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Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

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At least 19 records
All sourcesNASA NTRSDOE OSTI
DOE OSTI2025

Optimizing Energy For Delivery Drones - A Comprehensive Tool Set For Drone Energy Calculation And Drone Fleet Optimization

This tool is intended to be deployed for potential customers to compare the energy profiles across various drone types/classes. The primary factors considered were design of the drone, the weight of the drone, the weight of the payload, and how the drone is flown. It has energy comparison metrics like "Drone (A) vs Drone (B) ", "Drone vs Ground Vehicle", "Drone Energy from delivery via landing versus hovering". It also includes the ability to determine the number of drones and batteries needed to optimally deliver goods from a chosen location to a set of destinations.

Mendadhala, Rohit [Idaho National Laboratory (INL)↗
DOE OSTI2024

Droning on to Delivery: Examining the Energy Impacts of Using Drones for Moving Goods

The demand for fast, localized delivery has grown significantly in recent years. Whether for cheesy snacks, prepared food, medical solutions, or business deliveries, fast and efficient delivery is increasingly a demand and differentiator. Drone delivery in the freight sector offers to revolutionize last-mile logistics and improve services. This research analyzes the impacts of drone energy for delivery operations, aiming to compare various types of drones (large and small, rotary and VTOL) and various types of business methods. The project executes novel open-air and laboratory-based testing to look at the impacts of weights, operations, temperatures and weather conditions. The study combines this real-world experimental data with fleet optimization mathematical models to assess energy consumption of different scenarios as well as examine the minimum fleet size and additional battery requirements. These fleet optimization models compare energy between types of deployments with existing delivery methods. The model was also extended to look at mixed fleets of aerial drones and ground vehicles to accommodate different restrictions on drones or when weather prohibits their use. The analysis provided insights into factors such as drone design, payload weight, flight distance, weather conditions and operational parameters and impacts of each. It showed how to combine different types of vehicles to reduce energy and improve services. And it showed that drone speed, routing restrictions, and unfavorable weather significantly influence energy consumption. Our open experiment data and optimization models can assist stakeholders and industry in understanding drone package delivery and offers keys to improving deployment.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2021

Delivery drone route planning over a battery swapping network

Many enterprises invest on drone delivery research and development to drop off packages at consumers’ doorsteps in a matter of minutes. We study delivery drone route planning over a battery swapping network allowing farther reach by penetrating current battery capacity constraints. A mixed-integer nonlinear programming model is created to plan efficient drone routing over the swapping machines by minimizing the delivery lead time. We develop an exact solution method, evaluate its performance, and compare it with a straightforward nonlinear solver application. A case study highlights the applicability of the model. Data and source code to the solver are publicly shared.

drone battery swapping↗
DOE OSTI2023

Toward Autonomous Field Inspection of CSP Collectors With a Polarimetric Imaging Drone

We developed a polarimetric imaging drone to perform field inspections of heliostats and carried out field tests at Sandia’s National Solar Thermal Test Facility (NSTTF). The preliminary results show that Degree of Linear Polarization (DOLP) and Angle of Polarization (AOP) images greatly enhanced the edge detection results compared with the conventional visible images, supporting fast and accurate detection of heliostat mirror edges and cracks. The system holds the promise to enable future automated detection of heliostats optical errors and mirror defects.

