System Partitioning Research Articles

Fuzzy Logic Energy Management Strategy of a Multiple Latent Heat Thermal Storage in a Small-Scale Concentrated Solar Power Plant

Latent heat thermal energy storage (LHTES) systems allow us to effectively store and release the collected thermal energy from solar thermodynamic plants; however, room for improvements exists to increase their efficiency when in operation. For this reason, in this work, a smart management strategy of an innovative LHTES in a micro-scale concentrated solar combined heat and power plant is proposed and numerically investigated. The novel thermal storage system, as designed and built by the partners within the EU funded Innova MicroSolar project, is subdivided into six modules and consists of 3.8 tons of nitrate solar salt kNO3/NaNO3, whose melting temperature is in the range 216 ÷ 223 °C. In this study, the partitioning of the storage system on the performance of the integrated plant is evaluated by applying a smart energy management strategy based on a fuzzy logic approach. Compared to the single thermal energy storage (TES) configuration, the proposed strategy allows a reduction in storage thermal losses and improving of the plant’s overall efficiency especially in periods with limited solar irradiance. The yearly dynamic simulations carried out show that the electricity produced by the combined heat and power plant is increased by about 5%, while the defocus thermal losses in the solar plant are reduced by 30%.

Orbital Alignment for Accurate Projection-Based Embedding Calculations along Reaction Paths

In projection-based embedding (PbE) the subsystem partitioning of a chemical system is based on localized orbitals. We demonstrate how the localization step can lead to inconsistent orbital spaces along reaction paths, with severe consequences for reaction barriers and energies. We propose an orbital alignment procedure that resolves this problem without manual input. The usefulness of this alignment is demonstrated for a reaction benchmark set in combination with a direct orbital selection approach to automatize PbE calculations of double hybrid-in-nonhybrid density functional theory (DFT) and wave-function-in-DFT type for reaction energies and barriers. We show how the embedded calculations are accelerated in comparison to the corresponding supersystem calculations for realistic example reactions, using a new implementation of domain-based local pair natural orbital coupled cluster with single, double, and perturbative triple excitations [DLPNO-CCSD(T0)]. The embedded calculations yield results within an error margin below 4 kJ mol-1 for the reaction barrier and energy when compared to the supersystem calculation. The calculations can be executed in a user-friendly, black-box-like fashion with minimal manual input.

3-D Partitioning for Large-Scale Graph Processing

Disk I/O is the major performance bottleneck of existing out-of-core graph processing systems. We found that the total I/O amount can be reduced by loading more vertices into memory every time. Although task partitioning of a graph processing system is traditionally considered equivalent to the graph partition problem, this assumption is untrue for many Machine Learning and Data Mining (MLDM) problems: instead of a single value, a vector of data elements is defined as the property for each vertex/edge. By dividing each vertex into multiple sub-vertices, more vertices can be loaded into memory every time, leading to less amount of disk I/O. To explore this new opportunity, we propose a category of 3-D partitioning algorithm that considers the hidden dimension to partition the property vector. The 3-D partitioning algorithm provides a new tradeoff to reduce communication costs, which is adaptive to both distributed and out-of-core scenarios. Based on it, we build a distributed graph processing system CUBE and an out-of-core system SINGLECUBE. Since network traffic is significantly reduced, CUBE outperforms state-of-the-art graph-parallel system PowerLyra by up to 4.7X. By largely reducing the disk I/O amount, the performance of SINGLECUBE is significantly better than state-of-the-art out-of-core system GridGraph (up to 4.5X).

