Publications

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Physics-constrained machine learning is emerging as an important topic in the field of machine learning for physics. One of the most significant advantages of incorporating physics constraints into machine learning methods is that the resulting model requires significantly less data to train. By incorporating physical rules into the machine learning formulation itself, the predictions are expected to be physically plausible. Gaussian process (GP) is perhaps one of the most common methods in machine learning for small datasets. In this paper, we investigate the possibility of constraining a GP formulation with monotonicity on three different material datasets, where one experimental and two computational datasets are used. The monotonic GP is compared against the regular GP, where a significant reduction in the posterior variance is observed. The monotonic GP is strictly monotonic in the interpolation regime, but in the extrapolation regime, the monotonic effect starts fading away as one goes beyond the training dataset. Imposing monotonicity on the GP comes at a small accuracy cost, compared to the regular GP. The monotonic GP is perhaps most useful in applications where data are scarce and noisy, and monotonicity is supported by strong physical evidence.

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Polymer concrete (PC) has been used to replace cement concrete when harsh service conditions exist. Polymers have a high carbon footprint when considering their life cycle analysis, and with increased climate change concerns and the need to reduce greenhouse gas emission, bio-based polymers could be used as a sustainable alternative binder to produce PC. This paper examines the development and characterization of a novel bio-polymer concrete (BPC) using bio-based polyurethane used as the binder in lieu of cement, modified with benzoic acid and carboxyl-functionalized multi-walled carbon nanotubes (MWCNTs). The mechanical performance, durability, microstructure, and chemical properties of BPC are investigated. Moreover, the effect of the addition of benzoic acid and MWCNTs on the properties of BPC is studied. The new BPC shows relatively low density, appreciable compressive strength between 20–30 MPa, good tensile strength of 4 MPa, and excellent durability resistance against aggressive environments. The new BPC has a low carbon footprint, 50% lower than ordinary Portland cement concrete, and can provide a sustainable concrete alternative in infrastructural applications.

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Marine energy generation technologies such as wave and tidal power have great potential in meeting the need for renewable energy in the years ahead. Yet, many challenges remain associated with marine-based systems because of the corrosive environment. Conventional materials like metals are subject to rapid corrosive breakdown, crippling the lifespan of structures in such environments. Fiber-reinforced polymer composites offer an appealing alternative in their strength and corrosion resistance, but can experience degradation of mechanical properties as a result of moisture absorption. An investigation is conducted to test the application of a technique for micromechanical analysis of composites, known as multicontinuum theory and demonstrated in past works, as a mechanism for predicting the effects of prolonged moisture absorption on the performance of fiber-reinforced composites. Experimental tensile tests are performed on composite coupons with and without prolonged exposure to a salt water solution to obtain stiffness and strength properties. Multicontinuum theory is applied in conjunction with micromechanical modeling to deduce the effects of moisture absorption on the behavior of constituent materials within the composites. The results are consistent with experimental observations when guided by known mechanisms and trends from previous studies, indicating multicontinuum theory as a potentially effective tool in predicting the long-term performance of composites in marine environments.

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Laser propagation experiments using four beams of the National Ignition Facility to deliver up to 35 kJ of laser energy at 351 nm laser wavelength to heat magnetized liner inertial fusion-scale (1 cm-long), hydrocarbon-filled gas pipe targets to ∼keV electron temperatures have demonstrated energy coupling >20 kJ with essentially no backscatter in 15% critical electron density gas fills with 0-19 T applied axial magnetic fields. The energy coupling is also investigated for an electron density of 11.5% critical and for applied field strengths up to 24 T at both densities. This spans a range of Hall parameters 0 < ω c e τ e i ≲2, where a Hall parameter of 0.5 is expected to reduce electron thermal conduction across the field lines by a factor of 4-5 for the conditions of these experiments. At sufficiently high applied field strength (and therefore Hall parameter), the measured laser propagation speed through the targets increases in the measurements, consistent with reduced perpendicular electron thermal transport; this reduces the coupled energy to the target once the laser burns through the gas pipe. The results compare well with a 1D analytic propagation model for inverse Bremsstrahlung absorption.

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