Identification of the safety operating envelope of a novel subsea shuttle tanker

A baseline Subsea Shuttle Tanker (SST) was proposed as a cost-efficient maritime transportation method. It is designed to be a 164 m length, 17 m beam autonomous underwater vessel with a cargo capacity of over 16,000 m3. One of the crucial topics for such underwater vehicles is recoverability during undesired malfunctions. A Safety Operating Envelope (SOE) must be identified for military submarines. It considers the submersibles' malfunctions, including partial flooding, jam-to-rise, and jam-to-dive. This paper aims to identify the SOE to enclose the safety operation zones of the SST. In this work, a planar SST manoeuvring simulation model considering the combined contributions from hydrodynamic loads, compensation tank blowing, propeller thrust, and control planes is derived based on semi-empirical formulas. Second, standard operating procedures of recovery actions are established to cope with each malfunction. After that, free-running simulations are conducted. Three cases are presented to discuss SST recovery responses during each incident. Finally, the SOE of the SST is identified. This established SOE determines the SST's feasible speed and depth excursion ranges from an operational safety perspective. The safety depth is sufficient for the SST to recover from a jam-to-rise failure. Moreover, the study found that the existing safety factor on the structural design suggested by the Norwegian classification society Det Norske Veritas (DNV) naval submarine code is exceedingly conservative and potentially leads to a heavy and complex SST structure. The SOE helps reduce the designed collapse depth from the operational safety perspective and contributes to reduced material cost and considerable payload capacity. Also, this work fills in the blanks of SOE analysis on commercial submersibles.publishedVersio

Trajectory Envelope of a Subsea Shuttle Tanker Hovering in Stochastic Ocean Current - Model Development and Tuning

A subsea shuttle tanker (SST) concept for liquid carbon dioxide transportation was recently proposed to support studies evaluating the ultra-efficient underwater cargo submarine concept. One important topic is the position keeping ability of SST during the offloading process. In this process, the SST hovers above the well and connects with the wellhead using a flowline. This process takes around 4 h. Ocean currents can cause tremendous drag forces on the subsea shuttle tanker during this period. The flow velocities over hydroplanes are low throughout this process, and the generated lift forces are generally insufficient to maintain the SST’s depth. The ballast tanks cannot provide such fast actuation to cope with the fluctuation of the current. It is envisioned that tunnel thrusters that can provide higher frequency actuation are required. This paper develops a maneuvering model and designs a linear quadratic regulator that facilitates the SST station-keeping problem in stochastic current. As case studies, the SST footprints at 0.5 m/s, 1.0 m/s, and 1.5 m/s mean current speeds are presented. Numerical results show that the designed hovering control system can ensure the SST’s stationary during offloading. The required thrust from thrusters and the propeller are presented. The presented model can serve as a basis for obtaining a more efficient design of the SST and provide recommendations for the SST operation.acceptedVersio

Station keeping of a subsea shuttle tanker system under extreme current during offloading

This paper presents the station keeping challenge of the subsea shuttle tanker (SST) design during underwater loading and offloading at a subsea well under an extreme current environment. The paper investigates the movement of the SST during offloading with extreme current speeds, i.e. above 1.6 m/s, in the surge, heave and pitch motions, respectively. A linear quadratic regulator (LQR) is used for SST motion control. The LQR’s primary focus is to achieve the target for the SST during the offloading process. Then, the average exceedance rate method is used to predict the maximum and minimum potential depth excursion. This extreme value prediction result will serve as a basis for obtaining a cost-efficient design of the subsea shuttle tanker and provide recommendations for the decision-makers upon SST operation.publishedVersio

