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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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At least 217 records · Page 12
All sourcesNASA NTRSDOE OSTI
DOE OSTI2022

Trade off-free entanglement stabilization in a superconducting qutrit-qubit system

Abstract Quantum reservoir engineering is a powerful framework for autonomous quantum state preparation and error correction. However, traditional approaches to reservoir engineering are hindered by unavoidable coherent leakage out of the target state, which imposes an inherent trade off between achievable steady-state state fidelity and stabilization rate. In this work we demonstrate a protocol that achieves trade off-free Bell state stabilization in a qutrit-qubit system realized on a circuit-QED platform. We accomplish this by creating a purely dissipative channel for population transfer into the target state, mediated by strong parametric interactions coupling the second-excited state of a superconducting transmon and the engineered bath resonator. Our scheme achieves a state preparation fidelity of 84% with a stabilization time constant of 339 ns, leading to a 54 ns error-time product in a solid-state quantum information platform.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2021

A trade-off between plant and soil carbon storage under elevated CO 2

Terrestrial ecosystems remove about 30 per cent of the carbon dioxide (CO 2 ) emitted by human activities each year, yet the persistence of this carbon sink depends partly on how plant biomass and soil organic carbon (SOC) stocks respond to future increases in atmospheric CO 2 . Although plant biomass often increases in elevated CO 2 (eCO 2 ) experiments, SOC has been observed to increase, remain unchanged or even decline. The mechanisms that drive this variation across experiments remain poorly understood, creating uncertainty in climate projections. In this study we synthesized data from 108 eCO 2 experiments and found that the effect of eCO 2 on SOC stocks is best explained by a negative relationship with plant biomass: when plant biomass is strongly stimulated by eCO 2 , SOC storage declines; conversely, when biomass is weakly stimulated, SOC storage increases. This trade-off appears to be related to plant nutrient acquisition, in which plants increase their biomass by mining the soil for nutrients, which decreases SOC storage. We found that, overall, SOC stocks increase with eCO 2 in grasslands (8 ± 2 per cent) but not in forests (0 ± 2 per cent), even though plant biomass in grasslands increase less (9 ± 3 per cent) than in forests (23 ± 2 per cent). Ecosystem models do not reproduce this trade-off, which implies that projections of SOC may need to be revised.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2023

Tuning the thermal activation atmosphere breaks the activity–stability trade-off of Fe–N–C oxygen reduction fuel cell catalysts

Fe–N–C catalysts are the most promising platinum group metal-free oxygen-reduction catalysts, but they suffer from a low density of active metal sites and the so-called activity–stability trade-off. Here we report an Fe–N–C catalyst prepared by adding an optimal amount of H 2 to the traditional inert atmosphere during the thermal activation. The presence of H 2 significantly increases the total density of FeN 4 sites, suppressing the unstable pyrrolic-N-coordinated S1 sites and favouring the stable pyridinic-N-coordinated S2 sites with shortened Fe–N bond lengths. Here, we propose that the intrinsically stable S2 sites are probably arranged in well-graphitized carbon layers, and the S1 sites exist in less-graphitized carbon. H 2 could remove unstable S1 sites and retain stable S2 sites during the pyrolysis to break the challenging activity–stability trade-off. The Fe–N–C catalyst in membrane electrode assemblies maintains a current density of 67 mA cm -2 at 0.8 V (H 2 –air) after 30,000 voltage cycles (0.60 to 0.95 V under H 2 –air), achieving encouraging durability and performance simultaneously.

25 ENERGY STORAGE↗
DOE OSTI2025

Application-specific machine-learned interatomic potentials: exploring the trade-off between DFT convergence, MLIP expressivity, and computational cost

Machine-learned interatomic potentials (MLIPs) are revolutionizing computational materials science and chemistry by offering an efficient alternative to ab initio molecular dynamics (MD) simulations. However, fitting high-quality MLIPs remains a challenging, time-consuming, and computationally intensive task where numerous trade-offs have to be considered, e.g., How much and what kind of atomic configurations should be included in the training set? Which level of ab initio convergence should be used to generate the training set? Which loss function should be used for fitting the MLIP? Which machine learning architecture should be used to train the MLIP? The answers to these questions significantly impact both the computational cost of MLIP training and the accuracy and computational cost of subsequent MLIP MD simulations. In this study, we use a configurationally diverse beryllium dataset and quadratic spectral neighbor analysis potential. We demonstrate that joint optimization of energy versus force weights, training set selection strategies, and convergence settings of the ab initio reference simulations, as well as model complexity can lead to a significant reduction in the overall computational cost associated with training and evaluating MLIPs. This opens the door to computationally efficient generation of high-quality MLIPs for a range of applications which demand different accuracy versus training and evaluation cost trade-offs.

