This paper reports the results of a theoretical study into an innovative scramjet-like hypersonic propulsion system. The method, named ASPIRE (Air-breathing Supersonic Pellet Injection Rotary Engine), aims to overcome one of the main limitations of the traditional scramjet cycle – poor mixing and therefore inadequate combustion of air and fuel. The proposed system achieves this by redirecting the main airflow, injecting encapsulated or localised fuel throughout the engine-duct mixing volume and then switching the main airflow back on – this then engulfs and mixes with the fuel. The results presented here include design calculations, simulations and mathematical modelling. These show that the system performs well in simulation and gives excellent theoretical results for airfuel mixing and airflow dynamics. A road-map to a working engine is also outlined.

This paper reviews work on two aspects of scramjet and similar ducted hypersonic propulsion. The first of these is air-fuel mixing techniques - the problem of mixing is considered the most intractable in scramjet design. The second aspect is the use of innovative techniques to supplement mixing or provide an alternative to combustion altogether. The paper outlines the mixing problem and reviews both classic treatments and newer, more innovative, work. In the light of this it also outlines conclusions and points out gaps in current knowledge and areas where more research is needed.

Inertial Electrostatic Confinement (IEC) is an alternative approach to nuclear fusion which uses electrostatically accelerated ions instead of hot plasmas. The best known device that utilises the principle is the Farnsworth-Hirsch Fusor. It has been argued that such devices have potential applications in spaceflight because of weight and other advantages. However, like other fusion reactors, practical machines have not yet been forthcoming. This paper builds on previous work to suggest an alternative topology for IEC-like reactors. This topology inverts the normal machine structure, potentially has significant advantages over standard designs and may achieve the reaction-rates necessary for practical power generation.

The Artificial Reaction Network (ARN) is an artificial chemistry inspired by Cell Signalling Networks. Its purpose is to represent chemical circuitry and to explore the computational properties responsible for generating emergent high-level behaviour. In this paper, the design and application of ARN-based cell-like agents termed “Cytobots” are explored. Such agents provide a facility to explore the dynamics and emergent properties of multicellular systems. The Cytobot ARN is constructed by combining functional motifs found in real biochemical networks. By instantiating this ARN, multiple Cytobots are created, each of which is capable of recognising environmental patterns, stigmergic communication with others and controlling its own trajectory. Applications in biological simulation and robotics are investigated by first applying the agents to model the life-cycle phases of the cellular slime mould D. discoideum and then to simulate an oil-spill clean-up operation. The results demonstrate that an ARN-based approach provides a powerful tool for modelling multi-agent biological systems and also has application in swarm robotics.

In this paper the concept of a pulsed Scramjet using pelletized fuel is examined. The aim is to overcome the main technical problem inherent in more traditional designs - poor air-fuel mixing. In this new paradigm, airflow is diverted away from the main engine duct and fuel pellets are injected at timing intervals which ensure that they are evenly distributed throughout the mixing volume. The main airflow is then permitted to enter the duct and the inertia of the pellets allows the air to envelop and then vaporise them - and so their fuel load is evenly spread through the duct volume. The paper outlines the basic concept and calculations and simulations are used to demonstrate its feasibility.

The Artificial Reaction Network (ARN) is a Cell Signalling Network inspired connectionist representation belonging to the branch of A-Life known as Artificial Chemistry. Its purpose is to represent chemical circuitry and to explore computational properties responsible for generating emergent high-level behaviour associated with cells. In this paper, the computational mechanisms involved in pattern recognition and spatio-temporal pattern generation are examined in robotic control tasks. The results show that the ARN has application in limbed robotic control and computational functionality in common with Artificial Neural Networks. Like spiking neural models, the ARN can combine pattern recognition and complex temporal control functionality in a single network, however it offers increased flexibility. Furthermore, the results illustrate parallels between emergent neural and cell intelligence.

This article outlines a view of the quantum mechanical wave-function as the limit of classical mechanics. It argues that the region inside the wavefunction is physically unrelated to that outside and represents a fundamentally different reality. A review of published evidence for this view is presented and it is argued that these ideas (and indeed more traditional physics and mathematics, even if the interpretation is incorrect) lead logically to the possibility of interstellar travel and teleportation though quantum mechanisms. Some of the practical implications of this are also explored.

The Artificial Reaction Network (ARN) is an Artificial Chemistry representation inspired by cell signaling networks. The ARN has previously been applied to the simulation of the chemotaxis pathway of Escherichia coli and to the control of limbed robots. In this paper we discuss the design of an ARN control system composed of a combination of network motifs found in actual biochemical networks. Using this control system we create multiple cell-like autonomous agents capable of coordinating all aspects of their behavior, recognizing environmental patterns and communicating with other agent's stigmergically. The agents are applied to simulate two phases of the life cycle of Dictyostelium discoideum: vegetative and aggregation phase including the transition. The results of the simulation show that the ARN is well suited for construction of biochemical regulatory networks. Furthermore, it is a powerful tool for modeling multi agent systems such as a population of amoebae or bacterial colony.

