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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 163 records · Page 9
All sourcesNASA NTRSDOE OSTI
DOE OSTI2025

Optimization of Species-Selective Reversible Proteasome Inhibitors for the Treatment of Malaria

Abstract Malaria remains a critical global health challenge, with increasing resistance to frontline therapies necessitating novel drug targets. The proteasome has emerged as a promising target for antimalarial drug discovery. This study describes efforts to optimize a series of species-selective reversible inhibitors targeting the Plasmodium falciparum 20S proteasome. Starting from the carboxypiperidine scaffold identified through a high-throughput viability screen, we conducted iterative structure–activity relationship studies, leading to the development of highly potent and selective inhibitors with good oral bioavailability. Lead compounds demonstrated nanomolar potency against P. falciparum blood-stage parasites and selective inhibition of the parasite proteasome over the human counterpart. Cryo-EM structural studies confirmed binding at the β5 subunit, while in vivo pharmacokinetic studies identified promising candidates for further development. These findings support proteasome inhibition as a viable strategy for novel antimalarial drug development.

Gahalawat, Suraksha [UT Southwestern Medical Cente↗
DOE OSTI2021

Analyzing Gas Turbine-Generator Performance of the Hybrid Power System

We report kinetic energy available from the turbomachinery may boost short-term ramp rates during load shifting between generator and fuel cell that may be exploited to optimize the performance of turbine-fuel cell hybrid systems during load following. The paper starts with modeling a gas turbine, gearbox, and generator sitting on the rigid shafts as a two-mass model primarily for estimating the rotational inertia available from the turbogenerator. The gas turbine and its environment impose constraints of available space allowances, measurements from high-speed rotating parts, lack of accessibility to the shaft of measure, making replacements and/or required installation adjustments of the torque transducer difficult. It is demonstrated that in the absence of torque measurement, the data containing only a slow excursion of the rotor motion in response to a slight load change is sufficient to determine inertia and damping coefficients from the model. Furthermore, the generator’s reactive power capability was investigated because it serves as an asset in integrated hybrid systems to meet dynamic reactive power demands, reducing the burden on the fuel cell’s power electronics control in generating reactive power.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2024

Final cooling with thick wedges for a muon collider

In the final cooling stages for a muon collider, the transverse emittances are reduced while the longitudinal emittance is allowed to increase. In previous studies, Final 4-D cooling used absorbers within very high field solenoids to cool low-momentum muons. Simulations of the systems did not reach the desired cooling design goals. In this study, we develop and optimize a different conceptual design for the final 4D cooling channel, which is based on using dense wedge absorbers. We used G4Beamline to simulate the channel and Python to generate and analyze particle distributions. We optimized the design parameters of the cooling channel and produced conceptual designs (corresponding to possible starting points for the input beam) which achieve transverse cooling in both x and y by a factor of ~3.5. These channels achieve a lower transverse and longitudinal emittance than the best design previously published.

43 PARTICLE ACCELERATORS↗
DOE OSTI2023

Nuclear-Renewable-Storage Systems: Enhancing Planning and Operations of Integrated Energy Systems

Nuclear-renewable-storage integrated energy systems (IES) are multi-carrier energy systems that include not only electricity but also other forms of demands. Because individual IES components must observe their thermo-physical limits, including ramp rates, start-up, and shut-down time, we formulate operations of the IES as an optimization model by minimizing the total operations costs subject to physical limits of all constituent components. In addition, we develop a data-driven approach to improve the computational performance of the economic dispatch model by using reinforcement learning, where an agent is rewarded by meeting demands and penalized otherwise when shifting to the next state.

25 ENERGY STORAGE↗
DOE OSTI2024

Hot Weather Impacts on Battery-Electric Transit Buses

Transit fleets considering integrating battery-electric buses (BEBs) can start by examining the impact of hot weather on BEBs and identifying key factors to optimize bus performance in high temperatures.

