Design Of Large-scale Systems Research Articles

Pyrolysis characteristics and kinetic parameters assessment of typical agricultural residues using high heating photothermal TGA

Understanding the complexity of thermal degradation characteristics, kinetic behavior, and thermodynamic parameters is pertinent for the efficient design and process optimization of large-scale conversion systems. Comparative assessment of pyrolysis characteristics and thermodynamic parameters of corn cobs (CC), peanut shells (PS) and sugarcane bagasse (SB) are investigated using high heating rates (100–1000 ℃/min) TGA results. Despite the effect of the heating rate increase on the degradation process of individual samples, increasing heating rates affect pyrolysis characteristics due to compositional differences. With increasing heating rates, the comprehensive pyrolysis index increased sporadically for CC (2.54–44.09 ×104%2/min2 ℃3) and PS (1.36–31.13 ×104%2/min2 ℃3), indicating easier release of volatiles at higher pyrolysis heating rates. The pyrolysis stability index (Rw), which relates to thermal degradation stability, presented an increasing trend (CC>PS>SB) with a heating rate increase (≥106%). Kinetic parameters estimated using model-free methods showed that the activation energies of CC and PS were significantly lower than that of SB. Comparative evaluation of fractional conversions and kinetic parameters using traditional and modified parallel models showed that the double random pore model (DRPM) fitted the best for simulating the experimental results at higher heating rates than other models. The change in Gibbs free energy (ΔG) trend for PS and SB showed a deviation, suggesting reaction difficulties due to higher temperature demands.

Generating simplified ammonia reaction model using genetic algorithm and its integration into numerical combustion simulation of 1 MW test facility

A small reaction model for ammonia is necessary for the design and development of large-scale ammonia combustion systems using computational fluid dynamics (CFD) simulation. The present study generated a simplified reaction model for ammonia using a genetic algorithm (14 species and 45 reactions). Optimization was applied to reproduce the combustion properties of ignition delay times, mole fractions of ammonia and nitric oxide at the exit of a single perfectly stirred reactor (PSR), those of a two-staged PSR, and laminar flame speed. The generated simplified reaction model for ammonia was applied to combustion CFD simulation for a 1 MW boiler test facility. Results of CFD simulation reproduced the characteristics of two-staged combustion well. Nitrous oxide emissions predicted by the simulation agreed well with experimental results.

A machine-learning digital-twin for rapid large-scale solar-thermal energy system design

In many industrialized regions of the world, large-scale photovoltaic systems now contribute a significant part to the energy portfolio during daylight operation. However, as energy demands peak shortly before sunset and persist for several hours afterwards, the integration of solar-thermal systems is extremely advantageous as a green “bridge” energy source. Accordingly, this work develops a digital-twin model to track and optimize the flow of incoming solar power through a complex solar-thermal storage system, consisting of a large array of adaptable mirrors, an optical-receiver and a power distribution system for customers to extract energy. Specifically, the solar power flow is rapidly computed with a reduced order model of Maxwell’s equations, based on a high-frequency decomposition of the irradiance into multiple rays that experience mirror reflections, losses and ultimately receiver absorption and customer delivery. The method allows for rapid testing (in microseconds) of the performance of large numbers of mirror-receiver layout configurations in design space, over extremely long time periods, such as weeks, months and years, using a genetic-based machine-learning digital-twin framework, which integrates submodels for: •optics and tracking of the Fresnel multi-mirror system,•thermal absorption of the optical energy by the receiver and•optimal operating temperatures balancing radiative losses with heat storage. The overall machine-learning digital-twin optimizes the configuration layout to balance meeting customer demands and operational efficiency. Numerical examples are provided to illustrate the approach. Finally, a deep-learning algorithm is developed and applied to the create an Artificial Neural-Net representation, which allows for even further simulation speedup.

A novel robust extended dissipativity state feedback control system design for interval type-2 fuzzy Takagi-Sugeno large-scale systems

Recently, systems have become large in their model and dynamic. To apply control algorithms, serious problems appear that need to be solved. Two significant problems are modelling the dynamics of the large-scale system and reducing effects of perturbations. In this paper, we use the advantage of large-scale systems modelling based on the type-2 fuzzy Takagi–Sugeno model to cover the uncertainties caused by large-scale systems modelling. The advantage of using membership function information is the reduction of conservatism resulting from stability analysis. Also, this paper uses the extended dissipativity robust control performance index to reduce the effect of external perturbations on the large-scale system, which is a generalization of , , passive and dissipativity performance indexes and control gains can be achieved through solving linear matrix inequalities (LMIs). Hence, the whole closed-loop system is asymptotically stable. Finally, the effectiveness of the proposed method is demonstrated by two practical examples.

