Engineering Papers⌕ Search

SEARCH · Engineering Papers

Results for “artificial”

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 523 records · Page 29
All sourcesNASA NTRSDOE OSTI
NASA NTRS2014

Mechanisms of Orthostatic Tolerance Improvement Following Artificial Gravity Exposure Differ Between Men and Women

We recently determined that a short exposure to artificial gravity (AG) improved the orthostatic tolerance limit (OTL) of cardiovascularly deconditioned subjects. We now seek to determine the mechanisms of that improvement in these hypovolemic men and women. Methods. We determined the orthostatic tolerance limit (OTL) of 9 men and 8 women following a 90 min exposure to AG compared to 90 min of head down bed rest (HDBR). In both cases (21 days apart), subjects were made hypovolemic (low salt diet plus 20 mg intravenous furosemide). Orthostatic tolerance was determined from a combination of head up tilt and increasing lower body negative pressure until presyncope. Mean values and correlations with OTL were determined for heart rate, blood pressure, stroke volume, cardiac output and peripheral resistance (Finometer), cerebral artery blood velocity (DWL), partial pressure of carbon dioxide (Novametrics) and body segmental impedance (UFI THRIM) were measured during supine baseline, during OTL to presyncope and during supine recovery Results. Orthostatic tolerance of these hypovolemic subjects was significantly greater on the day of AG exposure than on the HDBR day. Regression of OTL on these variables identified significant relationships on the HDBR day that were not evident on the AG day: resting TPR correlated positively while resting cerebral flow correlated negatively with OTL. On both days, women's resting stroke volume correlated positively with orthostatic tolerance. Higher group mean values of stroke volume and cerebral artery flow and lower values of blood pressure, peripheral vascular and cerebrovascular resistance both at control and during OTL testing were observed on the AG day. Even though regression of OTL on resting stroke volume was significant only in women, presyncopal stroke volume reached the same level on each day of study for both men and women while the OTL test lasted 30% longer in men and 22% longer in women. Cerebral artery flow appeared to follow stroke volume and absolute values of cerebral flow did not correlate with the development of presyncope. Women responded to AG exposure with elevated cerebral flow at resting control and throughout the OTL test, implying a loss of autoregulation in deconditioned (hypovolemic) women following AG exposure. Conclusions. Before countermeasures to space flight cardiovascular deconditioning are established, gender differences in cardiovascular responses to orthostatic stress, in general, and to orthostatic stress following exposure to artificial gravity, in particular, need to be determined. Since, in both men and women, a single, acute bout of AG exposure improved orthostatic tolerance, the feasibility of short exposures to AG during longer spaceflights or prior to entry into a gravity (Earth or Mars) environment, should be explored. Given the known beneficial effects of AG on other organ systems, the present study indicates that the positive effects of AG on cardiac stroke volume make AG a likely candidate for maintaining cardiovascular conditioning.

Evans, J. M.↗
NASA NTRS2014

Hybrid Wing Body Shielding Studies Using an Ultrasonic Configurable Fan Artificial Noise Source Generating Typical Turbofan Modes

An Ultrasonic Configurable Fan Artificial Noise Source (UCFANS) was designed, built, and tested in support of the NASA Langley Research Center's 14- by 22-ft wind tunnel test of the Hybrid Wing Body (HWB) full 3-D 5.8 percent scale model. The UCFANS is a 5.8 percent rapid prototype scale model of a high-bypass turbofan engine that can generate the tonal signature of proposed engines using artificial sources (no flow). The purpose of the test was to provide an estimate of the acoustic shielding benefits possible from mounting the engine on the upper surface of an HWB aircraft using the projected signature of the engine currently proposed for the HWB. The modal structures at the rating points were generated from inlet and exhaust nacelle configurations--a flat plate model was used as the shielding surface and vertical control surfaces with correct plan form shapes were also tested to determine their additional impact on shielding. Radiated acoustic data were acquired from a traversing linear array of 13 microphones, spanning 36 in. Two planes perpendicular, and two planes parallel, to the axis of the nacelle were acquired from the array sweep. In each plane the linear array traversed four sweeps, for a total span of 168 in. acquired. The resolution of the sweep is variable, so that points closer to the model are taken at a higher resolution. Contour plots of Sound Pressure Levels, and integrated Power Levels, from nacelle alone and shielded configurations are presented in this paper; as well as the in-duct mode power levels

