Engineering Papers⌕ Search

SEARCH · Engineering Papers

Results for “efficiency.”

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 919 records · Page 51
All sourcesNASA NTRSDOE OSTI
DOE OSTI2025

Trade‐Off Between Toxicity and Efficiency in Tin‐ versus Lead‐Based Halide Perovskites

Toxicity remains one of the major challenges that prevent Pb-based halide perovskites from widespread utilization. Ideally, non-toxic alternatives can be identified while still maintaining the superior power conversion efficiency of the Pb-based perovskite solar cells. Using the currently most promising candidate, the Sn-based halide perovskites, as an example, we show that a trade-off exists between toxicity and efficiency in the Sn- versus Pb-based halide perovskites. Indeed, the dominant nonradiative recombination center in the Sn-based halide perovskites differs from the one in its Pb-based counterparts, resulting in the nonradiative capture coefficient in CsSnI 3 being an order of magnitude higher than that in CsPbI 3 . We attribute this difference to the band alignment. Here, our results indicate that development of halide perovskites beyond the Pb and Sn bases is essential for efficient yet environmentally friendly perovskite solar cells.

36 MATERIALS SCIENCE↗
DOE OSTI2020

Xylose Assimilation for the Efficient Production of Biofuels and Chemicals by Engineered Saccharomyces cerevisiae

Microbial conversion of plant biomass into fuels and chemicals offers a practical solution to global concerns over limited natural resources, environmental pollution, and climate change. Pursuant to these goals, researchers have put tremendous efforts and resources towards engineering the yeast Saccharomyces cerevisiae to efficiently convert xylose, the second most abundant sugar in lignocellulosic biomass, into various fuels and chemicals. Here, we summarize recent advances in metabolic engineering of yeast to address bottlenecks on xylose assimilation, and to enable simultaneous co-utilization of xylose and other carbon sources in lignocellulosic hydrolysates. We also highlight distinct characteristics of xylose metabolism that can be harnessed to produce advanced biofuels and chemicals. Although many challenges remain, recent research investments have facilitated efficient fermentation of xylose and simultaneous co-consumption of xylose and glucose. In particular, understanding of xylose-induced metabolic rewiring in engineered yeast has encouraged use of xylose as a carbon source for producing various non-ethanol bioproducts. Furthermore, to boost the lignocellulosic biomass-based bioeconomy, much attention is expected to promote xylose-utilizing efficiency via reprogramming cellular regulatory networks, to attain robust co-fermentation of xylose and other cellulosic carbon sources under industrial conditions, and to exploit the advantageous traits of yeast xylose metabolism for producing diverse fuels and chemicals.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2021

FPGA-based HPC accelerators: An evaluation on performance and energy efficiency

Hardware specialization is a promising direction for the future of digital computing. Reconfigurable technologies enable hardware specialization with modest non-recurring engineering cost, but their performance and energy efficiency compared to state-of-the-art processor architectures remain an open question. In this article, we use FPGAs to evaluate the benefits of building specialized hardware for numerical kernels found in scientific applications. In order to properly evaluate performance, we not only compare Intel Arria 10 and Xilinx U280 performance against Intel Xeon, Intel Xeon Phi, and NVIDIA V100 GPUs, but we also extend the Empirical Roofline Toolkit (ERT) to FPGAs in order to assess our results in terms of the Roofline model. We show design optimization and tuning techniques for peak FPGA performance at reasonable hardware usage and power consumption. As FPGA peak performance is known to be far less than that of a GPU, we also benchmark the energy efficiency of each platform for the scientific kernels comparing against microbenchmark and technological limits. Results show that while FPGAs struggle to compete in absolute terms with GPUs on memory- and compute-intensive kernels, they require far less power and can deliver nearly the same energy efficiency.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2021

High foliar K and P resorption efficiencies in old-growth tropical forests growing on nutrient-poor soils

Resorption is the active withdrawal of nutrients before leaf abscission. This mechanism represents an important strategy to maintain efficient nutrient cycling; however, resorption is poorly characterized in old-growth tropical forests growing in nutrientpoor soils. We investigated nutrient resorption from leaves in 39 tree species in two tropical forests on the Guiana Shield, French Guiana, to investigate whether resorption efficiencies varied with soil nutrient, seasonality, and species traits. The stocks of P in leaves, litter, and soil were low at both sites, indicating potential P limitation of the forests. Accordingly, mean resorption efficiencies were higher for P (35.9%) and potassium (K; 44.6%) than for nitrogen (N; 10.3%). K resorption was higher in the wet (70.2%) than in the dry (41.7%) season. P resorption increased slightly with decreasing total soil P; and N and P resorptions were positively related to their foliar concentrations. We conclude that nutrient resorption is a key plant nutrition strategy in these old-growth tropical forests, that trees with high foliar nutrient concentration reabsorb more nutrient, and that nutrients resorption in leaves, except P, are quite decoupled from nutrients in the soil. Seasonality and biochemical limitation played a role in the resorption of nutrients in leaves, but species-specific requirements obscured general tendencies at stand and ecosystem level.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2021

