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The simulation of large complex dynamical systems such as a fire in road tunnels is necessary but costly. Therefore, there is a crucial need to design efficient models. Coupling of computational fluid dynamics (CFD) models and 1D network modeling simulations of a fire event, a multiscale method, can be a useful tool to increase the computational efficiency while the accuracy of simulations is maintained. The boundary between a CFD model (near field) and a 1D model (far field) plays a key role in the accuracy of simulations of large systems. The research presented in this paper develops a novel methodology to select the interface boundary between the 3D CFD model and a 1D model in the multiscale simulation of vehicle fire events in a tunnel. The development of the methodology is based on the physics of the fluid structure, turbulent kinetic energy of the dynamical system, and the vortex dynamics. The methodology was applied to a tunnel with 73.73 m(2) cross section and 960 m in length. Three different vehicle fire scenarios were investigated based on two different heat reslease rates (10 MW and 30 MW) and two different inlet velocities (1.5 m/s and 5 m/s). all parameters upstream and downstream of the fire source in all scenarios were investigated at t = 900 s. The effect of changes in heat release rate (HRR) and air velocity on the selection of an interface boundary was investigated. The ratio between maximum longitudinal and transversal velocities was within a range of 10 to 20 in the quasi-1D region downstream of the fire source. The selected downstream interface boundary was 12D(h) m downstream of the fire for the simulations. The upstream interface boundary was selected at 0.5 D-h m upstream the tip of the object when the velocity was greater than equal to the V-c. In the simulations with backlayering (V < V-c), the interface boundary was selected 10 m further from the tip of the backlayering (1.2 D-h). An indirect coupling strategy was utilized to couple CFD models to 1D models at the selected interface boundary; then, the coupled models results were compared to the full CFD model results. The calculated error between CFD and coupled models for mean temperature and velocity at different cross sections were calculated at less than 5%. The findings were used to recommend a modification to the selection of interface boundary in multiscale fire simulations in the road tunnels and more complex geometries such as mines. National Institute for Occupational Safety and Health (NIOSH) [200-2014-59669] This research was developed under Contract No. 200-2014-59669, awarded by the National Institute for Occupational Safety and Health (NIOSH). The findings and conclusions in this report are those of the authors and do not reflect the official policies of the Department of Health and Human Services; nor does mention of trade names, commercial practices, or organizations imply endorsement by the U.S. Government.

A method of sensor fusion was developed to combine long-wavelength infrared (LWIR) stereo vision and a spinning LIDAR for improved rangefinding in smoke-obscured environments. This method allows rangefinding in clear and smoke conditions, relying on LIDAR’s high accuracy in clear conditions and the perception ability of LWIR cameras in smoke. Sensor data were combined using evidential (Dempster–Shafer) theory in a 3D multi-resolution voxel domain for occupied and free space states. A heuristic method was produced for separating significantly attenuated and low-attenuation LIDAR returns using return intensity and distance. A sensor model was developed to apply free space state information from LIDAR high-attenuation returns. Sensor models were developed for applying occupied and free space state information from LIDAR low-attenuation returns and from LWIR stereo vision points. The fusion method was evaluated in two fire environments: a room-hallway scenario with a range of clear to dense-smoke conditions and a shipboard fire scenario. Room-hallway tests were evaluated by assessing performance against baseline rangefinding. For the occupied state, the fusion method and LIDAR are within typically 5% to 10% for clear conditions, and the fusion method is more accurate than the LIDAR by typically 5% to 10% for smoke conditions, with LIDAR providing no data in the densest smoke. For the free space state, the fusion method outperformed the LIDAR in smoke conditions by as much as 40% and was typically within 5% of the LIDAR in clear conditions.

© 2017 Wiley Periodicals, Inc. The Electric Series Compliant Humanoid for Emergency Response (ESCHER) platform represents the culmination of four years of development at Virginia Tech to produce a full-sized force-controlled humanoid robot capable of operating in unstructured environments. ESCHER's locomotion capability was demonstrated at the DARPA Robotics Challenge (DRC) Finals when it successfully navigated the 61 m loose dirt course. Team VALOR, a Track A team, developed ESCHER leveraging and improving upon bipedal humanoid technologies implemented in previous research efforts, specifically for traversing uneven terrain and sustained untethered operation. This paper presents the hardware platform, software, and control systems developed to field ESCHER at the DRC Finals. ESCHER's unique features include custom linear series elastic actuators in both single and dual actuator configurations and a whole-body control framework supporting compliant locomotion across variable and shifting terrain. A high-level software system designed using the robot operating system integrated various open-source packages and interfaced with the existing whole-body motion controller. The paper discusses a detailed analysis of challenges encountered during the competition, along with lessons learned that are critical for transitioning research contributions to a fielded robot. Empirical data collected before, during, and after the DRC Finals validate ESCHER's performance in fielded environments.

