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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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Volterra (now Maxim), Sandia National Laboratories, and the National Renewable Energy Laboratory are entering into a cooperative research and development agreement (CRADA) in order to perform validation testing of Volterra’s PV modules through the DOE Regional Test Centers (RTCs).
Currently, NASA has engaged industry to develop a series of small to medium capacity landers with payload capacities of up to 5-9 tons by the mid to late 2020s. This contrasts with the former Constellation program, where the Altair lunar lander was targeting a payload capability of roughly 14-20 tons. Investment in smaller landers may present future challenges in delivering habitat modules larger than lunar lander cabins or small logistics modules to the lunar surface. Additionally, given a projected SLS flight rate of 1-2 launches per year, a lunar surface buildup from small elements seems problematic at best. While commercial launchers provide a supplement to SLS, many of the current and projected launch vehicles deliver less than 20 tons to a Trans-Lunar Injection – even fewer to the lunar surface. However, a possible solution could emerge if the lander itself could be launched in pieces with a buildup in Cislunar space. Thus, launchers with these capacities could contribute to a lunar lander capable of delivering 30 tons or more to the lunar surface. This paper introduces the notional concept of a Joinable Undercarriage to Maximized Payload (JUMP) lander. Key elements of a proposed JUMP lander concept will be discussed, followed by recommendations and forward work.
Abstract A facile ultrasonication‐assisted wet chemistry method for preparing multicomponent alloy nanoparticles (NPs) including high‐entropy alloys (HEAs) is reported. PtAuPdRhRu alloy (HEA), quaternary PtAuPdRh alloy, and ternary PtAuPd alloy NPs are produced with ≈3 nm in diameter. Taking advantage of the acoustic cavitation phenomenon in ultrasonication process, noble metal precursors could be co‐reduced by chemical reductants and transform to alloy structures under operation at room conditions. The instantaneous massive energy (≈5000 °C, 2000 atm) occurring in momentary timespans (≤10 −9 s) contributes to the formation of multimetallic mixed nanomaterials driven by entropy maximization. Owing to strong synergistic effects, the catalysts with the HEA NPs supported on carbons exhibit prominent electrocatalytic activities for hydrogen evolution reaction.
We report that the discovery of solid-phase, inexpensive transition-metal-based water oxidation catalysts is a central goal for renewable energy, and has led to a general consensus that a partially populated metal e g d-electronic state is desirable, leading to favorable catalysis for certain elements in specific oxidation states. In manganese systems, the key species is manganese(III), whose high-spin d 4 electronic configuration places an unpaired electron in the e g orbital, which is postulated to contribute to electronic and structural features that support catalysis. Based on density functional theory calculations, it is predicted that electron transfer would be facilitated by a catalyst with alternating low- and high-Mn III -content sheets, which positions neighboring band edges in closer energetic proximity. The preparation of such catalysts is demonstrated for the first time and it is shown that the catalytic activity is maximized in these systems over more uniform, but more Mn III -rich systems. The best catalyst possesses alternating high-and low-average oxidation state sheets with interlayer Cs + ions, and has an overpotential of 450 mV at 10 mA, which represents an improvement of 250 mV over the best unmodified synthetic potassium birnessites. Using scanning tunneling spectroscopy, bandgap modulations consistent with the theoretically predicted band edge shifts are detected.
Abstract Maximizing lithium (Li) utilization is crucial for enhancing the long‐term stability of Li‐based batteries. In anode‐free Li batteries (AFLBs), although the initial formation of solid electrolyte interphase (SEI) is beneficial on limiting further reaction between Li and electrolyte, large volume change of Li metal anode (LMA) during cycling often leads to continuous breakdown and re‐formation of SEI and formation of inactive Li, hindering its efficiency. In this study, a novel strategy is presented to protect Li metal by applying a floatable protection layer (FPL) on a copper substrate, enabling large Li particles to be primarily deposited below FPL, but SEI to be primarily formed above FPL. This approach effectively minimizes the direct contact between electrolyte and freshly deposited Li, therefore mitigating the continuous side reactions and early failure of AFLBs. As a result, Li consumption is minimized, and the overall stability of the battery is significantly enhanced. Further development of this approach can also be used to improve the performance of other Li‐based batteries for large‐scale applications.