Sutliff, Daniel l.↗
NASA NTRS2014

Artificial Intelligence Based Control Power Optimization on Tailless Aircraft

Traditional methods of control allocation optimization have shown difficulties in exploiting the full potential of controlling large arrays of control devices on innovative air vehicles. Artificial neutral networks are inspired by biological nervous systems and neurocomputing has successfully been applied to a variety of complex optimization problems. This project investigates the potential of applying neurocomputing to the control allocation optimization problem of Hybrid Wing Body (HWB) aircraft concepts to minimize control power, hinge moments, and actuator forces, while keeping system weights within acceptable limits. The main objective of this project is to develop a proof-of-concept process suitable to demonstrate the potential of using neurocomputing for optimizing actuation power for aircraft featuring multiple independently actuated control surfaces. A Nastran aeroservoelastic finite element model is used to generate a learning database of hinge moment and actuation power characteristics for an array of flight conditions and control surface deflections. An artificial neural network incorporating a genetic algorithm then uses this training data to perform control allocation optimization for the investigated aircraft configuration. The phase I project showed that optimization results for the sum of required hinge moments are improved by more than 12% over the best Nastran solution by using the neural network optimization process.

Gern, Frank↗
NASA NTRS2014

Shielding Characteristics Using an Ultrasonic Configurable Fan Artificial Noise Source to Generate Modes - Experimental Measurements and Analytical Predictions

An Ultrasonic Configurable Fan Artificial Noise Source (UCFANS) was designed, built, and tested in support of the NASA Langley Research Center's 14x22 wind tunnel test of the Hybrid Wing Body (HWB) full 3-D 5.8% scale model. The UCFANS is a 5.8% rapid prototype scale model of a high-bypass turbofan engine that can generate the tonal signature of proposed engines using artificial sources (no flow). The purpose of the program was to provide an estimate of the acoustic shielding benefits possible from mounting an engine on the upper surface of a wing; a flat plate model was used as the shielding surface. Simple analytical simulations were used to preview the radiation patterns - Fresnel knife-edge diffraction was coupled with a dense phased array of point sources to compute shielded and unshielded sound pressure distributions for potential test geometries and excitation modes. Contour plots of sound pressure levels, and integrated power levels, from nacelle alone and shielded configurations for both the experimental measurements and the analytical predictions are presented in this paper.

Artifical Noise↗
NASA NTRS2014

Hybrid Wing Body Shielding Studies Using an Ultrasonic Configurable Fan Artificial Noise Source Generating Typical Turbofan Modes

An Ultrasonic Configurable Fan Artificial Noise Source (UCFANS) was designed, built, and tested in support of the NASA Langley Research Center's 14x22 wind tunnel test of the Hybrid Wing Body (HWB) full 3-D 5.8% scale model. The UCFANS is a 5.8% rapid prototype scale model of a high-bypass turbofan engine that can generate the tonal signature of proposed engines using artificial sources (no flow). The purpose of the test was to provide an estimate of the acoustic shielding benefits possible from mounting the engine on the upper surface of an HWB aircraft using the projected signature of the engine currently proposed for the HWB. The modal structures at the rating points were generated from inlet and exhaust nacelle configurations - a flat plate model was used as the shielding surface and vertical control surfaces with correct plan form shapes were also tested to determine their additional impact on shielding. Radiated acoustic data were acquired from a traversing linear array of 13 microphones, spanning 36 inches. Two planes perpendicular, and two planes parallel, to the axis of the nacelle were acquired from the array sweep. In each plane the linear array traversed 4 sweeps, for a total span of 168 inches acquired. The resolution of the sweep is variable, so that points closer to the model are taken at a higher resolution. Contour plots of Sound Pressure Levels, and integrated Power Levels, from nacelle alone and shielded configurations are presented in this paper; as well as the in-duct mode power levels.

