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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 559 records · Page 31
All sourcesNASA NTRSDOE OSTI
DOE OSTI2022

The synergistic effects of central core size and end group engineering on performance of narrow bandgap nonfullerene acceptors

Understanding the relationship between the molecular structure and photoelectric properties of fused-ring non-fullerene acceptors (NFAs) is of far-reaching significance to the development of organic solar cells (OSCs). Herein, six NFAs based on multiple thiophenes (4 T, 6 T and 8 T) are employed to systematically probe the synergistic effects of extending central core size and terminal fluorination. The absorption results manifest that the molecular absorption is comprehensively affected by molecular crystallinity, planarity, conjugation length and intramolecular electron push–pull effect, simultaneously. The intensity of electron push–pull effect is not only related to the electron-donating ability of central core and the electron-withdrawing ability of end group, but also may be related to the distance between the positive and negative centers. The extension of central core leads to the more planar backbone and stronger crystallinity of NFAs, and less energy loss (E loss ) in its based OSC. Compared with the extension of central core, terminal fluorination has a greater impact on molecular photoelectric properties. The terminal fluorination significantly enhances the push–pull effect, lowers the energy levels, and slightly increases the vibrational relaxation. As a result, the strongest crystallinity and coplanarity of 8TIC-4F lead to a low vibrational relaxation of 0.18 eV, which makes PTB7-Th:8TIC-4F device exhibit a small E loss of 0.51 eV and a high efficiency of 10.4%. In addition, the fluorinated 6TIC-4F with suitable core size exhibits suitable energy level, absorption, crystallization, and phase separation morphology, making its as-cast device up to 11.61% efficiency. Finally, to the best of the authors’ knowledge, the PCE of 11.61% for PTB7-Th:6TIC-4F based device without any treatment is one of the highest values reported for the NAFs with over 1000 nm absorption.

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

Plastic shrinkage of mortars cured with a paraffin-based compound – Bimodal neutron/X-ray tomography study

Early-age drying (immediately after casting) of mortars and the corresponding plastic shrinkage were studied using bimodal neutron/X-ray computed tomography. This novel, correlative 3D imaging mode enabled studying simultaneously and without any source of spurious perturbation the water migration and loss processes together with the corresponding deformations due to plastic shrinkage. Bimodal imaging opens up new possibilities for studying dynamic processes of coupled water transport and deformations in porous solids. The measurements were carried out on model systems (cylindrical mortar specimens with height of 19 mm). The study focused on the effect of a paraffin-based curing compound. Our results confirm that when the curing compound was applied directly onto the drying surface in a sufficient amount, both the evaporation rate and the rate of vertical displacement (settlement) were substantially reduced. The results shed a new light on the mechanisms of plastic shrinkage and the action of curing compounds.

36 MATERIALS SCIENCE↗
DOE OSTI2021

3D-printable engineered cementitious composites (3DP-ECC): Fresh and hardened properties

3D printing (3DP) is an emerging digital construction method for concrete materials. A major impediment to efficient 3D concrete printing (3DCP) is the need for steel reinforcement, the placement of which is incompatible with the 3DP process. Unlike plain concrete, ductile self-reinforced engineered cementitious composite (ECC) holds promise to remove the dependence on steel reinforcement. The objective of this research is to develop a 3D-printable ECC (3DP-ECC). The fresh rheological and hardened mechanical properties of 3DP-ECC are investigated. The robotically printed tensile specimens demonstrated the familiar multiple microcracking and strain-hardening behavior of conventionally cast ECC. Significant orthotropy is revealed in the compressive properties. The interface between printed layers is found to be toughened by a printed groove-tongue joint. The developed 3DP-ECC was used to print a twisted column with 150 layers, reaching a height of 1.5 m. This research lays the groundwork for efficient robotically 3D-printed structures of complex shapes.

