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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 235 records · Page 13
All sourcesNASA NTRSDOE OSTI
DOE OSTI2020

Bismuth Enables the Formation of Disordered Birnessite in Rechargeable Alkaline Batteries

Recent advances in rechargeable Zn/MnO$_2$ alkaline batteries have shown promise for scalable energy storage systems which provide a safe, low-cost alternative with a demonstrated lifetime over thousands of cycles. This cathode technology is based on a 2-electron Mn redox process where a layered birnessite-type phase has been shown to form after the first cycle with excellent reversibility between the discharge product, Mn(OH)$_2$. Herein, we investigate the reversible reaction between birnessite and Mn(OH)$_2$ with and without a Bi$_2$O$_3$ additive using multimodal structural characterization techniques during active battery cycling. Diffraction results provide evidence of Bi$^{3+}$ residing in the interlayer of birnessite which prevents irreversible Mn$_3$O$_4$ formation by limiting Mn$^{3+}$ diffusion within the crystal lattice. Also, upon charge no MnOOH intermediate phases are observed. Instead, X-ray absorption and Raman spectroscopy indicate a disordered, non-crystalline birnessite-type phase consisting of mostly neutral H$_2$O within the interlayer. Birnessite phases will reform without Bi$_2$O$_3$ present, but Mn$_3$O$_4$ formation severely polarizes the potential they are formed at, leading to capacity fade. Also, we discuss the reversible Bi$_2$O$_3$ conversion to Bi$^0$ and its contribution to the observed capacity. We expect the results will provide crucial insight into the development of aqueous, rechargeable battery systems utilizing MnO 2 .

25 ENERGY STORAGE↗
DOE OSTI2024

Reduce-Order Modeling of Multigroup Neutron Cross Sections for High-Temperature Gas-cooled Reactors

Deterministic neutronics calculations rely on multigroup neutron cross section libraries, which usually consists of a database of tabulated values, used to calculate the cross sections through multivariate linear interpolation. However, interpolation of the multidimensional cross section data becomes memory inefficient and time consuming as the number of tabulations increases, significantly slowing down the neutronics calculation, especially in the case of micro cross section libraries where every isotope (on the order of hundreds) has its own set of specific reactions and cross sections. To address this challenge, this work constructs efficient and robust reduced-order models (ROMs) of the multi-group cross sections to support the Griffin simulation of high-temperature gas-cooled reactors (HTGRs). The first part of the study investigates the linearity of the multi-group cross section data across isotopes, reaction types and energy groups on pre-generated datasets for the purpose of dimensionality reduction. Secondly, a down-selection of ROM techniques is presented on representative classical machine learning (ML) techniques, including variants of linear regression, kernel-based methods, tree-based algorithms, and artificial neural networks. The selection criteria jointly consider the memory efficiency, predictive accuracy, prediction speed, and scalability in comparison to the multidimensional interpolation. Among all the ML techniques, deep neural networks (DNNs) have proven to be the best selection with sufficient accuracy, high robustness, good memory efficiency, great scalability, and superior flexibility. DNNs for have been trained for all isotopes in this work and systematic Griffin testing is ongoing at this moment to ensure the feasibility of this ROM technique for cross section predictions.

42 - ENGINEERING↗
DOE OSTI2024

Reduced-Order Modeling of Multigroup Neutron Cross Sections for High-Temperature Gas-cooled Reactors

Abstract – Deterministic neutronics calculations rely on multigroup neutron cross section libraries, which consist of databases of tabulated values, used to calculate the neutron cross sections through multivariate linear interpolation. However, interpolation of the multidimensional cross section data becomes memory inefficient and time consuming as the number of tabulations increases, significantly slowing down the neutronics calculation, especially in the case of microscopic cross section libraries where every isotope (on the order of hundreds) has its own set of specific reactions and cross sections. In order to address this challenge, this work constructs efficient and robust reduced-order models (ROMs) of the multi-group cross sections to support the Griffin simulation of high-temperature gas-cooled reactors (HTGRs). The first part of the study investigates the linearity of the multigroup cross section data across isotopes, reaction types, and energy groups on pre-generated datasets for the purpose of dimensionality reduction. Secondly, a down-selection of ROM techniques is presented on representative classical machine learning (ML) techniques, including variants of linear regression, kernel-based methods, tree-based algorithms, and artificial neural networks. The selection criteria jointly consider the memory efficiency, predictive accuracy, prediction speed, and scalability in comparison to the multidimensional interpolation. Among all the ML techniques, deep neural networks (DNNs) have proven to be the best selection with sufficient accuracy, high robustness, good memory efficiency, great scalability, and superior flexibility. DNNs have been trained for all isotopes in this work and systematic Griffin testing is ongoing to ensure the feasibility of this ROM technique for predicting cross section and reducing memory requirements without a significant sacrifice in computational performance.