ADVANCED PROPULSION SYSTEMS↗
DOE OSTI2024

The Value of Hyperparameter Optimization in Phase-Picking Neural Networks

The effectiveness of neural networks for picking seismic phase arrival times has been demonstrated through several case studies, and seismic monitoring programs are starting to adopt the technology into their workflows. However, published models were designed and trained using rather arbitrary choices of hyperparameters, limiting their performance. In this study, we use phase picks from both routine and template-matching analyses from multiple regions (Ridgecrest, California; Kilauea, Hawaii; Yellowstone, Wyoming–Montana–Idaho) to test a hyperparameter optimization scheme for phase-picking neural networks and to evaluate their performance. We show that a published model, namely PhaseNet (Zhu and Beroza, 2019), can be simplified and improved with reasonable effort and there are preferred choices of hyperparameters that increase the performance. We also show that models optimized based on the arrival times reported in routine event catalogs consistently perform well when picking arrival times of smaller events, which is crucial for many tasks from microseismicity to explosion monitoring.

58 GEOSCIENCES↗
DOE OSTI2023

"Reducing Detailed Vehicle Energy Dynamics to Physics-Like Models"

The energy demand of vehicles, particularly in unsteady drive cycles, is affected by complex dynamics internal to the engine and other powertrain components. Yet, in many applications, particularly macroscopic traffic flow modeling and optimization, structurally simple approximations to the complex vehicle dynamics are needed that nevertheless reproduce the correct effective energy behavior. This work presents a systematic model reduction pipeline that starts from complex vehicle models based on the Autonomie software and derives a hierarchy of simplified models that are fast to evaluate, easy to disseminate in open-source frameworks, and compatible with optimization frameworks. The pipeline, based on a virtual chassis dynamometer and subsequent approximation strategies, is reproducible and is applied to six different vehicle classes to produce concrete explicit energy models that represent an average vehicle in each class and leverage the accuracy and validation work of the Autonomie software.

Khoudari, Nour↗
DOE OSTI2024

Opportunities and Challenges for Machine Learning-Assisted Enzyme Engineering

Enzymes can be engineered at the level of their amino acid sequences to optimize key properties such as expression, stability, substrate range, and catalytic efficiency or even to unlock new catalytic activities not found in nature. Because the search space of possible proteins is vast, enzyme engineering usually involves discovering an enzyme starting point that has some level of the desired activity followed by directed evolution to improve its “fitness” for a desired application. Recently, machine learning (ML) has emerged as a powerful tool to complement this empirical process. ML models can contribute to (1) starting point discovery by functional annotation of known protein sequences or generating novel protein sequences with desired functions and (2) navigating protein fitness landscapes for fitness optimization by learning mappings between protein sequences and their associated fitness values. In this Outlook, we explain how ML complements enzyme engineering and discuss its future potential to unlock improved engineering outcomes.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2023

Enabling Real-time Scattering Data Analysis with Scalable Optimization [Slides]

Diffraction experiments produce datasets with rich multidimensional physics information such as microstructure, equations of state, crystal structure, elastoplastic properties, and other key inputs to LANL mission-essential multiphysics models. This information is typically extracted through a process called Rietveld refinement, which involves selecting appropriate models of the instrument, crystal structure, and microstructure, identifying suitable starting values, and then fitting often hundreds of model parameters using a sequence of empirical parameter turnon/off sequences within a non-global gradient-based optimization. Extensive user expertise is required to properly setup a refinement, identify appropriate models, and select initial parameter values close to truth, such that the refinement will yield parameter values that are optimally predictive. This is a very tedious manual process performed far after the beamline campaign has ended. As facilities have become capable of generating larger volumes of data, the limitation in throughput due to Rietveld refinement has led to a dramatic increase in unanalyzed data as opposed to an intended increase in new science. In our FY22 TED, we demonstrated an integrated toolset providing near real-time automated Rietveld analysis. If this toolset can be optimized to provide automated Rietveld analysis in real-time, this could alleviate the bottleneck in unanalyzed diffraction data, aid in decision-making during experiments, and increase efficiency of the facility.

