SEARCH · Engineering Papers

Results for “efficiency maximization”

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.

SourceResearch connectionFrom yearThrough year

Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.

At least 1,405 records · Page 78

All sources NASA NTRS DOE OSTI

DOE OSTI2015

Theoretical limits of thermoelectric figure of merit in n -type TiO 2 polymorphs

We calculate the conduction-band structures and n-type thermoelectric transport properties for the Ti O2 polymorphs rutile, anatase, and brookite from first principles within the constant-relaxation-time approximation. Although the Seebeck coefficient is nearly isotropic in all polymorphs, the power factor is anisotropic and takes its largest values along [100] in rutile and anatase and along [010] in brookite. We also identify the free-carrier concentrations and temperatures that maximize the power factor. Furthermore, our results for the theoretical upper bounds of the figure of merit at high temperature show that optimized rutile exhibits thermoelectric conversion efficiency that is superior to anatase and brookite and can reach values desirable for waste-heat recovery applications.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗

DOE OSTI2017

Optimal Experimental Design Using a Consistent Bayesian Approach

We consider the utilization of a computational model to guide the optimal acquisition of experimental data to inform the stochastic description of model input parameters. Our formulation is based on the recently developed consistent Bayesian approach for solving stochastic inverse problems, which seeks a posterior probability density that is consistent with the model and the data in the sense that the push-forward of the posterior (through the computational model) matches the observed density on the observations almost everywhere. Given a set of potential observations, our optimal experimental design (OED) seeks the observation, or set of observations, that maximizes the expected information gain from the prior probability density on the model parameters. We discuss the characterization of the space of observed densities and a computationally efficient approach for rescaling observed densities to satisfy the fundamental assumptions of the consistent Bayesian approach. Finally, numerical results are presented to compare our approach with existing OED methodologies using the classical/statistical Bayesian approach and to demonstrate our OED on a set of representative partial differential equations (PDE)-based models.

42 ENGINEERING↗

DOE OSTI2014

SU-D-18A-01: Tumor Motion Tracking with a Regional Deformable Registration Model for Four Dimensional Radiation Treatment of Lung Cancer

Purpose: To develop a tumor motion model from four-dimensional computed tomography (4DCT) of thoracic patients and demonstrate its impact on 4D radiation therapy simulation. Methods: A regional deformable image registration algorithm was introduced to extract tumor motion out of patient's breathing cycle. The gross target volume (GTV) was manually delineated on a selected phase of 4DCT and a subregion with 10mm margin supplemented to the GTV was created on the Eclipse treatment planning system (Varian Medical Systems, Palo Alto, CA). Together with 4DCT the structures were exported into an inhouse research platform. A free form B-Spline deformable registration was carried out to map the subregion to other respiratory phases. The displacement vector fields were employed to propagate GTV contours with which the center of mass (CoM) of the GTV was computed for each breathing phase of 4DCT. The resultant GTV motion and its volumetric shape are utilized to facilitate 4D treatment planning. Five lung cancer patients undergoing stereotactic body radiation therapy were enrolled and their 4DCT sets were included in the study. Results: Application of the algorithm to five thoracic patients indicates that clinically satisfactory outcomes were achievable with a spatial accuracy better than 2mm for GTV contour propagation between adjacent phases, and 3mm between opposite phases. The GTV CoM was found to be in the range of 2.0mm through 2.5cm, depending upon the tumor location. Compared to the traditional whole image based registration, the computation of the regional model was found to be an order of magnitude more efficient. Conclusion: A regional deformable registration model was implemented to extract tumor motion. It will have widespread application in 4D radiation treatment planning in the future to maximally utilize the available spatial-tempo information.

