# AES: Autonomous Excavator System for Real-World and Hazardous Environments

Jinxin Zhao1, Pinxin Long2, Liyang Wang1, Lingfeng Qian2,
Feixiang Lu2, Xibin Song2, Dinesh Manocha3, Liangjun Zhang1∗

1Baidu USA, Sunnyvale, CA, 94089
2Baidu Research Institute, Beijing, China, 100085
3 University of Maryland, College Park, MD 20742

∗Corresponding Author. E-mail<EMAIL_ADDRESS>
> Excavators are widely used for material-handling applications in
> unstructured environments, including mining and construction. The size of
> the global market of excavators is $44.12$ Billion USD in 2018 and is
> predicted to grow to $63.14$ Billion USD by 2026 (?). Operating excavators
> in a real-world environment can be challenging due to extreme conditions and
> rock sliding, ground collapse, or exceeding dust. Multiple fatalities and
> injuries occur each year during excavations (?). An autonomous excavator
> that can substitute human operators in these hazardous environments would
> substantially lower the number of injuries and can improve the overall
> productivity. In this paper, we present an autonomous excavator system
> (AES), for material-loading and soil excavation tasks. Our system is capable
> of handling different environments and uncertainties and uses a novel
> architecture that combines perception and planning. We use multiple
> perception sensors along with advanced object material and texture detection
> algorithms. We present novel motion planning algorithms that combine
> learning-based techniques with optimization-based methods and are tightly
> coupled with the perception modules. We have evaluated the performance on
> compact and standard excavators in many complex indoor and outdoor scenarios
> corresponding to waste material handling, soil excavation, rock
> manipulation, etc. AES is the first autonomous excavator system that has
> been deployed for real-world operations for long periods and can operate
> robustly in challenging scenarios. We demonstrate that our novel
> architecture with integrated perception, planning and control components
> improves the efficiency and autonomously handle different scenarios, as
> compared with prior autonomous excavator systems. In practice, AES achieves
> $24$ HPI (Hours Per Intervention), i.e. the system can continuously operate
> for $24$ hours without any human assistance. Moreover, the amount of
> material handled by AES per hour is closely equivalent to that of an
> experienced human operator.

## Introduction

Excavation is the process of moving earth, rock, or other materials with
tools, explosives or heavy equipment. It is frequently used in different
applications corresponding to mining, exploration, environmental restoration,
archaeological sites, construction, etc. In fact, excavators are considered as
the most versatile heavy equipment with a huge market. In US, the excavator
market size is about $5.25$ billion USD in 2008 (?). And currently China is
the largest market for excavators. In China, around 1.6 million excavators
were in operation during $2018$ and a total of $380$ thousand new excavators
are projected to be sold in 2024 (?). However, excavating is recognized as one
of the most hazardous operations (?) and results in high number of injuries
and deaths each year. In USA, around $200$ casualties occur per year (?),
caused by cave-in, ground collapse or other excavation incidents (Figure 1).
The number of injuries and deaths would grow even larger with more excavators
being used.

Figure 1: (a)(b) Land-falls in mining sites that cause excavator accidents,
resulting in casualties or injuries of human operators. (c)(d) Excavators
operate at extreme working conditions, such as remote area, high and low
temperatures and exceeding dusty. Our AES system is designed to operate
without human operators in such challenging scenarios.

Currently, excavators are operated by human operators that have undergone
specific training (?). This prolonged training includes not only learning
excavating maneuvering techniques, but also safety regulations and standards.
In addition to the life-threatening incidents, human operators may also face
extreme working conditions. For example, a mining site (Figure 1 (c) and (d))
is usually located at a remote area, or even inside a desert, where severe
high or low temperatures can happen. Moreover, the remote location and long
distances from cities results in limited availability of on-site excavator
operators. Hence, each worker suffers from prolonged working hours and loads,
which can result in higher fatigue and more injuries (?). One solution is to
develop autonomous excavators that can operate in such challenging and
hazardous conditions without any human operators. An unmanned excavation
system would largely reduce the number of casualties or injuries during
excavation operation. Moreover, such an excavator could conduct tedious and
repetitive tasks tirelessly for extended hours, thereby increasing the overall
throughput.

Efficiency, robustness and generalizability are the three key requirements in
terms of designing an autonomous excavator (?). In other words, the autonomous
excavator should be able to operate without human intervention, while
finishing human-equivalent workload. Besides, the resulting excavator system
should be capable of perceiving the surrounding environment, such as
monitoring the target material status, detecting impurities and obstacles
under extreme conditions, environment obstacles, and other uncertainties.

Current excavators can be classified based on their sizes. A compact excavator
normally weights less than $6$ tons, a standard excavator weighs between $7$
to $45$ tons, while a large excavator weights above $45$ tons and at most
$900$ tons. Ideally, we want to develop autonomous systems for all these types
of excavators. The desired system should also be able to handle many types and
sizes of materials, operate during raining or dust, and generate feasible
motions while avoiding any collisions with obstacles.

In terms of designing an autonomous excavator architecture that can operate
robustly in real world scenarios, we address these challenges (?):

  * •

The system needs to operate under a large range of environment conditions that
vary by the terrain types, weather, temperatures, lighting conditions etc. It
is necessary that the perception module, which is responsible for
understanding the surrounding environments, should be able to function in all
such scenarios.

  * •

Many times, an excavator is used to load a pile of material onto trucks. We
refer to the material as the target material. Moreover, the shape of such
target material pile changes after each loop of scooping and dumping the
material. As a result, we need real-time techniques for online modeling of the
shape of the pile;

  * •

Scooping of material has to be successful regardless of the types or
characteristics of the materials, which vary based on the density, hardness or
texture;

  * •

After scooping the material, the excavator has to successfully dump the
material into a truck, while avoiding any collisions with the truck, material
pile or other obstacles in the environment;

  * •

The location of the dump truck body area could vary considerably during the
excavation. We need to develop robust online detection methods to compute the
truck pose.

Figure 2: Our autonomous excavator system is equipped with state-of-the-art
sensor hardware and drive-by-wire system. Our system is designed for material
loading and dumping excavation task in various hazardous and difficult
scenarios. We highlight the various sensors used in our system (right) and
being deployed on a real-world excavator (left). We evaluate the performance
on compact and standard excavators.

We present a novel system architecture and algorithms for autonomous excavator
(AES) for material loading and soil excavation tasks. Our system integrates
perception, planning and control and is centered around the perception module.
The planning and control modules are driven by perception. We develop a
perception system that fuses 3D-Lidar and camera outputs for perceiving 3D
environments of source material piles and the target dumping area, detecting
the material textures, impurities and blocking obstacles to improve the
robustness of the whole system. Moreover, the perception module detects
unexpected objects, such as animals, within the excavator operation zone and
can trigger emergency stop to ensure the safety. We also present a
hierarchical planning module, which is composed of one target selection layer
and one motion planning layer. Our planning approach combines inverse
reinforcement learning (IRL) and imitation learning (IL), data-driven
algorithms with optimization-based methods. The target selection layer tries
to learn the motion corresponding to excavation target selection, such as the
next scooping position, from human operator demonstrations. The motion
generation layer uncovers the motion pattern of human-operated excavation
movements offline and integrates this pattern with a stochastic optimization-
based algorithm for trajectory generation of scooping and dumping motion.
Additional obstacle avoidance constraints, corresponding to trucks or
buildings, are also added to our optimization formulation.