36 MATERIALS SCIENCE↗
DOE OSTI2020

Determination of latent dimensionality in international trade flow

Currently, high-dimensional data is ubiquitous in data science, which necessitates the development of techniques to decompose and interpret such multidimensional (aka tensor) datasets. Finding a low dimensional representation of the data, that is, its inherent structure, is one of the approaches that can serve to understand the dynamics of low dimensional latent features hidden in the data. Moreover, decomposition methods with non-negative constraints are shown to extract more insightful factors. Nonnegative RESCAL is one such technique, particularly well suited to analyze self-relational data, such as dynamic networks found in international trade flows. Particularly, non-negative RESCAL computes a low dimensional tensor representation by finding the latent space containing multiple modalities. Furthermore, estimating the dimensionality of this latent space is crucial for extracting meaningful latent features. Here, to determine the dimensionality of the latent space with non-negative RESCAL, we propose a latent dimension determination method which is based on clustering of the solutions of multiple realizations of non-negative RESCAL decompositions. We demonstrate the performance of our model selection method on synthetic data. We then apply our method to decompose a network of international trade flows data from International Monetary Fund and shows that with a correct latent dimension determination, the resulting features are able to capture relevant empirical facts from economic literature.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2015

Power and Performance Trade-offs for Space Time Adaptive Processing

Computational efficiency – performance relative to power or energy – is one of the most important concerns when designing RADAR processing systems. This paper analyzes power and performance trade-offs for a typical Space Time Adaptive Processing (STAP) application. We study STAP implementations for CUDA and OpenMP on two computationally efficient architectures, Intel Haswell Core I7-4770TE and NVIDIA Kayla with a GK208 GPU. We analyze the power and performance of STAP’s computationally intensive kernels across the two hardware testbeds. We also show the impact and trade-offs of GPU optimization techniques. We show that data parallelism can be exploited for efficient implementation on the Haswell CPU architecture. The GPU architecture is able to process large size data sets without increase in power requirement. The use of shared memory has a significant impact on the power requirement for the GPU. A balance between the use of shared memory and main memory access leads to an improved performance in a typical STAP application.

Gawande, Nitin A.↗
DOE OSTI2025

Understanding Reliability Trade-Offs in 1T-nC and 2T-nC FeRAM Designs

Ferroelectric random access memory (FeRAM) is a promising candidate for energy-efficient nonvolatile memory, particularly for logic-in-memory and compute-in-memory (CIM) applications. Among the available cell architectures, One-Transistor–n-Capacitor (1T-nC) and two-transistor–n-capacitor (2T-nC) FeRAMs each offer distinct trade-offs in density, scalability, and reliability. In this work, we present a comparative study of these two architectures under both dimensional scaling ( XY/Z shrinkage) and vertical integration (increasing stacked capacitors per cell). Using technology computer-aided design (TCAD) and circuit-level simulations, we analyze how scaling impacts ferroelectric capacitance, parasitic coupling, and floating-node (FN) dynamics, which together dictate sense margin (SM) and read stability. A key mitigation strategy—floating unselected capacitors—is applied to both architectures, effectively decoupling the SM from the number of stacked capacitors and enabling tractable analysis across scaling regimes. Results show that 1T-nC suffers more from charge sharing with the bitline (BL), while 2T-nC benefits from transistor isolation and stronger low-voltage sensing at the cost of increased area. By systematically evaluating these behaviors across scaling directions, this work establishes the reliability trade-offs of 1T-nC and 2T-nC cells and provides design guidelines for high-density, vertically integrated FeRAM systems.

