Search NASA⌕ Search

SEARCH · Search NASA

Results for “Paradigm”

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.

Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.

At least 469 records · Page 26
All sourcesNASA NTRSDOE OSTI
DOE OSTI2022

Social vulnerability and power loss mitigation: A case study of Puerto Rico

The increasing occurrence of extreme weather events urges us to reevaluate the resiliency and vulnerability aspects of our most critical infrastructures — such as power grids — as their failures result in both economic loss and severe human hardship. Seen through the lens of alleviating human suffering, it is crucial to be able to identify critical system components of the infrastructure for targeted hardening given resource constraints. This effort is of particular importance in islanded areas such as Puerto Rico where hurricanes are frequent and resources are limited, and where the spatially diverse effects of power loss on human suffering are all the more severe. Recent studies on evaluating infrastructure networks during extreme weather events have taken a simulation based approach that incorporates a variety of component models, such as weather realizations, topological network models, fragility models, and power flow models to estimate expected loss of service. Here, in this work, we expand such a Component Based Event Simulation (CBES) methodology proposed in the literature and integrate it with a social vulnerability modeling component. This paradigm-advancing approach of synthesizing the cutting edge capability of power network modeling and the social impacts of the power transmission network failure is demonstrated for the island of Puerto Rico. Our work exemplifies the efficacy of this integrated modeling framework in developing a decision metric for targeted transmission line hardening.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2022

A review of active probing-based system identification techniques with applications in power systems

System identification is becoming a relevant research area for numerous applications in power grids due to the increasing complexity of the system. A paradigm shift in power system infrastructure driven by renewable energy resources, controllable loads, and new power electronics technologies have given rise to new challenges in power systems operation and control. This increased complexity of power systems and unavailability of physics-based models of most inverter-based resources requires that traditional modeling of power systems approaches be complemented by system identification-based black or grey box modeling techniques. In light of the importance of system identification and little attention paid to the applications of these techniques in transforming power systems, this paper provides a comprehensive review of active probing-based system identification methods in the context of power system applications. It reviews applications of both linear and nonlinear system identification with discussion on their potential and key takeaways. To motivate a further practical use in power systems, the paper provides an example of system identification, to develop a state space model of an unknown plant with step-by-step details. The paper highlights the advantages of using modern power electronics-based sources in the identification process and discusses the emerging research directions for future.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2023

A hybrid data-driven and model-based approach for computationally efficient stochastic unit commitment and economic dispatch under wind and solar uncertainty

Stochastic unit commitment (UC) and economic dispatch (ED) are imperative in dealing with uncertainty in renewable forecast for power system operation and planning such that the overall expected production cost is minimized over the planning horizon. However, accurate calculation of the expected production cost requires assessment of a very large number of different scenarios of uncertain renewable resources, such as solar and wind, which is practically infeasible to simulate in real time. This article proposes a hybrid datadriven and physics-based model-predictive paradigm to efficiently solve for stochastic unit commitment and economic dispatch considering uncertainty in wind and solar power forecasts. Here, the novelty of the approach lies in decoupling the production cost estimation from the unit commitment and economic dispatch optimization problems under uncertainty without compromising on the fidelity of the solutions. A data-driven machine learning model is first developed to predict the mean optimal production cost. A physics-based inverse problem is then solved to get the stochastic UC and ED profiles from the expected cost. The presented approach considers, for the first time, solar uncertainty in UC/ED determination and enables efficient and accurate propagation of wind and solar uncertainty to estimate the statistics of the production cost. The effectiveness of the developed approach is demonstrated systematically on a stylized RTS-GMLC single-node system. The overall framework predicts the expected cost 62.5% more accurately than the existing state-of-the-art, on unforeseen days during the entire year, and yields, for the first time, the associated physically consistent UC and ED profiles. The solutions are also shown to be flexible in providing adequate daily reserves to address any statistical deviations from probabilistic power forecasts. The computational time associated with the presented method is only about 10 s compared to over 24 h needed for a conventional stochastic UC/ED determination under uncertainty on an Intel Core i9 processor with 32 GB of RAM.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗
DOE OSTI2026

