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At least 343 records · Page 19

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DOE OSTI2021

Model-Free Voltage Control of Active Distribution System with PVs Using Surrogate Model-Based Deep Reinforcement Learning

Accurate knowledge of the distribution system topology and parameters is required to achieve good voltage control performance, but this is difficult to obtain in practice. This paper proposes a physical-model-free voltage control method based on a surrogate-model-enabled deep reinforcement learning approach. Specifically, a surrogate model is trained in a supervised manner using the recorded limited number of historical data to learn the relationship between the power injections and voltage fluctuations of each node. Then, the deep reinforcement learning algorithm is applied to learn an optimal control strategy from the experiences obtained by continuous interactions with the surrogate model. The proposed method can achieve physical-model-free control of unbalanced distribution network and inform real-time decisions to deal with fast voltage fluctuations caused by the rapid variation of PV generation. Simulation results on an unbalance IEEE 123-bus system show that the proposed method can achieve similar performance as that of perfect physical-model-based approaches while being advantageous over other traditional methods.

active distribution network↗

DOE OSTI2013

Surface treated polypropylene (PP) fibres for reinforced concrete

Surface treatments on a polypropylene (PP) fibre have contributed to the improvement of fibre/concrete adhesion in fibre-reinforced concrete. The treatments to the PP fibre were characterized by contact angle measurements, ATR-IR and XPS to analyse chemical alterations. The surface topography and fibre/concrete interaction were analysed by several microscopic techniques, namely optical petrographic, and scanning electron microscopy. Treatment modified the surface chemistry and topography of the fibre by introducing sodium moieties and created additional fibre surface roughness. Modifications in the fibre surface led to an increase in the adhesion properties between the treated fibres and concrete and an improvement in the mechanical properties of the fibre-reinforced concrete composite as compared to the concrete containing untreated PP fibres. Compatibility with the concrete and increased roughness and mineral surface was also improved by nucleated portlandite and ettringite mineral association anchored on the alkaline PP fibre surface, which is induced during treatment.

36 MATERIALS SCIENCE↗

DOE OSTI2015

Corrosion products of carbonation induced corrosion in existing reinforced concrete facades

Active corrosion in reinforced concrete structures is controlled by environmental conditions and material properties. These factors determine the corrosion rate and type of corrosion products which govern the total achieved service life. The type and critical amount of corrosion products were studied by electron microscopy and X-ray diffractometry on concrete and reinforcement samples from existing concrete facades on visually damaged locations. The corrosion products in outdoor environment exposed concrete facades are mostly hydroxides (Feroxyhite, Goethite and Lepidocrocite) with a volume ratio to Fe of approximately 3. The results can be used to calibrate calculation of the critical corrosion penetration of concrete facade panels.

36 MATERIALS SCIENCE↗

DOE OSTI2021

Deep reinforcement learning control of hydraulic fracturing

Hydraulic fracturing is a technique to extract oil and gas from shale formations, and obtaining a uniform proppant concentration along the fracture is key to its productivity. Recently, various model predictive control schemes have been proposed to achieve this objective. But such controllers require an accurate and computationally efficient model which is difficult to obtain given the complexity of the process and uncertainties in the rock formation properties. In this article, we design a model-free data-based reinforcement learning controller which learns an optimal control policy through interactions with the process. Deep reinforcement learning (DRL) controller is based on the Deep Deterministic Policy Gradient algorithm that combines Deep-Q-network with actor-critic framework. In addition, we utilize dimensionality reduction and transfer learning to quicken the learning process. We show that the controller learns an optimal policy to obtain uniform proppant concentration despite the complex nature of the process while satisfying various input constraints.

42 ENGINEERING↗

DOE OSTI2025

RLGBS: Reinforcement Learning-Guided Beam Search for process optimization in a paper machine dryer section

Paper drying is responsible for over two-thirds of energy consumption in the U.S. pulp and paper industry, presenting significant potential for energy savings through optimization of process parameters. Current approaches often assume fixed operating conditions, neglecting dynamic ambient and process variations that limit achievable savings and real-world applicability. To this end, we develop a physics-based simulation environment for a paper machine dryer section and propose a reinforcement learning (RL) framework to minimize overall energy consumption by optimizing drying process parameters under diverse operating conditions. To mitigate overdrying and numerical instabilities caused by suboptimal local RL actions, we introduce Reinforcement Learning-Guided Beam Search (RLGBS), which explores multiple action sequences in parallel using beam search. Instead of making step-by-step decisions, RLGBS prioritizes solutions based on cumulative probability, reducing the impact of individual suboptimal actions. Experiments demonstrate that RLGBS achieves consistent energy savings under unseen operating conditions not encountered during training, outperforming conventional RL methods. While validated in drying optimization, this framework is broadly applicable to other RL-based industrial process control problems.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗

