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Before a droplet can contact a surface during impact, it must first displace the air beneath it. Over a wide range of impact velocities, the air is compressed into a thin film, slowing the progress of the liquid toward the surface. Below a critical impact velocity, the air film remains intact; the droplet rebounds off of the air film without making contact. Above this critical impact velocity, the droplet always makes contact. The initiation of contact formation occurs via a topological transition, whereby the initially connected gas domain is ruptured, and a liquid capillary bridge forms, binding the droplet to the surface. Here we probe this transition in detail around the critical impact velocity using calibrated total internal reflection (TIR) microscopy to monitor the air film profile at high speed during the impact process. Two air film rupture modalities are observed: nucleated contacts, which are isolated and don't correspond to the global minimum air film thickness, and spontaneous contacts, that occur always on a ring centered upon the impact axis where the air film reaches its global minimum. Our measurements show that for impact velocities exceeding the critical velocity for contact initiation, the air film ruptures at a nearly identical height h min ~ 20 nm, for two fluids - silicone oil and a water glycerol mixture. The height and time duration of the air film prior to contact, analyzed for over 180 droplet impact experiments, are consistent with a linear instability driven by van der Waals forces. Impact events of water solution droplets show different statistics for contact nucleation than the silicon oil; nevertheless, a critical impact velocity above which contact always occurs is identified for both liquids.

In this paper, we investigate the feasibility and physical consequences of cyber attacks against energy management systems (EMS). Within this framework, we have designed a complete simulation platform to emulate realistic EMS operations: it includes state estimation (SE), real-time contingency analysis (RTCA), and security constrained economic dispatch (SCED). This software platform allowed us to achieve two main objectives: 1) to study the cyber vulnerabilities of an EMS and understand their consequences on the system, and 2) to formulate and implement countermeasures against cyber-attacks exploiting these vulnerabilities. Our results show that the false data injection attacks against state estimation described in the literature do not easily cause base-case overflows because of the conservatism introduced by RTCA. For a successful attack, a more sophisticated model that includes all of the EMS blocks is needed; even in this scenario, only post-contingency violations can be achieved. Nonetheless, we propose several countermeasures that can detect changes due to cyber-attacks and limit their impact on the system.

It is possible to launch undetectable load-redistribution (LR) attacks against power systems, even in systems with protection schemes. Therefore, detecting LR attacks in power systems and establishing a corrective action to provide secured operating points are imperative. In this paper, we develop a systematic real-time LR threat analysis (RTLRTA) tool, which can flag LR attacks and identify all affected transmission assets. Since attackers might use random deviations to create LR attacks, we introduce an optimization model to generate random LR attacks. Hence, we can determine accurate thresholds for our detection index and test the tool's functionality when there are random LR attacks. Additionally, based on an estimation for the actual loads in the post-attack stage, we design a set of physical line flow security constraints (PLFSCs) and add it to the security-constrained economic dispatch (SCED) model. We call the new model cyber-physical SCED (CPSCED), which can appropriately respond to the identified LR attacks and provide secured dispatch points. We generate multiple scenarios of random LR attacks and noise errors for different target lines in the $2383$-bus Polish test system to validate our proposed methods' accuracy and functionality in detecting LR attacks and responding to them.

This paper presents a real-time non-probabilistic detection mechanism to detect load-redistribution (LR) attacks against energy management systems (EMSs). Prior studies have shown that certain LR attacks can bypass conventional bad data detectors (BDDs) and remain undetectable, which implies that presence of a reliable and intelligent detection mechanism to flag LR attacks, is imperative. Therefore, in this study a detection mechanism to enhance the existing BDDs is proposed based on the fundamental knowledge of the physics laws in the electric grid. A greedy algorithm, which can optimize the core LR attack problems, is presented to enable a fast mechanism to identify the most sensitive locations for critical assets. The main contribution of this detection mechanism is leveraging of power systems domain insight to identify an underlying exploitable structure for the core problem of LR attack problems, which enables the prediction of the attackers' behavior. Additional contribution includes the ability to combine this approach with other detection mechanisms to increase their likelihood of detection. The proposed approach is applied to 2383-bus Polish test system to demonstrate the scalability of the greedy algorithm, and it solved the attacker's problem more than 10x faster than a traditional linear optimization approach.