36 MATERIALS SCIENCE↗
DOE OSTI2021

Comparison on the properties of ITZs in fly ash-based geopolymer and Portland cement concretes with equivalent flowability

Highlights: • Modelled ITZs can effectively avoid the effects caused by different factors and test locations of ITZ. • ITZ properties of PC and geopolymer concretes are compared based on equivalent flowability of binders. • Geopolymer ITZ exhibits stronger bonding than that of PC, and higher mechanical properties than geopolymer paste. • Strong ITZs in geopolymer concrete are attributed to the layer of gel-rich paste with dense microstructures. • Elastic modulus and hardness properties of ITZs in PC concrete are increased due to the CH crystals. This paper aims to compare the properties of interfacial transition zones (ITZs) in Portland cement (PC) concrete and geopolymer concrete. Portland cement and geopolymer pastes were designed with the equivalent flowability to provide similar mix and casting condition of ITZs. Two types of modelled ITZs were prepared to facilitate the nanoindentations across ITZs, microstructural characterization, and comparison on the properties of ITZs with less influential factors. The results showed that the interfacial bonding of ITZs between geopolymer matrix and aggregate is relatively stronger than the counterpart in the PC concrete. There is a high amount of crystalline hydration products in the ITZs of PC concrete, but a layer gel-rich paste with denser microstructures in the ITZs of geopolymer concrete. Additionally, the interface morphology and nanoindentation analysis indicate that the property of ITZs in the modelled geopolymer concrete is not poorer than that of the corresponding geopolymer paste.

36 MATERIALS SCIENCE↗
DOE OSTI2021

Shrinkage-induced deformations and creep of structural concrete: 1-year measurements and numerical prediction

Highlights: • Extensive experimental study on drying shrinkage, creep and microcracking of concrete • All specimens prepared from a single batch of ordinary-strength structural concrete • 1st year of measurements of (not only) non-uniformly drying beams with span up to 3-m • The database is downloadable from free-to-use research data repository • Modified MPS model for concrete creep used in blind prediction of all experiments The material models for creep and shrinkage operating on the material point level in FEM are usually intended for challenging complex applications and structures, where the average cross-sectional approach does not suffice. The identification of the growing number of material parameters induced by increasing model capabilities relies on very specific and narrow-oriented yet interconnected experiments which are scarce. The presented comprehensive experiments aim to provide a clearer image of the complicated interaction among the basic phenomena: drying, shrinkage, creep, and microcracking. The cornerstone of this ongoing research is a unique set of 30 partially-sealed unreinforced concrete beams with span 1.75–3.0 m subjected to drying. To minimize material variation, all specimens in this study were cast from a single batch of ordinary strength structural concrete with slag-blended binder. The resulting experimental database will be suitable both for validation and development of the constitutive models.

36 MATERIALS SCIENCE↗
DOE OSTI2021

Modelling the development of capillary pressure in freshly 3D-printed concrete elements

3D concrete printing is a promising technology recently developed to automate construction. Since no formwork is used in this technology to support and protect fresh concrete, there are two aspects which considerably accelerate the development of capillary pressure in 3D-printed concrete in comparison to conventionally placed concrete: i) high stiffness of 3D-printed needed to provide sufficient buildability, and ii) very early and fast evaporation of pore water. Accelerated development of capillary pressure may lead to severe plastic shrinkage cracking in 3D-printed elements and, hence, need to be mitigated. This investigation aims at providing a poromechanical model for capillary pressure development in 3D-printed elements. To simulate the development of capillary pressure and plastic shrinkage, environmental factors, material properties, and element geometry need to be considered as a whole. The model inputs – coefficient of permeability, static bulk modulus, air entry pressure and chemical shrinkage rate – were determined experimentally. The model was validated for two fine-grained concretes. Both 3D-printed materials yielded faster capillary pressure increase in comparison to cast concrete, while partial substitution of cement with silica fume further accelerated the capillary pressure development. Furthermore, due to the lower permeability of the mixture containing silica fume, the gradient of capillary pressure between 3D-printed layers increased, as did the gradient of plastic shrinkage.

