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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 469 records · Page 26

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DOE OSTI2022

Data-Enabled Predictive Control for Building HVAC Systems

Model predictive control is widely used as a control technology for the computation of optimal control inputs of building heating, ventilating, and air conditioning (HVAC) systems. However, both the benefits and widespread adoption of model predictive control (MPC) are hindered by the effort of model creation, calibration, and accuracy of the predictions. In this paper, we apply the data-enabled predictive control (DeePC) algorithm for designing controls for building HVAC systems. The algorithm solely depends on input/output data from the system to predict future state trajectories without the need for system identification. The algorithm relies on the idea that a vector space of all input–output trajectories of a discrete-time linear time-invariant (LTI) system is spanned by time-shifts of a single measured trajectory, given the input signal is persistently exciting. Closed-loop simulations using EnergyPlus are performed to demonstrate the approach. The simulated building modeled in EnergyPlus is a modified commercial large office prototype building served by an air handling unit-variable air volume HVAC system. Temperature setpoints of zones are used as control variables to minimize the HVAC energy cost of the building considering a time-of-use electricity rate structure. Furthermore, sensitivity analysis is conducted to gain insights into the effect of parameter tuning on DeePC performance. Simulation results are used to illustrate the performance of the algorithm and compare the algorithm with model-based MPC and occupancy-based setpoint controller. Overall, DeePC achieves similar performance compared to MPC for lower engineering effort.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗

DOE OSTI2021

Toward polymer upcycling—adding value and tackling circularity

Plastics have revolutionized modern life, but have created a global waste crisis driven by our reliance and demand for low-cost, disposable materials. New approaches are vital to address challenges related to plastics waste heterogeneity, along with the property reductions induced by mechanical recycling. Chemical recycling and upcycling of polymers may enable circularity through separation strategies, chemistries that promote closed-loop recycling inherent to macromolecular design, and transformative processes that shift the life-cycle landscape. Polymer upcycling schemes may enable lower-energy pathways and minimal environmental impacts compared with traditional mechanical and chemical recycling. The emergence of industrial adoption of recycling and upcycling approaches is encouraging, solidifying the critical role for these strategies in addressing the fate of plastics and driving advances in next-generation materials design.

Korley, LaShanda T. J.↗

DOE OSTI2023

Autonomous alignment and healing in multilayer soft electronics using immiscible dynamic polymers

Self-healing soft electronic and robotic devices can, like human skin, recover autonomously from damage. While current devices use a single type of dynamic polymer for all functional layers to ensure strong interlayer adhesion, this approach requires manual layer alignment. In this study, we used two dynamic polymers, which have immiscible backbones but identical dynamic bonds, to maintain interlayer adhesion while enabling autonomous realignment during healing. These dynamic polymers exhibit a weakly interpenetrating and adhesive interface, whose width is tunable. When multilayered polymer films are misaligned after damage, these structures autonomously realign during healing to minimize interfacial free energy. Finally, we fabricated devices with conductive, dielectric, and magnetic particles that functionally heal after damage, enabling thin-film pressure sensors, magnetically assembled soft robots, and underwater circuit assembly.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2021

Smart Building Start (SBS) (CRADA 480)

The idea of Smart Building Start (SBS) is to manage the on/off scheduling of HVAC equipment in buildings in an optimal way in order to achieve comfort conditions at the designated times with the minimal use of energy. This concept is also known as Optimal Start and it has typically been used in buildings to start up equipment based on predictions made for a representative (or average) zone in a building. This means that zones that could have waited longer to receive conditioned air are activated too early leading to unnecessary energy loss. The approach proposed in this project is to develop and implement a distributed version of optimal start that will consider the individual requirements of all treated zones. The developed control strategy will enable smart start of the central plant as well as further localized control over each individual zone by means of setpoint adjustment so that zones are not conditioned too early thereby reducing energy use compared to the current state of the art. This project is a collaboration between PNNL and Verdicity with the goal being to bring the research concept of SBS to commercialization. The SBS algorithm(s) will be implemented and hosted on the PNNL-developed open-platform VOLTTRON which will accelerate bringing the research to practice. It is anticipated that this project will lead to new IP surrounding the algorithm and its decentralized deployment. The project will also further demonstrate the success of VOLTRON as a flexible platform for implementation of innovative algorithms for improving energy efficiency in buildings. Previous studies on smart building start carried out by PNNL have indicated potential energy savings on the order of 10-15% for multi-zone buildings compared to conventional strategies. Verdicity currently has a number of customers who can benefit from the use of this technology. PNNL will work with Verdicity to identify any key gaps in the integrated software and jointly work to enhance and customize it for Verdicity and to integrate it with their existing products for use at their customers’ sites. The proposed work will help previously funded Federal research and development technology investments find their way into a viable commercial market.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗

