Design and Prototyping of a Lightweight Wave Energy Converter for Marine Energy Harvesting and Self-powered Sensing
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An automobile vehicle crankshaft including a crankshaft casting of a nodular iron. The crankshaft casting includes multiple main journals coaxially aligned on a common crankshaft casting axis. Multiple crankpin journals are fixedly connected to the main journals by individual webs. Multiple lightening holes have individual ones of the multiple lightening holes integrally formed within individual ones of the crankpin journals during casting. A bubble space is located proximate to a mid-portion of selected ones of the multiple lightening holes of the crankpin journals. The bubble space locally increases a passage size of the selected ones of the multiple lightening holes and reduces a mass of the individual ones of the crankpin journals.
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Here, in the transportation industry, reducing component weight is an effective strategy to improve fuel efficiency and lower emissions. Martensitic AISI 52100 steel is commonly used in drivetrain bearing components due to its high strength and excellent tribological performance. To achieve lightweighting without sacrificing mechanical properties and tribological behavior, this study explored the 52100 steel alloy modified by introducing nominally 5 wt% aluminum. The addition of aluminum led to an approximately 6% density reduction, however, it resulted in reduced hardness, strength and wear resistance, in part due to stabilization of a substantial amount of ferrite, as suggested by microstructural examination. Introducing 0.15 wt% additional carbon reduced the ferrite and demonstrated feasibility of mitigating the mechanical and tribological degradation caused by aluminum. This case study provides fundamental insights into the balance between lightweighting and mechanical/tribological performance for steel alloys, serving as a reference for further development of lightweight bearing steels.
The demand for high-performance lightweight optics has driven interest in silicon carbide (SiC) due to its exceptional thermal stability, hardness, and strength-to-weight ratio. This study investigates the potential of robocasting, an additive manufacturing process, as a viable method for producing lightweighted SiC components for optical applications. Four samples with varied starting powder phases (α and β) and sintering conditions were fabricated and evaluated. Post-sintering surface and form were assessed using coherence scanning interferometry (CSI) and coordinate measuring machine (CMM) techniques. A three-stage grinding process was applied to each sample, with surface roughness assessed at each stage. Results demonstrate that samples with predominantly α-phase SiC and smaller particle sizes achieved superior surface finish, particularly sample D2-α-2135 °C, which displayed the lowest post-grinding Sq value of 0.178 µm. The analysis also indicated no significant print-through effect from the lightweighting structure, or print artifacts, at this stage of grinding. However, β-phase samples showed poorer grindability, increased surface roughness, and pitting. These findings suggest that phase composition and particle size are critical for achieving the desired surface quality in robocast SiC optics. Future work will incorporate additional samples and finer grinding wheels to refine surface quality further, supporting the development of SiC for high-precision optical applications.
Extending the concept of complex concentrated alloys (CCAs) to the refractory alloys (solidus temperature over 2000 °C) space potentially facilitates the design of lightweight structural alloys with service temperatures that exceed those of Ni and Co‐based alloys. However, the room and elevated temperature tensile properties of the current refractory‐CCAs (R‐CCAs) are inferior to those of the Ni/Co‐based alloys. Furthermore, the manufacturing scalability of R‐CCAs remains challenging, in that cracks are prevalent in all R‐CCAs when processed using near‐net shape manufacturing processes, such as fusion‐based additive manufacturing (F‐BAM). Still, mechanisms governing the poor F‐BAM processability of R‐CCAs remain unexplored. Here, to this end, this work unveils the atomistic mechanisms underlying F‐BAM process‐induced cracking in a NbTiTaMoHfZrC R‐CCA. The implications of light elements’ presence for intrinsic ductility and grain boundary cohesion, and subsequently for F‐BAM processability and mechanical behavior, are revealed. Leveraging the insights, we accomplish what is, to the best of the knowledge, the first instance of crack‐free F‐BAM processing of any R‐CCA. Additionally, the R‐CCA exhibits over 20% tensile ductility and ≈160 MPa tensile yield strength at 1200 °C. In addition to facilitating the design of lightweight R‐CCAs, findings enable scalable manufacturing of these ultra‐high temperature alloys for structural applications.