Modern Holstein-origin dairy cows within grassland-based systems partition more feed nitrogen into milk and excrete less in manure

The objective was to determine whether modern Holstein-origin dairy cows, when managed within grassland-based systems, partitioned more feed nitrogen (N) into milk and excreted less in manure, in comparison to an earlier population of Holstein-origin dairy cows. Data used were collated from total diet digestibility studies undertaken in Northern Ireland from 1990 to 2002 (old dataset, n = 538) and from 2005 to 2019 (new dataset, n = 476), respectively. An analysis of variance indicated that cows in the new dataset partitioned a significantly higher proportion of consumed N into milk and excreted a lower proportion in urine and total manure, compared to cows in the old dataset. A second analysis using the linear regression revealed that in comparison to the old dataset, the new dataset had a lower slope in the relationship between N intake and N excretion in urine or total manure, while a higher slope in the relationship between N intake and milk N output. A third analysis used the combined data from both datasets to examine if there was a relationship between experimental year and N utilization efficiency. Across the period from 1990 to 2019, urine N/N intake and manure N/N intake significantly decreased, while milk N/N intake increased. These results indicate that modern Holstein-origin dairy cows utilize consumed N more efficiently than earlier populations. Thus, N excretion is likely to be overestimated if models developed from the old data are used to predict N excretion for modern dairy herds. Therefore, the final part of analysis involved using the new dataset to develop prediction models for N excretion based on N intake and farm level data (milk yield, live weight and dietary N concentration). These updated models can be used to estimate N excretion from modern Holstein-origin dairy cows within grassland-based dairy systems.

Complexity Reduction in Density Functional Theory Calculations of Large Systems: System Partitioning and Fragment Embedding.

With the development of low order scaling methods for performing Kohn-Sham density functional theory, it is now possible to perform fully quantum mechanical calculations of systems containing tens of thousands of atoms. However, with an increase in the size of the system treated comes an increase in complexity, making it challenging to analyze such large systems and determine the cause of emergent properties. To address this issue, in this paper, we present a systematic complexity reduction methodology which can break down large systems into their constituent fragments and quantify interfragment interactions. The methodology proposed here requires no a priori information or user interaction, allowing a single workflow to be automatically applied to any system of interest. We apply this approach to a variety of different systems and show how it allows for the derivation of new system descriptors, the design of QM/MM partitioning schemes, and the novel application of graph metrics to molecules and materials.

Research on Transmission Network Expansion Planning Considering Splitting Control

A robust and reliable grid is one of the core elements for power network planning. Specifically, splitting is an effective way for power grid out-of-step oscillation. Since the cross-section of system out-of-step is mostly found on the weak connection lines, reducing the number of those lines can be conducive to the system partition, save the finding time of the optimal splitting cross-section, and improve the performance of the splitting control. This paper proposed an enhanced method based on slow coherence theory for weak connection lines’ identification and monitoring. The ratio of the number of weak connection lines to the number of all the lines, called weak connection coefficient, is considered as a crucial factor. A bi-level programming model, which perceives the minimum connection coefficient as the optimization goal, is built for the transmission network. Additionally, a fused algorithm, consisting of Boruvka algorithm and particle swarm optimization with adaptive mutation and inertia weight, is employed to solve the proposed method in the instances of an 18-node IEEE Graver system and a practical power grid in East China. Simulation results in PSD-BPA are conducted to verify the effectiveness of the weak connection monitoring method and transmission network planning model.

Stable and radioactive carbon isotope partitioning in soils and saturated systems: a reactive transport modeling benchmark study

This benchmark provides the first rigorous test of a three-isotope system [12C, 13C, and 14C] subject to the combined effects of radioactive decay and both stable equilibrium and kinetic fractionation. We present a series of problems building in complexity based on the cycling of carbon in both organic and inorganic forms. The key components implement (1) equilibrium fractionation between multiple coexisting carbon species as a function of pH, (2) radioactive decay of radiocarbon with associated mass-dependent speciation demonstrating appropriate correction of the Δ14C value in agreement with reporting convention, and (3) kinetic stable isotope fractionation due to the oxidation of organic carbon to inorganic forms as a function of time and space in an open, through-flowing system. Participating RTM codes are CrunchTope, ToughReact, Hytec, and The Geochemist’s Workbench. Across all problem levels, simulation results from all RTMs demonstrate good agreement.