As shown in Figure 3, multiple perception algorithms taking inputs of camera
images and Lidar point-clouds are integrated for different levels of
perception. Such combination provides detailed scene understanding of the
working zone, particularly the detailed condition of the target material. This
is used to increase the robustness of the system and enables the system to
operate without human operator assistance for prolonged hours. Based on the
rich information generated by perception module, the planning module takes
advantage of data-driven and optimization-based methods to improve the
efficiency. Our system architecture with closely-combined perception and
planning modules results in efficient excavation in different environments.

We use our modular architecture (Figure 3) to generalize the autonomous
excavator system for compact, standard and large excavators. This
generalization is achieved by parameterizing the size of the excavators within
the perception, planning and control modules of the system. Our autonomous
excavator system is extensively tested and evaluated under controlled real-
world scenarios, inside a designated test facility. The details of various
scenarios are highlighted in Table 1. During material loading excavation
operation, the autonomous excavator performs the normal scooping motion and
dumps the target material. At the same time, the autonomous system handles
terrain manipulation, obstacle avoidance and any water that may appear in the
scene. In this context, terrain manipulation indicates that some objects like
rocks or impurities could appear in the working area and block the normal
excavation motion. In these scenarios, the excavator needs to lift and remove
such kinds of objects, in order to successfully load the target material.
Obstacle avoidance is performed during the loading operation and ensures that
the excavator should not collide with any loading trucks, the material pile
after scooping as well as any non-targeted materials such as removed
impurities. We highlight these tasks being performed in our test scenarios.

Table 1: Scenarios Setup: We evaluated the performance of AES on $8$ different scenarios. Our system operates robustly in these scenarios. The first column of the table lists the names of each scenario, while the other columns indicate whether the corresponding tasks need to be conducted in the related scenario. “Y” means the task is conducted in the scenario, while “N” means the otherwise. Terrain manipulation, obstacle avoidance and water recognition are three common tasks an excavator needs to perform for successful loading operation.  Scenario Elements |  | Terrain
---
Manipulation
| Obstacle
---
Avoidance
| Handling
---
water
| Scenario 1:
---
Material loading and dumping
N | N | N
| Scenario 2:
---
Terrain manipulation
Y | N | N
| Scenario 3:
---
Obstacle avoidance
N | Y | N
| Scenario 4:
---
Loading with rain
N | N | Y
| Scenario 5:
---
Terrain manipulation
with obstacle avoidance
Y | Y | N
| Scenario 6:
---
Terrain manipulation
with water handling
Y | N | Y
| Scenario 7:
---
Obstacle avoidance
with water handling
N | Y | Y
| Scenario 8:
---
Full stack scenario
Y | Y | Y

We validate AES on two realistic applications. This includes recycling
pipeline of hazardous industrial solid waste material, produced by various
industrial activities. We demonstrate that our approach can handle industrial
waste, such as gypsum, dirt, gravel, or chemicals. In the second application,
we use AES to automate the material loading and dumping. We have extensively
tested AES on the waste material recycling pipeline for 24 hours continuous
operations in an indoor setting (see Fig. 6). We also compare the efficiency
of our autonomous excavator system with skillful human operators for mass
excavation and truck loading tasks. In terms of overall throughput, AES
efficiency is closely equivalent to that of an expert human operator.

We also evaluated AES for a mining application. Most mining sites are located
at remote location and consist of unconstrained environments, landfalls or
cave-ins. These hazardous scenarios require a robust perception module that
can identify stones or rocks, along with appropriate motion planning
algorithms that can be used to capture the rocks. We highlight the performance
on mining of handling blocking objects and obstacle avoidance in Scenario 2,
Scenario 5, Scenario 6 and Scenario 8 (Figure 5).

Overall, the novel components of our autonomous system (AES) include:

1) A perception-centered architecture that provides the excavator the
capability of handling all the scenarios, which results in high robustness of
the excavator system in various dangerous and difficult indoor and outdoor
environments.

2) A hierarchical planning module that combines data-driven method and
optimization-based algorithms.

3) Our autonomous excavator system is the first autonomous excavator that can
continuously operate for $24$ hours without human assistance. We have
thoroughly tested the performance of AES on multiple real-world scenarios. In
addition, the system has been deployed on an application site and is being
extensively tested by our partner.

Figure 3: Overview of our Autonomous Excavator System (AES): the sensor data
corresponding to Lidar and camera is captured by the perception module, which
parses the surrounding areas by checking for blocking obstacles, impurities of
the material and material textures; This information is passed into the task
planning module for determining the action corresponding to material or object
capturing; The motion planning module generates a feasible trajectory based on
these constraints; The controller follows the planned trajectory closely.

### Previous Methods

Several autonomous excavator related experimental systems and algorithms have
been developed and tested. Early development of autonomous excavators dates
back to $1990$s. In (?), an autonomous loading system (ALS) is presented. ALS
relies on a laser scan for perception to detect a single model of truck and
the system handles single type of uniform soil without any impurity or change
of texture. Following this innovation, other system architectures for
autonomous excavator have been proposed (?, ?, ?). For example, (?) introduces
a conceptual framework of a semi-autonomous earthwork system, while human
intervention is minimized. Moreover, many heavy equipment manufacturers such
as Caterpillar are also making effort to bring autonomy into construction
industry (?). Their current focus is on remote control and semi-auto

Along with the overall system integration, numerous methods and algorithms
have been investigated for individual modules, especially planning and
perception. (?) introduces a classic planner architecture for earth-moving
task. This architecture divides the planner into a coarse planner and a
refined planner, which is responsible for planning the scooping region and
excavation motion. Our autonomous excavator planning module uses a similar
pipeline but improves each layer with improved planning algorithms for real-
world deployment.

As for coarse task planner, (?) focuses on the design of excavation motion
sequencing problem to arrange an efficient schedule for the excavation task
based on coverage planning rules. A control method is discussed in (?) to
improve the motion trajectory tracking accuracy. Recently (?) propose an
advanced planning and control algorithm to generate digging motions for
excavation. This method is able to constrain the interaction force during the
digging motion. These methods provides in-depth insights of autonomous
excavator motion planning and control, yet they focus on only limited
scenarios and may not be able to handle complex real-world environments.

Recent advances in computer vision, deep learning and lidar sensors can be
used to develop better perception capabilities for these excavators. The
robustness and accuracy of perception results is crucial for an autonomous
excavator to understand the environment conditions and perform appropriate
planning operations. A rock detection and segmentation method is discussed in
(?), based on the images of Mars terrain. Moreover, (?) describes a safety
check perception module to detect abnormally of an excavator bucket. These
results focus on design and implementation of single perception functionality.
We propose a perception module architecture that satisfies the overall fully
automatic excavation perception requirements.

