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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 199 records · Page 11
All sourcesNASA NTRSDOE OSTI
DOE OSTI2015

H–J–B Equations of Optimal Consumption-Investment and Verification Theorems

We consider a consumption-investment problem on infinite time horizon maximizing discounted expected HARA utility for a general incomplete market model. Based on dynamic programming approach we derive the relevant H–J–B equation and study the existence and uniqueness of the solution to the nonlinear partial differential equation. By using the smooth solution we construct the optimal consumption rate and portfolio strategy and then prove the verification theorems under certain general settings.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2020

A novel improved model for building energy consumption prediction based on model integration

Building energy consumption prediction plays an irreplaceable role in energy planning, management, and conservation. Constantly improving the performance of prediction models is the key to ensuring the efficient operation of energy systems. Moreover, accuracy is no longer the only factor in revealing model performance, it is more important to evaluate the model from multiple perspectives, considering the characteristics of engineering applications. Based on the idea of model integration, this paper proposes a novel improved integration model (stacking model) that can be used to forecast building energy consumption. The stacking model combines advantages of various base prediction algorithms and forms them into “meta-features” to ensure that the final model can observe datasets from different spatial and structural angles. Two cases are used to demonstrate practical engineering applications of the stacking model. A comparative analysis is performed to evaluate the prediction performance of the stacking model in contrast with existing well-known prediction models including Random Forest, Gradient Boosted Decision Tree, Extreme Gradient Boosting, Support Vector Machine, and K-Nearest Neighbor. The results indicate that the stacking method achieves better performance than other models, regarding accuracy (improvement of 9.5%–31.6% for Case A and 16.2%–49.4% for Case B), generalization (improvement of 6.7%–29.5% for Case A and 7.1%-34.6% for Case B), and robustness (improvement of 1.5%–34.1% for Case A and 1.8%–19.3% for Case B). The proposed model enriches the diversity of algorithm libraries of empirical models.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2016

Specific energy consumption and quality of wood pellets produced using high-moisture lodgepole pine grind in a flat die pellet mill

In the present study a Box–Behnken experimental design was used to understand the effect of the moisture content of lodgepole pine grind (33–39%, w.b.), die speed (40–60 Hz) and preheating temperature (30–90 °C) on the pellet quality and specific energy consumption. The partially dried pellets produced had high-moisture content in the range of 19–28% (w.b.), and were further dried to <9% (w.b.) in a mechanical oven set at 70 °C for 3 h. Dried pellets were further evaluated for pellet moisture content, unit, bulk, tapped density, and durability. Response surface models developed for the product properties have adequately described the process based on coefficient of determination values. Surface plots developed indicated higher unit, bulk, and tapped density (1050, 520, 560 kg/m 3 ) are achievable at 33–35% (w.b.) moisture content of the lodgepole pine grind, die speed of 60 Hz and preheating temperature of 30–60 °C. Higher moisture content of 39% (w.b) reduced unit, bulk, and tapped density to <912, 396, and 452 kg/m 3 . Higher durability values of >95% were obtained at 33–35% (w.b.) at lower preheating temperatures of 30–50 °C and higher die speed of >50 Hz. At 33% (w.b.) moisture content of the lodgepole pine grind, preheating temperature of 90 °C, and die speed of 60 Hz, the observed specific energy consumption was <116 kW h/ton. As a result, scanning electron microscope studies indicated that lignin crosslinking is the primary reason for binding of the lodgepole pine grind at high-moisture content.

09 BIOMASS FUELS↗
DOE OSTI2017

Gradient boosting machine for modeling the energy consumption of commercial buildings

Accurate savings estimations are important to promote energy efficiency projects and demonstrate their cost-effectiveness. The increasing presence of advanced metering infrastructure (AMI) in commercial buildings has resulted in a rising availability of high frequency interval data. These data can be used for a variety of energy efficiency applications such as demand response, fault detection and diagnosis, and heating, ventilation, and air conditioning (HVAC) optimization. This large amount of data has also opened the door to the use of advanced statistical learning models, which hold promise for providing accurate building baseline energy consumption predictions, and thus accurate saving estimations. The gradient boosting machine is a powerful machine learning algorithm that is gaining considerable traction in a wide range of data driven applications, such as ecology, computer vision, and biology. In the present work an energy consumption baseline modeling method based on a gradient boosting machine was proposed. To assess the performance of this method, a recently published testing procedure was used on a large dataset of 410 commercial buildings. The model training periods were varied and several prediction accuracy metrics were used to evaluate the model's performance. The results show that using the gradient boosting machine model improved the R-squared prediction accuracy and the CV(RMSE) in more than 80 percent of the cases, when compared to an industry best practice model that is based on piecewise linear regression, and to a random forest algorithm.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2022

