Improved Performances in Polymer BHJ Solar Cells Through Frontier Orbital Tuning of Small Molecule Additives in Ternary Blends
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solar (photovoltaic), charge transport, materials ↗
SEARCH · Engineering Papers
Results for “performance tuning”
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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Hierarchal structures tuned electrocaloric and electromechanical performance in PVDF-based tetrapolymers
Ferroelectrics with multifunctionalities are gaining increased interest in self-actuated electrocaloric effect (ECE) refrigerators. However, achieving high ECE and electromechanical (EM) coupling concomitantly for maximum heat transfer remains challenging. Here we present the structure-property relationship for poly(vinylidene fluoride-co-trifluoroethylene-co-chlorofluoroethylene-co-double bond), P(VDF-TrFE-CFE-DB), tetrapolymer, which exhibited a high ECE entropy change of 66.5 J Kg −1 K −1 and EM strain of −6.1%. We show that thermal treatment can be a key factor influencing multifunctional properties. High-temperature annealing incorporates DB and CFE units into crystalline grains to form extended-chain crystals, enabling CFE units to induce relaxor behavior and DB units to induce large structural changes at low electric fields. This synergy leads to an enhancement in both ECE and EM performances. Furthermore, at an optimized temperature of 50 °C, the annealed films exhibit giant cross-energy coupling, achieving ECE and EM performances of 100.8 J Kg −1 K −1 and −7.6%. This study provides insights into developing new ferroelectric polymers with electroactive multifunctionalities.
36 MATERIALS SCIENCE↗
Performance Impact of Parameter Tuning on the CCSDS-123.0-B-2 Low-Complexity Lossless and Near-Lossless Multispectral and Hyperspectral Image Compression Standard
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Ian Blanes↗
Legacy Code Modernization
Over the past decade, high performance computing has evolved rapidly; systems based on commodity microprocessors have been introduced in quick succession from at least seven vendors/families. Porting codes to every new architecture is a difficult problem; in particular, here at NASA, there are many large CFD applications that are very costly to port to new machines by hand. The LCM ("Legacy Code Modernization") Project is the development of an integrated parallelization environment (IPE) which performs the automated mapping of legacy CFD (Fortran) applications to state-of-the-art high performance computers. While most projects to port codes focus on the parallelization of the code, we consider porting to be an iterative process consisting of several steps: 1) code cleanup, 2) serial optimization,3) parallelization, 4) performance monitoring and visualization, 5) intelligent tools for automated tuning using performance prediction and 6) machine specific optimization. The approach for building this parallelization environment is to build the components for each of the steps simultaneously and then integrate them together. The demonstration will exhibit our latest research in building this environment: 1. Parallelizing tools and compiler evaluation. 2. Code cleanup and serial optimization using automated scripts 3. Development of a code generator for performance prediction 4. Automated partitioning 5. Automated insertion of directives. These demonstrations will exhibit the effectiveness of an automated approach for all the steps involved with porting and tuning a legacy code application for a new architecture.
Hribar, Michelle R.↗
Quantum annealing for jet clustering with thrust
Quantum computing holds the promise of substantially speeding up computationally expensive tasks, such as solving optimization problems over a large number of elements. In high-energy collider physics, quantum-assisted algorithms might accelerate the clustering of particles into jets. In this study, we benchmark quantum annealing strategies for jet clustering based on optimizing a quantity called “thrust” in electron-positron collision events. Here, we find that quantum annealing yields similar performance to exact classical approaches and classical heuristics, after tuning the annealing parameters. Without tuning, comparable performance can be obtained through a hybrid quantum/classical approach.
72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
How Difficult is it to Reduce Low-Level Cloud Biases With the Higher-Order Turbulence Closure Approach in Climate Models?