We derive an asymptotic expansion for the log-likelihood of Gaussian mixture models (GMMs) with equal covariance matrices in the low signal-to-noise regime. The expansion reveals an intimate connection between two types of algorithms for parameter estimation: the method of moments and likelihood optimizing algorithms such as Expectation-Maximization (EM). We show that likelihood optimization in the low SNR regime reduces to a sequence of least squares optimization problems that match the moments of the estimate to the ground truth moments one by one. This connection is a stepping stone towards the analysis of EM and maximum likelihood estimation in a wide range of models. A motivating application for the study of low SNR mixture models is cryo-electron microscopy data, which can be modeled as a GMM with algebraic constraints imposed on the mixture centers. We discuss the application of our expansion to algebraically constrained GMMs, among other example models of interest. © 2022 The Authors. Communications on Pure and Applied Mathematics published by Wiley Periodicals LLC.
The recent comprehensive numerical study of critical points of the scalar potential of four-dimensional N = 8, SO(8) gauged supergravity using Machine Learning software in [1] has led to a discovery of a new N = 1 vacuum with a triality-invariant SO(3) symmetry. Guided by the numerical data for that point, we obtain a consistent SO(3) × Z 2 -invariant truncation of the N = 8 theory to an N = 1 supergravity with three chiral multiplets. Critical points of the truncated scalar potential include both the N = 1 point as well as two new non-supersymmetric and perturbatively unstable points not found by previous searches. Studying the structure of the submanifold of SO(3) × Z 2 -invariant supergravity scalars, we find that it has a simple interpretation as a submanifold of the 14-dimensional Z $3\atop{2}$ -invariant scalar manifold (SU(1, 1)/U(1)) 7 , for which we find a rather remarkable superpotential whose structure matches the single bit error correcting (7, 4) Hamming code. This 14-dimensional scalar manifold contains approximately one quarter of the known critical points. We also show that there exists a smooth supersymmetric domain wall which interpolates between the new N = 1 AdS4 solution and the maximally supersymmetric AdS4 vacuum. Using holography, this result indicates the existence of an N = 1 RG flow from the ABJM SCFT to a new strongly interacting conformal fixed point in the IR.
We study the interactions of systems of two and three nondegenerate mesons composed of pions and kaons at maximal isospin using lattice QCD, specifically $π^+K^+, π^+π^+K^+$ and $K^+K^+π^+$. Utilizing the stochastic LapH method, we determine the spectrum of these systems on two CLS $N_f$ = 2 + 1 ensembles with pion masses of 200 MeV and 340 MeV, and include many levels in different momentum frames. We constrain the K matrices describing two- and three-particle interactions by fitting the spectrum to the results predicted by the finite-volume formalism, including up to p waves. This requires also results for the $π^+π^+$ and $K^+K^+$ spectrum, which have been obtained previously on the same configurations. We explore different fitting strategies, comparing fits to energy shifts with fits to energies boosted to the rest frame, and also comparing simultaneous global fits to all relevant two- and three-particle channels to those where we first fit twoparticle channels and then add in the three-particle information. We provide the first determination of the three-particle K matrix in $π^+π^+K^+$ and $K^+K^+π^+$ systems, finding statistically significant nonzero results in most cases. We include s and $\textit{p}$ waves in the K matrix for $π^+K^+$ scattering, finding evidence for an attractive p-wave scattering length. We compare our results to Chiral Perturbation Theory, including an investigation of the impact of discretization errors, for which we provide the leading order predictions obtained using Wilson Chiral Perturbation Theory.
We use a combination of AdS/CFT and supersymmetric localization to study codimension-2 defects in 5d SCFTs and their gauge theory deformations. The 5d SCFTs are engineered by ( p , q ) 5-brane webs, with defects realized by D3-branes ending on the 5-brane webs. We obtain the defect free energies and find that gauge theory descriptions of the combined 5d/3d systems can be connected to the UV defect SCFTs through a form of F-maximization which extremizes over different gauge theory defects. This leads to a match between the defect free energies obtained from supersymmetric localization in the gauge theories on the one hand and string theory results on the other. We extend this match to defect RG flows.
We present a numerical exploration of the relativistic-field-theory (RFT) formalism for three pions with all possible values of non-maximal isospin, I πππ = 2, 1 and 0. Using the generic-isospin extension of the RFT formalism [1] and applying our open-source Python library to implement the framework, we predict a range of three-pion energies for illustrative values of the two-to-two scattering amplitudes for various finite-volume irreps also with non-zero total momentum P in the finite-volume frame. The results restrict attention to the case of a vanishing intrinsic three-body interaction so that the spectra can be understood as a baseline. In future lattice QCD calculations, deviations from these values will be translated into evidence for intrinsic three-body effects in the various scattering channels.
