We present an innovative design for a two-head, gas-cooled multi-slab high-energy, high-repetition-rate amplifier aimed at mitigating thermally induced depolarization in a wide-bandwidth neodymium-doped glass gain medium. This architecture employs two quartz rotators (QRs) with opposite-handedness, strategically positioned within each multi-slab amplifier head, to enhance depolarization compensation. Theoretical modeling of this amplifier configuration demonstrates a 20× reduction in depolarization losses for a 70 mm beam operating at the central wavelength, compared to conventional approaches that utilize a single QR positioned between the amplifier heads. In addition, for a wide bandwidth source, the integration of QRs with opposite-handedness yields a 9× improvement in depolarization losses at the spectral extremes compared to the use of two QRs exhibiting the same optical handedness in both amplifier heads.

A liquefied natural gas (LNG) facility often incorporates replicate liquefaction trains. The performance of equivalent units across trains, designed using common numerical models, might be expected to be similar. In this article, we discuss statistical analysis of real plant data to validate this assumption. Analysis of operational data for end flash vessels from a pair of replicate trains at an LNG facility indicates that one train produces 2.8%–6.4% more end flash gas than the other. We then develop statistical models for train operation, facilitating reduced flaring and hence a reduction of up to 45% in CO2 equivalent flaring emissions, noting that flaring emissions for a typical LNG facility account for ~4%–8% of the overall facility emissions. We recommend that operational data-driven models be considered generally to improve the performance of LNG facilities and reduce their CO2 footprint, particularly when replica units are present.

This study introduces an advanced reinforcement learning (RL)-based control strategy for heating, ventilation, and air conditioning (HVAC) systems, employing a soft actor-critic agent with a customized reward mechanism. This strategy integrates time-varying outdoor temperature-dependent weighting factors to dynamically balance thermal comfort and energy efficiency. Our methodology has undergone rigorous evaluation across two distinct test cases within the building optimization testing (BOPTEST) framework, an open-source virtual simulator equipped with standardized key performance indicators (KPIs) for performance assessment. Each test case is strategically selected to represent distinct building typologies, climatic conditions, and HVAC system complexities, ensuring a thorough evaluation of our method across diverse settings. The first test case is a heating-focused scenario in a residential setting. Here, we directly compare our method against four advanced control strategies: an optimized rule-based controller inherently provided by BOPTEST, two sophisticated RL-based strategies leveraging BOPTEST’s KPIs as reward references, and a model predictive control (MPC)-based approach specifically tailored for the test case. Our results indicate that our approach outperforms the rule-based and other RL-based strategies and achieves outcomes comparable to the MPC-based controller. The second scenario, a cooling-dominated environment in an office setting, further validates the versatility of our strategy under varying conditions. The consistent performance of our strategy across both scenarios underscores its potential as a robust tool for smart building management, adaptable to both residential and office environments under different climatic challenges.

Millimeter wave antenna arrays are essential components of modern communication and radar systems. To produce these devices in large quantities, manufacturers require fast and reliable measurement equipment. The measurement equipment needs to ensure the quality, interoperability, and adherence to regulatory norms of the produced devices. In this work, we present an active probe array structure (PAS), which enables fast, compact, and reliable over-the-air (OTA) measurements of radiation characteristics. No relative movement between the antenna under test (AUT) and the active PAS is required, making the system very suitable for cost-effective large-scale characterization and commercial production test scenarios. We demonstrate and discuss how a near-field (NF) OTA measurement performed by this active PAS system can be used to reconstruct the far-field (FF) antenna radiation behavior of AUTs using an NF to FF correlation approach.