DOE OSTI2020

Manufacturing and characterization of continuous fiber-reinforced thermoplastic tape overmolded long fiber thermoplastic

Light-weight construction, design freedom, integration of functions, and compelling cost element are desired by Original Equipment Manufacturers (OEMs) and their suppliers. The emergence of overmolding of continuous-discontinuous reinforcement enables design freedom and ability to tailor stiffness, strength, and damage tolerance for structural applications. In this work, long fiber thermoplastics (LFT) are overmolded with continuous fiber-reinforced thermoplastic (CFRTP) tape are combined using extrusion compression molding process to evaluate the structural performance. The CFRTP tape overmolded LFT samples were characterized using nondestructive and destructive techniques to track fiber alignment, fiber distribution, manufacturing defects, interfacial bonding of the tape – LFT and effect of CFRTP tape on LFT. Mode 1 fracture toughness, G 1c for tape overmolded LFT was higher by 25–30% compared to literature reported G 1c . This response was attributed to excellent fiber distribution, good fiber wetting, and absence of voids at the interface. Three-point bend test indicated that CFRTP tape overmolded LFT composites were better able to resist damage under the bending load compared to constituent LFT composite. Flexural strength of the overmolded composite was higher by 119–142%, and modulus higher by 77–65% compared to constituent LFT composite. Simulated flexural results accurately represents the mechanical behavior of composites. Here, the penetration energy of tape overmolded LFT composites determined by LVI test was in the range of 27.66–30.15 J, which is significantly higher than constituent LFT composite, 7.76 J. CFRTP tape overmolded LFT composite exhibits progressive fiber fracture, matrix cracking, and interfacial debonding failure, whereas, constituent LFT composite showed catastrophic fiber fracture.

42 ENGINEERING↗

DOE OSTI2024

Understanding interfacial crystallization dynamics on carbon fiber reinforced polypropylene composite manufacturing

Reinforcing polymers with discontinuous fibers improves mechanical properties, such as strength and stiffness, and in some cases achieve isotropic properties, rendering them suitable for various engineering applications. Matrix materials are generally highly engineered thermosets (e.g. crosslinked epoxies), bonded to the fiber periphery by proprietary surface and sizing chemistries. Semicrystalline thermoplastic matrices are less utilized due to poor fiber-matrix bonding resulting in inefficient interfacial load-transfer in reinforced composites. However, flexibility with melt-processing or molding conditions can be leveraged to promote non-covalent interfacial bonding between matrix and fiber via crystallization of the matrix onto fiber surface. In the present study, we utilize a co-mingle chopped carbon and isotactic polypropylene fibers to form isotropic composites, tailoring interfacial immobilized matrix or interphase morphology to optimize performance through precise control of thermal processing/molding windows. Calorimetry and optical microscopy were employed to investigate the impact of carbon fiber at various volume fractions (10, 20, and 30 %) on isotactic polypropylene crystallization and mechanical performance. Variations in mechanical properties correspond to the structural evolution of the interfacial region and are correlated to underlying microstructural attributes using wide-angle X-ray scattering, thermal analysis, and low-field nuclear magnetic resonance spectroscopy. These results provide a practical framework for the manufacturing of thermoplastic matrix composites. In conclusion, the results presented provide a guide for the strategic optimization of interphase design, showcasing tailorable tensile strengths which outperform any isotactic polypropylene carbon fiber composites previously reported in literature.

36 MATERIALS SCIENCE↗

DOE OSTI2020

Predictive multiscale modeling for Unidirectional Carbon Fiber Reinforced Polymers

This paper presents a predictive multiscale modeling scheme for Unidirectional (UD) Carbon Fiber Reinforced Polymers (CFRP). A bottom-up modeling procedure is discussed for predicting the performance of UD structures. UD material responses are computed from high fidelity Representative Volume Elements (RVEs). A data-driven Reduced Order Modeling approach compresses RVEs into Reduced Order Models so material responses can be computed in a concurrent fashion along with the structural level simulation. The approach presented in this paper is validated against experimental data and is believed to provide design guidance for future fiber reinforced polymers development.

