=O (carbonyl) bonds in directing preferential salt reduction, we introduce a novel strategy that leverages the modulation of the binder, a long-term overlooked pivotal components in the electrode process. Specifically, Polymethyl methacrylate (PMMA), abundant in C=O groups, is partially substituted for PVDF, ensuring robust adhesion of the electrode material to the current collector while preserving superior mechanical properties. The accurate combination of two binders with delightful compatibility in the state-of-art electrode process, can promote a uniform formation of the SEI on the hard carbon surface enriched in inorganic components, which can ensure long-term interfacial stability and suppresses excessive solvent decomposition and facilitates Na + transfer at the interface. Consequently, the initial Coulombic efficiency of the hard carbon anode with 70 %PMMA binder achieves 86 %, with prominent cycling stability (88 % capacity retention over 500 cycles) at a high current density of 1.2 A g −1 . When paired with high loading cathodes to assemble the pouch cell, it also demonstrates stable operational scenarios.

25 ENERGY STORAGE↗