1T-nC↗
DOE OSTI2021

Pricing and Energy Trading in Peer-to-Peer Zero Marginal-Cost Microgrids

Efforts to utilize 100% renewable energy in community microgrids require new approaches to energy markets and transactions to efficiently address periods of scarce energy supply. In this paper we contribute to the promising approach of peer-to-peer (P2P) energy trading in two main ways: analysis of a centralized, welfare-maximizing economic dispatch that characterizes optimal price and allocations, and a novel P2P system for negotiating energy trades that yields physically feasible and at least weakly Pareto-optimal outcomes. Our main results are 1) that optimal pricing is insufficient to induce agents with batteries to take optimal actions, 2) a novel P2P algorithm to addresses this while keeping private information, 3) a formal proof that this algorithm converges to the centralized solution in the case of two agents negotiating for a single period, and 4) numerical simulations of the P2P algorithm performance with up to 10 agents and 24 periods that show it converges on average to total welfare within 0.1% of the social optimum in on the order of 10s to 100s of iterations, increasing with the number of agents, time periods, and total storage capacity.

batteries↗
DOE OSTI2019

Effects of group size and group density on trade–offs in resource selection by a group–territorial central–place foraging woodpecker

Trade-offs in resource selection by central-place foragers are driven by the need to balance the benefits of selecting resources against the costs of travel from the central place. For group-territorial central-place foraging birds, trade-offs in resource selection are likely to be complicated by a competitive advantage for larger groups at high group density that may limit accessibility of high-quality distant resources to small groups. We used the group-territorial, central-place foraging Red-cockaded Woodpecker Leuconotopicus borealis (RCW) as a case study to test predictions that increases in group density lead to differences in foraging distances and resource selection for groups of different sizes. We used GPS tracking and LiDAR-derived habitat data to model effects of group size on foraging distances and selection for high-quality pines (≥ 35.6 cm diameter at breast height (dbh)) and lower quality pines (25.4–35.6 cm dbh) by RCW groups across low ( n = 14), moderate ( n = 10) and high group density ( n = 10) conditions. At low and moderate group density, all RCW groups selected distant high-quality pines in addition to those near the central place because competition for resources was low. In contrast, at high group density, larger groups travelled further to select high-quality pines, whereas smaller groups selected high-quality pines only when they were close to the central place and, conversely, were more likely to select lower quality pines at greater distances from the central place. Selection for high-quality pines only when close to the cavity tree cluster at high group density is important to long-term fitness of small RCW groups because it allows them to maximize benefits from both territorial defence and selecting high-quality resources while minimizing costs of competition. Furthermore these relationships suggest that intraspecific competition at high group density entails substantive costs to smaller groups of territorial central-place foragers by limiting accessibility of distant high-quality foraging resources.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2023

Ectomycorrhizal fungi enhance pine growth by stimulating iron‐dependent mechanisms with trade‐offs in symbiotic performance

Iron (Fe) is crucial for metabolic functions of living organisms. Plants access occluded Fe through interactions with rhizosphere microorganisms and symbionts. Yet, the interplay between Fe addition and plant–mycorrhizal interactions, especially the molecular mechanisms underlying mycorrhiza-assisted Fe processing in plants, remains largely unexplored. Here, we conducted mesocosms in Pinus plants inoculated with different ectomycorrhizal fungi (EMF) Suillus species under conditions with and without Fe coatings. Meta-transcriptomic, biogeochemical, and X-ray fluorescence imaging analyses were applied to investigate early-stage mycorrhizal roots. While Fe addition promoted Pinus growth, it concurrently reduced mycorrhiza formation rate, symbiosis-related metabolites in plant roots, and aboveground plant carbon and macronutrient content. This suggested potential trade-offs between Fe-enhanced plant growth and symbiotic performance. However, the extent of this trade-off may depend on interactions between host plants and EMF species. Interestingly, dual EMF species were more effective at facilitating plant Fe uptake by inducing diverse Fe-related functions than single-EMF species. This subsequently triggered various Fe-dependent physiological and biochemical processes in Pinus roots, significantly contributing to Pinus growth. However, this resulted in a greater carbon allocation to roots, relatively reducing the aboveground plant carbon content. Our study offers critical insights into how EMF communities rebalance benefits of Fe-induced effects on symbiotic partners.

