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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 397 records · Page 22

All sources NASA NTRS DOE OSTI

DOE OSTI2023

Potential Cooling Energy Savings of Economizer Control and Artificial-Neural-Network-Based Air-Handling Unit Discharge Air Temperature Control for Commercial Building

Heating, ventilation, and air-conditioning (HVAC) systems play a significant role in building energy consumption, accounting for around 50% of total energy usage. As a result, it is essential to explore ways to conserve energy and improve HVAC system efficiency. One such solution is the use of economizer controls, which can reduce cooling energy consumption by using the free-cooling effect. However, there are various types of economizer controls available, and their effectiveness may vary depending on the specific climate conditions. To investigate the cooling energy-saving potential of economizer controls, this study employs a dry-bulb temperature-based economizer control approach. The dry-bulb temperature-based control strategy uses the outdoor air temperature as an indicator of whether free cooling can be used instead of mechanical cooling. This study also introduces an artificial neural network (ANN) prediction model to optimize the control of the HVAC system, which can lead to additional cooling energy savings. To develop the ANN prediction model, the EnergyPlus program is used for simulation modeling, and the Python programming language is employed for model development. The results show that implementing a temperature-based economizer control strategy can lead to a reduction of 7.6% in annual cooling energy consumption. Moreover, by employing an ANN-based optimal control of discharge air temperature in air-handling units, an additional 22.1% of cooling energy savings can be achieved. In conclusion, the findings of this study demonstrate that the implementation of economizer controls, especially the dry-bulb temperature-based approach, can be an effective strategy for reducing cooling energy consumption in HVAC systems. Additionally, using ANN prediction models to optimize HVAC system controls can further increase energy savings, resulting in improved energy efficiency and reduced operating costs.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗

DOE OSTI2023

Integrated Electrocoagulation, Ultrafiltration, Membrane Distillation, and Crystallization for Treating Produced Water

Produced water (PW) generated from hydraulic fracturing operations was treated using an integrated electrocoagulation, ultrafiltration, membrane distillation, and crystallization process (EC UF MDC). The aim was to determine the viability of this integrated process for maximizing water recovery. The results obtained here indicate that optimizing the various unit operations could lead to increased recovery of PW. Membrane fouling limits all membrane separation processes. A pretreatment step to suppress fouling is essential. Here, removal of total suspended solids (TSS) and total organic carbon (TOC) was achieved by electrocoagulation (EC) followed by ultrafiltration (UF). The hydrophobic membrane used in membrane distillation may be fouled by dissolved organic compounds. Reducing membrane fouling is essential to increase the long-term durability of the membrane distillation (MD) system. In addition, combining membrane distillation with crystallization (MDC) can help reduce scale formation. By inducing crystallization in the feed tank, scale formation on the MD membrane was suppressed. The integrated EC UF MDC process can impact Water Resources/Oil & Gas Companies. Conservation of surface and groundwater is possible by treating and reusing PW. Additionally, treating PW reduces the amount of PW disposed in Class II disposal wells and promotes more environmentally sustainable operations.

42 ENGINEERING↗

NASA NTRS1973

A theoretical and experimental study of sound attenuation in an annular duct.

The flow duct used in the experiments for this study was described in an earlier paper by Plumblee and Dean (1972). Angular and radial standing modes were excited in isolation, and the modal attenuation and phase speed were measured in an annular duct with locally reacting liners. Contour maps of constant attenuation and phase speed were computed in the wall impedance plane for no-flow and several flow conditions, and experimental data were compared with some of these calculations. The effect of flow on modal attenuation and phase speed was measured and is theoretically verified. The major result of this study is the verification of theoretical predictions which ultimately can lead to a duct liner design optimization without resort to expensive and lengthy test programs.