Low-level clouds cover nearly half of the Earth and play a critical role in regulating the energy and hydrological cycle. Despite the fact that a great effort has been put to advance the modeling and observational capability in recent years, low-level clouds remains one of the largest uncertainties in the projection of future climate change. Low-level cloud feedbacks dominate the uncertainty in the total cloud feedback in climate sensitivity and projection studies. These clouds are notoriously difficult to simulate in climate models due to its complicated interactions with aerosols, cloud microphysics, boundary-layer turbulence and cloud dynamics. The biases in both low cloud coverage/water content and cloud radiative effects (CREs) remain large. A simultaneous reduction in both cloud and CRE biases remains elusive. This presentation first reviews the effort of implementing the higher-order turbulence closure (HOC) approach to representing subgrid-scale turbulence and low-level cloud processes in climate models. There are two HOCs that have been implemented in climate models. They differ in how many three-order moments are used. The CLUBB are implemented in both CAM5 and GDFL models, which are compared with IPHOC that is implemented in CAM5 by our group. IPHOC uses three third-order moments while CLUBB only uses one third-order moment while both use a joint double-Gaussian distribution to represent the subgrid-scale variability. Despite that HOC is more physically consistent and produces more realistic low-cloud geographic distributions and transitions between cumulus and stratocumulus regimes, GCMs with traditional cloud parameterizations outperform in CREs because tuning of this type of models is more extensively performed than those with HOCs. We perform several tuning experiments with CAM5 implemented with IPHOC in an attempt to produce the nearly balanced global radiative budgets without deteriorating the low-cloud simulation. One of the issues in CAM5-IPHOC is that cloud water content is much higher than in CAM5, which is combined with higher low-cloud coverage to produce larger shortwave CREs in some low-cloud prevailing regions. Thus, the cloud-radiative feedbacks are exaggerated there. The turning exercise is focused on microphysical parameters, which are also commonly used for tuning in climate models. The results will be discussed in this presentation.
Xu, Kuan-Man↗
Extremum-seeking control for an Ultrasonic/Sonic Driller/Corer (USDC) driven at high-power
Future NASA exploration missions will increasingly require sampling, in-situ analysis and possibly the return of material to Earth for further tests. One of the challenges to addressing this need is the ability to drill using for low axial loading while operating from light weight platforms (e.g., lander, rover, etc.) as well as operate at planets with low gravity. For this purpose, the authors developed the Ultrasonic/Sonic Driller/Corer (USDC) jointly with Cybersonics Inc. Studies of the operation of the USDC at high power have shown there is a critical need to self-tune to maintain the operation of the piezoelectric actuator at resonance. Performing such tuning is encountered with difficulties and to address them an extremum-seeking control algorithm is being investigated. This algorithm is designed to tune the driving frequency of a time-varying resonating actuator subjected to both random and high-power impulsive noise disturbances. Using this algorithm the performance of the actuator is monitored on a time-scale that is compatible with its slowly time-varying physical characteristics. The algorithm includes a parameter estimator, which estimates the coefficients of a function that characterizes the quality factor of the USDC. Since the parameter estimator converges sufficiently faster than the time-varying drift of the USDC's physical parameters, the proposed extremum-seeking estimation and control algorithm is potentially applicable for use as a closed-loop health monitoring system. Specifically, this system may be programmed to automatically adjust the duty-cycle of the sinusoidal driver signal to guarantee that the quality factor of the USDC does not fall below a user-defined set-point. Such fault-tolerant functionality is especially important in automated drilling applications where it is essential not to inadvertently drive the piezoelectric ceramic crystals of the USDC beyond their capacities. The details of the algorithm and experimental results will be described and discussed in this paper.
Peek-seeking estimation and control↗
TunIO: An AI-powered Framework for Optimizing HPC I/O
I/O operations are a known performance bottleneck of HPC applications. To achieve good performance, users often employ an iterative multistage tuning process to find an optimal I/O stack configuration. However, an I/O stack contains multiple layers, such as high-level I/O libraries, I/O middleware, and parallel file systems, and each layer has many parameters. These parameters and layers are entangled and influenced by each other. The tuning process is time-consuming and complex. In this work, we present TunIO, an AI-powered I/O tuning framework that implements several techniques to balance the tuning cost and performance gain, including tuning the high-impact parameters first. Furthermore, TunIO analyzes the application source code to extract its I/O kernel while retaining all statements necessary to perform I/O. It utilizes a smart selection of high-impact configuration parameters of the given tuning objective. Finally, it uses a novel Reinforcement Learning (RL)-driven early stopping mechanism to balance the cost and performance gain. Experimental results show that TunIO leads to a reduction of up to ≈73% in tuning time while achieving the same performance gain when compared to H5Tuner. It achieves a significant performance gain/cost of 208.4 MBps/min (I/O bandwidth for each minute spent in tuning) over existing approaches under our testing.
