The Space–Time Computational Flow Analysis (STCFA) enabled the virtual test and evaluation of spacecraft parachutes and helped reach the final design of the parachutes used in the Orion spacecraft landing in April 2026. Computational fluid–structure interaction (FSI) analysis of spacecraft parachutes requires overcoming a number of formidable challenges. (i) Because the parachute dynamics involves very large air masses compared to the parachute structure mass, the coupled fluid and structure equations need to be solved with the most robust coupling method practically realizable. Otherwise, the computation can generate nonphysical parachute-shape fluctuations, which would be mistaken for actual, physical shape fluctuations. That would, of course, throw off the design process if the design objective is to minimize the physical fluctuations. (ii) Spacecraft parachute canopies have a “ringsail” construction, creating a “geometric porosity” with hundreds of ring gaps and sail slits that the flow goes through to enhance the canopy shape stability, making the FSI problem seemingly intractable. (iii) Spacecraft parachutes are typically used in cluster configurations, where the parachute canopies interact with each other, creating flow computation challenges associated with large mesh deformations and contact between moving structural surfaces. (iv) The final parachute design had, beyond the hundreds of gaps and slits, a “modified geometric porosity,” aimed at enhancing the canopy shape stability. In FSI computations with such modified geometric porosity, the flow through the “windows” created by the removal of the panels and the wider gaps created by the removal of the sails has to be represented in a different way than the flow through the gaps and slits, because they are larger and their sizes are design parameters. Together with the core STCFA methods, a number of ancillary methods played an essential role in overcoming these challenges, leading to the first-ever FSI computation of the Orion spacecraft final-design parachute clusters, which was reported in 2013. We provide an overview of the core and ancillary STCFA methods that enabled the computations reported in 2013 and summarize the computations carried out for various parachute designs tried before reaching the final design and for other types of parachutes involved in the deceleration of the Orion spacecraft as it returns to Earth. We also provide an overview of the methods we introduced since 2013 to increase the fidelity and scope of spacecraft parachute computational flow analysis and summarize the computations carried out since then. These new developments include extending the computations to compressible flows and upgrading the discretization from finite elements to isogeometric analysis, first with NURBS and then with T-splines.
Accurately predicting the response of concrete and steel structures to blast loading remains challenging due to the complex interplay of numerous parameters. Conventional approaches experimental tests, analytical methods, and numerical simulations suffer from high costs, safety constraints, oversimplified assumptions, or excessive computational demands. This paper presents a critical review of recent advances in theoretical, numerical, and machine learning (ML) methods. A systematic literature search following PRISMA guidelines was conducted, covering 386 studies published. Analytical methods, particularly single-degree-of-freedom models and pressure-impulse diagrams, are evaluated for their efficiency and fundamental limitations they offer rapid predictions (seconds to minutes) but cannot capture local failures and exhibit displacement errors of 8–25
Expansive soils represent a major challenge to civil engineering infrastructure because of their volumetric instability and poor engineering characteristics. This review is based on a systematic discussion of the way fly ash improves engineering properties of expansive soil. This study focuses on the fundamental behaviour of expansive soils in terms of their mineralogy, swelling phenomenon and engineering characteristics. The review summarises the contributions of studies on the improvements of engineering properties of significance such as Atterberg limits, compaction characteristics, strength parameters, volume change behaviour and hydraulic properties. The quantitative synthesis of data published in the literature indicates that optimum fly ash contents by weight typically range from 15 to 25
Large Language Models (LLMs) have emerged as a transformative technology in artificial intelligence, significantly advancing natural language understanding, generation, and reasoning capabilities. This survey reviews the evolution of language models from early statistical approaches to modern Transformer-based architectures and summarizes key developments, including attention mechanisms, scaling laws, alignment techniques, and efficient inference methods. The paper further explores the growing impact of LLMs on everyday life and a wide range of application domains, including healthcare, finance, education, agriculture, marketing, software engineering, and scientific research. To provide a systematic perspective, LLM applications are categorized according to their maturity level and integration across major artificial intelligence subfields, such as natural language processing, multimodal learning, intelligent decision support, autonomous agents, and knowledge-based systems. The survey highlights how these models enhance automation, data-driven decision-making, personalized services, and human–AI interaction across both consumer and industrial environments. Despite their remarkable capabilities, LLMs face several critical challenges, including high computational costs, limited interpretability, hallucinations, privacy and security risks, ethical concerns, and environmental sustainability issues. Existing mitigation approaches and recent advancements are reviewed to assess their effectiveness and limitations. Finally, the paper outlines key future research directions, including trustworthy and explainable AI, efficient model architectures, domain-specific adaptation, multimodal intelligence, and human-centered alignment. This survey provides a comprehensive overview of the current landscape, challenges, and future prospects of LLMs, serving as a valuable reference for researchers and practitioners.
The rising growth of digital media forces digital images as a significant medium for information exchange, indicating the need for stronger security over open networks. However, conventional cryptographic algorithms designed to encode text data are not optimal when applied to images due to high redundancy, strong dependency of pixels, and big files. Although various algorithms are being researched in the domain of cryptography, further investigation is required to analyze the shift from deterministic ciphers to modern AI-based methods and evaluate new deep learning and biology-inspired encryption structures. This review examines current advances in encryption techniques used to secure image transfer over open networks. In particular, it presents an overview of six classes of image encoding algorithms: classical cryptography, chaos-based, biology-inspired schemes, deep learning, transformer-based and hybrid encryption solutions. The paper focuses on high-quality sources published in the last five years and assesses the strengths and limitations of their structures in terms of robustness, key security, and differential resistance. Additionally, the review discusses performance metrics, the balance between speed and complexity, and appropriate use cases for each technique, providing recommendations on possible changes to enhance encryption processes and optimize performance. Overall, the findings indicate that future development in image encryption should focus on time-efficient processing of image data and domain-specific methods. It suggests focusing on lightweight architectures (e.g., State Space Models) and implementing hybrid encryption based on classical ciphers combined with AI-driven feature extraction.