36 MATERIALS SCIENCE↗
DOE OSTI2025

Bias-Variance Trade-Off in Physics-Informed Neural Networks with Randomized Smoothing for High-Dimensional PDEs

Physics-Informed Neural Networks (PINNs) have triggered a paradigm shift in scientific computing, leveraging mesh-free properties and robust approximation capabilities. While proving effective for low-dimensional partial differential equations (PDEs), the computational cost of PINNs remains a hurdle in high-dimensional scenarios. This is particularly pronounced when computing high-order and high-dimensional derivatives in the physics-informed loss. Randomized Smoothing PINN (RS-PINN) introduces Gaussian noise for stochastic smoothing of the original neural net model, enabling the use of Monte Carlo methods for derivative approximation, which eliminates the need for costly automatic differentiation. Despite its computational efficiency, especially in the approximation of high-dimensional derivatives, RS-PINN introduces biases in both loss and gradients, negatively impacting convergence, especially when coupled with stochastic gradient descent (SGD) algorithms. We present a comprehensive analysis of biases in RS-PINN, attributing them to the nonlinearity of the Mean Squared Error (MSE) loss as well as the intrinsic nonlinearity of the PDE itself. We propose tailored bias correction techniques, delineating their application based on the order of PDE nonlinearity. The derivation of an unbiased RS-PINN allows for a detailed examination of its advantages and disadvantages compared to the biased version. Specifically, the biased version has a lower variance and runs faster than the unbiased version, but it is less accurate due to the bias. To optimize the bias-variance trade-off, we combine the two approaches in a hybrid method that balances the rapid convergence of the biased version with the high accuracy of the unbiased version. In addition to methodological contributions, we present an enhanced implementation of RS-PINN. Extensive experiments on diverse high-dimensional PDEs, including Fokker-Planck, Hamilton-Jacobi-Bellman (HJB), viscous Burgers’, Allen-Cahn, and Sine-Gordon equations, illustrate the bias-variance trade-off and highlight the effectiveness of the hybrid RS-PINN. Empirical guidelines are provided for selecting biased, unbiased, or hybrid versions, depending on the dimensionality and nonlinearity of the specific PDE problem.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2019

Cloud System Evolution in the Trades (CSET): Following the Evolution of Boundary Layer Cloud Systems with the NSF–NCAR GV

The Cloud System Evolution in the Trades (CSET) study was designed to describe and explain the evolution of the boundary layer aerosol, cloud, and thermodynamic structures along trajectories within the North Pacific trade winds. The study centered on seven round trips of the National Science Foundation–National Center for Atmospheric Research (NSF–NCAR) Gulfstream V (GV) between Sacramento, California, and Kona, Hawaii, between 7 July and 9 August 2015. The CSET observing strategy was to sample aerosol, cloud, and boundary layer properties upwind from the transition zone over the North Pacific and to resample these areas two days later. Global Forecast System forecast trajectories were used to plan the outbound flight to Hawaii with updated forecast trajectories setting the return flight plan two days later. Two key elements of the CSET observing system were the newly developed High-Performance Instrumented Airborne Platform for Environmental Research (HIAPER) Cloud Radar (HCR) and the high-spectral-resolution lidar (HSRL). Together they provided unprecedented characterizations of aerosol, cloud, and precipitation structures that were combined with in situ measurements of aerosol, cloud, precipitation, and turbulence properties. The cloud systems sampled included solid stratocumulus infused with smoke from Canadian wildfires, mesoscale cloud–precipitation complexes, and patches of shallow cumuli in very clean environments. Ultraclean layers observed frequently near the top of the boundary layer were often associated with shallow, optically thin, layered veil clouds. In conclusion, the extensive aerosol, cloud, drizzle, and boundary layer sampling made over open areas of the northeast Pacific along 2-day trajectories during CSET will be an invaluable resource for modeling studies of boundary layer cloud system evolution and its governing physical processes.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2025

Quantum Advantage in Trading: A Game-Theoretic Approach

Quantum games, like quantum algorithms, exploit quantum entanglement to establish strong correlations between strategic player actions. This paper introduces quantum game-theoretic models applied to trading and demonstrates their implementation on an ion-trap quantum computer. The results showcase a quantum advantage, previously known only theoretically, realized as higher-paying market Nash equilibria. This advantage could help uncover alpha in trading strategies, defined as excess returns compared to established benchmarks. These findings suggest that quantum computing could significantly influence the development of financial strategies.

