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Advancing the TRL of technologies developed during the NASA 3D Printed Habitat Centennial Challenge. Focused on the design and construction of a lunar unpressurized radiation protection shelter called LINA.
Machine Learning (ML) has seen an exponential growth in its applications due to its advanced data driven prediction capabilities. The study presents a data-driven approach as a preliminary attempt to predict the power at which departure from nucleate boiling (DNB) occurs in pressurized water reactors (PWRs) by constructing an advanced ML algorithm that takes outlet pressure, inlet temperature and inlet mass flux as the input features. DNB is a critical heat flux (CHF) phenomenon seen in PWRs. The experimental data from the PWR subchannel and bundle tests (PSBT) benchmark is first used to train an artificial neural network (ANN) to predict the DNB power, which produces a root mean square error (RMSE) of 6.89 kW/m when tested on a blind subset of the PSBT data. Since the PSBT dataset is relatively small to train an accurate ANN, a data augmentation methodology based on generative adversarial networks (GANs) is used to expand the training dataset. By assuming that the real data follows a certain distribution, GANs try to learn that underlying distribution to generate similar synthetic data to augment the database and to improve the predictive capabilities of the ANN. The data generated from GANs are validated using 1-nearest neighbor and kernel maximum mean discrepancy. To further ensure data from GAN is similar to PSBT, the data is tested and filtered out using the sub-channel thermal-hydraulic code CTF. The results indicate that with the addition of 120 data points from GAN the RMSE reduces to 4.84 kW/m showing promising results for future developments.
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This presentation summarizes the project sites for zero-energy homes in multifamily and mixed-used buildings.
The building sector is the one of the largest consumers of raw materials and producers of waste in the United States. NREL is working to minimize waste from building construction by conducting research on new materials, construction, and building durability, as well as developing new design principles and modeling tools. These efforts are designed to help transform the "linear economy" - where materials are simply used and disposed - into a circular one.
Introduction: The Lightweight Surface Manipulation System (LSMS) AutoNomy capabilities Development for surface Operations and construction (LANDO) project is an Early Career Initiative selected for funding by NASA Space Technology Mission Directorate. LANDO is developing a general-purpose autonomy framework applicable to serial and tension-actuated manipulation agents, that will be validated using an existing prototype of the LSMS-L35 (35-kg wrist lift capacity on the lunar surface [Fig. 1], sized for a Commercial Lunar Pay-load Services (CLPS) mission). The autonomous LSMS-L35 will be used to demonstrate autonomous payload handling capabilities for Lunar and other planetary surfaces, directly addressing STMD capability gaps in autonomous excavation and construction operations, advanced robotics and spacecraft autonomy technologies, and technologies supporting emerging space industries including the In-Space Servicing, Assembly and Manufacturing national strategy. LSMS: The LSMS is a tension actuated robotic agent that is scalable (reach and lifting capacity in different gravity environments), versatile (types of surface operations), and reusable. Compared to serial arms, the LSMS provides significantly higher structural efficiency and mechanical advantage, enabling a greater payload lift capacity at a lower system mass. The LSMS is envisioned to be a crucial part of the excavation and construction portfolio, capable of supporting a variety of activities on the lunar surface. Autonomous payload handling is one of the first activities the LSMS can support that develops capabilities that are extensible to other surface operations. Payload handling is required to: remove payloads from a lander; place payloads on mobile agents for transport from a lander to construction site/assembly point; emplace payloads in their operational configuration, and aggregate components to create an asset. As an example of this critical gap, manifested CLPS missions do not currently have a ubiquitous payload offloading capability; payloads (excluding rovers) are designed to remain on the lander. Why Autonomy? Autonomous robotic systems capable of carrying out excavation and construction operations are a fundamental and critical capability required for realizing the NASA Artemis program vision to “emplace and build the infrastructure, systems, and robotic missions that can enable a sustained lunar surface presence.” While teleoperation is still feasible for lunar surface operations, increased latency at Mars will require validated supervised autonomous technologies capable of operating with minimal human involvement (human-on-the-loop) unless an unexpected event occurs requiring human intervention. Autonomy reduces operator burden, allows operations to continue during uncrewed periods, in-creases the safety of operations by automatically detecting and handling faults, and allows operating in high latency environments. Development Activities: LANDO is extending critical autonomous operations to the manipulation domain and creating an integrated system, based on reusable software modules, that is capable of planning and executing payload handling and autonomous surface operations without requiring hu-man intervention beyond a supervisory role. The priority features under development are 1) autonomously offload payloads from a tilted lander deck without buckling the LSMS; 2) sensing whether a payload is safe to lift and handle; and 3) integrate with Astrobotic’s CLPS lander. The poster presentation will highlight current development activities over the past year on LSMS-L35 prototype hard-ware design, and autonomy software.