DOE OSTI2024

Portable HCAL reconstruction in the CMS detector using the Alpaka library

CMS has deployed a number of different GPU algorithms at the High-Level Trigger (HLT) in Run 3. As the code base for GPU algorithms continues to grow, the burden for developing and maintaining separate implementations for GPU and CPU becomes increasingly challenging. To mitigate this, CMS has adopted the Alpaka (Abstraction Library for Parallel Kernel Acceleration) library as the performance portability solution to provide a single-code base for parallel execution on both GPUs and CPUs in CMS software (CMSSW). A direct CUDA version of HCAL energy reconstruction, called Minimization At Hcal, Iteratively (MAHI), has been deployed at the HLT in the 2022-2023 data taking period. This contribution will describe how the CUDA version is converted into a portable implementation using the Alpaka library. We will discuss the porting experience from CUDA to Alpaka, the validation process and the performance of the Alpaka version in CPU and GPU.

Kwok, Martin↗

DOE OSTI2025

Additive manufacturing of amorphous metal soft magnetic composites

Soft magnet alloys are used as magnetic cores for electric motors, transformers, wind turbines and other power generation systems. Soft magnetic cores are expensive and time consuming to manufacture in the complex shapes required for next-generation devices using conventional press and sinter powder metallurgy. The objective of this effort is to additively manufacture high performance soft magnets, with reduced cost and reduced material waste and 10x lower energy (core) loss at the high operating frequencies of many electric machines. Laser powder bed fusion additive manufacturing is used as the fabrication method. Electrical steel and amorphous alloys atomized powders are used as a feedstock materials. Magnetic cores are printed in topology optimized structures such as the Hilbert curve because this has been shown to reduce energy losses by minimizing the eddy currents that circulate within the magnet at high frequencies. In our project, we succeed in printing FeSi 3.5wt% and FeSi 6.5wt% electrical steels, and iron-based soft magnetic amorphous alloys in the shape of Hilbert and Peano curve topology optimized structures. We found that the Peano curve has a higher cut-off frequency than the Hilbert curve, and that amorphous alloys have high cut-off frequencies and higher mechanical hardness than electrical steels. Processing conditions such as laser power, scan speed, and hatching pattern were optimized to achieve high density prints, and optimize magnetic performance. We find that the printing of amorphous alloy soft magnetic cores may be technoeconomically feasible for large scale applications such as transformers, for which supply chain issues and the labor costs of manual fabrication of magnetic cores is prohibitive in some cases.

36 MATERIALS SCIENCE↗

DOE OSTI2020

A New Concept for Kilotonne Scale Liquid Argon Time Projection Chambers

We develop a novel Time Projection Chamber (TPC) concept suitable for deployment in kilotonne-scale detectors, with a charge-readout system free from reconstruction ambiguities, and a robust TPC design that reduces high-voltage risks while increasing the coverage of the light-collection system and maximizing the active volume. This novel concept could be used as a far detector module in the Deep Underground Neutrino Experiment (DUNE). For the charge-readout system, we used the charge-collection pixels and associated application-specific integrated circuits currently being developed for the liquid argon (LAr) component of the DUNE Near Detector design, ArgonCube. In addition, we divided the TPC into a number of shorter drift volumes, reducing the total voltage used to drift the ionization electrons, and minimizing the stored energy per TPC. Segmenting the TPC also contains scintillation light, allowing for precise trigger localization and a more expansive light-readout system. Furthermore, the design opens the possibility of replacing or upgrading components. These augmentations could substantially improve the reliability and the sensitivity, particularly for low-energy signals, in comparison to traditional monolithic LArTPCs with projective-wire charge readouts.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗

DOE OSTI2021

Control systems and methods to enable autonomous drilling

Buerger, Stephen↗

DOE OSTI2022

Occupancy-Based Controls for an All-Electric Residential Community in a Cold Climate: Preprint

