An application of bi-directional evolutionary structural optimisation for optimising energy absorbing structures using a material damage model

The Bi-directional Evolutionary Structural Optimisation (BESO) method is a numerical topology optimisation method developed for use in finite element analysis. This paper presents a particular application of the BESO method to optimise the energy absorbing capability of metallic structures. The optimisation objective is to evolve a structural geometry of minimum mass while ensuring that the kinetic energy of an impacting projectile is reduced to a level which prevents perforation. Individual elements in a finite element mesh are deleted when a prescribed damage criterion is exceeded. An energy absorbing structure subjected to projectile impact will fail once the level of damage results in a critical perforation size. It is therefore necessary to constrain an optimisation algorithm from producing such candidate solutions. An algorithm to detect perforation was implemented within a BESO framework which incorporated a ductile material damage model.

On the potential of significance-driven execution for energy-aware HPC

Dynamic Voltage and Frequency Scaling (DVFS) exhibits fundamental limitations as a method to reduce energy consumption in computing systems. In the HPC domain, where performance is of highest priority and codes are heavily optimized to minimize idle time, DVFS has limited opportunity to achieve substantial energy savings. This paper explores if operating processors Near the transistor Threshold Volt- age (NTV) is a better alternative to DVFS for break- ing the power wall in HPC. NTV presents challenges, since it compromises both performance and reliability to reduce power consumption. We present a first of its kind study of a significance-driven execution paradigm that selectively uses NTV and algorithmic error tolerance to reduce energy consumption in performance- constrained HPC environments. Using an iterative algorithm as a use case, we present an adaptive execution scheme that switches between near-threshold execution on many cores and above-threshold execution on one core, as the computational significance of iterations in the algorithm evolves over time. Using this scheme on state-of-the-art hardware, we demonstrate energy savings ranging between 35% to 67%, while compromising neither correctness nor performance.

The use of theranostic gadolinium-based nanoprobes to improve radiotherapy efficacy

A new efficient type of gadolinium-based theranostic agent (AGuIX®) has recently been developed for MRI-guided radiotherapy (RT). These new particles consist of a polysiloxane network surrounded by a number of gadolinium chelates, usually 10. Owing to their small size (<5 nm), AGuIX typically exhibit biodistributions that are almost ideal for diagnostic and therapeutic purposes. For example, although a significant proportion of these particles accumulate in tumours, the remainder is rapidly eliminated by the renal route. In addition, in the absence of irradiation, the nanoparticles are well tolerated even at very high dose (10 times more than the dose used for mouse treatment). AGuIX particles have been proven to act as efficient radiosensitizers in a large variety of experimental in vitro scenarios, including different radioresistant cell lines, irradiation energies and radiation sources (sensitizing enhancement ratio ranging from 1.1 to 2.5). Pre-clinical studies have also demonstrated the impact of these particles on different heterotopic and orthotopic tumours, with both intratumoural or intravenous injection routes. A significant therapeutical effect has been observed in all contexts. Furthermore, MRI monitoring was proven to efficiently aid in determining a RT protocol and assessing tumour evolution following treatment. The usual theoretical models, based on energy attenuation and macroscopic dose enhancement, cannot account for all the results that have been obtained. Only theoretical models, which take into account the Auger electron cascades that occur between the different atoms constituting the particle and the related high radical concentrations in the vicinity of the particle, provide an explanation for the complex cell damage and death observed.

Predictive Maintenance for Improved Sustainability - An Ion Beam Etch Endpoint Detection System Use Case

In modern semiconductor manufacturing facilities maintenance strategies are increasingly shifting from traditional preventive maintenance (PM) based approaches to more efficient and sustainable predictive maintenance (PdM) approaches. This paper describes the development of such an online PdM module for the endpoint detection system of an ion beam etch tool in semiconductor manufacturing. The developed system uses optical emission spectroscopy (OES) data from the endpoint detection system to estimate the RUL of lenses, a key detector component that degrades over time. Simulation studies for historical data for the use case demonstrate the effectiveness of the proposed PdM solution and the potential for improved sustainability that it affords.

Promoting Community Renewable Energy in a Corporate Energy World

Small-scale, decentralized and community-owned renewable energy is widely acknowledged to be a desirable feature of low carbon futures, but faces a range of challenges in the context of conventional, centralized energy systems. This paper draws on transition frameworks to investigate why the UK has been an inhospitable context for community-owned renewables and assesses whether anything fundamental is changing in this regard. We give particular attention to whether political devolution, the creation of elected governments for Scotland, Wales and Northern Ireland, has affected the trajectory of community renewables. Our analysis notes that devolution has increased political attention to community renewables, including new policy targets and support schemes. However, these initiatives are arguably less important than the persistence of key features of socio-technical regimes: market support systems for renewable energy and land-use planning arrangements that systemically favour major projects and large corporations, and keep community renewables to the margins. There is scope for rolling out hybrid pathways to community renewables, via joint ownership or through community benefit funds, but this still positions community energy as an adjunct to energy pathways dominated by large, corporate generation facilities

Energy and energy flux in axisymmetric slow and fast waves

Aims: We aim to calculate the kinetic, magnetic, thermal, and total energy densities and the flux of energy in axisymmetric sausage modes. The resulting equations should contain as few parameters as possible to facilitate applicability for different observations. 