Khan, Faisal Shah [Taqtics LLC, USA, Rethinc. Labs↗
DOE OSTI2023

Data for Climate vs Energy Security: Quantifying the Trade-offs of BECCS Deployment and Overcoming Opportunity Costs on Set-Aside Land

Bioenergy with carbon capture and storage (BECCS) sits at the nexus of the climate and energy security. We evaluated trade-offs between scenarios that support climate stabilization (negative emissions and net climate benefit) or energy security (ethanol production). Our spatially explicit model indicates that the foregone climate benefit from abandoned cropland (opportunity cost) increased carbon emissions per unit of energy produced by 14–36%, making geologic carbon capture and storage necessary to achieve negative emissions from any given energy crop. The toll of opportunity costs on the climate benefit of BECCS from set-aside land was offset through the spatial allocation of crops based on their individual biophysical constraints. Dedicated energy crops consistently outperformed mixed grasslands. We estimate that BECCS allocation to land enrolled in the Conservation Reserve Program (CRP) could capture up to 9 Tg C year–1 from the atmosphere, deliver up to 16 Tg CE year–1 in emissions savings, and meet up to 10% of the US energy statutory targets, but contributions varied substantially as the priority shifted from climate stabilization to energy provision. Our results indicate a significant potential to integrate energy security targets into sustainable pathways to climate stabilization but underpin the trade-offs of divergent policy-driven agendas.

Field Data↗
DOE OSTI2022

Performance trade-offs in reconfigurable networks for HPC

Designing efficient interconnects to support high-bandwidth and low-latency communication is critical toward realizing high performance computing (HPC) and data center (DC) systems in the exascale era. At extreme computing scales, providing the requisite bandwidth through overprovisioning becomes impractical. These challenges have motivated studies exploring reconfigurable network architectures that can adapt to traffic patterns at runtime using optical circuit switching. Despite the plethora of proposed architectures, surprisingly little is known about the relative performances and trade-offs among different reconfigurable network designs. We aim to bridge this gap by tackling two key issues in reconfigurable network design. First, we study how cost, power consumption, network performance, and scalability vary based on optical circuit switch (OCS) placement in the physical topology. Specifically, we consider two classes of reconfigurable architectures: one that places OCSs between top-of-rack (ToR) switches—ToR-reconfigurable networks (TRNs)—and one that places OCSs between pods of racks—pod-reconfigurable networks (PRNs). Second, we tackle the effects of reconfiguration frequency on network performance. Our results, based on network simulations driven by real HPC and DC workloads, show that while TRNs are optimized for low fan-out communication patterns, they are less suited for carrying high fan-out workloads. PRNs exhibit better overall trade-off, capable of performing comparably to a fully non-blocking fat tree for low fan-out workloads, and significantly outperform TRNs for high fan-out communication patterns.

Teh, Min Yee↗
DOE OSTI2013

International Trade of Wood Pellets [Brochure]

The production of wood pellets has increased dramatically in recent years due in large part to aggressive emissions policy in the European Union; the main markets that currently supply the European market are North America and Russia. However, current market circumstances and trade dynamics could change depending on the development of emerging markets, foreign exchange rates, and the evolution of carbon policies. This fact sheet outlines the existing and potential participants in the wood pellets market, along with historical data on production, trade, and prices.

09 BIOMASS FUELS↗
DOE OSTI2017

[Europa Lander] Decontamination Methods Trade Study

The following trade study was done to answer the following task from the Sandia JPL Collaboration for Europa Lander Statement of Work: Perform a trade study to assess the feasibility of other sterilization/decontamination methods for reducing forward biological contamination on S/C and assess their suitability for PP applications

42 ENGINEERING↗
DOE OSTI2022

Cost Efficient-by-Design Microreactors: Trade-offs between cost, technical, and regulatory factors

This research evaluates a cost reduction investigation through adoption of a functional containment approach on the microreactor system and structure. Trade-offs between microreactor system designs, fuels and reactor module sizes are evaluated based on performance-based and risk-informed design procedures. Cost savings are evaluated against trade-offs in the reliability of passive heat removal systems, reactivity control, and radioactive material containment. Results can inform the Microreactor Program and reactor designs on “sweet spots” for microreactors that are cost-efficient while meeting required safety limits.

21 SPECIFIC NUCLEAR REACTORS AND ASSOCIATED PLANTS↗