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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 757 records · Page 42
All sourcesNASA NTRSDOE OSTI
DOE OSTI2023

A robust interface finite element formulation for modeling brittle material failure problems

Abstract Failure of many brittle materials and structures can be modeled using interface‐oriented finite elements combined with intrinsic cohesive zone models. The discontinuous Galerkin (DG) finite element method provides an innovative framework for modeling brittle crack propagation with zero‐thickness interface elements, which can accommodate extrinsic cohesive laws to avoid the artificial compliance required in intrinsic cohesive models. However, robust formulations and implementations of DG methods are critical in alleviating the well‐known convergence issues for both crack nucleation and propagation with reduced instability. This paper presents a robust interface element formulation by modifying the incomplete interior penalty Galerkin (IIPG) method, which successfully avoids the initial element interface penetration across elements that occurs prior to crack nucleation, and thereby greatly reduces the instability issue as cracks open. We further verified and validated our implementation by using a bar tension test and a beam fracturing benchmark. The robustness of our proposed interface element method was demonstrated by a micromechanics fiber/matrix debonding problem with 64 fibers embedded in a bulk matrix.

Liu, Ruijie↗
DOE OSTI2021

Predicting Low Toxicity and Scalable Solvent Systems for High–Speed Roll–to–Roll Perovskite Manufacturing

Printed lead-based perovskite photovoltaics (PV) have gained interest due to their potential to be manufactured with scalable roll-to-roll techniques. In industrial scale-up, toxicity of inks can constrain roll-to-roll manufacturing due to the added cost of managing toxic effluents. Due to solvent toxicity, few perovskite solution chemistries in published works are scalable to gigawatt production capacity at low cost. Herein, it is shown that for scalable PV production, the use of aprotic polar solvents should be avoided due to their overall toxicity. Compliance with worldwide worker safety regulations for solvent exposure limits could require additional air handling requirements for some solvents, which in turn would affect cost-effectiveness. It is shown that costs associated with handling of hazardous substances can be significant and estimate an added cost of ¢3.7/W for dimethylformamide (DMF)-based inks. To solve this problem, a new perovskite ink solvent system is developed that is composed entirely of ether and alcohol, which has an effective exposure limit 14× higher than DMF, making it suitable for industrial coating processes. Here, it is shown that the new ink solvent system is capable of fabricating high-efficiency perovskite solar cells processed in 1 min on a standard roll-to-roll system.

36 MATERIALS SCIENCE↗
DOE OSTI2024

Challenges for monitoring and data analytics in a leadership public data repository

The availability and disposition of data has assumed increasing importance in large-scale computational science. Data repositories are evolving to meet new classes of requirements: compliance with government access guidelines, support for reproducibility of experimental results, and long-term availability of data products. The Constellation public data repository at the Oak Ridge Leadership Computing Facility faces these issues while being situated in one of the most productive data centers in the world. While monitoring and operational data analysis are ingrained in the operation of the OLCF’s large-scale high performance computing platforms, data repositories do not have this history of support. Problems faced by Constellation range from data size (over 7 petabytes in current holdings) to analytic complexity (detailed curation is both absolutely necessary for many data sets and absolutely impossible for humans to accomplish in any practical manner) to deployment environment (OLCF storage resources are oriented toward the needs of the compute platforms). In this paper we describe some of the challenges for collecting monitoring and analytic data from a leadership public data repository. We also discuss various strategies we are pursuing in order to address these challenges, from manual data collection to plans for introducing machine learning-based curatorial techniques.

Widener, Patrick [ORNL] (ORCID:0000000258820816)↗
DOE OSTI2022

Parton distribution function for topological charge at one loop

We present results for the gg-part of the one-loop corrections to the recently introduced “topological charge” GPD F~(x, q 2 ). In particular, we give expression for its evolution kernel. To enforce strict compliance with the gauge invariance requirements, we have used on-shell states for external gluons, and have obtained identical results both in Feynman and light-cone gauges. No “zero mode” δ(x) terms were found for the twist-4 gluon GPD F~(x, q 2 ).