While some of these prototype systems can perform certain tasks under
restricted conditions, rarely any autonomous excavators have been deployed in
real-world scenarios. Our autonomous excavator (AES) is the first unmanned
excavator system that has been deployed in real world scenarios and used by
manufacturers of heavy machinery and the construction industry. We develop a
novel perception system that fuses 3D-Lidar and camera outputs for perceiving
the 3D environment. The combination of Lidar and camera helps detect both the
type and location of the targets. Our excavator planning module takes
advantage of the advanced data-driven methods and stochastic optimization
techniques to account for the uncertainties in sensed environment and the
system itself. More importantly, our planning module is integrated and fully
tested with our perception module. The combination results in a robust and
efficient autonomous excavator that can operate for long hours without human
intervention.

## Results

Figure 4: (a) Scenario 1: Our excavator system is operating autonomously in a
basic scenario for excavator loading. In this scenario, the material soil is
uniform. AES is able of handle the rough terrain and elevation. The perception
sensors (red) and motion sensors (green) are marked; (b) Scenario 2: Our
autonomous excavator captures and removes the rock when it appears in the
working zone and blocks the space for scooping the material. The working zone
is the intersection of the location of the target material and the excavator
range of motion; (c) Scenario 3: Our system successfully loads the material
while avoiding the region with impurities. In case the bucket directly
contacts the impenetrable impurity, the excavator arm will stop; (d) Scenario
4: In case a puddle or water appears in the scene, our perception module also
segments these areas and feed their location to the planning module. In this
case, our planner module generates the motion that avoids scooping into this
area.

Our autonomous excavation system has been evaluated under multiple controlled
real-world testing scenarios. Aiming to fully test the system capability, we
set up $8$ scenarios in a closed test field, mimicking the common real-world
use cases for an excavator.

Based on the successful test results in these scenarios, we also evaluated the
efficiency and robustness of the system in one of our deployment sites, a
waste disposal factory. In this working scenario, we have shown that our AES
system is able to continuously operate for $24$ hours without any human
intervention. Meanwhile, our autonomous excavator system could achieve
efficiency close to that of a human operator, measured by the amount of
material handled during the same amount of time. In addition, the excavator
system is currently test and evaluated in a remote mining site, where the
environment is substantially more hazardous and a human operator would
confront many difficult conditions.

### System Verification Using Different Excavation Scenarios

#### Scenario 1: Material loading and dumping

This is a fundamental test scenario, where the material is free of impurities
and there is no obstacle or water in the scene. This scenario helps us to
evaluate terrain modeling perception and basic excavation motion generation
for loading and dumping the target material into a dump truck (see Figure 4
(a)).

In this scenario, we further conduct comparison experiment to investigate the
excavator efficiency between our autonomous system and human operator. In
terms of average time used for loading and dumping task, our autonomous system
takes around $6\%$ more than a human operator, while achieving larger bucket
fill rate that corresponds to the percentage of the bucket that is loaded.
Specifically, we divide each loading and dumping loop into four phases
corresponding to “scoop”, “swing to truck”, “dump” and “swing back”. “Scoop”
indicates the time taken to load the excavator bucket with target material;
“swing to truck” indicates the time used for swinging the excavator bucket to
align with the truck; “dump” means the time to unload the material from bucket
to truck bed; “swing back” means the time used for swinging the bucket back to
face the working area. The performance for each period is also provided in
Table 2.

Table 2: Efficiency comparison between our autonomous excavator and a human operator in terms of average time used for each loading loop and bucket fill rate. Our autonomous system takes $6\%$ more average time for a loading loop, while loading more amount of material during each iteration measured by the bucket fill rate. Overall, the amount of material handled by AES per hour is closely equivalent to that of an experienced human operator.  |  | Scoop
---
| Swing to truck
---
Dump |  | Swing back
---
Total |  | Bucket fill rate
---
AES | 5.5 s | 8.5 s | 5.4 s | 5.7 s | 25.0 s | 105 %
Human | 5.8 s | 6.2 s | 5.8 s | 5.7 s | 23.6 s | 86 %

#### Scenario 2: Terrain manipulation

In this scenario (Figure 4 (b)), the excavator has to lift stone or rock that
blocks its working area for further excavation operation. The scenario is
designed to extensively test the stone identification module and perform
appropriate task/motion planning. The stone identification perception module
outputs the 3D bounding boxes for the target stone or rocks, while the motion
planning module generates a feasible trajectory for the excavator arm motion,
in order to capture the object.

#### Scenario 3: Obstacle avoidance

In this scenario, the detection of obstacles and collision-free trajectory
generation are being evaluated, as shown in Figure 4 (c). Moreover, the
obstacles not only correspond to the loading trucks, surrounding buildings as
well as material impurities and possibly the material that has been piled. The
reason to avoid impurity is that these impenetrable impurities can jam or
block the excavator arm motion. The reason to avoid the material pile is that
the excavator arm should avoid any contact with the pile after filling up the
bucket and before dumping the material into the truck.

#### Scenario 4: Loading with rain

In many circumstances, an excavator need to operate during rainy days. It is
highly possible a puddle would appear around the working zone. In order to
avoid scooping into the water, AES is capable of identifying the area filled
with water. Based on these working conditions, we design a test scenario and
verify the performance of our system, as shown in Figure 4 (d).

#### Scenario 5: Combined scenario of terrain manipulation with obstacle
avoidance

This test scenario is closer to the realistic mining scenario, where Scenario
2 and Scenario 3 are combined. For robust mining operation, our perception
module is designed to label the blocking objects and segment the obstacles.
Our task planner first decides whether to remove the blocking objects and then
the motion planner module generates the collision-free trajectory of the arm.

#### Scenario 6: Combined scenario of terrain manipulation with water handling

When excavation happens during wet weather, the successful detection of both
water and rocks is necessary. Hence we set up a new scenario that is a
combination of Scenario 2 and Scenario 4. In this case, the perception module
reliably identifies the blocking objects as well as segments the water area.

#### Scenario 7: Combined scenario of obstacle avoidance with water handling

In order to segment the obstacles as well as the water area, our perception
module takes advantage of the semantic segmentation method. During rainy days
in scenarios with obstacles and impurities in the scene, we verify these
segmentation capabilities of AES perception module.

Figure 5: In this realistic mining site scenario, AES removes blocking
obstacles if necessary and loads the material with success, even with water
and impurities appearing in the scene. This highlights the robust capabilities
in terms of handling such complex scenarios

#### Scenario 8: Full stack scenario

This is the most difficult but realistic scenario, where all these challenges
are combined. In this scenario, AES has to decide between manipulating the
terrain and scooping the material, while avoiding the obstacles and any water
area. We are able to evaluate the robust capabilities of our perception,
planning, and control modules.

### AES Deployment

#### Waste Disposal Recycle

AES is the first autonomous excavator system used for such a complex,
commercial application, and can run continuously for $24$ hours without any
human intervention. In this application, the excavator is assigned to load the
waste disposal material into a designated area. Afterwards, the material is
transferred and recycled after loading. However, during the excavation
process, the material could consist of excessive dust, which can be toxic to
human beings. In addition, the material pile is not stable and could collapse,
which is another threat to the human operators. The pipeline of the waste
recycle is shown in Figure 6. The speed of material loading by the excavator
has to coordinate with the speed of the belt conveyor and the material
processing. Hence, there is a high efficiency requirement for our autonomous
excavator. In addition to satisfying the efficiency requirement, our
autonomous excavator system can handle both dry and wet material. Meanwhile,
AES is able to function during night time.