Predicting industrial building energy consumption with statistical and machine-learning models informed by physical system parameters

The industrial sector consumes about one-third of global energy, making them a frequent target for energy use reduction. Variation in energy usage is observed with weather conditions, as space conditioning needs to change seasonally, and with production, energy-using equipment is directly tied to production rate. Previous models were based on engineering analyses of equipment and relied on site-specific details. Others consisted of single-variable regressors that did not capture all contributions to energy consumption. Further, new modeling techniques could be applied to rectify these weaknesses. Applying data from 45 different manufacturing plants obtained from industrial energy audits, a supervised machine-learning model is developed to create a general predictor for industrial building energy consumption. The model uses features of air enthalpy, solar radiation, and wind speed to predict weather-dependency; motor, steam, and compressed air system parameters to capture support equipment contributions; and operating schedule, production rate, number of employees, and floor area to determine production-dependency. Results showed that a model that used a linear regressor over a transformed feature space could outperform a support vector machine and utilize features more representative of physical systems. Using informed parameters to build a reliable predictor will more accurately characterize a manufacturing facility's energy savings opportunities.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2024

LogPath: Log data based energy consumption analysis enabling electric vehicle path optimization

Vehicle navigation and path optimization require a more meticulous approach when it deals with EVs (electric vehicles) and SDVs (software-defined vehicles), due to lengthy charging times and the lack of charging infrastructure. Long-distance freight EV trucking needs path guidance with accurate energy consumption estimates to prevent charging-related failures. We developed a novel energy consumption estimation approach that only uses battery log data to extract major vehicle parameters to increase EV navigation accuracy without additional sensors. This is enabled by extracting multiple drive modes from the log data for analysis. The system provides 1) routes, 2) charge locations, 3) charging times, and 4) optimal vehicle speeds that guarantee the shortest travel time. Here we successfully validated the system using log data collected from an EV and Tesla's Supercharging map in the US and compared it with the commercially available navigation system, Tesla's trip planner, whose capabilities solely include charging time and routing.

EV (Electric vehicles) navigation↗
DOE OSTI2025

Tunneling Barrier-Integrated Gold Nanofilms for Negative Strain Gauging with Near-Zero Energy Consumption

Wireless strain sensors with minimal power needs are essential for long-term monitoring in energy-limited environments. We present a soft tunneling barrier-integrated gold thin film for negative strain sensing with near-zero energy consumption. The device features a strain-induced transition from an insulating to a metallic state, increasing conductivity by 9 orders of magnitude under a controlled strain. It consists of Au-PDMS-Au nanofilm layers, where the Au structures are near the percolation threshold and the PDMS layer acts as a tunneling barrier. Under strain, thinning due to the Poisson effect lowers the barrier’s potential height, enabling electron tunneling and forming an electrical path. Further, with a standby power consumption of ~10 –5 mW over 10 6 times lower than conventional sensors (~12.5 mW), this device is ideal for real-time, long-term stationary structural monitoring in multiple locations.

77 NANOSCIENCE AND NANOTECHNOLOGY↗
DOE OSTI2023

Investigation of Reactive Silver Ink Formula for Reduced Silver Consumption in Silicon Heterojunction Metallization

Silver is the most expensive non-silicon component in photovoltaic cells. This is particularly salient for silicon heterojunction (SHJ) cells, which rely on large quantities of low-temperature silver pastes (LT-SP). SHJ cells would benefit greatly from an industrially scalable metallization process that simultaneously offers low silver consumption, low finger resistivities (<20 μΩ·cm), and low processing temperatures. Printed reactive silver inks (RSI) are an innovative candidate to this end. Furthermore, this work furthers the research on RSI metallization by investigating the impact of ink formula on properties pertinent to SHJ cells, including electrical properties, line width, ink splatter, silver consumption, cell performance, and adhesion. We introduce a scalable, high-throughput flexible needle contact printing approach for metallization that solves many of the issues associated with drop-on-demand printing. The printed silver fingers are characterized using electrical measurements and top-down and cross-sectional microscopy. The best performing ink, consisting of silver acetate, ethylamine, and formic acid, achieved silver fingers with total resistivities of 3.1 μΩ·cm and contact resistivities of 3.2 mΩ·cm 2 when printed at 61 °C. This ink metallized a full-sized 156 mm × 156 mm SHJ cell. This is the first reported data for RSI metallization of a full-sized SHJ cell and shows how an optimized RSI can achieve similar performances to LT-SP while consuming 80–90% less silver.

