14 Dec 2021

Phase-Field Modeling of Nonequilibrium Solidification Processes in Additive Manufacturing

Abstract: This project models dendrite growth during nonequilibrium solidification of binary alloys using the phase-field method (PFM). Understanding the dendrite formation processes is important because the microstructural features directly influence mechanical properties of the produced parts. An improved understanding of dendrite formation may inform design protocols to achieve optimized process parameters for controlled microstructures and enhanced properties of materials. To this end, this work implements a phase-field model to simulate directional solidification of binary alloys. For applications involving strong nonequilibrium effects, a modified antitrapping current model is incorporated to help eject solute into the liquid phase based on experimentally calibrated, velocity-dependent partitioning coefficient. Investigated allow systems include SCN, Si-As, and Ni-Nb. The SCN alloy is chosen to verify the computational method, and the other two are selected for a parametric study due to their different diffusion properties. The modified antitrapping current model is compared with the classical model in terms of predicted dendrite profiles, tip undercooling, and tip velocity. Solidification parameters—the cooling rate and the strength of anisotropy—are studied to reveal their influences on dendrite growth. Computational results demonstrate effectiveness of the PFM and the modified antitrapping current model in simulating rapid solidification with strong nonequilibrium at the interface.

10 Dec 2021

Assessing the Feasibility of Detecting Epileptic Seizures Using Non-Cerebral Sensor Data

Abstract: This paper investigates the feasibility of using non-cerebral, time-series data to detect epileptic seizures. Data were recorded from fifteen patients (7 male, 5 female, 3 not noted, mean age 36.17 yrs), five of whom had a total of seven seizures. Patients were monitored in an inpatient setting using standard video-electroencephalography (vEEG), while also wearing sensors monitoring electrocardiography, electrodermal activity, electromyography, accelerometry, and audio signals (vocalizations). A systematic and detailed study was conducted to identify the sensors and the features derived from the non-cerebral sensors that contribute most significantly to separability of data acquired during seizures from non-seizure data. Post-processing of the data using linear discriminant analysis (LDA) shows that seizure data are strongly separable from non-seizure data based on features derived from the signals recorded. The mean area under the receiver operator characteristic (ROC) curve for each individual patient that experienced a seizure during data collection, calculated using LDA, was 0.9682. The features that contribute most significantly to seizure detection differ for each patient. The results show that a multimodal approach to seizure detection using the specified sensor suite is promising in detecting seizures with both sensitivity and specificity. Moreover, the study provides a means to quantify the contribution of each sensor and feature to separability. Development of a non-electroencephalography (EEG) based seizure detection device would give doctors a more accurate seizure count outside of the clinical setting, improving treatment and the quality of life of epilepsy patients.

9 Dec 2021

Evaluation of Geocell-Reinforced Backfill for Airfield Pavement Repair

Abstract: After an airfield has been attacked, temporary airfield pavement repairs should be accomplished quickly to restore flight operations. Often, the repairs are made with inadequate materials and insufficient manpower due to limited available resources. Legacy airfield damage repair (ADR) methods for repairing bomb damage consist of using bomb damage debris to fill the crater, followed by placement of crushed stone or rapid-setting flowable fill backfill with a foreign object debris (FOD) cover. While these backfill methods have provided successful results, they are heavily dependent on specific material and equipment resources that are not always readily available. Under emergency conditions, it is desirable to reduce the logistical burden while providing a suitable repair, especially in areas with weak subgrades. Geocells are cellular confinement systems of interconnected cells that can be used to reinforce geotechnical materials. The primary benefit of geocells is that lower quality backfill materials can be used instead of crushed stone to provide a temporary repair. This report summarizes a series of laboratory and field experiments performed to evaluate different geocell materials and geometries in combinations with a variety of soils to verify their effectiveness at supporting heavy aircraft loads. Results provide specific recommendations for using geocell technology for backfill reinforcement for emergency airfield repairs.

9 Dec 2021

Semi-Automated Land Cover Mapping Using an Ensemble of Support Vector Machines with Moderate Resolution Imagery Integrated into a Custom Decision Support Tool

Abstract: Land cover type is a fundamental remote sensing-derived variable for terrain analysis and environmental mapping applications. The currently available products are produced only for a single season or a specific year. Some of these products have a coarse resolution and quickly become outdated, as land cover type can undergo significant change over a short time period. In order to enable on-demand generation of timely and accurate land cover type products, we developed a sensor-agnostic framework leveraging pre-trained machine learning models. We also generated land cover models for Sentinel-2 (20m) and Landsat 8 imagery (30m) using either a single date of imagery or two dates of imagery for mapping land cover type. The two-date model includes 11 land cover type classes, whereas the single-date model contains 6 classes. The models’ overall accuracies were 84% (Sentinel-2 single date), 82% (Sentinel-2 two date), and 86% (Landsat 8 two date) across the continental United States. The three different models were built into an ArcGIS Pro Python toolbox to enable a semi-automated workflow for end users to generate their own land cover type maps on demand. The toolboxes were built using parallel processing and image-splitting techniques to enable faster computation and for use on less-powerful machines.

