Engineering topics

Schmidt, David P.

Publications and source records attributed to Schmidt, David P..

4 records

DOE OSTI2024

Martensitic transformation induced strength-ductility synergy in additively manufactured maraging 250 steel by thermal history engineering

Maraging steels are known for their exceptional strength but suffer from limited work hardening and ductility. Here, in this study, we report an intermittent printing strategy to tailor the microstructure and mechanical properties of maraging 250 steel via tuning the thermal history during wire-arc directed energy deposition. By introducing a dwell time between adjacent layers, the maraging 250 steel is cooled below the martensite start temperature, triggering thermally-driven martensitic transformation during the printing process. Thermal cycling during subsequent layer deposition results in the formation of reverted austenite which shows a refined microstructure and induces elemental segregation between martensite and reverted austenite. The Ni enrichment in the austenite promotes stabilization of the reverted austenite upon cooling to room temperature. The reverted austenite is metastable during deformation, leading to strain-induced martensitic transformation under loading. Specifically, a 3 min interlayer dwell time produces a maraging 250 steel with approximately 8% reverted austenite, resulting in improved work hardening via martensitic transformation induced plasticity during deformation. Meanwhile, the higher cooling rate and refined prior austenite grains lead to substantially refined martensitic grains (by approximately fivefold) together with an increased dislocation density. With 3 min interlayer dwell time, the yield strength of the printed maraging 250 steel increases from 836 MPa to 990 MPa, and the uniform elongation is doubled from 3.2% to 6.5%. This intermittent deposition strategy demonstrates the potential to tune the microstructure of maraging steels for achieving strength-ductility synergy by engineering the thermal history during additive manufacturing.

Additive manufacturing↗

DOE OSTI2022

Validation and parameterization of a novel physics-constrained neural dynamics model applied to turbulent fluid flow

We report, in fluid physics, data-driven models to enhance or accelerate time to solution are becoming increasingly popular for many application domains, such as alternatives to turbulence closures, system surrogates, or for new physics discovery. In the context of reduced order models of high-dimensional time-dependent fluid systems, machine learning methods grant the benefit of automated learning from data, but the burden of a model lies on its reduced-order representation of both the fluid state and physical dynamics. In this work, we build a physics-constrained, data-driven reduced order model for Navier–Stokes equations to approximate spatiotemporal fluid dynamics in the canonical case of isotropic turbulence in a triply periodic box. The model design choices mimic numerical and physical constraints by, for example, implicitly enforcing the incompressibility constraint and utilizing continuous neural ordinary differential equations for tracking the evolution of the governing differential equation. We demonstrate this technique on a three-dimensional, moderate Reynolds number turbulent fluid flow. In assessing the statistical quality and characteristics of the machine-learned model through rigorous diagnostic tests, we find that our model is capable of reconstructing the dynamics of the flow over large integral timescales, favoring accuracy at the larger length scales. More significantly, comprehensive diagnostics suggest that physically interpretable model parameters, corresponding to the representations of the fluid state and dynamics, have attributable and quantifiable impact on the quality of the model predictions and computational complexity.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗

DOE OSTI2021

Floating platform effects on power generation in spar and semisubmersible wind turbines

The design and financing of commercial-scale floating offshore wind projects require a better understanding of how power generation differs between newer floating turbines and well-established fixed-bottom turbines. In floating turbines, platform mobility causes additional rotor motion that can change the time-averaged power generation. In this work, OpenFAST simulations examine the power generated by the National Renewable Energy Laboratory's 5-MW reference turbine mounted on the OC3-UMaine spar and OC4-DeepCWind semisubmersible floating platforms, subjected to extreme irregular waves and below-rated turbulent inflow wind from large-eddy simulations of a neutral atmospheric boundary layer. For these below-rated conditions, average power generation in floating turbines is most affected by two types of turbine displacements: an average rotor pitch angle that reduces power, caused by platform pitch; and rotor motion upwind-downwind that increases power, caused by platform surge and pitch. The relative balance between these two effects determines whether a floating platform causes power gains or losses compared to a fixed-bottom turbine; for example, the spar creates modest (3.1%–4.5%) power gains, whereas the semisubmersible creates insignificant (0.1%–0.2%) power gains for the simulated conditions. Furthermore, platform surge and pitch motions must be analyzed concurrently to fully capture power generation in floating turbines, which is not yet universal practice. Finally, a simple analytical model for predicting average power in floating turbines under below-rated wind speeds is proposed, incorporating effects from both the time-averaged pitch displacement and the dynamic upwind-downwind displacements.

17 WIND ENERGY↗

DOE OSTI2020

Collaborative investigation of the internal flow and near-nozzle flow of an eight-hole gasoline injector (Engine Combustion Network Spray G)

The internal details of fuel injectors have a profound impact on the emissions from gasoline direct injection engines. However, the impact of injector design features is not currently understood, due to the difficulty in observing and modeling internal injector flows. Gasoline direct injection flows involve moving geometry, flash boiling, and high levels of turbulent two-phase mixing. In order to better simulate these injectors, five different modeling approaches have been employed to study the engine combustion network Spray G injector. Here these simulation results have been compared to experimental measurements obtained, among other techniques, with X-ray diagnostics, allowing the predictions to be evaluated and critiqued. The ability of the models to predict mass flow rate through the injector is confirmed, but other features of the predictions vary in their accuracy. The prediction of plume width and fuel mass distribution varies widely, with volume-of-fluid tending to overly concentrate the fuel. All the simulations, however, seem to struggle with predicting fuel dispersion and by inference, jet velocity. This shortcoming of the predictions suggests a need to improve Eulerian modeling of dense fuel jets.

ECN↗