36 MATERIALS SCIENCE↗
DOE OSTI2021

A review of the effect of nanoclays on the fresh and hardened properties of cement-based materials

This paper provides an overview of the use of nanoclays in cement-based materials. The effect of nanoclays on shear rheological properties will be discussed, with a focus on structural build-up behavior. This macroscale flow behavior will then be tied to the effect of nanoclays on the fresh microstructure as characterized via in-situ particle size measurements and rheological techniques. This will provide insight on the mechanisms underlying the effect of nanoclays on fresh state properties through an improved understanding of their effect on flocculation and coagulation behavior. In addition, secondary effects nanoclays have on the hardening and hardened properties will also be discussed. Examples of casting applications in which nanoclays have been effectively utilized as rheological modifiers, i.e. reducing self-consolidating concrete formwork pressure, extrusion, semi-flowable concrete for slipform paving, and 3D concrete printing, will be presented. Finally, some thoughts on potential directions for future work will be shared.

36 MATERIALS SCIENCE↗
DOE OSTI2021

Cu- and Zn-doped alkali activated mortar – Properties and durability in (bio)chemically aggressive wastewater environments

Metakaolin-based alkali activated mortars (AAM) - with and without CuSO{sub 4}·5H{sub 2}O and ZnO addition (mass ratio M{sup n+}/solid binder 0.08% to 1.7%) - were casted and exposed within an extensive long-term field campaign over the period of 20 months to a sewer basin, strongly affected by biogenic acid corrosion. (Un-)exposed AAM were tested regarding their physicochemical and microstructural properties, bioreceptivity and overall durability. Metal addition led to a retarding effect during alkali-activation reaction, as well as to an increase in open porosity of up to 3.0% and corresponding lower compressive strength of up to 10.9%. Reduced microbial colonization and diversity were observed on AAM with Cu, while Zn addition led to increased biodiversity. We propose that the observed higher durability of Cu-doped AAM is due to antibacterial effects and associated reduction of biogenic acid production, superseding overall negative effects of metal-dosage on physical material properties. Observed lower durability of Zn-doped AAM was related to combined negative physicochemical and microbial effects.

36 MATERIALS SCIENCE↗
DOE OSTI2024

Investigation of B 4 C for inhibiting crystallization in silica at high temperatures

Amorphous silica has numerous high temperature applications due to its inherent thermal shock resistance and low coefficient of thermal expansion (CTE). However, at the high temperatures required for processing (>1200 °C), the metastable β-cristobalite phase preferentially forms and is accompanied by a volume change, and potential cracking, upon conversion to the low temperature α-cristobalite phase. The CTE of the final crystalline phase is an order of magnitude higher than its amorphous counterpart. Here experimental results demonstrate that small additions of B 4 C (3.5 wt%) effectively inhibit silica crystallization in powder and sintered gel-cast forms up to 22h at temperatures as high as 1500°C as confirmed via x-ray diffraction. Further, the oxidation of B 4 C to B 2 O 3 and its subsequent melt and evaporation disrupts the nucleation and growth of the cristobalite phase. The mechanism for crystallization inhibition is further explored through optical microscopy to probe changes in surface morphology.

36 MATERIALS SCIENCE↗
DOE OSTI2020

An error-in-constitutive equations strategy for topology optimization for frequency-domain dynamics

This paper presents a topology optimization formulation for frequency-domain dynamics to reduce solution dependence upon initial guess and considered loading conditions. Due to resonance phenomena in undamped steady-state dynamics, objectives measuring dynamic response possess many local minima that may represent poor solutions to a design problem, an issue exacerbated for design with respect to multiple frequencies. In this work, we propose an extension of the modified error-in-constitutive-equations (MECE) method, used previously in material identification inverse problems, as a new approach for frequency-domain dynamics topology optimization to mitigate these issues. The main idea of the proposed framework is to incorporate an additional penalty-like term in the objective function that measures the discrepancy in the constitutive relations between stresses and strains and between inertial forces and displacements. Then, the design problem is cast within a PDE-constrained optimization formulation in which we seek displacements, stresses, inertial forces, and a density-field solution that minimize our new objective subject to conservation of linear momentum plus some additional constraints. We show that this approach yields superior designs to conventional gradient-based optimization approaches that solely use a functional of displacements as the objective, while strictly enforcing the constitutive equations. The MECE strategy integrates into a density-based topology optimization scheme for void–solid or two-phase material structural design. We highlight the merits of our approach in a variety of scenarios for direct frequency response design, considering multiple frequency load cases and structural objectives.

