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Delplanque, Jean-Pierre

Publications and source records attributed to Delplanque, Jean-Pierre.

11 records
All sourcesNASA NTRSDOE OSTI
DOE OSTI2022

Model for the Solid–Liquid Interfacial Free Energy at High Pressures

The free energy involved in the formation of an interface between two phases (e.g., a solid–liquid interface) is referred to as the interfacial free energy. For the case of solidification, the interfacial free energy dictates the height of the energy barrier required to nucleate stable clusters of the newly forming solid phase and is essential for producing an accurate solidification kinetics model using classical nucleation theory (CNT)-based methods. While various methods have been proposed for modeling the interfacial free energy for solid–liquid interfaces in prior literature, many of these formulations involve making restrictive assumptions or approximations, such as the system being at or near equilibrium (i.e., the system temperature is approximately equal to the melt temperature) or that the system is at pressures close to atmospheric. However, these approximations and assumptions may break down in highly non-equilibrium situations, such as in dynamic-compression experiments where metastable liquids that are undercooled by hundreds of kelvin or over-pressurized by several gigapascals or more are formed before eventually solidifying. We derive a solid–liquid interfacial free-energy model for such high-pressure conditions by considering the enthalpies of interactions between pairs of atoms or molecules. We also consider the contribution of interface roughness (disordering) by incorporating a multilayer interface model known as the Temkin n-layer model. Our formulation is applicable to a diverse variety of materials, and we demonstrate it by developing models specifically for two different materials: water and gallium. We apply our interfacial free-energy formulation to CNT-based kinetics simulations of several suites of dynamic-compression experiments that cause liquid water to solidify to the high-pressure solid polymorph ice VII and have found good agreement to the observed kinetics with only minor empirical fitting.

36 MATERIALS SCIENCE↗
DOE OSTI2020

Drive-pressure optimization in ramp-wave compression experiments through differential evolution

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. Furthermore, 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.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
DOE OSTI2020

Ducted Fuel Injection vs. Free-Spray Injection: A Study of Mixing and Entrainment Effects Using Numerical Modeling

Diesel engines are an important technology for transportation of both people and goods. However, historically they have suffered a significant downside of high soot and nitrogen oxides (NO x ) emissions. Recently, ducted fuel injection (DFI) has been demonstrated to attenuate soot formation in compression-ignition engines and combustion vessels by 50% to 100%. This allows for diesel engines to be run at low-NO x emissions that would have otherwise produced significantly more soot due to the soot/NO x tradeoff. Currently the root causes of this soot attenuation are not well understood. To be able to better optimize DFI for use across a variety of engines and conditions, it is important to understand clearly how it works. This study expands on the current understanding of DFI by using numerical modeling under nonreacting conditions to provide insights about the roles of entrainment and mixing that would have been much more challenging to obtain experimentally. We report this study found that DFI enhances charge gas entrainment upstream of the duct and blocks entrainment inside of the duct. Mixing is enhanced by the duct, which results in lower peak equivalence ratios at the exit of the duct.

42 ENGINEERING↗
DOE OSTI2019

Numerical modeling of solid-cluster evolution applied to the nanosecond solidification of water near the metastable limit

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. In conclusion, 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.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
DOE OSTI2018

Creation of a Robust and Generalizable Machine Learning Classifier for Patient Ventilator Asynchrony

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.

97 MATHEMATICS AND COMPUTING↗
NASA NTRS2004

Spacecraft Fire Suppression: Testing and Evaluation

The objective of this project is the testing and evaluation of the effectiveness of a variety of fire suppressants and fire-response techniques that will be used in the next generation of spacecraft (Crew Exploration Vehicle, CEV) and planetary habitats. From the many lessons learned in the last 40 years of space travel, there is common agreement in the spacecraft fire safety community that a new fire suppression system will be needed for the various types of fire threats anticipated in new space vehicles and habitats. To date, there is no single fire extinguishing system that can address all possible fire situations in a spacecraft in an effective, reliable, clean, and safe way. The testing conducted under this investigation will not only validate the various numerical models that are currently being developed, but it will provide new design standards on fire suppression that can then be applied to the next generation of spacecraft extinguishment systems. The test program will provide validation of scaling methods by conducting small, medium, and large scale fires. A variety of suppression methods will be tested, such as water mist, carbon dioxide, and nitrogen with single and multiple injection points and direct or distributed agent deployment. These injection methods cover the current ISS fire suppression method of a portable hand-held fire extinguisher spraying through a port in a rack and also next generation spacecraft units that may have a multi-point suppression delivery system built into the design. Consideration will be given to the need of a crew to clean-up the agent and recharge the extinguishers in flight in a long-duration mission. The fire suppression methods mentioned above will be used to extinguish several fire scenarios that have been identified as the most relevant to spaceflight, such as overheated wires, cable bundles, and circuit boards, as well as burning cloth and paper. Further testing will be conducted in which obstructions and ventilation will be added to represent actual spacecraft conditions (e.g., a series of cards in a card rack).

Abbud-Madrid, Angel↗