Optimal CO 2 storage management considering safety constraints in multi-stakeholder multi-site GCS projects: A Markov game perspective

Geological carbon storage (GCS) projects could involve a diverse array of stakeholders or players from public, private, and regulatory sectors, each with different objectives and responsibilities. Given the complexity, scale, and long-term nature of GCS operations, determining whether individual stakeholders can independently optimize their interests — or whether collaborative coalition agreements are needed — remains a central question for effective GCS project planning and management. To access large, high-quality storage resources, future GCS deployment may increasingly occur in geologically connected sites, where shared geological features such as pressure space and reservoir pore capacity can lead to competitive behavior among stakeholders. In this work, we propose a paradigm based on Markov games to quantitatively investigate how different coalition structures affect the goals of stakeholders. We frame this multi-stakeholder multi-site problem as a multi-agent reinforcement learning problem with safety constraints. Our approach enables agents to learn optimal strategies while complying with safety regulations. We present an example where multiple operators are injecting CO 2 into their respective project areas in a geologically connected basin. To address the high computational cost of repeated simulations of high fidelity models, a previously developed surrogate model based on the Embed-to-Control (E2C) framework is employed. Our results demonstrate the effectiveness of the proposed framework in addressing optimal management of CO 2 storage when multiple stakeholders with different objectives and goals are involved.

58 GEOSCIENCES↗
DOE OSTI2020

Thermal coupling-decoupling mechanism of heat transfer across van der Waals interfaces in n-eicosane

Progress toward improving thermal transport properties of n-eicosane, a promising phase change material for thermal energy storage near human body temperature, is hindered by lack of a general theoretical framework to model thermal interfacial conductance (TIC) of the van der Waals (vdW) force bonded molecular interfaces, which have little compositional/structural mismatch. Combining molecular dynamics simulations at temperatures up to the melting point and a time-domain analysis of interfacial heat currents in individual molecules, we unveil that (1) the heat flux across a molecular vdW interface has an alternating coupling-decoupling pattern at the single molecule level due to thermal fluctuations, and (2) the net heat transfer during the thermally coupled periods is orders of magnitude larger than that during the thermally decoupled periods. Furthermore, we demonstrate that a vdW interface's TIC correlates almost linearly with two atomistic-scale coupling-decoupling parameters: αc and σc, that represent the duration and strength of the thermal coupling, respectively. This thermal coupling-decoupling mechanism proposes a new paradigm that explicitly accounts for the effects of structural flexibility and dynamical fluctuation on heat transfer across molecular interfaces, and will likely provide new insights into modeling thermal transport properties in a wide-ranging soft materials, including self-assembled monolayers, paraffins or lipids.

42 ENGINEERING↗
DOE OSTI2025

Designing slippery rough surfaces to enhance dropwise condensation of low surface tension fluid

Enhancing low surface tension liquid condensation is critical for achieving high energy efficiency and reducing the size of thermal energy systems. Extensive research has focused on promoting dropwise condensation of these liquids using state-of-the-art coatings on plain surfaces. However, maintaining dropwise condensation with low surface tension fluids is challenging due to rivulet formation, resulting in wetted tails that transition to filmwise condensation at elevated heat fluxes. Here, to address this issue, we uncover the role of surface structures and surface chemistry in the dropwise condensation of low surface tension ethanol on slippery rough surfaces (SRS). High-performance dropwise condensation has been achieved on slippery microchannels grafted with perfluoropolyether. The SRS uniquely facilitates rapid lateral droplet removal, enabling faster directional droplet shedding without rivulet formation. The resulting higher droplet removal frequency on SRS leads to heat transfer coefficients 100 % and 500 % higher than conventional dropwise and filmwise condensation on plain surfaces, respectively. Our findings uncover the pivotal role of rapid droplet removal through slippery microchannels in sustaining dropwise condensation of low surface tension liquids. This study introduces a new paradigm for promoting dropwise condensation using engineered SRS that incorporates surface structure and surface chemistry. The work will provide fundamental design guidelines to design efficient and compact condensers that use costly or flammable low global warming potential refrigerants in future refrigeration systems.