A computer vision-based autonomous fire suppression system with real-time feedback of fire size and spray direction is presented in this paper. The system has been developed for use in a firefighting robot for close-range, localized fire suppression tasks in enclosed environments. A probabilistic water classification method was developed for segmenting water spray in a pair of IR cameras. Stereo processing was performed to localize points along the spray path for use in yaw and pitch angle estimation. A Golden Section Search with linear least squares optimization was used to determine the optimal pitch angle of the spray position at each sampling time. Kalman filtering was used to remove noise from the angle measurements and obtain a better estimate of the current nozzle orientation. A decision tree was used to determine the correct nozzle positioning mode using image feedback to suppress the fire and accounts for errors in direction, fire size during suppression, and when to adjust the nozzle based on IR feedback. Through implementation of a PI controller, the system is able to correct for unknown disturbances causing erroneous targeting of a localized fire. Experiments are presented with the initial nozzle angled correctly and with forced offsets in the system to set the initial spray position incorrectly in order for the system to correct. Suppression times ranged from 7.2 s to 16.3 s with a standard deviation of 3.9 s and average time of 11.2 s. A total of 12 tests demonstrated performance of the system given a forced offset to the initial nozzle orientation resulting in an error between the spray location and the fire target. Suppression times ranged from 8.1 s to 27.9 s with a mean of 16.9 s and standard deviation of 6.2 s. The proposed system can be implemented on a robotic firefighting platform to autonomously detect a fire, choose a proper manipulation goal and suppress full scale fires given disturbances causing erroneous targeting.

An autonomous fire suppression system was developed for localized fire suppression in high and low visibility environments. The system contains a multispectral sensor suite, including UV sensors and infrared stereovision, to detect and target a fire for suppression. The UV sensor provides an alert to the system to begin fire detection. IR imagery is used to segment fire from the field of view and target the base of the fire and IR stereovision to determine the 3D coordinates of the fire. IR tracking provides continuously updated information on the size and intensity of the fire before and during suppression and alerts the system when to cease suppression activity. Visual servoing is used to correctly position a nozzle based on feedback of changes in the fire location and size. The autonomous system was used to suppress wood crib fires (40 kW to 50 kW) in high and low visibility environments and at varying distances (2.8 m to 5.5 m) and elevations (0.4 m to 1.3 m). The suppression time in clear conditions was 3.72 s ± 1.51 s and 4.49 s ± 1.62 s in low visibility conditions. To simulate wind effects and inaccurate initial target coordinates, forced offsets were input to the system to show effectiveness of the feedback control algorithm when an initial estimate of spray trajectory does not accurately spray the center base of the fire. System performance with a forced offset resulted in suppression times of 4.11 s ± 0.84 s.

Locating a fire inside of a structure that is not in the direct field of view of the robot has been researched for intelligent firefighting robots. By classifying fire, smoke, and their thermal reflections, firefighting robots can assess local conditions, decide a proper heading, and autonomously navigate toward a fire. Long-wavelength infrared camera images were used to capture the scene due to the camera’s ability to image through zero visibility smoke. This paper analyzes motion and statistical texture features acquired from thermal images to discover the suitable features for accurate classification. Bayesian classifier is implemented to probabilistically classify multiple classes, and a multiobjective genetic algorithm optimization is performed to investigate the appropriate combination of the features that have the lowest errors and the highest performance. The distributions of multiple feature combinations that have 6.70% or less error were analyzed and the best solution for the classification of fire and smoke was identified.