42 - ENGINEERING↗
DOE OSTI2024

Advanced Cross Section Library Generation using Reduced Order Models

Deterministic neutronics calculations rely on multigroup neutron cross section libraries, which consist of databases of tabulated values, used to calculate the neutron cross sections through multivariate linear interpolation. However, interpolation of the multidimensional cross section data becomes memory inefficient and time consuming as the number of tabulations increases, significantly slowing down the neutronics calculation, especially in the case of microscopic cross section libraries where every isotope (on the order of hundreds) has its own set of specific reactions and cross sections. In order to address this challenge, this work constructs efficient and robust reduced-order models (ROMs) of the multi-group cross sections to support the Griffin simulation of high-temperature gas-cooled reactors (HTGRs). The first part of the study investigates the linearity of the multigroup cross section data across isotopes, reaction types, and energy groups on pre-generated datasets for the purpose of dimensionality reduction. Secondly, a down-selection of ROM techniques is presented on representative classical machine learning (ML) techniques, including variants of linear regression, kernel-based methods, tree-based algorithms, and artificial neural networks. The selection criteria jointly consider the memory efficiency, predictive accuracy, prediction speed, and scalability in comparison to the multidimensional interpolation. Among all the ML techniques, deep neural networks (DNNs) have proven to be the best selection with sufficient accuracy, high robustness, good memory efficiency, great scalability, and superior flexibility. DNNs have been trained for all isotopes in this work and systematic Griffin testing is ongoing to ensure the feasibility of this ROM technique for predicting cross section and reducing memory requirements without a significant sacrifice in computational performance.

42 - ENGINEERING↗
DOE OSTI2020

Surface-modified and oven-dried microfibrillated cellulose reinforced biocomposites: Cellulose network enabled high performance

Microfibrillated cellulose (MFC) is widely used as a reinforcement filler for biocomposites due to its unique properties. However, the challenge of drying MFC and the incompatibility between nanocellulose and polymer matrix still limits the mechanical performance of MFC-reinforced biocomposites. In this study, we used a water-based transesterification reaction to functionalize MFC and explored the capability of oven-dried MFC as a reinforcement filler for polylactic acid (PLA). Remarkably, this oven-dried, vinyl laurate–modified MFC improved the tensile strength by 38 % and Young’s modulus by 71 % compared with neat PLA. Our results suggested improved compatibility and dispersion of the fibrils in PLA after modification. This study demonstrated that scalable water-based surface modification and subsequent straightforward oven drying could be a facile method for effectively drying cellulose nanomaterials. We find that the method helps significantly disperse fibrils in polymers and enhances the mechanical properties of microfibrillar cellulose-reinforced biocomposites.