Methods: The background equilibrium is a one-dimensional cylindrical flux tube model with a piecewise constant radial density profile. This enables us to use linearised magnetohydrodynamic equations to calculate the energy densities and the flux of energy for axisymmetric sausage modes. 

Results: The equations used to calculate the energy densities and the flux of energy in axisymmetric sausage modes depend on the radius of the flux tube, the equilibrium sound and Alfvén speeds, the density of the plasma, the period and phase speed of the wave, and the radial or longitudinal components of the Lagrangian displacement at the flux tube boundary. Approximate relations for limiting cases of propagating slow and fast sausage modes are also obtained. We also obtained the dispersive first-order correction term to the phase speed for both the fundamental slow body mode under coronal conditions and the slow surface mode under photospheric conditions.

Application-Level Energy Awareness for OpenMP

Power, and consequently energy, has recently attained first-class system resource status, on par with conventional metrics such as CPU time. To reduce energy consumption, many hardware- and OS-level solutions have been investigated. However, application-level information - which can provide the system with valuable insights unattainable otherwise - was only considered in a handful of cases. We introduce OpenMPE, an extension to OpenMP designed for power management. OpenMP is the de-facto standard for programming parallel shared memory systems, but does not yet provide any support for power control. Our extension exposes (i) per-region multi-objective optimization hints and (ii) application-level adaptation parameters, in order to create energy-saving opportunities for the whole system stack. We have implemented OpenMPE support in a compiler and runtime system, and empirically evaluated its performance on two architectures, mobile and desktop. Our results demonstrate the effectiveness of OpenMPE with geometric mean energy savings across 9 use cases of 15 % while maintaining full quality of service.

ALEA: Fine-Grain Energy Profiling with Basic Block Sampling

Energy efficiency is an essential requirement for all contemporary computing systems. We thus need tools to measure the energy consumption of computing systems and to understand how workloads affect it. Significant recent research effort has targeted direct power measurements on production computing systems using on-board sensors or external instruments. These direct methods have in turn guided studies of software techniques to reduce energy consumption via workload allocation and scaling. Unfortunately, direct energy measurements are hampered by the low power sampling frequency of power sensors. The coarse granularity of power sensing limits our understanding of how power is allocated in systems and our ability to optimize energy efficiency via workload allocation.
We present ALEA, a tool to measure power and energy consumption at the granularity of basic blocks, using a probabilistic approach. ALEA provides fine-grained energy profiling via sta- tistical sampling, which overcomes the limitations of power sens- ing instruments. Compared to state-of-the-art energy measurement tools, ALEA provides finer granularity without sacrificing accuracy. ALEA achieves low overhead energy measurements with mean error rates between 1.4% and 3.5% in 14 sequential and paral- lel benchmarks tested on both Intel and ARM platforms. The sampling method caps execution time overhead at approximately 1%. ALEA is thus suitable for online energy monitoring and optimization. Finally, ALEA is a user-space tool with a portable, machine-independent sampling method. We demonstrate two use cases of ALEA, where we reduce the energy consumption of a k-means computational kernel by 37% and an ocean modelling code by 33%, compared to high-performance execution baselines, by varying the power optimization strategy between basic blocks.

A Numerical and Experimental Assessment of the Use of a Turbine Utilizing Splitter Blades for an Automotive Variable Geometry Turbocharger

Automotive manufacturers require improved part load engine performance to further improve fuel economy. For a swing vane VGS (Variable Geometry Stator) turbine this means a more closed stator vane, to deal with the low MFRs (Mass Flow Rates), high PRs (Pressure Ratios) and low rotor rotational speeds. During these conditions the turbine is operating at low velocity ratios. As more energy is available at high pressure ratios and during lower turbocharger rotational speeds, a turbine which is efficient at these conditions is desirable. Another key aspect for automotive manufacturers is engine responsiveness. High inertia designs result in “turbo lag” which means an increased time before the target boost pressure is reached. Therefore, designs with improved performance at low velocity ratios, reduced inertia or an increased swallowing capacity are the current targets for turbocharger manufacturers.

To try to meet these design targets a CFD (Computational Fluid Dynamics) study was performed on a turbine wheel using splitter blades. A number of parameters were investigated. These included splitter blade merdional length, blade number and blade angle distribution.

The numerical study was performed on a scaled automotive VGS. Three different stator vane positions have been analysed. A single passage CFD model was developed and used to provide information on the flow features affecting performance in both the stator vanes and turbine.