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2022

Causality, unitarity, and the weak gravity conjecture

We consider the shift of charge-to-mass ratio for extremal black holes in the context of effective field theory, motivated by the Weak Gravity Conjecture. We constrain extremality corrections in different regimes subject to unitarity and causality constraints. In the asymptotic IR, we demonstrate that for any supersymmetric theory in flat space, and for all minimally coupled theories, logarithmic running at one loop pushes the Wilson coefficient of certain four-derivative operators to be larger at lower energies, guaranteeing the existence of sufficiently large black holes with Q > M. We identify two exceptional cases of nonsupersymmetric theories involving large numbers of light states and Planck-scale nonminimal couplings, in which the sign of the running is reversed, leading to black holes with negative corrections to Q/M in the deep IR, but argue that these do not rule out extremal black holes as the requisite charged states for the WGC. We separately show that causality and unitarity imply that the leading threshold corrections to the effective action from integrating out massive states, in any weakly coupled theory, can be written as a sum of squares and is manifestly positive for black hole backgrounds. Quite beautifully, the shift in the extremal Q/M ratio is directly proportional to the shift in the on-shell action, guaranteeing that these threshold corrections push Q > M in compliance with the WGC. Our results apply for black holes with or without dilatonic coupling and charged under any number of U(1)s.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2021

One-loop structure of parton distribution for the gluon condensate and “zero modes”

We present results for one-loop corrections to the recently introduced “gluon condensate” PDF F(x). In particular, we give expression for the gg-part of its evolution kernel. To enforce strict compliance with the gauge invariance requirements, we have used on-shell states for external gluons, and have obtained identical results both in Feynman and light-cone gauges. No “zero mode” δ(x) terms were found for the twist-4 gluon PDF F(x). However a q 2 δ(x) term was found for the ξ = 0 GPD F(x, q 2 ) at nonzero momentum transfer q. Overall, our results do not agree with the original attempt of one-loop calculations of F(x) for gluon states, which sets alarm warning for calculations that use matrix elements with virtual external gluons and for lattice renormalization procedures based on their results.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2025

A simple introduction to the SiMPL method for density-based topology optimization

We introduce a novel method for solving density-based topology optimization problems: Sigmoidal Mirror descent with a Projected Latent variable (SiMPL). The SiMPL method (pronounced as “the simple method”) optimizes a design using only first-order derivative information of the objective function. The bound constraints on the density field are enforced with the help of the (negative) Fermi–Dirac entropy, which is also used to define a non-symmetric distance function called a Bregman divergence on the set of admissible designs. This Bregman divergence leads to a simple update rule that is further simplified with the help of a so-called latent variable. Because the SiMPL method involves discretizing the latent variable, it produces a sequence of pointwise-feasible iterates, even when high-order finite elements are used in the discretization. Numerical experiments demonstrate that the method outperforms other popular first-order optimization algorithms. In conclusion, to outline the general applicability of the technique, we include examples with (self-load) compliance minimization and compliant mechanism optimization problems.

Calculus of Variations and Optimization↗
DOE OSTI2021

A Time-Dependent Directional Damage Theory for Brittle Rocks Considering the Kinetics of Microcrack Growth

In this work, a novel microcrack damage theory for describing the time-dependent behavior of brittle rocks is proposed. Instead of using scalar or tensorial variables to approximate the effect of microcracks, the concept of directional damage is introduced by upscaling the directional distribution density of microcracks through the noninteracting homogenization scheme. The contribution of an individual crack on global strain is first derived from the crack-opening displacements including sliding, closure, and dilation. The model is then cast in the free energy form, and the range of parameters ensuring compliance with the second law of thermodynamics is derived rigorously. Considering that the subcritical propagation of microcracks is the dominant mechanism behind the time-dependent deformation and failure of brittle rocks, the driving force for directional damage is defined as the corresponding energy release rate, and the effect of time is introduced through a damage evolution law inspired from the kinetics of subcritical crack growth. The model is applied to simulate the behavior of basalt rock under various loading conditions. In addition to predicting the stress–strain–time response, the model offers enhanced resolution in representing the anisotropic characteristics of microcrack-induced damage in brittle rocks and their time-dependent evolutions.