UiS Subsea-Freight Glider: A large buoyancy-driven autonomous cargo glider

This study presents the baseline design for the autonomous subsea vehicle capable of traveling at a lower speed of 1 m/s with an operating range of 400 km. Owing to UiS subsea-freight glider’s (USFG) exceedingly economical and unique propulsion system, it can transport various types of cargo over variable distances. The primary use-case scenario for the USFG is to serve as an autonomous transport vessel to carry CO2 from land-based facilities to subsea injection sites. This allows the USFG to serve as a substitute for weather-dependent cargo tankers and underwater pipelines. The length of the USFG is 50.25 m along with a beam of 5.50 m, which allows the vessel to carry 518 m3 of CO2 while serving the storage needs of the carbon capture and storage (CCS) ventures on the Norwegian continental shelf. The USFG is powered by battery cells, and it only consumes a little less than 8 kW of electrical power. Along with the mechanical design of the USFG, the control design is also presented in the final part of the paper. The maneuvering model of the USFG is presented along with two operational case studies. For this purpose, a linear quadratic regulator (LQR)- and proportional-integral-derivative (PID)-based control system is designed, and a detailed comparison study is also shown in terms of tuning and response characteristics for both controllers.acceptedVersio

An evaluation of key challenges of CO2 transportation with a novel Subsea Shuttle Tanker

Recently, a novel Subsea Shuttle Tanker (SST) concept has been proposed to transport carbon dioxide (CO2) from ports to offshore oil and gas fields for either permanent storage or enhanced oil recovery (EOR). SST is a large autonomous underwater vehicle that travels at a constant water depth away from waves. SST has some key advantages over subsea pipelines and tanker ships when employed at marginal fields. It enables carbon storage in marginal fields which do not have sufficient volumes to justify pipelines. Further, in contrast to ships, SST does not require the use of a permanently installed riser base. This paper will evaluate the key challenges of using such vessel for CO2 transportation. It discusses the most important properties such as thermodynamic properties, purity, and hydrate formation of CO2 at different vessel-transportation states in relation to cargo sizing, material selection, and energy consumption.publishedVersio

Dynamic design and analysis of subsea CO2 discharging flowline for cargo submarines used for CCS in low-carbon and renewable energy value chains

Developing offshore low carbon and renewable energy value chains to realize a net-zero energy future requires combining offshore renewable energy and carbon capture storage (CCS) solutions. The subsea shuttle tanker (SST) was presented in recently published works to accelerate the adoption of offshore CCS systems. The SST is a novel underwater vessel designed to transport CO2 autonomously from offshore facilities to subsea wells for direct injection at marginal fields using a flowline connected. The SST will be subjected to stochastic currents and experience dynamic responses during this offloading process. The offloading flowline must be designed to handle this dynamic response. As such, this paper establishes the baseline design for this flowline. The cross-section and global configuration designs drive the flowline design. For the cross-section design, the pressure containment, collapse and local buckling criteria defined in DNV-OS-F101 are applied to validate the required structural capacity at specified water depths. For the configuration design, the principle factors concerning the water depth, internal flow rate, and current speed are investigated to further validate the stress capacity according to the allowed von Mises stress level for a more robust baseline design. Finally, the flowline connecting and disassembly methodology is proposed, and the critical factor of well-coordinated speed between flowline and SST is investigated to avoid overbending during the lifting and lowering phases.publishedVersio

Generative Watermarking Against Unauthorized Subject-Driven Image Synthesis

Large text-to-image models have shown remarkable performance in synthesizing high-quality images. In particular, the subject-driven model makes it possible to personalize the image synthesis for a specific subject, e.g., a human face or an artistic style, by fine-tuning the generic text-to-image model with a few images from that subject. Nevertheless, misuse of subject-driven image synthesis may violate the authority of subject owners. For example, malicious users may use subject-driven synthesis to mimic specific artistic styles or to create fake facial images without authorization. To protect subject owners against such misuse, recent attempts have commonly relied on adversarial examples to indiscriminately disrupt subject-driven image synthesis. However, this essentially prevents any benign use of subject-driven synthesis based on protected images. In this paper, we take a different angle and aim at protection without sacrificing the utility of protected images for general synthesis purposes. Specifically, we propose GenWatermark, a novel watermark system based on jointly learning a watermark generator and a detector. In particular, to help the watermark survive the subject-driven synthesis, we incorporate the synthesis process in learning GenWatermark by fine-tuning the detector with synthesized images for a specific subject. This operation is shown to largely improve the watermark detection accuracy and also ensure the uniqueness of the watermark for each individual subject. Extensive experiments validate the effectiveness of GenWatermark, especially in practical scenarios with unknown models and text prompts (74% Acc.), as well as partial data watermarking (80% Acc. for 1/4 watermarking). We also demonstrate the robustness of GenWatermark to two potential countermeasures that substantially degrade the synthesis quality