36 MATERIALS SCIENCE↗
DOE OSTI2024

Self-Assembling Cell-Free Systems for Scalable Bioconversion

This project focused on developing cell-free systems to directly express multi-enzyme catalysts and perform CO2 bioconversions for industrial chemical production. The use of cell-free expression (CFE) systems derived from bacterial lysates is emerging as a promising approach for biomanufacturing. CFEs are genetically programmable, permit the expression of toxic enzymes, and allow for rapid prototyping of metabolic pathways. Research Contributions: 1. Understanding the Area Investigated: This research advances the understanding of cell-free systems by demonstrating their capability to perform complex multi-enzyme reactions. By directly expressing multi-gene systems, CFEs avoid the high costs and inefficiencies associated with producing and purifying enzymes for multi-step pathways. 2. Technical Effectiveness and Economic Feasibility: The project successfully engineered a CFE-based multienzyme biocatalyst for the de novo synthesis of serine and glycine from CO2 equivalents (formate and bicarbonate) and ammonia. This method achieved a 30% conversion rate of formate into these industrially important amino acids. Additionally, an 8-gene CFE biocatalyst was developed to produce malate, conserving 43% of carbon that would otherwise be lost as CO2. This approach has the potential to reach higher carbon efficiency than microbial production. 3. Public Benefit: The cell-free production of chemicals like serine, glycine, and malate using electrochemically generated formate could significantly reduce CO2 emissions. For example, satisfying the global malate market with this method could avoid approximately 400,000 tons of CO2 emissions annually. This work demonstrates the potential of CFE systems to produce platform chemicals, contributing to environmental sustainability and reducing reliance on petrochemicals. Future Prospects: The CFE-based biocatalyst process could be extended to produce a variety of chemicals, including other industrial di-acids, aromatics, terpenes, alcohols, and polymers. This project showcases the capabilities of cell-free expression systems for prototyping carbon-conserving pathways and sustainably bioproducing platform chemicals, marking a significant step towards economically-viable industrial processes.

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

Sulfide-Based Anode-Free Solid-State Batteries: Key Challenges and Emerging Solutions

Sulfide-based anode-free solid-state batteries (AFSSBs) have emerged as a transformative technology for next-generation energy storage, offering compelling advantages in energy density, safety, and manufacturing scalability. However, these batteries face significant challenges, particularly rapid capacity degradation that currently limits their practical implementation. This comprehensive review critically examines three fundamental issues affecting AFSSBs: nonuniform lithium nucleation on bare current collectors, unstable interfaces between plated lithium and sulfide electrolytes, and formation of interfacial voids during cycling. We systematically evaluate recent strategic advances in addressing these challenges, including metal seed coatings, conversion reaction-based compounds, and carbon-based interlayers. The review also analyzes the crucial role of advanced characterization techniques, from cryo-FIB-SEM to operando methods, in understanding failure mechanisms and validating improvement strategies. Finally, we present a forward-looking perspective on research directions necessary for commercialization. This work provides a thorough framework for understanding and advancing sulfide-based AFSSBs toward practical applications in next-generation energy storage systems.

25 ENERGY STORAGE↗
DOE OSTI2021

A Micrometer‐Sized Silicon/Carbon Composite Anode Synthesized by Impregnation of Petroleum Pitch in Nanoporous Silicon

Porous silicon (Si)/carbon nanocomposites have been extensively explored as a promising anode material for high-energy lithium (Li)-ion batteries (LIBs). However, shrinking of the pores and sintering of Si in the nanoporous structure during fabrication often diminishes the full benefits of nanoporous Si. Herein, a scalable method is reported to preserve the porous Si nanostructure by impregnating petroleum pitch inside of porous Si before high-temperature treatment. The resulting micrometer-sized Si/C composite maintains a desired porosity to accommodate large volume change and high conductivity to facilitate charge transfer. It also forms a stable surface coating that limits the penetration of electrolyte into nanoporous Si and minimizes the side reaction between electrolyte and Si during cycling and storage. A Si-based anode with 80% of pitch-derived carbon/nanoporous Si enables very stable cycling of a Si||Li(Ni0.5Co0.2Mn0.3)O 2 (NMC532) battery (80% capacity retention after 450 cycles). It also leads to low swelling in both particle and electrode levels required for the next generation of high-energy LIBs. In conclusion, the process also can be used to preserve the porous structure of other nanoporous materials that need to be treated at high temperatures.

anode materials↗
DOE OSTI2026

Progress Towards Synthesis of Uranium Chloride Fuel Salts Using Zinc Chloride

Reliable, scalable methods for producing high-purity actinide chloride salts are needed to support molten salt reactor fuel development and deployment. This report describes the continued development and demonstration of a bench-scale chlorination and purification apparatus using a zinc chloride-based method for synthesizing uranium chloride fuel salts. In this approach, uranium metal is chlorinated by ZnCl2 to produce LiCl-KCl-UCl3. Reaction with three aliquots of added uranium metal was used to generate a target uranium concentration of 30 wt %. While this concentration was chosen for initial testing of the apparatus and method, the final uranium concentration is not limited to 30 wt %. The zinc metal generated in the reaction forms an immiscible layer that was removed by volatilization at moderately high temperatures. Electrochemical measurements confirmed the removal of zinc and applied sensing methods indicated the uranium concentration to be approximately 25 wt %. These initial results demonstrate that the bench-scale chlorination apparatus is an effective platform for the synthesis and purification of uranium chloride salts using ZnCl2. This method shows promise for application to industry-relevant salt systems such as NaCl-UCl3. Further development is recommended to optimize reagent loading, zinc removal, and avoid possible U-Zn alloy formation.