In this scenario, AES is able to operate a whole day of $24$ hours without any
human intervention. Meanwhile, AES can perform $145$ operations of excavation
and dumping per hour; the amount of the handled material is as much as $36.25$
$m^{3}$ per hour for the $6.5$-ton excavator, which is closely equivalent to
human operators’ performance. Furthermore, AES performs consistently over the
time, while the performance of human operators may have the variation.

#### Mining

Figure 6: Robust and Prolonged Operation of AES: Excavator is used by the
industry in waste recycle pipeline for loading various materials including
toxic material for indoor dusty and smelly environments, which are harmful for
human operators. In addition, the excavator sits next to a large pile of
material, where landfall could happen. The pipeline needs to run 24 hours per
day. Any malfunction of the excavator will stall the entire pipeline. During
our stress test of the system, AES is able to operate a whole day of $24$
hours without any human intervention. Furthermore, the performance of AES is
closely equivalent to human operators’ performance. We show the operation
during daytime and nights, handling wet and dry materials.

Another challenging scenario that is used to evaluate our system is mining
site excavation (Figure 1 (d)). In this mining benchmark, the excavator
loading process includes the following tasks: isolate the larger-sized rocks
and remove them from the scene; load the smaller-sized rocks onto the loading
truck. Our advanced perception module can reliably identify the pose and size
of the rocks. Given these perception results, the motion planning module
generates excavator arm trajectories corresponding to the rock captures. Our
system is currently being used at the mining site.

## Discussion and Conclusion

Our newly designed autonomous excavator system is extensively tested for
robustness and efficiency, and its effectiveness has been demonstrated above.
Our system is the first ever autonomous excavator system put into deployment
for prolonged operations. In the waste disposal application, our autonomous
excavator system substitutes the human operator from the hazardous condition
and achieves $24$ HPI (hours per intervention). Meanwhile, our system is
currently used for a mining application, which is hazardous due to possible
rock-falls.

Compared with existing unmanned excavator system developed before, our overall
architecture largely relies on the perception module for fine-grained
understanding of the surrounding environment. Thus, our performance is mostly
governed by the accuracy of perception algorithms and the sensor hardware. In
heavy machinery applications, the reliability of the sensor hardware plays an
important role. The recent developments in terms of better Lidar and camera
sensors, along with improved computer vision algorithms, can further improve
the performance of our perception module. Moreover, we currently have not
considered the scenarios where snow or ice appears. Such scenarios introduce
many challenges for not only the perception module, but also for the planning
and control modules. Our goal is to further evaluate the performance in more
challenging weather and illumination conditions.

## Methods and Materials

### Software Architecture

There are four software modules in our system. The perception module takes
care of sensing various obstacles, modeling the terrain and classifying the
material, as well as locating the loading truck. The planning module generates
the excavator arm motion according to the perception output. Meanwhile, the
control module calculates the commands that sent to the excavator in order to
track the desired motion. The application layer of the software adjusts the
other modules based on the application.

All modules run simultaneously as nodes under the ROS (?) framework. In this
section, we provide more details on perception and planning modules, which are
the key components that enable our system to be deployed in real-world
scenarios. The overall architecture of AES is shown in Figure 3.

### Perception

The perception module for an autonomous excavator focuses on understanding the
condition of the unstructured working zones. More specifically, the perception
module mainly needs to determine the status of the material, i.e.

  * •

Identify the blocking obstacles that need to be captured and removed;

  * •

Detect the impenetrable portion of the material to avoid direct contact with
the excavator arm;

  * •

Recognize the texture of the material and model the shape of the material pile
to perform the loading operation.

We need to satisfy all these goals for different materials, under difference
weather and lighting conditions.

Towards this end, our perception module uses an hierarchical approach for
detection and identification of larger-sized objects, small-sized targets and
material textures. Our perception module exploits extensively on state-of-the
art algorithms like semantic segmentation (?) (?) and super pixel (?).

#### Blocking Objects Detection

In many excavation scenarios, blocking objects indicate the large-sized rocks
or accumulated undesired material impurities that can usually cause the
failure of the excavation motion. Human operators are able to remove these
kind of objects after certain amount training. In order to robustly remain in
autonomous operation without human assistance, AES needs to have equivalent
ability of detecting these objects. This part shows the results corresponding
to blocking obstacle detection, where the pose and size of an obstacle are
obtained through RGBD camera and Lidar. The information is fed to the planning
module to compute the trajectory to perform the task of rock-removing
movement.

We employ the Mask R-CNN (?) algorithm to identify the blocking obstacles in
the scene with RGB input. Based on the segmentation results, the depth
information from camera and Lidar are combined and used for fitting the 3D
bounding boxed to each detected obstacles. An example of stone pose estimation
is shown in Figure 8. For training the Mask r-cnn model, the training data-set
contains $4000$ rock or stone instances. The training period took $2$ hours
using $4$ Nvidia 1080 GPU.

Figure 7: AES architecture: The perception module provides different layers of
results to task planning and motion planning module. This perception-in-center
layout helps provide rich information of the surrounding environment.

#### Obstacle Identification

Another difficulty that commonly happens during the excavation operation is to
avoid collisions with obstacles. Such obstacles include the material
impurities, the loading truck, the material pile after the scooping operation,
etc. When the material contains hard impurities, they will prevent the further
movement of the excavator arm after direct contact. This is more likely to
happen when the end-tip or the end-effector of the excavator bucket contacts
such impurities. In this case, human operator normally performs reasonable
choice in terms of excavation motion that can avoid direct contact between the
impurities and the end-tip of the bucket. TTo identify such scenarios, we
perform semantic segmentation as shown in Figure 8.

Figure 8: Obstacles Segmentation: Obstacles such as impurities, stone and
water need to be avoided during the excavation operation. With obstacles being
labeled through semantic segmentation, our autonomous excavator system is able
to avoid any direct contacts with these obstacles. Our perception exploits
Mask R-CNN model for semantic segmentation from RGB images, the segmentation
results are combined with depth information to generate the bounding boxes.

#### Texture Classification

The properties of the loading material such as the density and hardness could
largely affect the excavation motion. We employ a visual texture classifier
based on the RGB image inputs to predict the type of the material. In
specific, to well train the texture classifier, we collect a texture dataset
which are captured from two environments: wild environment and chemical wastes
disposal. The dataset contains 8 classes, named, wild environment: dry soil,
wet soil, stone and mud stone and chemical wastes: phosphorus powder,
phosphorus lump, titanium and mixture of titanium and phosphorus. The dataset
contains $7563$ images, and $6720$ images for training and $843$ images for
testing. We employ Deep Encoding Pooling Network (?) with attention strategy
for texture classification, and accuracy of $100\%$ can be achieved. The
offline training period takes 1 hour using an Nividia $V100$ GPU.

#### Loading Truck Pose Estimation

As for truck pose estimation, a template truck model with 3D point cloud
representation is computed offline. This is performed by scanning the truck
from multiple perspectives and recording the point clouds. Next, we segment
each point cloud that corresponds to the truck and compose the resulting point
clouds together to obtain a truck template. During online truck pose
estimation, the truck template model is matched with the point cloud from
Lidar scan using iterative closest point (ICP) based algorithm. Finally, the
estimated truck pose is used to determine the bucket location for material
dumping.

