Real-time prediction of projectile penetration to laminates by training machine learning models withfinite element solver as the trainer

Studies on ballistic penetration to laminates is complicated, but important for design effective protection of structures. Experimental means of study is expensive and can often be dangerous. Numerical simu-lation has been an excellent supplement, but the computation is time-consuming. Main aim of this thesis was to develop and test an effective tool for real-time prediction of projectile penetrations to laminates by training a neural network and a decision tree regression model. A large number of finite element models were developed;the residual velocities of projectiles fromfinite element simulations were used as the target data and processed to produce sufficient number of training samples. Study focused on steel 4340tpolyurea laminates with various configurations. Four different 3D shapes of the projectiles were modeled and used in the training. The trained neural network and decision tree model was tested using independently generated test samples using finite element models. The predicted projectile velocity values using the trained machine learning models are then compared with thefinite element simulation to verify the effectiveness of the models. Additionally, both models were trained using a published experimental data of projectile impacts to predict residual velocity of projectiles for the unseen samples. Performance of both the models was evaluated and compared. Models trained with Finite element simulation data samples were found capable to give more accurate predication, compared to the models trained with experimental data, becausefinite element modeling can generate much larger training set, and thus finite element solvers can serve as an excellent teacher. This study also showed that neural network model performs better with small experimental dataset compared to decision tree regression model.     

Damage mechanics and energy absorption capabilities of naturalfiber reinforced elastomeric based bio composite for sacrificial structural applications

The present study deals with the experimental, finite element (FE) and analytical assessment of low ballistic impact response of proposedflexible'green' composite make use of naturally available jute and rubber as the constituents of the composite with stacking sequences namely jute/rubber/jute (JRJ), jute/rubber/rubber/jute (JRRJ) and jute/rubber/jute/rubber/jute (JRJRJ). Ballistic impact tests were carried out by firing a conical projectile using a gas gun apparatus at lower range of ballistic impact regime. The ballistic impact response of the proposed flexible composites are assesses based on energy absorption and damage mechanism. Results revealed that inclusion of natural rubber aids in better energy ab-sorption and mitigating the failure of the proposed composite. Among the three different stacking se-quences of flexible composites considered, JRJRJ provides better ballistic performance compared to its counterparts. The damage study reveals that the main mechanism of failure involved in flexible com-posites is matrix tearing as opposed to matrix cracking in stiff composites indicating that the proposedflexible composites are free from catastrophic failure. Results obtained from experimental, FE and analytical approach pertaining to energy absorption and damage mechanism agree well with each other. The proposed flexible composites due to their exhibited energy absorption capabilities and damage mechanism are best suited as claddings for structural application subjected to impact with an aim of protecting the main structural component from being failed catastrophically.     

Mechanical properties and thermal conductivity of pristine andfunctionalized carbon nanotube reinforced metallic glass composites:A molecular dynamics approach

This work uses the molecular dynamics approach to study the effects of functionalization of carbon nanotubes (CNTs) on the mechanical properties of Cu64Zr36 metallic glass (MG). Three types of functional groups, carboxylic, vinyl and ester were used. The effect of CNT volume fraction (Vf) and the number of functional groups attached to CNT, on the mechanical properties and thermal conductivity of CNT-MG composites was analysed using Biovia Materials Studio. At lower values of Vf (from 0 to 5％), the per-centage increase in Young's modulus was approximately 66％. As the value of Vf was increased further (from 5 to 12％), the rate of increase in Young's modulus was reduced to 16％. The thermal conductivity was found to increase from 1.52 W/mK at Vf=0％to 5.88 W/mK at Vf=12％, thus giving an increase of approximately 286％. Functionalization of SWCNT reduced the thermal conductivity of the SWCNT-MG composites.     

Decision support model for effects estimation and proportionality assessment for targeting in cyber operations

Cyber operations are relatively a new phenomenon of the last two decades.During that period,they have increased in number,complexity,and agility,while their design and development have been processes well kept under secrecy.As a consequence,limited data(sets)regarding these incidents are available.Although various academic and practitioner public communities addressed some of the key points and dilemmas that surround cyber operations(such as attack,target identification and selection,and collateral damage),still methodologies and models are needed in order to plan,execute,and assess them in a responsibly and legally compliant way.Based on these facts,it is the aim of this article to propose a model that i))estimates and classifies the effects of cyber operations,and ii)assesses proportionality in order to support targeting decisions in cyber operations.In order to do that,a multi-layered fuzzy model was designed and implemented by analysing real and virtual realistic cyber operations combined with interviews and focus groups with technical-military experts.The proposed model was evaluated on two cyber operations use cases in a focus group with four technical-military experts.Both the design and the results of the evaluation are revealed in this article.     