Numerical solutions of partial differential equations require expensive simulations, limiting their application in design optimization, model-based control, and large-scale inverse problems. Surrogate modeling techniques aim to decrease computational expense while retaining dominant solution features and characteristics. Existing frameworks based on convolutional neural networks and snapshot-matrix decomposition often rely on lossy pixelization and data-preprocessing, limiting their effectiveness in realistic engineering scenarios. Recently, coordinate-based multilayer perceptron networks have been found to be effective at representing 3D objects and scenes by regressing volumetric implicit fields. These concepts are leveraged and adapted in the context of physical-field surrogate modeling. Two methods toward generalization are proposed and compared: design-variable multilayer perceptron (DV-MLP) and design-variable hypernetworks (DVH). Each method utilizes a main network which consumes pointwise spatial information to provide a continuous representation of the solution field, allowing discretization independence and a decoupling of solution and model size. DV-MLP achieves generalization through the use of a design-variable embedding vector, while DVH conditions the main network weights on the design variables using a hypernetwork. The methods are applied to predict steady-state solutions around complex, parametrically defined geometries on non-parametrically-defined meshes, with model predictions obtained in less than a second. The incorporation of random Fourier features greatly enhanced prediction and generalization accuracy for both approaches. DVH models have more trainable weights than a similar DV-MLP model, but an efficient batch-by-case training method allows DVH to be trained in a similar amount of time as DV-MLP. A vehicle aerodynamics test problem is chosen to assess the method’s feasibility. Both methods exhibit promising potential as viable options for surrogate modeling, being able to process snapshots of data that correspond to different mesh topologies.

The flexible delivery of single-frequency lasers is far more challenging than that of conventional lasers due to the onset of stimulated Brillouin scattering (SBS). Here we present the successful delivery of 100 W single-frequency laser power through 100 m of anti-resonant hollow-core fiber (AR-HCF) in an all-fiber configuration, with the absence of SBS. By employing a custom-designed AR-HCF with a mode-field diameter matching that of a large-mode-area panda fiber, the system achieves high coupling efficiency without the need for free-space components or fiber post-processing. The AR-HCF attains a transmission efficiency of 92%, delivering an output power of 100.3 W with a beam quality factor (M2) of 1.22. The absence of SBS is confirmed through monitoring backward light, which shows no increase in intensity. This all-fiber architecture ensures high stability, compactness and efficiency, potentially expanding the application scope of single-frequency lasers in high-precision metrology, optical communication, light detection and ranging systems, gravitational wave detection and other advanced applications.

Understanding the physics of electromagnetic pulse (EMP) emission and nozzle damage is critical for the long-term operation of laser experiments with gas targets, particularly at facilities looking to produce stable sources of radiation at high repetition rates. We present a theoretical model of plasma formation and electrostatic charging when high-power lasers are focused inside gases. The model can be used to estimate the amplitude of gigahertz EMPs produced by the laser and the extent of damage to the gas jet nozzle. Looking at a range of laser and target properties relevant to existing high-power laser systems, we find that EMP fields of tens to hundreds of kV/m can be generated several metres from the gas jet. Model predictions are compared with measurements of EMPs, plasma formation and nozzle damage from two experiments on the VEGA-3 laser and one experiment on the Vulcan Petawatt laser.

This research proposes a low-complexity, low-profile square-shaped quad-band dual-sense circularly polarized (CP) perturbed slot antenna with stepped microstrip feed for C-band radar and satellite applications. The proposed antenna is characterized by characteristic mode analysis. The proposed design has a square-shaped slot with diagonally opposite symmetric rectangular corner extensions. Multiband resonance is achieved by exciting the split ring resonator (SRR), cross strips and annular ring structure using the stepped microstrip line-fed slot radiator. The slot antenna and a metallic ring resonate at 1.64 and 8.2 GHz, respectively, showing left-hand circular polarization response, whereas the SRR and cross strips resonate at 3.6 and 6.6 GHz, respectively, exhibiting right-hand circular polarization radiation at these resonance bands. Hence, the proposed design shows quad-band performance with dual-sense CP behavior. Furthermore, the proposed antenna allows for independent tuning of polarization sense at resonance frequencies. The proposed design uses a low-cost FR-4 material as a substrate of dimensions 60 × 60 × 1.6 mm3. The experimentally measured results are in close agreement with the simulated performance parameters of the prototype.