In plant genomic experiments, correlations among various biological traits (phenotypes) give new insights into how genetic diversity may have tuned biological processes to enhance fitness under diverse conditions. Consequently, knowing how the correlations are affected by genetic (G) and environmental (E) factors helps develop climate-resilient plants. However, the current literature lacks any method for assessing the effect of predictors on pairwise correlations among multiple phenotypes together with easily interpretable model parameters. To address this need, we propose to model pairwise correlations directly in terms of G and E and develop a computationally efficient inference procedure. Two major novelties in our methodology are (1) the use of a composite pairwise likelihood method to avoid the positive definiteness restriction on the correlation matrix and (2) the use of a novel Minorize–Maximize (MM) algorithm for the efficient estimation of a large number of parameters. The proposed method shows excellent numerical performance on synthetic datasets. Here, the analysis of the motivating data on cowpea reveals that the rates of solar energy storage by photosynthesis (the aggregate trait) are differentially affected by different genetic loci through two distinct processes: “photoinhibition” which results from photodamage caused by excess light, and “photoprotection” which protects plants from photodamage but also results in energy loss.
Maximizing the sum of two generalized Rayleigh quotients (SRQ) can be reformulated as a one-dimensional optimization problem, where the function value evaluations are reduced to solving semi-definite programming (SDP) subproblems. In this paper, we first use the dual SDP subproblem to construct an explicit overestimation and then propose a branch-and-bound algorithm to globally solve (SRQ). Numerical results demonstrate that it is even more efficient than the recent SDP-based heuristic algorithm.
In this paper, using generalized resolvents and retractions onto shrinking closed subsets, we present new extragradient algorithms for finding the solution of a J-variational-like inequality, the zero points of a family of maximal monotone operators, and the solution set of mixed equilibrium problems for a J-α-inverse-strongly monotone-like operator in a 2-uniformly convex and 2-uniformly smooth Banach space. By introducing the new definitions, we prove strong convergence of generated iterates in the extragradient methods. Using FMINCON optimization toolbox in MATLAB, we give some numerical examples and compare them with several existence results in the literature to illustrate the usability of our results.
Maximizing energy efficiency through waste heat recovery (WHR) processes is crucial for sustainable and ecofriendly operations across multiple industries, notably in wastewater treatment plants (WWTPs). This work proposes a comprehensive approach for assessing the WHR feasibility in WWTPs, structured in two main objectives. Firstly, an Artificial Neural Network (ANN) model is developed to accurately predict WHR based on operational data, including biogas temperature, biogas pressure, daily production in kWh, and WHR values in kWh th . The second objective focuses on economically evaluating the WHR feasibility based on the estimated WHR values obtained by the ANN model, and then realistically assessing the economic feasibility of integrating the Organic Rankine Cycle (ORC) and Seasonal Thermal Energy Storage (STES) systems. With an application to the As-Samra WWTP located in Jordan, the developed ANN model demonstrates promising results in the validation phase, with a root mean square error (RMSE) of 2206 kWh/day, a mean absolute error (MAE) of 1674 kWh/day, and an R-squared (R 2 ) value of 68%. On the other hand, the economic analysis reveals that an optimal ORC system of 412.14 kW e capacity yields a Net Present Value (NPV) of 2.09 million US dollars, a Levelized Cost of Energy (LCOE) of 0.0749 USD/kWh, a Payback Period (PBP) of 4.8 years, and annual revenues of 428 kUSD. This work also investigates the techno-economic feasibility of integrating ORC-STES. Results indicate that the LCOE and PBP are highly affected by the ORC's capital cost, and integrating STES increases the LCOE to 0.0824 USD/kWh, rendering its integration with ORC infeasible. In conclusion, this study aims to advance the understanding and application of WHR in WWTPs, paving the way for more efficient and sustainable practices in the field.