36 MATERIALS SCIENCE↗

DOE OSTI2020

Development of high-early-strength fiber-reinforced self-compacting concrete

High-early-strength fiber-reinforced self-compacting concrete (FRSCC) is a practical material for the precast concrete industry to double its production capacity. In this study, we developed high-early-strength FRSCC mixtures that achieved a compressive and flexural strength of at least 24 MPa and 3.5 MPa, respectively, at 6 h, using commercially available components, typical mixing procedures, and air curing. A mix design process was developed based on the literature and experimental results. The effect of six different fiber reinforcements on the fresh and mechanical properties of concrete was analyzed. The cost limit was set at 523 USD/m 3 to make the concrete mix cost-effective.

36 MATERIALS SCIENCE↗

DOE OSTI2023

Microgrid energy scheduling under uncertain extreme weather: Adaptation from parallelized reinforcement learning agents

Microgrids are useful solutions for integrating renewable energy resources and providing seamless green electricity to minimize carbon footprint. In recent years, extreme weather events happened often worldwide and caused significant economic and societal losses. Such events bring uncertainties to the microgrid energy scheduling problems and increase the challenges of microgrid operation. Traditional optimization approaches suffer from the inaccuracy of the uncertain microgrid model and the unseen events. Existing reinforcement learning (RL) - based approaches are also hampered by the limited generalization and the increasing computational burden when stochastic formulations are required to accommodate the uncertainties. This paper proposes a new parallelized reinforcement learning (PRL) method based on the probabilistic events to handle the microgrid energy uncertainties. Specifically, several local learning agents are employed to interact with pertinent microgrid environments in a distributed manner and report outcomes to the global agent, which will optimize microgrid energy resources online during extreme events. The stochastic microgrid energy optimization problem is reformulated to include all possible scenarios with probabilities. The advantage estimate functions of learning agents are designed with a backward sweep to transfer the outcomes to the value function updating process. Two simulation studies, stochastic optimization and online testing, are performed to compare with several existing RL approaches. Results substantiate that the proposed PRL method can achieve up to 20% optimization performance improvement with 4 and 28 times less computation cost than Q-learning with experience replay and multi-agent Q-learning approaches, respectively.

24 POWER TRANSMISSION AND DISTRIBUTION↗

DOE OSTI2024

Dyn$\mathrm{AMO}$: Multi-agent reinforcement learning for dynamic anticipatory mesh optimization with applications to hyperbolic conservation laws

Here we introduce DynAMO, a reinforcement learning paradigm for Dynamic Anticipatory Mesh Optimization. Adaptive mesh refinement is an effective tool for optimizing computational cost and solution accuracy in numerical methods for partial differential equations. However, traditional adaptive mesh refinement approaches for time-dependent problems typically rely only on instantaneous error indicators to guide adaptivity. As a result, standard strategies often require frequent remeshing to maintain accuracy. In the DynAMO approach, multi-agent reinforcement learning is used to discover new local refinement policies that can anticipate and respond to future solution states by producing meshes that deliver more accurate solutions for longer time intervals. By applying DynAMO to discontinuous Galerkin methods for the linear advection and compressible Euler equations in two dimensions, we demonstrate that this new mesh refinement paradigm can outperform conventional threshold-based strategies while also generalizing to different mesh sizes, remeshing and simulation times, and initial conditions.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2013

Effect of re-melting on particle distribution and interface formation in SiC reinforced 2124Al matrix composite