Plumblee, H. E.↗

NASA NTRS1974

Digital adaptive flight controller development

A design study of adaptive control logic suitable for implementation in modern airborne digital flight computers was conducted. Two designs are described for an example aircraft. Each of these designs uses a weighted least squares procedure to identify parameters defining the dynamics of the aircraft. The two designs differ in the way in which control law parameters are determined. One uses the solution of an optimal linear regulator problem to determine these parameters while the other uses a procedure called single stage optimization. Extensive simulation results and analysis leading to the designs are presented.

Kaufman, H.↗

NASA NTRS1996

Balanced Placement Of Vibration Sensors And Actuators

Mathematical approach that involves Hankel singular values leads to method for estimation of optimal placement of relatively few vibration sensors and actuators in multivariable flexible structure. Approach extends and complements previous developments regarding approximate decomposition, to Hankel singular values, of singular values of controllability and observability grammian matrices of multivariable flexible structure. Mathematical constructs also described in "Designing An Approximately Balanced LQG Compensator" (NPO-19000).

Gawronski, Wodek↗

NASA NTRS2002

Development of Chemically and Thermally Robust Lithium Fast Ion Conducting Chalcogenide Glasses

In this project, a new research thrust into the development of an entirely new class of FIC glasses has begun that may lead to a new set of optimized thin-film lithium ion conducting materials. New chemically robust FIC glasses are being prepared that are expected to exhibit unusually high chemical and electrochemical stability. New thermally robust FIC glasses are being prepared that exhibit softening points in excess of 500 C which will dramatically expand the usable operating temperature range of batteries, fuel-cells, and sensors using such electrolytes. Glasses are being explored in the general compositional series xLi2S+ yGa2S3 + (1-x-y)GeS2. Li2S is added as the source of the conductive lithium ions. GeS2 is the base glass-forming phase and the trivalent sulfides, Ga2S3, is added to increase the "refractoniness" of the glass, that is to significantly increase the softening point of the glass as well as its chemical stability. By optimizing the composition of the glass, new glasses and glass-ceramic FIC materials have been prepared with softening points in excess of 500 C and conductivities above 10(exp -3)/Ohm cm at room temperature. These latter attributes are currently not available in any FIC glasses to date.

Martin, Steve W.↗

NASA NTRS2004

Clouds Versus Carbon: Predicting Vegetation Roughness by Maximizing Productivity

Surface roughness is one of the dominant vegetation properties that affects land surface exchange of energy, water, carbon, and momentum with the overlying atmosphere. We hypothesize that the canopy structure of terrestrial vegetation adapts optimally to climate by maximizing productivity, leading to an optimum surface roughness. An optimum should exist because increasing values of surface roughness cause increased surface exchange, leading to increased supply of carbon dioxide for photosynthesis. At the same time, increased roughness enhances evapotranspiration and cloud cover, thereby reducing the supply of photosynthetically active radiation. We demonstrate the optimum through sensitivity simulations using a coupled dynamic vegetation-climate model for present day conditions, in which we vary the value of surface roughness for vegetated surfaces. We find that the maximum in productivity occurs at a roughness length of 2 meters, a value commonly used to describe the roughness of today's forested surfaces. The sensitivity simulations also illustrate the strong climatic impacts of vegetation roughness on the energy and water balances over land: with increasing vegetation roughness, solar radiation is reduced by up to 20 W/sq m in the global land mean, causing shifts in the energy partitioning and leading to general cooling of the surface by 1.5 K. We conclude that the roughness of vegetated surfaces can be understood as a reflection of optimum adaptation, and it is associated with substantial changes in the surface energy and water balances over land. The role of the cloud feedback in shaping the optimum underlines the importance of an integrated perspective that views vegetation and its adaptive nature as an integrated component of the Earth system.