14 SOLAR ENERGY↗
DOE OSTI2022

Advances in Secure 5G Network for a Nationwide Drone Corridor

Recent research has validated the proposal to add a separate set of antennas for 5G coverage in the air, while the conventional set of antennas continues to provide coverage on the ground, for a nationwide drone corridor for 5G cellular drones. More importantly, this drone corridor can be made secure and reliable by adapting the drone trajectories to avoid interference and security attacks, and with advanced precoding and physical layer security. Energy efficiency can also be improved with low-resolution massive multiple-input multiple-output (MIMO) systems that utilize low resolution digital to analog converters. This paper describes additional research findings to further support the creation of this nationwide drone corridor. We design optimal drone trajectory within the drone corridor to improve safety for pedestrians and vehicles on the ground. We derive the optimum antenna uptilt angle to minimize outage probability for a given drone corridor. We also study the placement of intelligent reflector surfaces in an urban drone corridor in order to improve the multi-path scattering and hence the spatial multiplexing gains for serving drones. We calculate trajectories to maximize data rate in the presence of smart interference when drones are used as relays and each drone may be deployed in the paths of data flows from multiple BSs to multiple UEs. Next we demonstrate how the use of the additional set of antennas along with the 3GPP standard based subframe blanking method can minimize the interference from ground reflection of the radio frequency (RF) radiation from the downtilted antennas. The paper concludes with plans to continue with experimental studies to advance this work further.

99 GENERAL AND MISCELLANEOUS↗
DOE OSTI2024

Aerial drone fleet deployment optimization with endogenous battery replacements for direct delivery of time-sensitive products

Aerial drones offer a distinct potential to reduce the delivery time and energy consumption for the delivery of time-sensitive and small products. However, there is still a need in the relevant industry to understand the performance of drone-based delivery under different business needs and drone operating conditions. We studied a drone deployment optimization problem for direct delivery of time-sensitive products with release dates to customers maintaining a specified time window. This paper presents a new mixed-integer programming model, new valid inequalities, a new greedy heuristic algorithm, and a Genetic algorithm to help business owners optimally schedule and route their drone fleet minimizing the required fleet size, the required number of additional batteries, and total energy consumption. A realistic feature of the optimization method is that instead of replacing the drone battery after each return to the depot, it keeps track of the remaining energy in the drone battery and decides on battery replacements accounting for the drone routing and the user-specified minimum required battery energy. Numerical results based on real data from drone flight tests and prepared food delivery industry provide insights into the effect of different practical drone operating parameters on the required fleet size, the required number of battery replacements, and energy consumption. Here, results demonstrate that the proposed heuristic algorithm substantially outperforms the accelerated CPLEX in runtime while sacrificing the solution quality by a small amount. Additionally, results show that using a mixed fleet of hexacopter and quadcopter drones reduces the total energy consumption by 48.52% compared to using a homogeneous fleet of only hexacopters.

Drone energy consumption↗
DOE OSTI2021

Optimization of battery swapping infrastructure for e-commerce drone delivery

Drone delivery is widely-researched to alter the current e-commerce delivery convention for providing short delivery lead times. Yet, flight range of drones, constrained by the available battery technology, sets a milestone toward realizing the sole-drone delivery. To tackle with the flight range limitation, locating automated battery swapping machines (ABSM) have been proposed and a few studies modeled the problem. Using the ABSMs, drones can be loaded with fully-charged batteries along the route to a demand location. Here, we introduce a mixed-integer nonlinear program to model the problem. The objective of the program is to optimally select ABSM locations, determine the delivery-mode choices (drone-only, truck-only, and mixed delivery) of demand locations, find drone delivery routes, and approximate the baseline requirements for the number of drones and batteries needed. The program minimizes the overall delivery system costs including: ABSM, delivery, drone ownership, battery inventory, and service congestion. A cutting-plane method is developed to find exact solutions in finite iterations. Computational experiments showed that the method quickly yields the optimal solution to instances with less than 60 ABSM candidates and 20 demand locations. A case study shows that the optimal drone delivery infrastructure can save almost 20% cost compared to the conventional truck-only delivery. Sensitivity analyses were conducted to reveal the impact of key parameters in the decision-making and found that a decrease in the ABSM and drone costs highly affect the system cost.

