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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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Objectives: 1) Store cryogenic propellants in a manner that maximizes their availability for use regardless of mission duration. 2) Efficiently transfer conditioned cryogenic propellant to an engine or tank situated in a microgravity environment. 3) Accurately monitor and gauge cryogenic propellants situated in a microgravity environment.
(1) Store cryogenic propellants in a manner that maximizes their availability for use regardless of mission duration; (2) Efficiently transfer conditioned cryogenic propellant to an engine or tank situated in a microgravity environment; and (3) Accurately monitor and gauge cryogenic propellants situated in a microgravity environment
An adaptive shape servoing control method is presented in this article to manipulate a deformable object into a desired shape in 3-D. A finite-point-based representation of the deformable object is used and the deformation Jacobian matrix is approximated using Fourier series basis functions. The unknown parameters of the deformation Jacobian are learned by using the velocity applied to a control point on the object and corresponding change of positions of the points describing the entire object. An integral concurrent learning (ICL)-based parameter update law is designed along with a constrained controller to satisfy the state constraints on the motion of the control point using Barrier Lyapunov function analysis. ICL-based parameter update law uses data history of velocity and corresponding positions of the points along with their current values. An efficient algorithm to update the history stack using singular value maximization is proposed based on the structure of the regressor matrix. Simulations using a physical simulator and experiments using a robot platform are performed to validate the performance of the proposed controller on two different deformable objects.
The Lunar Architecture Team Phase 2 study defined and assessed architecture options for a Lunar Outpost at the Moon's South Pole. The Habitation Focus Element Team was responsible for developing concepts for all of the Habitats and pressurized logistics modules particular to each of the architectures, and defined the shapes, volumes and internal layouts considering human factors, surface operations and safety requirements, as well as Lander mass and volume constraints. The Structures Subsystem Team developed structural concepts, sizing estimates and mass estimates for the primary Habitat structure. In these studies, the primary structure was decomposed into a more detailed list of components to be sized to gain greater insight into concept mass contributors. Structural mass estimates were developed that captured the effect of major design parameters such as internal pressure load. Analytical and empirical equations were developed for each structural component identified. Over 20 different hard-shell, hybrid expandable and inflatable soft-shell Habitat and pressurized logistics module concepts were sized and compared to assess structural performance and efficiency during the study. Habitats were developed in three categories; Mini Habs that are removed from the Lander and placed on the Lunar surface, Monolithic habitats that remain on the Lander, and Habitats that are part of the Mobile Lander system. Each category of Habitat resulted in structural concepts with advantages and disadvantages. The same modular shell components could be used for the Mini Hab concept, maximizing commonality and minimizing development costs. Larger Habitats had higher volumetric mass efficiency and floor area than smaller Habitats (whose mass was dominated by fixed items such as domes and frames). Hybrid and pure expandable Habitat structures were very mass-efficient, but the structures technology is less mature, and the ability to efficiently package and deploy internal subsystems remains an open issue.
Wind turbines in a wind farm typically operate individually to maximize their own performance regardless of the impact of aerodynamic interactions on neighboring turbines. Properly coordinating turbines, by operating some turbines suboptimally, within a wind farm has the potential to improve overall wind farm performance. Computing the optimal control strategy under varying atmospheric conditions can be computationally intense for large wind farms. As wind power farms increase in size and related models become more complex, computationally efficient algorithms are needed to perform real-time optimization and control. This study proposes a distributed optimization framework and computationally efficient wake steering wind farm control strategy that uses the yaw angle of a turbine to alter the behavior of a turbine wake and minimize turbine interactions. This computational efficiency allows the strategy to be feasible for real-time control.
In July 2022, President Cyril Ramaphosa announced an emergency response plan to address the growing electricity crisis in South Africa, which included the necessity for customers of Eskom, South Africa’s primary electricity supplier, to implement demand-side energy efficiency measures. This report is the second of two reports that provides the foundation for a strategic Demand-Side Management (DSM) approach to reduce load shedding and maximize energy savings in South Africa, though inter-governmental collaboration and increased coordination of energy efficiency activities. The first report focuses on the experience of Eskom’s rate-funded Integrated Demand Management (IDM) programme, from its beginning in 2004 to today, highlighting the factors that led to failed support for the programme. This second report focuses on addressing the challenges identified in the first report and describes the elements of a policy framework to develop a sustained DSM program to meet the government’s energy efficiency goals in terms of greenhouse gas emissions reduction and social equity and inclusion to achieve a just transition.
In July 2022, President Cyril Ramaphosa announced an emergency response plan to address the growing electricity crisis in South Africa, which included the necessity for customers of Eskom, South Africa’s primary electricity supplier, to implement demand-side energy efficiency measures. This report is the first of two reports that provides the foundation for a strategic Demand-Side Management (DSM) approach to reduce load shedding and maximize energy savings in South Africa, through inter-governmental collaboration and increased coordination of energy efficiency activities. This first report focuses on the experience of Eskom’s rate-funded Integrated Demand Management (IDM) programme, from its beginning in 2004 to today, highlighting the factors that led to failed support for the programme, and emphasizing the measures that will need to be addressed to meet the government’s energy efficiency (EE) goals, as well as other socio-economic and environmental priorities, such as job creation, affordability and greenhouse gas (GHG) emission reduction goals.