### Planning

Our planning module is closely related to perception module, as shown in
Figure 7. To handle the real-world material loading task, the planning module
needs to explicitly consider the terrain shape as well as the positions of the
obstacles. Our hierarchical planning module automatically selects the target
of excavation based on the perception results. The detailed arm motion is
generated based on this target. Thus, the planning module is composed of a
task planning algorithm and a motion generation algorithm. This de-coupling
structure allows us to combine data-driven method with an optimization-based
algorithm.

#### Autonomous Excavation Target Selection

We use a data-driven method for the target selection module. This module aims
to learn the routine of a human operator according to the terrain observation
of the excavation working zone. In this application, the terrain observation
is selected as the 2.5D grid-based height map (?) of the working area. The
predicted output are the location of point of attack (POA) and bucket travel
length, where POA represents the point that excavator bucket first contacts
the material, the travel length means the distance the bucket travels before
lifting the material. A neural network model is designed for the learning task
and is described as

$\displaystyle
y=f_{core}(x),~{}~{}~{}\theta=y^{T}M,~{}~{}~{}z_{x}=f_{lon}(\theta
M),~{}~{}~{}z_{y}=f_{lat}(\theta M),~{}~{}~{}z_{l}=f_{l}(\theta M),$

where $x$ is the observation, $f_{core}$, $f_{lon}$, $f_{lat}$ and $f_{l}$ are
multilayer perceptron (MLP) with trainable parameters, $M$ is a matrix with
trainable elements, $z_{x}$ and $z_{y}$ are the longitudinal, lateral
coordinates of the POA, $z_{l}$ is the length that the excavator bucket
travels before lifting. This model follows the idea of neural programming
interpreter (?).

Figure 9: The figure shows the examples of the data-set used for texture
classification. Images in the top row correspond to textures in the wild
environment, while images in the bottom row represent mean different type of
chemical wastes. We trained a classification model for identify the class
labels for each material type.

#### Excavation Motion Generation

This motion generation module outputs the trajectory in the excavator joint
space based on the target selection module. For this part, we leverage a data-
driven method to uncover the human operated excavation movement pattern. The
pattern is later implemented into a optimization-based method for generating
the motion trajectory.

We use inverse reinforcement learning (IRL) (?) algorithm to uncover the
motion pattern. Given a trajectory $\tau$, the cumulative cost $C(\tau)$ is
defined as

$C(\tau)=w^{T}\Psi(\tau),$

where $\Psi$ is a user defined feature function and $w$ is the weight vector
associated with the feature. Given multiple collected human operated
excavation motion trajectories, the target is to learn the feature weight
vector $w$, which can be obtained by solving a convex optimization problem

$\displaystyle
minimize_{w}\sum_{i=1}^{D}\log\sum_{k=1}^{K}e^{-w^{T}(\Psi_{i,k}-\Psi_{i}^{*})},$

where $K$ is the number of sampling points of one trajectory and $D$ is the
number of demonstrated trajectories. Based on the determined POA and bucket
travel length, the start and end excavator arm configuration can be calculated
through inverse kinematics. The features we define are the squared error
between certain configuration and the end configuration w.r.t. each joint, as
well as the changing rates of the errors. This learning process is done
offline.

During the online motion generation, the concluded feature weights and cost
features are used in a stochastic trajectory optimization for motion planning
(STOMP) (?). In addition to the human pattern, STOMP allows us to consider the
obstacles in the scene as well. Recall that there always exist impenetrable
impurities in the material and our perception module is able to identify the
locations of such impurities. Therefore, additional cost features related to
these obstacles are added to the overall cost function is the STOMP algorithm.

### System Hardware

In this application, the robot platform (Figure 2) are hydraulic excavators
equipped with drive-by-wire system. Currently we have developed and tested on
two different sizes of excavators, one is a $6.5$-ton compact excavator and
one is a $49$-ton standard excavator. These excavation platforms offer
enormous output power to successfully conduct various excavation tasks. The
manufacturer provides a control interface through CAN bus, so the entire unit
can be controlled by software. To ensure safety, a fall-back human control
mechanism is implemented in case of an emergency.

To sense the excavator locations and motions, multiple sensors are installed.
We use a Huace real-time kinematic (RTK) positioning device for providing the
location of the excavator. Honeywell inclinometers are used for measuring the
angles of different joints of the excavator. During the hardware tests, such
sensors have shown relatively accurate readings amid the excessive hardware
vibrations. A combination of Livox mid-100 light detection and ranging (LIDAR)
sensors and ZED RGBD cameras collect the environmental information for the
perception module to process and analyze for understanding the surroundings.

One Intel i7 computer with $8$ CPUs and $2$ Nvidia GPUs, running Linux system
hosts the perception, planning and control sofrware modules. An industrial
control board based on STM32F407 arm micro-controller is used for low-level
communication between the computing unit and the excavator.

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# Assessment of Detached Eddy Simulation and Sliding Mesh Interface in
Predicting Tiltrotor Performance in Helicopter and Airplane Modes

Feilin Jia111Research Scientist, Department of Computational Fluid Dynamics.
and John Moore222Chief Executive Officer, Department of Computational Fluid
Dynamics. FlexCompute, 130 Trapelo Road, Belmont, MA, 02478  Qiqi
Wang333Associate Professor, Department of Aeronautics and Astronautics, AIAA
Associate Fellow. Massachusetts Institute of Technology, 77 Mass Ave,
Cambridge, MA, 02139

###### Abstract

This paper presents numerical investigation on performance and flow field of
the full-scale XV-15 tiltrotor in both helicopter mode (hovering flight and
forward flight) and aeroplane propeller mode using Detached Eddy Simulation,
in which the movement of the rotor is achieved using a Sliding Mesh Interface.
Comparison of our CFD results against experiment data and other CFD results is
performed and presented.

##  1 Nomenclature

$R$ = | rotor disk radius
---|---
$A$ = | rotor disk area, $\pi R^{2}$
$c$ = | blade chord
$c_{ref}$ = | reference blade chord
$C_{p}$ = | surface pressure coefficient
$C_{Q}$ = | rotor torque coefficient, $Q/\rho(\Omega R)^{2}AR$
$C_{T}$ = | rotor thrust coefficient, $T/\rho(\Omega R)^{2}A$
FoM = | figure of merit, FoM=$\frac{C_{T}^{3/2}}{\sqrt{2}C_{Q}}$
$T$ = | rotor thrust
$Q$ = | rotor torque
$\Omega$ = | rotor angular speed
$\tau_{wall}$ = | wall shear stress