25 ENERGY STORAGE↗
DOE OSTI2023

Comparing Regional Energy Consumption for Direct Drone and Truck Deliveries

Drone delivery, once thought of as fictitious, is becoming a reality with the efforts of both forward-looking enterprises and supportive government policies. This emerging mode of e-commerce delivery raises many concerns. One important concern is the energy efficiency of direct delivery drones compared with conventional delivery trucks at a regional systems level. Here, in this study, we develop and apply methods to quantify the regional energy impacts of drone delivery, then we assess these impacts and compare them with the impacts of truck delivery. To study this problem, we develop an optimization model that determines an optimal set of fulfillment centers (FCs) with variable service capacities that allow drones to make direct e-commerce deliveries. We adopt two drone delivery energy estimation models from the literature and use them as inputs to demonstrate the potential range of energy needs. We also develop another optimization model to account for the energy consumption of diesel trucks (DTs) and battery electric vehicles (BEVs). We test the models using validated simulation data for the Chicago metropolitan area in the U.S. to quantify the energy implications of these three delivery modes. For drone delivery, we further extend our analyses by considering the impact of wind speed and flight patterns. Our results show that direct delivery drones require 15.8% more energy than BEVs on an average windy day, and they need 15% more energy than DTs on a very windy day. We provide essential parameter values for reproducibility and list relevant open problems.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2018

Energy use and life cycle greenhouse gas emissions of drones for commercial package delivery

Here, the use of automated, unmanned aerial vehicles (drones) to deliver commercial packages is poised to become a new industry, significantly shifting energy use in the freight sector. Here we find the current practical range of multi-copters to be about 4 km with current battery technology, requiring a new network of urban warehouses or waystations as support. We show that, although drones consume less energy per package-km than delivery trucks, the additional warehouse energy required and the longer distances traveled by drones per package greatly increase the life-cycle impacts. Still, in most cases examined, the impacts of package delivery by small drone are lower than ground-based delivery. Results suggest that, if carefully deployed, drone-based delivery could reduce greenhouse gas emissions and energy use in the freight sector. To realize the environmental benefits of drone delivery, regulators and firms should focus on minimizing extra warehousing and limiting the size of drones.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2019

Drone Strikes: An Overview, Articulation and Assessment of the United States' Position Under International Law

The United States unmanned aerial vehicle (UAV or drone) program has origins dating back to the early part of the twentieth century. From crude target tug vessels to sophisticated Hellfire missile delivery vehicles, United States drones have undergone a gradual metamorphosis from their early days as an obscure technology to their present status as a center piece in global counterterrorism operations. Since the attacks of September 11, 2001 and the onset of the war on terror, weaponized drones have effectuated thousands of strikes in numerous countries spanning multiple continents in the course of a claimed non-international armed conflict with “al-Qaida, the Taliban and their associated forces.” A plethora of scholarship has evaluated aspects of the legal underpinnings of the nearly two decade-long campaign and its utilization of weaponized drones. This article builds upon that analytic foundation to date in articulating and assessing the present United States legal position vis-à-vis its lethal drone strike operations under relevant principles of international law. It identifies numerous instances in which the United States position departs to varying degree from general international understanding and concludes that, in the aggregate, these deviations are sufficient to pose serious international rule of law concerns. It then goes further to briefly consider possible alternatives for mitigating these concerns, but notes their challenges given prevailing political circumstances. The article is organized in four parts. Part I provides a condensed history of the development of drone capabilities in the United States and their use under the administrations of Clinton, Bush, Obama and Trump. Part II surveys principles of international law relevant to the analysis of United States drone strike operations abroad. Part III draws on key government documents to articulate the United States legal position, including its jus ad bellum justifications and stance on international humanitarian and international human rights law principles, and offers a legal assessment. Part IV briefly considers and evaluates possible alternatives to the present position.

45 MILITARY TECHNOLOGY, WEAPONRY, AND NATIONAL DEF↗
DOE OSTI2025

Optimization of a Mixed Fleet of Aerial Drones for Medical Supplies: A Case Study of Blood Delivery Logistics

