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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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Integrating multi-sensor identification with machine learning significantly enhances bioenergy preprocessing by using non-destructive, multi-spectral sensors to improve feedstock purity and quality. This approach not only reduces preprocessing costs but also ensures a more efficient and sustainable bioenergy production process.
Hazardous nuclear and industrial facilities are rarely designed for robots. Work in these domains demand precise manipulation and robust mobility in cluttered, constrained spaces where off-the-shelf platforms struggle and “one-size-fits-all” machines become costly and complex. Idaho National Laboratory (INL) is developing an autonomous, multi-robot inspection system that coordinates task-specific platforms rather than relying on a single omni-tool robot. An electric truck serves as a power and compute hub for a custom manipulator co-developed with Florida International University (FIU), a commercial mini crawler, a pan–tilt–zoom camera, and a Nexxis Argus LiDAR mapping system. Working in concert, these robots generate spatial, radiation, and temperature maps of the pit environments at the Hanford Waste Tank Farms. These systems will capture visual records and environmental telemetry to allow for analysis post inspection. The system architecture uses Robot Operating System 2 (ROS 2) for publish/subscribe integration, NVIDIA Isaac Sim and Unity for simulation and visualization, and algorithms such as NVBlox to fuse data into unified 3D overlays. This robot-agnostic approach reduces operator burden by enabling autonomy across heterogeneous platforms and lets each robot be used where it is strongest. Having autonomous functions means operators don’t have to fully control multiple different components. The ease of use could allow for more widespread adoption of advanced robotics at waste management sites that see continued use. By coordinating simpler, purpose-built mechanisms, the approach lowers design and manufacturing complexity, reduces capital risk in contaminated settings, and improves controllability for complex inspection and manipulation tasks. We present the architecture, early results, and lessons learned from building and deploying this coordinated multi-robot system, with the goal of accelerating safe, cost-effective adoption of advanced robotics at waste-management sites.
The electric field assisted sintering (EFAS) process involves tightly coupled physics that influence microstructural evolution in the particles being compacted. It is also an inherently multi-scale phenomenon, with the microstructure of the compact influencing the subsequent engineering-scale response of the sintering system. To improve understanding of how processing parameters influence microstructural evolution, we have developed a multi-scale modeling approach that couples a continuum-level model of the sintering system with a phase-field model for microstructural evolution of particles within the compact. The phase-field model couples the effect of chemical and electrical driving forces on microstructural evolution and includes the effect of charged defect segregation to surfaces and grain boundaries; this segregation leads to enhanced defect transport and heat generation at these interfaces in response to applied electric field. The effect of enhanced heat generation on particle neck growth and the influence of microstructural evolution on the engineering-scale model are demonstrated.
— A propulsion system design is exhibited here that comprises of an X-type GaN power module based dual multi-level inverter and a PM Synchronous Reluctance motor. With the critical benefits of multi-level operation, namely low common mode EMI noise, low switching loss, low current Total Harmonic Distortion (THD), smoother torque and lower iron loss, this topology targets high system-level efficiency and high power density while using reduced rare earth (RE) elements in its motor. General Motors and Purdue University jointly designed and developed this 800V class, highly scalable traction drive that can deliver 200+ kW of power suitable for C-SUV to truck vehicle class. The 3-phase electric machine design utilizes dual winding with 6 terminals to work in line with the GaN X-type inverter, and results in a tractive solution that ensures higher machine efficiency while reducing cost and uncertainty related to use and availability of heavy rare earth/rare earth elements in its rotor.
Multi-split variable refrigerant flow (VRF) systems are highly energy-efficient HVAC (heating, ventilation and air conditioning) technologies that connect a single outdoor unit to multiple independent indoor terminal units using a common refrigerant circuit and a variable-speed compressor. Building energy simulations that incorporate VRF systems help model their unique operational characteristics and predict energy consumption in specific building designs. Traditionally, EnergyPlus models these systems by employing multiple sets of performance curves to characterize both individual terminal units and the outdoor unit. However, producing these curves is labor intensive and error prone, and they often do not capture all the key input and output variables. This paper introduces a novel approach that uses multi-dimensional performance maps to model VRF systems in building environments for space cooling. In this approach, performance maps are developed at the component level—separately for the outdoor unit and for each indoor terminal. The new modeling method is validated within EnergyPlus via a Python plug-in that contains a simple solver loop to coordinate the component-level, indoor, and outdoor unit maps. Furthermore, because performance maps can span more variables than traditional performance curves, they offer the opportunity to implement advanced controls, such as enhanced dehumidification and compressor modulation. A VRF air conditioner’s hardware system was modeled using the DOE/ORNL Heat Pump Design Model, which was automated to produce extensive performance maps for both the indoor and outdoor units.