Planning for the curation of samples from future sample return missions must begin during the initial planning stages of a mission. Waiting until the samples have been returned to Earth, or even when you begin to physically build the spacecraft is too late. A lack of proper planning could lead to irreversible contamination of the samples, which in turn would compromise the scientific integrity of the mission. For example, even though the Apollo missions first returned samples in 1969, planning for the curation facility began in the early 1960s, and construction of the Lunar Receiving Laboratory was completed in 1967. In addition to designing the receiving facility and laboratory that the samples will be characterized and stored in, there are many aspects of contamination that must be addressed during the planning and building of the spacecraft: planetary protection (both outbound and inbound); cataloging, documenting, and preserving the materials used to build spacecraft (also known as coupons); near real-time monitoring of the environment in which the spacecraft is being built using witness plates for critical aspects of contamination (known as contamination control); and long term monitoring and preservation of the environment in which the spacecraft is being built for most aspects of potential contamination through the use of witness plates (known as contamination knowledge). The OSIRIS REx asteroid sample return mission, currently being built, is dealing with all of these aspects of contamination in order to ensure they return the best preserved sample possible. Coupons and witness plates from OSIRIS REx are currently being studied and stored (for future studies) at the Johnson Space Center. Similarly, planning for the clean room facility at Johnson Space Center to house the OSIRIS-REx samples is well advanced, and construction of the facility should begin in early 2017 (despite a nominal 2023 return date for OSIRIS-REx samples). Similar development is being done, in concert with JAXA, for the return of Hayabusa 2 samples (nominally in 2020). We are also actively developing advanced techniques like cold curation and organically clean curation in anticipation of future sample return missions such as comet nucleus sample return and Mars sample return.
Ion nitriding and carburizing are plasma activated thermodynamic processes for the production of case hardened surface layers not only for ferrous materials, but also for an increasing number of nonferrous metals. When the treatment variables are properly controlled, the use of nitrogenous or carbonaceous glow discharge medium offers great flexibility in tailoring surface/near-surface properties independently of the bulk properties. The ion nitriding process has reached a high level of maturity and has gained wide industrial acceptance, while the more recently introduced ion carburizing process is rapidly gaining industrial acceptance. The current status of plasma mass transfer mechanisms into the surface regarding the formation of compound and diffusion layers in ion nitriding and carbon build-up ion carburizing is reviewed. In addition, the recent developments in design and construction of advanced equipment for obtaining optimized and controlled case/core properties is summarized. Also, new developments and trends such as duplex plasma treatments and alternatives to dc diode nitriding are highlighted.
The United States is committed to achieving a 50 to 52 percent reduction from 2005 levels in economy-wide net greenhouse gas pollution by 2030, creating a carbon pollution-free power sector by 2035, and achieving net zero emissions economy-wide by no later than 2050. Nuclear energy is essential to meeting those goals. That includes the existing fleet and advanced reactors that are moving forward for deployment and under development. Nuclear power plants produce 20 percent of the total electricity supply in the United States today and are the largest source of carbon-free energy. However, we have an opportunity to expand significantly nuclear energy’s percentage of electricity generated in the United States. The U.S. Department of Energy (DOE) Office of Nuclear Energy (NE) report - developed in response to President Biden’s Executive Order “America’s Supply Chains” signed in 2021 - describes the nuclear supply chain and investigates challenges for continued operation of today’s 93 reactors and construction of advanced reactors. The supply chain is critical for successfully enabling the continued operation of the existing domestic fleet of light-water reactors as well as supporting deployment of advanced nuclear technologies.