Performing fire endurance tests of railcar floor assemblies in accordance with NFPA 130 is expensive given the minimum size requirements of 3.7 m (12 ft) in length and the full vehicle width. Often it is not financially viable to conduct such tests on several iterations of designs for the purpose of design optimization. Simulations of the fire endurance tests can be performed in place of experiments to provide predictions of floor assembly response of multiple designs at much lower cost. However, capturing the thermo-structural response of the floor assembly requires the ability to model the relevant physical phenomena including softening and weakening of the steel frame, degradation of the fire insulation, and failure of the composite floor. A methodology for performing such simulations was developed under this research addressing each of these phenomena. Temperature dependent thermal and mechanical properties of all modeled materials captures material softening and weakening. Degradation of the insulation was handled through a novel temperature dependent shrinkage approach. Failure models for the sandwich composite floor panels were obtained from literature to predict shear fracture of the core based on a maximum principal shear stress approach and delamination of the core/facesheet based on a maximum strain energy approach. The developed methodology was applied to the simulation of a fire endurance test of an exemplar railcar floor assembly using the commercial finite element solver Abaqus. The assembly was known to hold a passing rating for a 30-minute fire endurance test according to NFPA 130. The floor assembly consisted of a stainless-steel frame, fiberglass insulation, and a ply-metal composite floor. Sequentially coupled thermal and structural models were developed to predict the thermostructural response of the floor assembly for a 30-minute exposure to the ASTM E119 prescriptive fire curve. User-subroutines were utilized to implement the sandwich composite failure models developed for predicted core shear fracture and core/facesheet delamination. The predicted temperature rise on the unexposed surface of the floor assembly after a 30-minute exposure ranged from 50°C to 90°C. The floor assembly was also predicted to maintain structural integrity with the applied crush load, having a center-point vertical deflection of 161 mm after the 30-minute exposure. This resulted in a predicted pass rating for a 30-minute exposure which agrees with the floor assembly’s actual fire rating.

Large scale flammability performance of interior finish used on railcars has been evaluated in previous studies using the NFPA 286 room corner fire test, which has a cross-section similar to a railcar. In some studies, the wall containing the door was removed to account for the shorter length of the room compared to the railcar length. The focus of this study is to assess whether the NFPA 286 standard room-corner test with a door represents conditions that developed inside a railcar during a fire. Fire Dynamics Simulator (FDS) was used to model the fire growth in a NFPA 286 standard room-corner test with a door, NFPA 286 room without the wall containing the door, and railcar geometry with a single door open. All three cases had the same exposure fire in a corner and the same lining material. In predictions of the NFPA 286 room-corner test with a door, gas temperature, heat release rate, and time to flashover agreed well with available NFPA 286 standard test data. The simulation results of fire growth inside a railcar with one side door open produced similar conditions and fire growth compared with the standard NFPA 286 room with a door. For simulations on the NFPA 286 room with the wall containing the door removed, it was found that removal of the wall with the door resulted in non-conservative fire growth conditions with the gas temperature and heat release rate under-estimated compared to the standard NFPA 286 room with a door. These simulations indicate that the standard NFPA 286 room-corner test with a door is representative of conditions that would develop inside of a railcar.

A method was developed to measure full-field, transient heat flux from a fire onto a surface using infrared (IR) thermography. This research investigated metal plates that were directly exposed to fire while the unexposed side temperature of the plate was measured using IR thermography. These temperatures were then used in a two-dimensional finite difference inverse heat transfer analysis to quantify the heat flux. The method was demonstrated through a series of experiments with direct fire exposures onto vertical and horizontal plates. Fires were produced using a propane sand burner and ranged from 20 to 100 kW. Point heat flux measurements were also measured using a Schmidt–Boelter heat flux gauge. It was found that heat fluxes obtained via IR thermography were within one standard deviation of those from the Schmidt–Boelter gauge. The effect of plate material was studied both numerically and experimentally for stainless steel and aluminum plates. It was found that although precision is affected by material, appropriate resolutions can be selected to obtain similar precision for both materials. Spatial and temporal resolution effects were also investigated and it was found that the precision of the heat flux measurement is inversely proportional to both spatial and temporal resolutions.