Radial storage efficiency for CO 2 injection: Quantifying effectiveness of local flow control methods

Basin-scale geologic carbon sequestration will require hundreds of injection wells, each of which has costs related to property rights and regulatory requirements that correlate with the areal size of the carbon dioxide plume. These surface-footprint-related issues motivate maximizing storage efficiency radially away from each well. Radial storage efficiency (RSE) is defined here as the ratio of the volumetrically weighted carbon dioxide free-phase saturation within a given radius away from the injection well to the available pore space within that same radius. Maximizing RSE effectively minimizes the radial extent of the CO 2 plume. Optimizing RSE around individual wells requires local flow control injection strategies that can increase the uniformity of the filling of pore space around the well over the entire length of the perforated injection interval despite differences in local formation transmissivity. The goal of uniform filling of the storage reservoir starting from the well outward is to maximize carbon dioxide sweep and trapping locally outward from the well in all of the layers of the storage region before buoyancy forces predominate and drive carbon dioxide upward where it will spread laterally under lower-permeability layers. Example simulations of carbon dioxide injection into a layered storage system with and without local flow control are presented to show the advantage of uniformly filling all layers and how RSE can be used to quantify storage efficiency for the two different injection approaches. Published 2021. This article is a U.S. Government work and is in the public domain in the USA.

03 NATURAL GAS↗
DOE OSTI2022

Benzotriazole‐Based Nonfused Ring Acceptors for Efficient and Thermally Stable Organic Solar Cells

Abstract Nonfused ring acceptors (NFRAs) have attracted significant attention for nonfullerene organic solar cells (OSCs) owing to their chemical tunability and facile synthesis. In this study, a benzotriazole‐based NFRA with chlorinated end groups (Triazole‐4Cl) is developed to realize highly efficient and thermally stable NFRA‐based OSCs; an analogous NFRA with nonchlorinated end groups (Triazole‐H) is synthesized for comparison. Triazole‐4Cl film exhibits the high‐order packing structure and the near‐infrared absorption capability, which are advantageous in charge transport and light harvesting of the resulting OSCs. In particular, the strong crystalline behavior of Triazole‐4Cl results in enhanced self‐aggregation, leading to high charge carrier mobility. Owing to these properties, a PBDB‐T (polymer donor):Triazole‐4Cl OSC demonstrates a high short‐circuit current, fill factor, and power conversion efficiency (PCE = 10.46%), outperforming a PBDB‐T:Triazole‐H OSC (PCE = 7.65%). In addition, the thermal stability of a PBDB‐T:Triazole‐4Cl OSC at an elevated temperature of 120 °C exceeds that of a PBDB‐T:Triazole‐H OSC. This is mainly attributed to the significantly higher cold crystallization temperature of Triazole‐4Cl (205.9 °C). This work provides useful guidelines for the design of NFRAs to achieve efficient and thermally stable NFRA‐based OSCs.

Polymer Science↗
DOE OSTI2021

A minimally invasive, efficient method for propagation of full-field uncertainty in solid dynamics

In this work, we present a minimally invasive method for forward propagation of material property uncertainty to full-field quantities of interest in solid dynamics. Full-field uncertainty quantification enables the design of complex systems where quantities of interest, such as failure points, are not known a priori. The method, motivated by the well-known probability density function (PDF) propagation method of turbulence modeling, uses an ensemble of solutions to provide the joint PDF of desired quantities at every point in the domain. A small subset of the ensemble is computed exactly, and the remainder of the samples are computed with approximation of the evolution equations based on those exact solutions. Although the proposed method has commonalities with traditional interpolatory stochastic collocation methods applied directly to quantities of interest, it is distinct and exploits the parameter dependence and smoothness of the driving term of the evolution equations. The implementation is model independent, storage and communication efficient, and straightforward. We demonstrate its efficiency, accuracy, scaling with dimension of the parameter space, and convergence in distribution with two problems: a quasi-one-dimensional bar impact, and a two material notched plate impact. For the bar impact problem, we provide an analytical solution to PDF of the solution fields for method validation. With the notched plate problem, we also demonstrate good parallel efficiency and scaling of the method.