Sutliff, Daniel L.↗
NASA NTRS2016

Conventional and Bimodal Nuclear Thermal Rocket (NTR) Artificial Gravity Mars Transfer Vehicle Concepts

A variety of countermeasures have been developed to address the debilitating physiological effects of zero-gravity (0-g) experienced by cosmonauts and astronauts during their approximately 0.5 to 1.2 year long stays in low Earth orbit (LEO). Longer interplanetary flights, combined with possible prolonged stays in Mars orbit, could subject crewmembers to up to approximately 2.5 years of weightlessness. In view of known and recently diagnosed problems associated with 0-g, an artificial gravity (AG) spacecraft offers many advantages and may indeed be an enabling technology for human flights to Mars. A number of important human factors must be taken into account in selecting the rotation radius, rotation rate, and orientation of the habitation module or modules. These factors include the gravity gradient effect, radial and tangential Coriolis forces, along with cross-coupled acceleration effects. Artificial gravity Mars transfer vehicle (MTV) concepts are presented that utilize both conventional NTR, as well as, enhanced bimodal nuclear thermal rocket (BNTR) propulsion. The NTR is a proven technology that generates high thrust and has a specific impulse (Isp) capability of approximately 900 s-twice that of today's best chemical rockets. The AG/MTV concepts using conventional Nuclear Thermal Propulsion (NTP) carry twin cylindrical International Space Station (ISS)- type habitation modules with their long axes oriented either perpendicular or parallel to the longitudinal spin axis of the MTV and utilize photovoltaic arrays (PVAs) for spacecraft power. The twin habitat modules are connected to a central operations hub located at the front of the MTV via two pressurized tunnels that provide the rotation radius for the habitat modules. For the BNTR AG/MTV option, each engine has its own closed secondary helium(He)-xenon (Xe) gas loop and Brayton Rotating Unit (BRU) that can generate 10s of kilowatts (kWe) of spacecraft electrical power during the mission coast phase eliminating the need for large PVAs. A single inflatable TransHab-type habitation module is also used with multiple vertical floors oriented radial to the MTV spin axis. The BNTR MTV's geometry-long and linear-is naturally compatible with AG operation. By rotating the vehicle about its center-of-mass (CM) and perpendicular to its flight vector at approximately 3.0 to 5.2 rpm, a centrifugal force and AG environment corresponding to approximately 0.38 to 1.0 g can be established to help maintain crew fitness out to Mars and back. Vehicles using NTP/ Bimodal Nuclear Thermal Propulsion (BNTP) can more readily accommodate the heavier payload mass and increased RCS propellant loading associated with AG operation, and can travel faster to and from Mars thereby reducing the crew's exposure to galactic cosmic radiation and solar flares. Mission scenario descriptions, key vehicle features and operational characteristics for each propulsion option are presented using the lift capability and payload volumes estimated for the Space Launch System (SLS)-1B and followon Heavy Lift Vehicle (HLV).

Nuclear Propulsion↗
NASA NTRS2020

Artificial Gravity in Mars Orbit for Crew Acclimation

NASA’s current baseline plan for a crewed Mars mission anticipates a transit time of up to three hundred days in microgravity and 3-14 days on the Martian surface for gravity acclimation before the crew can safely perform their first Extra-Vehicular Activity (EVA). While there are multiple options for how initial surface operations will be performed, all current designs involve acclimation on the surface, and the impacts on the mission schedule, required supplies, and crew lander systems are significant. This paper proposes an alternative option utilizing artificial gravity, which offers benefits in terms of mission scope, mass savings, crew health, and long-term strategic vision. By moving the acclimation requirement to the orbiting habitat’s existing systems, rather than adding redundant systems to the lander, the Mars Descent Vehicle (MDV) can be a much smaller, simpler, and lighter design. Rather than the lander being designed to support crew for days, it would be mere hours. While ambitious, the concept of pre-acclimation in orbit can be not only safe and feasible, but done with fairly minimal changes to the planned architecture and overall mass requirements. The data used draws on decades of established research and demonstrates how this capability can be not only used for pre-acclimation, but also to support crew during early orbital-only missions, surface abort contingency scenarios, return-to-orbit abort scenarios, and as an early proof of capability into larger and more ambitious artificial gravity designs needed for extended exploration missions in the future.