A thermal–mechanical model for calculating the residual stiffness and strength of fire-exposed sandwich composite structures is presented. The model computes the unsteady-state heat flow and decomposition of a sandwich composite exposed to one-sided radiant heating representative of a fire scenario. The model also computes the residual tension and compression properties of a fire-exposed sandwich composite at room temperature. The accuracy of the model is assessed using post-fire stiffness and failure stress property data for a sandwich composite beam consisting of face skins of E-glass/vinyl ester laminate and a core of balsa wood. Experimental testing reveals that the residual tension and compression properties of the sandwich composite decrease rapidly due mainly to thermal decomposition to the fire-exposed skin. It is demonstrated that the model can accurately predict the residual stiffness and strength properties of fire-exposed sandwich composites. The model reveals that the post-fire tension properties are controlled by char damage to the entire sandwich composite whereas the post-fire compression properties are only dependent on charring to the front skin, resulting in a more rapid loss in stiffness and strength than the tension properties.

An integrated infrared thermography and 3-D digital image correlation (TDIC) technique has been developed which allows for simultaneous measurement of spatial and temporal distributions of temperatures and displacements. For this, a novel technique was developed to calibrate the IR thermal cameras with a stereo-vision digital image correlation (DIC) system using the standard pin-hole stereo calibration model. This method fuses thermal and displacement information and compensates for the difference in camera resolutions. Several high temperature black and white paints were evaluated to determine their characteristics including the temperature-dependent emissivity of each paint, the mixed emissivity of both paints in the speckle pattern, and optical thickness. The advantages of evaluating linked full-field temperatures and strain measurements through the TDIC technique are demonstrated through measurements obtained on an E-glass/vinyl ester/balsa wood sandwich composite subjected to simultaneous one-sided heating and compressive loading.

A real-time probabilistic classification method was developed for identifying fire, smoke, their thermal reflections, and other objects in infrared images. This algorithm was formulated for use on a robot that will autonomously locate fires inside of a structure where the fire is outside the robot field of view. Thermal images were used to extract features due to the fact that long wavelength infrared is capable of imaging through zero visibility environments. For an autonomous navigation under fire environments, robots need to be able to differentiate between desired characteristics, such as fire and smoke, and those that may lead the robot in the incorrect direction, such as thermal reflections and other hot objects. The probabilistic classification method in this paper provides a robust, real-time algorithm that uses thermal images to classify fire and smoke with high accuracy. The algorithm is based on four statistical texture features identified through this work to characterize and classify the candidates. Based on classification of candidates from large-scale test data, the classification performance error was measured to be 6.8% based on validation using the test dataset not included in the original training dataset. In addition, the precision, recall, F-measure, and G-measure were 93.5–99.9% for classifying fire and smoke using the test dataset.

Robotic firefighting is an area of increased focus as a way of limiting the exposure of firefighters to hazardous environments. A suppression system must incorporate multiple functionalities to allow for closed-loop firefighting control. One area of development is classifying water spray as a way of correcting errors between suppressant placement and fire location. An IR vision system is presented which is capable of identifying water. Image segmentation is performed, followed by a process that classifies regions of interest as water or non-water objects. A probabilistic classification method, using Naïve Bayes classifier, was applied on a varied dataset of differing water temperatures and sprays. Objects were segmented using frame differencing with image intensity and difference thresholds. Segments were manually labeled to create a training dataset. Precision, recall, F-measure, and G-measure results of the classifier on a separate test dataset ranged from 86.1-97.4% for classifying water objects using the test dataset with water classification alone having 94.2-97.4% accuracy.

This paper presents a fire heading estimation for solving the autonomous navigation problem of a firefighting robot in smoke-filled indoor fire environment. In smoke-filled fire environments, firefighters and firefighting robots experience difficulty maintaining direction while finding the fire source. To solve this, the statistical texture features in thermal images were analyzed and fused by using Bayesian estimation to compute the vertical and horizontal fire heading. For its validation, a large-scaled test-bed was built with a hallway and two rooms, with one of the rooms having a real size fire generating dense and dark smoke. The proposed method probabilistically computed the fire-heading toward the entrance of the hallway then guided the robot to the room with the actual fire, all while navigating in a smoke-filled situation. The experimental results have demonstrated the effectiveness of this method in indoor fire environments.