We consider the problem of distributed downlink beam scheduling and power allocation for millimeter-Wave (mmWave) cellular networks where multiple base stations (BSs) belonging to different service operators share the same unlicensed spectrum with no centralized control or explicit coordination among them. Our goal is to design efficient distributed beam scheduling and power allocation schemes such that the network-level payoff, defined as the weighted sum of the total throughput and a power penalization term, can be maximized. To this end, we propose a distributed scheduling approach to power allocation and adaptation for efficient interference management over the shared spectrum by modeling each BS as an independent Q-learning agent. As a baseline, we compare the proposed approach to the state-of-the-art non-cooperative game-based approach which was previously developed for the same problem. We conduct extensive experiments under various scenarios to verify the effect of multiple factors on the performance of both approaches. Experiment results show that the proposed approach adapts well to different interference conditions by learning from experience and thus can achieve higher payoff than the game-based approach. The proposed approach can also be integrated into our previously developed Lyapunov stochastic optimization framework for the purpose of network utility maximization with optimality guarantee. As a result, the weights in the payoff function can be automatically and optimally determined by the virtual queue values from the sub-problems derived from the Lyapunov optimization framework.
This program aims to overcome these challenges and develop high-efficiency (24-25%) double-side (DS) TOPCon solar cells by maximizing passivation on both sides while mitigating light absorption losses. To achieve this, the program will implement either thin (≤ 20 nm) homogeneous n-TOPCon on the entire front surface or selective area thick (≥ 100 nm) n-TOPCon only underneath the front metal contact with ~90% field region composed of dielectric passivated textured n-Si in between the poly-Si/metal grid. The rear side will feature ~250 nm-thick full-area planar p-TOPCon, which functions as the rear junction. Recombination and parasitic absorption losses in the front and rear TOPCon layers will be minimized by tailoring their doping profiles and thickness. Additionally, the device performance will be further enhanced through the optimization of bulk parameters, including the carrier lifetime, resistivity, and thickness of the n-type Si absorber. Finally, advanced metallization techniques, such as fine-line printing, and floating busbar or busbar-less designs, will be employed to reduce recombination, resistive, and optical losses. The program started with the development of a technology roadmap for DS-TOPCon cells to achieve target efficiency.
ABSTRACT The standard genetic code is robust to mutations during transcription and translation. Point mutations are likely to be synonymous or to preserve the chemical properties of the original amino acid. Saturation mutagenesis experiments suggest that in some cases the best-performing mutant requires replacement of more than a single nucleotide within a codon. These replacements are essentially inaccessible to common error-based laboratory engineering techniques that alter a single nucleotide per mutation event, due to the extreme rarity of adjacent mutations. In this theoretical study, we suggest a radical reordering of the genetic code that maximizes the mutagenic potential of single nucleotide replacements. We explore several possible genetic codes that allow a greater degree of accessibility to the mutational landscape and may result in a hyperevolvable organism that could serve as an ideal platform for directed evolution experiments. We then conclude by evaluating the challenges of constructing such recoded organisms and their potential applications within the field of synthetic biology. IMPORTANCE The conservative nature of the genetic code prevents bioengineers from efficiently accessing the full mutational landscape of a gene via common error-prone methods. Here, we present two computational approaches to generate alternative genetic codes with increased accessibility. These new codes allow mutational transitions to a larger pool of amino acids and with a greater extent of chemical differences, based on a single nucleotide replacement within the codon, thus increasing evolvability both at the single-gene and at the genome levels. Given the widespread use of these techniques for strain and protein improvement, along with more fundamental evolutionary biology questions, the use of recoded organisms that maximize evolvability should significantly improve the efficiency of directed evolution, library generation, and fitness maximization.
This paper addresses two key parallelization challenges the unstructured mesh-based ocean modeling code, MPAS-Ocean, which uses a mesh based on Voronoi tessellations: (1) load imbalance across processes, and (2) unstructured data access patterns, that inhibit intra- and inter-node performance. Our work analyzes the load imbalance due to naive partitioning of the mesh, and develops methods to generate mesh partitioning with better load balance and reduced communication. Furthermore, we present methods that minimize both inter- and intranode data movement and maximize data reuse. Our techniques include predictive ordering of data elements for higher cache efficiency, as well as communication reduction approaches. We present detailed performance data when running on thousands of cores using the Cray XC30 supercomputer and show that our optimization strategies can exceed the original performance by over 2×. Additionally, many of these solutions can be broadly applied to a wide variety of unstructured grid-based computations.