Rowe, Justin↗
NASA NTRSDate not supplied

Aeroacoustic Characterization of Optimum Hovering Rotors using Artificial Neural Networks

This work illustrates the use of artificial neural network modeling in the aerodynamic and aeroacoustic characterization of optimum hovering rotors over a broad range of design and operating conditions. Design of Experiments was used to create input feature spaces over eight input factors: the number of rotor blades, rotor radius, rotor rotation rate, design thrust condition, collective pitch, airfoil camber, the location of maximum camber, and the airfoil thickness. A low-fidelity tool chain was then used at the discrete data points defined by the designed input feature spaces to analytically design optimum hovering rotors and simulate aerodynamic and aeroacoustic quantities. This allowed for the generation of data sets over which to train and test the artificial neural network prediction models. Prediction models were trained over the data sets for the actual thrust generated by the rotor, power loading, tonal thickness and loading noise at the fundamental blade passage frequency, and broadband self-noise at seventeen one-third octave bands between 1 kHz and 40 kHz. These prediction models were validated by testing over data previously unseen by the models to quantify their capability for generalization to new data within the design feature space. The models were then used to study the effect each input feature had on the aeroacoustics and aerodynamics of optimum hovering rotors and physical insights were concluded to further explain the effect of each input. This characterization study showed that tonal noise and power loading were most sensitive to the number of rotor blades and the rotor rotation rate and that broadband noise was most sensitive to collective pitch and the design thrust condition.

Christopher S Thurman↗
NASA NTRSDate not supplied

Artificial Generation of 2-D Fiber Reinforced Composite Microstructures with Statistically Equivalent Features

Fiber reinforced composites are used widely for their high strength and low weight advantages in various aerospace and automotive applications. While their use may be sought after, modeling of these material requires increasing fidelity at the lower scales to capture accurate material behavior under loading. The first steps in creating statistically equivalent models to real life cases is developing a method of rapid evaluation and artificial microstructure generation. The outlined work is capable of tracking microscale fiber positions and determining regions of localized volume fraction extrema (high and low end). Groupings of high and low volume fraction regions are called clusters and their geometry is used to characterize the microstructure. These cluster features can be evaluated for both artificial models and actual scans, allowing correlation to be established which can ultimately be used to regenerate statistically equivalent models. The results of this work show that if one feature is to be correlated, a model can be generated which matches almost exactly. But once more features are equally taken into account, the regeneration loses accuracy.

micromechanics↗
NASA NTRSDate not supplied

Validation of Rendered Natural and Artificial Lighting Environments in Real Time Lunar South Pole Simulations

NASA’s Artemis campaign is making heavy use of simulation to help return humans to the lunar surface by the end of the decade. There are several aspects of the lunar surface and its environment which must be accurately modeled before these simulations can be relied upon to influence decisions being made under these programs. Digital Lunar Exploration Sites, a paper submitted to the 2022 IEEE Aerospace Conference, outlined the process used to generate the lunar surface in a digital environment. This paper will expand upon this topic and delve into the steps being taken by the NASA Exploration Systems Simulations (NExSyS) team at NASA’s Johnson Space Center (JSC) to properly verify and validate these simulations, with a focus on the visual aspects of the environment. Natural lighting validation relies in part on the wealth of data generated during the Apollo program. Many images taken by Apollo astronauts on the lunar surface have been replicated in the simulated environments to gain confidence in the accuracy of terrain and lighting models. However, because the environment the Artemis astronauts will experience at the Lunar South Pole (LSP) is dissimilar from the near-equatorial Apollo sites, other validation techniques must be applied. At the LSP, the sun crests only about three degrees above the horizon and when combined with the lack of a lunar atmosphere, lighting in this region is often very different than what a human would experience on Earth. Solar illumination, earthshine, human eye response, solar blooming, lunar regolith optical properties, and shadows cast by rocks and crater walls will play a significant role in an astronaut’s ability to safely conduct an Extra-Vehicular Activity (EVA) or perform a traverse with a lunar rover. Approaches for validation of these aspects of the rendered LSP environment are considered in this paper. In addition to natural lighting, approaches for the validation of artificial lighting models at the LSP are discussed. The JSC Lighting Lab has been studying the illumination profile of the Exploration Infomatics Subsystem (xINFO) lighting on the Exploration EVA Mobility Unit (xEMU). How these lights interact with the solar illumination and the shadows being cast on the lunar surface is of particular interest, so the validity of models representing these lights in a human-in-the-loop virtual reality environment becomes very important. This paper also touches on some of the simulation performance considerations when a Human in the Loop (HITL) is present, which drives the need for real time rendering of the environment. Natural and artificial lighting will play a crucial role to decisions being made when planning and executing missions at the Lunar South Pole (LSP) and it is vitally important to understand the LSP environment before we return.