The quest for game changers - Review of new trends and innovations in the design of large-scale energy systems

This paper presents a subjective review of emerging trends and innovations in the design of large-scale energy systems. The main research question is how societies are responding to serious concerns about climate change, access to resources, access to energy, and economic development. The aim of the study is twofold. Firstly, it aims at the identification of major changes and emerging novelties that may have an impact on the functioning of modern societies and the future shape of energy systems. Secondly, the aim is to present the increasing complexity and multidimensionality of the issues related to the planning and design of energy systems. In order to achieve the objectives, a short development history and the key features of large-scale energy systems are presented in the first place. Then, the AARTT review framework is proposed, which groups trends and innovations into five respective categories, namely: Awareness, Approaches, Resources, Technologies, and Tools. Within each category up to date literature, reports and projects have been reviewed, and as a result, a mind map has been built, which depicts key innovations. In the last part of the paper, a vision of the future energy system is created based on key findings of the literature review. It addresses the identified innovations and development trends as well as the fact that the global energy transition is noticeably accelerating. It is concluded that the fate of our future doesn't depend solely on technologies but rather a holistic approach and concerted multi- and interdisciplinary activities are required.

Scale-up analysis of a twin-bed PSA pilot plant

Small-scale pressure swing adsorption (PSA) plants, also referred to as pilot plants, are commonly exploited for studying separation processes in favour of the development of mathematical models and scale-up strategies. The applicability of a lately presented mathematical model, which was developed based on experimental data acquired from a high-purity twin-bed N2-PSA pilot plant, is verified in this paper for the design of large-scale systems by an analysis of the mass transfer zone development at different PSA cycle times. Effects of the PSA scale-up factor, adsorber aspect ratio, packed-bed density, and flow resistances along the piping system on the process performance are studied numerically. These considerations are particularly relevant for the scale-up of bank-type PSA units as well as for skid-mounted systems fitted to local space limitations, where the standard scale-up concept of keeping the gas velocity constant often cannot be fully realised. It is demonstrated that the sensitivity of the PSA performance to studied factors increases along with the required product purity level. Therefore, recommendations for adequate dimensions of pilot plants depending on the desired gas purity level can be derived. Limitations of the gas velocity through the adsorber shall be observed to generate reliable simulation data. The agreement between experimental results obtained from an industrial-scale system on one hand, and the outcome of a dynamic simulation on the other hand, is confirmed—provided that realistic pressure profiles are generated by a proper adjustment of flow resistances along the piping.

Clustering-based average state observer design for large-scale network systems

This paper addresses the aggregated monitoring problem for large-scale network systems with a few dedicated sensors. Full state estimation of such systems is often infeasible due to unobservability and/or computational infeasibility; therefore, through clustering and aggregation, a tractable representation of a network system, called a projected network system, is obtained for designing a minimum-order average state observer. This observer estimates the average states of the clusters, which are identified under explicit consideration of estimation error. Moreover, given the clustering, the proposed observer design algorithm exploits the structure of the estimation error dynamics to achieve computational tractability. Simulations show that the computation of the proposed algorithm is significantly faster than the usual H2/H∞ observer design techniques. On the other hand, compromise on the estimation error characteristics is shown to be marginal.