The pyrolyzing behavior of solid fuel mixtures was quantified from the physicochemical, kinetic and energetic perspectives using scanning electron microscope and simultaneous thermogravimetric analyzer. The effects of water leaching on biomass pyrolysis and coal–biomass co-pyrolysis were investigated simultaneously from all three perspectives and the behaviors were cross-examined. The water leaching of the biomass significantly decreased the inorganic content and reduced its char yield. The activation energy for the leached biomass was calculated to be higher than the untreated counterparts, and the heat of pyrolysis of biomass increased on water leaching due to mitigation of secondary reactions. The co-pyrolysis of coal and biomass fuel mixtures exhibited a weighted additive physicochemical, kinetic and energetic behavior for the conditions tested in this study. It was shown that the interactions between leached biomass and coal during co-pyrolysis were minimal by successful simulation of co-pyrolysis kinetics using the individual kinetic parameters of coal and biomass pyrolysis reactions. The energetic properties of the fuel samples, viz. heat of pyrolysis and heat of gasification, were calculated on per unit volatile mass basis using a pyrolysis mathematical model. It was demonstrated that these energetic properties were additive in nature for coal and leached biomass co-pyrolysis.

Firefighting robots are actively being researched to reduce firefighter injuries and deaths as well as increase their effectiveness on performing tasks. There has been difficulty in making firefighting robots autonomous because the commonly used sensors for autonomous robot navigation do not perform well in fire smoke-filled environments where low visibility and high temperature are present. In order to overcome these limitations, a multi-spectral vision system was developed that uses sensor fusion between stereo thermal infrared (IR) vision and frequency modulated-continuous wave (FMCW) radar to locate objects through zero visibility smoke in real-time. In this system, the stereo IR vision was used to obtain 3-D information about the scene while the radar provided more accurate distances of objects in the field of view. Through globally matching radar objects with those in the 3-D image, the accuracy of the stereo IR vision map was updated removing the far-field inaccuracy of the stereo IR as well as ghost objects created due to stereo mismatch. The system was sufficiently fast to provide real-time matching of objects in the scene allowing for dynamic reaction object tracking and locating. Through large-scale fire experiments with and without smoke in the field of view, the distance error for the stereo IR vision was reduced from 1% to 19.0% to 1% to 2% due to sensor fusion of the stereo IR with FMCW radar.

An experimental study was performed to quantify the performance of eleven common robotic navigation rangefinding technologies and camera systems in fire smoke environments. Instruments evaluated included two IR cameras, two visible cameras, two sonar systems, radar, a single-echo LIDAR, a multi-echo LIDAR, a Kinect™ depth sensor, and night vision. Small-scale smoke layer experiments were performed to isolate the effect of smoke visibility and gas temperature on instrument performance. Dense, low temperature smoke tests were used to evaluate instrument performance as the smoke visibility dropped below 1 m while the smoke temperature remained below 100°C. Light, high temperature smoke tests were used to evaluate instrument performance as the smoke reached a temperature above 250°C with the visibility above 5 m. Results from the tests show that radar systems and infrared cameras outperform the other rangefinders and cameras tested for these scenarios. A series of large-scale experiments were then performed to locate objects in a smoke filled room and hallway. Distances from the LIDAR were subject to error when the visibility reduced below 4 m. Infrared stereo vision and radar could locate the distance to target objects immersed in the smoke to within 10% and 1%, respectively, independent of smoke visibility level.

A thermo-structural model was developed and validated to predict the failure of compressively loaded fiber-reinforced polymer (FRP) laminates during one-sided heating from a fire. The model consists of a one-dimensional pyrolysis model to predict the thermal response of a decomposing material and an integral structural model based on the bending equation. The thermo-structural model predicts temperatures, out-of-plane deflections, and compressive failure of laminates exposed to fire conditions. Model results were validated with intermediate-scale compression load failure tests with a one-sided heat flux exposure. Through a sensitivity analysis of the model predictions to input parameters, the residual elastic modulus was determined to be of utmost importance to both prediction of out-of-plane deflection and time-to-failure.

A thermo-structural model was previously developed and validated for predicting the failure of compressively loaded fiber-reinforced polymer (FRP) laminates by one-sided heating in fire. The model consists of a one-dimensional pyrolysis model to predict the temperature and decomposition response. An integrated structural model uses the thermal predictions to predict thermally-induced bending caused by one-sided heating. Failure is predicted based on a localized failure criterion using the compressive strength of the material. The analysis was performed by slightly perturbing the thermal and mechanical properties to determine their effect on predictions of the out-of-plane deflection and time-to-failure. The predicted out-of-plane deflections were affected by several properties, including the in-plane thermal expansion and residual elastic modulus. The residual elastic modulus also had a significant effect on time-to-failure predictions. This demonstrates the sensitivity of the model to these parameters in predicting both the time-to-failure and deflection behavior of the laminate.

