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Category: Publications: Information Technology Laboratory (ITL)
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  • A Comprehensive Review on Wood Chip Moisture Content Assessment and Prediction

    Abstract: Wood chips are the primary sources of raw materials for numerous industries, including pelleting mills, biorefineries, pulp-and-paper industries, and biomass-based power generation facilities. Unfortunately, when wood chips are utilized as a renewable and environmentally friendly resource, industries are constantly challenged by the consistency of the wood chip qualities (e.g., moisture/ash contents, size distributions) - a historically recognized problem on a global scale. Among other wood chip quality attributes, the moisture content is considered the most pressing one as it directly impacts the energy content, storage stability, and handling properties of the raw and finished products. Therefore, accurate wood chip moisture content prediction can help optimize the drying process and reduce energy consumption. In this review, a survey was conducted on various techniques and models employed for predicting wood chip moisture content. The advantages and limitations of these approaches, as well as their potential applications and future directions were also discussed. This review aims to provide a comprehensive overview of the current state-of-the-art in wood chip moisture content prediction and to highlight the challenges and opportunities for further research and development in this field.
  • RISC TAMER Framework: Resilient Installation Support Against Compound Threats Analysis and Mitigation for Equipment and Resources Framework

    Every day, decision-makers must allocate resources based on the best available information at the time. Military installations face a variety of threats which challenge sustained functionality of their supporting and supported deployable systems. Considering the compounding and interdependent impacts of the threats, both specified (what is known) and unspecified (what is not known) and the investments needed to address these threats adds value to the decision-making process. Current risk management practices are generally evaluated via scenario analyses that do not consider compound threats, resulting in limited risk management solutions. Current practices also challenge the ability of decision-makers to increase resilience against such threats. The Resilient Installation Support against Compound Threats Analysis and Mitigation for Equipment and Resources (RISC TAMER) Framework establishes a decision support structure to identify and categorize system components, compound threats and risks, and system relationships to provide decision-makers with more complete and comprehensive information from which to base resilience-related decisions, for prevention and response. This paper focuses on the development process for RISC TAMER framework to optimize resilience enhancements for a wide variety of deployable systems in order to implement resilience strategies to protect assets, to increase adaptability, and to support power projection and global operations.
  • Dockerization of the Coastal Model Test Bed Toolkit

    Purpose: The purpose of this technical note is to document and describe changes made to the Coastal Model Test Bed (CMTB) suite of software in conjunction with the version 2 (V2) update.
  • ERDC-PT: A Multidimensional Particle Tracking Model

    Abstract: This report describes the technical engine details of the particle- and species-tracking software ERDC-PT. The development of ERDC-PT leveraged a legacy ERDC tracking model, “PT123,” developed by a civil works basic research project titled “Efficient Resolution of Complex Transport Phenomena Using Eulerian-Lagrangian Techniques” and in part by the System-Wide Water Resources Program. Given hydrodynamic velocities, ERDC-PT can track thousands of massless particles on 2D and 3D unstructured or converted structured meshes through distributed processing. At the time of this report, ERDC-PT supports triangular elements in 2D and tetrahedral elements in 3D. First-, second-, and fourth-order Runge-Kutta time integration methods are included in ERDC-PT to solve the ordinary differential equations describing the motion of particles. An element-by-element tracking algorithm is used for efficient particle tracking over the mesh. ERDC-PT tracks particles along the closed and free surface boundaries by velocity projection and stops tracking when a particle encounters the open boundary. In addition to passive particles, ERDC-PT can transport behavioral species, such as oyster larvae. This report is the first report of the series describing the technical details of the tracking engine. It details the governing equation and numerical approaching associated with ERDC-PT Version 1.0 contents.
  • Toxic Industrial Chemical / Material Intelligence Tool (TICMINT) User Guide

    Abstract: The Toxic Industrial Chemical / Material Intelligence Tool (TICMINT) is a web application that provides critical chemical and toxicological information to users quickly and efficiently for the purpose of enacting safe maneuvers in areas of operations. It provides an in-depth look at the makeup, properties, and hazardous effects of nearly 400 toxic chemicals of interest. It also provides background on the chemical makeup of a bevy of building materials, enabling soldiers in areas of operation to determine the toxicological risks associated with the combustion of those materials in their environment. This document’s purpose is to demonstrate the functionality of the TICMINT web application and provide instructional material for those managing its content.
  • Discover ERDC 101 and 201 Training Modules User’s Guide