Rajesh, Neeraj↗
Automatic tuning of the Community Atmospheric Model (CAM5) by using short-term hindcasts with an improved downhill simplex optimization method
Traditional trial-and-error tuning of uncertain parameters in global atmospheric general circulation models (GCMs) is time consuming and subjective. This study explores the feasibility of automatic optimization of GCM parameters for fast physics by using short-term hindcasts. An automatic workflow is described and applied to the Community Atmospheric Model (CAM5) to optimize several parameters in its cloud and convective parameterizations. We show that the auto-optimization leads to 10 % reduction of the overall bias in CAM5, which is already a well-calibrated model, based on a predefined metric that includes precipitation, temperature, humidity, and longwave/shortwave cloud forcing. The computational cost of the entire optimization procedure is about equivalent to a single 12-year atmospheric model simulation. The tuning reduces the large underestimation in the CAM5 longwave cloud forcing by decreasing the threshold relative humidity and the sedimentation velocity of ice crystals in the cloud schemes; it reduces the overestimation of precipitation by increasing the adjustment time in the convection scheme. The physical processes behind the tuned model performance for each targeted field are discussed. Limitations of the automatic tuning are described, including the slight deterioration in some targeted fields that reflect the structural errors of the model. It is pointed out that automatic tuning can be a viable supplement to process-oriented model evaluations and improvement.
54 ENVIRONMENTAL SCIENCES↗
Automatic tuning of the Community Atmospheric Model CAM5.3 by using short-term hindcasts with an improved downhill simplex optimization method
Traditional trial-and-error tuning of uncertain parameters in global atmospheric General Circulation Models (GCM) is time consuming and subjective. This study explores the feasibility of automatic optimization of GCM parameters for fast physics by using short-term hindcasts. An automatic workflow is described and applied to the Community Atmospheric Model (CAM5) to optimize several parameters in its cloud and convective parameterizations. We show that the auto-optimization leads to 10 % reduction of the overall bias in CAM5, which is already a well calibrated model, based on a pre-defined metric that includes precipitation, temperature, humidity, and longwave/shortwave cloud forcing. The computational cost of the entire optimization procedure is about equivalent to about a single 12-year atmospheric model simulation. The tuning reduces the large underestimation in the CAM5 longwave cloud forcing by decreasing the threshold relative humidity and the sedimentation velocity of ice crystals in the cloud schemes; it reduces the overestimation of precipitation by increasing the adjustment time in the convection scheme. The physical processes behind the tuned model performance for each targeted field are discussed. Limitations of the automatic tuning are described, including the slight deterioration in some targeted fields that reflect the structural errors of the model. It is pointed out that automatic tuning can be a viable supplement to process-oriented model evaluations and improvement.
54 ENVIRONMENTAL SCIENCES↗
Performance Profile of Transformer Fine-Tuning in Multi-GPU Cloud Environments
The study presented here focuses on performance characteristics and trade-offs associated with running machine-learning tasks in multi-GPU environments on both on-site cloud computing resources and commercial cloud services (Azure). Specifically, this study examines these tradeoffs by examining the performance of training and fine-tuning of transformer-based deep-learning (DL) networks on clinical notes and data, a task of critical importance in the medical domain. To this end, we perform DL-related experiments on the widely deployed NVIDIA V100 GPUs and on the newer A100 GPUs connected via NVLink or PCIe. This study analyzes the execution time of major operations to train DL models and investigate popular options to optimize each of them. We examine and present the findings on the impacts that various operations (e.g. data loading into GPUs, training, fine-tuning), optimizations, and system configurations (single vs. multi-GPU, NVLink vs. PCIe) have on the overall training performance.
Begoli, Edmon↗
Autonomous Performance Monitoring System: Monitoring and Self-Tuning (MAST)
Maintaining the long-term performance of software onboard a spacecraft can be a major factor in the cost of operations. In particular, the task of controlling and maintaining a future mission of distributed spacecraft will undoubtedly pose a great challenge, since the complexity of multiple spacecraft flying in formation grows rapidly as the number of spacecraft in the formation increases. Eventually, new approaches will be required in developing viable control systems that can handle the complexity of the data and that are flexible, reliable and efficient. In this paper we propose a methodology that aims to maintain the accuracy of flight software, while reducing the computational complexity of software tuning tasks. The proposed Monitoring and Self-Tuning (MAST) method consists of two parts: a flight software monitoring algorithm and a tuning algorithm. The dependency on the software being monitored is mostly contained in the monitoring process, while the tuning process is a generic algorithm independent of the detailed knowledge on the software. This architecture will enable MAST to be applicable to different onboard software controlling various dynamics of the spacecraft, such as attitude self-calibration, and formation control. An advantage of MAST over conventional techniques such as filter or batch least square is that the tuning algorithm uses machine learning approach to handle uncertainty in the problem domain, resulting in reducing over all computational complexity. The underlying concept of this technique is a reinforcement learning scheme based on cumulative probability generated by the historical performance of the system. The success of MAST will depend heavily on the reinforcement scheme used in the tuning algorithm, which guarantees the tuning solutions exist.