58 GEOSCIENCES↗
DOE OSTI2024

The Influence of Residual Stress on Fatigue Crack Growth Rates in Stainless Steel Processed by Different Additive Manufacturing Methods

The properties and microstructure of Type 304L stainless steel produced by two additive manufacturing (AM) methods—directed energy deposition (DED) and powder bed fusion (PBF)—are evaluated and compared. Localized heating and steep temperature gradients of AM processes lead to significant residual stress and distinctive microstructures, which may be process-specific and influence mechanical behavior. Test data show that materials produced by DED and PDF have small differences in tensile strengths but clear differences in residual stress and microstructural features. Measured fatigue crack growth rates (FCGRs) for cracks propagating parallel to and perpendicular to the build directions differ between the two AM materials. To separate the influences of residual stress and microstructure, K-control test procedures with decreasing and constant stress intensity factor ranges are used to measure FCGRs in the near-threshold regime (crack growth rates ≤ 1 × 10 −8 m/cycle). Residual stress is quantified by the residual stress intensity factor, K res , measured by the online crack compliance method. Correcting the FCGR data for differences in K res brings results for specimens of the two AM materials into agreement with each other and with results for wrought specimens, when the latter are corrected for crack closure. Differences in microstructure and tensile strength have an insignificant influence on FCGRs in these tests.

additive manufacturing↗
DOE OSTI2025

Leveraging artificial intelligence and advanced food processing techniques for enhanced food safety, quality, and security: a comprehensive review

Artificial intelligence is emerging as a transformative force in addressing the multifaceted challenges of food safety, food quality, and food security. This review synthesizes advancements in AI-driven technologies, such as machine learning, deep learning, natural language processing, and computer vision, and their applications across the food supply chain, based on a comprehensive analysis of literature published from 1990 to 2024. AI enhances food safety through real-time contamination detection, predictive risk modeling, and compliance monitoring, reducing public health risks. It improves food quality by automating defect detection, optimizing shelf-life predictions, and ensuring consistency in taste, texture, and appearance. Furthermore, AI addresses food security by enabling resource-efficient agriculture, yield forecasting, and supply chain optimization to ensure the availability and accessibility of nutritious food resources. This review also highlights the integration of AI with advanced food processing techniques such as high-pressure processing, ultraviolet treatment, pulsed electric fields, cold plasma, and irradiation, which ensure microbial safety, extend shelf life, and enhance product quality. Additionally, the integration of AI with emerging technologies such as the Internet of Things, blockchain, and AI-powered sensors enables proactive risk management, predictive analytics, and automated quality control. By examining these innovations' potential to enhance transparency, efficiency, and decision-making within food systems, this review identifies current research gaps and proposes strategies to address barriers such as data limitations, model generalizability, and ethical concerns. These insights underscore the critical role of AI in advancing safer, higher-quality, and more secure food systems, guiding future research and fostering sustainable food systems that benefit public health and consumer trust.

AI↗
DOE OSTI2024

Can Wholesale Electricity Markets Achieve Resource Adequacy and High Clean Energy Generation Targets in the Presence of Self-Interested Actors?

Wholesale electricity markets are intended to incentivize system generation investments and operations outcomes that meet evolving system needs. In this work, we evaluate the effectiveness of wholesale market structures, rules and policies in achieving system resource adequacy (RA) and clean energy targets in the presence of self-interested generation investors using the Electricity Markets and Investment Suite Agent-based Simulation (EMIS-AS) model. Results highlight that both capacity markets and operating reserve demand curves (ORDCs) can help achieve a reliable system but with different RA compliance timelines and distribution of generation technologies. Structures with capacity markets tend to favor more capital-intensive peaking technologies while reducing wind and solar build-outs due to suppressed energy and clean energy market prices, particularly in the absence of strong clean energy targets. Conversely, ORDCs improve the commitment of available generation units, but this comes at the expense of higher system costs and renewable generation curtailment. We also find that well-calibrated static capacity demand curves can yield similar reliability and total cost compared to capacity market demand curves informed dynamically by resource adequacy while also yielding stable annual capacity prices. Different approaches to formulating ORDC curves can also yield key trade-offs, namely that a more efficient treatment of storage chronology results in lower ORDC curves and prices, yielding less investment and cost but at the expense of reliability. Finally, the effectiveness of wholesale electricity markets in practically achieving very high clean energy generation targets highly depends on the cost-competitiveness of clean energy technologies that can support critical balancing needs across multiple timescales.

capacity expansion↗
DOE OSTI2026

Revolutionizing thermal Management in Next-Generation AI data centers: Challenges and breakthrough innovations

Data centers (DCs) serve as critical infrastructure for powering the growth and evolution of AI. Next-generation AI DCs present unique challenges in thermal management driven by unprecedented computational demands. This paper provides a comprehensive summary of key stakeholder perspectives on technology gaps, infrastructure requirements, test bed needs, emerging opportunities, and preliminary solutions related to thermal management for AI DCs. It establishes six strategic pillars of thermal management for next generation AI DC: reliability, deployability, efficiency, resilience, measurability, and valorization. The discussion spans a range of critical topics, including advanced cooling technologies, thermal strategies for emerging modular and edge DCs, system-level optimization and control frameworks, infrastructure planning and grid integration designs, benchmarking approaches, and pathways for waste heat recovery and reuse. The proposed research, development, and demonstration efforts are aimed at accelerating the deployment of AI DCs while ensuring energy efficiency, reliability, safety, and regulatory compliance.