Historic levels of funding for energy efficiency and clean energy projects from the Bipartisan Infrastructure Law and Inflation Reduction Act will be flowing through State Energy Offices (SEOs) in the coming years, including funds specifically for supporting workforce development efforts. This report offers a workforce evaluation framework that they can use to track and measure the progress, performance, and effectiveness of energy efficiency and clean energy workforce programs that they fund and/or implement. This evaluation step is essential to ensuring that SEOs' workforce efforts remain relevant to the evolving needs of the energy efficiency and clean energy sector and advance a workforce that is inclusive and diverse thus maximizing the success of their programs.
Optical metasurfaces are regular quasi-planar nanopatterns that can apply diverse spatial and spectral transformations to light waves. But, metasurfaces are no longer adjustable after fabrication, and a critical challenge is to realise a technique of tuning their optical properties that is both fast and efficient. Here, we experimentally realise an ultrafast tunable metasurface consisting of subwavelength gallium arsenide nanoparticles supporting Mie-type resonances in the near infrared. In using transient reflectance spectroscopy, we demonstrate a picosecond-scale absolute reflectance modulation of up to 0.35 at the magnetic dipole resonance of the metasurfaces and a spectral shift of the resonance by 30 nm, both achieved at unprecedentedly low pump fluences of less than 400 μJ cm –2 . Our findings thereby enable a versatile tool for ultrafast and efficient control of light using light.
Discovery of physical models of binary compositions using structured Gaussian Process (sGP) with physics-informed mean functions, optimizing materials post-discovery to enhance design and application efficiency.
Significance This work establishes a semicontinuous process for efficient and complete upgrading of low-strength acetic acid into lipids. By implementing control strategies in response to the time-varying nature of cell metabolism, we achieved and sustained lipid yields close to the theoretical maximum while maintaining high productivities. The control algorithm was designed to respond to readily available real-time measurements and basic principles, which is ideal for industrial applications. Overall, our process scheme can directly take in dilute nutrient streams that are difficult and costly to purify and is able to biologically convert them into highly concentrated value-added products. This design significantly reduces the energy costs associated with processes using dilute feedstocks, which typically require initial concentration of the feed.
Many quantum algorithms seek to output a specific bitstring solving the problem of interest—or a few if the solution is degenerate. It is the case for the quantum approximate optimization algorithm (QAOA) in the limit of large circuit depth, which aims to solve quadratic unconstrained binary optimization problems. Hence, the expected final state for these algorithms is either a product state or a low-entangled superposition involving a few bitstrings. What happens in between the initial N -qubit product state | 0 〉 ⊗ N and the final one regarding entanglement? Here, we consider the QAOA algorithm for solving the paradigmatic MaxCut problem on different types of graphs. We study the entanglement growth and spread resulting from randomized and optimized QAOA circuits and find that there is a volume-law entanglement barrier between the initial and final states. We also investigate the entanglement spectrum in connection with random matrix theory. In addition, we compare the entanglement production with a quantum annealing protocol aiming to solve the same MaxCut problems. Finally, we discuss the implications of our results for the simulation of QAOA circuits with tensor network-based methods relying on low-entanglement for efficiency, such as matrix product states.
We propose an operating-envelope-aware, prosumer-centric, and efficient energy community that aggregates individual and shared community distributed energy resources and transacts with a regulated distribution system operator (DSO) under a generalized net energy metering tariff design. To ensure safe network operation, the DSO imposes dynamic export and import limits, known as dynamic operating envelopes, on end-users' revenue meters. Given the operating envelopes, we propose an incentive-aligned community pricing mechanism under which the decentralized optimization of community members' benefit implies the optimization of overall community welfare. The proposed pricing mechanism satisfies the cost-causation principle and ensures the stability of the energy community in a coalition game setting. Numerical examples provide insights into the characteristics of the proposed pricing mechanism and quantitative measures of its performance.
Wind farm layout optimization (WFLO) studies often aim to maximize the annual energy production (AEP) of a wind farm by choosing an arrangement of turbines that minimizes wake interactions. One way to reduce the cost of WFLO studies is by using more computationally efficient AEP models. The cost of standard AEP modeling approaches, based on the numerical integration of low-fidelity engineering wake models, scales poorly with the number of simulated discrete wind conditions. A second way to reduce cost when using a gradient-based algorithm is to supply exact gradient information instead of finite-difference estimates. However, analytical functions for the derivatives of AEP with respect to turbine positions are not always available in the conventional modeling approach. FLOWERS is a computationally inexpensive, analytical model for wind farm AEP that is specifically developed for WFLO applications. In this paper, we analyze the performance of the FLOWERS AEP model with analytic gradients in a layout optimization study compared with a reference optimization framework across three wind farm case studies. We find that the FLOWERS-based approach reduces computation time by a factor of 50–4000 and improves optimal AEP by about 0.3% with less than half of the variability in AEP across instances with randomized initial conditions. We also find the optimal layouts to be insensitive to model parameter tuning, making FLOWERS-based layout optimization a streamlined, user-friendly approach.