A recently developed measure-theoretic framework solves a stochastic inverse problem (SIP) for models where uncertainties in model output data are predominantly due to aleatoric (i.e., irreducible) uncertainties in model inputs (i.e., parameters). The subsequent inferential target is a distribution on parameters. Another type of inverse problem is to quantify uncertainties in estimates of “true” parameter values under the assumption that such uncertainties should be reduced as more data are incorporated into the problem, i.e., the uncertainty is considered epistemic. A major contribution of this work is the formulation and solution of such a parameter identification problem (PIP) within the measure-theoretic framework developed for the SIP. The approach is novel in that it utilizes a solution to a stochastic forward problem (SFP) to update an initial density only in the parameter directions informed by the model output data. In other words, this method performs “selective regularization” only in the parameter directions not informed by data. Further, the solution is defined by a maximal updated density (MUD) point where the updated density defines the measure-theoretic solution to the PIP. Another significant contribution of this work is the full theory of existence and uniqueness of MUD points for linear maps with Gaussian distributions. Data-constructed Quantity of Interest (QoI) maps are also presented and analyzed for solving the PIP within this measure-theoretic framework as a means of reducing uncertainties in the MUD estimate. We conclude with a demonstration of the general applicability of the method on two problems involving either spatial or temporal data for estimating uncertain model parameters. The first problem utilizes spatial data from a stationary partial differential equation to produce a MUD estimate of an uncertain boundary condition. The second problem utilizes temporal data obtained from the state-of-the-art ADvanced CIRCulation (ADCIRC) model to obtain a MUD estimate of uncertain wind drag coefficients for a simulated extreme weather event near the Shinnecock Inlet located in the Outer Barrier of Long Island, NY, USA.
Here, this paper demonstrates a novel modular distributed framework that uses optimal energy-dispatching strategies to enable greater flexibility and profitability in nuclear-renewable integrated energy systems (NR-IES). Hydrogen is used as a commodity in this framework since its production can improve grid stability and system operational flexibility, decarbonize heavy industry, and create an additional revenue stream for electricity generators, particularly nuclear power plants with high operational expenses. The proposed solution addresses the challenges associated with merging multiple software and services from various domains by using functional mock-up units (FMU) to co-simulate diverse subsystems designed in various platforms. The tightly coupled integrated energy system (IES) is optimized to maximize revenue by utilizing the deep reinforcement learning (DRL) technique to make smart dispatching decisions based on variable electricity prices and the availability of renewable energy. Proximal policy optimization (PPO) algorithm is used in training and testing the DRL agent. Over a period of 120 days, the proposed hydrogen-based IES framework showed about 10% revenue boost compared to a non-hydrogen generating baseline IES while also providing an easily-adoptable framework which can help to improve the flexibility of future generation nuclear power plants.
Advances in appliance and equipment systems' technologies also cause changes in their technical characteristics that can affect energy consumption and associated greenhouse gas emissions. Energy performance evaluation methods that use testing and metrics can evolve to account for energy consumption contributed from new technological characteristics. However, there is no systematic effort to enable the advancement of test procedures and performance metrics to maximize the energy, climate, and financial benefits of disruptive technologies. Therefore, it is important for countries to regularly update their energy-efficiency policy programs, such as standards and labels, by improving test procedures and metrics to reflect the innovation in emerging technologies as well as to mitigate the risk of deploying obsolete technology. This paper shows how energy performance metrics have been improving in selected appliances and equipment and provides insights to design a systematic effort to improve metrics and test procedures at the speed of ongoing changes in technologies and markets.
Ultrafiltration (UF) has been widely utilized as water pretreatment for different applications especially in water reuse. The UF system operation is characterized by a filtration phase, where particles accumulate on the membrane surface resulting in an increase in the transmembrane pressure (TMP) and a cleaning phase, where foulants are removed through cleaning cycles including physical backwash and chemical-enhanced backwash (CEB). In this study, data from an engineering-scale UF system treating reclaimed wastewater were used to assess the impact of backwashing on the filtration process. TMP backwash trigger, backwash duration, and CEB frequency were purposely varied for a cycle-by-cycle investigation on the net water production, water recovery, initial operating TMP, and filtration cycle duration. As the TMP backwash trigger was varied between 62 and 145kPa, the maximum net water production (63 m 3 /d) was achieved at 103kPa and water recovery remained relatively constant at approximately 92%. Backwash durations of 45, 65, and 85s were performed where both net water production and water recovery yielded similar results (~63 m 3 /d and ~ 91%) compared with 103kPa TMP backwash trigger. The CEB frequency was also lowered from one every three backwashes (1/3) to 1/6 and 1/12 and resulted in decreased net water production and water recovery while the initial TMP increased. Interestingly, the total number of CEBs remained approximately constant regardless of their frequency. In conclusion, results suggest that CEB is an important fouling control process to maximize water production.