The interface between metal matrix and ceramic reinforcement particles plays an important role in improving properties of the metal matrix composites. Hence, it is important to find out the interface structure of composite after re-melting. In the present investigation, the 2124Al matrix with 10 wt.% SiC particle reinforced composite was re-melted at 800 °C and 900 °C for 10 min followed by pouring into a permanent mould. The microstructures reveal that the SiC particles are distributed throughout the Al-matrix. The volume fraction of SiC particles varies from top to bottom of the composite plate and the difference increases with the decrease of re-melting temperature. The interfacial structure of re-melted 2124Al–10 wt.%SiC composite was investigated using scanning electron microscopy, an electron probe micro-analyzer, a scanning transmission electron detector fitted with scanning electron microscopy and an X-ray energy dispersive spectrometer. It is found that a thick layer of reaction product is formed at the interface of composite after re-melting. The experimental results show that the reaction products at the interface are associated with high concentration of Cu, Mg, Si and C. At re-melting temperature, liquid Al reacts with SiC to form Al{sub 4}C{sub 3} and Al–Si eutectic phase or elemental Si at the interface. High concentration of Si at the interface indicates that SiC is dissociated during re-melting. The X-ray energy dispersive spectrometer analyses confirm that Mg- and Cu-enrich phases are formed at the interface region. The Mg is segregated at the interface region and formed MgAl{sub 2}O{sub 4} in the presence of oxygen. The several elements identified at the interface region indicate that different types of interfaces are formed in between Al matrix and SiC particles. The Al–Si eutectic phase is formed around SiC particles during re-melting which restricts the SiC dissolution. - Highlights: • Re-melted composite shows homogeneous particle distribution • Thick reaction products are observed at the interface • Carbide is identified at interface due to SiC dissolution at high temperature • Reaction products are also Si enrich phase • Mg and Cu segregated and formed MgAl{sub 2}O{sub 4}, CuMgAl{sub 2} phase at grain boundary.

36 MATERIALS SCIENCE↗

DOE OSTI2018

Cold rolling texture evolution of TiB2 particle reinforced Al-based composites by Neutron Diffraction and EBSD analysis

Highlights: • Texture development of in-situ TiB{sub 2} particle reinforced Al matrix composites after cold rolling were characterized. • The sum of rolling texture volume fractions has been significantly reduced by the weak texture in particle deformation zone. • Recrystallization nuclei with the Cube and Rotated-Cube orientations were observed in the particle deformation zones. • Small TiB{sub 2} particles within Al grains kept the parallel relationship with surrounding matrix during the deformation. - Abstract: Texture development of particle-reinforced metal matrix materials (P-MMCs) has not been understood well, especially the effects of second-phase particles during the deformation. In this work, we have investigated the texture evolution of TiB{sub 2}/Al composites during cold rolling by Neutron Diffraction and EBSD analysis. Two forms of TiB{sub 2} particles influenced the texture evolution in different ways. The particle-clusters aggregated along grain boundaries led to a reduction of texture volume fraction; recrystallization nuclei with weak texture appeared around these particle-clusters. Small non-shearable TiB{sub 2} particles dispersed inside Al matrix contributed to strong Copper texture component at high strains; the individual particles rotated together with Al matrix during the deformation. These revealed effects of particles on the texture development in TiB{sub 2}/Al composites provide a reference for the texture control of P-MMCs.

36 MATERIALS SCIENCE↗

DOE OSTI2018

Ameliorated mechanical and thermal properties of SiC reinforced Al matrix composites through hybridizing carbon nanotubes

Highlights: • The carbon nanotubes were hybridized into SiC reinforced Al based composites. • Hybridization of CNTs leads to the uniform dispersion of SiC in the Al matrix. • Hybridization of CNTs enhances the strength and ductility of the composites. • CNTs can help to decrease the coefficient of thermal expansion of the composites. - Abstract: By hybridizing carbon nanotubes (CNTs) into SiC reinforced Al matrix composites, a novel composite was fabricated to achieve excellent mechanical properties and low coefficient of thermal expansion simultaneously. The study demonstrated that the CNTs can help to acquire the uniform dispersion of SiC particles in the Al matrix, because of their interaction during ball milling and hot rolling processes. In addition, the results revealed that such design optimizes both the mechanical and thermal properties, due to homogeneous microstructure as well as tight and uniform interfaces induced by the addition of CNTs. Strengthening mechanism and Sideridis's model were used to discuss the effects of the CNTs on the mechanical and thermal properties. This study opens up an unexplored avenue for simultaneous improvement of the mechanical and thermal properties of metal matrix composites.

36 MATERIALS SCIENCE↗

DOE OSTI2018

High elongation achieved by band-like distribution of reinforcements in aluminum matrix composites

Highlights: • The high elongation of composites with high reinforcement content has been achieved by band-like distribution of ABO. • The band-like distribution of ABO could lead to more homogenous strain distribution by scattering the damages. • The cracks played a key role in the strain non-localization process, but have negligible contribution to the tensile strain. • A model of the relationship between the ductility enhancement and the band-like distribution of ABO was established. • The residual stresses induced by band-like distribution of ABO were theoretically calculated and experimentally measured. - Abstract: Ductile aluminum matrix composites reinforced by Al{sub 18}B{sub 4}O{sub 33} whisker (ABO) with band-like distribution (Band-like Aluminum Matrix Composites, BAMCs) were fabricated by powder metallurgy and extrusion followed by annealing. The cracks tend to nucleate in the ABO-rich bands, but the band-like structure induces compressive residual stress then suppresses the crack propagation and improves the strain transferring ability in the BAMCs. The denser and smaller cracks formed in the BAMCs with higher ABO volume fraction during tensile test. These cracks play an important role in the strain non-localization process, but have negligible contribution to the tensile strain.