##  2 Introduction

Over the past decades, tiltrotor has attracted more and more attention because
of the emerging demands for a new type of flying vehicle that has capabilities
of both vertical take-off/landing (VTOL) and high-speed cruise. After this
technology was demonstrated with the Bell XV-3 in 1955 for the first time [1],
a joint program was launched by the NASA Ames Research Center and Bell
Helicopters to develop a new tiltrotor named XV-15 in the late 1960s and early
1970s. The XV-15 tiltrotor research aircraft took its first flight in 1977,
and it has been substantially utilized to support many other tiltrotor
research activities since then, e.g. Bell-Boeing V-22 Osprey[2, 3] and the
AW609[4]. Unlike typical helicopter rotors, tiltrotor blades demand a
compromised design to operate efficiently in both helicopter and propeller
modes, so the blades of XV-15 tiltrotor have high twist and solidity, along
with small rotor radius. Because of its complexity in geometry and widely
varying operation conditions, many experimental investigations of the rotor
performance of the XV-15 tiltrotor have been done over the years. The first
experiment of the full-scale XV-15 rotor was employed in the NASA
40-by-80-Foot Wind Tunnel[5] in 1970. In 1980, Weiberg et al. [6] used the
same facility to measured integrated rotor loads in helicopter, aeroplane, and
transition-coordidor modes. However, the measured force and moment included
the contribution from the airframe. In 1985, a comprehensive experimental
study of the XV-15 tiltrotor in helicopter mode was conducted by Felker et al.
[7] at the NASA-Ames Outdoor Aeronautical Research Facility (OARF). In 1997,
Light [8] conducted hover and forward flight tests in the 80-fit by 120-ft
Wind Tunnel, but the number of tested operation conditions are relatively
limited. To fill the gap, in 2002, Betzina did a series of experiments on
hovering flight and propulsive/descending forward flight in helicopter mode,
and then an extensive data set was published [9]. It should be noted that all
of the above cited experiments didn’t include measurement of surface skin
friction, which is very useful to find the transition location and identify
regions of reversed flow to facilitate the design of blades. Therefore,
Wadcook et al. [10] measured skin friction on a hovering full-scale XV-15
tiltrotor.

In recent decades, both computer hardware and numerical algorithms have
evolved and advanced dramatically, which makes computational fluid dynamics
(CFD) a powerful tool to predict rotor performance. Significant efforts have
been performed to develop various numerical methods to improve the accuracy of
CFD simulations on tiltrotors compared with the published experimental data.
The simplest method was analytical models based on the actuator disk theory
and the blade element momentum theory [11], but it ignores the blade-vortex
interaction and the effect of rotor wake. To overcome the above drawback,
several prescribed wake models were proposed [12, 13]. However, because these
models don’t take the effects of tip shape, nonlinear blade twist and flow
separation into account rigorously, today they are only used for preliminary
performance estimates in some low-fidelity CFD codes. In order to simulate the
near blade flow at higher degree of accuracy on limited computing resources,
some hybrid solvers were developed, which combined a Navier-Stokes solver in
the unsteady viscous flow regions near the blades and a potential flow solver
for wake convection in the farfield [14, 15]. Due to the emergence of parallel
computers, modern high fidelity approaches based on full-domain numerical
simulation of the Navier-Stokes equations were gradually employed. Kaul et al.
[16, 17] and Yoon et al. [18] conducted simulations on the hovering XV-15 main
rotor blade using OVERFLOW CFD code. Sheng et al. [19] used the U2NCLE and
Helios CFD solvers to assess the impact of transition models on prediction of
the hover figure of merit on the XV-15 blade. Gates [20] and Garcia et al [21]
used Helicopter Multi-Block (HMB) CFD solver to perform detailed performance
analyses of the hover and propeller modes of the XV-15 blades. However, there
hasn’t been a comprehensive numerical study on propulsive/descending forward
flight helicopter modes of XV-15 tiltrotor.

In this paper, the hovering and forward flights of helicopter mode and
airplane mode were investigated numerically by our CFD solver: Flow360. In
Sec.3, the geometry and the mesh generation methodology of multi-block meshes
are introduced. In Sec.4, the numerical methods used in the present study are
briefly reviewed. In Sec.5, the various operating conditions for the hover and
forward helicopter modes and airplane mode are listed. In Sec.6, the numerical
results are shown and compared with published experimental data followed by
some detailed studies and discussions. Finally, we conclude the paper with
some major findings in Sec.7.

##  3 XV-15 Rotor Geometry and Mesh

###  3.1 XV-15 Rotor Geometry

The CFD model of XV-15 rotor was generated based on the experiment by Felker
[7]. The rotor blade consists of 5 NACA 6-series aerofoil sections, which are
reported in Table 2.

Table 2: Radial location of the XV-15 rotor blade aerofoils r/R |  Aerofoil
---|---
0.09 |  NACA 64-935
0.17 |  NACA 64-528
0.51 |  NACA 64-118
0.80 |  NACA 64-(1.5)12
1.00 |  NACA 64-208

In the present paper, the tool to create the geometry file of the blade is The
Engineering Sketch Pad V1.18 [22]. The radius of the three-bladed rotor is 150
inches. The inboard aerodynamic section starts at 9.1% radius with a chord of
16.6 inches, linearly tapering a chord of 14 inches by 25% radius. The chord
keeps a constant 14 inches from 25% radius to the tip. Each blade has a
structural twist angle of $-40.25\degree$ from the root cutoff to the tip. The
main geometric characteristics of the XV-15 rotor blades are listed in Table
3. Also, a detailed sketch of the XV-15 blade radial twist and chord
distributions are shown in Figure 1. Different from the conventional
helicopter blades, tiltrotor blades are characterised by high twist and
solidity, along with a small rotor radius.

Table 3: Geometric properties of the full-scale XV-15 rotor Parameter |  Value
---|---
Number of blades, $N_{b}$ |  3
Rotor radius, $R$ |  150 inches
Reference blade chord, $c_{ref}$ |  14 inches
Aspect ratio, $R/c_{ref}$ |  10.71
Rotor solidify, $\sigma$ |  0.089
Linear twist angle, $\Theta$ |  -40.25°
Figure 1: XV-15 rotor blade’s twist and chord radial distribution [7].

###  3.2 XV-15 Rotor Mesh

A multi-block unstructured mesh was generated for the aerodynamic study of the
XV-15 rotor. The entire domain was splitted into multiple blocks for meshing,
shown in Figure 2. The farfield block acts as the stationary domain, and the
nearfield block, containing the three blades, acts as the rotational domain.
For the farfield block, the boundaries were extended to $33.5R$ above and
below the rotor disk plane in the axial direction, and to $48.8R$ from the
center of domain in the radial direction, which are far enough to assure an
independent solution with the boundary conditions employed. For the nearfield
block, because CFD simulations of the XV-15 tiltrotor need to be conducted
over a range of blade collective angles from $0$ to $18$ degrees, it is more
flexible to split the nearfield block into 4 parts: 1 cylindrical off-body
mesh (nearfield background) and 3 cylindrical body-fitted meshes containing
the blades. It should be noted that, to avoid non-conforming meshes, the nodes
on the interfaces between the nearfield background mesh and body-fitted meshes
are one-to-one matched. The nearfield background mesh could be reused for all
body-fitted meshes with various blade collective angles. It is extended to
0.1667R above and below the rotor plane in the axial direction and to 1.1667R
in the radial direction. For the body-fitted meshes, one cylindrical body-
fitted mesh was generated first. Its axial direction is along the radial
direction of the computational domain. It expanded from 0.1367R to 1.0333R in
the radial direction and the radius of its circular face is 0.1024R. Then it
was rotated by 120 degree and -120 degree respectively along the axial
direction of the computational domain to obtain the other two body-fitted
meshes by our mesh transformation tool. Finally, the off-body mesh and the 3
body-fitted meshes were merged together to obtain the nearfield block by our
efficient mesh merging tool. All the overlapped nodes from the above meshes
are detected and merged into one single node, so the resulting merged
nearfield block is a single-block conforming mesh. During the simulation, the
inner block rotates along the axial direction of the domain and outer block
remains stationary. The solution is communicated through the interface of the
above two blocks via interpolation.