Peterson, Chariya↗
Improvement of Oxygen Reduction Performance in Alkaline Media by Tuning Phase Structure of Pd–Bi Nanocatalysts
Tuning the crystal phase of bimetallic nanocrystals offers an alternative avenue to improving their electrocatalytic performance. Herein, we present a facile and one-pot synthesis approach that is used to enhance the catalytic activity and stability toward oxygen reduction reaction (ORR) in alkaline media via control of the crystal structure of Pd-Bi nanocrystals. By merely altering the types of Pd precursors under the same conditions, the monoclinic structured Pd 5 Bi 2 and conventional face-centered cubic (fcc) structured Pd 3 Bi nanocrystals with comparable size and morphology can be precisely synthesized, respectively. Interestingly, the carbon-supported monoclinic Pd 5 Bi 2 nanocrystals exhibit superior ORR activity in alkaline media, delivering a mass activity (MA) as high as 2.05 A/mg Pd . After 10,000 cycles of ORR durability test, the monoclinic structured Pd 5 Bi 2 /C nanocatalysts still remain a MA of 1.52 A/mg Pd , which is 3.6 times, 16.9 times, and 21.7 times as high as those of the fcc Pd 3 Bi/C counterpart, commercial Pd/C, and Pt/C electrocatalysts, respectively. Moreover, structural characterizations of the monoclinic Pd 5 Bi 2 /C nanocrystals after the durability test demonstrate the excellent retention of the original size, morphology, composition, and crystal phase, greatly alleviating the leaching of the Bi component. Overall, this work provides new insight for the synthesis of multimetallic catalysts with a metastable phase and demonstrates phase-dependent catalytic performance.
37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
Conceptual Design of a Tiltduct Reference Vehicle for Urban Air Mobility
NASA is establishing a fleet of conceptual air vehicle designs to support research and development for Urban Air Mobility (UAM). This fleet of vehicles will enable examination of the sensitivity of UAM vehicle designs to technology assumptions, identify key research and development needs for UAM aircraft, and provide the UAM community with reference vehicles that are publicly available and based upon known assumptions. To date, five six-passenger reference vehicles have been published: a quadrotor, a side-by-side, a lift-plus-cruise, a single-main-rotor helicopter, and a tiltwing. To increase the breadth of vehicle technologies encapsulated in the fleet of NASA UAM reference vehicles, this paper establishes a tiltduct vehicle as an addition to the fleet. The fleet will continue to evolve as future analyses and trade studies are performed. The tiltduct reference vehicle has six tilting ducted proprotors. This paper describes the initial configuration downselection; discusses ducted proprotor design rules of thumb as they applied to the conceptual design of the reference vehicle; describes the vehicle sizing, trade studies, and tuning of models performed; and finally, compares the resulting tiltduct vehicle against the other six-passenger NASA UAM reference vehicles. The high-level analyses performed for this study did not indicate significant differences in performance between the tiltduct and tiltwing reference vehicles, and so vehicle performance alone may not be a key driver in the selection of a tiltduct vehicle over a tiltwing vehicle. However, if ducts are found to have significant acoustical benefits, then acoustical priorities may provide a compelling reason to incorporate ducted proprotors. One significant limitation of the design presented in this paper is that the ducted proprotor performance was tuned based upon performance characteristics observed during historical tests with disk loadings (defined as thrust divided by proprotor disk area) of 125-250 lb/ft^2. The tiltduct vehicle designed in this study has a disk loading of 30 lb/ft^2, to be more representative of UAM vehicles; further studies to understand performance of ducted proprotors at representative disk loadings are warranted.
