• USACE stands up first National Occupational Health Center in Mobile

    It is said that success breeds success. Whatever your business or endeavor, if you create something innovative and set yourself apart from others, the opportunities for further success usually come.
  • USACE hosts open house May 2 in Mansfield Center, Conn., for Mansfield Hollow Lake Master Plan revision

    The U.S. Army Corps of Engineers, New England District will host an open house May 2, 2024, in
  • USACE hosts open house May 1 in Charlton, Mass., for Buffumville Lake Master Plan revision

    The U.S. Army Corps of Engineers, New England District will host an open house May 1, 2024, in
  • USACE hosts open house April 30 in Sturbridge, Mass., for East Brimfield Lake Master Plan revision

    The U.S. Army Corps of Engineers, New England District will host an open house April 30, 2024, in
  • USACE, City of Cincinnati developing plan to improve Cincinnati’s riverfront

    The U.S. Army Corps of Engineers Louisville District is working in coordination with the City of Cincinnati and Hamilton County to help improve Cincinnati’s Riverfront along the Ohio River in downtown Cincinnati, Ohio. USACE and the Cincinnati Park Board are partnering on a $2.5 million feasibility study to improve and revitalize the Ohio River’s edge along Smale Riverfront Park.
  • Media Advisory: Atlantic Ocean Channel Phase II Groundbreaking Ceremony

    The U.S. Army Corps of Engineers Norfolk District, Port of Virginia and the City of Virginia Beach will host a groundbreaking ceremony for the commencement of the Atlantic Ocean Channel Phase II on Thursday, April 25, 2024, from 10:00 am to 11:00 am.
  • Standardized NEON Organismal Data for Biodiversity Research

    Abstract: Understanding patterns and drivers of species distribution and abundance, and thus biodiversity, is a core goal of ecology. Despite advances in recent decades, research into these patterns and processes is limited by a lack of standardized, high-quality, empirical data spanning large spatial scales and long time periods. The NEON fills this gap by providing freely available observational data generated during robust and consistent organismal sampling of several sentinel taxonomic groups within 81 sites distributed across the US and will be collected for at least 30 years. The breadth and scope of these data provide a unique resource for advancing biodiversity research. To maximize the potential of this opportunity, however, it is critical that NEON data be accessible and easily integrated into investigators’ workflows and analyses. To facilitate its use for biodiversity research and synthesis, we created a workflow to process and format NEON organismal data into the ecocomDP (ecological community data design pattern) format available through the ecocomDP R package; provided the standardized data as an R data package (neonDivData). We briefly summarize sampling designs and data wrangling decisions for the major taxonomic groups included. Our workflows are open-source so the biodiversity community may: add additional taxonomic groups; modify the workflow to produce datasets appropriate for their own analytical needs; and regularly update the data packages as more observations become available. Finally, we provide two simple examples of how the standardized data may be used for biodiversity research. By providing a standardized data package, we hope to enhance the utility of NEON organismal data in advancing biodiversity research and encourage the use of the harmonized ecocomDP data design pattern for community ecology data from other ecological observatory networks.
  • Neural Ordinary Differential Equations for Rotorcraft Aerodynamics

    Abstract: High-fidelity computational simulations of aerodynamics and structural dynamics on rotorcraft are essential for helicopter design, testing, and evaluation. These simulations usually entail a high computational cost even with modern high-performance computing resources. Reduced order models can significantly reduce the computational cost of simulating rotor revolutions. However, reduced order models are less accurate than traditional numerical modeling approaches, making them unsuitable for research and design purposes. This study explores the use of a new modified Neural Ordinary Differential Equation (NODE) approach as a machine learning alternative to reduced order models in rotorcraft applications—specifically to predict the pitching moment on a rotor blade section from an initial condition, mach number, chord velocity and normal velocity. The results indicate that NODEs cannot outperform traditional reduced order models, but in some cases they can outperform simple multilayer perceptron networks. Additionally, the mathematical structure provided by NODEs seems to favor time-dependent predictions. We demonstrate how this mathematical structure can be easily modified to tackle more complex problems. The work presented in this report is intended to establish an initial evaluation of the usability of the modified NODE approach for time-dependent modeling of complex dynamics over seen and unseen domains.
  • ERDC’s Ship/Tow Simulator used to assist the Francis Scott Key Bridge Response

    Francis Scott Key Bridge Response Unified Command is utilizing simulation resources from the U.S. Army Engineer Research and Development Center’s (ERDC) Coastal and Hydraulics Laboratory (CHL) to test shipping runs around the Key Bridge accident site.
  • Artificial Intelligence (AI)–Enabled Wargaming Agent Training

    Abstract: Fiscal Year 2021 (FY21) work from the Engineer Research and Development Center Institute for Systems Engineering Research lever-aged deep reinforcement learning to develop intelligent systems (red team agents) capable of exhibiting credible behavior within a military course of action wargaming maritime framework infrastructure. Building from the FY21 research, this research effort sought to explore options to improve upon the wargaming framework infrastructure and to investigate opportunities to improve artificial intelligence (AI) agent behavior. Wargaming framework infrastructure enhancements included updates related to supporting agent training, leveraging high-performance computing resources, and developing infrastructure to support AI versus AI agent training and gameplay. After evaluating agent training across different algorithm options, Deep Q-Network–trained agents performed better compared to those trained with Advantage Actor Critic or Proximal Policy Optimization algorithms. Experimentation in varying scenarios revealed acceptable performance from agents trained in the original baseline scenario. By training a blue agent against a previously trained red agent, researchers successfully demonstrated the AI versus AI training and gameplay capability. Observing results from agent gameplay revealed the emergence of behavior indicative of two principles of war, which were economy of force and mass.