Dropwise condensation↗
DOE OSTI2021

Towards data-driven constitutive modelling for granular materials via micromechanics-informed deep learning

The analytical description of path-dependent elastic-plastic responses of a granular system is highly complicated because of continuously evolving microstructures and strain localisation within the system undergoing deformation. This study offers an alternative to the current analytical paradigm by developing micromechanics-informed machine-learning based constitutive modelling approaches for granular materials. A set of critical variables associated with the constitutive behaviour of granular materials are identified through an incremental stress-strain relationship analysis. Depending on the strategy to exploit the priori micromechanical knowledge, three different training strategies are explored. The first model uses only the measurable external variables to make stress predictions; the second model utilises a directed graph to link all the external strain sequences and internal microstructural evolution variables into a single prediction model comprised of a series of sub-mappings, and the third model explicitly integrates the physically important non-temporal properties with external strain paths into training through an enhanced Gated Recurrent Unit (GRU). These three models show satisfactory agreement with unseen test specimens based on multi-directional loading cases. The basic features and potential applications of each model are explained. Lastly, the key factors for constitutive training and limitations of the current work are also discussed in detail.

36 MATERIALS SCIENCE↗
DOE OSTI2021

Use of Multi-Site Radiation Therapy for Systemic Disease Control

Metastatic cancer is a heterogeneous entity, some of which could benefit from local consolidative radiation therapy (RT). Although randomized evidence is growing in support of using RT for oligometastatic disease, a highly active area of investigation relates to whether RT could benefit patients with polymetastatic disease. This article highlights the preclinical and clinical rationale for using RT for polymetastatic disease, proposes an exploratory framework for selecting patients best suited for these types of treatments, and briefly reviews potential challenges. The goal of this hypothesis-generating review is to address personalized multimodality systemic treatment for patients with metastatic cancer. The rationale for using high-dose RT is primarily for local control and immune activation in either oligometastatic or polymetastatic disease. However, the primary application of low-dose RT is to activate distinct antitumor immune pathways and modulate the tumor stroma in efforts to better facilitate T cell infiltration. We explore clinical cases involving high- and low-dose RT to demonstrate the potential efficacy of such treatment. We then group patients by extent of disease burden to implement high- and/or low-dose RT. Patients with low-volume disease may receive high-dose RT to all sites as part of an oligometastatic paradigm. Subjects with high-volume disease (for whom standard of care remains palliative RT only) could be treated with a combination of high-dose RT to a few sites for immune activation, while receiving low-dose RT to several remaining lesions to enhance systemic responses from high-dose RT and immunotherapy. We further discuss how emerging but speculative concepts such as immune function may be integrated into this approach and examine therapies currently under investigation that may help address immune deficiencies. The review concludes by addressing challenges in using RT for polymetastatic disease, such as concerns about treatment planning workflows, treatment times, dose constraints for multiple-isocenter treatments, and economic considerations.

62 RADIOLOGY AND NUCLEAR MEDICINE↗
DOE OSTI2021

A Framework for Patient-Centered Pathways of Care for Radiopharmaceutical Therapy: An ASTRO Consensus Document