Scalable distributed optimal control of vibrating modular structures

A scalable optimal control method for structural vibration mitigation is studied. The method relies on a structure's partitioning that leads to a set of dynamically interconnected subsystems. Each subsystem is operated with an individual subcontroller that collects the local state information and collaborates with the neighboring subcontrollers to estimate a short time prediction of the interconnecting forces defining the subsystem's boundary conditions. Using the extended model that represents the subsystem's dynamics together with the evolution of its boundary conditions, each subcontroller computes the control decision based on the solution to a finite-time horizon optimal control problem. In order to cope with the changes in the boundary conditions, the optimal solution is computed repetitively according to the receding horizon scheme. The method is validated numerically for a cantilever structure equipped with actively controlled electromagnetic actuators and subjected to a variety of initial condition scenarios. The performance of the designed controller is tested by comparisons to the centralized and isolated decentralized controllers. The introduced system partitioning and distributed controller allow performing parallel computing which makes the method fully scalable and applicable to large-scale structures. The computational complexity of the designed distributed control is studied for different settings in the modeling of the subsystem's boundary conditions.

Monitoring technical lignin partition in aqueous/alcohol biphasic systems according to pH: influences of the molecular structure and solvent characteristics

This study deals with the comprehension of lignin partition in biphasic systems constituted of an alkaline aqueous phase containing dissolved lignin and a non-miscible alcohol phase.

Mercury partitioning in oil and gas production systems - design optimisation and risk mitigation through advanced simulation

Operators are increasingly producing fields with challenging operational environments, including fluids with higher concentrations of mercury. Mercury is harmful to personnel and the environment and contaminates most of the production plant that it contacts through physical adsorption, potentially creating hazardous wastes through the operating lifetime and subsequent decommissioning. The assessment of mercury removal locations requires careful consideration at the design stage. Mercury exists in various chemical forms that readily partition between streams in the production process. Mercury partitioning simulations are an essential step in managing the operational, safety, environmental, production and decommissioning risks associated with mercury. Accurate assessment of mercury species during welltests is an essential step towards successful mercury risk management; providing the basis to model partitioning into vapour, liquid, aqueous and solid phases through production. Adsorption modelling is also necessary to understanding the propagation of mercury through offshore and onshore systems, identifying release points to the environment. Only once form, flow and accumulation locations are understood, can the adequate design of mercury removal facilities be confidently completed. Experience in thermodynamic modelling and verification through laboratory research and plant analysis is required to fully understand modelling limitations, capabilities and applications to proposed or existing infrastructure. Changes in inlet stream conditions may affect propagation of mercury through an operating plant thus the influence of predicted conditions through the full life of the field should be considered.

Reflection Coefficient Stability Criterion for Multi-Bus Multi-VSC Power Systems

This paper proposes a reflection coefficient stability criterion for multi-bus power systems with multiple voltage-source converters (VSCs). In the method, VSCs are modeled as two-port networks, which are then characterized by the incident and reflected power waves, as well as their ratios, i.e. reflection coefficients. Two superior features over the impedance-based stability analysis are provided, which are no need for system partitioning to avoid unstable subsystems, and the identification of relative stability and the critically oscillating frequency at each bus of the power system. Case studies with experimental results demonstrate the effectiveness of the approach.

Segmentation and termination of the surface rupture zone produced by the 1932 Ms 7.6 Changma earthquake: New insights into the slip partitioning of the eastern Altyn Tagh fault system

AbstractThe 1932 Ms 7.6 earthquake struck the active Changma fault in the NE Tibetan Plateau, and produced a distinct surface rupture along the fault zone. However, the segmentation and termination of the surface rupture zone are still unclear. In this paper, the active tectonic analyses of multiple satellite images complemented by field investigations present the 120-km-long surface rupture zone, which can be divided into five discrete first-order segments, ranging from 14.4 to 39.56 km in length, linked by step-overs. Our results also indicate that the 1932 rupture zone could jump across step-overs 0.3–4.5 km long and 2.2–5.4 km wide in map view, but was terminated by a 6.3-km-wide restraining step-over at the eastern end. The left-lateral slip rates along the mid-eastern and easternmost segments of the Changma fault are 3.43 ± 0.5 mm/yr and 4.49 ± 0.5 mm/yr since 7–9 ka, respectively. The proposed tectonic models suggest that the slip rates on the Changma fault are similar to the slip rate on the eastern segment of the Altyn Tagh fault system near the junction point with the Changma fault. These results imply that the Changma fault plays a leading role in the slip partitioning of the easternmost segment of the Altyn Tagh fault system.