There is a current need for new aluminum alloy design strategies to target applications requiring high strength and conductivity with reductions in mass. A new lightweight Al-2Ni-0.5Zr (wt. %) conductor alloy was fabricated using laser powder bed fusion. A design of experiments probed the alloy's solidification cracking susceptibility. It was observed that solidification cracking was generally reduced with fast scan speeds, above 1500 mm/s, and smaller hatch spacings. The different cooling rates throughout the melt pool produced a heterogeneous distribution of cellular and equiaxed Al 3 Ni precipitates in the as-printed alloy. Additionally, the rapid solidification characteristic of laser powder bed fusion created a super-saturated Zr solid solution. An aging heat treatment at 375 °C for 24 h imparted strengthening through the precipitation of L1 2 -Al 3 Zr nanoprecipitates, which counteracted the softening caused by the fragmentation and coarsening of Al 3 Ni precipitates. The yield strength increased from 138 MPa in the as-printed condition to 168 MPa after aging, while the ductility remained constant at ∼21%. The aging treatment simultaneously increased the electrical conductivity from 40.8% IACS (International Annealed Copper Standard) to 53.5% IACS. Modeling of the strengthening mechanisms and electrical conductivity contributions rationalized the simultaneous increase in strength and conductivity upon aging. Furthermore, the strengthening efficacy of the Al 3 Ni and L1 2 -Al 3 Zr precipitates, combined with the low Ni and Zr solubility in the FCC Al matrix, facilitated both high strength and electrical conductivity. Overall, the combination of strength and electrical conductivity positions this alloy as a suitable choice for additively manufactured lightweight conductors.
We present JACC (Julia for Accelerators), the first high-level, and performance-portable model for the just-in-time and LLVM-based Julia language. JACC provides a unified and lightweight front end across different back ends available in Julia, enabling the same Julia code to run efficiently on many HPC CPU and GPU targets. We evaluated the performance of JACC for common HPC kernels as well as for the most computationally demanding kernels used in applications, HPCCG, a supercomputing benchmark test for sparse domains, and HARVEY, a blood flow simulator to assist in the diagnosis and treatment of patients suffering from vascular diseases. We carried out the performance analysis on the most advanced US DOE supercomputers: Aurora, Frontier, and Perlmutter. Overall, we show that JACC has a negligible overhead versus vendor-specific solutions, reporting GPU speedups with no extra cost to programmability.
Existing frameworks to forecast time-series photovoltaic (PV) output power and consumer load for microgrid operations and controls assume a near-continuous availability of real-time input features from the field assets such as PV inverters, energy meters, and weather station. These incoming data points are used to periodically retrain models and update forecast snapshots over a moving horizon window, be it one hour-ahead, one-day ahead, or one-week ahead. However, such frameworks are not resilient to disruptions in data availability caused by losses in communications between the field sensors and data loggers. Hence, there is a need for programs that assume no availability of real-time microgrid asset data and still make reliable forecasts that can be used for decision-making. Such programs would be apt to function in extreme weather events such as hurricanes and would use lightweight recursive time-series models to independently forecast solar irradiance and ambient temperature, then compute PV power from those forecasts, as well as independently forecast consumer load. The codebase performs forecasting for the scenario of when the microgrid does not have a reliable access to forecasts or real-time observations of solar irradiance (I) and ambient temperature (AT) and load (Load) to be able to adequately forecast, in real-time, the PV power production or a business' load. In this case, using historical values of PV power and load, a univariate forecasting of generation and consumption are respectively made. The use-case in particular has two sub-scenarios: one, a normal 7-day ahead forecast where the unavailability of real-time data is assumed due to infrastructure issues such as loss of communication or sensor maintenance or service downtimes. Whereas a hurricane-caused unavailability of real-time data requires a second model trained specifically on historical hurricane days to be able to capture the extreme day behavior of generation in particular, and load if applicable. A gradient boosted regression tree comprises an ensemble of additive models that map between the input of historical values (be it irradiance, temperature, or load) and their corresponding output forecasts of a given horizon such that the individual learner predictions are summed up over the total number of such learners in the ensemble to produce an aggregate forecast. A weighting mechanism is applied to the training data in each iteration, where actual and forecast values are compared to penalize incorrect forecasts by increasing the weight and reducing it to reward correct forecasts. The code's benefits are that it: (a) accounts for a contingency where communication loss renders newly measured real-time data unavailable for model tuning and snapshot updates; (b) presents blind forecasting that recursively determines the next time-step value in a horizon using the forecast of the same attribute from a prior step; and (c) employs lightweight models that, once trained, can reliably generalize for different horizons, which make them suitable for enhancing the resilience of field microgrids prone to extreme events that encounter disruptions to data availability.
