The New Technological Frontiers of CO2 and Hydrogen Transportation Via Pipelines

COVID-19 pandemic is accelerating the transition to decarbonized energy systems. In this context, major Operators and Contractors are bound to promote innovation and technological development. The paper describes how this is being applied to the design of offshore pipelines that are now required to transport not only Hydrocarbons but also anthropogenic CO2 and low-carbon Hydrogen. In order to evaluate all the new technical challenges presented in designing CO2 and H2 pipelines, a state of art has been carried out and is here presented focusing on all the new technical aspects associated to the main disciplines involved in the pipeline network design. Different technical aspects (such as performances evaluation of Equation of State in CCS, Design Standards application to both CO2 and hydrogen pipelines, energy capacity of hydrogen pipelines and others) have been also analytically or numerically addressed simulating credible pipeline operating scenarios. To achieve that, an intensive engineering effort is being dedicated to the development of knowledge, engineering tools, methods and procedures that will be the basis for the execution of future projects concerning H2 and CO2 transportation and storage. A particular focus has been dedicated to offshore pipeline design both for new installation and repurposing of existing ones. In parallel, the cooperation started between Operators, Contractors, Manufacturers, Institutions and Universities, as described in the present paper, acts as a "booster" for the consolidation of knowledge and for the advancing of technology to put in place to overcome those new challenges. Recommendations are made in relation to the gaps found in experimental evidence present in literature and gaps in Standards coverage for the proper pipeline design in those new scenarios.

Development of a Comprehensive Multi-Factor Method for Comparing Batting Performances in One-Day International Cricket

In cricket, one-day-international (ODI) batsmen have traditionally been compared on the dimensions of batting average (BA) and strike rate (SR). The conventional method of computing BA assumes that runs scored by a batsman follow an exponential or geometric distribution. This results in unreasonably equating batting inconsistency with batting mean. Our study shows that a Weibull distribution model gives a very sensible assessment of a batsman’s inconsistency, independent of his BA. It also provides a superior fit to batting scores of ODI batsmen. We also introduce a measure for ‘quality-runs’ scored by a batsman which takes into account the difficulty level of opposition. Additionally, longevity index and opposition diversity index are defined to make comparisons more holistic. A substantial amount of data engineering effort is made in segregating available data into home, away and neutral matches. The measures proposed in this paper are more comprehensive and granular than those found in the literature. Various combinations of these six criteria are used to rank a select group of great ODI batsmen by assigning objective weights derived from principal component analysis. Finally, multivariate statistical outlier detection procedure produces different lists of outstanding players corresponding to different combinations of criteria. Our proposed methodology may be gainfully used by a team management to select best batsmen in a given situation.

Accelerated First Oil With Integrated Execution Model, Digital FEED Platform And Product Standardization

This paper illustrates how a subsea integrated ("SURF – Subsea Umbilicals Risers Flowlines" and "SPS – Subsea Production systems") execution model can be significantly improved in further reducing the time to first oil. It illustrates the use of digital platforms for FEEDs (Front End Engineering and Design) and Configure to Order (CTO) product designs to provide further schedule improvements resulting in an enhanced execution model. The time to first oil is considerably improved by cruising through the Front-End design gates using digital FEED platforms. CTO designs enable a project to reach the manufacturing gate faster by reducing detailed engineering effort significantly and in some cases eliminating it. The paper provides an introduction to the conventional execution model (Split SURF and SPS scopes), the integrated execution model (combined SURF and SPS scopes) and the enhanced integrated execution model (using digital FEED platform and CTO products). A comparison has been made on project delivery schedules between the three models illustrating the schedule savings through use of digital FEED platforms and CTO designs. It concludes that digitalization of FEEDs and standardization of product configurations create value for the operators in context of faster return on investment. Digital FEEDs are a new concept and still in its infancy. CTO design is a known concept; however, in context of a subsea project, how it may improve schedule delivery is not well established. These factors are truly an enhancement to the integrated execution approach and the additional value gets delivered to the end customer by further accelerating the time to first oil.

GPU acceleration of the FESOM-2 ocean and sea-ice model

<p>FESOM-2 is a finite volume ocean circulation and sea ice model developed by the Alfred Wegener Institute (AWI). It solves the primitive equations using the hydrostatic and Bousinessq approximations on an unstructured grid, allowing seamless mesh resolution increase towards eddy-resolving scales in regions of high variability or along coast lines. FESOM-2 is a highly optimized MPI-parallel Fortran code that displays excellent scaling to tens of thousands of cores. In the context of ESiWACE-2 services, we have explored the benefits of GPU acceleration of FESOM-2 in a six-month engineering effort. We have determined the flux-corrected tracer transport, and in particular the advection of temperature and salinity, to be a dominant factor in the application profile and we have ported this routine to GPUs using both OpenACC and CUDA-C. We conclude that the memory access patterns in FESOM-2 are suitable to map onto GPU accelerators and that both strategies are viable options, giving significant speedups for tracer advection in high-resolution mesh configurations. We have benchmarked the ported application on Nvidia Kepler, Volta and Ampere architectures and observe that our tuned kernels can approach the peak memory bandwidth, and we also see that OpenACC offers a competitive performance with less development and maintenance effort. We conclude that an expansion of the OpenACC directives is the most promising road to utilize upcoming GPU-equipped exascale machines for FESOM-2.</p>

Microgrid System Design Based on Model Based Systems Engineering: the Case Study in the Amazon Region