Lunar↗
NASA NTRS2021

Anaerobic Expression and Purification of Holo-CCIS, an Artificial Iron-sulfur Protein

Iron-sulfur proteins are ubiquitous among all living organisms and are indispensable for almost all metabolic pathways ranging from photosynthesis, respiration, nitrogen, and carbon dioxide cycles. The iron-sulfur clusters primarily serve as electron acceptors and donors and transfer electrons to active sites of various enzymes, thus driving the energy metabolism. Prokaryotes like E. coli have ISC and SUF pathways that help in the assembly and maturation of iron-sulfur proteins. These ironsulfur proteins, especially with [4Fe-4S] clusters, are highly sensitive to molecular oxygen, and it would be advantageous if the de novo proteins and native proteins having iron-sulfur binding sites are expressed and isolated under anaerobic conditions. Bacterially assembled iron-sulfur proteins, when isolated and purified anaerobically, exhibit improved biochemical and biophysical stabilities in comparison to the counterparts expressed and purified aerobically and reconstituted under anaerobic conditions. This protocol outlines the expression and purification of the artificial protein, Coiled-Coil IronSulfur (CCIS). It may be deployed to both natural and artificial [4Fe-4S] proteins when heterologously expressed in E. coli.

Bhanu P. Jagilinki↗
NASA NTRSDate not supplied

Harnessing Artificial Intelligence for Medical Diagnosis and Treatment During Space Exploration Missions

From May 8th to June 9th, 2023, I had the opportunity to participate in an experiential learning experience at Johnson Space Center in Houston, TX with Exploration Medical Capability (ExMC), an element of the NASA Human Research Program. During this research experience, I was not only able to work on the above titled research project, but also gain an immense exposure to the field of aerospace medicine, make numerous connections within the field, tour NASA facilities, as well as travel to the Aerospace Medical Association Annual Conference (AsMA) in New Orleans. To briefly introduce my project, it is well understood that the medical capabilities available to crew medical officers (CMOs) on the International Space Station will be different than the capabilities available and needed during deep space exploration missions to the Moon, Mars, and beyond. Ground support is particularly limited due to distance, communication delays (or lack of communication), and lack of resupply. Therefore, to support medical care by CMOs on these missions, robust clinical decision support systems (CDSSs) must be designed. The recent publication and public launch of generative artificial intelligence (AI) tools based upon large language models (LLM) such as ChatGPT provides the opportunity to create a smart assistant for onboard triage, diagnosis, and treatment of medical conditions. Ultimately, the overall purpose of the project was to research what AI tools currently exist or are in development, and to see how they might be implemented onboard during exploration class spaceflights of the future. The ExMC element is actively developing several tools to be used in preparation for and during deep space exploration missions. One of those tools, known as IMPACT, is a probabilistic risk assessment model which can be used to propose a desired medical system (based on mass and volume) and suggest the clinical outcomes likely to occur for a design reference mission (DRM). The group recently presented the IMPACT model and a DRM of interest titled “Modified Long Duration Lunar Orbital and Lunar Surface” (mLDLOLS) at the recent AsMA conference. The mLDLOLS mock mission is a 9 month and 6-day deep space exploration mission consisting of time in Moon’s orbit (3 months on the Gateway space station), on the lunar surface (3 months within habitat), and another 3 months on Gateway before return to Earth. For this DRM, IMPACT ultimately outlined a preferred medical system that was then associated with medical conditions considered to be most likely based on frequency, most likely to cause astronaut task time loss (TTL), most likely to cause return to definitive care (RTDC), and most likely cause loss of crew life (LOCL). IMPACT also highlighted the medical capabilities/skills that would be required to care for those medical conditions, such as performing a history of present illness or musculoskeletal exam with ultrasound. The primary objective of the project was to perform a survey of the AI tools and systems applicable to the conditions outlined for the proposed mLDLOLS mission. Using PubMed (including most relevant MeSH terms) and Google Scholar, we then created a robust annotated bibliography organized by condition. The 56-page and over 500 reference annotated bibliography was subsequently used to create a review outline that would become the basis for drafting of a future publication. For the review outline, we took those medical conditions researched within the annotated bibliography (condition-based approach) and deployed a systems-based approach, combining those medical conditions and related tools into ten categories. These categories included general/all-purpose CDSSs, tools to diagnose or manage respiratory, dermatologic, neurologic, auditory and vestibular, ophthalmic, musculoskeletal, infection-associated, and gynecologic conditions, as well as tools that could be deployed in the setting of trauma/emergency. With the completion of the 30-page outline, we then began drafting the review paper. To conclude the research experience, I presented the findings from our survey to the ExMC Clinical and Science team. With these objectives, I ultimately learned about the number of AI tools that exist today to assist medical professionals with the triage, diagnosis, and management of several medical conditions. These tools can span from chatbot assistants to help triage knee pain to vision transformer models that can identify ophthalmic conditions based on ocular surface images captured with a cell phone. We also highlighted the current gaps that exist in the literature alongside the advancements that are needed to make the desired CDSS for deep space exploration missions. With this experience, I certainly confirmed an existing career goal and identified several additional skills needed to become an aerospace medical doctor including knowledge of critical care in an extreme medicine setting, aerospace engineering and human integration systems, artificial intelligence, machine learning, and risk models. I also identified numerous transferable skills for this career goal including the basic knowledge of medicine (MD), deployment of the scientific method for critical thought about new scientific questions (PhD), review of published literature, including creating an annotated bibliography (PhD), as well as detailed scientific writing (PhD). The results of my research will likely guide the design of an all-encompassing onboard medical assistant for use during deep space exploration missions of the future. I plan on sharing the outcomes from this experience with my peers at a student seminar in the Fall semester on August 30th. During the seminar, I will detail the project, my experience at NASA and AsMA, as well as offer best practice guidelines for students entertaining similar experiences or careers. In conclusion, I would like to thank the WVU School of Medicine, Research and Graduate Education office, as well as NASA ExMC for the unwavering support of this life-changing experience.