Olsen, Lola M.↗

NASA NTRS2016

Annular Air Leaks in a Liquid Hydrogen Storage Tank

Large liquid hydrogen (LH2) storage tanks are vital infrastructure for NASA, the DOD, and industrial users. Over time, air may leak into the evacuated, perlite filled annular region of these tanks. Once inside, the extremely low temperatures will cause most of the air to freeze. If a significant mass of air is allowed to accumulate, severe damage can result from nominal draining operations. Collection of liquid air on the outer shell may chill it below its ductility range, resulting in fracture. Testing and analysis to quantify the thermal conductivity of perlite that has nitrogen frozen into its interstitial spaces and to determine the void fraction of frozen nitrogen within a perlite-frozen nitrogen mixture is presented. General equations to evaluate methods for removing frozen air, while avoiding fracture, are developed. A hypothetical leak is imposed on an existing tank and a full analysis of that leak is detailed. This analysis includes a thermal model of the tank and a time-to-failure calculation. Approaches to safely remove the frozen air are analyzed, leading to the conclusion that the optimal approach is to allow the frozen air to melt and use a water stream to prevent the outer shell from chilling.

Krenn, A. G.↗

NASA NTRS2022

Computing Sensitivities in Evolutionary Systems: A Real-time Reduced Order Modeling Strategy

We present a new methodology for computing sensitivities in evolutionary systems using a model-driven low-rank approximation. To this end, we formulate a variational principle that seeks to minimize the distance between the time derivative of the reduced approximation and sensitivity dynamics. The first order optimality condition of the variational principle leads to a system of closed form evolution equations for an orthonormal basis and corresponding sensitivity coefficients. This approach allows for the computation of sensitivities with respect to a large number of parameters in an accurate and tractable manner by extracting correlations between different sensitivities on the fly. The presented method requires solving forward evolution equations, sidestepping the restrictions imposed by the forward/backward workflow of adjoint sensitivities. For example, the presented method, unlike the adjoint equation, does not impose any input/output load and can be used in applications in which real-time sensitivities are of interest. We demonstrate the utility of the method for three test cases: (1) computing sensitivity with respect to model parameters in the Rössler system, (2) computing sensitivity with respect to an infinite-dimensional forcing parameter in the chaotic Kuramoto--Sivashinsky equation, and (3) computing sensitivity with respect to reaction parameters for species transport in a turbulent reacting flow.

Reduced order model↗

DOE OSTI2022

Commercial Building Sensors and Controls Systems: Barriers, Drivers, and Costs

Building sensors and controls systems, including building automation systems, consists of the sensor-based devices installed in buildings and the control and automation of those devices. Optimized sensors and controls systems could lead to 29% annual energy savings in commercial buildings and are integral to the growth of grid-interactive efficient buildings. Only 8% of small commercial buildings, however, have installed sensors and controls systems. This is largely due to cost barriers. This work seeks to increase the transparency of system costs and identify specific barriers and drivers for increased adoption. To gather industry insights, the team reached out to building owners, vendors, and contractors and conducted 20 interviews with the goal of collecting cost data and market characteristics regarding building sensors and controls. The qualitative interview data was analyzed using grounded theory to identify overarching concepts, such as barriers, drivers, and future directions on the field. From this analysis, primary barriers were found to be complexity, a lack of knowledge, and money. Primary drivers were found to be the knowledge of data, savings, and remote access. The qualitative cost data was collected in the form of invoices during the interviews. The cost values were used to develop a percentage-based cost stack which identifies the average fraction of the total cost attributed to each category (hardware, software, labor, fees, and taxes). This greater understanding of costs and barriers associated with commercial building sensors and controls systems lays the groundwork for future steps in increasing system adoption, reducing energy consumption, and market transformation.

