Motion of Droplets Along Thin Fibers With Temperature Gradient

Liquid n-decane, n-undecane, n-dodecane, and n-hexadecane formed tiny symmetrical droplets on a partially wettable cylindrical fiber. When a temperature gradient was created along the fiber, the droplets began to move along the fiber toward the cold region. An explanation of the phenomenon is related to the thermocapillary motion. Other possible mechanisms were ruled out. The theoretical results and experimental data agree reasonably well. (C) 2002 American Institute of Physics

Taylor Cone and Jetting from Liquid Droplets in Electrospinning of Nanofibers

Sessile and pendant droplets of polymer solutions acquire stable shapes when they are electrically charged by applying an electrical potential difference between the droplet and a flat plate, if the potential is not too large. These stable shapes result only from equilibrium of the electric forces and surface tension in the cases of inviscid, Newtonian, and viscoelastic liquids. In liquids with a nonrelaxing elastic force, that force also affects the shapes. It is widely assumed that when the critical potential phi (0*) has been reached and any further increase will destroy the equilibrium, the liquid body acquires a conical shape referred to as the Taylor cone, having a half angle of 49.3 degrees. In the present work we show that the Taylor cone corresponds essentially to a specific self-similar solution, whereas there exist nonself-similar solutions which do not tend toward a Taylor cone. Thus, the Taylor cone does not represent a unique critical shape: there exists another shape, which is not self-similar. The experiments of the present work demonstrate that the observed half angles are much closer to the new shape. In this article a theory of stable shapes of droplets affected by an electric field is proposed and compared with data acquired in our experimental work on electrospinning of nanofibers from polymer solutions and melts. (C) 2001 American Institute of Physics

Bending Instability of Electrically Charged Liquid Jets of Polymer Solutions in Electrospinning

Nanofibers of polymers were electrospun by creating an electrically charged jet of polymer solution at a pendent droplet. After the jet flowed away from the droplet in a nearly straight line, it bent into a complex path and other changes in shape occurred, during which electrical forces stretched and thinned it by very large ratios. After the solvent evaporated, birefringent nanofibers were left. In this article the reasons for the instability are analyzed and explained using a mathematical model. The rheological complexity of the polymer solution is included, which allows consideration of viscoelastic jets. It is shown that the longitudinal stress caused by the external electric field acting on the charge carried by the jet stabilized the straight jet for some distance. Then a lateral perturbation grew in response to the repulsive forces between adjacent elements of charge carried by the jet. The motion of segments of the jet grew rapidly into an electrically driven bending instability. The three-dimensional paths of continuous jets were calculated, both in the nearly straight region where the instability grew slowly and in the region where the bending dominated the path of the jet. The mathematical model provides a reasonable representation of the experimental data, particularly of the jet paths determined from high speed videographic observations. (C) 2000 American Institute of Physics. [S0021-8979(00)03609-4]

Models of polymer solutions in electrified jets and solution blowing

Fluid flows hosting electrical phenomena make the subject of a fascinating and highly interdisciplinary scientific field. In recent years, the extraordinary success of electrospinning and solution blowing technologies for the generation of polymer nanofibers has motivated vibrant research aiming at rationalizing the behavior of viscoelastic jets under applied electric fields or other stretching fields including gas streams. Theoretical models unveiled many original aspects in the underpinning physics of polymer solutions in jets, and provided useful information to improve experimental platforms. This article reviews advances in the theoretical description and numerical simulation of polymer solution jets in electrospinning and solution blowing. Instability phenomena of electrical and hydrodynamic origin are highlighted, which play a crucial role in the relevant flow physics. Specifications leading to accurate and computationally viable models are formulated. Electrohydrodynamic modeling, theories for the jet bending instability, recent advances in Lagrangian approaches to describe the jet flow, including strategies for dynamic refinement of simulations, and effects of strong elongational flow on polymer networks are reviewed. Finally, the current challenges and future perspectives of the field are outlined and discussed, including the task of correlating the physics of the jet flows with the properties of realized materials, as well as the development of multiscale techniques for modelling viscoelastic jets.Comment: 135 pages, 42 figure

Determining the region of origin of blood spatter patterns considering fluid dynamics and statistical uncertainties