Ryan A. Lacinski↗
NASA NTRSDate not supplied

Quantitative Phenotypic Analysis of Arabidopsis Thaliana Grown in Microgravity Using Soap, an Applied Artificial Intelligence

Phenotypic analysis is an essential step in studying the gravitropic responses and gravitational stress experienced by plants grown in microgravity. Many of the phenotypic traits analyzed in gravitropism studies, such as root length, leaf area, secondary root count, and number of root hairs, currently rely upon manual measurement methods for quantification. However, new advances in data analysis technology using artificial intelligence offer an opportunity for more efficient phenotypic analysis and a reduction of time spent in the data collection phase. In this project, the ability of a new artificially intelligent data collection software, SOAP (Simple Object Access Protocol), to collect and quantify phenotypic traits of Arabidopsis thaliana will be assessed. This project will test the measurements taken by an initial draft of the software. SOAP will take measurements of shoot length, a key phenotype used to assess A. thaliana stress response when grown in microgravity conditions. Shoot length measurements made by SOAP will be compared against a series of manual shoot length measurements. The comparison between the two methods of data measurement will provide valuable insight into the relative accuracy of SOAP and the margin of human error when conducting lab measurements.

Arabidopsis↗
NASA NTRS2025

OnAIR: Applications of The NASA On-Board Artificial Intelligence Research Platform

Infusing artificial intelligence algorithms into production aerospace systems can be challenging due to costs, timelines, and a risk averse industry. We introduce the Onboard Artificial Intelligence Research (OnAIR) platform, an open source software pipeline and cognitive architecture tool which enables full life cycle AI research for on-board intelligent systems. We begin a description and user walk-through of the OnAIR tool. Next we describe four use cases of OnAIR for both research and deployed onboard applications, detailing their use of OnAIR and the benefits it provided to development and function of each respective scenario. We conclude with remarks on future work, future planned deployments and goals for forward progression of OnAIR as a tool to enable larger AI and aerospace research community.