Tiltduct↗
Conceptual Design of a Tiltduct Reference Vehicle for Urban Air Mobility
NASA is establishing a fleet of conceptual air vehicle designs to support research and development for Urban Air Mobility (UAM). This fleet of vehicles will enable examination of the sensitivity of UAM vehicle designs to technology assumptions, identify key research and development needs for UAM aircraft, and provide the UAM community with reference vehicles that are publicly available and based upon known assumptions. To date, five six-passenger UAM reference vehicles have been published: a single-main-rotor helicopter, a side-by-side helicopter, a quadrotor, a lift-plus-cruise, and a tiltwing. To increase the breadth of vehicle technologies encapsulated in the fleet of NASA UAM reference vehicles, this paper establishes a tiltduct vehicle as an addition to the fleet. The fleet will continue to evolve as future analyses and trade studies are performed. The tiltduct UAM reference vehicle has six tilting ducted proprotors. This paper describes the initial configuration downselection; discusses ducted proprotor design rules of thumb as they applied to the conceptual design of the reference vehicle; describes the vehicle sizing, trade studies, and tuning of models performed; and finally, compares the resulting tiltduct vehicle against the other six-passenger NASA UAM reference vehicles. The high-level analyses performed for this study did not indicate significant differences in performance between the tiltduct and tiltwing reference vehicles, and so vehicle performance alone may not be a key driver in the selection of a tiltduct vehicle over a tiltwing vehicle. However, if ducts are found to have significant acoustical benefits, then acoustical priorities may provide a compelling reason to incorporate ducted proprotors. One significant limitation of the design presented in this paper is that the ducted proprotor performance was tuned based upon performance characteristics observed during historical tests with disk loadings (defined as thrust divided by proprotor disk area) of 125-250 lb/ft$^2$. The tiltduct vehicle designed in this study has a disk loading of 30 lb/ft$^2$, to be more representative of UAM vehicles; further studies to understand performance of ducted proprotors at representative disk loadings are warranted.
Tiltduct↗
Simulation-Based Inference for Neutrino Interaction Model Parameter Tuning
High-energy physics experiments studying neutrinos rely heavily on simulations of their interactions with atomic nuclei. Limitations in the theoretical understanding of these interactions typically necessitate ad hoc tuning of simulation model parameters to data. Traditional tuning methods for neutrino experiments have largely relied on simple algorithms for numerical optimization. While adequate for the modest goals of initial efforts, the complexity of future neutrino tuning campaigns is expected to increase substantially, and new approaches will be needed to make progress. In this paper, we examine the application of simulation-based inference (SBI) to the neutrino interaction model tuning for the first time. Using a previous tuning study performed by the MicroBooNE experiment as a test case, we find that our SBI algorithm can correctly infer the tuned parameter values when confronted with a mock data set generated according to the MicroBooNE procedure. This initial proof-of-principle illustrates a promising new technique for next-generation simulation tuning campaigns for the neutrino experimental community.
Tame-Narvaez, Karla Maria [Fermilab]↗
A generalized reusable guidance algorithm for optimal aerobraking
A practical real-time guidance algorithm was developed for guiding aerobraking vehicles in such a way that the maximum heating rate, the maximum structural loads, and the post-aeropass delta-V requirements (for post-aeropass orbit insertion) are all minimized. The algorithm is general and reusable in the sense that a minimum of assumptions are made, thus minimizing the number of gains and mission-dependent parameters that must be laboriously determined prior to a particular mission. A particularly interesting feature is that inplane guidance performance is tuned by simply adjusting one mission-dependent parameter, the bank margin; similarly, the out-of-plane guidance performance is turned by simply adjusting a plane controller time constant. Other objectives in the algorithm development are simplicity, efficiency, and ease of use. The algorithm is developed for, but not necessarily restricted to, a single pass mission and a trimmed vehicle with a bank angle modulation as the method of trajectory control. Guidance performance is demonstrated via results obtained using this algorithm integrated into an aerobraking test-bed program. Comparisons are made with numerical results from a version of the aerobraking guidance algorithm that was to be flown onboard NASA's aeroassist flight experiment (AFE) vehicle. Promising results are obtained with a minimum of development effort.
Dukeman, G. A.↗
Simulation-based inference for neutrino interaction model parameter tuning
High-energy physics experiments studying neutrinos rely heavily on simulations of their interactions with atomic nuclei. Limitations in the theoretical understanding of these interactions typically necessitate ad hoc tuning of simulation model parameters to data. Traditional tuning methods for neutrino experiments have largely relied on simple algorithms for numerical optimization. While adequate for the modest goals of initial efforts, the complexity of future neutrino tuning campaigns is expected to increase substantially, and new approaches will be needed to make progress. In this paper, we examine the application of simulation-based inference (SBI) to the neutrino interaction model tuning for the first time. Using a previous tuning study performed by the MicroBooNE experiment as a test case, we find that our SBI algorithm can correctly infer the tuned parameter values when confronted with a mock data set generated according to the MicroBooNE procedure. This initial proof-of-principle illustrates a promising new technique for next-generation simulation tuning campaigns for the neutrino experimental community.
Tame-Narvaez, Karla [Fermilab] (ORCID:000000022249↗