74 ATOMIC AND MOLECULAR PHYSICS↗
DOE OSTI2024

Paired Hydro-Battery Hybrid System Operations Using MIQP Based Multi-Objective Optimization

Hybridizing a hydropower plant with a battery energy storage system is often a very expensive decision. So to make the case for feasibility of hybridization the cost benefit analysis must be comprehensive. Most literature found on this subject only uses one out of many different available value streams to carry out a feasibility analysis. To that end, in this study we propose a multi-objective optimization formulation and a mixed-integer quadratic programming optimization engine that considers multiple different value streams and optimizes hydro-battery hybrid system paired operations to maximize revenue generated from energy arbitrage while simultaneously minimizing the total hydro turbine mileage thus reducing turbine starts and stops and improving turbines life all while following all environmental constraints and not violating any limitations either regulatory or preferential (i.e., to support recreational activities like white water rafting, etc.). In this study we also implemented the developed optimization methodology to a real-world case study of Bagnell dam hydropower facility (8 units totaling 240 MW rated power capacity) which is owned and operated by our industry partner Ameren Energy Inc. The case study outcomes shows that by hybridizing the Bagnell dam hydropower facility with a 60 MW x 2-hr battery energy storage system, the annual benefits can be increased over \$6 million while reducing the annual mileage averaged per turbine and number of start/stops by over 98\% and 85\% respectively.

Chalishazar, Vishvas H.↗
DOE OSTI2021

Advanced Light-Duty Spark Ignition Engine Research: Co-Optimization of Fuels and Engines and Partnership to Advance Combustion Engines (FY2020 Annual Progress Report)

This report covers recent progress on research tasks that support both the Co-Optimization of Fuels and Engines (Co-Optima) initiative and the Partnership to Advance Combustion Engines (PACE) consortium. The Co-Optima tasks further the science-base needed by industry stakeholders to co-evolve the next generation of highly efficient direct injection spark ignition (DISI) engines and new gasoline-type fuels. The research emphasis is on fuel effects on multimode spark ignition (SI) engine operation, which uses traditional non-dilute stoichiometric operation for peak load and power but reverts to lean operation at lower loads to provide higher fuel economy. This work focuses on determining desirable fuel specifications in terms of well-established metrics like research octane number (RON) and motor octane number, but it also involves the assessment of new fuel metrics, including fuel sooting propensity and phi-sensitivity. The PACE task supports the development of predictive computational fluid dynamics (CFD) modeling, which promises to unlock new strategies for high-efficiency combustion while minimizing tailpipe emissions. Here, the primary fuel is a regular E10 gasoline (i.e., a regular gasoline blend containing 10% ethanol), and focus is on fuel-spray dynamics and soot emissions. Soot-formation pathways are studied to determine how the pathways change with injection strategies and the thermal state of the engine (i.e., cold-starting vs. fully warmed-up operation). This PACE task also contributed to the development of an optimal E10 gasoline surrogate fuel, as reported in detail elsewhere

33 ADVANCED PROPULSION SYSTEMS↗
DOE OSTI2022

Optimizing the shape of photometric redshift distributions with clustering cross-correlations

We present an optimization method for the assignment of photometric galaxies to a chosen set of redshift bins. This is achieved by combining simulated annealing, an optimization algorithm inspired by solid-state physics, with an unsupervised machine learning method, a self-organizing map (SOM) of the observed colours of galaxies. Starting with a sample of galaxies that is divided into redshift bins based on a photometric redshift point estimate, the simulated annealing algorithm repeatedly reassigns SOM-selected subsamples of galaxies, which are close in colour, to alternative redshift bins. We optimize the clustering cross-correlation signal between photometric galaxies and a reference sample of galaxies with well-calibrated redshifts. Depending on the effect on the clustering signal, the reassignment is either accepted or rejected. By dynamically increasing the resolution of the SOM, the algorithm eventually converges to a solution that minimizes the number of mismatched galaxies in each tomographic redshift bin and thus improves the compactness of their corresponding redshift distribution. This method is demonstrated on the synthetic Legacy Survey of Space and Time cosmoDC2 catalogue. We find a significant decrease in the fraction of catastrophic outliers in the redshift distribution in all tomographic bins, most notably in the highest redshift bin with a decrease in the outlier fraction from 57 percent to 16 percent.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2022

