Jean-Pierre Delplanque

We present a multiscale simulation framework that couples the finite-element method with molecular dynamics. Bypassing traditional equations of state (EOS) by using in-line atomistic simulations, the method offers the advantage of incorporating detailed microscale physics not easily represented with coarse-grained models. Coupling consistency with the continuum code is ensured through the use of lifting and restriction operators, in line with heterogeneous multiscale methods. The concurrent continuum-atomistic framework is validated through comparison with experimental results and conventional EOS models, and demonstrated in a shock-driven hydrodynamic flow simulation under extreme conditions. We further evaluate the framework's usability by comparing it to state-of-the-art EOS models of deuterium. A computational performance study reveals that the atomistic EOS evaluation is a feasible alternative to conventional approaches, and demonstrates a weak scaling of 99% efficiency. These results highlight the framework's potential for large-scale multiscale modeling across a broad range of materials and conditions.

The focus of this paper is the physics of supercritical water film boiling through the simulation of natural convection of supercritical water in a rectangular cavity. It has been widely believed that above a fluid's critical point, only one phase exists. However, recent research has indicated the existence of distinct gas-like and liquid-like regions separated by the Widom line, which is the locus of the maxima of the specific heat. Along the Widom line, density decreases sixfold, viscosity drops by a factor of two, while specific heat spikes by an order of magnitude. These variations, specifically in density and viscosity, produce a thick pseudo-interface and flow dynamics behavior akin to film-boiling, denoted here as pseudo-two-phase flow dynamics. When the cavity is heated from the bottom, a pseudo-film of gas-like fluid is formed at the heated wall, and the flow complexity evolves, characterized by the formation of multiple vortices. Results demonstrate the ability of modern conjugate heat transfer computational fluid dynamics to capture the pseudo two-phase regime, with the formation of a pseudo-film at the heated wall. Further, the boiling curve is studied, showing an increase in heat flux with an increase in the temperature differential between the wall and the pseudo-critical temperature. Results also show a previously uncharacterized instability, where the convection cells in the cavity oscillate and can flip directions under certain conditions.

Ramp-wave dynamic-compression experiments are used to examine quasi-isentropic loading paths in materials. The gradual and continuous increase in pressure created by ramp waves make these types of experiments ideal for studying nonequilibrium material behavior, such as solidification kinetics. In ramp-wave compression experiments, the input drive pressure to the experimental setup may be exerted through one of a number of different mechanisms (e.g., magnetic fields, gas-gun-driven impactors, or high-energy lasers) and is generally required for simulating such experiments. Yet, regardless of the specific mechanism, this drive pressure cannot be measured directly (measurements are generally taken at a location near the back of the experimental setup through a transparent window), leading to an inverse problem where one must determine the drive pressure at the front of the experimental setup (i.e., the input) that corresponds to the particle velocity (the output) measured near the back of the experimental setup. We solve this inverse problem using a heuristic optimization algorithm, known as differential evolution, coupled with a multiphysics, hydrodynamics code that simulates the compression of the experimental setup. By running many rounds of forward simulations of the experimental setup, our optimization process iteratively searches for a drive pressure that is optimized to closely reproduce the experimentally measured particle velocity near the back of the experimental setup. While our optimization methodology requires a significant number of hydrodynamics simulations to be conducted, many of these can be performed in parallel, which greatly reduces the time cost of our methodology. One novel aspect of our method for determining the drive pressure is that it does not require physical modeling of the drive mechanism and can thus be broadly applied to many types of ramp-compression experiments, regardless of the drive mechanism.

Session 2171 Initiating a Program on Humanitarian Engineering: Rationale, Implementation, Problems, and Perceptions Jean-Pierre Delplanque, Joan Gosink, and Juan Lucena Division of Engineering Division of Liberal Arts and International Studies Colorado School of Mines, Golden, CO 80401 Abstract With the support of a grant from the William and Flora Hewlett Foundation, faculty at the Colorado School of Mines (CSM) are initiating a new program in Humanitarian Engineering (http://humanitarian.mines.edu/). Our specific goals are to: create a culture of acceptance for humanitarian service endeavors among engineering students and faculty; increase the number of engineering graduates that enter occupations and /or internships in service related industries and organizations; and, increase the recruitment and retention of women and minority students. The project includes the development of a minor degree program in Humanitarian Engineering (HE). The minor degree involves extensive collaboration and participation of faculty from the Division of Liberal Arts and International Studies (LAIS) at CSM. First envisioned as a minor degree available only to students with civil, electrical, environmental and mechanical engineering, the concept has evoked widespread interest among students and faculty at CSM, resulting in the planning of a second minor degree in Humanitarian Studies and Technology, which would serve students in applied science and economics. In this paper, we present some of the problems and issues associated with this multidisciplinary endeavor, involving engineering, the sciences and the humanities. 1. Introduction The 21st century has brought Americans a new awareness of anguish and discontent in lower income countries, and an emerging recognition of the need for U.S. participation to ameliorate this suffering. Attendant with these issues is the demand for enhanced security, safety, and equity for the disadvantaged. At the same time, engineering graduates shy away from political life, community service, and international work in the non-profit sector.4 Engineering talent is not making sufficient contributions toward the solutions of major human needs such as access to clean water, nutrition, as well as health and education facilities. It is also important to sustain both human systems and natural systems within an ethical framework which recognizes the disproportionate impact of engineering and applied science in contemporary society.9 Indeed the site for resource development is often within countries noted for autocratic or even dictatorial leadership where the public, and especially the poor have little impact on decisions related to land or water usage. Proceedings of the 2004 American Society for Engineering Education Annual Conference & Exposition Copyright © 2004, American Society for Engineering Education

