Engineering topics

Cousins, Dylan S.

Publications and source records attributed to Cousins, Dylan S..

6 records

DOE OSTI2024

Empirical Characterization and Modeling of Cohesive – to – Adhesive Shear Fracture Mode Transition due to Increased Adhesive Layer Thicknesses of Fiber Reinforced Composite Single – Lap Joints

Here, to ensure a strong adhesive bond, most standards and adhesive manufacturers specify a maximum adhesive gap of 1 mm when bonding fiber reinforced composite structures. In manufacturing large components, such as joining two halves of wind turbine blades, meeting this gap tolerance specification is impractical; gaps larger than 10 mm are common in large adhesively bonded composite structures using state-of-the-art manufacturing techniques. Currently, there is a lack of fundamental understanding of the failure mechanics of adhesive gaps larger than 3 mm. To create such understanding, glass fiber - acrylic thermoplastic composite panels bonded using different epoxy adhesives within single-lap joint samples with adhesive thicknesses of 0.1 mm, 0.3 mm, 1 mm, 3 mm, 5 mm, and 10 mm were sheared to failure. A transition from cohesive to adhesive failure was observed to occur about 1 mm to 3 mm joint thicknesses. Plotting the shear stress normalized by the ratio of the joint width to thickness as a function of the joint thickness normalized by the joint length is shown to result in the ability to fit simple empirically derived models of the cohesive-to-adhesive failure transition, regardless of the adhesive. Furthermore, using these normalized variables, all the observed cohesively failed specimens collapse to a single master curve, as do the adhesively failed specimens.

36 MATERIALS SCIENCE↗

DOE OSTI2023

NMR relaxometry characterization of water adsorption in corn stover anatomical fractions

Nuclear magnetic resonance (NMR) relaxometry is applied to provide direct measurement of water adsorption in anatomical fractions of corn stover. NMR transverse T 2 relaxation time distribution measurements indicate multiple water populations, which vary with anatomical fraction and water adsorption. Measured T 2 data are used to calculate thermodynamic properties of Brunauer-Emmet-Teller adsorption theory using a model to estimate mono and bilayer relaxation. T 2 data are used directly to determine rotational diffusion correlation times indicating adsorption interaction strength. T 1 -T 2 longitudinal-transverse relaxation time correlation measurements quantify differences in the molecular level structural order of the adsorbate surface water as a function of water activity, i.e. relative humidity or water vapor partial pressure. The T 1 /T 2 ratio provides a measure of the surface energy related to the adsorption strength and surface diffusive mobility of the water adsorbate and differentiates the anatomical fractions. Furthermore, the results indicate that direct measurement of NMR relaxation times can be used to characterize corn stover biomass water adsorption, data relevant to processing and handling considerations.

09 BIOMASS FUELS↗

DOE OSTI2023

Infusible Thermoplastic Composites for Wind Turbine Blade Manufacturing: Fatigue Life of Thermoplastic Laminates under Ambient and Low-Temperature Conditions

Traditionally, thermoset resins such as polyesters (PE) and epoxies are used as the polymer matrix for construction of wind turbine blades. However, concern about their end-of-life treatment garners interest to use thermoplastics for increased recyclability. However, the high viscosity of molten thermoplastics inhibits their use in manufacturing wind turbine blades with injection or compression molding. A recently developed, infusible, reactive thermoplastic resin overcomes this technological barrier. Toward verifying that this recyclable resin is suitable for use in wind turbine blades, a dataset of R?=?0.1 and R?=?10 fatigue data for glass fiber-reinforced acrylic composites is provided and equal fatigue life to industry standard epoxy and unsaturated PE resin systems is demonstrated. Specifically, R?=?0.1 fatigue data for acrylic composites at room temperature and -30?degrees C for verification of low-temperature performance are tabulated. To elucidate failure mechanisms, in situ mechanical testing with X-ray computed tomography demonstrates that damage accumulation occurs by crack propagation along the fiber-matrix interface under cyclic loading. Infrared (IR) thermography predicts failure points in composites specimens with porosity defects introduced from nonideal manufacturing processes. Furthermore, these manufacturing defects are shown to compromise the fatigue life of the acrylic laminates by an order of magnitude.

effect of defects↗

DOE OSTI2022

Particle classification by image analysis improves understanding of corn stover degradation mechanisms during deconstruction