More than 40 layers of Hexahedral cells are used near the blade to resolve the
viscous boundary layer. Beyond that, tetrahedron cells are used in most
places, while prismatic and pyramidal cells are used in between. The surface
mesh points are directionally clustered near the blade leading edge, trailing
edge and tip regions. The number of nodes on each blade’s surface is 58,641.
The height of the first mesh layer above the blade surface was set to
$7.143\text{\times}{10}^{-7}$ $\text{Chord}_{ref}$, which yields the $y^{+}$
less than 1.0 all over the blade. Figure 3 shows the volumetric grid
distributions and the surface mesh resolutions as well as the boundary-layer
mesh near the blade tip. The number of nodes in the farfield/nearfield
background meshes and the body-fitted mesh are listed in Table 4

(a) Dimensions of the farfield background domain

(b) Dimensions of the nearfield background domain

(c) Dimensions of the body-fitted domain

(d) Planform of the XV-15 rotor blade

Figure 2: Sketch of the domains of farfield background, nearfield background and body-fitted for meshing and the Planform of the XV-15 rotor blade. Table 4: Meshing parameters for the XV-15 rotor mesh |  Number of Nodes
---|---
Blade mesh |  6.27M
Nearfield background mesh |  2.69M
Farfield background mesh |  15.10M

Figure 3: Cutting planes of volumetric mesh of 3-bladed XV-15 computational
domain and surface mesh near blade tip.

##  4 The Numerical Methods

We used Detached Eddy Simulation (DES) with Spalart–Allmaras (SA) turbulence
model in the present study [23]. Figure 4 shows the approach in which the
solution is communicated from the stationary mesh to the rotating mesh on the
sliding interface. At any moment during the rotation, every single receiver
node on rotating mesh detects its two ’neighboring’ nodes as donor nodes. The
receiver node’s solution is linearly interpolated from solutions of the two
donor nodes. The communication from rotating mesh to stationary mesh is in the
same manner.

Figure 4: Overview of data communication from stationary mesh to rotating mesh
on a sliding interface.

##  5 Test Conditions

Table 5 summarises the flow conditions employed in the present study.

Table 5: Flow conditions for the full-scale XV-15 tiltrotor blade. |  Hover Mode |  Airplane Mode |  Forward Mode
---|---|---|---
Blade-tip Mach number ($M_{tip}$) |  0.69 |  0.54 |  0.69
Reynolds number ($Re$) |  $4.95\text{\times}{10}^{6}$ |  $4.50\text{\times}{10}^{6}$ |  $5.65\text{\times}{10}^{6}$
Blade pitch angle ($\theta_{75}$) |  0°,3°,5°,10°,13° |  26°,27°,28°,28.8° |  2°$\sim$10°
Angle of attack ($\alpha$) |  |  -90° |  -5°,0°,5°
Advance ratio |  |  0.337 |  0.170
Grid size (# of nodes) |  36.6M |  36.6M |  39.83M

##  6 Results and Discussions

###  6.1 Hovering Flight of Helicopter Mode

####  6.1.1 Overall Blade Loads

The figure of merit and torque coefficient $C_{Q}$, as functions of the thrust
coefficient $C_{T}$, are shown in Figure 5 and Figure 6 respectively. Three
sets of experimental data of the full-scale XV-15 rotor are also shown,
carried out by Felker et al. [7] at OARF, and Light [8] and Betzina [9] at the
NASA 80$\times$120ft wind tunnel. The results from Flow360 present a good
agreement with the experimental data.

Figure 5: Figure of merit - Thurst coefficient of hovering helicopter mode.
Figure 6: Torque coefficient - Thrust coefficient of hovering helicopter mode

####  6.1.2 Surface Pressure Coefficient and Skin Friction

To validate the predictive capability of Flow360 in greater physical detail,
surface pressure and viscous friction distributions are investigated. For
surface pressure, because there is no experimental measurements on it, the CFD
data by OVERFLOW2 [16] and HMB3 [21] is used as reference for comparison shown
in Figure 7. Three radial stations were selected for the collective pitch
angle = 10°. The surface pressure coefficient is calculated based on the local
rotating velocity at each radial station in Eq.1:

$C_{P}=\frac{P-P_{\infty}}{1/2\rho_{\infty}\bigl{(}\Omega r\bigr{)}^{2}}$ (1)

(a) Surface pressure coefficient

(b) $r/R$=0.72

(c) $r/R$=0.83

(d) $r/R$=0.94

Figure 7: Comparison of predicted surface pressure coefficient among Flow360,
OVERFLOW2 and HMB3 at collective pitch angle $\theta_{75}=10\degree$.

In order to provide finer assessment of the Flow360 in capturing physics
phenomenon, the computed skin friction coefficients, defined in Eq.2, with
experimental data by Wadcock et al. [10] at 6 blade span stations: $r/R$ =
0.17, 0.28, 0.50, 0.72, 0.84 and 0.94 for collective pitch angle
$\theta_{75}=10\degree$ are shown in Figure 8.

$C_{f}=\frac{\tau_{wall}}{1/2\rho_{\infty}\bigl{(}\Omega r\bigr{)}^{2}}$ (2)

At the outboard station $r/R$ = 0.94, the flow is fully turbulent shown by the
measured data and the computed $C_{f}$ matches the measured data well. At
other stations, the experimental data shows natural transition phenomenon. In
turbulent flow regions of these stations, the computed $C_{f}$ matches the
measured data well, except the region $x/C_{x}>0.6$ at the station $r/R$=0.28,
which will be investigated further. In laminar regions, because no transition
model was employed in the present study, the computed $C_{f}$ over predicts
the skin friction as expected. This result shows the capability of capturing
the natural transition phenomenon is crucial to accurately predict the skin
friction on the rotor blades, which is one of the future works.

(a) $r/R=0.28$

(b) $r/R=0.50$

(c) $r/R=0.72$

(d) $r/R=0.83$

(e) $r/R=0.94$

Figure 8: Comparison of predicted skin friction coefficient with the
experimental data of Wadcock et al.[10]. Conditions employed: $M_{tip}=0.69$,
$Re=4.95\cdot 10^{6}$, and $\theta_{75}=10\degree$.

####  6.1.3 Sectional Blade Loads

To further understand the flow physics near the rotor blades, the sectional
loading was investigated. The sectional thrust coefficient $C_{t}$ and torque
coefficient $C_{q}$ are distributions along the radius $r$. They are defined
in Eq.3 and Eq.4 respectively.

