Search results for: computational intelligence

Authors: Todd H. Buxton, Yong G. Lai

Abstract:

Temperature management in regulated rivers can involve significant expenditures of water to meet the cold-water requirements of species in summer. For this purpose, flows released from Lewiston Dam on the Trinity River in Northern California are 12.7 cms with temperatures around 11oC in July through September to provide adult spring Chinook cold water to hold in deep pools and mature until spawning in fall. The releases are more than double the flow and 10oC colder temperatures than the natural conditions before the dam was built. The high, cold releases provide springers the habitat they require but may suppress the stream food base and limit future populations of salmon by reducing the juvenile fish size and survival to adults via the positive relationship between the two. Field and modeling research was undertaken to explore whether lowering summer releases from Lewiston Dam may promote thermal stratification in river pools so that both the cold-water needs of adult salmon and warmer water requirements of other organisms in the stream biome may be met. For this investigation, a three-dimensional (3D) computational fluid dynamics (CFD) model was developed and validated with field measurements in two deep pools on the Trinity River. Modeling and field observations were then used to identify the flows and temperatures that may form and maintain thermal stratification under different meteorologic conditions. Under low flows, a pool was found to be well mixed and thermally homogenous until temperatures began to stratify shortly after sunrise. Stratification then strengthened through the day until shading from trees and mountains cooled the inlet flow and decayed the thermal gradient, which collapsed shortly before sunset and returned the pool to a well-mixed state. This diurnal process of stratification formation and destruction was closely predicted by the 3D CFD model. Both the model and field observations indicate that thermal stratification maintained the coldest temperatures of the day at ≥2m depth in a pool and provided water that was around 8oC warmer in the upper 2m of the pool. Results further indicate that the stratified pool under low flows provided almost the same daily average temperatures as when flows were an order of magnitude higher and stratification was prevented, indicating significant water savings may be realized in regulated streams while also providing a diversity in water temperatures the ecosystem requires. With confidence in the 3D CFD model, the model is now being applied to a dozen pools in the Trinity River to understand how pool bathymetry influences thermal stratification under variable flows and diurnal temperature variations. This knowledge will be used to expand the results to 52 pools in a 64 km reach below Lewiston Dam that meet the depth criteria (≥2 m) for spring Chinook holding. From this, rating curves will be developed to relate discharge to the volume of pool habitat that provides springers the temperature (<15.6oC daily average), velocity (0.15 to 0.4 m/s) and depths that accommodate the escapement target for spring Chinook (6,000 adults) under maximum fish densities measured in other streams (3.1 m3/fish) during the holding time of year (May through August). Flow releases that meet these goals will be evaluated for water savings relative to the current flow regime and their influence on indicator species, including the Foothill Yellow-Legged Frog, and aspects of the stream biome that support salmon populations, including macroinvertebrate production and juvenile Chinook growth rates.

Keywords: 3D CFD modeling, flow regulation, thermal stratification, chinook salmon, foothill yellow-legged frogs, water managment

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Authors: L. Diogo, A. Legrand, J. Nguyen-Cao, J. Rogeau, S. Bornhofen

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Lenia is a system of cellular automata with continuous states, space and time, which surprises not only with the emergence of interesting life-like structures but also with its beauty. This paper reports ongoing research on a GPU implementation of Lenia using CUDA C++ and OpenGL Interoperability. We demonstrate how CUDA as a low-level GPU programming paradigm allows optimizing performance and memory usage of the Lenia algorithm. A comparative analysis through experimental runs with existing implementations shows that the CUDA implementation outperforms the others by one order of magnitude or more. Cellular automata hold significant interest due to their ability to model complex phenomena in systems with simple rules and structures. They allow exploring emergent behavior such as self-organization and adaptation, and find applications in various fields, including computer science, physics, biology, and sociology. Unlike classic cellular automata which rely on discrete cells and values, Lenia generalizes the concept of cellular automata to continuous space, time and states, thus providing additional fluidity and richness in emerging phenomena. In the current literature, there are many implementations of Lenia utilizing various programming languages and visualization libraries. However, each implementation also presents certain drawbacks, which serve as motivation for further research and development. In particular, speed is a critical factor when studying Lenia, for several reasons. Rapid simulation allows researchers to observe the emergence of patterns and behaviors in more configurations, on bigger grids and over longer periods without annoying waiting times. Thereby, they enable the exploration and discovery of new species within the Lenia ecosystem more efficiently. Moreover, faster simulations are beneficial when we include additional time-consuming algorithms such as computer vision or machine learning to evolve and optimize specific Lenia configurations. We developed a Lenia implementation for GPU using the C++ and CUDA programming languages, and CUDA/OpenGL Interoperability for immediate rendering. The goal of our experiment is to benchmark this implementation compared to the existing ones in terms of speed, memory usage, configurability and scalability. In our comparison we focus on the most important Lenia implementations, selected for their prominence, accessibility and widespread use in the scientific community. The implementations include MATLAB, JavaScript, ShaderToy GLSL, Jupyter, Rust and R. The list is not exhaustive but provides a broad view of the principal current approaches and their respective strengths and weaknesses. Our comparison primarily considers computational performance and memory efficiency, as these factors are critical for large-scale simulations, but we also investigate the ease of use and configurability. The experimental runs conducted so far demonstrate that the CUDA C++ implementation outperforms the other implementations by one order of magnitude or more. The benefits of using the GPU become apparent especially with larger grids and convolution kernels. However, our research is still ongoing. We are currently exploring the impact of several software design choices and optimization techniques, such as convolution with Fast Fourier Transforms (FFT), various GPU memory management scenarios, and the trade-off between speed and accuracy using single versus double precision floating point arithmetic. The results will give valuable insights into the practice of parallel programming of the Lenia algorithm, and all conclusions will be thoroughly presented in the conference paper. The final version of our CUDA C++ implementation will be published on github and made freely accessible to the Alife community for further development.