60 APPLIED LIFE SCIENCES↗

DOE OSTI2024

The Case for Co-Designing Model Architectures with Hardware

While GPUs are responsible for training the vast majority of state-of-the-art deep learning models, the implications of their architecture are often overlooked when designing new deep learning (DL) models. As a consequence, modifying a DL model to be more amenable to the target hardware can significantly improve the runtime performance of DL training and inference. In this paper, we provide a set of guidelines for users to maximize the runtime performance of their transformer models. These guidelines have been created by carefully considering the impact of various model hyperparameters controlling model shape on the efficiency of the underlying computation kernels executed on the GPU. We find the throughput of models with “efficient” model shapes is up to 39% higher while preserving accuracy compared to models with a similar number of parameters but with unoptimized shapes.

Yin, Junqi↗

DOE OSTI2018

Gamma-ray raster imaging with robotic data collection. FY18 report

This project has developed an alternative imaging capability for measuring radioactive materials in situ. This approach uses a robotic-mounted gamma-ray detector which can move around an area of interest, sampling the space at a high frequency. By rastering across the area, an image can be created with no collimation and a high efficiency. A regression model was developed to map out material distribution as the robot moves through the environment. An informative path planning algorithm was also developed to maximize the information collected throughout the movement path. The use of a robotic mount allows data collection for long periods of time unattended, and it will also eliminate uncertainties in positioning typically introduced by personnel. This approach will be particularly relevant for gloveboxes, shielded cells, or process piping which may have complex, non-uniform distributions of material.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗

DOE OSTI2023

Phosphate and Fluoride Processing Options for Hanford Sludge

The Direct Feed High-Level Waste (DFHLW) strategy represents an alternative flowsheet to bypass the Hanford Waste Treatment and Immobilization Plant (WTP) Pretreatment Facility. Key processing functions previously captured during baseline pretreatment operations include leaching and washing prior to solids concentration and should be considered in the new DFHLW flowsheet to maximize waste feed loading, minimize high-level waste (HLW) volume, and mitigate corrosion challenges associated with vitrification of high phosphate and fluoride concentrations. The effectiveness and efficiency of sludge washing can have a substantial impact on DST space and mission duration. Two target species that benefit significantly from washing are phosphate and fluoride. Commonly present in many high-level wastes at Hanford, phosphate and fluoride are found predominately in the form of salt precipitates and can have adverse and detrimental effects on glass waste loading. Additionally, fluoride produces melter off-gas that creates corrosion risks in the off-gas system piping. Determining the solubilities of fluoride and phosphate salts, as well as the dissolution kinetics under potential HLW processing conditions will help to mitigate future operational risks and mature flowsheet technical bases.

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗

DOE OSTI2023

The role of photorespiration in plant immunity

To defend themselves in the face of biotic stresses, plants employ a sophisticated immune system that requires the coordination of other biological and metabolic pathways. Photorespiration, a byproduct pathway of oxygenic photosynthesis that spans multiple cellular compartments and links primary metabolisms, plays important roles in defense responses. Hydrogen peroxide, whose homeostasis is strongly impacted by photorespiration, is a crucial signaling molecule in plant immunity. Photorespiratory metabolites, interaction between photorespiration and defense hormone biosynthesis, and other mechanisms, are also implicated. An improved understanding of the relationship between plant immunity and photorespiration may provide a much-needed knowledge basis for crop engineering to maximize photosynthesis without negative tradeoffs in plant immunity, especially because the photorespiratory pathway has become a major target for genetic engineering with the goal to increase photosynthetic efficiency.

59 BASIC BIOLOGICAL SCIENCES↗

NASA NTRS1992

Improved Depiction Of Computed Unsteady Flows

PLOT4D and STREAKER programs increase ease of production and utility of animated depictions of unsteady flows simulated numerically by advanced computational fluid dynamics. Programs developed to maximize information displayed while minimizing amount stored. PLOT4D, an extended version of PLOT3D, used to depict steady three-dimensional flows, incorporates features making it more computationally efficient than to visualize unsteady flow as succession of steady flows, animating frame by frame. PLOT4D postprocesses numerical output of fluid-dynamical computation into maps of flow quantities on two-coordinate surfaces depicting cut through flow field or conform to body surface. STREAKER generates streaklines, close approximations of patterns traced by streams of dye or smoke released into simulated flows. Streaklines defined via spatial succession of endpoints of pathlines of massless particles released into flow. STREAKER computes endpoints by fourth-order Runge-Kutta integration of velocity vectors.

