554,161 research outputs found
Advancing Building Energy Modeling with Large Language Models: Exploration and Case Studies
The rapid progression in artificial intelligence has facilitated the
emergence of large language models like ChatGPT, offering potential
applications extending into specialized engineering modeling, especially
physics-based building energy modeling. This paper investigates the innovative
integration of large language models with building energy modeling software,
focusing specifically on the fusion of ChatGPT with EnergyPlus. A literature
review is first conducted to reveal a growing trend of incorporating of large
language models in engineering modeling, albeit limited research on their
application in building energy modeling. We underscore the potential of large
language models in addressing building energy modeling challenges and outline
potential applications including 1) simulation input generation, 2) simulation
output analysis and visualization, 3) conducting error analysis, 4)
co-simulation, 5) simulation knowledge extraction and training, and 6)
simulation optimization. Three case studies reveal the transformative potential
of large language models in automating and optimizing building energy modeling
tasks, underscoring the pivotal role of artificial intelligence in advancing
sustainable building practices and energy efficiency. The case studies
demonstrate that selecting the right large language model techniques is
essential to enhance performance and reduce engineering efforts. Besides direct
use of large language models, three specific techniques were utilized: 1)
prompt engineering, 2) retrieval-augmented generation, and 3) multi-agent large
language models. The findings advocate a multidisciplinary approach in future
artificial intelligence research, with implications extending beyond building
energy modeling to other specialized engineering modeling
Development of a novel 3D culture system for screening features of a complex implantable device for CNS repair
Tubular scaffolds which incorporate a variety of micro- and nanotopographies have a wide application potential in tissue engineering especially for the repair of spinal cord injury (SCI). We aim to produce metabolically active differentiated tissues within such tubes, as it is crucially important to evaluate the biological performance of the three-dimensional (3D) scaffold and optimize the bioprocesses for tissue culture. Because of the complex 3D configuration and the presence of various topographies, it is rarely possible to observe and analyze cells within such scaffolds in situ. Thus, we aim to develop scaled down mini-chambers as simplified in vitro simulation systems, to bridge the gap between two-dimensional (2D) cell cultures on structured substrates and three-dimensional (3D) tissue culture. The mini-chambers were manipulated to systematically simulate and evaluate the influences of gravity, topography, fluid flow, and scaffold dimension on three exemplary cell models that play a role in CNS repair (i.e., cortical astrocytes, fibroblasts, and myelinating cultures) within a tubular scaffold created by rolling up a microstructured membrane. Since we use CNS myelinating cultures, we can confirm that the scaffold does not affect neural cell differentiation. It was found that heterogeneous cell distribution within the tubular constructs was caused by a combination of gravity, fluid flow, topography, and scaffold configuration, while cell survival was influenced by scaffold length, porosity, and thickness. This research demonstrates that the mini-chambers represent a viable, novel, scale down approach for the evaluation of complex 3D scaffolds as well as providing a microbioprocessing strategy for tissue engineering and the potential repair of SCI
Molecular dynamics simulation in concrete research: A systematic review of techniques, models and future directions
This paper presents a comprehensive review of the application of molecular dynamics simulation in concrete research. The study addresses the background and significance of the topic, providing an overview of the principles, applications, and types of molecular dynamics simulation, with a particular focus on its role in enhancing the understanding of concrete properties. Moreover, it critically examines existing research studies that employ molecular dynamics simulation in concrete research, highlighting the associated benefits and limitations. The paper further investigates various simulation techniques and models employed in concrete research, offering a comparative analysis of their effectiveness. Additionally, the study explores future directions and identifies research needs in the field of molecular dynamics simulation in concrete, while also discussing the potential impact of this approach on the sustainability of the construction industry. By providing a comprehensive overview and critical analysis, this review serves as a valuable resource for researchers and practitioners interested in leveraging molecular dynamics simulation for advancing concrete science and engineering
Energy-based Analysis of Biochemical Cycles using Bond Graphs
Thermodynamic aspects of chemical reactions have a long history in the
Physical Chemistry literature. In particular, biochemical cycles - the
building-blocks of biochemical systems - require a source of energy to
function. However, although fundamental, the role of chemical potential and
Gibb's free energy in the analysis of biochemical systems is often overlooked
leading to models which are physically impossible. The bond graph approach was
developed for modelling engineering systems where energy generation, storage
and transmission are fundamental. The method focuses on how power flows between
components and how energy is stored, transmitted or dissipated within
components. Based on early ideas of network thermodynamics, we have applied
this approach to biochemical systems to generate models which automatically
obey the laws of thermodynamics. We illustrate the method with examples of
biochemical cycles. We have found that thermodynamically compliant models of
simple biochemical cycles can easily be developed using this approach. In
particular, both stoichiometric information and simulation models can be
developed directly from the bond graph. Furthermore, model reduction and
approximation while retaining structural and thermodynamic properties is
facilitated. Because the bond graph approach is also modular and scaleable, we
believe that it provides a secure foundation for building thermodynamically
compliant models of large biochemical networks
- …