Research

Space travel to planets and moons with a sensible atmosphere requires an atmospheric entry vehicle to deliver payloads safely from orbit to the surface. The entry vehicle generally has a blunt forebody to withstand heating during the high-speed entry phase. However, blunt-body vehicles become dynamically unstable once they slow down to supersonic and transonic speeds. The instabilities cause the angle-of-attack to oscillate, gain amplitude in time and diverge to a point where the vehicle tumbles, resulting in a catastrophic event. The physical mechanisms leading to the dynamic stability and its characteristics remain challenging after decades of meticulous work due to massive flow separation, complex wake flow, and unsteady pressure field of dramatically changing flight and flow conditions of the descending and decelerating vehicle. This proposed research aims to develop hybrid physics-data modeling approaches for space exploration. We focus on innovating a holistic physics-guided machine learning framework for characterizing the dynamic stability and performance of reentry vehicle systems. Our framework is, therefore, motivated to provide a trustworthy learning platform with enhanced model fusion, feature engineering, and symbolic regression capabilities. We will explore the feasibility of new learning approaches to elucidate new physical insights in describing vehicle stability and identify how to utilize multimodal resources extracted from experiments and high-fidelity simulations effectively.

Advances in artificial intelligence have led to a renaissance in learning and extracting patterns from complex data. Despite successes in other areas, applying machine learning techniques in the field of fluid mechanics is relatively new. Most efforts are primarily focused on finding parameters for existing turbulence models. This project explores a big data approach that learns from physical constraints without assuming any heuristics for underlying turbulence physics. Our overall research program will develop novel physics-reinforced data-driven approaches for geophysical turbulence. The research will also involve deep learning approaches that can discover closure models for complex multiscale systems. Research insights will facilitate the building of improved numerical weather prediction models and better parameterization strategies for DOE mission-relevant challenges.

Mathematical models are a fundamental tool for improving our knowledge of natural and industrial processes. Their use in practice depends on their reliability and efficiency. Reliability requires a fine-tuning of the model parameters and an accurate assessment of the sensitivity to noisy inputs. Efficiency is particularly critical in optimization problems, where the computational procedure identifies the best working conditions of a complex system. These requirements lead to solving many times models with millions or even billions of unknowns. This process may require days or weeks of computations on high-performance computing facilities. To mitigate these costs, we need new modeling strategies that allow model-runs in minutes to hours on local computing facilities (such as a laptop). Reduced order models (ROMs) are extremely low-dimensional approximations that can decrease the computational cost of current computational models by orders of magnitude. Having in mind biomedical and wind-engineering applications, this project proposes novel methods of model reduction. Data and numerical results from the expensive (or high-fidelity) models are combined with machine learning approaches, to obtain ROMs that attain both efficiency and accuracy at an unprecedented level. The new data-driven ROM framework will finally make possible the numerical simulation of aortic dissections, pediatric surgery, or wind farm optimization on a laptop in minutes, and aims at becoming a critical and trustworthy tool in decision-making processes.

To enable maximum energy utilization and grid flexibility, building energy use needs to be well understood. Comfort and ventilation systems and equipment represent 50% of building energy use (roughly 20% of total US energy use) and are therefore the most critical to target. The inability of the existing equipment models to accurately account for the building performance is a main limitation in achieving the set energy conservation goals. The actual building energy conservation is twice the predicted energy utilization for many residential and office building spaces. Additionally, the effect of varying load conditions and transient operation of the system is less understood, which influences grid flexibility. This project will provide the necessary transient building modeling tools and experimental datasets to empower the industry for equipment designing of next generation buildings. In this project, we will address these limitations by first generating the needed dynamic datasets of unitary equipment utilizing Hardware-In-the-Loop (HIL) techniques in OSU's state-of-the-art facilities. These datasets will then be used to create high-fidelity dynamic models. Using the high-fidelity model, in parallel, a reduced order model will be developed and the experimental data generated in this project will be used to validate the reduced-order model developed and quantify the effect of model order reduction.  

The Centres for Environment-friendly Energy Research (FME) carry out long-term research targeted towards renewable energy, energy efficiency, CCS and social science aspects of energy research. The centres selected for funding must demonstrate the potential for innovation and value creation. Research activities are carried out in close collaboration between research groups, trade and industry, and the public administration, and key tasks include international cooperation and researcher training. The centres are established for a period of maximum eight years (5 + 3). FME NorthWind will bring forward outstanding research and innovation to reduce the cost of wind energy, facilitate its sustainable development, create jobs and grow exports.  

Effective hole cleaning during the drilling process is one of the prerequisites to reducing the incurred economic and environmental cost. Current practice is mostly based on sophisticated physics-based calculations done before the operation starts, in some operations with real-time update during operations, and on human assessment of a limited number of measured parameters like for example trends in hook load when picking up and slacking off the drill string when making connections. Introduction of high bandwidth data transmission from sensors at many positions along the string, calls for methods that make full use of the increasing number of measured parameters to determine hole cleaning status more accurately and reliably. Accordingly, this project proposes to develop novel hybrid modelling approaches that will combine the interpretability, robust foundation and understanding of a physics-based modelling approach with the accuracy, efficiency and automatic pattern-identification capabilities of advanced machine learning and artificial intelligence algorithms for an efficient and improved monitoring of the hole cleaning process during drilling operations.

The project scope covers developing the necessary testing methods for the mixers, developing new mixers and air samplers, developing their performance, and as a final step, evaluate overall in-situ performance of the newly developed devices with coil tests of 1.5 ton, 3 ton, and 5 ton size. The objective of this project is to provide (i) design recommendations for measuring bulk air conditions and (ii) methods for validating the performance of a sampler and mixer (where a mixer can be utilized) combination that would provide the most accurate bulk temperature and humidity measurement at indoor air inlet and indoor air outlet.

Investigation of the effects of inlet duct design onto fan power, optimum static pressure measurement location, and air flow profile at exit of inlet duct. This project offers specific guidelines for the design of the inlet ductwork to reduce the design space towards inlet duct designs that are known to have little effect onto the equipment performance, as shown by comparable outlet flow profiles obtained from a validated CFD method and experiments.

Turbulence models are a key tool in understanding complex flow physics in many applications. In this project, we have explored the feasibility of a heuristics-free turbulence modeling framework, with the goal of developing robust and accurate closure models for the coarse-grained simulations of flows in massively separated turbulent flow regimes.