Guest Editorial Cognitive Cyber-Physical Systems With AI Based Solutions in Medical Informatics

All six papers in this special section engage in different streams but extremely relevant domain vectors of Cognitive Cyber-Physical Systems (CCPS) with artificial intelligence (AI) based solutions in medical informatics. Highlights recent trends in the scientific community and presents emergent technologies, implementations, applications concerning the CPSS. CPSS is witnessing rapid transformation as an interdisciplinary technology that blends physical components and computing devices to enable AI-based solutions. CCPS will be playing a significant role that integrates machine learning/AI techniques and resulting in dramatic improvements for medical informatics and the future of human-augmentation. CPHMS coordinates supervisory medical systems and medical resources everywhere; there is a great scope towards health consciousness and healthy society. Medical Cyber-Physical Systems (MCPS) in healthcare towards critical integration in network of medical devices. MCPS is the next generation computing that is comprised of tightly coupled computational and communication components of medical automation systems such as clinical decision, early detection of health infectious, disease prevention, rapid analysis of health hazards and so on. CCPS and MCPS research would be created new models, new design, and integration models for large scale systems in comprehensive, holistic medical automation systems. With recent enlargements in the big data processing, cognitive data science and AI, it is now possible to create even more realistic digital twins that properly model different operating situations and characteristics to process the medical intelligence systems.

Comparison of Measured and Derived Thermal Conductivities in the Unsaturated Soil Zone of a Large-Scale Geothermal Collector System (LSC)

The design, energetic performance, and thermal impact of large-scale geothermal collector systems (LSCs) are dependent on the thermal conductivity of unsaturated soils (λ). The aim of this study was to investigate the benefits of two different λ measurement methods using single-needle sensor measuring devices on a laboratory scale. Since large-scale determinations are required in the context of LSCs, the potential for deriving λ from electrical resistivity tomography measurements (ERTs) was also examined. Using two approaches—the continuous evaporation method and the punctual method—thermal conductivities of soil samples from Bad Nauheim (Germany) were measured. The results were compared with averaged λ derived from three ERT sections. With the evaporation method, significant bulk density changes were observed during the experimental procedure, which were caused by the clay content and the use of repacked samples. The punctual method ensures a sufficiently constant bulk density during the measurements, but only provides a small number of measurement points. The thermal conductivities derived from ERTs show largely minor deviations from the laboratory measurements on average. If further research confirms the results of this study, ERTs could provide a non-invasive and unelaborate thermal exploration of the subsurface in the context of large-scale infrastructure projects such as LSCs.

Improvements for decomposition based methods utilized in the development of multi-scale energy systems

The optimal design of large-scale energy systems can be found by posing the problem as an integrated multi-period planning and scheduling mathematical programming problem. Due to the complexity of the accompanying mathematical programming problem, decomposition techniques are often required, unfortunately they to are often plagued with converge issues. To address these issues we have derived a set of valid inequalities to strengthen the formulation, and we have developed a machine learning framework to identify which subproblems should be solved in each iteration of the decomposition procedure. We illustrate the effectiveness of the valid inequalities and the machine learning framework through the use of a case study wherein we examine the development of an industrial scale hydrogen-based energy system. The results show that the valid inequalities and the machine learning framework significantly reduce the computational burden of the procedure.

A New Model Reduction Technique for the Simplification and Controller Design of Large-Scale Systems

In this article, a new method for the simplification and controller design for large-scale linear dynamic plants is proposed. This method is applicable for both large-scale single input single output (SISO) and multiple input multiple output (MIMO) models. The diminution procedure is simple and it assurances the stability of the lower-order plant provided that the higher-order system (HOS) is stable. The characteristic equation of the desired system is evaluated by using the stability equation technique and the coefficients of the numerator polynomial are calculated by applying a simple mathematical process as mentioned in the proposed scenario. This algorithm is suggested for circumventing the limitations of the stability equation scheme and preserving its important features such as stability, passivity, etc. The proposed method ensures the retention of stability and static characteristic of the higher-order plant in the reduced model. For validating the accuracy and effectiveness of the proposed technique, it is implemented on the three standard real-time systems. These systems are further used for the comparison of the proposed method with some other popular existing algorithms. By using the proposed lower-order plant, a controller is obtained for the large-scale system with the help of the moment matching technique. The design of the controller is demonstrated and validated by a real-time system taken from the literature.

Codesign for Extreme Heterogeneity: Integrating Custom Hardware With Commodity Computing Technology to Support Next-Generation HPC Converged Workloads

The future of high-performance computing (HPC) will be driven by the convergence of physical simulation, artificial intelligence, machine learning, and data science computing capabilities. While computational performance gains afforded by technology scaling, as predicted by Moore's Law, have enabled large-scale HPC system design and deployment using commodity CPU and GPU processing components, emerging technologies will be required to effectively support such converged workloads. These emerging technologies will integrate commodity computing components with custom processing and networking accelerators into ever-more heterogeneous architectures resulting in a diverse ecosystem of industry technology developers, university, and U.S. Government researchers. In this article, we describe efforts at the U.S. Department of Energy's Pacific Northwest National Laboratory to construct an end-to-end codesign framework that lays a groundwork for such an ecosystem, including notable outcomes, remaining challenges, and future opportunities.

