Engineering Papers⌕ Search

SEARCH · Engineering Papers

Results for “mining operations”

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.

Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.

At least 181 records · Page 10
All sourcesNASA NTRSDOE OSTI
NASA NTRS2007

Journal of Air Transportation, Volume 12, No. 1

Topics discussed include: a) Data Mining Methods Applied to Flight Operations Quality Assurance Data: A Comparison to Standard Statistical Methods; b) Financial Comparisons across Different Business Models in the Canadian Airline Industry; c) Carving a Niche for the "No-Frills" Carrier, Air Arabia, in Oil-Rich Skies; d) Situational Leadership in Air Traffic Control; and e) The Very Light Jet Arrives: Stakeholders and Their Perceptions.

Bowers, Brent D.↗
DOE OSTI2022

Performance and Total Cost of Ownership of a Fuel Cell Hybrid Mining Truck

The main objective of this work was to investigate the potential of hydrogen and fuel cells replacing diesel and internal combustion engines in the ultraclass haul trucks deployed in the mining sector. Performance, range, durability, and cost are the main criteria considered for comparing the two fuels and engine options. Fuel cell system (FCS) performance is characterized in terms of heat rejection, efficiency, and fuel consumption for a hybrid platform equivalent to a 3500 hp diesel engine operating on a representative open pit mining duty cycle. A hybrid platform was chosen because the heat rejection, with a constrained radiator frontal area, limits the maximum fuel cell-rated power by about 50% compared to that of the diesel truck. The hybrid powertrain was 81–88% more efficient than the diesel powertrain on the truck duty cycle. A liquid hydrogen storage system is required for an equal range or time between refilling, but the packaging remains a challenge. Fuel cell and battery durability were evaluated for their performance degradation and lifetime. Achieving a fuel cell lifetime comparable to the time between major overhauls for diesel trucks necessitates the oversizing of the membrane-active area, catalyst overloading, and voltage clipping. For an equal lifetime, the battery must be oversized to control its depth of discharge and charge/discharge rates. A total cost of ownership (TCO) analysis considering the initial capital expenditures, as well as the lifetime cost of fuel, operation, and maintenance, indicates that fuel cells and hydrogen can compete with diesel. A breakeven fuel cost for TCO parity is obtained if H2 is available at USD 5.79–6.85/kg vs. diesel at USD 3.25/gal and the FCS-specific cost is USD 323/kW e relative to USD 250/kW for a diesel genset. Volume manufacturing is required for FCS cost reduction. High volume is possible through the standardization, modularity, and proliferation of class 8 long-haul truck systems across different heavy-duty applications.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗
NASA NTRS1979

SEASAT demonstration experiments with the offshore oil, gas and mining industries

Despite its failure, SEASAT-1 acquired a reasonable volume of data that can be used by industrial participants on a non-real-time basis to prove the concept of microwave sensing of the world's oceans from a satellite platform. The amended version of 8 experimental plans are presented, along with a description of the satellite, its instruments, and the data available. Case studies are summarized for the following experiments: (1) Beaufort Sea oil, gas, and Arctic operations; (2) Labrador Sea oil, gas, and sea ice; (3) Gulf of Mexico pipelines; (4) U.S. East Coast offshore oil and gas; (5) worldwide offshore drilling and production operations; (6) Equatorial East Pacific Ocean mining; (7) Bering Sea ice project; and (8) North Sea oil and gas.

Mourad, A. G.↗
NASA NTRS2015

Regolith Advanced Surface Systems Operations Robot (RASSOR) Phase 2 and Smart Autonomous Sand-Swimming Excavator

The Regolith Advanced Surface Systems Operations Robot (RASSOR) Phase 2 is an excavation robot for mining regolith on a planet like Mars. The robot is programmed using the Robotic Operating System (ROS) and it also uses a physical simulation program called Gazebo. This internship focused on various functions of the program in order to make it a more professional and efficient robot. During the internship another project called the Smart Autonomous Sand-Swimming Excavator was worked on. This is a robot that is designed to dig through sand and extract sample material. The intern worked on programming the Sand-Swimming robot, and designing the electrical system to power and control the robot.