Keywords: artificial life, cellular automaton, GPU optimization, Lenia, comparative analysis.

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Authors: Lan Cheng, Harry Qin, Yang Wang

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Background: COVID-19 has induced the largest digital learning experiment in history. There is also emerging research evidence that students have paid a high cost of learning loss from virtual learning. University-wide survey results demonstrate that digital learning remains difficult for students who struggle with learning challenges, isolation, or a lack of resources. Large-scale efforts are therefore increasingly utilized for digital education. To better prepare students in higher education for this grand scientific and technological transformation, STEM education has been prioritized and promoted as a strategic imperative in the ongoing curriculum reform essential for unfinished learning needs and whole-person development. Building upon five key elements identified in the STEM education literature: Problem-based Learning, Community and Belonging, Technology Skills, Personalization of Learning, Connection to the External Community, this case study explores the potential of pedagogical innovation that integrates computational and experimental methodologies to support, enrich, and navigate STEM education. Objectives: The goal of this case study is to create a high-fidelity prototype design for STEM education with knowledge transfer technology that contains a Cooperative Multi-Agent System (CMAS), which has the objectives of (1) conduct assessment to reveal a virtual learning mechanism and establish strategies to facilitate scientific learning engagement, accessibility, and connection within and beyond university setting, (2) explore and validate an interactional co-creation approach embedded in project-based learning activities under the STEM learning context, which is being transformed by both digital technology and student behavior change,(3) formulate and implement the STEM-oriented campaign to guide learning network mapping, mitigate the loss of learning, enhance the learning experience, scale-up inclusive participation. Methods: This study applied a case study strategy and a methodology informed by Social Network Analysis Theory within a cross-disciplinary communication paradigm (students, peers, educators). Knowledge transfer technology is introduced to address learning challenges and to increase the efficiency of Reinforcement Learning (RL) algorithms. A co-creation learning framework was identified and investigated in a context-specific way with a learning analytic tool designed in this study. Findings: The result shows that (1) CMAS-empowered learning support reduced students’ confusion, difficulties, and gaps during problem-solving scenarios while increasing learner capacity empowerment, (2) The co-creation learning phenomenon have examined through the lens of the campaign and reveals that an interactive virtual learning environment fosters students to navigate scientific challenge independently and collaboratively, (3) The deliverables brought from the STEM educational campaign provide a methodological framework both within the context of the curriculum design and external community engagement application. Conclusion: This study brings a holistic and coherent pedagogy to cultivates students’ interest in STEM and develop them a knowledge base to integrate and apply knowledge across different STEM disciplines. Through the co-designing and cross-disciplinary educational content and campaign promotion, findings suggest factors to empower evidence-based learning practice while also piloting and tracking the impact of the scholastic value of co-creation under the dynamic learning environment. The data nested under the knowledge transfer technology situates learners’ scientific journey and could pave the way for theoretical advancement and broader scientific enervators within larger datasets, projects, and communities.