14 SOLAR ENERGY↗
DOE OSTI2025

Estimating energy consumption and GHG emissions in the U.S. food supply chain for net-zero

This work provides a database of the U.S. food system’s energy consumption and GHG emissions at the national and state levels by food supply chain (FSC) stage, fuel type, and food commodity. We estimate that the U.S. FSC consumed a total 4660 TBTU (4900 PJ) of site energy, 7130 TBTU (7500 PJ) of primary energy, and generated 970 MMT of GHG emissions in 2016. Among all the stages, on-farm production is the largest energy consumer (31% primary energy) and GHG emissions contributor (70%), largely due to raising animals. Optimizing distribution can reduce the stage’s energy consumption and GHG emissions and increase products’ shelf-life. Reducing food loss and waste is another good option, as it decreases the amount of food necessary to grow, thus impacting the overall FSC. The database can help stakeholders identify stage- and region-specific strategies and measures to curtail the environmental footprint of the U.S. food system.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2015

Numerical prediction of energy consumption in buildings with controlled interior temperature

New European directives bring strong requirement to the energy consumption of building objects, supporting the renewable energy sources. Whereas in the case of family and similar houses this can lead up to absurd consequences, for building objects with controlled interior temperature the optimization of energy demand is really needed. The paper demonstrates the system approach to the modelling of thermal insulation and accumulation abilities of such objetcs, incorporating the significant influence of additional physical processes, as surface heat radiation and moisture-driven deterioration of insulation layers. An illustrative example shows the numerical prediction of energy consumption of a freezing plant in one Central European climatic year.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗
DOE OSTI2015

The effect of output-input isolation on the scaling and energy consumption of all-spin logic devices

All-spin logic (ASL) is a novel approach for digital logic applications wherein spin is used as the state variable instead of charge. One of the challenges in realizing a practical ASL system is the need to ensure non-reciprocity, meaning the information flows from input to output, not vice versa. One approach described previously, is to introduce an asymmetric ground contact, and while this approach was shown to be effective, it remains unclear as to the optimal approach for achieving non-reciprocity in ASL. In this study, we quantitatively analyze techniques to achieve non-reciprocity in ASL devices, and we specifically compare the effect of using asymmetric ground position and dipole-coupled output/input isolation. For this analysis, we simulate the switching dynamics of multiple-stage logic devices with FePt and FePd perpendicular magnetic anisotropy materials using a combination of a matrix-based spin circuit model coupled to the Landau–Lifshitz–Gilbert equation. The dipole field is included in this model and can act as both a desirable means of coupling magnets and a source of noise. The dynamic energy consumption has been calculated for these schemes, as a function of input/output magnet separation, and the results show that using a scheme that electrically isolates logic stages produces superior non-reciprocity, thus allowing both improved scaling and reduced energy consumption.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2015

Detecting the relationship between economic growth, CO{sub 2} and energy consumption by using panel data approach

Many scholars have shown their interest into the relationship between energy consumption (EC), gross domestic product (GDP) and emissions. The main objective of this study is to investigate the relationship between GDP, EC and CO{sub 2} within multivariate model by using panel data method in Asian countries; Korea, Malaysia, Japan and China for annually data during the period 1960 to 2010. The main finding shows that CO{sub 2} can be explained more than 86% & 78% by EC and GDP in each of cross section model and period model respectively. As a result of that, CO{sub 2} emissions should be considered as an important factor in energy consumption and gross domestic product by policy maker.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗
DOE OSTI2014

Power Consumption During Neuronal Computation

Maintaining the ability of the nervous system to perceive, remember, process, and react to the outside world necessitates a continuous energy supply. However, the overall power consumption is remarkably low, which has inspired engineers to mimic nervous systems in designing artificial cochlea, retinal implants, and brain–computer interfaces (BCIs) to improve the quality of life in patients. Such neuromorphic devices are both energy efficient and increasingly able to emulate many functions of the human nervous system. We explore the energy constraints of neuronal signaling within biology, review the quantitative tradeoff between energy use and information processing, and ask whether the biophysics and design of nerve cells minimizes energy consumption.

42 ENGINEERING↗
DOE OSTI2020

Electricity Produced from Photovoltaic Systems in Apartment Buildings and Self-Consumption: Comparison of the Situation in Various IEA PVPS Countries

Over the last two decades, grid-connected solar photovoltaic systems have increased from a niche market to one of the leading power generation capacity additions annually. In 2018 the total installed photovoltaic electricity generation capacity exceeded 500 GW. Another doubling is forecasted until the early 2020s. Therefore, the further development is coupled with the question at what prices solar photovoltaic electricity can be provided and delivered to the customers. The installation of PV systems for self-consumption is already now an interesting option for many people but in general limited to those who have access to a rooftop they own or can use. A new envelopment to enable residents of multi apartment buildings to commonly use electricity generated by a PV system (collective self-consumption) is a relatively new development and is still facing a lot of administrative and regulatory challenges.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗
DOE OSTI2019