Regolith↗
NASA NTRSDate not supplied

ICE-RASSOR: Intelligent Capabilities Enhanced Regolith Advanced Surface Systems Operations Robot

NASA’s Regolith Advanced Surface Systems Operations Robot (RASSOR) is principally designed to mine and deliver regolith for In-Situ Resource Utilization (ISRU)processing. RASSOR’s design enables it to efficiently collect and deposit regolith, return collected material for processing, and myriad related ISRU activities. To reliably perform these operations on the lunar surface, RASSOR software and sensory systems need to be robust and maximize the information extracted from a reduced sensor payload. Herein, we present preliminary findings from the Intelligent Capabilities Enhanced RASSOR project. We apply supervised learning using real data to estimate the soil mass collected without the need for mass flow rate monitors or other explicate sensing techniques. We also create a reduced-order simulation environment to develop autonomous trenching controllers via reinforcement learning and prototype state estimation architectures. Our initial results suggest that excavated regolith mass can be inferred within 2.9% RMS error of full scale, and reinforcement learning for autonomous operations has learned viable trenching strategies and helped identify desirable sensing capabilities, arrangements, and considerations. Future work includes regolith mass estimation during dynamic operation, expanding our simulation to more complex environments, and transfer learning from simulation to hardware.

machine learning↗
NASA NTRSDate not supplied

ICE-RASSOR: Intelligent Capabilities Enhanced Regolith Advanced Surface Systems Operations Robot

NASA’s Regolith Advanced Surface Systems Operations Robot (RASSOR) is principally designed to mine and deliver regolith for In-Situ Resource Utilization (ISRU) processing. RASSOR’s design enables it to efficiently collect and deposit regolith, return collected material for processing, and myriad related ISRU activities. To reliably perform these operations on the lunar surface, RASSOR software and sensory systems need to be robust and maximize the information extracted from on-board sensory. Herein, we present preliminary findings from the Intelligent Capabilities Enhanced RASSOR project. We apply supervised learning using real data to estimate the soil mass collected without the need for mass flow rate monitors or other explicate sensing techniques. We also create a reduced-order simulation environment to develop autonomous trenching controllers via reinforcement learning and proto-type state estimation architectures. Our initial results suggest that excavated regolith mass can be inferred within 2.9% RMS error of full scale, and reinforcement learning for autonomous operations has learned viable trenching strategies and helped identify desirable sensing capabilities, arrangements, and considerations. Future work includes regolith mass estimation during dynamic operation, expanding our simulation to more complex environments, and transfer learning from simulation to hardware.