Design and Implementation of a Damage Assessment System for Large-Scale Surface Warships Based on Deep Learning

Artificial intelligence technology and image recognition technology are playing an increasingly important role in information warfare, while battlefield image recognition and information processing are at the heart of information processing in warfare. This research will use deep learning image recognition technology and QT development platform, combined with target damage tree analysis and Bayesian network inference method, to research and develop the design of large-scale surface warships damage assessment system. A large-scale surface warships damage assessment system was designed. The system can quickly identify the target large-scale surface warships type with an accuracy rate of over 91%. On this basis, damage assessment is carried out in terms of target vulnerability, combatant power analysis, and bullet-eye rendezvous. A new damage classification is established. The system can improve the efficiency of large-scale surface warships damage assessment, can be well combined with the front-line information collection pictures to assess, and overcome the traditional large-scale surface warships damage assessment and problems of slow and inaccurate manual processing of raw data. It provides a new way of thinking for large-scale surface warships damage assessment research.

Novel principle for characterizing the material-level parameters of a thermoelectric generator module

• A high-fidelity transient model considering all related effects is built and verified. • A modified characterization method using stepped temperature rise is proposed. • The modified method improves the implementation efficiency by 108 times in theory. • A generally consistent accuracy in both characterized results is validated by tests. Accurate characterization of all the three material-level parameters, i.e., thermal conductivity, Seebeck coefficient and electrical resistivity, is a premise for performance evaluation or degradation analysis of both the lab-made and commercial thermoelectric generator (TEG) modules. The recently reported quasi-steady-state (QSS) method can directly derive all the three temperature-dependent parameters through in situ TEG module characterization but shows a poor efficiency. This paper presents a modified QSS method based on stepped temperature rise for a high implementation efficiency. Both the optimal ‘width’ and ‘height’ of each step are elaborately specified. Its feasibility is then validated using a high-fidelity transient TEG model, which considers all the related effects and key irreversible factors. According to the simulations, the modified method has a 108 times higher efficiency than that of the original QSS method. Finally, the practicality of this modified method is explored by tests, which shows a consistent accuracy compared to the original method. Therefore, the modified QSS method is more practical, because it can greatly improve the efficiency on the premise of ensuring the accuracy with its similarity to the original QSS method. All the principle, method and conclusions can assist TEG performance estimation and guide the design of large-scale power systems.

Large-scale simulation of bird localization systems in forests with distributed microphone arrays

Acoustic wildlife monitoring systems are important tools for capturing information about animal habitation in ecosystems. Previous work has demonstrated the effectiveness of audio-based bird localization techniques. However, few studies have investigated the performance and robustness of distributed systems in large forests. Here, the performance of distributed microphone arrays for localizing birds is examined by simulating forest scenes with added reverberation, ambient noise, and measurement errors. The simulation revealed the importance of the signal-to-noise ratio and the spectral weighting in the localization algorithm. These results may guide the design of large-scale wildlife monitoring systems and suggest promising directions for further improvements.

Foundations and Applications of Information Systems Dynamics

Although numerous advances have been made in information technology in the past decades, there is still a lack of progress in information systems dynamics (ISD), owing to the lack of a mathematical foundation needed to describe information and the lack of an analytical framework to evaluate information systems. The value of ISD lies in its ability to guide the design, development, application, and evaluation of large-scale information system-of-systems (SoSs), just as mechanical dynamics theories guide mechanical systems engineering. This paper reports on a breakthrough in these fundamental challenges by proposing a framework for information space, improving a mathematical theory for information measurement, and proposing a dynamic configuration model for information systems. In this way, it establishes a basic theoretical framework for ISD. The proposed theoretical methodologies have been successfully applied and verified in the Smart Court SoSs Engineering Project of China and have achieved significant improvements in the quality and efficiency of Chinese court informatization. The proposed ISD provides an innovative paradigm for the analysis, design, development, and evaluation of large-scale complex information systems, such as electronic government and smart cities.