Optimization of Water Distribution Systems: A Review

A water distribution system (WDS), being a system of interconnected hydraulic elements ensures water distribution and supply to satisfy demands while optimization is applied in many systems and situations, thus making it an important paradigm in technology. When we try to optimize, we either minimize (resource consumption, cost) or maximize (profit, system performance). This paper reviews optimization of water distribution systems and presents different aspects of WDSs. It also presents the different optimization methods in a detailed manner. Finally, modern sophisticated optimization methods are refined enough to solve complex real-world problems such as WDS design and operations. The categories of challenges being solved include: new WDSs design; expansion and revamping of existing WDSs; pump operation; water quality management; calibration and system partitioning. Reliability, robustness and resilience considerations are often encountered in WDS literature and are still open to analysis because there is no generally acceptable model for permanent inclusion in WDSs optimization.

INSTalytics

We present the design, implementation, and evaluation of INSTalytics , a co-designed stack of a cluster file system and the compute layer, for efficient big-data analytics in large-scale data centers. INSTalytics amplifies the well-known benefits of data partitioning in analytics systems; instead of traditional partitioning on one dimension, INSTalytics enables data to be simultaneously partitioned on four different dimensions at the same storage cost, enabling a larger fraction of queries to benefit from partition filtering and joins without network shuffle. To achieve this, INSTalytics uses compute-awareness to customize the three-way replication that the cluster file system employs for availability. A new heterogeneous replication layout enables INSTalytics to preserve the same recovery cost and availability as traditional replication. INSTalytics also uses compute-awareness to expose a new sliced-read API that improves performance of joins by enabling multiple compute nodes to read slices of a data block efficiently via co-ordinated request scheduling and selective caching at the storage nodes. We have built a prototype implementation of INSTalytics in a production analytics stack, and we show that recovery performance and availability is similar to physical replication, while providing significant improvements in query performance, suggesting a new approach to designing cloud-scale big-data analytics systems.

Generalized Many-Body Expanded Full Configuration Interaction Theory.

Facilitated by a rigorous partitioning of a molecular system's orbital basis into two fundamental subspaces-a reference and an expansion space, both with orbitals of unspecified occupancy-we generalize our recently introduced many-body expanded full configuration interaction (MBE-FCI) method to allow for electron-rich model and molecular systems dominated by both weak and strong correlation to be addressed. By employing minimal or even empty reference spaces, we show through calculations on the one-dimensional Hubbard model with up to 46 lattice sites, the chromium dimer, and the benzene molecule how near-exact results may be obtained in an entirely unbiased manner for chemical and physical problems of not only academic but also applied chemical interest. Given the massive parallelism and overall accuracy of the resulting method, we argue that generalized MBE-FCI theory possesses an immense potential to yield near-exact correlation energies for molecular systems of unprecedented size, composition, and complexity in the years to come.

On the convex cones arising from classifications of partial entanglement in the three qubit system**Both KHH and SHK were partially supported by NRF-2017R1A2B4006655, Korea.

In order to classify partial entanglement of multi-partite states, it is natural to consider the convex hulls, intersections and differences of basic convex cones obtained from partially separable states with respect to partitions of systems. In this paper, we consider convex cones consisting of -shaped three qubit states arising in this way. The class of -shaped states includes important classes like Greenberger–Horne–Zeilinger diagonal states. We find all the extreme rays of those convex cones to exhibit corresponding partially separable states. We also give characterizations for those cones which give rise to necessary criteria in terms of diagonal and anti-diagonal entries for general three qubit states.