Aerial drones have emerged as an innovative solution for faster transportation of time-sensitive items (e.g., emergency medical supplies), potentially reducing the transmission of contagious diseases and enhancing healthcare availability through contactless autonomous delivery. We study fleet sizing and efficient scheduling of a mixed fleet of drones for delivering time-sensitive medical items having distinct release and due times to minimize the required fleet size and fleet composition, the required number of additional batteries, and the total energy consumption. We continuously track the remaining battery energy of drones to determine the optimal timing for battery replacement, rather than replacing the battery at each node. Using actual drone flight test data, we employed a machine learning (ML) method to estimate the energy consumption of different drone types during flight segments for different operating parameters. We present a novel mixed-integer programming model to efficiently formulate the problem that integrates the estimated energy consumption functions from ML. We propose a new greedy heuristic (GH) algorithm and a customized genetic algorithm (GA) for solving large-scale instances of this problem faster. Results demonstrate that the GH algorithm is substantially faster than the accelerated CPLEX and the GA, while sacrificing the solution quality by a small amount. Results based on an actual blood sample delivery case study from Pendleton, Oregon, United States, show that using a mixed fleet of drones reduces the total cost and total energy consumption up to 18.18% and 28.7%, respectively, compared to using a homogeneous fleet.

29 - ENERGY PLANNING, POLICY AND ECONOMY↗
DOE OSTI2023

Channel Rank Improvement in Urban Drone Corridors Using Passive Intelligent Reflectors

Multiple-input multiple-output (MIMO) techniques can help in scaling the achievable air-to-ground (A2G) channel capacity while communicating with drones. However, spatial multiplexing with drones suffers from rank deficient channels due to the unobstructed line-of-sight (LoS), especially in millimeter wave (mmWave) frequencies that use narrow beams. One possible solution is utilizing low-cost and low-complexity metamaterial based intelligent reflecting surfaces (IRS) to enrich the multipath environment, taking into account that the drones are restricted to fly only within well-defined drone corridors. A hurdle with this solution is placing the IRSs optimally. In this study, we propose an approach for IRS placement with a goal to improve the spatial multiplexing gains, and hence to maximize the average channel capacity in a predefined drone corridor. Our results at 6 GHz, 28 GHz and 60 GHz show that the proposed approach increases the average rates for all frequency bands for a given drone corridor, when compared with the environment where there are no IRSs present, and IRS-aided channels perform close to each other at sub-6 and mmWave bands.

99 GENERAL AND MISCELLANEOUS↗
DOE OSTI2024

A Behavior Tree Approach for Battery-Aware Inspection of Large Structures Using Drones

Electric multi-rotor drones have been used to inspect several structures, including large buildings and dams. In these inspections, energy consumption is a concern. To prevent the drone from running out of battery, commercial drones usually come back to their home position when the battery level reaches a minimum threshold. The pilots then need to replace the battery and use their own experience to restart the inspection mission approximately from where it ended before the drone returned home. Instead of relying on the human operator, in this paper, we automate this process using behavior trees, which is an effective way to perform autonomous mission control and supervision. By integrating battery management strategies into a behavior tree framework, this paper demonstrates the drone’s adaptive and resilient decision-making when confronted with limited power constraints. We implemented our methodology using a commercial drone and tested the proposed ideas in a photogrammetry-based inspection task.

42 ENGINEERING↗
DOE OSTI2020

Designing a drone delivery network with automated battery swapping machines

Drones are projected to alter last-mile delivery, but their short travel range is a concern. In this study, we propose a drone delivery network design using automated battery swapping machines (ABSMs) to extend ranges. The design minimizes the long-term delivery costs, including ABSM investment, drone ownership, and cost of the delivery time, and locates ABSMs to serve a set of customers. We build a mixed-integer nonlinear program that captures the nonlinear waiting time of drones at ABSMs. To solve the problem, we create an exact solution algorithm that finds the globally optimal solution using a derivative-supported cutting-plane method. To validate the applicability of our program, we conduct a case study on the Chicago Metropolitan area using cost data from leading ABSM manufacturer and geographical data from the planning and operations language for agent-based regional integrated simulation (more commonly known as POLARIS). A sensitivity analysis identifies that ABSM service times and costs are the key parameters impacting the long-term adoption of drone delivery.