This study compiles the research that has been conducted on thermal conditions produced by fires under ceilings as well as additional new data for fires beneath ceilings in a corridor configuration. This includes fires in corridor/tunnel, unbounded ceiling, and corner configurations. For each configuration, the thermal conditions produced for both initiating fires impinging on the ceiling and burning ceiling fires are discussed. Thermal characterization includes flame length and heat flux at the ceiling, and correlations are proposed to predict these quantities for each configuration. Comparison of available data in the different configurations indicates that the highest heat flux conditions are produced with initiating fires impinging on a corridor ceiling.

An experimental study was performed to quantify the response and failure of 5083-H116 and 6082-T6 aluminum plates under compression load while being subjected to a constant heat flux representing a fire exposure. Using an intermediate scale loading frame with integrated heating, the study evaluated the effects of geometry, aluminum type, fire exposure, load, and fire protection. Intermediate scale aluminum panels which were more than 0.7 m high and 0.2 m wide were used to gain insights into the structural behavior of large structural sections exposed to fire. Failure temperatures were measured to range from 100 to 480 °C and were dependent on applied stress and aluminum type. This indicates that the use of a single temperature criterion in fire resistance without load as typically done is not sufficient for evaluating structural response during fire. An empirical failure model was developed to account for fire exposure conditions, aluminum type, and geometry.

Intermediate-scale, one-sided heating tests were performed on compressively loaded E-glass vinyl ester composite laminates. The tests were designed to investigate the effect of varying the applied stress, applied heat flux, and laminate dimensions on structural response. Three failure modes were observed in testing: large-scale buckling, localized kinking, and forced-response deflection. The failure modes were dependent on applied stress and independent of applied heating. The times-to-failure of the laminates exhibited an inverse relationship with the applied stress and heating levels. The use of a single temperature was incapable of quantifying laminate failure due to variations in temperature at failure for a given stress level. A dimensionless relationship was developed as a function of temperature for the applied stress and slenderness ratio. This relationship compares the applied stress, slenderness ratio, and laminate temperature at failure and may be used in design of composite laminate structures to determine failure.

This paper presents a finite-element (FE) modeling approach to predict the deformation, softening, and failure of compression-loaded aluminum structures exposed to fire. A fully coupled thermal-mechanical FE model is outlined. The FE model can analyze the thermal profile and deformation as well as the initial and final plastic collapse of aluminum structures in fire. It calculates the temperature profile of an aluminum structure exposed to unsteady-state heating conditions representative of fire. Using the temperature profile, the elastic and plastic deformations together with the loss in the compression load capacity of an aluminum structure caused by elastic softening, time-independent plastic (yield) softening, and time-dependent plastic (creep) softening effects are analyzed by using a mechanics-based FE solution. The modeling approach is validated by structural tests on an aluminum alloy (5083 Al) plate supporting an applied compression load while locally heated at different radiant heat flux (temperature) levels. The modeling approach can estimate the deformations, initiation of plastic collapse, and final failure of the aluminum test article for heat flux levels representative of different fire types. The FE model described in this paper can be used as the basis for performing complex deformation and failure analysis of compression-loaded aluminum (and other metallic) structures in fire.

FRP composite panels, with single skin E-glass/vinylester and cored architectures, were subjected to combined compressive and thermal loading. Failure via panel collapse was monitored as a function of thermally induced material degradation at various levels of applied loads. Thermal loads were caused by exposure to either IMO A.754(18) or UL 1709 flames. The applied load levels were of modest magnitudes and in particular were in the range of 20% of the collapse loads for undegraded panels. As the application of such modest loads led to a stronger time dependence of failure, as well as a temperature dependence, a time dependent failure model was proposed to analyze the observations of collapse. Our results indicate that the time dependence as well as the temperature dependence of material degradation mediates the failure, and thus the performance, of FRP panels during fire.