9 Dec 2021

Field Jet Erosion Tests on Benbrook Dam, Texas

Abstract: This report summarizes the results of eight field Jet Erosion Tests (JETs) performed on Benbrook Dam, TX. The results from these tests will be used by the U.S. Army Corps of Engineers, Fort Worth District, in assessments of the erosion resistance of the Benbrook Dam with regards to possible overtopping by extreme flooding. The JETs were performed at four different locations, i.e., two locations at the lowest crest elevation and two locations at the mid-slope face of the downstream embankment. Variations in estimated critical hydraulic shear stress and erosion rate values may have been caused by differences in soil composition, i.e., when the material changed from silt/sand to clay. The resulting values of the Erodibility Coefficient, Kd, and Critical Stress, τc, are very useful information in assessing the stability of Benbrook Dam during an overtopping event. Because of the observed natural variability of the materials, combining the erosion parameters presented in this report with the drilling logs and local geology will be imperative for assessing erosion-related failure modes of Benbrook Dam.

3 Dec 2021

Data Collection Tools for River Geomorphology Studies: LiDAR and Traditional Methods

Abstract: The purpose of this review is to highlight LiDAR data usage for geomorphic studies and compare to other remote sensing technologies. This review further identifies survey efficiencies and issues that can be problematic in using LiDAR digital elevation models (DEMs) in completing surveys and geomorphic analysis. US Army Corps of Engineers (USACE) geospatial data collection guidance (EM 1110-1-1000) (USACE 2015) aligns with the American Society for Photogrammetry and Remote Sensing Positional Accuracy Standards for Digital Geospatial Data (ASPRS 2014). Geomorphic assessment technologies are rapidly evolving, and LiDAR data collection methods are at the forefront. The FluvialGeomorph (FG) toolbox, developed to support USACE watershed planning, is a recent example of the use of LiDAR high-resolution terrain data to provide a new, efficient approach for rapid watershed assessments (Haring et al. 2020; Haring and Biedenharn 2021). However, there are advantages and disadvantages in using LiDAR data compared to other remote sensing technologies and traditional topographic field survey methods.

1 Dec 2021

Development of Smartphone-Based Semi-Prepared Runway Operations (SPRO) Models and Methods

Abstract: The U.S. Army Engineer Research and Development Center (ERDC) has developed a method for predicting surface friction response by use of ground vehicles equipped with deceleration-based measurement devices. Specifically, the ERDC has developed models and measurement methods between the Findlay Irvine Mk2 GripTester and a variety of deceleration measurement devices: Bowmonk AFM2 Mk3, Xsens MTi-G-710, two Android smartphones, and two iOS smartphones. These models show positive correlation between ground vehicle deceleration and fixed-slip surface continuous surface friction measurement. This effort extends prior work conducted by the U.S. Army ERDC in developing highly correlative models between the Findlay Irvine Mk2 GripTester and actual C-17 braking deceleration, measured via the runway condition rating (RCR) system. The models and measurement methods detailed here are of considerable use to semi-prepared airfield managers around the world needing to measure safe landing conditions following inclement weather. This work provides the tools necessary for airfield managers to quantify safe landing conditions for C-17 aircraft by using easily obtainable equipment and simple test standards.

29 Nov 2021

A Detailed Approach to Autonomous Vehicle Control through Ros and Pixhawk Controllers

Abstract: A Polaris MRZR military utility vehicle was used as a testing platform to develop a novel, low cost yet feature-rich, approach to adding remote operation and autonomous driving capability to a military vehicle. The main concept of operation adapts steering and throttle output from a low cost commercially available Pixhawk autopilot controller and translates the signal into the necessary inputs for the Robot Operating System (ROS) based drive by wire system integrated into the MRZR. With minimal modification these enhancements could be applied to any vehicle with similar ROS integration. This paper details the methods and testing approach used to develop this autonomous driving capability.

22 Nov 2021

Imagery Classification for Autonomous Ground Vehicle Mobility in Cold Weather Environments

Abstract: Autonomous ground vehicle (AGV) research for military applications is important for developing ways to remove soldiers from harm’s way. Current AGV research tends toward operations in warm climates and this leaves the vehicle at risk of failing in cold climates. To ensure AGVs can fulfill a military vehicle’s role of being able to operate on- or off-road in all conditions, consideration needs to be given to terrain of all types to inform the on-board machine learning algorithms. This research aims to correlate real-time vehicle performance data with snow and ice surfaces derived from multispectral imagery with the goal of aiding in the development of a truly all-terrain AGV. Using the image data that correlated most closely to vehicle performance the images were classified into terrain units of most interest to mobility. The best image classification results were obtained when using Short Wave InfraRed (SWIR) band values and a supervised classification scheme, resulting in over 95% accuracy.

22 Nov 2021

Methodology for the Analysis of Geospatial and Vehicle Datasets in the R Language

Abstract: The challenge of autonomous off-road operations necessitates a robust understanding of the relationships between remotely sensed terrain data and vehicle performance. The implementation of statistical analyses on large geospatial datasets often requires the transition between multiple software packages that may not be open-source. The lack of a single, modular, and open-source analysis environment can reduce the speed and reliability of an analysis due to an increased number of processing steps. Here we present the capabilities of a workflow, developed in R, to perform a series of spatial and statistical analyses on vehicle and terrain datasets to quantify the relationship between sensor data and vehicle performance in winter conditions. We implemented the R-based workflow on datasets from a large, coordinated field campaign aimed at quantifying the response of military vehicles on snow-covered terrains. This script greatly reduces processing times of these datasets by combining the GIS, data-assimilation and statistical analyses steps into one efficient and modular interface.