Contemporary advancements in low-cost automation and computation, reduced barrier to entry in developing artificial intelligence/machine learning (AI/ML), and increased ability to represent complex materials in digital form have led to a number of accelerated materials discovery platforms. However, many of these approaches operate with completely rigid vertical integration in an isolated feedback loop using limited modalities. In order to make a substantial impact on discovering new energy materials, AI-driven experiments must operate collaboratively with each other and researchers and over multiple measurement modalities. Herein, we describe the potential for an “internet of things” approach to self-driving enterprise beamlines that merges core information technologies, robotics, and multi-modal AI. The approach will enable full utility of light sources, collaborate effectively with other remote materials acceleration platforms, and help stride toward the world’s energy future.
This is a presentation on the Design and Implementation of a Novel Multi-Frac Stimulation Concept by The University of Oklahoma, presented by Ahmad Ghassemi. This slide presentation video discusses the design and implementation of a reservoir stimulation concept improving near-wellbore and well-to-well conductivity while enhancing the SRV and promoting self-propping and heat exchange. This presentation was featured in the Utah FORGE R&D Annual Workshop on August 13-15, 2024.
Emitted smoke composition is determined by properties of the biomass burning source and ambient ecosystem. However, conditions that mediate the partitioning of black carbon (BC) and brown carbon (BrC) formation, as well as the spatial and temporal factors that drive particle evolution, are not understood adequately for many climate and air-quality related modeling applications. In situ observations provide considerable detail about aerosol microphysical and chemical properties, although sampling is extremely limited. Satellites offer the frequent global coverage that would allow for statistical characterization of emitted and evolved smoke, but generally lack microphysical detail. However, once properly validated, data from the National Aeronautics and Space Administration (NASA) Earth Observing System’s Multi-Angle Imaging Spectroradiometer (MISR) instrument can create at least a partial picture of smoke particle properties and plume evolution. We use in situ data from the Department of Energy’s Biomass Burning Observation Project (BBOP) field campaign to assess the strengths and limitations of smoke particle retrieval results from the MISR Research Aerosol (RA) retrieval algorithm. We then use MISR to characterize wildfire smoke particle properties and to identify the relevant aging factors in several cases, to the extent possible. The RA successfully maps qualitative changes in effective particle size, light absorption, and its spectral dependence, when compared to in situ observations. By observing the entire plume uniformly, the satellite data can be interpreted in terms of smoke plume evolution, including size-selective deposition, new-particle formation, and locations within the plume where BC or BrC dominates.
The need for new approaches to the consistent simulation of related phenomena at multiple levels of resolution is great. While many fields of application would benefit from a complete and approachable solution to this problem, such solutions have proven extremely difficult. We present a multi-resolution simulation methodology that uses numerical optimization as a tool for maintaining external consistency between models of the same phenomena operating at different levels of temporal and/or spatial resolution. Our approach follows from previous work in the disparate fields of inverse modeling and spacetime constraint-based animation. As a case study, our methodology is applied to two environmental models of forest canopy processes that make overlapping predictions under unique sets of operating assumptions, and which execute at different temporal resolutions. Experimental results are presented and future directions are addressed.
A 640x512 pixel, long-wavelenghth multi-band and a broad-band quantum well infrared photo-detector (QWIP) focal plane array having a 15.4 um cutoff have been demonstrated.