This paper describes the mechanical design of ESCHER, a series elastic humanoid developed to compete in the DARPA Robotics Challenge (DRC). The design methodology was informed by preliminary experimental results obtained using the THOR humanoid, a prototype platform developed for the DRC Trials, relying heavily on an accurate model of the torque-controlled robot in the Gazebo simulation environment. The redesigned lower body features a unique double actuated knee; by driving the single degree of freedom joint with two identical linear series elastic actuators (SEAs), the lower body is able to meet the necessary speed and torque requirements for locomotion on rough terrain. Experimental results demonstrating ESCHER's ability to step onto a 23 cm block, representative of the stairs task at the DRC Finals, validating the proposed approach. Joint torques measured on the hardware platform approximate those in simulation, validating the proposed design methodology.

This paper provides an overview of the bipedal walking controller implemented on ESCHER, a new torque-controlled humanoid designed by Virginia Tech to compete in the DARPA Robotics Challenge (DRC). The robot's compliant control approach relies on an optimization-based inverse dynamics solver proposed in a previous publication. This work presents two unique features to improve stability on soft and uncertain terrain, developed in preparation for the dirt track and stairs task at the DRC Finals. First, a step adjustment algorithm is introduced to modify the swing foot position based on the divergent component of motion (DCM) error. Second, a simple heuristic is introduced to improve stability on compliant surfaces such as dirt and grass by modifying the design of the center of pressure (CoP) trajectory. The proposed approach is validated through DRC-related experiments demonstrating the robot's ability to climb stairs and traverse soft terrain.

A fire growth model was developed to predict the flame spread and total heat release rate of a fire in a corner configuration with a combustible lining. Input data for the combustible lining were developed using small-scale test data from the ASTM E1354 cone calorimeter and ASTM E1321 LIFT. The fire growth model includes a flame spread model linked with a two zone compartment fire model, CFAST Version 3.1.2. At a user selected time interval, the flame spread model uses the gas temperature from CFAST to predict the heat release rate of the fire at that time interval, and then provides CFAST with a new heat release rate to predict conditions during the next time step. The flame spread model is an improved version of the flat wall flame spread model previously developed for the US Navy. The model is capable of predicting flame spread in a variety of configurations including a flat wall, a corner with a ceiling, flat wall with a ceiling, unconfined ceiling, and parallel walls. The model has been validated against ISO 9705 test data and was used in this study to simulate conditions that develop in three open corner tests each with a different lining material. The model was able to predict the heat release rate of the fire and provide a reasonable estimate of the flame fronts and flame lengths during the growing fire.

Concerns have been raised about the environmental impact of fluorocarbon-based fire fighting foams. To aid in developing alternative fire fighting foam formulations, fire suppression models are being constructed to evaluate the performance of new, innovative formulations. Some of the input parameters for the fire suppression models are specific to the foam and need to be measured experimentally. A small-scale test apparatus was developed in this study to measure the foam loss mechanisms (i.e., solution evaporated and drained) as well as the time to fuel ignition. In this test, the mass evaporated and drained is continuously measured for a static layer of foam exposed to a fixed irradiance level. The test apparatus was used to quantify the behavior of a single formulation of MIL-SPEC 6% aqueous film-forming foam (AFFF) for a variety of test conditions. Methods for using the data from the small-scale testing in fire suppression models were developed.

The flammability of interior finish materials for many applications is regulated using the ISO 9705 large-scale room corner fire test. To aid designers in developing new materials, a fire growth model has been developed to predict material performance in the ISO 9705 test using small-scale test data from ASTM E1354 cone calorimeter. The fire growth model includes a flame spread model linked with a two zone compartment fire model, CFAST Version 3.1.2. At a user selected time interval, the flame spread model uses the gas temperature from CFAST to predict the heat release rate of the fire at that time interval, and then provides CFAST with a new heat release rate to predict conditions during the next time step. The flame spread model is an improved version of the flat wall flame spread model previously developed for the U.S. Navy. The model is capable of predicting flame spread in a variety of configurations including a flat wall, a corner with a ceiling, flat wall with a ceiling, unconfined ceiling, and parallel walls. The fire growth model was used to predict the heat release rate and smoke production rate measured in eight ISO 9705 room corner fire tests on composite materials used in marine applications.