Wang, Pengtao [ORNL] (ORCID:0000000214713429)↗
DOE OSTI2026

Ten questions concerning Large Language Models (LLMs) for building applications

Large Language Models (LLMs) are emerging as powerful AI tools capable of transforming how building information is collected, processed, analyzed, and applied across diverse research areas. Their capabilities can help building operators, facility managers and other stakeholders such as designers, architects and engineers by providing actionable insights for decision-making across planning, construction, operations, and maintenance of buildings and facilities. This paper explores ten key questions concerning the role of LLMs in shaping sustainable, intelligent, and human-centric buildings. From fundamental definitions to advanced applications, we examine how LLMs facilitate decision-making across the life cycle of buildings and energy systems. LLMs can enhance life cycle assessments (LCA), building energy simulations, and real-time data integration, empowering more efficient and adaptive human-AI environments. They can also contribute to streamlining regulatory compliance, improving post-occupancy evaluations, and fostering more inclusive and participatory design processes. Additionally, this paper addresses the ethical challenges posed by LLMs, such as bias, data privacy, and environmental impacts, and explores their potentials in advancing intelligent digital twins (DT) for ongoing building operations and maintenance. Built upon our applied research using LLMs and the review of tools, datasets, and research gaps, we provide a forward-looking perspective on how LLMs can drive innovation, collaboration, and productivity in the built environment while supporting ethical and effective implementation.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2024

Reactivity of net-zero carbon alcohol fuels and their corresponding aldehyde intermediates on PGM-based commercial oxidation catalysts for lean-burn emissions control

Alcohol net-zero carbon fuels will play a significant role in decarbonization of the hard-to-electrify transportation sectors. However, the combustion process in alcohol-fueled engines generate toxic aldehydes. For commercialization of net-zero carbon fuels on engines, compliance with the stringent U.S. EPA regulations is necessary. Here, this contribution focuses on reactivity of alcohols and aldehydes on commercial diesel oxidation catalysts (DOC) to inform selection of future net-zero carbon fuels. Alcohol (and aldehyde) light-off temperatures were measured on aged DOCs under full synthetic engine-exhaust conditions. Methanol was the most reactive, while ethanol completely oxidized at higher temperatures. Alcohols typically formed less reactive aldehyde intermediates. DRIFTS revealed strongly adsorbing surface formates and acetates during methanol and ethanol oxidation, respectively, resulting in inhibition effects. Preliminary alcohol oxidation mechanisms combining flow reactor and DRIFTS observations are presented. Aldehydes inhibited CO oxidation. Minimal N 2 O formation was observed for the alcohols investigated.

Alcohol oxidation↗
DOE OSTI2025

Techno-economic evaluation of emission control configurations for AMP/PZ-based post-combustion CO 2 capture

Minimizing the environmental impacts of amine-based post-combustion carbon capture technologies is essential for regulatory compliance and public acceptance. Experimental campaigns at RWE's CO 2 capture pilot plant in Niederaussem using CESAR1 demonstrated that combining existing emission abatement technologies can substantially reduce the concentration of amines and degradation products in the CO 2 -depleted flue gas to below the detection limit of an infrared spectrometer. The campaign confirmed that a proprietary dry bed technology (OEASE aerozone™) or a second water wash can reduce AMP and PZ emissions to below 1 mg/Nm 3 . However, an acid or chemically active wash downstream of the water wash is necessary to reduce NH 3 emissions to very low levels (<2 mg/Nm 3 ). Combining a dry bed with an acid wash significantly reduces both the required acid solution and the resulting acid waste. The techno-economic analysis indicates that implementing emission mitigation technologies provides substantial environmental benefits for the CESAR1 process, with only a marginal increase (<1 €/tCO 2 ) in both the carbon capture cost (CCC) and CO 2 avoidance cost (CAC). The additional capital cost associated with extra column packing is offset by operational cost savings from reduced solvent losses. For stringent emission permits targeting NH 3 , a configuration combining a dry bed upstream of the water wash followed by an acid wash achieves the best balance between emission control and cost efficiency. This configuration results in a Levelized Cost of Electricity (LCOE) of 143 €/MWh, a CCC of 45.4 €/tCO 2 , and a CAC of 88.4 €/tCO 2 .