The Artificial Reaction Network (ARN) is a cell signaling network inspired representation belonging to the branch of A-Life known as Artificial Chemistry. It has properties in common with both AI and Systems Biology techniques including Artificial Neural Networks, Petri Nets, Random Boolean Networks and S-Systems. The ARN has been previously applied to control of limbed robots and simulation of biological signaling pathways. In this paper, multiple instances of independent distributed ARN controlled agents function to find the global minima within a set of simulated environments characterized by benchmark problems. The search behavior results from the internal ARN network, but is enhanced by collective activities and stigmergic interaction of the agents. The results show that the agents are able to find best fitness solutions in all problems, and compare well with results of cell inspired optimization algorithms. Such a system may have practical application in distributed or swarm robotics.

This paper describes some innovative methods for achieving enhanced fuel-air mixing and combustion in Scramjet-like spaceplane engines. A multimodal approach to the problem is discussed; this involves using several concurrent methods of forced mixing. The paper concentrates on Electromagnetic Activation (EMA) and Electrostatic Attraction as suitable techniques for this purpose - although several other potential methods are also discussed. Previously published empirical data is used to draw conclusions about the likely effectiveness of the system and possible engine topologies are outlined.

The Artificial Reaction Network (ARN) is a bio-inspired connectionist paradigm based on the emerging field of Cellular Intelligence. It has properties in common with both AI and Systems Biology techniques including Artificial Neural Networks, Petri Nets, and S-Systems. This paper discusses the temporal aspects of the ARN model using robotic gaits as an example and compares it with properties of Artificial Neural Networks. The comparison shows that the ARN based network has similar functionality.

The Artificial Reaction Network (ARN) is a bio-inspired connectionist paradigm based on the emerging field of Cellular Intelligence. It has properties in common with both AI and Systems Biology techniques including Artificial Neural Networks, Petri Nets, and S-Systems. In this paper, elements of temporal dynamics and pattern recognition are combined within a single ARN control system for a quadrupedal robot. The results show that the ARN has similar applicability to Artificial Neural Network models in robotic control tasks. In comparison to neural Central Pattern Generator models, the ARN can control gaits and offer reduced complexity. Furthermore, the results show that like spiky neural models, the ARN can combine pattern recognition and complex temporal control functionality in a single network.

Inertial Electrostatic Confinement (IEC) is an approach to nuclear fusion which utilises the properties of electrostatically accelerated ion-beams instead of hot plasmas. The best known device which uses the principle is the Farnsworth-Hirsch fusor. It has been argued that such devices have some potential advantages in spaceflight and in-particular as power-supplies for trans-atmospheric propulsion. This paper builds on previous work in the field and focuses on how the fixing of the fuel for such reactors in a solid, liquid or encapsulated form may provide a high enough energy-density to make such devices practical power sources. Several methods of fixing the fuel are discussed; theoretical calculations are presented and applicable literature is reviewed. Finally, there is a discussion of practical issues and feasibility, together with suggestions for further work.

Connectionist models are usually based on artificial neural networks. However, there is another route towards parallel distributed processing. This is by considering the origins of the intelligence displayed by the single celled organisms known as protoctists. Such intelligence arises by means of the biochemical interactions within the animal. An artificial model of this might therefore be termed an artificial biochemical network or ABN. This paper describes the attributes of such networks and illustrates their abilities in pattern recognition problems and in generating time-varying signals of a type which can be used in many control tasks. The flexibility of the system is explained using legged robots as an example. The networks are trained using back propagation and evolutionary algorithms such as genetic algorithms.

Exploration and production activities have moved into more challenging deep-water and subsea environments. Many of the clastic reservoirs in these environments are characterized by thick overburden, HP-HT and largely unconsolidated formations with challenging sand management issues. For effective overall field/reservoir management, it is crucial to know if and when sand would fail and be ultimately produced. Field-life sanding potential evaluation and analysis, which seeks to evaluate the sanding potential of reservoir formations during the appraisal stage and all through the development to the abandonment stage, is therefore necessary so that important reservoir/field management decisions regarding sand control deployment can be made. Recent work has identified Unconfined Compressive Strength (UCS) as a key parameter required for the evaluation and analysis of sanding potential of any reservoir formation. There is therefore the need to be able to predict this important sanding potential parameter accurately and in real time to reduce the level of uncertainties usually associated with sanding potential evaluation and analysis. In this work, neural network coded in C++ was trained with log-derived petrophysical, geomechanical and textural data to develop a stand-alone model for predicting UCS. Real-time functionality of this model is guaranteed by real time data gathering via logging while drilling (LWD) and other measurement while drilling (MWD) tools. The choice of neural network over and above other methods and techniques which have been widely used in the industry was informed by its ability to better resolve the widely known complex relationship between petrophysical, textural and geomechanical strength parameters.

Most present-day research into Nuclear Fusion concentrates on high-temperature plasmas combined with Inertial or Magnetic Confinement. However, there exists another body of less well-known work based on Electrostatic Acceleration and Confinement. The most thoroughly researched of these devices is known as the Farnsworth Fusor. This paper reviews the technique and then argues that, with development, similar technologies would be particularly suited to space-borne applications, due to their safety, simplicity and light weight. The paper then goes on to suggest several possible directions for new research into such devices which might result in a working machine.