36 MATERIALS SCIENCE↗

DOE OSTI2017

Spall damage of a Ta particle-reinforced metallic glass matrix composite under high strain rate loading

We investigate deformation and damage of a Zr-based bulk metallic glass (BMG) and its Ta particle-reinforced composite (MGMC) under impact loading, as well as quasi-static tension for comparison. Yield strength, spall strength, and damage accumulation rate are obtained from free-surface velocity histories, and MGMC appears to be more damage-resistant. Scanning electron microscopy, electron back scattering diffraction and x-ray computed tomography, are utilized for characterizing microstructures, which show features consistent with macroscopic measurements. Different damage and fracture modes are observed for BMG and MGMC. Multiple well-defined spall planes are observed in BMG, while isolated and scattered cracking around reinforced particles dominates fracture of MGMC. Particle–matrix interface serves as the source and barrier to crack nucleation and propagation under both quasi-static and impact loading. Finally, deformation twinning and grain refinement play a key role in plastic deformation during shock loading but not in quasi-static loading. In addition, 3D cup-cone structures are resolved in BMG, but not in MGMC due to its heterogeneous stress field.

36 MATERIALS SCIENCE↗

DOE OSTI2021

Analytical models of the strength and ductility of CNT reinforced metal matrix nano composites under elevated temperatures

Carbon nanotubes (CNTs) can greatly enhance the strength of metal matrix composites while resulting in ductility loss. This strength-ductility tradeoff dilemma always confines the development of material design and real-life applications. Here in this work, a temperature dependent strengthening analytical model is proposed for CNT reinforced metal matrix composites which considers three common strengthening mechanisms: Orowan looping effect, thermal expansion mismatch effect, and load bearing effect. The proposed model can predict composite material strength with different volume fractions of CNTs and under different temperatures. Combining the strengthening model with the stress based modified Mohr-Coulomb (sMMC) ductile fracture model, a ductility analytical model is then derived. This ductility analytical model includes the influences of temperature, multi-axial stress loading conditions, as well as the aforementioned three strengthening mechanisms. A good agreement has been achieved between literature published experimental data and analytical predictions for both composite material strength and ductility loss. The proposed two analytical models can provide a straightforward way to study the strength-ductility relationship for CNT reinforced metal matrix composites over a wide range of temperatures and different stress states, and then provide guidance on new material design and processing.

36 MATERIALS SCIENCE↗

DOE OSTI2026

Effect of fiber sizing and glass fiber laminate hybridization on vibration damping and mechanical properties of banana fiber reinforced polypropylene composites

Modern automotive applications demand lightweight, multifunctional materials to reach mileage goals and natural fiber reinforced composites (NFRCs) are one of the classes of materials proposed as a solution. NFRCs exhibit good vibration damping properties and have low density, but are often limited by processing challenges, poor-fiber matrix compatibility and variable performance. Herein, we investigate non-woven wet-lay of comingled banana fiber (BF), recycled glass fiber (rGF), and polypropylene (PP) fibers to in situ sizing and preparation of composite feedstocks for compression molding. BF and rGF hybrids were prepared by stacking rGF layers during compression molding to produce composites with various fiber ratios. The effect of fiber content, in-situ sizing and ratio of BF to rGF on tensile, flexural and vibration damping performance are investigated. Key results are the significant increase in tensile strength by in situ sizing (40 % sized at 60 wt% BF) and in flexural modulus (+58 % sized at 60 wt% BF) and flexural strength (+41 % sized 60 wt% BF) compared to the unsized equivalent. For BF-rGF hybrid composites with40 wt% total fiber content, flexural strength and modulus were improved by 51 % and 231 % respectively for a 1:1 ratio BF:rGF compared to BF reinforced system. Lastly, identifying the cross-over point where damping and stiffness are optimized for a hybrid composite. These findings demonstrate that these composites can be used as alternative to synthetic fiber or mineral filled composites in automotive applications, particularly where weight reduction, vibration damping and stiffness are desired.

Banana fiber↗