Capgemini (France)

In the context of energy transition and the search for sustainable industrial solutions, waste heat recovery is a promising strategy to improve energy efficiency and reduce greenhouse gas emissions. This study investigates the integration of Organic Rankine Cycle (ORC) systems for waste heat recovery through a comprehensive 3E (energy, exergy, and environmental) analysis. A Python 3.10-based simulation framework was employed to model ORC performance under varying operating conditions and working fluids. Two case studies were considered: (i) a metallurgical application (specifically, an aluminium production plant) and (ii) two large marine engines (Man S60-MC6 and Wärtsilä 46DF), evaluated in electricity-only and combined heat-and-power (CHP) modes. Results show that neopentane is the optimal fluid for the aluminum industry, achieving 3.5 MW of net power output with zero environmental penalties. For marine engines, efficiency gains reached 7–8% for the Man engine and over 10% for the Wärtsilä engine in electricity mode, with thermal efficiencies exceeding 35% under CHP operation. The study demonstrates the relevance of ORC systems for the energy recovery of waste heat and the integration of sustainable technologies into industrial processes. It helps improve energy efficiency, reduce environmental impact, and support the energy transition by recovering waste heat.

This paper presents a fault-tolerant control (FTC) strategy for a six-degree-of-freedom (DoF) anthropomorphic manipulator operating under actuator faults and complex friction dynamics. The proposed framework integrates a backstepping control methodology with LuGre friction modeling and a feedforward neural network (FFNN) for friction estimation. Actuator faults are considered in the form of multiplicative efficiency losses and additive disturbances. An adaptive control law is developed to estimate and compensate for both friction and actuator faults in real time. The stability of the closed-loop system is guaranteed through Lyapunov theory. The simulation results validate the effectiveness and robustness of the proposed approach in ensuring precise trajectory tracking despite faults and friction uncertainties.

Abstract Bipolar electrochemistry is a high‐throughput corrosion testing method capable of applying a quasi‐linear potential gradient across test specimens. This study employs—bipolar electrochemistry corrosion testing to investigate the influence of gravity on pitting corrosion of type 304L and 420 stainless steel across a broad range of applied potentials. Gravity modifies the distribution of current density on the bipolar electrode without altering the potential distribution. The impact of gravity on pitting corrosion is achieved through its effects on the dilution of the electrolyte and the removal of the salt film within the pits. Pits oriented in a face up position demonstrate smoother morphologies, larger cross‐sectional areas and pit volumes. In contrast, pits oriented in perpendicular and facedown positions exhibit pit shape. Under conditions governed by diffusion and activation control, pits can up to over 100 μm. Additionally, crystallographic pits are observed to form in areas subjected to high applied potentials.

This paper addresses the peak-to-average power ratio (PAPR) reduction of Orthogonal Frequency Division Multiplexing (OFDM) waveforms under unit modulus constraint for Integrated Sensing and Communication systems (ISAC). We conduct a comparative analysis of radar and communication performance for waveforms optimized using classical PAPR reduction techniques, primarily Tone Reservation (TR) and Selected Mapping (SLM), as well as their state-of-the-art sensing-aware variants, across radar- and communication-centric scenarios. Additionally, we investigate the pilot-aided case with pseudorandom pilot tones. The results reveal a trade-off between PAPR reduction, radar sensing capability, and computational cost, offering practical insights for efficient OFDM-based ISAC system design.

Aviation emissions contribute to climate change, one of the key contributors being contrail cirrus clouds. The importance of the impact is strongly dependent on their formation and persistence. A condensation trail - or contrail - is composed of ice crystals which form behind the aircraft engine exhaust at high altitudes when local weather conditions are favorable. The formation is also influenced by the engine technology and operating conditions, and by the fuel type. The contrail persists and evolves as long as it remains in an ice supersaturated region - or ISSR-, a local atmospheric air mass characterized by a low temperature and a humidity level that is saturated versus ice. Only persistent contrails are considered as having a climate effect.Weather forecast or reanalysis datasets were leveraged to understand if current data are sufficient to predict ISSRs and contrails, and to support the preparation of in-flight measurement campaigns. Statistics on ISSRs using multiple years of ERA5 ECMWF data will be presented for different months, and geographical areas. Results clearly help to identify geographical areas where the frequency of ISSRs is more important, as well as the seasonal, diurnal and vertical evolution of these frequencies. Using these results and incorporating information on annual meteorological changes, such as El Niño, a region and time of year was selected to conduct a contrail-related flight measurement campaign in Minnesota for two weeks of December 2023.During this period, NOAA GFS forecasts were used to identify ISSRs and plan aircraft flight paths. The forecast for a flight will be presented and compared to what was observed during this flight. The use of different resolutions to establish this forecast will also be discussed.

This paper deals with an energy management strategy for a catenary-free modular train using a combination of fuel cell (FC), battery (BAT) and supercapacitor (SC). The objective is to identify the best sizing with compromising functional constraints to each source appropriately. The strategy first uses an equivalent consumption minimization (ECMS) algorithm to minimize the fuel consumption (hydrogen) while keeping the state of charge of different components in a safe range. Then, particle swarm optimization (PSO) algorithm is applied to optimize the sizing of the embedded sources. The compared results, simulated in Matlab/ Simulink environment, prove that PSO contributes to effectively reduce the embedded weight by 10% compared to the ECMS sizing results while achieving an appropriate power distribution.