$C_{t}\bigl{(}r\bigr{)}=\frac{\int\bigl{(}(p-p_{\infty})\vec{n}+\bm{\tau}\cdot\vec{n}\bigr{)}_{axial}\,ds}{\frac{1}{2}\rho_{\infty}\left((\Omega
r)^{2}+V_{\infty}^{2}\right)\text{chord}_{\text{local}}}\cdot\frac{r}{R}$ (3)
$C_{q}\bigl{(}r\bigr{)}=\frac{\int\left[\bigl{(}(p-p_{\infty})\vec{n}+\bm{\tau}\cdot\vec{n}\bigr{)}\times\vec{r}\right]_{axial}\,ds}{\frac{1}{2}\rho_{\infty}\left((\Omega
r)^{2}+V_{\infty}^{2}\right)\text{chord}_{\text{local}}R}\cdot\frac{r}{R}$ (4)

Due to the lack of experimental data on the sectional loading, CFD data of
HMB3 [21] is shown as comparison for multiple collective pitch angles
$\theta_{75}=3\degree,5\degree,10\degree,13\degree$ in Figure 9. It shows the
two solvers agree well with each other in the entire span for all collective
pitch angles. Both of them predict the spike of thrust near the tip,
$r/R=0.9$, due to the blade vortex interaction.

(a) Sectional thrust coefficient

(b) Sectional torque coefficient

Figure 9: Blade sectional thrust and torque coefficient for the full-scale
XV-15 rotor in helicopter mode.

####  6.1.4 Flowfield Details

Flowfield visualization of the rotor wake for the full-scale XV-15 rotor blade
in hovering flight of helicopter mode using the Q criterion is given in Figure
10. The interaction between blade and vortex can be seen evidently.

Figure 10: Wake visualization of the hovering XV-15 blades using Q-criterion
shaded by contours of Mach at collective pitch $\theta_{75}=10\degree$.

###  6.2 Airplane Mode

####  6.2.1 Overall Blade Loads

For airplane mode, simulations were performed for medium advance ratio
$\mu=0.337$ at collective pitch angles of 26°, 27°, 28°, 28.8°, and tip Mach
number of 0.54. The $C_{Q}$ as a function of $C_{T}$ is shown in Fig. 11. In
this mode, the indicator to measure the rotor efficiency is the propeller
propulsive efficiency, defined as the ratio between the useful power output of
the propeller and the absorbed power expressed in Eq.(5). The $\eta$ as a
function of $C_{T}$ is shown in Fig.12.

$\eta=\frac{C_{T}V_{\infty}}{C_{Q}V_{tip}}$ (5)

Figure 11: Torque coefficient - Thrust coefficient of airplane mode. Figure
12: Propeller propulsive efficiency - Thrust coefficient of airplane mode.

####  6.2.2 Surface Pressure Predictions

Figure 13 shows the predicted pressure coefficient distribution of the upper
surface of a XV-15 blade at the advance ratio $\mu=0.337$. As the collective
pitch angle increased, the span-wise blade loading becomes more uniform.

(a) Blade collective pitch angle $\theta_{75}=26\degree$

(b) Blade collective pitch angle $\theta_{75}=28.8\degree$

Figure 13: Contours of pressure coefficient for one XV-15 rotor blade in
airplane mode.

####  6.2.3 Flowfield Details

Flowfield visualization of the rotor wake of two collective pitch angles in
airplane mode, $\theta_{75}=26\degree$ and $28.8\degree$, using the Q
criterion is given in Figure 14. It can be seen that the vortices are stronger
with a higher collective pitch angle. Moreover, because of the non-zero axial
velocity of freestream, the blade vortex interaction is much less than the
hovering flight, leading to simpler tip vortex trajectories.

(a) Blade collective pitch angle $\theta_{75}=26\degree$

(b) Blade collective pitch angle $\theta_{75}=28.8\degree$

Figure 14: Wake visualization of the airplane mode using Q-criterion shaded by
contours of Mach at collective pitch of $\theta_{75}=26\degree$ (left) and
$\theta_{75}=28.8\degree$ (right).

###  6.3 Forward Flight Helicopter Mode

In the study of forward flight of helicopter mode, the shaft angle (angle of
attack of the rotor tip-path-plane) varied from $-5\degree$ to $5\degree$ to
simulate a wide range of both propulsive and descending forward flight
conditions. The flow in this mode is much more complex because of the more
intensive blade-vortex interactions. The advance ratio ($V_{\infty}/\Omega R$)
is 0.17 mentioned in Table 5. The detailed information of tested collective
pitch angles for different shaft angle is listed in Table 6. The experimental
data reported here were from Ref. [9], and they were already corrected to
exclude the hub and tares effects.

Table 6: Collective pitch angles tested in forward flight of helicopter mode. shaft angle $\bm{\alpha}$ |  collective pitch angles $\bm{\theta_{75}}$
---|---
-5° |  4°, 5°, 6°, 7°, 8°, 9°, 10°
0° |  3°, 5°, 6°, 7°, 8°, 9°
5° |  2°, 3°, 4°, 5°, 6°, 7°

####  6.3.1 Overall Blade Loads and Flowfield details

The relation between thrust and lift to torque is shown in Figure 15 and
Figure 16. The predicted rotor performance has $4\%-14\%$ relative error
compared with the experimental data. Also, Figure 17 shows the rotor wake in
helicopter forward flight mode using the $Q$ criterion. It can be seen that
the tip vortices have more interaction with the downstream blades. Because of
the uncertainties in both simulations and measurements, a comparison between
prediction and experimental data in greater physical detail should be done to
find the reason for they discrepancy. However, due to lack of more precise
experimental data, e.g. pressure distribution and skin friction, to verify and
validate the Flow360 further, as a future work, an investigation on the impact
of grid resolution and time integration needs to be done to study their
impacts on capturing the underlying physics.

Figure 15: Comparison of measured and predicted torque coefficient versus
thrust coefficient in forward flight helicopter mode with advance ratio
$\mu=0.17$. Figure 16: Comparison of measured and predicted torque coefficient
versus lift coefficient in forward flight helicopter mode with advance ratio
$\mu=0.17$.

(a) Angle of attack of rotor disk $\alpha=-5\degree$

(b) Angle of attack of rotor disk $\alpha=5\degree$

Figure 17: Wake visualization of the helicopter forward flight mode using
Q-criterion shaded by contours of Mach at propulsive $\alpha=-5\degree$ (left)
and $\alpha=5\degree$ (right).

##  7 Conclusions

The helicopter mode (hovering flight and forward flight) and airplane mode of
a full-scale XV-15 tiltrotor are numerically studied using the Flow360 solver.
All of the meshes involved are multi-block unstructured meshes, which are very
efficient to create meshes with various collective pitch angles
($0\degree-28.8\degree$ in the present study). The following conclusions are
given the present study:

  1. 1.

The predicted integrated performance, e.g. $C_{T}$, $C_{Q}$, in hovering
flight shows excellent agreement with the wind tunnel tests in [7, 8, 9].

  2. 2.

The predicted blade sectional thrust and torque of hovering flight of
helicopter mode capture the effect of tip vortices, which are close to the CFD
data of HMB3.

  3. 3.

In hovering flight of helicopter mode, the predicted skin friction can match
the measured data in most of turbulent flow regions, but a transition model is
necessary to capture the natural transition process, which is a future area of
research.

  4. 4.

In airplane mode with a medium advance ratio ($\mu=0.337$), the predicted
integrated $C_{T}$ and $C_{Q}$ show good agreement with the experiment in [5].

  5. 5.

In forward flight helicopter mode, the predicted $C_{T}$ and $C_{Q}$ matches
the measured data fairly well. A grid resolution study and time integration
study are useful to investigate the reasons of discrepancy.

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