One of many challenges in the implementation of multiple exciter testing is establishing a reasonable set of test metrics to measure the quality of testing. This is especially true in the application of Impedance Matched Multi-Axis Testing; in that it is possible to have very large spectral density matrices that serve as reference criteria. While there exist plotting schemes to view a spectral density matrix, it is often necessary to break the overlay of reference and test results into subsections of the matrices to get sufficient resolution to interpret the data. In addition, as one attempts to control multiple locations on a structure, implementation of classical single degree-of freedom test tolerances across all channels and associated cross spectra is simply not feasible. Hence it is challenging to evaluate overall test quality. The use of spectral views of the dominant singular values from the singular value decomposition of the spectral density matrices and metrics based upon them is proposed for establishing a set of compact metrics for evaluating test quality. A laboratory experiment will be included to demonstrate this proposed technique.
Abstract—Frequency domain (FD) multi-user detection (MUD) and precoding has been shown to be an effective means of approaching the theoretical per-user capacity for single carrier modulation (SCM) schemes in massive MIMO scenarios with highly dispersive channels. When a cyclic prefix is added to the SCM waveform, the circulant structures of the resulting convolutional channel matrix allows for relatively simple expressions for the FD detection and precoding. In this paper, we further refine the computationally efficient minimum mean squared error (MMSE) FD-MUD technique in a time-division duplexing (TDD) massive MIMO setup by pre-computing the scale factor needed to produce an unbiased estimate. The resulting scale factor and the matrix inverse that was calculated for the uplink is then reused for the downlink to form a computationally efficient precoding scheme with superior performance compared to zero-forcing (ZF) precoding. Performance analysis is provided and confirmed through simulation results.
Cyclic Prefix Direct Sequence Spread Spectrum (CP-DSSS) is a promising solution for futuristic 6G ultra-reliable low latency communications (URLLC) and massive machine type communication (mMTC) applications, where the CP-DSSS waveform would operate as a secondary network at the same frequencies as the primary network but at much lower SNR. In this paper, we show per-user capacity for multi-user scenarios, where simple matched filtering (MF) is performed on the uplink (UL) and time-reversal (TR) precoding is used on the downlink (DL). When operating in the low SNR regime, CP-DSSS achieves per-user capacity near the optimum single-user capacity by using a MF detector at the receiver for the UL. TR precoding converges to the optimal capacity as the number of antennas at the hub/gateway increases. Given the near-optimal performance of MF detection and TR precoding for each of the users, CP-DSSS can be implemented with simple device transceiver structures, reducing per-unit cost for massively deployed 6G networks.
Multiplexed gas detection at room temperature is critical for practical applications, such as for tracking the complex chemical environments associated with food decomposition and spoilage. Here, an integrated array of multiple silicon-based, chemical-sensitive field effect transistors (CSFETs) is presented to realize selective, sensitive, and simultaneous measurement of gases typically associated with food spoilage. CSFETs decorated with sensing materials based on ruthenium, silver, and silicon oxide are used to obtain stable room-temperature responses to ammonia (NH 3 ), hydrogen sulfide (H 2 S), and humidity, respectively. For example, one multi-CSFET sensor signal changes from its baseline by 13.34 in response to 1 ppm of NH 3 , 724.45 under 1 ppm H 2 S, and 23.46 under 80% relative humidity, with sensitive detection down to 10 ppb of NH 3 and H 2 S. To demonstrate this sensor for practical applications, the CSFET sensor array is combined with a custom-printed circuit board into a compact, fully integrated, and portable system to conduct real-time monitoring of gases generated by decomposing food. By using existing silicon-based manufacturing methodologies, this room-temperature gas sensing array can be fabricated reproducibly and at low cost, making it an attractive platform for ambient gas measurement needed in food safety applications.
Currently, few porous vanadium metal-organic frameworks (V-MOFs) are known and even fewer are obtainable as single crystals, resulting in limited information on their structures and properties. Here this work demonstrates remarkable promise of V-MOFs by presenting an extensible family of V-MOFs with tailorable pore geometry and properties. The synthesis leverages inter-modular synergy on a tri-modular pore-partitioned platform. New V-MOFs show a broad range of structural features and sorption properties suitable for gas storage and separation applications for C 2 H 2 /CO 2 , C 2 H 6 /C 2 H 4 , and C 3 H 8 /C 3 H 6 . The c/a ratio of the hexagonal cell, a measure of pore shape, is tunable from 0.612 to 1.258. Other tunable properties include pore size from 5.0 to 10.9 Å and surface area from 820 to 2964 m 2 g –1 . With C 2 H 2 /CO 2 selectivity from 3.3 to 11 and high uptake capacity for C 2 H 2 from 65.2 to 182 cm 3 g –1 (298K, 1 bar), an efficient separation is confirmed by breakthrough experiments. The near-record high uptake for C 2 H 6 (166.8 cm 3 g –1 ) contributes to the promise for C 2 H 6 -selective separation of C 2 H 6 /C 2 H 4 . The multi-module pore expansion enables transition from C 3 H 6 -selective to more desirable C 3 H 8 -selective separation with extraordinarily high C 3 H 8 uptake (254.9 cm 3 g –1 ) and high separation potential (1.25 mmol g –1 ) for C 3 H 8 /C 3 H 6 (50:50 v/v) mixture.