Biomass feedstock heterogeneity is a principal roadblock to implementation of the biorefinery concept. Even within an identical cultivar of corn stover, different bales contain not only varying abundance moisture, ash, glucan, and other chemical compounds, but also varying abundance of tissue anatomies (e.g., leaf, husk, cob, or stalk). These different anatomical components not only differ in their response to pretreatment and enzymatic hydrolysis to glucose, but also vary in their mechanical and conveyance properties. Although this heterogeneous nature of corn stover feedstock has been identified as a challenge, a fundamental knowledge gap of how these tissues behave during biorefining processing remains. Here, we demonstrate the use of a commercial fiber image analyzer typically used for wood fiber characterization to monitor the particle size and shapes of non-woody feedstock during milling, pretreatment, and hydrolysis. Additionally, we present novel use of Gaussian process classification to distinguish bundle, parenchyma, and fiber particles to an accuracy of 96.4%. Quantitative probability distribution plots for characteristics such as length and roundness allow elucidation of particle morphology as pretreatment and enzymatic hydrolysis progress. In both stalk pith and stalk rind, particles peel into individual cells whose walls are subsequently fragmented during enzymatic hydrolysis.

09 BIOMASS FUELS↗

DOE OSTI2022

Predictive models enhance feedstock quality of corn stover via air classification

Feedstock heterogeneity is a fundamental obstacle to cost-competitive biobased products. Agricultural products like corn stover have anatomical components that vary in their chemical composition, mechanical properties, structure, and response to chemical and biological treatments. A technique that can enrich streams in select anatomical fractions would allow a tailored deconstruction approach to increase overall process efficiency. Air classification can be leveraged for such refining, however, fundamental characterization and understanding of the particle properties that underly the physics of air classification are only modestly documented. Here, we determine fundamental particle properties including mass-to-area ratio, drag coefficient, and partition velocity that describe how anatomical tissues of corn stover behave during air classification. In this work, mass-to-area ratios of anatomical tissues vary by nearly two orders of magnitude from 2.3 mg/mm 2 for cob to 0.04 mg/mm 2 for leaf. Drag coefficients of longer, fibrous materials (i.e., rind, husk, and sheath) are shown to correlate with particle area (p-value < 0.001) whereas granular tissues (i.e., cob, pith, and leaf) correlate better with mass-to-area ratio (p-values < 0.001). When compared to experimental observations, a simulated two-stage air classification and size reduction scenario predicts the overall partitioning of anatomical tissues within 15% for pith, husk, rind, and cob tissues. The model predicts an air-classified fraction preferentially enriched in cob (purity = 20%), rind (purity = 74%), and pith (purity = 4.5%) with a mass yield of 47%. Empirical relations for these properties can be used to predict the partitioning of corn stover during air classification based on anatomical type and size.

09 BIOMASS FUELS↗

DOE OSTI2022

Near-Infrared Spectroscopy can Predict Anatomical Abundance in Corn Stover

Feedstock heterogeneity is a key challenge impacting the deconstruction and conversion of herbaceous lignocellulosic biomass to biobased fuels, chemicals, and materials. Upstream processing to homogenize biomass feedstock streams into their anatomical components via air classification allows for a more tailored approach to subsequent mechanical and chemical processing. Here, we show that differing corn stover anatomical tissues respond differently to pretreatment and enzymatic hydrolysis and therefore, a one-size-fits-all approach to chemical processing biomass is inappropriate. To inform on-line downstream processing, a robust and high-throughput analytical technique is needed to quantitatively characterize the separated biomass. Predictive correlation of near-infrared spectra to biomass chemical composition is such a technique. Here, we demonstrate the capability of models developed using an “off-the-shelf,” industrially relevant spectrometer with limited spectral range to make strong predictions of both cell wall chemical composition and the relative abundance of anatomical components of the corn stover, the latter for the first time ever. Gaussian process regression (GPR) yields stronger correlations (average R 2 v = 88% for chemical composition and 95% for anatomical relative abundance) than the more commonly used partial least squares (PLS) regression (average R 2 v = 84% for chemical composition and 92% for anatomical relative abundance). In nearly all cases, both GPR and PLS outperform models generated using neural networks. These results highlight the potential for coupling NIRS with predictive models based on GPR due to the potential to yield more robust correlations.

09 BIOMASS FUELS↗