Smith, Merritt H.↗

NASA NTRS2000

Robustness via Run-Time Adaptation of Contingent Plans

In this paper, we discuss our approach to making the behavior of planetary rovers more robust for the purpose of increased productivity. Due to the inherent uncertainty in rover exploration, the traditional approach to rover control is conservative, limiting the autonomous operation of the rover and sacrificing performance for safety. Our objective is to increase the science productivity possible within a single uplink by allowing the rover's behavior to be specified with flexible, contingent plans and by employing dynamic plan adaptation during execution. We have deployed a system exhibiting flexible, contingent execution; this paper concentrates on our ongoing efforts on plan adaptation, Plans can be revised in two ways: plan steps may be deleted, with execution continuing with the plan suffix; and the current plan may be merged with an "alternate plan" from an on-board library. The plan revision action is chosen to maximize the expected utility of the plan. Plan merging and action deletion constitute a more conservative general-purpose planning system; in return, our approach is more efficient and more easily verified, two important criteria for deployed rovers.

Bresina, John L.↗

NASA NTRS2002

The exercise and environmental physiology of extravehicular activity

Extravehicular activity (EVA), i.e., exercise performed under unique environmental conditions, is indispensable for supporting daily living in weightlessness and for further space exploration. From 1965-1996 an average of 20 h x yr(-1) were spent performing EVA. International Space Station (ISS) assembly will require 135 h x yr(-1) of EVA, and 138 h x yr(-1) is planned for post-construction maintenance. The extravehicular mobility unit (EMU), used to protect astronauts during EVA, has a decreased pressure of 4.3 psi that could increase astronauts' risk of decompression sickness (DCS). Exercise in and repeated exposure to this hypobaria may increase the incidence of DCS, although weightlessness may attenuate this risk. Exercise thermoregulation within the EMU is poorly understood; the liquid cooling garment (LCG), worn next to the skin and designed to handle thermal stress, is manually controlled. Astronauts may become dehydrated (by up to 2.6% of body weight) during a 5-h EVA, further exacerbating the thermoregulatory challenge. The EVA is performed mainly with upper body muscles; but astronauts usually exercise at only 26-32% of their upper body maximal oxygen uptake (VO2max). For a given ground-based work task in air (as opposed to water), the submaximal VO2 is greater while VO2max and metabolic efficiency are lower during ground-based arm exercise as compared with leg exercise, and cardiovascular responses to exercise and training are also different for arms and legs. Preflight testing and training, whether conducted in air or water, must account for these differences if ground-based data are extrapolated for flight requirements. Astronauts experience deconditioning during microgravity resulting in a 10-20% loss in arm strength, a 20-30% loss in thigh strength, and decreased lower-body aerobic exercise capacity. Data from ground-based simulations of weightlessness such as bed rest induce a 6-8% decrease in upper-body strength, a 10-16% loss in thigh extensor strength, and a 15-20% decrease in lower-body aerobic exercise capacity. Changes in EVA support systems and training based on a greater understanding of the physiological aspects of exercise in the EVA environment will help to insure the health, safety, and efficiency of working astronauts.