Evaluation of different scale-up strategies for Haematococcus pluvialis cultivation in airlift photobioreactors

Large scale algal biomass production can be very challenging due to the potential issues of sustainability, environmental ethics, and economic concerns. A strategic approach to the transition from the laboratory to the industrial scale allows the prediction of process characteristics, design and analysis of large scale systems, and reduction of extra costs. In this study, a scale-up procedure that considered different approaches was carried out by selecting the Haematococcus pluvialis as a model organism. Three scale-up parameters (constant mixing time (tm), volumetric power consumption rate (P/V), and oxygen mass transfer coefficient (kL a)) were tested for biomass production in a 2-L airlift photobioreactor and they were compared with those obtained from a 1-L aerated cultivation bottle. Among three strategies, the maximum cell concentration, 4.60±0.20×105 cells/mL, was obtained in a constant volumetric power consumption rate experiment. Also, total carotenoid amount showed similar changes with the cell concentration and reached the maximum concentration of 2.02±0.11 mg/L under constant P/V experiment. However, the cultivation bottle presented the highest biomass amount of 0.62 g/L and specific growth rate of 0.38 day-1 of all of the photobioreactors. This result might be attributed to the low aeration rates or improper configuration of the system, which created a non-homogenous culture medium and led to ineffective mass transfer. 

Compositional Design and Verification of Large-Scale Systems Using Dissipativity Theory: Determining Stability and Performance from Subsystem Properties and Interconnection Structures

A major problem for safety-critical engineering systems is to certify the required stability and performance properties using analytical and computational models of a physical system. The methods for such certification are severely limited in their scalability, that is, their ability to cope with a large number of physical components and the complexity of their interactions. Rather than tackling the system model as a whole, this article advocates a compositional approach that derives system-level guarantees from dissipativity properties of the subsystems and their interconnection structure. This method is particularly suitable when the subsystems are amenable to standard analytical and computational methods. However, a monolithic model of the interconnection is beyond the reach of these techniques. The results reviewed here were motivated by several application problems, such as resource allocation in communication networks, motion coordination in multiagent systems, and dynamical analysis of biochemical reaction networks. Several examples are drawn from these applications to illustrate the results.

Evaluation and Design of Large-Scale Solar Adsorption Cooling Systems Based on Energetic, Economic and Environmental Performance

In hot and dry regions such as the Gulf Cooperation Council (GCC) countries, the cooling demand is often responsible for more than 70% of electricity consumption, which places a massive strain on the electricity grid and leads to significant emissions. Solar thermal driven Silica-Gel/Water adsorption chillers, used for space cooling, could provide low carbon emission cooling and reduce the reliance on grid electricity. However, a meticulous design is required to make this both economically and environmentally beneficial. This paper aims to evaluate the solar thermal adsorption chiller performance based on large-scale cooling demand through a TRNSYS simulation for 1 year of operation. The proposed system consists of two main parts: first, the solar loop with evacuated tube solar collectors; and second, the adsorption cooling system with a silica-gel/water adsorption chiller. A neighbourhood of 80 typical 197 m2 villas in Riyadh, the capital city of the Kingdom of Saudi Arabia (KSA), was taken as a case study. The solar adsorption cycle’s performance has been compared to the conventional vapour compression cycle in terms of energy, economic and environmental performance. In addition, a parametric study has been performed for the main design parameters. Results reveal that the system can reach a solar fraction of 96% with solar collector area of 5500 m2 and a storage tank volume between 350 and 400 m3. Furthermore, the annual energy cost can be reduced by 74% for the solar adsorption system compared to the conventional vapour compression cycle. Meanwhile, the CO2 saving percentage for the solar adsorption cycle was approximately 75% compared to the conventional vapour compression cycle. Carefully designed solar thermal cooling systems can reduce greenhouse gas emissions while covering a large scale of cooling demands. This can reduce the strain on the electricity grid as well as greenhouse gas emissions.

Conceptual Design of Large-Scale Thermal Energy Storage System Utilizing Surplus Renewable Energy for steam power generation

Conceptual Design of Large-Scale Thermal Energy Storage System Utilizing Surplus Renewable Energy for steam power generation