Microgrid is a technically and economically viable opportunity to meet the demands of populations that, for various reasons, do not have access to electricity. The complexity of Smart Grid (SG) systems requires considerable engineering effort in the design process. Designing this type of complex system requires new approaches, methods, concepts and engineering tools. Where, requirements analysis plays a major role in better characterizing, understanding and specifying the domain of application that SG systems should solve. This work presents a systemic proposal based specifically on System Systems (SoS) which anticipates the formalization of requirements, aiming to understand, analyze and design SG within the scope of Model Based Systems Engineering (MBSE). The definition of a microgrid from the SoS perspective is presented in order to provide a complete view of its life cycle. Requirements would be represented in an Objective Oriented Requirements Engineering (GORE) approach, specifically using visual diagrams based on the Keep All Objectives Satisfied (KAOS) method, where network operation and control will be formally represented. A case study for small communities in the equatorial Amazon forest is used as a case study for the proposed method.

Perception of Complexity in Engineering Design

This paper evaluates perception of complexity in a novel explanatory model that relates product performance and engineering effort. Complexity is an intermediate factor with two facets: it enables desired product performance but also requires effort to achieve. Three causal mechanisms explain how exponential growth bias, excess complexity, and differential perception lead to effort overruns. Secondary data from a human subject experiment validates the existence of perception of complexity as a context-dependent factor that influences required design effort. A two-level mixed effects regression model quantifies differences in perception among 40 design groups. Results summarize how perception of complexity may contribute to effort overruns and outline future work to further validate the explanatory model and causal mechanisms.

Towards Semi-Automatic Generation of a Steady State Digital Twin of a Brownfield Process Plant

Researchers have proposed various models for assessing design alternatives for process plant retrofits. Due to the considerable engineering effort involved, no such models exist for the great majority of brownfield process plants, which have been in operation for years or decades. This article proposes a semi-automatic methodology for generating a digital twin of a brownfield plant. The methodology consists of: (1) extracting information from piping and instrumentation diagrams, (2) converting the information to a graph format, (3) applying graph algorithms to preprocess the graph, (4) generating a simulation model from the graph, (5) performing manual expert editing of the generated model, (6) configuring the calculations done by simulation model elements and (7) parameterizing the simulation model according to recent process measurements in order to obtain a digital twin. Since previous work exists for steps (1–2), this article focuses on defining the methodology for (3–5) and demonstrating it on a laboratory process. A discussion is provided for (6–7). The result of the case study was that only few manual edits needed to be made to the automatically generated simulation model. The paper is concluded with an assessment of open issues and topics of further research for this 7-step methodology.

Developing a Smart Infusion Pump Dedicated to Infusion Safety

A large-volume infusion pump is a medical device with a big job: infuse patients with life-sustaining fluids and medications at a known and controlled rate. And, do it safely. Because infusions are frequently administered therapies, the opportunity for use error–induced adverse events is amplified. To develop a safer infusion pump, Ivenix, Inc., committed to a comprehensive usability engineering effort that included over 400 hours of usability testing. As a result, the pump’s design includes risk controls for mitigating potential use errors not available on today’s pumps. The resulting product was the winner of the 2019 Stanley Caplan User-Centered Design Award.

How to Build a Biological Machine Using Engineering Materials and Methods

We present work in 3D printing electric motors from basic materials as the key to building a self-replicating machine to colonise the Moon. First, we explore the nature of the biological realm to ascertain its essence, particularly in relation to the origin of life when the inanimate became animate. We take an expansive view of this to ascertain parallels between the biological and the manufactured worlds. Life must have emerged from the available raw material on Earth and, similarly, a self-replicating machine must exploit and leverage the available resources on the Moon. We then examine these lessons to explore the construction of a self-replicating machine using a universal constructor. It is through the universal constructor that the actuator emerges as critical. We propose that 3D printing constitutes an analogue of the biological ribosome and that 3D printing may constitute a universal construction mechanism. Following a description of our progress in 3D printing motors, we suggest that this engineering effort can inform biology, that motors are a key facet of living organisms and illustrate the importance of motors in biology viewed from the perspective of engineering (in the Feynman spirit of “what I cannot create, I cannot understand”).

audiomath: a Neuroscientist's Sound Toolkit

In neuroscientific experiments and applications, working with auditory stimuli demands software tools for generation and acquisition of raw audio, for composition and tailoring of that material into finished stimuli, for precisely timed presentation of the stimuli, and for experimental session recording. Numerous programming tools exist to approach these tasks, but their differing specializations and conventions demand extra time and effort for integration. In particular, verifying stimulus timing requires extensive engineering effort when developing new applications.We present audiomath (https://pypi.org/project/audiomath ), a sound software library for Python that prioritizes the needs of neuroscientists. It minimizes programming effort by providing a simple object-oriented interface that unifies functionality for audio generation, manipulation, visualization, decoding, encoding, recording, and playback. It also incorporates specialized tools for measuring and optimizing stimulus timing.We provide an overview of the challenges and possible approaches to the problem of recording stimulus timing. We then report audio latency measurements across a range of hardware, operating systems and settings, to illustrate the ways in which hardware and software factors interact to affect stimulus presentation performance, and the resulting pitfalls for the programmer and experimenter. In particular, we highlight the potential conflict between demands for low latency, low variability in latency ("jitter"), cooperativeness and robustness. We report the ways in which audiomath can help to map this territory and provide a simplified path toward each application's particular priority.By unifying audio-related functionality and providing specialized diagnostic tools, audiomath both simplifies and potentiates the development of neuroscientific applications in Python.