Dulovic, Stephanie↗
DOE OSTI2026

Bifunctional Electrocatalysts with High-Entropy Alloys: Bridging Hydrogen Evolution and Oxygen Reduction

High-entropy alloys (HEAs) have emerged as a promising class of bifunctional electrocatalysts capable of simultaneously driving the hydrogen evolution reaction (HER) and the oxygen reduction reaction (ORR) with high activity and durability. Their near-equiatomic multicomponent compositions give rise to unique physicochemical characteristics, including lattice distortion, sluggish diffusion, high-entropy stabilization, and pronounced electronic heterogeneity, that collectively generate diverse and synergistic active sites inaccessible in conventional alloys. This review summarizes recent progress in HEA-based bifunctional electrocatalysis, with a focus on the fundamental mechanisms governing HER and ORR activity, stability, and selectivity. We discuss advances in synthesis strategies, ranging from confined growth and step-alloying to scalable continuous-flow methods, that enable precise control over composition, size, and surface structure. Complementary computational and data-driven approaches, including density functional theory, machine-learning-assisted screening, and descriptor development, are highlighted as essential tools for navigating the vast HEA design space and establishing structure−property relationships. Particular attention is paid to adsorption-energy distributions, multisite cooperativity, and environmental effects under realistic electrochemical conditions. Finally, we outline current challenges and future opportunities for integrating mechanistic understanding with AI-guided, closed-loop design frameworks to accelerate the discovery of next-generation HEA bifunctional electrocatalysts for sustainable energy conversion.

Alloys↗
DOE OSTI2020

Scalable Synthesis of Ti 3 C 2 T x MXene

Scaling the production of synthetic 2D materials to industrial quantities has faced significant challenges due to synthesis bottlenecks whereby few have been produced in large volumes. These challenges typically stem from bottom‐up approaches limiting the production to the substrate size or precursor availability for chemical synthesis and/or exfoliation. In contrast, MXenes, a large class of 2D transition metal carbides and/or nitrides, are produced via a top‐down synthesis approach. The selective wet etching process does not have similar synthesis constraints as some other 2D materials. The reaction occurs in the whole volume; therefore, the process can be readily scaled with reactor volume. Herein, the synthesis of 2D titanium carbide MXene (Ti 3 C 2 T x ) is studied in two batch sizes, 1 and 50 g, to determine if large‐volume synthesis affects the resultant structure or composition of MXene flakes. Characterization of the morphology and properties of the produced MXene using scanning electron microscopy, X‐ray diffraction, dynamic light scattering, Raman spectroscopy, X‐ray photoelectron spectroscopy, UV–visible spectroscopy, and conductivity measurements show that the materials produced in both batch sizes are essentially identical. This illustrates that MXenes experience no change in structure or properties when scaling synthesis, making them viable for further scale‐up and commercialization.

Shuck, Christopher E.↗
DOE OSTI2024

Spatially Precise Light‐Activated Dedoping in Wafer‐Scale MoS 2 Films

2D materials, particularly transition metal dichalcogenides (TMDCs), have shown great potential for microelectronics and optoelectronics. However, a major challenge in commercializing these materials is the inability to control their doping at a wafer scale with high spatial fidelity. Interface chemistry is used with the underlying substrate oxide and concomitant exposure to visible light in ambient conditions for photo-dedoping wafer scale MoS 2 . It is hypothesized that the oxide layer traps photoexcited holes, leaving behind long-lived electrons that become available for surface reactions with ambient air at sulfur vacancies (defect sites) resulting in dedoping. Additionally, high fidelity spatial control is showcased over the dedoping process, by laser writing, and fine control achieved over the degree of doping by modulating the illumination time and power density. This localized change in MoS 2 doping density is very stable (at least 7 days) and robust to processing conditions like high temperature and vacuum. The scalability and ease of implementation of this approach can address one of the major issues preventing the “Lab to Fab” transition of 2D materials and facilitate its seamless integration for commercial applications in multi-logic devices, inverters, and other optoelectronic devices.