Keywords: co-creation, cross-disciplinary, knowledge transfer, STEM education, social network analysis

Procedia PDF Downloads 100

Authors: Reza Nematirad, Anil Pahwa, Balasubramaniam Natarajan

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Time series data consist of successive data points collected over a period of time. Accurate prediction of future values is essential for informed decision-making in several real-world applications, including electricity load demand forecasting, lifetime estimation of industrial machinery, traffic planning, weather prediction, and the stock market. Due to their critical relevance and wide application, there has been considerable interest in time series forecasting in recent years. However, the proliferation of sensors and IoT devices, real-time monitoring systems, and high-frequency trading data introduce significant intricate temporal variations, rapid changes, noise, and non-linearities, making time series forecasting more challenging. Classical methods such as Autoregressive integrated moving average (ARIMA) and Exponential Smoothing aim to extract pre-defined temporal variations, such as trends and seasonality. While these methods are effective for capturing well-defined seasonal patterns and trends, they often struggle with more complex, non-linear patterns present in real-world time series data. In recent years, deep learning has made significant contributions to time series forecasting. Recurrent Neural Networks (RNNs) and their variants, such as Long short-term memory (LSTMs) and Gated Recurrent Units (GRUs), have been widely adopted for modeling sequential data. However, they often suffer from the locality, making it difficult to capture local trends and rapid fluctuations. Convolutional Neural Networks (CNNs), particularly Temporal Convolutional Networks (TCNs), leverage convolutional layers to capture temporal dependencies by applying convolutional filters along the temporal dimension. Despite their advantages, TCNs struggle with capturing relationships between distant time points due to the locality of one-dimensional convolution kernels. Transformers have revolutionized time series forecasting with their powerful attention mechanisms, effectively capturing long-term dependencies and relationships between distant time points. However, the attention mechanism may struggle to discern dependencies directly from scattered time points due to intricate temporal patterns. Lastly, Multi-Layer Perceptrons (MLPs) have also been employed, with models like N-BEATS and LightTS demonstrating success. Despite this, MLPs often face high volatility and computational complexity challenges in long-horizon forecasting. To address intricate temporal variations in time series data, this study introduces Times2D, a novel framework that parallelly integrates 2D spectrogram and derivative heatmap techniques. The spectrogram focuses on the frequency domain, capturing periodicity, while the derivative patterns emphasize the time domain, highlighting sharp fluctuations and turning points. This 2D transformation enables the utilization of powerful computer vision techniques to capture various intricate temporal variations. To evaluate the performance of Times2D, extensive experiments were conducted on standard time series datasets and compared with various state-of-the-art algorithms, including DLinear (2023), TimesNet (2023), Non-stationary Transformer (2022), PatchTST (2023), N-HiTS (2023), Crossformer (2023), MICN (2023), LightTS (2022), FEDformer (2022), FiLM (2022), SCINet (2022a), Autoformer (2021), and Informer (2021) under the same modeling conditions. The initial results demonstrated that Times2D achieves consistent state-of-the-art performance in both short-term and long-term forecasting tasks. Furthermore, the generality of the Times2D framework allows it to be applied to various tasks such as time series imputation, clustering, classification, and anomaly detection, offering potential benefits in any domain that involves sequential data analysis.

Keywords: derivative patterns, spectrogram, time series forecasting, times2D, 2D representation

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Authors: Andrew Gennett

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Designing for fungal development means embracing the symbiotic relationship between the living system and built environment. The potential of mycelium post-colonization is explored for the fabrication of advanced pure mycelium products, going beyond the conventional methods of aggregating materials. Fruiting induction imparts desired material properties such as enhanced environmental resistance. Production approach allows for simultaneous generation of multiple products while scaling up raw materials supply suitable for architectural applications. The following work explores the integration of fungal environmental perception with computational design of built fruiting chambers. Polyporales, are classified by their porous reproductive tissues supported by a wood-like context tissue covered by a hard waterproofing coat of hydrobpobins. Persisting for years in the wild, these species represent material properties that would be highly desired in moving beyond flat sheets of arial mycelium as with leather or bacon applications. Understanding the inherent environmental perception of fungi has become the basis for working with and inducing desired hyphal differentiation. Working within the native signal interpretation of a mycelium mass during fruiting induction provides the means to apply textures and color to the final finishing coat. A delicate interplay between meeting human-centered goals while designing around natural processes of living systems represents a blend of art and science. Architecturally, physical simulations inform model design for simple modular fruiting chambers that change as fungal growth progresses, while biological life science principles describe the internal computations occurring within the fungal hyphae. First, a form filling phase of growth is controlled by growth chamber environment. Second, an initiation phase of growth forms the final exterior finishing texture. Hyphal densification induces cellular cascades, in turn producing the classical hardened cuticle, UV protective molecule production, as well, as waterproofing finish. Upon fruiting process completion, the fully colonized spent substrate holds considerable value and is not considered waste. Instead, it becomes a valuable resource in the next cycle of production scale-up. However, the acquisition of new substrate resources poses a critical question, particularly as these resources become increasingly scarce. Pursuing a regenerative design paradigm from the environmental perspective, the usage of “agricultural waste” for architectural materials would prove a continuation of the destructive practices established by the previous industrial regime. For these residues from fields and forests serve a vital ecological role protecting the soil surface in combating erosion while reducing evaporation and fostering a biologically diverse food web. Instead, urban centers have been identified as abundant sources of new substrate material. Diverting the waste from secondary locations such as food processing centers, papers mills, and recycling facilities not only reduces landfill burden but leverages the latent value of these waste steams as precious resources for mycelium cultivation. In conclusion, working with living systems through innovative built environments for fungal development, provides the needed gain of function and resilience of mycelium products. The next generation of sustainable fungal products will go beyond the current binding process, with a focus upon reducing landfill burden from urban centers. In final considerations, biophilic material builds to an ecologically regenerative recycling production cycle.