machine learning↗
NASA NTRSDate not supplied

Towards Autonomous Lunar Resource Excavation via Reinforcement Learning

To continue on a sustainable and flexible path, NASA needs to address the challenge of collecting and moving large amounts of regolith at the destination. NASA’s Regolith Advanced Surface Systems Operations Robot (RASSOR) is principally designed to mine and deliver regolith for In-Situ Resource Utilization (ISRU) processing. RASSOR’s design enables it to efficiently collect and deposit regolith, return collected material for processing, and myriad related ISRU activities. To reliably perform these operations on the lunar surface, RASSOR software and sensory systems need to be robust and maximize the information extracted from a reduced sensor payload. Herein, we present preliminary findings from the Intelligent Capabilities Enhanced RASSOR project. We created reduced-order simulation environments to develop autonomous trenching controllers via reinforcement learning and prototype state estimation architectures. The goal of reinforcement learning is for an agent to learn a policy (task strategy) through interactions with an environment. When the agent performs an action, a change occurs in environment state and a numerical reward is received which informs the agent whether the action performed was good or not. Since reinforcement learning algorithms learn through trial-and-error, a simulation is a desirable first environment for development and learning. We developed two simulations, the first is a 2D excavation simulation developed to facilitate parameter selection, and a 3D simulation developed using a game physics engine, to simulate simplified soil interactions and increase the fidelity of the dynamic models of the robotic agents. The development of this 3D simulation has enabled the training of additional sensing capabilities and research both at the granular mechanics and operations levels. We experimented with various virtual sensor payloads to identify a combination that enabled efficient excavation operation and learning. Our reward function is based on how much material is excavated per step. A penalty is also received for leaving the dig site and to smooth the acceleration of the drum arms. We implemented pseudo time-of-flight sensors to report distance from each drum to ground and the height above ground which was found to be more efficient than existing solutions. Our findings suggest that reinforcement learning for autonomous operations has learned viable trenching strategies within 3000 training episodes in our simplified 2D environment and helped identify desirable sensing capabilities, arrangements, and considerations such as the positioning of time-of-flight sensors. Future work includes expanding our simulation to more complex environments and scenarios, and transfer learning from simulation to RASSOR 2.0 hardware for deployment in the Regolith Test Bin at NASA's Kennedy Space Center.

rassor↗
NASA NTRSDate not supplied

ICE-RASSOR: Intelligent Capabilities Enhanced

NASA’s Regolith Advanced Surface Systems Operations Robot (RASSOR) is principally designed to mine and deliver regolith for In-Situ Resource Utilization (ISRU) processing. RAS-SOR’s design enables it to efficiently collect and deposit regolith, return collected material for processing, and myriad related ISRU activities. To reliably perform these operations on the lunar sur-face, RASSOR software and sensory systems need to be robust and maximize the information extracted from on-board sensing. Herein, we present preliminary findings from the Intelligent Capabilities Enhanced RASSOR project. We apply supervised learning using real data to estimate the soil mass collected without the need for mass flow rate monitors or other explicate sensing techniques. We also create a reduced-order simulation environment to develop autonomous trenching controllers via reinforcement learning and proto-type state estimation architectures. Our initial results suggest that excavated regolith mass can be inferred within 2.9% RMS error of full scale, and reinforcement learning for autonomous operations has learned viable trenching strategies and helped identify desirable sensing capabilities, arrangements, and considerations. Future work includes regolith mass estimation during dynamic operation, expanding our simulation to more complex environments, and transfer learning from simulation to hardware.

machine learning↗
NASA NTRS2015

RASSOR - Regolith Advanced Surface Systems Operations Robot

The Regolith Advanced Surface Systems Operations Robot (RASSOR) is a lightweight excavator for mining in reduced gravity. RASSOR addresses the need for a lightweight (<100 kg) robot that is able to overcome excavation reaction forces while operating in reduced gravity environments such as the moon or Mars. A nominal mission would send RASSOR to the moon to operate for five years delivering regolith feedstock to a separate chemical plant, which extracts oxygen from the regolith using H2 reduction methods. RASSOR would make 35 trips of 20 kg loads every 24 hours. With four RASSORs operating at one time, the mission would achieve 10 tonnes of oxygen per year (8 t for rocket propellant and 2 t for life support). Accessing craters in space environments may be extremely hard and harsh due to volatile resources - survival is challenging. New technologies and methods are required. RASSOR is a product of KSC Swamp Works which establishes rapid, innovative and cost effective exploration mission solutions by leveraging partnerships across NASA, industry and academia.

Regolith↗
NASA NTRS1993

Lunar surface operations. Volume 4: Lunar rover trailer

The purpose of the project was to design a lunar rover trailer for exploration missions. The trailer was designed to carry cargo such as lunar geological samples, mining equipment and personnel. It is designed to operate in both day and night lunar environments. It is also designed to operate with a maximum load of 7000 kilograms. The trailer has a ground clearance of 1.0 meters and can travel over obstacles 0.75 meters high at an incline of 45 degrees. It can be transported to the moon fully assembled using any heavy lift vehicle with a storage compartment diameter of 5.0 meters. The trailer has been designed to meet or exceed the performance of any perceivable lunar vehicle.