Metasurfaces offer a compact platform for optical vapor sensing, but their practical deployment has been limited by weak evanescent light–matter interactions and reliance on spectrally resolved instrumentation. Here, we report porous silicon (pSi) metasurfaces for spectrometer-free quantitative detection of volatile organic compounds (VOCs) with strongly enhanced light–matter interaction. The engineered porosity increases sensitivity by >100× relative to non-porous dielectric metasurfaces, enabling limits of detection of 1.65 ppm for methanol and 9.1 ppm for ethanol across a broad dynamic range (<10 ppm to >103 ppm). Imaging-based readout provides a lightweight, spectrometer-free pathway for real-time quantitative sensing. Beyond quantitative detection, the mesoporous architecture introduces adsorption–desorption kinetics as an additional information channel. Analysis of the resulting spatiotemporal signatures enables kinetic fingerprinting without reliance on infrared spectral features or surface functionalization, and a lightweight machine-learning classifier differentiates acetone, methanol, ethanol, and isopropanol with 91.6% accuracy. These results establish porous metasurfaces as spatiotemporal sensing elements that couple quantitative vapor detection with kinetic fingerprinting through real-time dynamical responses, enabling low-cost, high-performance optical sensors.
Modern multimaterial vehicles require joining of various lightweight materials, such as aluminum (Al) and magnesium (Mg) alloys and carbon fiber reinforced polymers (CFRP), with advanced high-strength steels together to form a high-performance and lightweight body structure. A variety of joining methodologies (e.g., resistance spot welding, adhesive bonding, linear fusion welding, hemming, clinching, bolting, riveting) have been attempted by the automotive industry to join different materials. Often, these joining techniques are limited to only certain material combinations. For capital and operational cost, automobile original equipment manufacturers need to limit the number of joining technologies implemented on an assembly line.
This report summarizes the development of a new class of recyclable multi-functional composite materials for production of lightweight smart structures and surfaces. Functional high stiffness conductive composites were processed using molding methods that integrated continuous fiber and additively manufactured features. Methods for integration of sensing functionality and controls were also developed to reduce system cost while providing a new capability for structural health monitoring. This new class of composites is applicable to a broad range of vehicle interior, exterior and battery enclosure systems. By way of demonstration, a vehicle instrument panel cross car beam was developed that provided a 38% mass savings compared to steel while maintaining a cost penalty competitive to alternate lightweight material solutions. These technologies were validated for implementation by a uniquely qualified project team comprising a US automotive OEM, Tier 1 and Tier 2 supplier, with key contributions from Oak Ridge National Lab, Purdue University and Michigan State University.
The pursuit of batteries capable of extreme fast charging (XFC), that also satisfy high energy and safety criteria, poses a significant challenge to current lithium-ion battery technologies. Additionally, the increasing demand for aluminum (Al) and copper (Cu) in electrification, and vehicle lightweighting is driving these metals towards near-critical status in the medium term. This study introduced metalized polymer films by depositing an Al or Cu thin layer onto two sides of a polyethylene terephthalate (PET) film – named mPET/Al and mPET/Cu, as lightweight, cost-effective alternatives to traditional metal current collectors in LIBs.
This document summarizes the progress of VTO Materials R&D projects supported during the fiscal year 2023. The Propulsion Materials portfolio is closely aligned with other VTO subprograms to identify critical materials needs for next-generation high-efficiency powertrains for both heavy- and light-duty vehicles. The Lightweight Materials portfolio works closely with industry through the U.S. DRIVE Partnership to understand light-duty vehicle structural weight-reduction goals and identify technical challenges that prevent the deployment of lightweight materials.