In residential buildings, rapid improvements in sensors, communication, and information technology have enabled occupancy-based building controls. These controls utilize occupancy information and modify the operation of the heating, ventilation, and air-conditioning (HVAC) system to minimize excess HVAC energy use, especially when the building is unoccupied. This reduces the total building energy consumption and utility bills while maintaining thermal comfort. In this paper, we present two novel occupancy-driven controls - reactive control and predictive control - and compare their performance. We model an all-electric residential community based on a 27- home community in Basalt, Colorado, in the United States. We simulated various scenarios, considering different temperature setback and control algorithms, to analyze the community-scale impact of these occupancy-based controls. The results show that total HVAC energy savings in a building ranges from 1%- 20% compared to the baseline scenario without occupancy-based controls. The energy-saving potential is highly correlated with the occupancy pattern and temperature setback in the building.

ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATION↗

DOE OSTI2022

Advanced adsorption processes for separation of bio-derived products

Disclosed herein are methods for the recovery of target bio-base carboxylic acid products using a sorption-based technology with a mixed elution solvent optimized for minimized downstream distillation energy input. The sorption-based technology includes absorbing the targeted bio-base carboxylic acid products onto a non-ionic resin and eluting the targeted bio-base carboxylic acid products with a mixed elution solvent. The mixed elution solvent includes a first solvent and a second solvent. The first solvent has a boiling point that is lower than the targeted bio-base carboxylic acid products and the second solvent is selected from the group consisting of a phosphine oxide or tertiary amine.

Saboe, Patrick Owen↗

DOE OSTI2022

Control systems and methods to enable autonomous drilling

A system or method for drilling includes autonomously controlling a rotary or percussive drilling process as it transitions through multiple materials with very different dynamics. The method determines a drilling medium based on real-time measurements and comparison to prior drilling data, and identifies the material type, drilling region, and approximately optimal setpoint based on data from at least one operating condition. The controller uses these setpoints initially to execute an optimal search to maximize performance by minimizing mechanical specific energy. Near-bit depth-of-cut estimations are performed using a machine learning prediction deployed in an embedded processor to provide high-speed ROP estimates. The sensing capability is coupled with a near-bit clutching mechanism to support drilling dysfunction mitigation.

Buerger, Stephen↗

DOE OSTI2022

Autonomous Eco-Driving with Traffic Light and Lead Vehicle Constraints: An Application of Best Constrained Interpolation

Eco-Driving is a critical technology for improving automotive transportation efficiency. It is achieved by modifying the driving trajectory over a particular route to minimize required propulsion energy. Eco-Driving can be approached as an optimal control problem subject to driving constraints such as traffic lights and positions of other vehicles. Best interpolation in a strip is a problem in approximation theory and optimal control. The solution to this problem is a cubic spline. In this research, we demonstrate the connection between Eco-Driving and best interpolation in the strip. By exploiting this connection, we are able to generate optimal Eco-Driving trajectories that could be driven with an autonomous system. Using the simulation tool FASTSim, we can evaluate the trajectories using conventional, hybrid electric, and fully electric vehicle models. Our results quantify the energy efficiency improvements that can be achieved with the proposed approach.

Mahmoud, Yara Hazem↗

DOE OSTI2022

Autonomous Eco-Driving with Traffic Light and Lead Vehicle Constraints: An Application of Best Constrained Interpolation

Mahmoud, Yara Hazem↗

DOE OSTI2023

Control systems and methods to enable autonomous drilling

Buerger, Stephen↗

DOE OSTI2026

Data for A Hybrid Biophysical-Machine Learning Framework for Diurnal Surface Energy Flux Estimation Using Proximal Sensing

Thermal infrared-based remote sensing of surface energy fluxes has traditionally relied on high spatial resolution satellite data with revisit frequencies on the order of weeks. In this study, we evaluate a biophysics-based analytical surface energy balance model for predicting latent energy (LE) and sensible heat (H) fluxes using proximal sensing observations. The Surface Temperature Initiated Closure (STIC1.2) model has been extensively validated across a wide range of spatial and temporal scales using various satellite-derived thermal infrared data sets. Here we extend this validation by applying STIC at sub-hourly temporal resolution over multiple growing seasons for four distinct agricultural systems. We further develop and evaluate novel STIC variants that incorporate machine learning (ML) techniques to eliminate the need for surface energy balance observations, specifically net radiation and soil heat flux, thereby enhancing model applicability in data-sparse settings. The integration of a ML component to estimate surface available energy is shown to have strong predictive performance for both LE (R2 = 0.81–0.94) and H (R2 = 0.46–0.72) across all agricultural systems examined here, demonstrating the potential of hybrid biophysical-machine learning approaches for surface energy balance modeling with minimal data requirements. This study concludes with a novel application of explainable machine learning (exML) to diagnose sources of model error. This exML framework attributes residual prediction errors to both model input variables and environmental drivers not explicitly included in the simulation experiments. This approach provides a new pathway for improving model design and integrating previously overlooked yet influential variables into future model iterations.