National restrictions in multinational military operations: A conceptual framework

Recent scholarship in security studies has started to explore the causes and consequences of various forms of national restrictions in multinational military operations (MMOs). This article makes a conceptual contribution to this literature by developing a theoretical framework of national restrictions in MMOs that distinguishes between structural, procedural, and operational restrictions. I argue that these types of restrictions are governed by different causal mechanisms. Structural restrictions are relatively stable over time and effect deployment decisions irrespective of other factors. Procedural restrictions, on the other hand, can constitute veto points against deployment only in combination with distinct political preferences. Finally, operational restrictions directly affect the rules of engagement of troop contributing countries. The article illustrates the three types of restrictions and their interaction with empirical examples from a range of countries and sketches their impact on MMO deployment decisions and mandates.     

Intelligence and military doctrine: paradox or oxymoron?

This article examines the evolution of the current British military joint intelligence doctrine. We argue that military intelligence doctrine is dogged by an intrinsic tension between the ethos and expectations of military doctrine and those of the professional practice of intelligence. We further argue not only that prior iterations of UK joint intelligence doctrine failed to effectively deal with this intelligence doctrine dilemma, but also that measures in the current doctrine to address this problem directly created their own problems. Moreover, as a result, otherwise sound innovations in the current UK intelligence doctrine have proven unsuitable to wider diffusion in more recent intelligence doctrine such as the new NATO intelligence doctrine which, otherwise, draws extensively on its British precursor.     

The Somali National Army: an assessment

To engage properly with the Somali National Army, to understand it in the hope of improving stability and the lives of over 12 million Somalis, good basic information on its composition and characteristics is necessary. Authoritative accounts on the subject have been scarce for over 25 years. This account seeks to detail the army’s dispositions across southern Somalia, and, more importantly, the brigades’ clan compositions and linkages. Clan ties supersede loyalties to the central government. The army as it stands is a collection of former militias which suffer from ill-discipline and commit crime along with greater atrocities. Estimates of numbers are unreliable, but there might be 13,000 or more fighters in six brigades in the Mogadishu area and five beyond.     

Honour,Comradeship and Betrayal: Lord Capel’s Sword

The sword which is the subject of this article and which will be referred to as the Capel sword, is a fine example of what has become known as an Irish basket-hilt. Whilst there are other examples in existence, what makes this sword unique and particularly important is the fact that it is known to have been owned by two important Royalist military leaders, Lord Arthur Capel and Sir John Owen. These two extraordinary men came from different backgrounds but their fate was linked through circumstances beyond their control. The story this sword tells is one of comradeship, loyalty, total belief in a cause, betrayal and ultimate sacrifice.     

Fire truck relocation during major incidents

The effectiveness of a fire department is largely determined by its ability to respond to incidents in a timely manner. To do so, fire departments typically have fire stations spread evenly across the region, and dispatch the closest truck(s) whenever a new incident occurs. However, large gaps in coverage may arise in the case of a major incident that requires many nearby fire trucks over a long period of time, substantially increasing response times for emergencies that occur subsequently. We propose a heuristic for relocating idle trucks during a major incident in order to retain good coverage. This is done by solving a mathematical program that takes into account the location of the available fire trucks and the historic spatial distribution of incidents. This heuristic allows the user to balance the coverage and the number of truck movements. Using extensive simulation experiments we test the heuristic for the operations of the Fire Department of Amsterdam‐Amstelland, and compare it against three other benchmark strategies in a simulation fitted using 10 years of historical data. We demonstrate substantial improvement over the current relocation policy, and show that not relocating during major incidents may lead to a significant decrease in performance.     

Interdiction models for delaying adversarial attacks against critical information technology infrastructure

Information technology (IT) infrastructure relies on a globalized supply chain that is vulnerable to numerous risks from adversarial attacks. It is important to protect IT infrastructure from these dynamic, persistent risks by delaying adversarial exploits. In this paper, we propose max‐min interdiction models for critical infrastructure protection that prioritizes cost‐effective security mitigations to maximally delay adversarial attacks. We consider attacks originating from multiple adversaries, each of which aims to find a “critical path” through the attack surface to complete the corresponding attack as soon as possible. Decision‐makers can deploy mitigations to delay attack exploits, however, mitigation effectiveness is sometimes uncertain. We propose a stochastic model variant to address this uncertainty by incorporating random delay times. The proposed models can be reformulated as a nested max‐max problem using dualization. We propose a Lagrangian heuristic approach that decomposes the max‐max problem into a number of smaller subproblems, and updates upper and lower bounds to the original problem via subgradient optimization. We evaluate the perfect information solution value as an alternative method for updating the upper bound. Computational results demonstrate that the Lagrangian heuristic identifies near‐optimal solutions efficiently, which outperforms a general purpose mixed‐integer programming solver on medium and large instances.