42 ENGINEERING↗
DOE OSTI2026

Hybrid Quantum–Classical Graph Transformers for Efficient Sentiment Analysis

Quantum Machine Learning (QML) offers a promising paradigm that leverages quantum computing principles to develop efficient and expressive models for learning from complex and structured data. Recent advances in natural language processing (NLP) and artificial intelligence (AI) have demonstrated capabilities in understanding, generating, and reasoning over linguistic and multimodal information. In this work, we present the Quantum Graph Transformer (QGT), a hybrid quantum–classical architecture that extends graph transformer capabilities through quantum self-attention. The QGT models variable-length sentences as token graphs, where both the embedding encoding and the self-attention mechanisms are implemented using parameterized quantum circuits (PQCs), enabling efficient contextual learning with significantly fewer trainable parameters. We train QGT using both fully connected and 𝑘 -nearest-neighbor graph structures and evaluate it on five benchmark sentiment-classification datasets. Experimental results show that QGT consistently achieves higher or comparable accuracy to existing quantum NLP models and outperforms a Classical Graph Transformer (CGT) baseline with identical architecture, achieving 29.4 × fewer parameters while requiring 3–5 × fewer samples to reach comparable performance. These findings highlight the potential of graph-based quantum models as scalable and data-efficient architectures for natural language understanding.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2023

Improving Cooperative Interactions Between Halogenated Aromatic Additives and Aromatic Side Chain Acceptors for Realizing 19.22% Efficiency Polymer Solar Cells

Processing additive plays an important role in the standard operation procedures for fabricating top performing polymer solar cells (PSCs) through efficient interactions with key photovoltaic materials. However, improving interaction study of acceptor materials to high performance halogenated aromatic additives such as diiodobenzene (DIB) is a widely neglected route for molecular engineering toward more efficient device performances. Here, in this work, two novel Y-type acceptor molecules of BTP-TT and BTP-TTS with different aromatic side chains on the outer positions are designed and synthesized. The resulting aromatic side chains significantly enhanced the interactions between the acceptor molecules and DIB through an arene/halogenated arene interaction, which improved the crystallinity of the acceptor molecules and induced a polymorph with better photovoltaic performances. Thus, high power conversion efficiencies (PCEs) of 18.04% and 19.22% are achieved in binary and ternary blend devices using BTP-TTS as acceptor and DIB as additive. Aromatic side chain engineering for improving additive interactions is proved to be an effective strategy for achieving much higher performance photovoltaic materials and devices.

36 MATERIALS SCIENCE↗
DOE OSTI2020

High‐Efficiency Organic Solar Cells with Wide Toleration of Active Layer Thickness

Industrial printing production of organic solar cells (OSCs) requires high power conversion efficiency (PCE) with wide toleration of active layer thickness. Herein, high‐efficiency thick‐film OSCs are demonstrated using a polymer fibril network strategy (FNS), which involves a donor polymer (PT2) that can self‐assemble into fibril nanostructure, and two nonfullerene acceptors with different crystalline properties. The fibril network can form a high‐speed hole transport channel and the addition of IDIC as a third component in active layers can improve the electron transport. As a result, the OSCs show high PCEs of ≈12% with wide toleration of active layer thickness (from 100 to 500 nm). The results indicate that FNS is a promising approach for the fabrication of highly efficient thick‐film PSCs, which can facilitate the commercialization of OSCs.

Weng, Kangkang↗
DOE OSTI2022

Scalable Two–Step Production of High–Efficiency Perovskite Solar Cells and Modules

Perovskite solar cells (PSCs) fabricated in laboratories have already achieved power conversion efficiency (PCE) comparable to market-dominant crystalline silicon solar cells. However, this promising photovoltaic technology suffers from severe loss of PCE during scaling up, limiting its progress toward commercialization. One critical question is to develop scalable, low-cost, high throughput, and well-controlled production methods to deposit high-quality perovskite films. Among various approaches, two-step sequential deposition methods have their unique advantages but have been long overlooked. This review provides an overview of two-step methods for fabricating efficient and stable perovskite solar modules (PSMs). We first discuss the mechanisms of two-step perovskite conversion and advanced engineering approaches to modulate the perovskite formation process. We survey the progress of efficient PSCs prepared by different two-step methods and compare the advantages and disadvantages of each method for scalable production of PSMs. Particularly, we highlight that the vapor-based two-step methods are promising for high-throughput in-line production of PSMs. Finally, we provide insights into the challenges and outlook of the industrialization of two-step processes for producing PSMs.