Although the interest in the use of artificial intelligence (AI) and machine learning (ML) in nuclear energy is increasing rapidly, at present their implementation is limited. This rapid increase in interest is not surprising considering that implementing AI and ML technology would allow for continuous monitoring, facilitate the implementation of predictive maintenance with optimized staffing plans, enable automation and autonomy opportunities that could drastically reduce fixed operation and maintenance costs, and provide training for operations and maintenance. Other industries are using AI for construction, and in the nuclear arena AI could provide great benefit in decommissioning activities. The ability of AI and ML to operate in real time vastly increases their potential impact. Before AI can be used in design, operations, or as a regulatory tool, the specifics on the regulations applicable to the use of AI for nuclear power applications need to be established. The difficulty is that the specific use cases will dictate the applicability of regulations. For example, even within the application domain associated with operations, the regulations might vary if the AI is used to create a virtual reference for plant operations or is used for training, optimization of maintenance intervals, prioritization of maintenance activities, etc. Different still is if the AI is to be used for design or setting technical specifications, which will introduce additional requirements. US Nuclear Regulatory Commission (NRC) licensing reviews are based on an applicant’s design meeting its performance assessment based on (1) safety goals and objectives, (2) deterministic and/or probabilistic analysis of accident scenarios, and (3) quantitative assessment of design alternatives against the safety goals and objectives using accepted engineering tools, methodologies, and performance criteria. The current regulatory framework does not explicitly address AI or autonomous control. However, as implementing AI technology will require the use of a digital platform, it must meet the requirements of an instrumentation and control (I&C) system. The regulatory requirements for AI, which will be incorporated into the I&C system, will be very dependent on how it is used (i.e., its functionality, safety classification, etc.). The licensing process is primarily risk-based with the identification of components and systems as nonsafety, important to safety, or safety related. A risk-informed approach allows further gradation of components and systems based on risk metrics such as core damage frequency or large early release fractions. Thus, the use cases and the risk categorization of impacted systems and components will determine the regulatory requirements. Regardless of how AI is used it presents new opportunities for risk-informing operating, maintenance, and regulatory decisions. Trustworthiness, transparency, and the ability to validate and verify the results will be paramount in showing that the systems and plant still meet their performance requirements. This report describes the results of research to identify regulatory implications of AI technologies and their uses. Specifically, this report reviews current regulatory guidance relevant to the application of AI for design (including design changes or new designs including advanced reactors), construction, operations, training, maintenance, research, testing, and as a regulatory tool. AI can be automated at different levels from purely informative purposes to autonomous controls. The focus of this review included determination of constraints on the application of AI technology, identification of any regulatory gaps or uncertainties, and clarification of anticipated technical basis information likely to be important for regulatory acceptance of these technologies. Currently, any use of AI at nuclear power plants is focused on nonsafety-related applications. The NRC and other regulatory bodies are evaluating providing guidance to address gaps rather than create new regulations to address the use of AI and ML. This approach seems to be the best to encourage AI development without adding regulatory uncertainty.
Abstract Constructing all‐aqueous systems with tailored geometries can generate a new class of biomimetic materials, a fascinating but challenging goal to achieve. Here, by taking advantage of the interfacial complexation of a polyelectrolyte (PE) and cellulose nanocrystals (CNCs), a unique interfacial PE/CNC complex is demonstrated for the stabilization of aqueous two‐phase systems and for the fabrication of all‐aqueous double emulsions and 3D constructs. The thickness of PE/CNC complex can be effectively adjusted by tuning the osmotic stress imbalance between the two aqueous phases and, during the formation and thickening of PE/CNC complex, individual assemblies can be connected to design hierarchical all‐aqueous structures. This new platform affords tremendous potential for engineering biomimetic constructs with advanced functionality, that can be used for chemical separation, delivery, and biphasic cascading reaction vessels.
In today’s nuclear industry, momentum towards the design, licensing, and construction of advanced nuclear demonstration plants, including fast reactors, is at a remarkably high level. However, there are currently no dedicated fast spectrum irradiation test facilities in the United States to support the development of fast spectrum technologies. As a result, a unique situation is developing where most of these plants will likely be designed by leveraging historic nuclear material technologies, but where the further optimization and advancement is impeded by the lack of fast neutron irradiation test facilities. While these circumstances present a challenge, there are some near-term opportunities that, if seized, can still help develop advanced fast reactor materials to a meaningful level of readiness to support future commercial fast reactors. Here, in this paper, we assess the feasibility of using thermal neutron filtering materials in existing experiment positions in the Advanced Test Reactor (ATR) at Idaho National Laboratory and the High Flux Isotope Reactor (HFIR) at Oak Ridge National Laboratory to simulate fast reactor test environments for nonfuel test specimens. Items investigated include the incident neutron flux (both fast and thermal), the total neutron fluence and cumulative atom displacements, helium production rate due to thermal neutron capture in nickel, and the potential impact that the thermal neutron filter material has on the cycle length of a given reactor. It is concluded that while HFIR provides the highest fast flux of all the options investigated, it is limited in the amount of thermal neutron filtering material that can be introduced into an experiment position without significantly affecting the operation of the reactor. Irradiation in Outboard-A positions in the ATR was found to be the most realistic near-term experiment avenue due to having ample space for several capsules in a moderately fast flux.