AMP↗
DOE OSTI2023

Cerium oxide impact on fresh and hardened properties of cementitious materials

Cements used in global nuclear waste treatment, storage, and disposal require thorough testing of fresh and cured properties to demonstrate regulatory compliance. Using actual radioactive wastes is often cost-prohibitive during early scoping tests necessitating the use of non-radiological surrogates. Cerium (Ce) is often used as a surrogate for actinides uranium (U) and plutonium (Pu) in spent nuclear fuel, yet few studies have explored how Ce impacts the properties used to qualify a cement for use. Here, this work compares the fresh and cured properties of three classes of cements – ordinary portland cements, ultra-high performance cement composites, and magnesium phosphate cements – each with and without ceria (CeO 2 ). Due to CeO 2 sorption of water (54 vol%) that effectively decreases the water available to hydrate and form binding cement phases, variations in key cement properties were detected that suggest surrogate CeO 2 –cementitious matrices may not be representative of matrix behaviors when using U and Pu sources.

36 MATERIALS SCIENCE↗
DOE OSTI2022

$\mathrm{IH}$-$\mathrm{GAN}$: A conditional generative model for implicit surface-based inverse design of cellular structures

Variable-density cellular structures can overcome connectivity and manufacturability issues of topologically optimized structures, particularly those represented as discrete density maps. However, the optimization of such cellular structures is challenging due to the multiscale design problem. Past work addressing this problem generally either only optimizes the volume fraction of single-type unit cells but ignoring the effects of unit cell geometry on properties, or considers the geometry–property relation but builds this relation via heuristics. In contrast, we propose a simple yet more principled way to accurately model the property to geometry mapping using a conditional deep generative model, named Inverse Homogenization Generative Adversarial Network (IH-GAN). It learns the conditional distribution of unit cell geometries given properties and can realize the one-to-many mapping from properties to geometries. Here we further reduce the complexity of IH-GAN by using the implicit function parameterization to represent unit cell geometries. Results show that our method can 1) generate various unit cells that satisfy given material properties with high accuracy (R 2 -scores between target properties and properties of generated unit cells >98%) and 2) improve the optimized structural performance over the conventional variable-density single-type structure. In the minimum compliance example, our IH-GAN generated structure achieves a 79.7% reduction in concentrated stress and an extra 3.03% reduction in displacement. In the target deformation examples, our IH-GAN generated structure reduces the target matching error by 86.4% and 79.6% for two test cases, respectively. We also demonstrated that the connectivity issue for multi-type unit cells can be solved by transition layer blending.

42 ENGINEERING↗
DOE OSTI2023

Implementation of extrinsic cohesive zone model (ECZM) in 2D finite-discrete element method (FDEM) using node binding scheme

The combined finite-discrete element method (FDEM) has been widely used for rock fracturing simulations. Conventionally, FDEM is realized using the intrinsic cohesive zone model (ICZM); however, it has the drawback of artificial compliance and high computational expense. As a complement, the extrinsic cohesive zone model (ECZM) is seen to be realized in FDEM recently, whereas the node splitting scheme utilized is cumbersome. Here, within the framework of ICZM-based FDEM, we propose a node binding scheme to efficiently bind the pre-discretized finite elements and thus guarantee the continuum behavior of materials in the elastic stage. The yield surfaces, controlled by ECZM, are dynamically embedded by invoking the pre-inserted cohesive elements. The effectiveness and efficiency of the proposed approach are validated and tested by performing a suite of numerical experiments. Compared with ICZM-based FDEM, the proposed approach can correctly capture material deformation and reduce the computation cost. In contrast to the existing ECZM-based FDEM, the proposed approach can overcome the frequent and complex element topology updating. Finally, this work provides a novel perspective that fully inherits the advantages of both ICZM and ECZM, but circumvents their shortcomings, which guarantees a more efficient and effective simulation of brittle material evolution from continuum to discontinuum.

58 GEOSCIENCES↗