advanced building controls↗

DOE OSTI2023

Commercial Building Sensors and Controls Systems: Barriers and Drivers: Preprint

Building sensors and controls systems, including building automation systems, comprise the sensor-based devices installed in buildings as well as the control and automation of those devices. Optimized sensors and controls systems could lead to 29% annual energy savings in commercial buildings and are integral to the growth of grid-interactive efficient buildings. Only 13% of small commercial buildings, however, have installed sensors and controls systems, largely because of cost barriers. To accelerate adoption, this work seeks to increase the transparency of system costs and identify specific barriers and drivers. To gather industry insights, the team reached out to building owners, vendors, and contractors and conducted 21 interviews with the goal of collecting cost data and market characteristics regarding building sensors and controls. We collected the cost data in the form of invoices and used it to develop a percentage-based cost category breakdown. The interview data were analyzed using grounded theory to identify overarching concepts such as barriers, drivers, and future directions. From this analysis, we found the primary barriers to be complex and confusing systems, lack of user skills, and financial concerns, and the primary drivers to be operational benefits, insight into operations, and remote access to data. The future directions analysis highlighted the potential technological solutions to address gaps and barriers, as well as predicted drivers to increase adoption. This greater understanding of the costs, barriers, and drivers associated with commercial building sensors and controls systems lays the groundwork for increasing system adoption, reducing energy consumption, and transforming the market.

building automation system↗

DOE OSTI2026

ResSR: A Computationally Efficient Residual Approach to Super-Resolving Multispectral Images

Multispectral imaging (MSI) plays a critical role in material classification, environmental monitoring, and remote sensing. However, MSI sensors typically have wavelength-dependent resolution, which limits downstream analysis. MSI super-resolution (MSI-SR) methods address this limitation by reconstructing all bands at a common high spatial resolution. Existing methods can achieve high reconstruction quality but often rely on spatially-coupled optimization or large learning-based models, leading to significant computational cost and limiting their use in large-scale or time-critical settings. In this paper, we introduce ResSR, a computationally efficient, model-based MSI-SR method that achieves high-quality reconstruction without supervised training or spatially-coupled optimization. Notably, ResSR decouples spectral and spatial processing into two sequential steps. ResSR first computes a spectrally-informed high-resolution estimate of the MSI using singular value decomposition together with a spatially-decoupled approximate forward model. It then applies a residual correction step to restore low-frequency spatial consistency while preserving high-frequency detail recovered by the spectral reconstruction. ResSR achieves comparable or improved reconstruction quality relative to existing MSI-SR methods while being

Sullivan, Haley [ORNL] (ORCID:0000000274069217)↗

DOE OSTI2020

Machine learning coupled multi-scale modeling for redox flow batteries

The reaction distribution in macro or device-scale has been studied for redox flow batteries. The reaction distribution on electrode pore-scale structure however is not well understood, lacking especially on how the reaction distribution on the pore-scale may impact the overall performance of a flow battery. This study introduces for the first time a framework of a multi-scale model that provides understanding of the relationship between the pore-scale electrode structure reaction and the device-scale electrochemical reaction uniformity within the flow battery. A reduced order model is constructed based on 128 pore-scale simulations, which provide a quantitative relationship between the battery operation conditions (inlet velocity, current density, inlet concentration) and the surface reaction uniformity for the pore-scale sample. The multi-scale framework upscales this pore-scale surface reaction uniformity to device-scale combined uniformity. Based on the multi-scale model, a time-varying optimization of the inlet velocity is established, leading to significant reduction on pump power consumption with targeted surface reaction uniformity. The multi-scale model establishes the critical link between the micro-structure of a flow battery component and its performance at the macro-scale, therefore providing rationale for further operational or material optimization.

flow batteries, machine learning, multi-scale mode↗

NASA NTRS1987

Problem size, parallel architecture and optimal speedup

The communication and synchronization overhead inherent in parallel processing can lead to situations where adding processors to the solution method actually increases execution time. Problem type, problem size, and architecture type all affect the optimal number of processors to employ. The numerical solution of an elliptic partial differential equation is examined in order to study the relationship between problem size and architecture. The equation's domain is discretized into n sup 2 grid points which are divided into partitions and mapped onto the individual processor memories. The relationships between grid size, stencil type, partitioning strategy, processor execution time, and communication network type are analytically quantified. In so doing, the optimal number of processors was determined to assign to the solution, and identified (1) the smallest grid size which fully benefits from using all available processors, (2) the leverage on performance given by increasing processor speed or communication network speed, and (3) the suitability of various architectures for large numerical problems.