Failure Probability Constrained AC Optimal Power Flow

Despite cascading failures being the central cause of blackouts in power transmission systems, existing operational and planning decisions are made largely by ignoring their underlying cascade potential. This paper posits a reliability-aware AC Optimal Power Flow formulation that seeks to design a dispatch point which has a low operator-specified likelihood of triggering a cascade starting from any single component outage. By exploiting a recently developed analytical model of the probability of component failure, our Failure Probability-constrained ACOPF (FP-ACOPF) utilizes the system's expected first failure time as a smoothly tunable and interpretable signature of cascade risk. Here, we use techniques from bilevel optimization and numerical linear algebra to efficiently formulate and solve the FP-ACOPF using off-the-shelf solvers. Extensive simulations on the IEEE 118-bus case show that, when compared to the unconstrained and N-1 security-constrained ACOPF, our probability-constrained dispatch points can significantly lower the probabilities of long severe cascades and of large demand losses, while incurring only minor increases in total generation costs.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2023

Opportunities for wave energy in bulk power system operations

Wave energy resources have high, yet largely untapped potential as candidate generation technology. In this paper, we perform a data-driven analysis to characterize the impact of wave energy integration on bulk-scale power systems and market operations. Through data-driven sensitivity studies centered on an optimization-based production cost modeling formulation, our work characterizes the inflection point beyond which wave integration starts impacting power system operations, considering present day transmission infrastructure. Furthermore, our analysis also considers the joint effects of wave energy integration and system-wide transmission expansion. Finally, potential resilience scenarios such as wildfire-driven transmission contingencies and heat wave events are investigated, whereby the contributions of grid-integrated wave energy in alleviating the effects of the resilience events are analyzed. As our demonstration test bed, we consider a reduced-order network topology for the U.S. Western Interconnection with wave energy generation integrated at carefully selected sites across the coastal areas of Washington, Oregon, and northern California. Our results indicate that over a representative year of operations, wave energy integration systematically reduces locational marginal prices (LMPs) of energy and price volatility, especially during periods of high wave resource availability (winter months for the U.S. west coast). Average, maximum, and minimum of hourly LMPs over a typical year of operation was reduced by 2.95, 51.28, and 1.13 $\$$/MWh respectively (over a baseline scenario with no wave energy integration), when the selected network model had a total of 5000 MW wave power installed capacity during the representative year of study. The effects of wave energy integration can remain localized with existing transmission infrastructure (identified to be most pronounced in the Pacific Northwest region in the example we studied). However, with concurrent transmission expansion, the impacts of wave energy integration are likely to have a higher geographical spread. Our results also indicate that wave energy may be able to assist power system operations during resilience events such as major transmission contingencies and heat wave events, although such benefits might be dependent on factors such as proximity of affected area to wave resources, availability of adequate resource potential and adequate transmission capacity.

16 TIDAL AND WAVE POWER↗
DOE OSTI2023

Accelerating and stabilizing the convergence of vibrational self-consistent field calculations via the direct inversion of the iterative subspace (vDIIS) algorithm

The vibrational self-consistent field (VSCF) method yields anharmonic states and spectra for molecular vibrations, and it serves as the starting point for more sophisticated correlated-vibration methods. Convergence of the iterative, non-linear optimization in VSCF calculations can be erratic or altogether unsuccessful, particularly for chemical systems involving low-frequency motions. In this work, a vibrational formulation of the Direct Inversion of the Iterative Subspace method of Pulay is presented and investigated. This formulation accounts for distinct attributes of the vibrational and electronic cases, including the expansion of each single-mode vibrational wavefunction in its own basis set. The resulting Direct Inversion of the Iterative Subspace method is shown to substantially accelerate VSCF convergence in all convergent cases as well as rectify many cases where Roothaan-based methods fail. Performance across systems ranging from small, rigid molecules to weakly bound molecular clusters is investigated in this analysis.