Radiopharmaceutical therapy (RPT) is an area of projected growth and importance with several agents in clinical use, new agents in late-phase clinical trials, and many others under testing and development. This article proposes a framework for developing pathways of care that can be broadly applied to all RPTs, representing the current status of RPT. It suggests foundational elements for many pathways of care for patients with cancer and concludes with areas in active development and the future horizon for RPT treatment centers. Developing a framework for patient-centered pathways of care is a critical step in establishing RPT as standard therapy for patients with a diverse spectrum of cancers. This expected increase in RPT treatment options will affect a much larger population of patients with complex cancer. It will also require enhanced coordination and collaboration among appropriately qualified personnel with diverse expertise in image acquisition, image interpretation, quantitative imaging, dosimetry calculation, radiation quality assurance and safety as well as oncology care and RPT-induced sequelae and response assessment. The essential role of this evolving RPT care team within multidisciplinary oncology care is a cornerstone of this framework for a patient-centered pathway of care for RPT. Given the status of current RPT practice and the horizon for future applications, this patient-centered pathway of care guidance is timely and should help inform future clinical RPT practice paradigms. A task force was recruited from the Theranostic Working Group of the American Society for Radiation Oncology (ASTRO) in May 2019 with equal representation from the nuclear medicine community. The task force expanded on a framework that was originally conceived by the Working Group for patient-centered care. This framework was developed to incorporate the strengths of both radiation oncologists and nuclear medicine physicians. The manuscript was then developed by the task force and posted on the ASTRO website for a 6-week public comment period ending in July 2020. Comments were adjudicated, and the draft was sent to external organizations for potential endorsement. This document was sent to the ASTRO Board of Directors in October 2020 for approval.

62 RADIOLOGY AND NUCLEAR MEDICINE↗
DOE OSTI2022

Patterns of Care, Tolerability, and Safety of the First Cohort of Patients Treated on a Novel High-Field MR-Linac Within the MOMENTUM Study: Initial Results From a Prospective Multi-Institutional Registry

High-field magnetic resonance-linear accelerators (MR-Linacs), linear accelerators combined with a diagnostic magnetic resonance imaging (MRI) scanner and online adaptive workflow, potentially give rise to novel online anatomic and response adaptive radiation therapy paradigms. The first high-field (1.5T) MR-Linac received regulatory approval in late 2018, and little is known about clinical use, patient tolerability of daily high-field MRI, and toxicity of treatments. Herein we report the initial experience within the MOMENTUM Study (NCT04075305), a prospective international registry of the MR-Linac Consortium.

62 RADIOLOGY AND NUCLEAR MEDICINE↗
DOE OSTI2024

Multiscale simulation of spatially correlated microstructure via a latent space representation

When deformation gradients act on the scale of the microstructure of a part due to geometry and loading, spatial correlations and finite-size effects in simulation cells cannot be neglected. We propose a multiscale method that accounts for these effects using a variational autoencoder to encode the structure–property map of the stochastic volume elements making up the statistical description of the part. In this paradigm the autoencoder can be used to directly encode the microstructure or, alternatively, its latent space can be sampled to provide likely realizations. Furthermore, we demonstrate the method on three examples using the common additively manufactured material AlSi10Mg in: (a) a comparison with direct numerical simulation of the part microstructure, (b) a push forward of microstructural uncertainty to performance quantities of interest, and (c) a simulation of functional gradation of a part with stochastic microstructure.

Elastoplasticity↗
DOE OSTI2021

Moving toward a framework for electricity and heat equivalence in energy systems analysis

Sustaining, maintaining, and upgrading the electricity grid, while meeting decarbonization goals is a challenge facing policymakers, regulators, grid operators, and investors. Simultaneously meeting demands for future capacity, retiring older inefficient technologies, and addressing externalities from energy production and use requires more diverse and inclusive technologies to avoid constraints and shortfalls in grid capability. Changing the energy production paradigm by encouraging alternative technologies was a key driver for FERC Order 2222. This stimulus for developing new small-scale generation will complement and supplement the existing fleet only if it attracts new investment. This investment must reflect technology that goes beyond the energy-only characteristics of traditional generation, creating systems where suites of energy-equivalent outputs are enhanced by environmental quality benefits and offsets. We use energy system designs to highlight the contribution that measuring and accounting for equivalency values provides net increases in capacity, electricity, and alternative fuels while simultaneously reducing carbon waste impacts.