A 20.5 TOPS Multicore SoC With DNN Accelerator and Image Signal Processor for Automotive Applications

Advanced driver-assistance systems (ADASs) that provide machine support to avoid critical accidents are already in wide use in vehicles in today's market. SoCs for such systems have several requirements: 1) high computational performance to run several advanced algorithms at low latency; 2) low power consumption to permit running under the extreme heat and power conditions of real-world vehicles; and 3) high reliability to reduce the risk of serious accidents caused by faults. This article presents a novel SoC for ADAS applications, which resolves these difficult challenges. To achieve high performance with low power consumption, the SoC adopts the heterogeneous architecture, with ten processors, four DSPs, and eight types of accelerators, including the DNN accelerator and the image signal processor (ISP). To achieve higher reliability, the SoC implements several safety mechanisms, including system partitioning to prevent fault propagation, diagnostic features to detect faults, and a dedicated controller to operate runtime built-in self-test (BIST) of the ISP's diagnostic features during the vertical blanking interval (VBLANK). The SoC is implemented in a 16-nm process, and its size is 94.52 mm <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> . Peak performance of 20.5 TOPS and low power consumption of 9.78 W are achieved.

Influence of the Coupling between Two Qubits in an Open Coherent Cavity: Nonclassical Information via Quasi-Probability Distributions

In this paper, we investigate the dynamics of two coupled two-level systems (or qubits) that are resonantly interacting with a microwave cavity. We examine the effects of the intrinsic decoherence rate and the coupling between the two qubits on the non-classicality of different system partitions via quasi-probability functions. New definitions for the partial Q-function and its Wehrl entropy are used to investigate the information and the quantum coherence of the phase space. The amount of the quantum coherence and non-classicality can be appropriately tuned by suitably adopting the rates of the intrinsic-decoherence and the coupling between the two qubits. The intrinsic decoherence has a pronounced effect on the negativity and the positivity of the Wigner function. The coupling between the two qubits can control the negativity and positivity of the quasi-probability functions.

Weight Partitioning for Dynamic Fixed-Point Neuromorphic Computing Systems

Neuromorphic computing systems consist of neurons and synapses with limited programmability, and neural networks are modified to be mapped for such a system. In order to map a perceptron with a large number of connections into a hardware neuron with a fixed, small number of synapses, it is decomposed into a tree of perceptrons, which substantially affects the neuron usage and predictive performance. In this paper, we propose two decomposition algorithms that take advantage of plastic connections and the dynamic scaling capability of neurons. One algorithm based on sorting considers the neuron usage first, and the other algorithm based on packing considers the predictive performance first. Our experimental results on two popular deep convolutional neural networks showed that the sorting-based algorithm substantially improved the accuracy at no cost of neurons compared to a previous work, and the packing-based algorithm improved it even further at a small cost of neurons.

Microcanonical Statistical Model for Fragmentation of Small Neutral Carbon Clusters

We present the microcanonical statistical model to study fragmentation of small neutral carbon clusters Cn (n ≤ 9). This model describes, at a given energy, the phase space associated with all the degrees of freedom accessible to the system (partition of the mass, translation and ro- tation, spin and angular momentum of the fragments). The basic ingredients of the model (cluster geometries, dissociation energies, harmonic frequencies) are obtained, for both the parent cluster and the fragments, by an ab initio calculation. The fragmentation channels probabilities obtained as a function of the excitation energy, were compared with the experimental data at the Orsay Tandem. The deposited energy distributions were adjusted so that the experimental measure- ments were optimally reproduced. Two algorithms were used: Non-Negative Least Squares and Bayesien backtracing. The comparison of the theoretical and experimental probabilities shows a good global agreement. Both algorithms result in deposited energy distributions showing peaks. These peaks could be the signatures of specific molecular states which may play a role in the clus- ter fragmentation.