Cognitive Architecture↗
DOE OSTI2019

Magnetic diode in artificial magnetic honeycomb lattice

A magnetic artificial honeycomb lattice comprising a multiplicity of connecting elements separated by hexagonal cylindrical pores, wherein: (a) the hexagonal cylindrical pores: (i) have widths that are substantially uniform and an average width that is in a range of about 15 nm to about 20 nm; and (ii) are substantially equispaced and have an average center-to-center distance that is in a range of about 25 nm to about 35 nm; and (b) the connecting elements comprise a magnetic material layer, and the connecting elements have: (i) lengths that are substantially uniform and an average length that is in a range of about 10 nm to about 15 nm; (ii) widths that are substantially uniform and an average width that is in a range of about 4 nm to 8 nm; and (iii) a thickness of the magnetic material layer that is substantially uniform and an average thickness that is in a range of about 2 nm to about 8 nm; and (c) the magnetic artificial honeycomb lattice has a surface area, disregarding the presence of the hexagonal cylindrical pores, that is in a range of about 100 mm.sup.2 to about 900 mm.sup.2.

Singh, Deepak Kumar↗
DOE OSTI2020

Artificial neural network based isotopic analysis of airborne radioactivity measurement for radiological incident detection

Responders need tools to rapidly detect and identify airborne alpha radioactivity during consequence management scenarios. Traditional continuous air monitor systems used for this purpose compute the net counts in various energy windows to determine the presence of specified isotopes, such as 235U, 239Pu, and 241Am. These calculations rely on having a well-calibrated detector, which is challenging in low-background environments. Here an alternative approach of using artificial neural networks to classify alpha spectra is presented. Two network architectures, fully connected and convolutional networks, were trained to classify alpha spectra into four categories: background and background plus the three isotopes above. Sources were injected into measured background at various fractions of the derived response level (DRL) corresponding to early-phase Protective Action Guides. The convolutional network identifies all sources at 1% of the DRL with average probability of detection of 95% and false alarm probability of 1%. Further, the network identifies sources ranging between 0.25% and 1% of the DRL with higher than 80% probability of detection and lower than 7% false alarm probability. Most significantly, the network performance improves in low-count background conditions, increasing its minimum probability of detection to 93% and reducing the false alarm probabilities to lower than 0.25%. These results show that, once trained on datasets representing a range of detection scenarios, artificial neural networks can accurately identify alpha isotopes of interest without the need for detector calibration.

Woldegiorgis, Surafel F.↗
DOE OSTI2020

New direction of nuclear code development: artificial intelligence

It has been a long-lasting aspiration of the nuclear power community to apply artificial intelligence (AI) technologies to nuclear power plants (NPPs), as AI is expected to substantially improve the efficiency, reliability, and continuity of NPPs. Along with the recent rise of AI featured with machining learning, there is an ever-increasing interest in utilizing the data-orientated technologies for nuclear power. In this chapter, we provide a brief summary of the history of AI and the previous efforts on introducing AI to nuclear reactor systems in 1980s. These histories suggest that although with a promising future, AI in NPPs also faces unique challenges, which are still not fully addressed and need special attention. We also introduced some recent studies that apply machine learning techniques to nuclear code development.Keywords: Nuclear power plants, code for nuclear power, artificial intelligence, expert system, machine learning

Lin, Lianshan↗
DOE OSTI2023

Operation Optimization Using Reinforcement Learning with Integrated Artificial Reasoning Framework

In large and complex systems, operational decision-making requires a systematic analysis with a vast amount of data from both process parameters and component status monitoring. In this paper, we present an integrated artificial reasoning approach for system state transition models that can help operational decision-making with explainable and traceable reasoning. The integrated artificial reasoning framework is a physics-based approach of defining the system structure in a Bayesian network, so we leveraged it in a Markov decision process (MDP) for finding optimal operational solutions. In our proposed framework, the MDP is implemented on a dynamic Bayesian network (DBN), which represents causalities in a system. The multilevel flow modeling was utilized in order to extract these causalities in a more efficient and objective manner. Since multilevel flow modeling is based on the fundamental energy and mass conservation laws, the target system is decomposed into several mass, energy, and information structures, which serve as the basis for a DBN. The MDP consists of the processes of finding a solution for the Bellman equation, which can be derived from the conditional probability equations of the constructed DBN. System operators can capture stochastic system dynamics as multiple subsystem state transitions based on their physical relations and uncertainties coming from the component degradation process or random failures. We analyzed a simplified example system to illustrate finding an optimal operational policy with this approach.

Kim, Junyung↗