This work constructs an advanced force field, the Completely Multipolar Model (CMM), to quantitatively reproduce each term of an energy decomposition analysis (EDA) for aqueous solvated alkali metal cations and halide anions and their ion pairings. We find that all individual EDA terms remain well-approximated in the CMM for ion-water and ion-ion interactions, except for polarization, which shows errors due to the partial covalency of ion interactions near their equilibrium. We quantify the onset of the dative bonding regime by examining the change in molecular polarizability and Mayer bond indices as a function of distance, showing that partial covalency manifests by breaking the symmetry of atomic polarizabilities while strongly damping them at short-range. This motivates an environment-dependent atomic polarizability parameter that depends on the strength of the local electric field experienced by the ions to account for strong damping, with anisotropy introduced by atomic multipoles. The resulting CMM model for ions provides accurate dimer surfaces and three-body polarization and charge transfer compared to EDA, and shows excellent performance on various ion benchmarks including vibrational frequencies and cluster geometries.
The methyl ketone is a central synthetic building block for the construction of advanced heteroaryl scaffolds and systems. Reactions, including oxidative cyclization strategies, are often predicated on efficient access to this ubiquitous moiety. In the context of arenes, standard approaches leveraging Markovnikov hydration/oxidation or oxidative cleavage of the C–C π bond often afford satisfactory performance. However, when the substrate contains an electron-deficient heteroaryl core, the traditional Malaprade reaction, and related oxidative-cleavage strategies, frequently result in diminished performance over carbon-based arenes. In this work we present the development and application of an oxidative cleavage reaction of various pyridinyl isoprenes towards accessing the downstream methyl ketone for utilization in advanced cyclizations for the preparation of soft-N-donor complexant scaffolds. Further, this efficient protocol parallels the principles of Green chemistry by exchanging KMnO 4 for the toxic OsO 4 and offers the end-user an efficient, more environmentally friendly option for accessing heteroaryl methyl ketones in one hour of reaction time using potassium permanganate and sodium paraperiodate as a synergistically potent oxidative cleavage system. The wide substrate scope defined access to simple, as well as advanced heteroaryl methyl ketones. Method development, optimization, substrate scope, preliminary mechanistic observations, and a scale up reaction are delineated herein.
To support the U.S. Department of Energy’s (DOE) Advanced Building Construction (ABC) Collaborative, the National Renewable Energy Laboratory (NREL) has been tasked with characterizing the U.S. building stock and developing a national typology of buildings. The potential use cases of such a typology are flexible and evolving, but in this initial phase, the primary intention is to help identify technology requirements and engineering solutions for moving the U.S. building stock toward a zero-carbon future by midcentury. This study was developed by NREL, with input from the ABC Analysis Working Group, which is a subset of the ABC Collaborative led by the Rocky Mountain Institute (RMI).
To support the U.S. Department of Energy's (DOE's) Advanced Building Construction (ABC) Collaborative, the National Renewable Energy Laboratory (NREL) has been tasked with characterizing the U.S. building stock and developing a national typology of buildings. The potential use cases of such a typology are flexible and evolving, but in this initial phase, the primary intention is to help identify technology requirements and engineering solutions for moving the U.S. building stock toward a zero-carbon future by mid-century. This typology will also support the development of appropriate ABC research goals for existing buildings, such as cost targets for new technology development, and in a later phase, the typology can be used to support the implementation of ABC solutions by informing market aggregation and business model development. The ABC Initiative invests in new technologies that enable high building performance, can be deployed quickly with minimal onsite construction time, and are affordable and appealing to building owners, investors, and occupants. Funding awardees use many innovations, including new building materials, 3D printing, offsite manufacturing, robotics, and digital art-to-part. Although the goals of ABC cover a broad range of objectives around energy, comfort, and health, the primary ABC-related application of this national building characterization study is the development of retrofit packages that can be applied to reduce thermal loads in buildings. Retrofit packages will be determined collaboratively by the DOE and the ABC Collaborative. We anticipate a range of upgrade measures covering envelope-, HVAC-, and water-heating-related loads.
The conceptual design of the FMR including the experimental and numerical verifications will lead to: Cost estimation of the FMR based on the conceptual design results and guidelines of advanced reactor construction. Submittal of the pre-application licensing plan to the NRC for review and feedback.
The U.S. Department of Energy (DOE) Office of Energy Efficiency and Renewable Energy (EERE) funded a series of research and training programs under the Advanced Building Construction with Energy Efficient Technologies and Practices (ABC) initiative. The aim is to develop new training programs to support the development of a skilled workforce in the building energy efficiency industry. In response, the University of Illinois Smart Energy Design Assistance Center (SEDAC), in collaboration with State Energy Offices in Illinois, Hawaii, and Nevada, developed the Community College Energy Code Training Program. The program is designed to prepare a new generation of energy-literate professionals that understand energy efficiency and its relation to energy codes for new construction and existing buildings through: a) the development of a curriculum and innovative pedagogical materials, b) dissemination through a national network of state energy offices: c) outreach targeting Community College students and instructors.