42 ENGINEERING↗
DOE OSTI2022

Probing surface-adsorbate interactions through active particle dynamics

Adsorbate molecules present in a reaction mixture may bind to and block catalytic sites. Measurement of the surface coverage of these molecules via adsorption isotherms is critical for modeling and design of catalytic reactions on surfaces. However, it is challenging to measure isotherms in solution in a way that is directly relevant to catalytic activity under reaction conditions, particularly since adsorbates may bind with an enormous range of surface affinity parameters. Here we used the motion of self-propelled catalytic Janus particles, which employ the decomposition of hydrogen peroxide fuel as a propulsion mechanism, to determine the effective surface coverage of thioglycerol, furfural, and ethanol on a platinum surface as a function of concentration in aqueous solution by measuring the decrease in active motion due to the blocking of active sites. For strongly adsorbing thioglycerol, this effective coverage was compared and contrasted to the total adsorbed amount measured using inductively-coupled plasma analysis. Demonstrating the broad applicability of this approach, the surface affinity of the three adsorbates spanned more than four orders of magnitude. For each species, the adsorbate-mediated attenuation of active motion occurred over a wide concentration range and was well-described by a Langmuir isotherm. The strongly interacting thioglycerol had the highest affinity towards the surface (K a = 15.5 ± 4.3 mM –1 ) and fully deactivated the active particle motion at surface saturation. Furfural had an intermediate affinity (K a = 0.42 ± 0.07 mM –1 ) but did not fully block H 2 O 2 access to the surface at apparent saturation, consistent with a maximum fractional surface coverage of θ max = 0.67. Ethanol exhibited even lower affinity (K a = 0.0025 ± 2x10 -4 mM –1 ) and its coverage saturated at only θ max = 0.38. Analysis of isotherms at elevated temperatures enabled direct extraction of the enthalpies of adsorption. The degree of surface coverage at adsorbate saturation appeared to correlate with the relative energies of adsorption for the different adsorbate species and was consistent with adsorbate saturation of one of multiple active site populations towards H 2 O 2 decomposition. Furthermore, computational investigations into solvent effects on furfural adsorption showed good quantitative agreement with the experimental results. This work leverages unique properties of active particles to explore fundamental catalysis questions and demonstrates a novel paradigm for significant and experimentally accessible multidisciplinary research.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2025

Dual-function lignin monomers enable high-performance graphene electrodes via interface confinement and proton transfer enhancement

Graphene oxide (GO)-based energy storage faces dual bottlenecks: unsustainable reduction methods and sluggish proton transfer kinetics. Here, we introduce a groundbreaking green strategy using lignin-derived vanillyl alcohol (VA) as a dual-function monomer to simultaneously address these challenges. By thermally annealing GO/VA films at mild temperatures (<100 °C), VA triggers an interface-confined reduction of GO while self-polymerizing into redox-active oligomers (P-VA) that intercalate between graphene layers. This dual role-reducing agent and proton highway enables a 3D conductive network with minimized graphene restacking, abundant redox sites, and rapid H + transport pathways. Density Functional Theory (DFT) reveals how P-VA optimizes proton dynamics, while the resulting rGO-P-VA4-T90 electrode achieves a record volumetric capacitance of 311.1F/cm 3 (777.8F/cm 2 ) and retains 87.8 % capacity after 10,000 cycles. Flexible solid-state supercapacitors deliver 94.2 μWh/cm 2 energy density at 63.8 μW/cm 2 , rivaling state-of-the-art devices. Furthermore, this work redefines sustainable graphene engineering, merging biomass valorization with high-performance energy storage in a scalable, eco-friendly paradigm.

Graphene oxide reduction↗
DOE OSTI2020

A linearity preserving nodal variation limiting algorithm for continuous Galerkin discretization of ideal MHD equations

A stabilized continuous Galerkin (CG) method for magnetohydrodynamics (MHD) is presented herein. Ideal, compressible inviscid MHD equations are discretized in space on unstructured meshes using piecewise linear or bilinear finite element bases to get a semi-discrete scheme. Stabilization is then introduced to the semi-discrete method in a strategy that follows the algebraic flux correction paradigm. This involves adding some artificial diffusion to the high order, semi-discrete method and mass lumping in the time derivative term. The result is a low order method that provides local extremum diminishing properties for hyperbolic systems. The difference between the low order method and the high order method is scaled element-wise using a limiter and added to the low order scheme. The limiter is solution dependent and computed via an iterative linearity preserving nodal variation limiting strategy. The stabilization also involves an optional consistent background high order dissipation that reduces phase errors. The resulting stabilized scheme is a semi-discrete method that can be applied to inviscid shock MHD problems and may be even extended to resistive and viscous MHD problems. To satisfy the divergence free constraint of the MHD equations, we add parabolic divergence cleaning to the system. Various time integration methods can be used to discretize the scheme in time. We demonstrate the robustness of the scheme by solving several shock MHD problems.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2022