The mechanical behavior and microstructural evolution of a BCC-phase NbTaTiV refractory multi-principal element alloy (RMPEA) is studied over a wide range of strain rates (10 −3 to 10 3 s −1 ) and temperatures (room temperature to 850 °C). The mechanical property of present RMPEA shows less strain-rate dependence and strong resistance to softening at high temperatures. Under high strain-rate loading, the formation of thin type-I twins is observed, which could lead to an increase in strain-hardening rates. However, this hardening mechanism competes with adiabatic heating effects, resulting in the deterrence of strain-hardening behaviors. In contrast, substantial strain-hardening occurs at cryogenic temperatures due to the formation of twins, which act as stronger barriers to dislocation motion and interact with each other. To further understand the different strain-hardening behaviors, density functional theory (DFT) calculations predict relatively low stacking fault energies and high twinning stress for the NbTaTiV RMPEA.
The multi-principal element alloy nanoparticles (MPEA NPs), a new class of nanomaterials, present a highly rewarding opportunity to explore new or vastly different functional properties than the traditional mono/bi/multimetallic nanostructures due to their unique characteristics of atomic-level homogeneous mixing of constituent elements in the nanoconfinements. Here, in this paper, the successful creation of NiCoCr nanoparticles, a well-known MPEA system is reported, using ultrafast nanosecond laser-induced dewetting of alloy thin films. Nanoparticle formation occurs by spontaneously breaking the energetically unstable thin films in a melt state under laser-induced hydrodynamic instability and subsequently accumulating in a droplet shape via surface energy minimization. While NiCoCr alloy shows a stark contrast in physical properties compared to individual metallic constituents, i.e., Ni, Co, and Cr, yet the transient nature of the laser-driven process facilitates a homogeneous distribution of the constituents (Ni, Co, and Cr) in the nanoparticles. Using high-resolution chemical analysis and scanning nanodiffraction, the environmental stability and grain arrangement in the nanoparticles are further investigated. Thermal transport simulations reveal that the ultrashort (≈100 ns) melt-state lifetime of NiCoCr during the dewetting event helps retain the constituent elements in a single-phase solid solution with homogenous distribution and opens the pathway to create the unique MPEA nanoparticles with laser-induced dewetting process.
Public transit agencies are focused on making their fixed-line bus systems more energy efficient by introducing electric (EV) and hybrid (HV) vehicles to their fleets. However, because of the high upfront cost of these vehicles, most agencies are tasked with managing a mixed-fleet of internal combustion vehicles (ICEVs), EVs, and HVs. In managing mixed-fleets, agencies require accurate predictions of energy use for optimizing the assignment of vehicles to transit routes, scheduling charging, and ensuring that emission standards are met. The current state-of-the-art is to develop separate neural network models to predict energy consumption for each vehicle class. Although different vehicle classes’ energy consumption depends on a varied set of covariates, we hypothesize that there are broader generalizable patterns that govern energy consumption and emissions. In this paper, we seek to extract these patterns to aid learning to address two problems faced by transit agencies. First, in the case of a transit agency which operates many ICEVs, HVs, and EVs, we use multi-task learning (MTL) to improve accuracy of forecasting energy consumption. Second, in the case where there is a significant variation in vehicles in each category, we use inductive transfer learning (ITL) to improve predictive accuracy for vehicle class models with insufficient data. As this work is to be deployed by our partner agency, we also provide an online pipeline for joining the various sensor streams for fixed-line transit energy prediction. Here, we find that our approach outperforms vehicle-specific baselines in both the MTL and ITL settings.