Review↗

DOE OSTI2019

Efficient Distributed Optimization of Wind Farms Using Proximal Primal-Dual Algorithms: Preprint

This paper presents a distributed approach to perform real-time optimization of large wind farms. Wind turbines in a wind farm typically operate individually to maximize their own performance regardless of the impact of aerodynamic interactions on neighboring turbines. This paper optimizes the overall power produced by a wind farm by formulating and solving a nonconvex optimization problem where the yaw angles are optimized to allow some turbines to operate in misaligned conditions and shape the aerodynamic interactions in a favorable way. The solution of the nonconvex smooth problem is tackled using a proximal primal-dual gradient method, which provably identifies a first-order stationary solution in a global sublinear manner. By adding auxiliary optimization variables for every pair of turbines that are coupled aerodynamically and properly adding consensus constraints into the underlying problem, a distributed algorithm with turbine-to-turbine message passing is obtained; this allows for turbines to be optimized in parallel using local information rather than information from the whole wind farm. This algorithm is computationally light, as it involves closed-form updates. This approach is demonstrated on a large wind farm with 60 turbines. The results indicate that similar performance can be achieved as with finite-difference gradient-based optimization at a fraction of the computational time and thus approach real-time control/optimization.

17 WIND ENERGY↗

DOE OSTI2022

Highly efficient small molecule multi-junction organic photovoltaic cells

A highly efficient multi junction photovoltaic device, such as a two, three, or four junction device, is disclosed. The multi-junction device may include a first subcell comprising a first photoactive region and a second subcell comprising a second photoactive region. The first and second photoactive regions are designed to minimize spectral overlap and maximize photocurrent across a broad absorption spectra, such as wavelengths ranging from 400 nm to 900 nm. The device may further include an inter-connecting layer, disposed between the first subcell and the second subcell, that is at least substantially transparent. By introducing a transparent interconnecting layer, a dual element (tandem) cell achieves a power conversion efficiency of 10.0±0.5%. By adding an additional (3rd) sub-cell that absorbs at the second order optical interference maximum within the stack. The triple junction cell significantly improves the quantum efficiency at shorter wavelengths, achieving a power conversion efficiency of 11.1±0.5%. Adding additional sub-cells has been shown to increase power conversion efficiency above 12%.

Che, Xiaozhou↗

DOE OSTI2020

Generation of reactive species in water film dielectric barrier discharges sustained in argon, helium, air, oxygen and nitrogen

Activation of liquids with atmospheric pressure plasmas is being investigated for environmental and biomedical applications. When activating the liquid using gas plasma produced species (as opposed to plasmas sustained in the liquid), a rate limiting step is transport of these species into the liquid. To first order, the efficiency of activating the liquid is improved by increasing the ratio of the surface area of the water in contact with the plasma compared to its volume—often called the surface-to-volume ratio (SVR). Maximizing the SVR then motivates the plasma treatment of thin films of liquids. In this paper, results are discussed from a computational investigation using a global model of atmospheric pressure plasma treatment of thin water films by a dielectric barrier discharge (DBD) sustained in different gases (Ar, He, air, N 2 , O 2 ). The densities of reactive species in the plasma activated water (PAW) are evaluated. The residence time of the water in contact with the plasma is increased by recirculating the PAW in plasma reactor. Longer lived species such as H 2 O 2aq and NO 3 - aq accumulate over time (aq denotes an aqueous species). DBDs sustained in Ar and He are the most efficient at producing H 2 O 2aq , DBDs sustained in argon produces the largest density of NO 3 - aq with the lowest pH, and discharges sustained in O 2 and air produce the highest densities of O 3aq . Finally, comparisons to experiments by others show agreement in the trends in densities in PAW including O 3aq , OH aq , H 2 O 2aq and NO 3 - aq , and highlight the importance of controlling desolvation of species from the activated water.