Change Diffusion: Change Detection Map Generation Based on Difference-Feature Guided DDPM

Deep learning (DL) approaches based on CNN-purely or Transformer networks have demonstrated promising results in bitemporal change detection (CD). However, their performance is limited by insufficient contextual information aggregation, as they struggle to fully capture the implicit contextual dependency relationships among feature maps at different levels. Additionally, researchers have utilized pre-trained denoising diffusion probabilistic models (DDPMs) for training lightweight CD classifiers. Nevertheless, training a DDPM to generate intricately detailed, multi-channel remote sensing images requires months of training time and a substantial volume of unlabeled remote sensing datasets, making it significantly more complex than generating a single-channel change map. To overcome these challenges, we propose a novel end-to-end DDPM-based model architecture called change-aware diffusion model (CADM), which can be trained using a limited annotated dataset quickly. Furthermore, we introduce dynamic difference conditional encoding to enhance step-wise regional attention in DDPM for bitemporal images in CD datasets. This method establishes state-adaptive conditions for each sampling step, emphasizing two main innovative points of our model: 1) its end-to-end nature and 2) difference conditional encoding. We evaluate CADM on four remote sensing CD tasks with different ground scenarios, including CDD, WHU, Levier, and GVLM. Experimental results demonstrate that CADM significantly outperforms state-of-the-art methods, indicating the generalization and effectiveness of the proposed model

DeepPredict : A zone preference prediction system for online lodging platforms

Publisher Copyright: © The author(s) 2021.Online lodging platforms have become more and more popular around the world. To make a booking in these platforms, a user usually needs to select a city first, then browses among all the prospective options. To improve the user experience, understanding the zone preferences of a user's booking behavior will be helpful. In this work, we aim to predict the zone preferences of users when booking accommodations for the next travel. We have two main challenges: (1) The previous works about next information of Points Of Interest (Pals) recommendation are mainly focused on users' historical records in the same city, while in practice, the historical records of a user in the same city would be very sparse. (2) Since each city has its own specific geographical entities, it is hard to extract the structured geographical features of accommodation in different cities. Towards the difficulties, we propose DeepPredict, a zone preference prediction system. To tackle the first challenge, DeepPredict involves users' historical records in all the cities and uses a deep learning based method to process them. For the second challenge, DeepPredict uses HERE places API to get the information of pals nearby, and processes the information with a unified way to get it. Also, the description of each accommodation might include some useful information, thus we use Sent2Vec, a sentence embedding algorithm, to get the embedding of accommodation description. Using a real-world dataset collected from Airbnb, DeepPredict can predict the zone preferences of users' next bookings with a remarkable performance. DeepPredict outperforms the state-of-the-art algorithms by 60% in macro Fl-score.Peer reviewe

Generated Graph Detection

Graph generative models become increasingly effective for data distribution approximation and data augmentation. While they have aroused public concerns about their malicious misuses or misinformation broadcasts, just as what Deepfake visual and auditory media has been delivering to society. Hence it is essential to regulate the prevalence of generated graphs. To tackle this problem, we pioneer the formulation of the generated graph detection problem to distinguish generated graphs from real ones. We propose the first framework to systematically investigate a set of sophisticated models and their performance in four classification scenarios. Each scenario switches between seen and unseen datasets/generators during testing to get closer to real-world settings and progressively challenge the classifiers. Extensive experiments evidence that all the models are qualified for generated graph detection, with specific models having advantages in specific scenarios. Resulting from the validated generality and oblivion of the classifiers to unseen datasets/generators, we draw a safe conclusion that our solution can sustain for a decent while to curb generated graph misuses.Comment: Accepted by ICML 202