Classical nucleation theory (CNT) is a promising way to predictively model the submicrosecond kinetics of phase transitions that occur under dynamic compression, such as the suite of experiments performed over the past two decades on the solidification of liquid water to the high-pressure ice VII phase. Myint et al. [Phys. Rev. Lett. 121, 155701 (2018)] presented the first CNT-based model for these types of rapid phase transitions, but relied on an empirical scaling parameter in their transient induction model to simulate the lag time that occurs prior to the onset of significant formation of ice VII clusters in the system. To build on that study, we model the liquid water–ice VII phase transformation using a numerical discretization scheme to solve the Zel’dovich–Frenkel partial differential equation, which is a fundamental CNT-based kinetic equation that describes the statistical time-dependent behavior of solid cluster formation. The Zel’dovich–Frenkel equation inherently accounts for transience in the nucleation kinetics and eliminates the need for the empirical scaling factor used by Myint et al. One major result of this research is that transience is found to play a relatively small role in the nucleation process for the dynamic-compression time scales of the liquid water–ice VII experiments being simulated. Instead, we show that it is possible to properly model the lag time using steady-state CNT by making small refinements to the interfacial free energy value. We have also developed a new dimensionless parameter that may be applied a priori to predict whether or not transient nucleation will be important in a given dynamic-compression experiment.

Summary: Background: As healthcare increasingly digitizes, streaming waveform data is being made available from an variety of sources, but there still remains a paucity of performant clinical decision support systems. For example, in the intensive care unit (ICU) existing automated alarm systems typically rely on simple thresholding that result in frequent false positives. Recurrent false positive alerts create distrust of alarm mechanisms that can be directly detrimental to patient health. To improve patient care in the ICU, we need alert systems that are both pervasive, and accurate so as to be informative and trusted by providers. Objective: We aimed to develop a machine learning-based classifier to detect abnormal waveform events using the use case of mechanical ventilation waveform analysis, and the detection of harmful forms of ventilation delivery to patients. We specifically focused on detecting injurious subtypes of patient-ventilator asynchrony (PVA). Methods: Using a dataset of breaths recorded from 35 different patients, we used machine learning to create computational models to automatically detect, and classify two types of injurious PVA, double trigger asynchrony (DTA), breath stacking asynchrony (BSA). We examined the use of synthetic minority over-sampling technique (SMOTE) to overcome class imbalance problems, varied methods for feature selection, and use of ensemble methods to optimize the performance of our model. Results: We created an ensemble classifier that is able to accurately detect DTA at a sensitivity/specificity of 0.960/0.975, BSA at sensitivity/specificity of 0.944/0.987, and non-PVA events at sensitivity/specificity of .967/.980. Conclusions: Our results suggest that it is possible to create a high-performing machine learning-based model for detecting PVA in mechanical ventilator waveform data in spite of both intra-patient, and inter-patient variability in waveform patterns, and the presence of clinical artifacts like cough and suction procedures. Our work highlights the importance of addressing class imbalance in clinical data sets, and the combined use of statistical methods and expert knowledge in feature selection.

In this work, we present a hydrophilic self-cleaning condenser surface for the collection of biological and environmental aerosol samples. The condenser is installed in a battery-operated hand-held breath sampling device. The device performance is characterized by the collection and analysis of exhaled breath samples from a group of volunteers. The exhaled breath condensate is collected on a subcooled condenser surface, transferred into a storage vial, and its chemical content is analyzed using mass spectrometric methods. The engineered surface supports upon it a continuous condensation cycle, and this allows the collection of liquid samples exceeding the saturation mass/area limit of a plain hydrophilic surface. The condenser surface employs two constituent parameters: a low surface energy barrier to enhance nucleation and condensation efficiency, and a network of surface microstructures to create a self-cleaning mechanism for fluid aggregation into a reservoir. Removal of the liquid condensate from the condenser surface prevents the formation of a thick liquid layer, and thus maintains a continuous condensation cycle with a minimum decrease in heat transfer efficiency as condensation occurs on the surface. The self-cleaning condenser surfaces may have a number of applications in the collection of biological, chemical, or environmental aerosol samples. Sample phase conversion to liquid can facilitate sample manipulation and chemical analysis of matrices with low concentrations. Here, we demonstrate the use of a self-cleaning microcondenser for the collection of exhaled breath condensate with a hand-held portable device. All breath collections with the two devices were performed with the same group of volunteers under UC Davis IRB protocol 63701-3.