Following the CFD investigation the design with the best compromise in terms of performance, inertia and increased MFP (Mass Flow Parameter) was selected for manufacture and testing. Tests were performed on a scaled, low temperature turbine test rig. The aerodynamic flow path of the gas stand was the same as that investigated during the CFD. The test results revealed a design which had similar performance at the closed stator vane positions when compared to the baseline wheel. At the maximum MFR stator vane condition a drop of −0.6% pts in efficiency was seen. However, 5.5% increase in MFP was obtained with the additional benefit of a drop in rotor inertia of 3.7%, compared to the baseline wheel.

Performance of sustainable SCC mixes with mineral additions for use in precast concrete industry

The aim of this research was to study the impact that different mineral powders have on the properties of self-compacting concrete (SCC) in order to obtain relations that make it possible to optimize their dosages for being used in precast concrete applications. Different combinations and contents of cement, mineral additions (active and inert), superplasticizers, and aggregates are considered. A new approach for determining the saturation point of superplasticizers is introduced. The fresh state performance was assessed by means of the following tests: slump flow, V-funnel, and J-ring. Concrete compressive strength values at different ages up to 56 days have been retained as representative of the materials’ performance in its hardened state. All these properties have been correlated with SCC proportioning. As a result, a number of recommendations for the precast concrete industry arise to design more stable SCC mixes with a reduced carbon footprint.

Energy and environmental forensic analysis for public buildings.

This paper outlines a forensic method for analysing the energy, environmental and comfort performance of a building. The method has been applied to a recently developed event space in an Irish public building, which was evaluated using on-site field studies, data analysis, building simulation and occupant surveying. The method allows for consideration of both the technological and anthropological aspects of the building in use and for the identification of unsustainable operational practice and emerging problems. The forensic analysis identified energy savings of up to 50%, enabling a more sustainable, lower-energy operational future for the building. The building forensic analysis method presented in this paper is now planned for use in other public and commercial buildings.

How useful is dual energy lateral vertebral assessment in a clinic setting?

Screening for osteoporotic vertebral fractures traditionally involves X-ray of the thoracic and lumbar spine. We evaluated use of dual energy X-ray technology in patients with osteoporosis. We found this technology useful in the clinic setting and it has advantages in that less radiation is delivered to the patient.

Exploiting Significance of Computations for Energy-Constrained Approximate Computing

Approximate execution is a viable technique for environments with energy constraints, provided that applications are given the mechanisms to produce outputs of the highest possible quality within the available energy budget. This paper introduces a framework for energy-constrained execution with controlled and graceful quality loss. A simple programming model allows developers to structure the computation in different tasks, and to express the relative importance of these tasks for the quality of the end result. For non-significant tasks, the developer can also supply less costly, approximate versions. The target energy consumption for a given execution is specified when the application is launched. A significance-aware runtime system employs an application-specific analytical energy model to decide how many cores to use for the execution, the operating frequency for these cores, as well as the degree of task approximation, so as to maximize the quality of the output while meeting the user-specified energy constraints. Evaluation on a dual-socket 16-core Intel platform using 9 benchmark kernels shows that the proposed framework picks the optimal configuration with high accuracy. Also, a comparison with loop perforation (a well-known compile-time approximation technique), shows that the proposed framework results in significantly higher quality for the same energy budget.

Behaviour-time budget and functional habitat use of a free-ranging European badger (Meles meles)

Background: The European badger (Melesmeles) is involved in the maintenance of bovine tuberculosis infection and onward spread to cattle. However, little is known about how transmission occurs. One possible route could be through direct contact between infected badgers and cattle. It is also possible that indirect contact between cattle and infected badger excretory products such as faeces or urine may occur either on pasture or within and around farm buildings. A better understanding of behaviour patterns in wild badgers may help to develop biosecurity measures to minimise direct and indirect contact between badgers and cattle. However, monitoring the behaviour of free-ranging badgers can be logistically challenging and labour intensive due to their nocturnal and semi-fossorial nature.We trialled a GPS and tri-axial accelerometer-equipped collar on a free-ranging badger to assess its potential value to elucidate behaviour-time budgets and functional habitat use. Results: During the recording period between 16:00 and 08:00 on a single night, resting was the most commonly identified behaviour (67.4%) followed by walking (20.9%), snuffling (9.5%) and trotting (2.3%).When examining accelerometer data associated with each GPS fix and habitat type (occurring 2 min 30 s before and after), walking was themost common behaviour in woodland (40.3%) and arable habitats (53.8%), while snuffling was themost common behaviour in pasture (61.9%). Several nocturnal resting periods were also observed. The total distance travelled was 2.28 km. Conclusions: In the present report, we demonstrate proof of principle in the application of a combined GPS and accelerometer device to collect detailed quantitative data on wild badger behaviour. Behaviour-time budgets allow us to investigate how badgers allocate energy to different activities and how thismight change with disease status. Such information could be useful in the development of measures to reduce opportunities for onward transmission of bovine tuberculosis from badgers to cattle.