14 SOLAR ENERGY↗
DOE OSTI2023

Layer-by-Layer-Processed Organic Solar Cells with 18.02% Efficiency Enabled by Regulating the Aggregation of Bottom Polymers

The fabrication of organic solar cells (OSCs) by a layer-by-layer (LBL) method has attracted growing attention in recent years. As already known, the pre-aggregates of conjugated polymers in solution have a profound impact on their microstructure morphology in films. Herein, by simply controlling the solution temperature and annealing processes, the pre-aggregation behavior of D18 polymer in solution can be fine-tuned and the microstructure of D18 bottom layer is well manipulated. The optimized D18 bottom layer can effectively regulate L8-BO upper-layer-forming suitable networks for efficient charge transportation. In addition, a vertical phase separation with a special D/D:A/A structure (P-i-N-type component distribution) is also formed. As a result, compared to the 16.43% power conversion efficiency (PCE) of the bulk heterojunction devices, such control enables bilayer OSC devices based on the polymer D18 and L8-BO to deliver an enhanced PCE of 18.02% with simultaneously improved short-circuit current density, open-circuit voltage, and fill factor. Importantly, it is also demonstrated in these results that the LBL deposition process utilizing the pre-aggregation of polymer and its fiber-network-forming ability is a very promising approach to improve charge dynamics, suppress carrier recombination, and fabricate highly efficient OSCs.

14 SOLAR ENERGY↗
DOE OSTI2023

Seed‐Assisted Growth for Scalable and Efficient Perovskite Solar Modules

Perovskite solar modules (PSMs) have shown remarkable photovoltaic potentials, but they still suffer from large power conversion efficiency (PCE) loss on scale‐up and instability due to inferior uniformity and crystallization over large areas. Herein, the scalable production of efficient and stable PSMs using a suite of all‐scalable fabrication methods featuring a two‐step blade/dip‐coating approach to deposit the perovskite absorber layer is demonstrated. Rubidium chloride is introduced to embed (PbI 2 ) 2 RbCl complex seeds in the first‐deposited PbI 2 precursor, which assists in uniform crystallization of the perovskite layer with high crystallinity and reduced defect density over large areas. Following the optimization of RbCl additives, a champion PSM with 17.9% PCE on a 7.6 × 7.6 cm 2 substrate with a 37 cm 2 aperture area is achieved. Moreover, the RbCl‐incorporated PSMs demonstrate excellent reproducibility and stability under continuous 1 sun illumination. This work shows that the two‐step blade/dip coating is a promising method for producing high‐efficiency and stable PSMs on an industrially relevant scale.

Energy & Fuels↗
DOE OSTI2025

Efficient Quantum Gibbs Samplers with Kubo–Martin–Schwinger Detailed Balance Condition

Lindblad dynamics and other open-system dynamics provide a promising path towards efficient Gibbs sampling on quantum computers. In these proposals, the Lindbladian is obtained via an algorithmic construction akin to designing an artificial thermostat in classical Monte Carlo or molecular dynamics methods, rather than being treated as an approximation to weakly coupled system-bath unitary dynamics. Recently, Chen, Kastoryano, and Gilyén (arXiv:2311.09207) introduced the first efficiently implementable Lindbladian satisfying the Kubo–Martin–Schwinger (KMS) detailed balance condition, which ensures that the Gibbs state is a fixed point of the dynamics and is applicable to non-commuting Hamiltonians. This Gibbs sampler uses a continuously parameterized set of jump operators, and the energy resolution required for implementing each jump operator depends only logarithmically on the precision and the mixing time. In this work, we build upon the structural characterization of KMS detailed balanced Lindbladians by Fagnola and Umanità, and develop a family of efficient quantum Gibbs samplers using a finite set of jump operators (the number can be as few as one), akin to the classical Markov chain-based sampling algorithm. Compared to the existing works, our quantum Gibbs samplers have a comparable quantum simulation cost but with greater design flexibility and a much simpler implementation and error analysis. Moreover, it encompasses the construction of Chen, Kastoryano, and Gilyén as a special instance.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2021

Efficient Trainability of Linear Optical Modules in Quantum Optical Neural Networks

The existence of “barren plateau landscapes” for generic discrete-variable quantum neural networks, which obstructs efficient gradient-based optimization of cost functions defined by global measurements, would be surprising in the case of generic linear optical modules in quantum optical neural networks due to the tunability of the intensity of continuous variable states and the relevant unitary group having exponentially smaller dimension. We demonstrate that coherent light in m modes can be generically compiled efficiently if the total intensity scales sublinearly with m, and extend this result to cost functions based on homodyne, heterodyne, or photon detection measurement statistics, and to noisy cost functions in the presence of attenuation. Here, we further demonstrate efficient trainability of m mode linear optical quantum circuits for variational mean field energy estimation of positive quadratic Hamiltonians for input states that do not have energy exponentially vanishing with m.