42 ENGINEERING↗
DOE OSTI2024

Data-driven projection pursuit adaptation of polynomial chaos expansions for dependent high-dimensional parameters

Uncertainty quantification (UQ) and inference involving a large number of parameters are valuable tools for problems associated with heterogeneous and non-stationary behaviors. The difficulty with these problems is exacerbated when these parameters are statistically dependent requiring statistical characterization over joint measures. Probabilistic modeling methodologies stand as effective tools in the realms of UQ and inference. Among these, polynomial chaos expansions (PCE), when adapted to low-dimensional quantities of interest (QoI), provide effective yet accurate approximations for these QoI in terms of an adapted orthogonal basis. These adaptation techniques have been cast as projection pursuits in Gaussian Hilbert space in what has been referred to as a projection pursuit adaptation (PPA) by Xiaoshu Zeng and Roger Ghanem (2023). The PPA method efficiently identifies an optimal low-dimensional space for representing the QoI and simultaneously evaluates an optimal PCE within that space. The quality of this approximation clearly depends on the size of the training dataset, which is typically a function of the adapted reduced dimension. Here, the complexity of the problem is thus mediated by the complexity of the low-dimensional quantity of interest and not the complexity of the high-dimensional parameter space.

Data-driven↗
DOE OSTI2024

An optimization-based approach to tailor the mechanical response of soft metamaterials undergoing rate-dependent instabilities

An optimization-based design framework is proposed to tune the response of soft metamaterials involving both geometric instabilities and nonlinear viscoelastic material behavior. Designing the response of soft metamaterials to harness instabilities and undergo large, tailored configuration changes will enable advancements in soft robotics, shock and vibration mitigation, and flexible electronics. In line with the metamaterial concept, the response of these materials is governed to a large extent by the geometric and topological makeup of their small-scale features. However, the link between structure and response is less intuitive for soft metamaterials due to their reliance upon highly nonlinear responses triggered by geometric instabilities. This is further complicated by the effects of viscoelastic relaxation, which recent studies have shown to alter the emergence of instabilities in non-intuitive ways. Here, these effects are accounted for in our framework to achieve various design objectives, including tailored force–displacement response and maximized energy absorption from both geometric and material effects. To fully automate this process, it is essential to have a completely robust equation solver for forward problems involving instabilities and viscoelastic relaxation. We achieve this by casting the search for stable mechanical equilibrium — i.e. the forward problem — as a minimization problem and utilize a trust region algorithm to robustly handle instabilities and follow energetically-favorable equilibrium paths through critical points.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2022

A parallel discrete dislocation dynamics/kinetic Monte Carlo method to study non-conservative plastic processes

Non-conservative processes play a fundamental role in plasticity and are behind important macroscopic phenomena such as creep, dynamic strain aging, loop raft formation, etc. In the most general case, vacancy-induced dislocation climb is the operating unit mechanism. While dislocation/vacancy interactions have been modeled in the literature using a variety of methods, the approaches developed rely on continuum descriptions of both the vacancy population and its fluxes. However, there are numerous situations in physics where point defect populations display heterogeneous concentrations and/or non-smooth kinetics. Here, a kinetic Monte Carlo (kMC) approach for modeling vacancy transport in response to arbitrary stress fields is used. Vacancies are treated as point particles and are coupled to the dislocation substructure representing a deformed material via an advection term defined by the local stress gradients. The stress fields and the dislocation substructure are evolved using a discrete dislocation dynamics (DDD) module. To extend the coupled model to the treatment of large systems, we have implemented it in the massively-parallel DDD code ParaDiS. To avoid numerical incompatibilities associated with merging deterministic (DDD) and stochastic (kMC) integration algorithms, we cast the entire elasto-plastic-diffusive problem within a single stochastic framework, taking advantage of a parallel kMC algorithm to evolve the system as a single event-driven process. The large-scale implementation enables the study of the evolution of a variety of dislocation-defect scenarios governed by non-conservative transport kinetics. After carrying out an exhaustive numerical and computational analysis of our parallel algorithm, we show results that emphasize situations where inhomogeneous vacancy dynamics are of relevance, and compare discrete kinetics to continuum solutions for several cases.

