Development of a Low-Cost Exhaust H2 Measurement Method for In-Use Vehicles
Bibliographic record
Abstract
Abstract Port-injected hydrogen (H2) can be used as a partial substitution of diesel fuel in compression-ignition engines to reduce GHG emissions. For port-injected H2 systems, incomplete combustion or valve overlap can result in H2 slip, which increases the brake-specific fuel consumption. In this study, a low-cost method is developed to measure the H2 slip in the exhaust of a heavy-duty truck under real-world operating conditions. The truck is equipped with a 2016 15L Detroit diesel engine converted to run in dual-fuel mode with port-injected H2 ignited by directly injected diesel. Existing H2 detecting methods used for steady-state laboratory tests either have slow response time or require well-controlled testing environments. To develop a method suitable for transient on-road H2 measurements, we utilized a low-cost semiconductor sensor. The output of the sensor is potentially influenced by temperature, relative humidity (RH), gas flow rate, as well as the sensor’s resistance in the ambient air (R0) and the pre-heating strategy. Firstly, the characteristics of R0 was investigated in controlled benchtop tests, where pre-heating time, gas temperature, and RH were monitored. Then, the sensor was calibrated using a standard gas mixture of H2 and nitrogen. Finally, a Portable Emission Measurement System (PEMS) was developed to control the conditions of the sample gas. The sensor output was recorded using a low-cost Raspberry Pi Data Acquisition (DAQ) system in combination with an analog HAT (Hardware attached on top) module at a frequency of 4Hz. The results from the benchtop tests show that RH and flow rate both have significant influences on the sensor’s output. To ensure a stable R0, thirty minutes of pre-heating time is required. After calibration, the sensor’s readings are within 15% difference compared with the actual values. Data from the on-road tests demonstrated the applicability of the system for in-use vehicle’s exhaust H2 measurement. It was found from this data that the sensor’s average response time to rising H2 concentrations is 4.5s, but that the response to decreasing concentrations is much slower. The exhaust H2 concentrations, together with the engine operating data, were used to generate H2 emission maps, which provide insight into the relationship between the engine load, engine speed and the H2 slip. With further sensor development and sample gas control, this method can achieve high accuracy and extended application in in-use vehicle’s H2 emission measurements.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".