47 OTHER INSTRUMENTATION↗
DOE OSTI2023

Efficient learning of power grid voltage control strategies via model-based deep reinforcement learning

Here this article proposes a model-based deep reinforcement learning (DRL) method to design emergency control strategies for short-term voltage stability problems in power systems. Recent advances show promising results for model-free DRL-based methods in power systems control problems. But in power systems applications, these model-free methods have certain issues related to training time (clock time) and sample efficiency; both are critical for making state-of-the-art DRL algorithms practically applicable. DRL-agent learns an optimal policy via a trial-and-error method while interacting with the real-world environment. It is also desirable to minimize the direct interaction of the DRL agent with the real-world power grid due to its safety-critical nature. Additionally, the state-of-the-art DRL-based policies are mostly trained using a physics-based grid simulator where dynamic simulation is computationally intensive, lowering the training efficiency. We propose a novel model-based DRL framework where a deep neural network (DNN)-based dynamic surrogate model (SM), instead of a real-world power grid or physics-based simulation, is utilized within the policy learning framework, making the process faster and more sample efficient. However, having stable training in model-based DRL is challenging because of the complex system dynamics of large-scale power systems. We addressed these issues by incorporating imitation learning to have a warm start in policy learning, reward-shaping, and multi-step loss in surrogate model training. Finally, we achieved 97.5% reduction in samples and 87.7% reduction in training time for an application to the IEEE 300-bus test system.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2026

Consequential improvement acquisition function for efficient multi-fidelity Bayesian optimization

Abstract Surrogate-based Bayesian optimization has been widely applied in design optimization to increase sampling efficiency. However, the cost for each evaluation of the objective function can still be very high when physical experiments or large-scale simulations are involved. Multi-fidelity Bayesian optimization is the new approach to further improve the sampling efficiency by reducing the number of expensive samples at the highest fidelity level and supplementing them with less expensive ones at low-fidelity levels. In this paper, a new consequential improvement (CI) acquisition function is proposed to allow for the simultaneous selection of the solution and the fidelity level in problems with a known hierarchy of fidelity levels. The new CI acquisition function incorporates the consequential effectiveness of objective improvement with the considerations of cost, accuracy, and validity differences between high- and low-fidelity samples in engineering practice. The new method of multi-fidelity Bayesian optimization based on the CI is demonstrated with several analytical and simulation-based design examples. In the simulation-based design optimization example, the results show that the CI acquisition function has a decisive advantage in the sampling efficiency over the other methods of multi-fidelity Bayesian optimization with simultaneous selection. The results indicate that the proposed method is particularly advantageous in solving high-dimensional problems and when large cost ratios between high- and low-fidelity evaluations exist and high-fidelity validation is mandatory. Furthermore, the method robustly avoids the prevalent issue of over sampling at low-fidelity levels.

Aydogdu, Ibrahim [Georgia Institute of Technology,↗
DOE OSTI2024

Pathway to Decarbonization Through Industrial Energy Efficiency: Micro and Macro Perspectives from Compressed Air Usage

Abstract Energy audits directly provided the industrial sector with reduced energy costs and avoided emissions. Still, they also lead to far-reaching indirect and induced local, regional, and national benefits. This paper aims to present the techno-economic-environmental analysis to achieve decarbonization through implementing industrial energy efficiency at micro and macro levels. An integrated techno-economic-environmental methodology is developed. Case studies of micro-level carbon reduction efforts through industrial energy efficiency technologies are presented. The broader macroeconomic and environmental effects of technology on society are analyzed using data from 206 energy audits of industrial compressed air systems conducted over 13 years. The impacts show that energy-efficient improvements lead to direct cost savings for manufacturers, boost economic activity across sectors, and affect carbon dioxide emissions both short-term and long-term in the region. Given their extensive benefits, energy audits significantly influence policymaking. We devised a methodology to link micro-level energy audit data with macroeconomic and environmental analyses to quantify these cascading benefits. The economic scenario analysis shows that $228 M has been saved from direct industrial energy savings from implementing all compressed air recommendations in the studied periods and the region. In addition, the investment made through manufacturers would create 2,025 jobs and $383 M annually, cascading regional economic impacts. The environmental analysis shows that the regional manufacturers have directly avoided about 2.8 M metric tons of carbon dioxide emissions.

Engineering↗