Chemistry↗
DOE OSTI2024

The Challenges of Modeling Distributed Energy Resources (DERs) as Blackstart Resources and for Volt-VAR Optimality

Modeling, testing and instituting Distributed Energy Resources (DERs) as Blackstart Resoucrces presents several challenges due to the fundamental differences between traditional black start resources (e.g., large generators) versus DERs like solar PV systems with battery storage. This paper addresses some of these key challenges. These include intermittency of coordinating DERs to provide continuous power during a black start event, especially during extended periods of cloud cover or when battery energy storage is depleted. This paper additionally addresses the collapsing voltage and stability control challenges specific to maintaining bulk power system stability during black start synchronization These physical and engineering limitations require careful engineering design, modeling and engineering to ensure that DERs can support critical loads and substations during black start events. A variety of additional challenges also exist. Additionally, there are challenges associated with feeder location and low voltage secondary system impacts on DER functions and settings. We compare typical functions and settings for DERs for power factor control and correction. We also demonstrate how voltag control via Volta-VAR power factor correction can be done

Mukherjee, Srijib↗
DOE OSTI2020

CCAMP: OpenMP and OpenACC Interoperable Framework

Heterogeneous systems have become a staple of the HPC environment. Several directive-based solutions, such as OpenMP and OpenACC, have been developed to alleviate the challenges of programming heterogeneous systems, and these standards strive to provide a single portable programming solution across heterogeneous environments. However, in many ways this goal has yet to be realized due to device-specific implementations and different levels of language support across compilers. In this framework we aim to analyze and address the different levels of optimization and compatibility between OpenACC and OpenMP programs and device compilers. We introduce the CCAMP framework, built on the OpenARC compiler, which implements language translation between OpenACC and OpenMP, with the goal of exploiting the maturity of different device-specific compilers to maximize performance for a given architecture. We show that CCAMP allows us to generate code for a specific device-compiler combination given a device-agnostic OpenMP or OpenACC program, allowing compilation and execution of programs with specific directives on otherwise incompatible devices. CCAMP also provides a starting point for a more advanced interoperable framework that can effectively provide directive translation and device, compiler, and application specific optimizations.

Lambert, Jacob↗
DOE OSTI2024

Studies of the outer-off-midplane lower hybrid wave launch scenario for plasma start-up on the TST-2 spherical tokamak

Abstract Establishment of an efficient central solenoid (CS) free tokamak plasma start-up method may lead to an economical fusion reactor. CS-free start-up using lower hybrid (LH) waves has been studied on the TST-2 spherical tokamak. Plasma current of about a quarter of CS-driven discharges has been obtained fully non-inductively using the outer-midplane and top LH launchers. Recently, an outer-off-midplane LH launcher was developed to achieve higher plasma current by optimizing for core absorption and minimal fast electron losses. Using the (outer-)off-midplane launcher, fully non-inductive plasma current start-up up to about 8 kA was achieved. Coupled ray-tracing and Fokker–Planck simulation was performed on equilibria reconstructed with an extended MHD model. It was found that the experimentally observed plasma current was in reasonable agreement with the numerical simulation. The simulation predicted appreciable orbit losses for the off-midplane launcher driven discharge at the present parameters, which was consistent with the experimentally observed x-ray radiation characteristics. The simulation showed that the current density was saturated for the present off-midplane launcher discharges and higher density and higher LH power was necessary to achieve higher plasma current.

Physics↗