36 MATERIALS SCIENCE↗
DOE OSTI2024

Deep operator network surrogate for phase-field modeling of metal grain growth during solidification

A deep operator network (DeepONet) has been constructed that generates accurate representations of phase-field model simulations for evolving two dimensional metal grain morphology growing from melt. These representations serve as lower resolution, computationally efficient stand-ins for quick parameter space exploration of solutions to the the Allen-Cahn equations that dictate the phase-field model simulations. The experimental target for the phase-field model is a uranium casting system cooling a 434 g uranium charge from a maximum temperature of 1400° C at an average rate of 30° C / min , traversing the crystallographic phases of the pure metal. Experimental parameters inform the phase-field model, whose higher resolution computational model solutions are used to train the DeepONet in a given parameter space with the aim of developing a faster, more efficient method for predicting the solidifying metal's microstructure at different potential experimental values. The final DeepONet generates high accuracy, lower resolution predictions with cumulative relative approximation error over all timesteps of less than 0.5%, while ensuring solutions remain within physically feasible ranges. Further, these relative error values are comparable with other state-of-the-art DeepONet models for microstructure evolution, while significantly reducing the amount of training data required. Training a convolutional neural network simultaneously with the DeepONet, enforcing realistic values at the complex metal grain boundaries, and mathematically encoding boundary conditions into the structure of the DeepONet improved prediction accuracy and computational efficiency over a standard DeepONet model.

36 MATERIALS SCIENCE↗
DOE OSTI2022

A spatial superstructure approach to the optimal design of modular processes and supply chains

Modularity is a design principle that aims to provide flexibility for spatio-temporal assembly/disassembly and reconfiguration of systems. This design principle can be applied to multiscale (hierarchical) manufacturing systems that connect units, processes, facilities, and entire supply chains. Designing modular systems is challenging because of the need to capture spatial interdependencies that arise between system components due to product exchange/transport between components and due to product transformation in such components. In this work, we propose an optimization framework to facilitate the design of modular manufacturing systems. Central to our approach is the concept of a spatial superstructure, which is a graph that captures all possible system configurations and interdependencies between components. The spatial superstructure is a generalization of the notion of a superstructure and of a p-graph used in process design, in that it encodes spatial (geographical) context of the system components. Here, we show that this generalization facilitates the simultaneous design and analysis of processes, facilities, and of supply chains. Our framework aims to select the system topology from the spatial superstructure that minimizes design cost and that maximizes design modularity. We show that this design problem can be cast as a mixed-integer, multi-objective optimization formulation. We demonstrate these capabilities using a case study arising in the design of a plastic waste upcycling supply chain.

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

Innovative large overbraided carbon fiber composite beams in historical Friendship Bell Pavilion

The International Friendship Bell Pavilion located in the City of Oak Ridge, Tennessee symbolizes the spirit of the peace and friendship shared by the city of Oak Ridge and city of Naka, Japan. The Bell Pavilion features an 8000-pound bronze cast bell along with futuristic materials and advanced manufacturing technologies. The team has developed innovative overbraided carbon fiber arched beams in conjunction with the bronze bell. Carbon fiber was utilized in these beams because of its lightweight, strong, stiff, and durable characteristics. A sandwich design used a high-performance foam core, overbraided with triaxial carbon fibers. This is the world’s largest overbraided composite structure for parks and recreation. For the first time in such an application, 17 free floating, 33-foot (10 m) long arched carbon fiber composite beams cantilever from a central concrete-steel superstructure over the bronze bell. The arc radius of the bottom of each beam is uniquely different giving an appearance of an open dome when standing underneath the structure.