AI/ML↗

DOE OSTI2021

EMOS (Energy Management Optimization System) [SWR-21-46]

EMOS software performs a real-time hierarchal optimal control for energy systems like multi-port electric vehicles charging site with distributed energy resources (DERs) and energy storage systems (ESSs). It gathers information in real-time from electric vehicles, power grid, and DERs, solves a multi-objective energy management optimization problem, and output setpoint for chargers, ESS converters, DERs converters, and grid converters. The software incorporates a novel integration of two control tasks: a) An Energy Management Optimization (EMO), which is the brain of EMOS controller that gathers information in real-time from EVs [e.g., battery size, state-of-charge (SOC), desired SOC, and charge acceptance curve], grid (e.g., electricity price, allowed feeder capacity, ramp rate limit, and reactive power), and DERs (e.g., prediction for solar generation for PV systems). It solves a control optimization problem in real-time to find optimal setpoint for ESSs power dispatch, EVs charging rate, and grid inverters. The objectives are to (1) minimize the charging cost considering grid energy, demand charges, and battery energy, (2) minimize charging time to meet fast charging criterion, (3) keep high energy level on ESSs at the end of an operating period, while satisfying constraints related to grid, EVs, and power converters. b) Real-Time Energy Management System (RT-EMS) is a rule-based algorithm that has faster response than EMO. It receives optimal setpoint from EMO and actual measurements from the system and modify the setpoint to compensate for any fast disturbance in the system, until a new optimum solution is received. Fast disturbances may include vehicle connect/disconnect, unpredicted variation in DERs profiles, variation in grid voltage, errors in PV generation prediction, and others. In addition, RT-EMS regulates voltage at point of common coupling (PCC) by managing reactive power of grid converters.

Mohamed, Ahmed↗

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 OSTI2025

Second-generation downscaled earth system model data using generative machine learning

The second-generation Sup3rCC dataset provides high-resolution meteorological data generated through the downscaling of multiple earth system models (ESMs) from the Coupled Model Intercomparison Project Phase 6 (CMIP6). This downscaling is performed through application of a generative machine learning approach called Super-Resolution for Renewable Resource Data (sup3r). This dataset builds on the first-generation Sup3rCC data by applying improved bias correction methods and adding downscaled precipitation to the output variables. As with the first Sup3rCC version, the data still include temperature, wind speed and direction at multiple heights, pressure, three components of downwelling solar radiation, and relative humidity—all at 4-kilometer (km) hourly resolution over the contiguous United States. This is a 25x spatial enhancement and 24x temporal enhancement of the source 100-km daily-average ESM data. This extension of the Sup3rCC dataset includes data from six ESMs from two shared socioeconomic pathways (SSPs) totaling 400 years of data with multiple future projections of changing meteorological conditions. The scenario selection was based on a structured evaluation of historical ESM skill and comprehensive representation of possible trajectories of future climate change in temperature, humidity, precipitation, solar irradiance, and near-surface wind speeds. The inclusion of multiple future projections is intended to enable users to assess key drivers of un 36 certainty and variability. All data are double-bias corrected, resulting in a product that can be used out-of-the-box for energy system analysis with minimal historical bias. The potential applications of Sup3rCC data extend to various topics in renewable energy resource assessment, energy systems modeling, and grid resilience studies. High-resolution future meteorological projections are critical for evaluating the effects of changing meteorological conditions on renewable energy generation, energy demand, and for optimizing energy storage and grid infrastructure. The 4-km hourly resolution of the downscaled data enables understanding of spatial and temporal variability at the scales necessary for energy system operational planning. In addition, the dataset can support risk assessments by providing detailed information on possible future extreme weather events and long-term meteorological variability at scales relevant to energy infrastructure. By offering an enhanced representation of possible future meteorological conditions, the second-generation Sup3rCC dataset enables more precise modeling of energy resilience and adaptation strategies in response to changing meteorological conditions.

24 POWER TRANSMISSION AND DISTRIBUTION↗