42 ENGINEERING↗

DOE OSTI2022

Multi-Pressure Rail System Design with Variable Pressure Control Strategy

This paper presents the design and the control method of a Multiple Pressure Rail system (MPR) with a variable pressure control logic that allows minimization of throttling loss, therefore, increasing the overall fluid power actuation efficiency. An MPR system allows controlling hydraulic actuators with a pressure control logic, as opposed to traditional flow control logics which are well established in mobile applications. A proper setting of the number of pressure rails and the pressure level in each rail permits optimizing the system efficiency of an MPR solution. MPR systems have been recently proposed with different purposes, but seldom adopt variable pressure control strategies to manage the pressure variations in the rails. Moreover, the related past work attempts to maximize the energy recovery during overrunning load conditions. This paper, instead, addresses the case of an MPR system that handles a multitude of hydraulic functions without significant overrunning loads, as it occurs in many mobile applications such as in agriculture. The paper first presents the design alternatives considered for the MPR system, including the supply, the control valves, and the controller. Second, a dedicated test rig is developed to support experimental activities and validations of a lumped parameter model developed within this research to develop the MPR control strategy. The results describe the operating features of the proposed MPR solution. By considering the case of a traditional LS system, the paper results also show how system efficiency can be highly improved, with energy savings in the order of 49%

24 POWER TRANSMISSION AND DISTRIBUTION↗

DOE OSTI2025

ExtremeMETA: High-speed Lightweight Image Segmentation Model by Remodeling Multi-channel Metamaterial Imagers

Deep neural networks (DNNs) have heavily relied on traditional computational units, such as CPUs and GPUs. However, this conventional approach brings significant computational burden, latency issues, and high power consumption, limiting their effectiveness. This has sparked the need for lightweight networks such as ExtremeC3Net. Meanwhile, there have been notable advancements in optical computational units, particularly with metamaterials, offering the exciting prospect of energy-efficient neural networks operating at the speed of light. Yet, the digital design of metamaterial neural networks (MNNs) faces precision, noise, and bandwidth challenges, limiting their application to intuitive tasks and low-resolution images. In this study, we proposed a large kernel lightweight segmentation model, ExtremeMETA. Based on ExtremeC3Net, our proposed model, ExtremeMETA maximized the ability of the first convolution layer by exploring a larger convolution kernel and multiple processing paths. With the large kernel convolution model, we extended the optic neural network application boundary to the segmentation task. To further lighten the computation burden of the digital processing part, a set of model compression methods was applied to improve model efficiency in the inference stage. The experimental results on three publicly available datasets demonstrated that the optimized efficient design improved segmentation performance from 92.45 to 95.97 on mIoU while reducing computational FLOPs from 461.07 MMacs to 166.03 MMacs. The large kernel lightweight model ExtremeMETA showcased the hybrid design’s ability on complex tasks.

large convolution kernel↗

DOE OSTI2017

Implicit sampling combined with reduced order modeling for the inversion of vadose zone hydrological data

Bayesian inverse modeling techniques are computationally expensive because many forward simulations are needed when sampling the posterior distribution of the parameters. Here, in this paper, we combine the implicit sampling method and generalized polynomial chaos expansion (gPCE) to significantly reduce the computational cost of performing Bayesian inverse modeling. There are three steps in this approach: (1) find the maximizer of the likelihood function using deterministic approaches; (2) construct a gPCE-based surrogate model using the results from a limited number of forward simulations; and (3) efficiently sample the posterior distribution of the parameters using implicit sampling method. The cost of constructing the gPCE-based surrogate model is further decreased by using sparse Bayesian learning to reduce the number of gPCE coefficients that have to be determined. We demonstrate the approach for a synthetic ponded infiltration experiment simulated with TOUGH2. The surrogate model is highly accurate with mean relative error that is < 0.035% in predicting saturation and < 0.25% in predicting the likelihood function. The posterior distribution of the parameters obtained using our proposed technique is nearly indistinguishable from the results obtained from either an implicit sampling method or a Markov chain Monte Carlo method utilizing the full mode.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2025

A Computational Framework to design 3D stiffness gradient acoustic metamaterials for impedance matching

