Rising energy expenses, the shift towards renewable sources, and grid congestion considerably affect the operations of container terminals. To tackle these challenges, it is necessary to implement energy-aware integrated operational planning which considers related uncertainties. This work proposes a two-stage stochastic mixed integer programming model to optimize container terminal operations planning and demand-responsive energy management. To this end, energy consumption is shifted whenever operationally possible and economically beneficial. We solve the proposed model by developing a dedicated progressive hedging algorithm. Operations considered in this model include vessel scheduling at berths, temperature control of refrigerated containers, and allocation of handling capacity of quay cranes, yard cranes, and automated guided vehicles to serve each vessel. Various scenarios for vessel arrival times and electricity prices are explored representing the uncertainty of energy demand and supply, respectively, based on a case study of the Altenwerder container terminal in Hamburg. Our results suggest potential cost savings of 5.9 per cent on average with a single energy price based on a long-term contract and 13.2 per cent when applying varying real-time electricity prices based on wholesale market rates. These findings underscore the substantial potential of demand response strategies for (electrified) container terminal operations.

Wind turbine technology has seen remarkable advancements in the last decades. Most notably, the rated power and size of wind turbines have grown considerably to reduce the cost of energy from wind. The increase in rotor diameters has pushed gearbox manufacturers to introduce multiple technological innovations to boost the torque density of current designs. One of the critical challenges of next-generation gearbox designs is to optimize structural components and gears. Complex models are needed to predict the gearbox components' load-carrying capacity and fatigue life. These tools need to be demonstrated and validated through experimental evaluation. Through physical testing, this study evaluates the structural calculation models used for a modern 6MW wind turbine gearbox. The measurement system is composed of fifty-four fiber Bragg gratings. Optical strain sensors have been used because they offer a higher signal-to-noise ratio, are immune to electromagnetic interference, and allow a more straightforward installation than conventional electrical strain gauges. A good correlation between the structural models and the test results in a full-scale back-to-back test bench has been achieved. This enables studying the effect of a range of design parameters through simulations. Hence, without the need to carry out physical testing for each design. Increasing the confidence in structural models through experimental data leads to more optimized gearbox designs and significant improvements in torque density and overall cost of the gearbox.

Detection of the magnetic signature of ships can be avoided by using a degaussing system; a set of on-board copper coils that compensates for the magnetic signature. High temperature superconductors (HTS) are currently investigated as a replacement for copper degaussing coils. By using HTS, we have to deal with higher currents and therefore with higher power supply losses. Also, large current leads are needed which introduces extra losses. This paper investigates different possible solutions to minimize these losses. Four H-bridge-based MOSFET topologies are presented that were designed to reduce the power supply and current lead losses. The first topology uses an H-bridge configuration so that the degaussing current can freewheel through the low-resistance MOSFETs. The second topology places the H-bridge inside the cryostat so that the current leads can be made smaller. The third topology includes a smoothing capacitor in the cryostat so that the current leads and input current are even smaller. The fourth topology uses a transformer so that the current leads can be eliminated. Measurements were done to determine the MOSFETs and capacitor performance in liquid nitrogen. The simulated losses of the four topologies are compared to determine the most energy-efficient option for supplying current to the HTS coils. It was found that by submerging multiple parallel MOSFETs in liquid nitrogen, the on-state resistance is decreased and the current supply can be made more efficient. Also, by placing a smoothing capacitor inside the cryostat, the current lead losses can be minimized significantly. The benefits of using a transformer do not outweigh the transformer losses.

As part of the National Agenda for Quantum Technology, QuTech (TU Delft and TNO) has agreed to make quantum technology accessible to society and industry via its full-stack prototype: Quantum Inspire. This system includes two different types of programmable quantum chips: circuits made from superconducting materials (transmons), and circuits made from silicon-based materials that localize and control single-electron spins (spin qubits). Silicon-based spin qubits are a natural match to the semiconductor manufacturing community, and several industrial fabrication facilities are already producing spin-qubit chips. Here, we discuss our latest results in spin-qubit technology and highlight where the semiconducting community has opportunities to drive the field forward. Specifically, developments in the following areas would enable fabrication of more powerful spin-qubit based quantum computing devices: circuit design rules implementing cryogenic device physics models, high-fidelity gate patterning of low resistance or superconducting metals, gate-oxide defect mitigation in relevant materials, silicon-germanium heterostructure optimization, and accurate magnetic field generation from on-chip micromagnets.

The mission of QuTech is to bring quantum technology to industry and society by translating fundamental scientific research into applied research. To this end we are developing Quantum Inspire (QI), a full-stack quantum computer prototype for future co-development and collaborative R&D in quantum computing. A prerelease of this prototype system is already offering the public cloud-based access to QuTech technologies such as a programmable quantum computer simulator (with up to 31 qubits) and tutorials and user background knowledge on quantum information science (www.quantum-inspire.com). Access to a programmable CMOS-compatible Silicon spin qubit-based quantum processor will be provided in the next deployment phase. The first generation of QI's quantum processors consists of a double quantum dot hosted in an in-house grown SiGe/²⁸Si/SiGe heterostructure, and defined with a single layer of Al gates. Here we give an overview of important aspects of the QI full-stack. We illustrate QI's modular system architecture and we will touch on parts of the manufacturing and electrical characterization of its first generation two spin qubit quantum processor unit. We close with a section on QI's qubit calibration framework. The definition of a single qubit Pauli X gate is chosen as concrete example of the matching of an experiment to a component of the circuit model for quantum computation.