Parks and Recreation↗
DOE OSTI2026

Queen bees offload pesticide burden to eggs when social buffering is overwhelmed

Honey bee colonies pollinate about one-third of the world’s food crops, and their rapid decline directly threatens agricultural productivity and ecosystem stability. Understanding how colony-level social defenses influence pesticide fate and the circumstances under which they fail is therefore a crucial question in pollinator biology. We used biological accelerator mass spectrometry (BioAMS), a sensitive radiotracer technique, to track the movement of a model pesticide through a small honey bee colony under laboratory conditions. We tested the hypothesis that social buffering protects honey bees from toxic accumulation and that this protection can be overcome, leading to maternal offloading of the pesticide to developing eggs. Consistent with this hypothesis, our results identified three key mechanisms governing chemical movement within a social insect colony: (1) worker bees initially decrease dietary pesticide levels by 95% through diet filtering and deposition in honeycombs, though this declines to 86% by day 10; (2) queen bees maintain markedly lower pesticide levels than workers but, over time, they accumulate the pesticide in their ovaries and transfer it into developing eggs, revealing a previously undocumented protective mechanism in reproductive individuals; and (3) the presence of a queen bee shifts colony-wide chemical distribution by concentrating worker exposure and increasing pesticide deposition in wax. Our findings show that honey bee colonies function as integrated detoxification networks, in which chemical fate depends on complex social behaviors and caste-specific physiology. When social buffering is overwhelmed, reproductive queens may survive by transferring their chemical burden to their offspring.

Biological and medical sciences↗
DOE OSTI2022

Digitalization of an experimental electrochemical reactor via the smart manufacturing innovation platform

The exponential increase in data produced over the last two decades has revolutionized the way we collect, store, process, analyze, model, and interpret information to improve profitability. Manufacturing is no exception. How- ever, Smart Manufacturing, the digital practice, organization, workforce, and infrastructure transformation for collection and deployment of data and models at scale and at all levels of manufacturing, is a complex, costly, and labor-intensive journey that is still seeing slow adoption. The Clean Energy Smart Manufacturing Innovation Institute (CESMII), a national Manufacturing USA public-private partnership sponsored by the Department of Energy, is addressing this scaled use of data and modeling in manufacturing. CESMII has focused on how to col- lect and use operating data for numerous applications that improve productivity, precision, and performance of manufacturing operations from factory floor to supply chain using process simulation, predictive analytics, mon- itoring and control, and real-time optimization. Because contextualized data are key, CESMII has developed the Smart Manufacturing Innovation Platform (SMIP) to lower the barriers to the data that are needed to accelerate data-based model building, improve data visualization, and more quickly gain insights. Reusable, standards-based ways of doing data collection, ingestion, and contextualization are particularly important for scaling access and use of data. The SMIP uses a standards-based definition and construct for reusable information models called an SM Profile. When an SM Profile is used in conjunction with the SMIP, the SMIP ensures the availability of contextualized, operational data for model building. The present work demonstrates Smart Manufacturing and the application of the SMIP for building several data-centered models for the operation and control of an ex- perimental electrochemical reactor that reduces carbon dioxide (CO 2 ) gas to valuable liquid and gas chemicals, such as alcohols, olefins, and syngas. We describe how the SMIP plays a central role in more effective model building and we demonstrate how the electochemical reactor can be controlled and optimized for the desired products. Use of the SMIP involves the transmission of real-time sensor measurements to a cloud resource so that the operating data are available to all model building experts. The data collection and transmission process is fully automated to greatly reduce the need for manual manipulation of the data. Data-driven machine learning models are used for advanced real-time state estimation, real-time optimization, and model-based feedback control for the reactor. The application models are implemented as a system to monitor the data flow and control the electrochemical reactor with a single visualization interface. SM Profiles are used to demonstrate reusability of the information models for the reactor and the instrumentation. The application packages, algorithms, and user interfaces developed are cast as Docker images in a library to facilitate reusability of the application models.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