    Abstract: Discover ERDC is a web-based tool that functions as a knowledge management hub by enabling employees of the US Army Engineer Research and Development Center (ERDC) to access valuable resources such as detailed employee profiles, organizational details, and links to other knowledge stores. This document covers the update of the ERDC 101 and 201 video player systems, the addition of a training component to those modules, and the integration of the systems into Discover ERDC. The updated video systems contain a collection of onboarding video presentations that give new employees critical information about their careers at ERDC. In addition, Discover ERDC 101 and 201 provide progress-tracking mechanics for asynchronous learning, as well as the ability to certify that employees have completed the training modules. This document serves as a user guide for these tools, providing an overview of the content and functionality.
  • Investigation of Steam Adsorption Chillers to Modernize Existing Central Steam Plant Systems

    Abstract: This report investigates the integration of steam adsorption chillers as a modernization strategy for conventional central steam plant systems. Our objective is to assess the feasibility, advantages, and challenges of incorporating steam adsorption chillers into existing steam plant setups to enhance energy efficiency and cooling capabilities. Central steam plant systems have historically been used for steam-based heating but often lack cooling capabilities, necessitating additional cooling infrastructure. Steam adsorption chillers offer a potential solution by using waste steam for cooling, optimizing energy utilization and reducing reliance on traditional cooling methods. Through a comprehensive analysis, this report evaluates the technical compatibility and potential cost implications of implementing steam adsorption chillers. It explores factors such as system integration, operational dynamics, and maintenance requirements to provide a holistic view of the feasibility and benefits of this modernization approach. The findings aim to offer valuable insights to decision-makers and Army facility managers seeking innovative ways to upgrade central steam plant systems. By considering the technical and economic aspects of adopting steam adsorption chillers, this report contributes to the knowledge base for sustainable and efficient energy utilization in central plant operations.
  • AI on Digital Twin of Facility Captured by Reality Scans

    Abstract: The power of artificial intelligence (AI) coupled with optimization algorithms can be linked to data-rich digital twin models to perform predictive analysis to make better informed decisions about installation operations and quality of life for the warfighters. In the current research, we developed AI connected lifecycle building information models through the creation of a data informed smart digital twin of one of US Army Corps of Engineers (USACE) buildings as our test case. Digital twin (DT) technology involves creating a virtual representation of a physical entity. Digital twin is created by digitalizing data collected through sensors, powered by machine learning (ML) algorithms, and are continuously learning systems. The exponential advance in digital technologies enables facility spaces to be fully and richly modeled in three dimensions and can be brought together in virtual space. Coupled with advancement in reinforcement learning and computer graphics enables AI agents to learn visual navigation and interaction with objects. We have used Habitat AI 2.0 to train an embodied agent in immersive 3D photorealistic environment. The embodied agent interacts with a 3D environment by receiving RGB, depth and semantically segmented views of the environment and taking navigational actions and interacts with the objects in the 3D space. Instead of training the robots in physical world we are training embodied agents in simulated 3D space. While humans are superior at critical thinking, creativity, and managing people, whereas robots are superior at coping with harsh environments and performing highly repetitive work. Training robots in controlled simulated world is faster and can increase their surveillance, reliability, efficiency, and survivability in physical space.
  • Integrating MOVEit Motion Constraints on a Novel Robotic Manipulator

    Abstract: MOVEit, a widely used Robot Operating System framework, plans composite tasks, where the high-level sequence of actions is fixed and known in advance. However, these tasks need to be tailored and adapted to the environmental context. This framework uses custom trajectory planners, known as controllers, to solve goals that are fully defined within the configuration space. Libraries, such as the Open Motion Planning Library, provide a collection of motion planners that can solve task-space goals. An exact spatial and joint replication of the robotic manipulator’s mechanics, typically Universal Robot Description Format and Semantic Robot Description Format files, is required. Common arms such as the Panda-Manipulator and OpenMANIPULATOR-X provide these files in their respective public repositories, but custom arms require significant modification or even a complete rewrite of these files.
  • Low Size, Weight, Power, and Cost (SWaP-C) Payload for Autonomous Navigation and Mapping on an Unmanned Ground Vehicle

    Abstract: Autonomous navigation and unknown environment exploration with an unmanned ground vehicle (UGV) is extremely challenging. This report investigates a mapping and exploration solution utilizing low size, weight, power, and cost payloads. The platform presented here leverages simultaneous localization and mapping to efficiently explore unknown areas by finding navigable routes. The solution utilizes a diverse sensor payload that includes wheel encoders, 3D lidar, and red-green-blue and depth cameras. The main goal of this effort is to leverage path planning and navigation for mapping and exploration with a UGV to produce an accurate 3D map. The solution provided also leverages the Robot Operating System