14 SOLAR ENERGY↗
DOE OSTI2022

The Generalized Spallation Model

The Generalized Spallation Model (GSM) event generator is an advanced, robust, and scalable Fortran code developed from an extended and improved version of the earlier event generator codes CEM03.03 and LAQGSM03.03. The GSM implements its algorithms within the context of cascade exciton, particle equilibration, particle evaporation, and particle disintegration models sampled via Monte-Carlo techniques. Coalescence of particles is also considered. The GSM assumes the incident particles has sufficient energy to satisfy the assumptions utilized in the GSM. The GSM calculates reactions induced by hadrons and nuclei incident on nuclear targets at incident energies ranging from ~20 MeV up to several TeV per nucleon. Usage of GSM is recommended however for particles with incident energies exceeding ~100 MeV to ensure model-assumptions are satisfied. The GSM additionally provides its clients with sub-model libraries to allow them to model desired physics within their own physical models.

Juneau, Chase↗
DOE OSTI2021

Prototype Data Acquisition and Slow Control Systems for the Mu2e Experiment

The Mu2e experiment at the Fermilab Muon Campus will search for the coherent neutrinoless conversion of a muon into an electron in the field of an aluminum nucleus with a sensitivity improvement by a factor of 10 000 over existing limits. Such a charged lepton flavor-violating reaction probes new physics at a scale unavailable with direct searches at either present or planned high-energy colliders. The Mu2e Trigger and Data Acquisition (TDAQ) system exploits otsdaq as its online Data Acquisition System (DAQ) solution. Furthermore, developed at Fermilab, otsdaq integrates both the artdaq DAQ and the art analysis frameworks for event transfer, filtering, and processing. otsdaq is an online DAQ software suite with a focus on flexibility and scalability and provides a multi-user, web-based, interface accessible through a web browser. The read out controllers (ROCs) stream out zero-suppressed data continuously from the detector subsystems to the data transfer controllers (DTCs). The data stream is then read over the peripheral component interconnect express (PCIe) bus to a software filter algorithm that selects events which are combined with the data flux coming from a cosmic-ray veto (CRV) system. The detector control system (DCS) has been developed using the experimental physics and industrial control system (EPICS) open source platform for monitoring, controlling, alarming, and archiving. The DCS has been integrated into otsdaq. A prototype of the TDAQ system and the DCS has been built at Fermilab's Feynman Computing Center. In this article, we report on the progress of the integration of this prototype in the online otsdaq software.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2025

Stability Enhancement in Na 0.67 Fe 0.2 Mn 0.8 O 2 Positive Electrodes via Spherical Coprecipitated Hydroxide Precursor Synthesis for Na-Ion Batteries

Mn/Fe-based layered transition metal oxides (LTMOs) are promising positive electrode materials for sodium-ion batteries (SIBs) due to their high abundance, low cost, and stable price fluctuations. At commercial scale, the fabrication of these materials commonly employs coprecipitation of hydroxide precursors, which allows for the scalable synthesis of uniform, dense particles with a tunable morphology. However, the common chelating agent (ammonia) forms unstable complexes with Fe 2+ ions, resulting in uncontrollable particle morphology and poor electrochemical properties. Here, in this study, three chelation strategies (no chelation, ammonia, oxalate) for Fe/Mn-based hydroxides are evaluated. It was found that oxalate chelation produced uniform, dense spherical hydroxide particles while particles via ammonia / no chelate routes exhibited no morphological control. The LTMOs synthesized from the oxalate-chelated hydroxide precursor formed uniform spherical particles, while the other two LTMOs showed greater variation in particle morphology. The oxalate-chelated LTMO electrode exhibited increased cycling stability due to reduced parasitic reactions with the electrolyte, as characterized by static leakage current measurements and electrochemical impedance spectroscopy.