We introduce a comprehensive data-driven framework aimed at enhancing the modeling of physical systems, employing inference techniques and machine-learning enhancements. As a demonstrative application, we pursue the modeling of cathodic electrophoretic deposition, commonly known as e-coating. Our approach illustrates a systematic procedure for enhancing physical models by identifying their limitations through inference on experimental data and introducing adaptable model enhancements to address these shortcomings. We begin by tackling the issue of model parameter identifiability, which reveals aspects of the model that require improvement. To address generalizability, we introduce modifications, which also enhance identifiability. However, these modifications do not fully capture essential experimental behaviors. To overcome this limitation, we incorporate interpretable yet flexible augmentations into the baseline model. These augmentations are parameterized by simple fully-connected neural networks, and we leverage machine-learning tools, particularly neural ordinary differential equations, to learn these augmentations. Our simulations demonstrate that the machine-learning-augmented model more accurately captures observed behaviors and improves predictive accuracy. Nevertheless, we contend that while the model updates offer superior performance and capture the relevant physics, we can reduce off-line computational costs by eliminating certain dynamics without compromising accuracy or interpretability in downstream predictions of quantities of interest, particularly film thickness predictions. The entire process outlined here provides a structured approach to leverage data-driven methods by helping us comprehend the root causes of model inaccuracies and by offering a principled method for enhancing model performance.

Friction stir welding (FSW) is a method of solid-state welding used to connect difficult-to-weld materials, such as aluminium alloy and magnesium alloy that cannot be joined using conventional welding techniques. This paper investigates the impact of FSW parameters on the corrosion characteristics of friction stir-welded AA2014-T6 aluminium alloy. Experiments were conducted in accordance with the Taguchi L9 orthogonal array by varying tool rotation speed, weld speed, and axial force across three levels. The FSW joints of the aluminium alloy AA2014-T6 were subjected to corrosion test using the electro-chemical workstation CHI660C. The Tafel plots and the corrosion rates were obtained from the corrosion tests. It was observed from the analysis of variance (ANOVA) results of the corrosion rates, that the tool rotation speed is the most persuading factor in controlling the corrosion rate. The scanning electron microscope (SEM) images of the corroded samples were analysed for the presence of pitting spots and its density.

Quantum technologies (QT) are being awaited with excitement. They are supported by many governments, the corporate sector, international bodies and technology forecasters. There is discursive investment as well in terms of creating expectations and laying down a vision for the ‘Second Quantum Revolution’. Science and technology studies are also playing their part to think of the quantum future along with philosophical discussions around it. These visions and expectations perform an implicit and latent function of steering policy proposals and governance. At the current stage of development of quantum technologies, a comprehensive and cogent legal framework is hard to envisage. As it is difficult to foresee the final shape of these technologies, a way to proceed can be to focus on the legal enquiry related to economic, political and policy factors which contribute to its material emergence. This can broaden the focus from thinking about its impact to contextualizing its production and development. Further, it allows a way of determining the extent to which social science and ethical frames can apply to the governance of QT, given the legal and practical realities of technology production and use. This article maps the myriad governance frameworks being envisaged to think about the future of QT. It zooms onto the discussion related to the access divide being framed for QT to understand the points of legal intervention. It uses the case of quantum computing to understand the way legal and practical policy solutions have been ideated. It highlights the way these solutions entrench power of digital infrastructure providers further. This seeks to motivate further work to expand the scope of a legal framework for QT.

Surrogate models of turbulent diffusive flames could play a strategic role in the design of liquid rocket engine combustion chambers. The present article introduces a method to obtain data-driven surrogate models for coaxial injectors, by leveraging an inductive transfer learning strategy over a U-Net with available multifidelity Large Eddy Simulations (LES) data. The resulting models preserve reasonable accuracy while reducing the offline computational cost of data-generation. First, a database of about 100 low-fidelity LES simulations of shear-coaxial injectors, operating with gaseous oxygen and gaseous methane as propellants, has been created. The design of experiments explores three variables: the chamber radius, the recess-length of the oxidizer post, and the mixture ratio. Subsequently, U-Nets were trained upon this dataset to provide reasonable approximations of the temporal-averaged two-dimensional flow field. Despite the fact that neural networks are efficient non-linear data emulators, in purely data-driven approaches their quality is directly impacted by the precision of the data they are trained upon. Thus, a high-fidelity (HF) dataset has been created, made of about 10 simulations, to a much greater cost per sample. The amalgamation of low and HF data during the the transfer-learning process enables the improvement of the surrogate model’s fidelity without excessive additional cost.