Energy Consumption for High-Frequency Switching of a Quantum-Dot Microdisk Laser

We have studied characteristics of a 23-μm-diameter microdisk laser operating in a regime of direct high-frequency modulation at a stabilized heat-sink temperature of 18°C. It is found that the minimum energy consumption in this regime amounts to ~1.6 pJ/bit and is achieved at a modulation frequency of 4.2 GHz. The maximum modulation frequency is 6.7 GHz, at which the minimum energy consumption is 3.3 pJ/bit.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
DOE OSTI2020

Modeling the External Effects of Air Taxis in Reducing the Energy Consumption of Road Traffic

Air taxis are currently being demonstrated. Few studies have quantified their external effects in reducing on-road vehicle fuel consumption. The hypothesis of this paper is that air taxis may divert some drivers away from congested traffic corridors, improve traffic speed and fuel economy, and reduce congestion-induced energy consumption. A model is developed that links several key components: mode choice, the relationship between travel demand and traffic speeds, the relationship between traffic speeds and fuel economies, and the heterogenous value of travel time. It is applied to the route from downtown Los Angeles to Los Angeles International Airport, where at peak hours 38,200 vehicles attempt to use the route that has an hourly capacity of 17,200 vehicles. The model estimates that, with conservative assumptions and near-term technologies, diverting 3.2% of the traffic to air taxis could produce a 15% reduction in traffic vehicle fuel use. With optimistic assumptions and mature technologies, the study estimates that diverting 20% of traffic could reduce the traffic vehicle fuel use by about 74%. The key insight is that if a small share of congested travelers switched to air taxis, motivated by private benefits of time savings, significant external benefits for other road travelers (time savings and fuel savings) and to society (reduced energy use and emissions), would ensue creating a win-win-win outcome. Overall, these estimates (which are not intended as predictions because of the stated limitations) strongly suggest the need to consider the external energy effect in future cost-benefit analyses of air taxi technologies.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2016

Control Strategies to Reduce the Energy Consumption of Central Domestic Hot Water Systems

Domestic hot water (DHW) heating is the second largest energy end use in U.S. buildings, exceeded only by space conditioning. Recirculation systems consisting of a pump and piping loop(s) are commonly used in multifamily buildings to reduce wait time for hot water at faucets; however, constant pumping increases energy consumption by exposing supply and return line piping to continuous heat loss, even during periods when there is no demand for hot water. In this study, ARIES installed and tested two types of recirculation controls in a pair of buildings in order to evaluate their energy savings potential. Demand control, temperature modulation controls, and the simultaneous operation of both were compared to the baseline case of constant recirculation. Additionally, interactive effects between DHW control fuel reductions and space conditioning (heating and cooling) were estimated in order to make more realistic predictions of the payback and financial viability of retrofitting DHW systems with these controls. Results showed that DHW fuel consumption reduced by 7% after implementing the demand control technique, 2% after implementing temperature modulation, and 15% after implementing demand control and temperature modulation techniques simultaneously; recirculation pump runtime was reduced to 14 minutes or less per day. With space heating and cooling interactions included, the estimated annual cost savings were 8%, 1%, and 14% for the respective control techniques. Possible complications in the installation, commissioning and operation of the controls were identified and solutions offered.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2016

Control Strategies to Reduce the Energy Consumption of Central Domestic Hot Water Systems

Domestic hot water (DHW) heating is the second largest energy end use in U.S. buildings, exceeded only by space conditioning. Recirculation systems consisting of a pump and piping loop(s) are commonly used in multifamily buildings to reduce wait time for hot water at faucets; however, constant pumping increases energy consumption by exposing supply and return line piping to continuous heat loss, even during periods when there is no demand for hot water. In this study, ARIES installed and tested two types of recirculation controls in a pair of buildings in order to evaluate their energy savings potential. Demand control, temperature modulation controls, and the simultaneous operation of both were compared to the baseline case of constant recirculation. Additionally, interactive effects between DHW control fuel reductions and space conditioning (heating and cooling) were estimated in order to make more realistic predictions of the payback and financial viability of retrofitting DHW systems with these controls. Results showed that DHW fuel consumption reduced by 7% after implementing the demand control technique, 2% after implementing temperature modulation, and 15% after implementing demand control and temperature modulation techniques simultaneously; recirculation pump runtime was reduced to 14 minutes or less per day. With space heating and cooling interactions included, the estimated annual cost savings were 8%, 1%, and 14% for the respective control techniques. Possible complications in the installation, commissioning and operation of the controls were identified and solutions offered.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