25 ENERGY STORAGE↗
DOE OSTI2025

Unsupported and carbon-supported silver catalysts for oxygen reduction reaction in alkaline media

Quick and easy Ag catalysts preparation via wet chemical synthesis method using only reducing agent (pure-Ag); reducing agent and citric acid as the capping agent (Ag-CA); and carbon support (KetjenBlack 600J), capping agent, and the reducing agent (Ag/C) is demonstrated. The Ag-based electrocatalysts are characterized by high-angle annular dark-field scanning transmission electron microscopy (HAADF-STEM) with energy-dispersive X-ray spectroscopy (EDS), scanning electron microscopy (SEM), X-ray diffraction (XRD) analysis, and X-ray photoelectron spectroscopy (XPS). The electrocatalytic activity of Ag catalysts for O 2 reduction reaction (ORR) in 1 M KOH is evaluated using the rotating (ring)-disc electrode method. SEM and HAADF-STEM results show that the unsupported pure-Ag and Ag-CA catalysts consist mainly of big agglomerates, and Ag/C has the smallest agglomerates and some sub-3 nm Ag nanoparticles. The XPS results reveal that Ag in all the catalysts is in the metallic form (Ag 0 ). Despite consisting of big agglomerates, the Ag-CA catalyst exhibits similar ORR electrocatalytic activity to that of Ag/C. Ag-CA (unsupported) shows the lowest hydrogen peroxide yield. These results are of great importance for the development of Ag-based catalysts that can be prepared in a fast, simple and easily up scalable fashion, for anion exchange membrane fuel cells.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2021

Scalable Synthesis of Pt/SrTiO 3 Hydrogenolysis Catalysts in Pursuit of Manufacturing-Relevant Waste Plastic Solutions

Here, an improved hydrothermal synthesis for shape-controlled, size-controlled 60 nm SrTiO 3 nanocuboid (STO NC) supports, which facilitates the scalable creation of platinum nanoparticles catalyst supported on STO (Pt/STO) for the chemical conversion of waste polyolefins, is reported herein. This synthetic method: 1) produces STO NC supports with average sizes ranging from 25 – 80 nm with narrow size distributions 2) demonstrates how SrCO 3 formation and variation in solution pH prevent the formation of STO NCs, and 3) establishes that STO nucleation prior to the hydrothermal treatment favors nanocuboid formation. The updated hydrothermal synthesis was scaled-up and conducted in a 4L batch reactor, resulting in STO NCs of comparable size and morphology (m = 22.5 g, d avg = 58.6 ± 16.2 nm) to those synthesized under standard hydrothermal conditions in a lab-scale 125 mL autoclave reactor. Size-controlled STO NCs, ranging in roughly 10 nm increments from the 25 nm to 80 nm, were used to support Pt deposited through strong electrostatic adsorption (SEA), a practical and scalable solution-based method. Using SEA techniques and a STO support with an average size of 39.3 ± 6.3 nm, a Pt/STO catalyst with 3.6 wt% Pt was produced and used for high-density polyethylene hydrogenolysis under previously-reported conditions (170 psi H 2 , 300°C, 96h; final product: M w = 2400, Ð = 1.03). As a well-established model system for studying the behavior of heterogeneous catalysts and their supports in reactions, the Pt/STO system detailed in this work presents a unique opportunity to simultaneously convert waste plastic into commercially-viable products while gaining fundamental insight into the mechanism of polyethylene hydrogenolysis.

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

Multiphysics simulation of recent experiments on alkali‐silica reaction expansion in reinforced concrete members

Alkali‐silica reaction (ASR) is an important degradation process that causes volumetric expansion and damage in concrete, and is affected significantly by the local temperature, moisture and stress conditions that often vary across the regions of a structure. Numerical simulation is essential to predict the progression and effects of ASR on the performance of structures. Because of the interactions between thermal and moisture transport and mechanical deformation, it is important for numerical models to represent all these physical phenomena and the coupling between them. Simulations of ASR in reinforced concrete (RC) structures are further complicated by the need to capture interactions between concrete and embedded reinforcing bars. Here, this paper describes the implementation of a scalable, coupled‐physics ASR model for simulating RC structures and assesses the ability of that model to predict ASR‐induced expansion in recent laboratory tests on RC block and beam specimens. These laboratory tests and the simulation approach were selected because of their applicability to RC structural‐scale simulations. This validation study helps builds confidence the ability of this approach to model ASR expansion in large, complex RC structures, which is a current high‐priority need.

36 MATERIALS SCIENCE↗