Westwood Aerogel investigated the reproducible production of an ambiently or electromagnetic spectrum assisted drying of extremely porous aerogel materials. Traditional aerogel processing is limited by low throughput and scalability due to supercritical drying. Westwood Aerogel’s advanced manufacturing process enables commercialization and deployment at scale, making aerogel materials more accessible to a range of industries and applications. We focused on shifting from the lab scale environment to manufacturing prototypes. The initial manufacturing model is based on the float glass process, which is the industry standard for glass window production, and the paper drying process. By incorporating an ambient drying process a continuous line model can be used from start to finish. The project team worked to develop a continuous manufacturing system to scale-up and reduce costs while maintaining the lightweight and insulative properties of aerospace-grade aerogel. While aerogels are extremely insulative, lightweight, and thin materials (making them ideal insulators across application spaces), high costs and stagnant innovation have hampered their wider adoption. Traditional manufacturing of aerogel requires supercritical extraction of pore fluid to obtain highly insulating materials, which is an energy intensive batch process and limits aerogel production output. Westwood Aerogel’s proprietary ambient drying process enables the continuous production of highly insulative aerogel materials without the use of supercritical extraction.
Thermoforming of short-fiber reinforced thermoplastic sheets offers a viable pathway for producing lightweight composite components; however, inherent anisotropy in fiber-reinforced sheets can limit structural performance under multidirectional loading. In this work, short carbon fiber, glass fiber, and hybrid fiber–reinforced PETG sheets were evaluated as candidate feedstock materials for thermoforming, with flexural and tensile testing performed both along the primary fiber direction and in the off-axis orientation to establish baseline stiffness, strength, and anisotropy. As expected, short carbon fiber PETG exhibited the highest stiffness and strength in the primary fiber direction, while all systems showed reduced performance in the off-axis direction. This off-axis performance reduction provides clear justification for the use of additive reinforcement when such thermoformed sheets are intended for structural applications. The intended manufacturing sequence involves thermoforming the reinforced sheet first, followed by the application of additively manufactured lattice reinforcement; therefore, the reinforcement strategy does not impose limitations on sheet formability during thermoforming. Post-forming lattice reinforcement significantly reduced load-normalized displacement by approximately 95–99% relative to non-reinforced sheets and improved weight-normalized stiffness by ~70%. These findings demonstrate that geometry-driven additive reinforcement can effectively compensate for off-axis property reductions in thermoformed PETG composites, enabling enhanced multidirectional structural performance without compromising manufacturability.
Pultrusion is a manufacturing process used to produce fiber-reinforced polymer composites with excellent mechanical, thermal, and chemical properties. The resulting materials are lightweight, durable, and corrosion-resistant, making them valuable in aerospace, automotive, construction, and energy sectors. However, conventional thermoset composites remain difficult to recycle due to their infusible and insoluble cross-linked structure. This review explores integrating vitrimer technology a novel class of recyclable thermosets with dynamic covalent adaptive networks into the pultrusion process. As only limited studies have directly reported vitrimer pultrusion to date, this review provides a forward-looking perspective, highlighting fundamental principles, challenges, and opportunities that can guide future development of recyclable high-performance composites. Vitrimers combine the mechanical strength (tensile strength and modulus) of thermosets with the reprocessability and reshaping of thermoplastics through dynamic bond exchange mechanisms. These polymers offer high-temperature reprocessability, self-healing, and closed-loop recyclability, where recycling efficiency can be evaluated by the recovery yield retention of mechanical properties and reuse cycles meeting the demand for sustainable manufacturing. Key aspects discussed include resin formulation, fiber impregnation, curing cycles, and die design for vitrimer systems. The temperature-dependent bond exchange reactions present challenges in achieving optimal curing and strong fiber–matrix adhesion. Recent studies indicate that vitrimer-based composites can maintain structural integrity while enabling recycling and repair, with mechanical performance such as flexural and tensile strength comparable to conventional composites. Incorporating vitrimer materials into pultrusion could enable high-performance, lightweight products for a circular economy. The remaining challenges include optimizing curing kinetics, improving interfacial adhesion, and scaling production for widespread industrial adoption.