Shields, William↗
DOE OSTI2025

Advancing nuclear forensics competencies at the National Nuclear Center of the Republic of Kazakhstan and conceptual development of a National Nuclear Forensics Library

Since 2021, the National Nuclear Center (NNC) of the Republic of Kazakhstan has been successfully developing a project dedicated to nuclear forensics. The relevance of this project is underscored by Kazakhstan’s leading position in the global uranium mining and export market. The country operates research reactors and is involved in the production of experimental nuclear fuel. The nuclear forensics project is being implemented in several key areas: the project will develop competencies in laboratory research of samples, interpretation of results, and develop a prototype concept for the National Nuclear Forensics Library. In implementing these challenges, the NNC experts draw on the experience of colleagues from world-leading scientific and research centers. Designing a prototype National Nuclear Forensics Library (NNFL) is a challenging project. In conclusion, the solution to this task is based on the IAEA’s approaches, recommendations, and the experience gained from the construction of similar systems in other countries.

and nuclear chemistry↗
DOE OSTI2023

Technical Feasibility of Recovering Critical Minerals from Oil and Gas Industry Produced Water

Economic recovery of critical minerals from solid and liquid streams produced from natural resource extraction operations including Oil and Gas production, Mining, geothermal, and other industries can help alleviate shortage of the minerals important to industry. It is important to develop techniques for extraction that can be utilized at large scale to be economically viable, and to minimize waste products that require special disposal procedures. NETL and Participant will collaborate to develop and evaluate at the lab-scale, a process to recover and concentrate critical minerals from oil and gas industry produced water using a combination of physical and chemical methods, so that it may be converted into a valuable product. This effort promotes a circular economy by converting a waste stream into marketable products.

02 PETROLEUM↗
NASA NTRS1987

The economics of mining the Martian moons

The costs for extracting and shipping volatiles such as water, carbon, and nitrogen that might be found on Phobos and Deimos are estimated. The costs are compared to the cost of shipping the same volatiles from earth, assuming the use of nuclear powered mining facilities and freighters. Mineral resources and possible products from the Martian moons, possible markets for these products, and the costs of transporting these resources to LEO or GEO or to transportation nodal points are examined. Most of the technology needed to mine the moons has already been developed. The need for extraterrestrial sources of propellants for ion propulsion systems and ways in which the mining of the moons would reduce the cost of space operations near earth are discussed. It is concluded that it would be commercially viable to mine the Martian moons, making a profit of at least a 10 percent return on capital.

Leonard, Raymond S.↗
NASA NTRS2004

Journal of Air Transportation, Volume 9, No. 1

The articles in this issue include: 1) Are Four Year Universities Better than Two-Year Colleges at Preparing Students to Pass the FAA Aircraft Mechanic Certification Written Examinations? 2) Assessing Perceived Risk of Consumers in Internet Airline Reservations Services; 3) Perceptions of Communication Training Among Collegiate Flight Educators; 4) Ethics Education in University Aviation Management Programs in the U.S.: Part Three - Qualitative Analysis and Recommendations; 5) Airline Flight Operations Internships Perspectives; 6) Applying Data Mining Techniques to Forecast Number of Airline Passengers in Saudi Arabia (Domestic and International Travels).

