To optimize the ignition and combustion performance of nanosecond pulsed surface dielectric barrier discharge (nSDBD) electrodes for NH3/air mixtures, an experimental investigation was conducted by varying the number of radial copper strips (number of conductive areas: CAN = 6, 9, 12) and width (width of conductive areas: CAW = 1 mm, 2 mm, 3 mm) on the powered electrode. Experiments were performed in a constant-volume combustion chamber under an NH3/air mixture with an initial temperature of 363 K, initial pressure of 2 bar, and an excess air ratio (lambda) of 1.0. Diagnostics were implemented using high-speed camera and pressure sensors, with nanosecond pulse parameters set to 100 pulses and a pulse repetition frequency of 20 kHz. The regulatory effects of electrode structural parameters on flame kernel development, flame propagation, and combustion characteristics were systematically analyzed. The results indicate that the CAN and CAW directly govern the flame kernel morphology and evolution by modulating the distribution of discharge energy. Under the optimal configuration of CAN = 6 and CAW = 3 mm, the electric field distribution is uniform with moderate energy density, leading to the stable generation of active free radicals (e.g., O, OH, H). Consequently, the synchronization of flame kernel ignition is maximized, with a consistent, stable number of 6 initial kernels, without obvious fusion or extinction. This configuration significantly compresses the flame development time (FDT = 8.5 ms) and flame rise time (FRT = 26.5 ms). Additionally, the combustion pressure peak occurs earliest (at 54 ms), and the cumulative heat release rate increases the fastest in the early stage. The findings demonstrate that the nSDBD electrode with CAN = 6 and CAW = 3 mm achieves efficient regulation of combustion performance in NH3/air mixtures by leveraging multi-scale synergy among "electric field distribution-energy deposition-active species generation-flame kernel development-combustion timing". This study provides critical technical parameters and theoretical support for the optimal design of ignition systems in ammonia-fueled engines.
With the addition of large numbers of distributed energy resources (DERs) to distribution networks comes the increasing risk that their operation may violate the safety constraints of these networks. The problem considered in this paper is that of combined siting, sizing and dispatch of these DERs, also known as distributed energy system (DES) design, to help meet electrical and heat loads within the network. Here, the operation of these DERs is modelled, along with the unbalanced three-phase alternating current (AC) power flow in the network. When this network power flow is considered, it admits a non-convex mixed-integer nonlinear program (MINLP) model formulation which scales poorly with network size in terms of solve time. To address this, the problem is decomposed into a series of algorithmic steps, between the binary decision variables and the nonlinear power flow constraints. Where solve times still remain unacceptably high for large problem sizes however, a further hybrid spatial/temporal decomposition strategy is presented, and solved with an alternating direction method of multipliers (ADMM)-based distributed optimisation method. The novelty of this approach therefore lies in enabling the applicability of this method to large-scale problem instances in a scalable manner. Results are presented for networks with up to 55 loads and 120 timepoints, with the ADMM approach showing speed-ups of up to 13x when considering parallel computation of the subproblems, for a maximum observed optimality gap of 0.61%.
We have developed a new array of organic glass scintillator detectors, consisting of up to 20 3cm×3cm×3cm scintillator cubes, each coupled to a photomultiplier tube. The array is sensitive to neutrons with energies ranging from a few hundred keV up to several tens of MeV. It was designed to be coupled with a recoil separator for inverse-kinematics direct measurements of astrophysical (α,n) or (p,n) reactions, where it provides a prompt start signal for identifying recoil-neutron coincidences from the recoil time of flight. In this article, we present the results of both source and in-beam measurements characterizing the properties of a subset of eight detectors used for a measurement of the 22Ne(α,n)25Mg reaction in inverse kinematics. We report specifically on the detectors’ energy and timing resolution and pulse shape discrimination capabilities. From the widths of γ-ray Compton-edge peaks at 311, 341, 477, and 1062 keVee, we find that the energy resolution as a function of energy is well described by FWHM(E)/E=A2/E+B2/E2+C2, with A=0.00(7), B=0.0618(54), and C=0.135(5). From analyzing the time difference spectrum between nearest-neighbor γ-γ coincidence events in the energy range ∼250-600 keVee, produced by a 22Na source, we find an FWHM coincidence timing resolution of 910(40) ps, implying a single-detector time resolution of 640(30) ps. Using mixed neutron-γ-ray fields produced by both the in-beam 22Ne(α,n)25Mg reaction and a 241AmBe source, we find that the pulse shape discrimination provides n-γ separation with a figure of merit >1 down to a threshold of around 100 keVee. Additionally, we demonstrate that the total neutron yield below 100 keVee can be extracted from two-Gaussian fits to energy-gated pulse-shape discrimination spectra. We also discuss the results of Monte Carlo simulations to determine the array’s neutron detection efficiency in its present configuration. Simulating the present 22Ne(α,n)25Mg measurements, we find a neutron singles detection efficiency of 1.56(45)%, with the uncertainty dominated by systematic effects related to the simulation’s input parameters.
Abstract This multiscale study investigates the influence of three-dimensional (3D) particle shape on the stress–dilatancy behavior of granular materials under low-stress simple shear conditions. High-resolution X-ray micro-computed tomography (micro-CT) was first used to quantify the particle shapes of 11 different sands, spanning silica, carbonate, and mixed compositions. The images with a reconstructed voxel size of 5.5 μm were segmented using a watershed-based algorithm, which isolated more than 10,000 grains per material. This enabled particle-level quantification of 3D shape descriptors for each sand. The analyzed particle-size distributions were validated against those measured from mechanical sieve analysis. In the second phase, direct simple shear tests were conducted on three selected materials, UWA superfine silica, ledge point, and glass beads, considering relative densities of 0.4 to 0.9 and normal pressures of 25 to 150 kPa. The results show that particle shape affects peak friction, excess friction, and dilation response, but the magnitude of this effect depends on density and normal stress. Particle-shape effects are strongest at lower density and lower normal stress, where particle rearrangement and dilation are more readily mobilized. As relative density or normal stress increases, the responses of the three materials converge, indicating that the mechanical response becomes less sensitive to particle shape. These results suggest that particle shape should be treated as a state-dependent modifier of stress–dilatancy behavior at low stress, rather than as a single fixed material correction.
Drawing on dynamic capability and resource-based theories, we theorize that team learning is a microfoundation through which firms develop artificial intelligence (AI) capabilities, and that team creativity is a value-realizing mechanism through which these capabilities generate service innovations and improve organizational performance. Study 1 examines how team learning contributes to the development of firms' AI capability and subsequent service innovations, using data from 600 managers in service firms in China. Study 2 investigates whether team creativity mediates the effects of AI capability on service innovation and performance, based on data from 276 managers in service firms in China. Across two studies, we found that teams contribute to the development and realization of AI capability. Specifically, team learning facilitates the development of AI capability, and team creativity enables firms to translate this capability into innovation and performance outcomes. Overall, this study advances the understanding of how human–AI collaboration enables firms to convert AI capability into tangible organizational value by integrating a microfoundational perspective with the resource-based logic of value realization.