Acoustic waves play a crucial role in various applications, including medical imaging, non-destructive testing, and sonar systems. One of the significant challenges in these applications is impedance matching, which is essential for minimizing reflections and maximizing the transfer of acoustic energy between different media. Acoustic metamaterials offer a promising solution to this challenge. In addition to impedance control, gradient stiffness can enhance structural efficiency and enable spatial control of wave propagation, making it a valuable feature in acoustic metamaterial design. In this pa- per, we present our developed computational method to design 3D stiffness gradient acoustic metamaterials for impedance matching. The key steps in our approach include generating initial designs using a periodic covariance function to provide unit cells that are both periodic on the boundaries and randomly formed inside the unit cell. Furthermore, we integrated manufacturing constraints into the design process, ensuring that the structures are interconnected for fabrication. We propose two computational optimization algorithms: GenUnit, based on a non-dominated sorting genetic algorithm (NSGA-II), and MLMatch, which leverages differentiable machine learning. The two approaches are not separate contributions but complementary com- ponents of a unified framework. GenUnit requires no training data and directly interfaces with physics-based simulations, making it highly accurate but slower for large-scale exploration. In contrast, MLMatch is data-hungry during training but, once trained, enables near-instantaneous inference and broad design-space coverage. Together, they form a hybrid strategy: ML- Match rapidly explores the global design space, and GenUnit provides local refinement with high-fidelity accuracy. This balance between training cost, inference time, and precision is the motivation for including both methods in the same study. We applied this dual-algorithm framework to generate two metallic-based metamaterial designs that match the acoustic impedance of water while exhibiting a controlled gradient in stiffness (from stiff to soft). The stiffness gradient is particularly advantageous in applications where one side of the structure must interface with soft or sensitive surfaces, such as human tissue or delicate components. Here, this work paves the way for improved materials in various acoustic applications, particularly in ultrasound devices, by providing better impedance.

Metamaterial↗

DOE OSTI2019

Two-stage ignition behavior and octane sensitivity of toluene reference fuels as gasoline surrogate

Current approaches to improve the efficiency of Spark-Ignition (SI) gasoline engines have been focusing on turbocharging, increasing the compression ratio, and pursuing advanced low-temperature combustion concepts. In order to maximize these strategies, it is important to optimize the knock resistance of the fuel, and therefore knowledge of the sensitivity of the ignition process under a wide range of engine operating conditions is required. Octane sensitivity (OS), which is defined as the difference between Research Octane Number (RON) and Motored Octane Number (MON), has been introduced to represent how fuel's ignition reactivity changes relative to the primary reference fuels (n-heptane/iso-octane) within RON/MON conditions. Previous works have indicated that OS is intimately related to low temperature reactivity of the fuel, which can be revealed as two-stage heat release characteristics during an ignition event. Prompted by these findings, in this paper, we investigate the relationship between two-stage ignition behavior and OS, using chemical kinetic simulations of 24 Toluene Reference Fuels (TRFs)/ethanol blends. TRFs are ternary mixtures of n-heptane/iso-octane/toluene, which is capable of capturing aromatic content and positive values of OS of real gasoline fuels. Simulation results show that fuels with weak or no two-stage ignition behavior tend to have high OS, due to their lack of Negative Temperature Coefficient (NTC) effect and high sensitivity in ignition delay time. Leveraging such observations, we develop a correlation between two-stage behavior and OS as an OS prediction method. Two metrics that represent the strength of the two-stage ignition behavior are proposed and used as OS predictors, which are Low Temperature Heat Release percentage (LTHR%) and Heat Release Rate at the end of first stage (HRR inf ) calculated from a simple kinetic simulation. Regression analysis shows a clear trend between decreases in the proposed two-stage behavior metrics and increases in the value of OS of the fuel. We also test the new metric (LTHR%) using simulation results of 0-D reactors with imposed pressure time histories obtained from engine experiments, as well as using different TRF kinetic mechanisms. Here, the results demonstrate the effectiveness of the metric as a representation of the two-stage ignition behavior in practical combustion systems, highlighting the importance of the proposed relationship, and its potential as a simple and effective OS predictor.

30 DIRECT ENERGY CONVERSION↗