Keywords: regenerative agriculture, mycelium fabrication, growth chamber design, sustainable resource acquisition, fungal morphogenesis, soil fertility

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Authors: Ali Kadir, S. R. Mishra, M. Shamshuddin, O. Anwar Beg

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Motivation- Rotating disk bio-reactors are fundamental to numerous medical/biochemical engineering processes including oxygen transfer, chromatography, purification and swirl-assisted pumping. The modern upsurge in biologically-enhanced engineering devices has embraced new phenomena including bioconvection of micro-organisms (photo-tactic, oxy-tactic, gyrotactic etc). The proven thermal performance superiority of nanofluids i.e. base fluids doped with engineered nanoparticles has also stimulated immense implementation in biomedical designs. Motivated by these emerging applications, we present a numerical thermofluid dynamic simulation of the transport phenomena in bioconvection nanofluid rotating disk bioreactor flow. Methodology- We study analytically and computationally the time-dependent three-dimensional viscous gyrotactic bioconvection in swirling nanofluid flow from a rotating disk configuration. The disk is also deformable i.e. able to extend (stretch) in the radial direction. Stefan blowing is included. The Buongiorno dilute nanofluid model is adopted wherein Brownian motion and thermophoresis are the dominant nanoscale effects. The primitive conservation equations for mass, radial, tangential and axial momentum, heat (energy), nanoparticle concentration and micro-organism density function are formulated in a cylindrical polar coordinate system with appropriate wall and free stream boundary conditions. A mass convective condition is also incorporated at the disk surface. Forced convection is considered i.e. buoyancy forces are neglected. This highly nonlinear, strongly coupled system of unsteady partial differential equations is normalized with the classical Von Karman and other transformations to render the boundary value problem (BVP) into an ordinary differential system which is solved with the efficient Adomian decomposition method (ADM). Validation with earlier Runge-Kutta shooting computations in the literature is also conducted. Extensive computations are presented (with the aid of MATLAB symbolic software) for radial and circumferential velocity components, temperature, nanoparticle concentration, micro-organism density number and gradients of these functions at the disk surface (radial local skin friction, local circumferential skin friction, Local Nusselt number, Local Sherwood number, motile microorganism mass transfer rate). Main Findings- Increasing radial stretching parameter decreases radial velocity and radial skin friction, reduces azimuthal velocity and skin friction, decreases local Nusselt number and motile micro-organism mass wall flux whereas it increases nano-particle local Sherwood number. Disk deceleration accelerates the radial flow, damps the azimuthal flow, decreases temperatures and thermal boundary layer thickness, depletes the nano-particle concentration magnitudes (and associated nano-particle species boundary layer thickness) and furthermore decreases the micro-organism density number and gyrotactic micro-organism species boundary layer thickness. Increasing Stefan blowing accelerates the radial flow and azimuthal (circumferential flow), elevates temperatures of the nanofluid, boosts nano-particle concentration (volume fraction) and gyrotactic micro-organism density number magnitudes whereas suction generates the reverse effects. Increasing suction effect reduces radial skin friction and azimuthal skin friction, local Nusselt number, and motile micro-organism wall mass flux whereas it enhances the nano-particle species local Sherwood number. Conclusions - Important transport characteristics are identified of relevance to real bioreactor nanotechnological systems not discussed in previous works. ADM is shown to achieve very rapid convergence and highly accurate solutions and shows excellent promise in simulating swirling multi-physical nano-bioconvection fluid dynamics problems. Furthermore, it provides an excellent complement to more general commercial computational fluid dynamics simulations.

Keywords: bio-nanofluids, rotating disk bioreactors, Von Karman swirling flow, numerical solutions

Procedia PDF Downloads 142