Trajectory reconstruction in bloodstain pattern analysis is currently performed by assuming that blood drop trajectories are straight along directions inferred from stain inspection. Recently, several attempts have been made at reconstructing ballistic trajectories backwards, considering the effects of gravity and drag forces. Here, we propose a method to reconstruct the region of origin of impact blood spatter patterns that considers fluid dynamics and statistical uncertainties. The fluid dynamics relies on defining for each stain a range of physically possible trajectories, based on known physics of how drops deform, both in flight and upon slanted impact. Statistical uncertainties are estimated and propagated along the calculations, and a probabilistic approach is used to determine the region of origin as a volume most compatible with the backward trajectories. A publicly available data set of impact spatter patterns on a vertical wall with various impactor velocities and distances to target is used to test the model and evaluate its robustness, precision, and accuracy. Results show that the proposed method allows reconstruction of bloodletting events with distances between the wall and blood source larger than ∼1 m. The uncertainty of the method is determined, and its dependency on the distance between the blood source and the wall is characterized. Causes of error and uncertainty are discussed. The proposed method allows the consideration of stains indicating impact velocities that point downwards, which are typically not used for determining the height of the origin. Based on the proposed method, two practical recommendations on crime scene documentation are drawn

Technology Readiness Levels for Machine Learning Systems

The development and deployment of machine learning (ML) systems can be executed easily with modern tools, but the process is typically rushed and means-to-an-end. The lack of diligence can lead to technical debt, scope creep and misaligned objectives, model misuse and failures, and expensive consequences. Engineering systems, on the other hand, follow well-defined processes and testing standards to streamline development for high-quality, reliable results. The extreme is spacecraft systems, where mission critical measures and robustness are ingrained in the development process. Drawing on experience in both spacecraft engineering and ML (from research through product across domain areas), we have developed a proven systems engineering approach for machine learning development and deployment. Our "Machine Learning Technology Readiness Levels" (MLTRL) framework defines a principled process to ensure robust, reliable, and responsible systems while being streamlined for ML workflows, including key distinctions from traditional software engineering. Even more, MLTRL defines a lingua franca for people across teams and organizations to work collaboratively on artificial intelligence and machine learning technologies. Here we describe the framework and elucidate it with several real world use-cases of developing ML methods from basic research through productization and deployment, in areas such as medical diagnostics, consumer computer vision, satellite imagery, and particle physics

Technology readiness levels for machine learning systems

The development and deployment of machine learning (ML) systems can be executed easily with modern tools, but the process is typically rushed and means-to-an-end. The lack of diligence can lead to technical debt, scope creep and misaligned objectives, model misuse and failures, and expensive consequences. Engineering systems, on the other hand, follow well-defined processes and testing standards to streamline development for high-quality, reliable results. The extreme is spacecraft systems, where mission critical measures and robustness are ingrained in the development process. Drawing on experience in both spacecraft engineering and ML (from research through product across domain areas), we have developed a proven systems engineering approach for machine learning development and deployment. Our Machine Learning Technology Readiness Levels (MLTRL) framework defines a principled process to ensure robust, reliable, and responsible systems while being streamlined for ML workflows, including key distinctions from traditional software engineering. Even more, MLTRL defines a lingua franca for people across teams and organizations to work collaboratively on artificial intelligence and machine learning technologies. Here we describe the framework and elucidate it with several real world use-cases of developing ML methods from basic research through productization and deployment, in areas such as medical diagnostics, consumer computer vision, satellite imagery, and particle physics

Technology readiness levels for machine learning systems

The development and deployment of machine learning systems can be executed easily with modern tools, but the process is typically rushed and means-to-an-end. Lack of diligence can lead to technical debt, scope creep and misaligned objectives, model misuse and failures, and expensive consequences. Engineering systems, on the other hand, follow well-defined processes and testing standards to streamline development for high-quality, reliable results. The extreme is spacecraft systems, with mission critical measures and robustness throughout the process. Drawing on experience in both spacecraft engineering and machine learning (research through product across domain areas), we’ve developed a proven systems engineering approach for machine learning and artificial intelligence: the Machine Learning Technology Readiness Levels framework defines a principled process to ensure robust, reliable, and responsible systems while being streamlined for machine learning workflows, including key distinctions from traditional software engineering, and a lingua franca for people across teams and organizations to work collaboratively on machine learning and artificial intelligence technologies. Here we describe the framework and elucidate with use-cases from physics research to computer vision apps to medical diagnostics