25 ENERGY STORAGE↗
DOE OSTI2022

A branch-and-price algorithm for a team orienteering problem with fixed-wing drones

This paper formulates a team orienteering problem with multiple fixed-wing drones and develops a branch-and-price algorithm to solve the problem to optimality. Fixed-wing drones, unlike rotary drones, have kinematic constraints associated with them, thereby preventing them to make on-the-spot turns and restricting them to a minimum turn radius. This paper presents the implications of these constraints on the drone routing problem formulation and proposes a systematic technique to address them in the context of the team orienteering problem. Furthermore, a novel branch-and-price algorithm with branching techniques specific to the constraints imposed due to fixed-wing drones are proposed. Extensive computational experiments on benchmark instances corroborating the effectiveness of the algorithms are also presented.

42 ENGINEERING↗
DOE OSTI2023

Base Station Antenna Uptilt Optimization for Cellular-Connected Drone Corridors

Reliable wireless coverage in drone corridors is critical to enable a connected, safe, and secure airspace. To support beyond visual line of sight (BVLOS) operations of aerial vehicles in a drone corridor, cellular base stations (BSs) can serve as a convenient infrastructure as they are widely deployed to provide seamless wireless coverage. However, antennas in the existing cellular networks are down-tilted to optimally serve their ground users, which results in coverage holes at higher altitudes when they are used to serve drones. In this paper, we consider the use of additional uptilted antennas at each cellular BS and optimize the uptilt angle to maximize the wireless coverage probability across a given drone corridor. Through numerical results, we can characterize the optimal value of the antenna uptilt angle for a given antenna pattern as well as the minimum/maximum altitudes of the drone corridor.

42 ENGINEERING↗
DOE OSTI2023

FY23 Drone RFID (Final Report)

The Drone RFID project successfully completed proof-of-concept testing to demonstrate the potential of a drone-based inventory system for high value remote materials at Los Alamos National Laboratory. Multiple drone platforms were procured, flight authorizations were received, and the vehicles were flown demonstrating what was initially perceived as the most difficult part of the project, successfully flying drones at the lab. Three commercial RFID inventory systems were also purchased, and ground testing performed to determine their suitability. Ultimately, one system was chosen for flight testing and successfully demonstrated the drone's ability to execute inventory with great potential for a relatively low-cost implementation.

42 ENGINEERING↗
DOE OSTI2018

Presentation for LDRD FY19 proposal: An Innovative Secure millimeter wave (mmWave) Machine to Machine (M2M) Communication Network for Operating Drones

We propose to prove the principle that a novel 5G wireless network using newly available millimeter wave (mmWave) frequency bands can operate Unmanned Aerial Vehicles (UAVs) with Machine to Machine (M2M) communication. If successful, this proposed network will provide a superior alternative to existing methods with improved radio frequency (RF) coverage and resiliency against cyberattacks. The use of UAVs or drones for non-military applications has been advancing rapidly for a large number of applications in the United States. They include public safety search and rescue of natural disaster victims, surveillance of remote critical infrastructure, surveys of environmental quality in protected regions, and detection of threats during major public events. The proposed approach involves research with modeling, simulation, and experimentation to validate the hypothesis in the following thrusts: 1) Using mmWave beams with antennas tilted upward for RF coverage in the sky to analyze the security, reliability, and spectral efficiency of this wireless network for UAV/drone operation; 2) Adding non orthogonal multiple access (NOMA) to analyze increase in security and spectral efficiency when compared to existing methods; 3) Adding intra-drone communication among the swarm of drones to analyze further increase in reliability and spectral efficiency; 4) Concluding the proof of principle/concept with modeling and experiment to demonstrate that a swarm of drones controlled with this advanced network can be effectively used during the simulated recovery of a critical infrastructure e.g. a cyber compromised power grid that requires a black restart. The experiments will be executed at the North Carolina State University (NCSU) using NCSU’s 28 GHz mmWave system. A conceptual Invention Record Disclosure (IDR) has been filed and we expect Intellectual Property (IP) from this proposed research to advance the IRD towards patent filing by INL with support from NCSU. Successful outcome from this work will pave the way for significant future research needed to mature the technology for a nationwide secure and reliable network supporting a large number of government and public safety drones.

99 GENERAL AND MISCELLANEOUS↗