Single-stage gradient-based stellarator coil design: Optimization for near-axis quasi-symmetry

Here we present a new coil design paradigm for magnetic confinement in stellarators. Our approach directly optimizes coil shapes and coil currents to produce a vacuum quasi-symmetric magnetic field with a target rotational transform on the magnetic axis. This approach differs from the traditional two-stage approach in which first a magnetic configuration with desirable physics properties is found, and then coils to approximately realize this magnetic configuration are designed. The proposed single-stage approach allows us to find a compromise between confinement and engineering requirements, i.e., find easy-to-build coils with good confinement properties. Using forward and adjoint sensitivities, we derive derivatives of the physical quantities in the objective, which is constrained by a nonlinear periodic differential equation. In two numerical examples, we compare different gradient-based descent algorithms and find that incorporating approximate second-order derivative information through a quasi-Newton method is crucial for convergence. We also explore the optimization landscape in the neighborhood of a minimizer and find many directions in which the objective is mostly flat, indicating ample freedom to find simple and thus easy-to-build coils.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2022

Accelerating high-order mesh optimization using finite element partial assembly on GPUs

In this paper we present a new GPU-oriented mesh optimization method based on high order finite elements. Our approach relies on node movement with fixed topology, through the Target-Matrix Optimization Paradigm (TMOP) and uses a global nonlinear solve over the whole computational mesh, i.e., all mesh nodes are moved together. A key property of the method is that the mesh optimization process is recast in terms of finite element operations, which allows us to utilize recent advances in the field of GPU-accelerated high order finite element algorithms. For example, we reduce data motion by using tensor factorization and matrix-free methods, which have superior performance characteristics compared to traditional full finite element matrix assembly and offer advantages for GPU based HPC hardware. Furthermore, we describe the major mathematical components of the method along with their efficient GPU-oriented implementation. In addition, we propose an easily reproducible mesh optimization test that can serve as a performance benchmark for the mesh optimization community.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2023

Uncertainty quantification in scientific machine learning: Methods, metrics, and comparisons

Neural networks (NNs) are currently changing the computational paradigm on how to combine data with mathematical laws in physics and engineering in a profound way, tackling challenging inverse and ill-posed problems not solvable with traditional methods. However, quantifying errors and uncertainties in NN-based inference is more complicated than in traditional methods. This is because in addition to aleatoric uncertainty associated with noisy data, there is also uncertainty due to limited data, but also due to NN hyperparameters, overparametrization, optimization and sampling errors as well as model misspecification. Although there are some recent works on uncertainty quantification (UQ) in NNs, there is no systematic investigation of suitable methods towards quantifying the total uncertainty effectively and efficiently even for function approximation, and there is even less work on solving partial differential equations and learning operator mappings between infinite-dimensional function spaces using NNs. In this work, we present a comprehensive framework that includes uncertainty modeling, new and existing solution methods, as well as evaluation metrics and post-hoc improvement approaches. Further, to demonstrate the applicability and reliability of our framework, we present an extensive comparative study in which various methods are tested on prototype problems, including problems with mixed input-output data, and stochastic problems in high dimensions. In the Appendix, we include a comprehensive description of all the UQ methods employed. Further, to help facilitate the deployment of UQ in Scientific Machine Learning research and practice, we present and develop in [1] an open-source Python library (github.com/Crunch-UQ4MI/neuraluq), termed NeuralUQ, that is accompanied by an educational tutorial and additional computational experiments.

11 physics-informed neural networks↗