MathWorks (United Kingdom)

G New Radio (NR) aims to provide a technological solution to the growing demand for faster data rates and lower latency in mobile communications through its improved features and flexibility; however, this comes at the cost of increased design complexity when targeting hardware devices. To address this, high-level design tools such as Simulink can be used with iterative model-based design to shorten development cycles and reduce human error. This paper demonstrates this design flow through the implementation of a hardware-compatible NR Physical Uplink Shared Channel (PUSCH) model, targeting the Zynq UltraScale+ Radio Frequency System-on-Chip (RFSoC) ZCU216 development board. By interfacing with the hardware through MATLAB, the design supports multiple symbol modulation schemes, and parameters such as the number of layers, antenna ports, and selected precoding matrix can be altered. The design was tested by targeting a 10 MHz bandwidth, 60 kHz subcarrier spacing (SCS) waveform with 132 active subcarriers and met timing with 0.276 ns Worst Negative Slack (WNS) for a 245.76 MHz clock frequency, demonstrating its standard compliancy. The Cyclic Prefix Orthogonal Frequency Division Multiplexing (CP-OFDM) modulated signal generated on the board was looped back to MATLAB and verified against an existing software implementation, and the hardware usage was recorded.

We examine some leading-order flow and stability properties of smectic A (SmA) liquid crystals (LCs) in two spatial dimensions by analysing a fully nonlinear continuum theory of these materials. We derive a system of equations for the dynamic variables describing the flow velocity and orientation of the material under suitable assumptions upon these quantities. This system can provide insight into the leading-order behaviour under quite general circumstances, and we provide an example of utilising this system to determine the flow induced by a constant pressure gradient applied normally to the smectic layers. We then consider the effect of oscillatory perturbations on a relaxed, stationary sample of SmA, and provide criteria under which one would expect to see the onset of instability in the form of inequalities between the material parameters and perturbative wave number. We find that instability occurs for physically realisable values of these quantities, and, in particular, that certain viscosities characterising the SmA phase can act as ‘destabilising agents’ such that one could, for a given sample with known parameter values, manipulate the behaviour of that sample.

The design of complex systems often requires reliability assessments involving a large number of uncertainties and low probability of failure estimations (in the order of 10−4). Estimating such rare event probabilities with crude Monte Carlo (CMC) is computationally intractable. Specific numerical methods to reduce the computational cost and the variance estimate have been developed such as importance sampling or subset simulation. However, these methods assume that the uncertainties are defined within the probability formalism. Regarding epistemic uncertainties, the interval formalism is particularly adapted when only their definition domain is known. In this paper, a method is derived to assess the reliability of a system with uncertainties described by both probability and interval frameworks. It allows one to determine the bounds of the failure probability and involves a sequential approach using subset simulation, kriging, and an optimization process. To reduce the simulation cost, a refinement strategy of the surrogate model is proposed taking into account the presence of both aleatory and epistemic uncertainties. The method is compared to existing approaches on an analytical example as well as on a launch vehicle fallout zone estimation problem.

Abstract This paper describes the use of an open innovation/collaboration model to provide strategic systemic solutions to complex problems in UK Defence. The model in question is the one evolved over time by the Niteworks Partnership, which was established in 2003 and now has over 160 organisational members drawn from government, academia and industry. One of the key principles of the model is ‘exploitation’, namely the capture and management of foreground ‘intellectual capital’ created within projects for subsequent re‐use by members of the Partnership. The volume and breadth of project activities, coupled with an enduring core team of technical experts, facilitates the identification of recurring problem types and solution approaches, which in some cases can be synthesised to address underlying root causes. An ever‐changing project portfolio supports testing and refinement of the developed approaches and helps to build a solid body of evidence for their efficacy. Although this process has been developed in the context of the UK Defence enterprise, it is applicable more generally. Furthermore, the strategic systemic methods emerging from the process are in themselves relevant to many domains. This paper presents the approach to developing strategic systemic solutions and describes specific methods emerging from this process, including: Continuous Capability Evolution; Styles of Architecting; Complex System Interventions, and; Capability Coherence.

We open this chapter by establishing the basic notation that will be used regularly throughout the book. We then provide some motivation behind what we aim to achieve in this book, namely to develop the theory and explore the applications of spherical basis functions (SBFs).

Complex system design can be supported by simulation studies. However, creating a system-level model that has the requisite level of complexity can be challenging. A model that is too simple can neglect important physical phenomena. Conversely a model that is too complex does not support tradeoff studies or design optimization. Applications encompassing multiple physical domains bring additional challenges if a single tool cannot simulate the complete system. The recently introduced Simscape™ language provides a platform aimed at addressing these needs. The paper demonstrates the use of the Simscape language to model a servomotor in the context of replacing a hydraulic ram with an electric actuation system.

Software time-domain simulation models are useful to the naval engineering community both for the system design of future vessels and for the in-service support of existing vessels. For future platforms, the existence of a model of the vessel's electrical power system provides a means of assessing the performance of the system against defined requirements. This could be at the stage of requirements definition, bid assessment or any subsequent stage in the design process. For in-service support of existing platforms, the existence of a model of the vessel's electrical power system provides a means of assessing the possible cause and effect of operational defects reported by ship's staff, or of assessing the possible future implications of some change in the equipment line-up or operating conditions for the vessel. Detailed high fidelity time-domain simulation of systems, however, can be problematic due to extended execution time. This arises from the model's mathematically stiff nature: models of Integrated Electric Propulsion systems can also require significant computational resource. A conventional time-domain software simulation model is only able to utilize a single computer processor at any one time. The duration of time required to obtain results from a software model could be significantly reduced if more computer processors were utilized simultaneously. This paper details the development of a distributed simulation environment. This environment provides a mechanism for partitioning a time-domain software simulation model and running it on a cluster of computer processors. The number of processors utilized in the cluster ranges between four and sixteen nodes. The benefit of this approach is that reductions in simulation duration are achievable by an appropriate choice of model partitioning. From an engineering perspective, any net timing reduction translates to an increase in the availability of data, from which more efficient analysis and design follows.