Bowen, Brent D.↗
NASA NTRS2015

Spacecube: A Family of Reconfigurable Hybrid On-Board Science Data Processors

SpaceCube is a family of Field Programmable Gate Array (FPGA) based on-board science data processing systems developed at the NASA Goddard Space Flight Center (GSFC). The goal of the SpaceCube program is to provide 10x to 100x improvements in on-board computing power while lowering relative power consumption and cost. SpaceCube is based on the Xilinx Virtex family of FPGAs, which include processor, FPGA logic and digital signal processing (DSP) resources. These processing elements are leveraged to produce a hybrid science data processing platform that accelerates the execution of algorithms by distributing computational functions to the most suitable elements. This approach enables the implementation of complex on-board functions that were previously limited to ground based systems, such as on-board product generation, data reduction, calibration, classification, eventfeature detection, data mining and real-time autonomous operations. The system is fully reconfigurable in flight, including data parameters, software and FPGA logic, through either ground commanding or autonomously in response to detected eventsfeatures in the instrument data stream.

reconfigurable computing↗
DOE OSTI2024

Super-Kamiokande Operations (Final Scientific/Technical Report)

The Super-Kamiokande experiment (SK, or Super-K) is based on a 50-kton water Cherenkov detector with 11,129 photomultiplier tubes. The detector is located in the Kamioka mine near Toyama, Japan. The detector has been operating since 1996. The collaboration is now around 165 people, mostly from the U.S. and Japan, but including Canada, the UK, S. Korea, Italy, China, Poland, France and Spain. Notable accomplishments so far include: the discovery of neutrino oscillations using atmospheric and solar neutrinos (and thus demonstrating that neutrinos have mass), the confirmation of these oscillations as the far detector of the K2K long baseline experiment, the first observation of v e appearance and non-zero θ 13 in a long baseline (T2K) beam, the world’s strictest limits on proton decay, the world’s best limits on indirect dark matter annihilation at moderate energies, sensitivity to a galactic supernova over most of the running time since 1996, the world’s best limit on diffuse supernova neutrinos, the first direct indication of matter effects on neutrino oscillations, and a number of other published results and theses in particle astrophysics.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
NASA NTRS2003

Data-Mining Toolset Developed for Determining Turbine Engine Part Life Consumption

The current practice in aerospace turbine engine maintenance is to remove components defined as life-limited parts after a fixed time, on the basis of a predetermined number of flight cycles. Under this schedule-based maintenance practice, the worst-case usage scenario is used to determine the usable life of the component. As shown, this practice often requires removing a part before its useful life is fully consumed, thus leading to higher maintenance cost. To address this issue, the NASA Glenn Research Center, in a collaborative effort with Pratt & Whitney, has developed a generic modular toolset that uses data-mining technology to parameterize life usage models for maintenance purposes. The toolset enables a "condition-based" maintenance approach, where parts are removed on the basis of the cumulative history of the severity of operation they have experienced. The toolset uses data-mining technology to tune life-consumption models on the basis of operating and maintenance histories. The flight operating conditions, represented by measured variables within the engine, are correlated with repair records for the engines, generating a relationship between the operating condition of the part and its service life. As shown, with the condition-based maintenance approach, the lifelimited part is in service until its usable life is fully consumed. This approach will lower maintenance costs while maintaining the safety of the propulsion system. The toolset is a modular program that is easily customizable by users. First, appropriate parametric damage accumulation models, which will be functions of engine variables, must be defined. The tool then optimizes the models to match the historical data by computing an effective-cycle metric that reduces the unexplained variability in component life due to each damage mode by accounting for the variability in operational severity. The damage increment due to operating conditions experienced during each flight is used to compute the effective cycles and ultimately the replacement time. Utilities to handle data problems, such as gaps in the flight data records, are included in the toolset. The tool was demonstrated using the first stage, high-pressure turbine blade of the PW4077 engine (Pratt & Whitney, East Hartford, CT). The damage modes considered were thermomechanical fatigue and oxidation/erosion. Each PW4077 engine contains 82 first-stage, high-pressure turbine blades, and data from a fleet of engines were used to tune the life-consumption models. The models took into account not only measured variables within the engine, but also unmeasured variables such as engine health parameters that are affected by degradation of the engine due to aging. The tool proved effective at predicting the average number of blades scrapped over time due to each damage mode, per engine, given the operating history of the engine. The customizable tools are available to interested parties within the aerospace community.

Litt, Jonathan S.↗