We have developed two different configurations of series elastic actuators to be used in the lower body of THOR, a full scale humanoid developed for the DARPA Robotics Challenge. Both designs utilize a ball screw transmission but use different output mechanisms. The THOR-Linear actuator uses a simple lever output while the THOR-Hoekens actuator uses a novel inversion of a Hoekens Linkage. The simpler design of the THOR-Linear actuator makes it well suited for parallel actuation applications while the THOR-Hoekens actuator features a larger range of motion and nearly constant mechanical advantage. In this video, we show early tests of the two actuator designs used in the THOR lower body. The video shows the two actuator designs, their range of motion on THOR, and a series of performance tests.

A thorough review was recently conducted to verify the correctness of equations being used to calculate heat release rate in standard test methods. The review incorporated 17 different standard test methods from American Society of Testing and Materials (ASTM), National Fire Protection Association (NFPA), Uniform Building Code (UBC), California Technical Bulletin (CA TB), International Standards Organization (ISO), and British Standards (BS). The standard test methods reviewed were ASTM D5424, ASTM D5537, ASTM E1354, ASTM E1537, ASTM E1590, ASTM E1623, ASTM E1822, NFPA 264, NFPA 265, NFPA 266, NFPA 267, CA TB 129, CA TB 133, UBC 8-2, UBC 26-8, ISO 5660, BS 476. Through this review, incorrect equations were found in 12 of the 17 standards with a total of 22 incorrect equations overall. The following paper provides the correct heat release rate equations and a summary of the review.

A simultaneous thermogravimetric analyzer (STA) and a microscale combustion calorimeter (MCC) were used to investigate the energetic properties of coal, biomass and mixture samples. The STA was used to measure the gravimetric and energetic response of a pyrolyzing sample under inert atmosphere. A pyrolysis mathematical model was used to calculate the heat of pyrolysis of samples from the STA data. The MCC was used to quantify the dynamic heat output from the combustion of the gases produced during the pyrolysis process. The measurement of heat output as a function of temperature was used to calculate the heat of combustion of fuels. It was found that the co-pyrolysis of coal and biomass fuel mixtures exhibited a weighted additive gravimetric and energetic behavior, both from pyrolysis and combustion aspects. It was further validated and concluded that the weighted sum prediction for the heat of pyrolysis and heat of combustion for the coal-biomass fuel mixtures must be conducted on the basis of their individual volatile mass contributions, and not on the basis of initial fuel mixture proportion.

Finding a fire fast is crucial in firefighting. For risky situations, it would be idealistic to send a firefighting robot that could quickly and efficiently find the fire and suppress it. This paper introduces an algorithm developed for an intelligent firefighting mobile robot to find a fire efficiently by fusing long wave infrared camera, ultraviolet radiation sensor, and LIDAR. For its validation, an experimental test-bed was constructed with a hallway and two rooms, with one of the rooms containing a real size fire created by propane gas. The robot immediately calculates its path towards the fire, moves towards it avoiding obstacles, and ultimately finds the fire. When the fire is out, the robot returns to its original starting place.

This paper discusses the application of thermal infrared stereo vision for distance mapping in fire environments for mobile robotic systems and humans. The capabilities of thermal infrared stereo vision are presented. The relationship between the development of thermal IR stereo vision systems and the development of visual stereo vision systems is discussed. Aspects of thermal infrared stereo vision that require a different approach than visual stereo vision systems are then presented with a discussion of techniques used to address these items. A comparison of a thermal IR stereo vision system with an off-the-shelf visual stereo vision system is provided.

Research on the effect of fire on bridge structures is still an area that has received little attention by the structural engineering community. In cases where bridges have been exposed to fire the need to quickly ascertain, re-open or replace the afflicted structure has limited detailed investigation of the effects of fire exposure. A modeling investigation on the behavior of bridges when exposed to simulated fires will be presented in this work. A modeling methodology was developed to integrate the fire dynamics simulation with a finite element model of the structural response. The discussion will cover the use of the Fire Dynamics Simulator in conjunction with the finite element software ABAQUS. The modeling methodology developed was utilized to simulate several fire scenarios with different parameters such as fire size and location altered. It was found that tanker fuel trucks were the only vehicle large enough to cause permanent deflections in the simulated events.

This paper presents the development of a far infrared stereo vision system for room mapping in low visibility firefighting scenarios. In this paper, the calibration and rectification of the cameras are presented, along with a discussion of the correspondence matching algorithm selected. Finally, the results from tests of the system in high visibility, clear conditions and low visibility smoke conditions are presented and compared to the ranging capability of LIDAR in the same environments.