Nicol, David M.↗

NASA NTRS1988

Problem size, parallel architecture, and optimal speedup

Nicol, David M.↗

DOE OSTI2024

Dose optimization of an adjuvanted peptide-based personalized neoantigen melanoma vaccine

The advancements in next-generation sequencing have made it possible to effectively detect somatic mutations, which has led to the development of personalized neoantigen cancer vaccines that are tailored to the unique variants found in a patient’s cancer. These vaccines can provide significant clinical benefit by leveraging the patient’s immune response to eliminate malignant cells. However, determining the optimal vaccine dose for each patient is a challenge due to the heterogeneity of tumors. To address this challenge, we formulate a mathematical dose optimization problem based on a previous mathematical model that encompasses the immune response cascade produced by the vaccine in a patient. We propose an optimization approach to identify the optimal personalized vaccine doses, considering a fixed vaccination schedule, while simultaneously minimizing the overall number of tumor and activated T cells. To validate our approach, we perform in silico experiments on six real-world clinical trial patients with advanced melanoma. We compare the results of applying an optimal vaccine dose to those of a suboptimal dose (the dose used in the clinical trial and its deviations). Our simulations reveal that an optimal vaccine regimen of higher initial doses and lower final doses may lead to a reduction in tumor size for certain patients. Our mathematical dose optimization offers a promising approach to determining an optimal vaccine dose for each patient and improving clinical outcomes.

59 BASIC BIOLOGICAL SCIENCES↗

DOE OSTI2022

Materials Design using an Active Subspace-based Batch Bayesian Optimization Approach

Integrated computational materials engineering (ICME) calls for integrating simulation tools and/or experiments to develop new materials and materials systems. However, implementation of ICME approaches is challenging mainly due to the considerable computational expense of such frameworks and large dimensionality of the design space. Addressing these challenges is thus critical to the success of ICME initiatives. We present here a specific Bayesian optimization framework designed to address these two challenges. In particular, we propose an active subspace batch Bayesian optimization framework. The framework makes use of dimension reduction via the active subspace method and makes use of the ability to query in parallel via the batch Bayesian optimization approach. Here, the integration of these techniques leads to significant efficiency improvements while maintaining accuracy.

36 MATERIALS SCIENCE↗

NASA NTRS2007

Optimizations of a Hardware Decoder for Deep-Space Optical Communications

The National Aeronautics and Space Administration has developed a capacity approaching modulation and coding scheme that comprises a serial concatenation of an inner accumulate pulse-position modulation (PPM) and an outer convolutional code [or serially concatenated PPM (SCPPM)] for deep-space optical communications. Decoding of this code uses the turbo principle. However, due to the nonbinary property of SCPPM, a straightforward application of classical turbo decoding is very inefficient. Here, we present various optimizations applicable in hardware implementation of the SCPPM decoder. More specifically, we feature a Super Gamma computation to efficiently handle parallel trellis edges, a pipeline-friendly 'maxstar top-2' circuit that reduces the max-only approximation penalty, a low-latency cyclic redundancy check circuit for window-based decoders, and a high-speed algorithmic polynomial interleaver that leads to memory savings. Using the featured optimizations, we implement a 6.72 megabits-per-second (Mbps) SCPPM decoder on a single field-programmable gate array (FPGA). Compared to the current data rate of 256 kilobits per second from Mars, the SCPPM coded scheme represents a throughput increase of more than twenty-six fold. Extension to a 50-Mbps decoder on a board with multiple FPGAs follows naturally. We show through hardware simulations that the SCPPM coded system can operate within 1 dB of the Shannon capacity at nominal operating conditions.

quadratic polynomial interleaver↗